Transport Policy & Economics
Enabling Effective Transdisciplinary Working with the Automotive Sector
Supervisor:
Linda Newnes

Over the past few decades transdisciplinary (TD) has been the subject of increased discourse in the context of large, complex, ill-defined, ‘wicked’ problems. However, there has been less consideration of the potential it offers within the practice of engineering. This research looks to create tools which enable effective TD working within the automotive sector. The Mobility Engineering 2030 FISTA White Paper identifies that changes within the sector mean that interdisciplinary working, involving groups formed from people working in similar disciplines, will not be sufficient. It recognises that in the future there will be a need for transdisciplinary working, which goes beyond the academic disciplines to understand the societal context. For example, legislation, standards, culture. However, achieving effective TD working within organisations is not simple. It requires the creation of tools (e.g. processes and methods) which enable clear communication and knowledge transfer within and beyond an organisation. This PhD will leverage input from the TREND (TRansdisciplinary ENgineering Designers) £1.8m platform grant (Dec 2017 – Dec 2022). The over-arching aim of TREND is to provide tools to assist engineers to work in a transdisciplinary manner and to identify the types of engineers that are transdisciplinary. Identifying what makes engineering teams in the automotive sector transdisciplinary and how to assess their current readiness level to be transdisciplinary is the focus of this PhD activity. The PhD will have a particular focus on ‘common’ characteristics and automotive design team behaviour within and across industry case studies. Mapping findings at various life cycle stages such as designer requirements, use of digital tools etc. for each case study/domain against the manufacturing life-cycle phases. This would be followed by cross case-study analysis. The analysis may use techniques such as input/output system modelling to map the designer requirements at each stage of the manufacturing life cycle, and/or socio-technical analysis could be used to classify and model the designer behaviour. In summary the PhD researcher will be required to create a structured framework to estimate the automotive sectors transdisciplinary readiness level. Specific objectives may include 1. Undertake a literature review to understand the state of TD working within the automotive sector. 2. Engage with stakeholders to gather information which informs the design of the TD readiness tool. 3. Create a TD readiness tool. 4. Validate the proof of concept tool within industry.

Digital Systems, Optimisation and Integration
Automated generation and parameterisation of physics-based propulsion system models
Supervisor:
Nic Zhang
Industry Partner: AVL

Accurate simulation models are vital for the implementation of digital development techniques. These models need to be a suitably accurate representation of the system behaviour, whilst also satisfying key requirements in terms of computational speed and scalability. Currently there are two major categories of models:

  1. those that are physically based which generally have strong scalability but also inherent inaccuracies due to the constraints of the underlying physical model.
  2. those that are data driven which have good accuracy within the range for which data is available, but poor scalability. This PhD will seek to create new algorithms that can automate the creation and parameterisation of physical and semi-physical models that are both scalable and accurate. The starting point for these will always be a known physical model and some limited measurements of the system performance. For physical systems, model parameters of interest are often distributed across a state space, which leads to a large number of parameters to identify, and restricts the usage of state-of-the-art identification procedures. For example, battery resistances will inherently depend on ambient temperature as well as the current state of charge. Your research will be focussing on localisation strategies to identify these distributed parameters independently, such that the identification procedure can be performed in a parallel fashion across the state space, and becomes computationally tractable. Your research will need to consider the future of automotive propulsion to ensure the approach is compatible with relevant technologies. Whilst there will be specialisms associated with the modelling of individual components (batteries, motors, engines, fuel cells…), you should seek to draw out the commonality of the mathematical approach that can be applied more generally. The outputs from this PhD would be expected to be integrated into a model factory engineering software tool that supports engineers in the creation of mathematical models
Digital Systems, Optimisation and Integration
Towards new statistical modelling techniques combining expert knowledge and experimental data for propulsion systems
Supervisor:
Nic Zhang
Industry Partner: AVL

Modern automotive powertrain labs create large amounts of data. The data include various key performance metrics, crank angle resolution cycle events and high frequency recordings of all channels in time traces. Historically, the experimental results in the form of lookup tables and scatter plots have not fully exploited the potential of the data and engineers are increasingly focusing on creating statistical models using the available dataset. High quality statistical models can replace some experimental work as the digital twin of physical systems for predictive analysis and can be embedded directly into automotive controllers for model-based control.

With the wide range of modelling tools available, automotive engineers would benefit from a framework of statistical modelling for specific powertrain systems in the form of an automated tool. This PhD will seek to create such a tool with a help of a large commercial database of experimental data. Familiarity with the physical models for individual components (batteries, motors, engines, fuel cells…) should be the starting point of the study. Open source machine learning libraries, such Keras, will then be used to explore the available dataset to investigate the predictive performance of statistical models, such as Neural Networks, compared to the physical models.

The technical know-how generated in this study is expected to provide the tool users with specific guidance such as:

whether important inputs are missing for specific technologies;

how to reduce the number of input variables of the problem for faster model training.

how to run iterations of training to eliminate irrelevant areas of the problem space and instead focus on areas of special interest.

which statistical models are most suitable for the specific engineering problem.

whether physically inspired rules should be included in the ML structure to improve the model performance.

A likely deficiency of this approach in highly non-linear systems is that the density for experimental data needed to allow the training of a Machine Learning structure would be impractical. If this proves to be the case, an alternative approach should be considered that seeks to embody the engineer’s understanding of the physical causality that underlies the unit under test. This can be represented in the form of physically inspired ‘rules’ or ‘toy models’ that can then be calibrated to represent the unit using an iterative training approach. Such a model could allow a more sparse dataset to be used without sacrificing predictive power.

The outputs from Johannes' PhD would be expected to be integrated into a model factory engineering software tool that supports engineers in the creation of mathematical models.

Digital Systems, Optimisation and Integration
Intelligent approaches to improve the system reliability of advanced testing methods
Supervisor:
Chris Brace
Industry Partner: AVL

Automotive propulsion system development processes have advanced greatly over the last years, blending physical experiments and high-fidelity simulation to provide a highly realistic environment in which to study system behavior.

The system needs to allow the Unit Under Test (UUT) to interact with simulated components just as they would in a vehicle. Take the example of a hybrid vehicle transmission undergoing physical test. The simulation of the remaining parts of the system – engine, vehicle, battery etc. need to cater for conditions such as start, warm up, shut down and error states in a way that is rarely called for in a pure simulation environment.

The preparation of models and test rooms for these blended test scenarios is therefore complex and time consuming. The two main areas that lead to errors are errors in the software implantation (bugs) and errors in the simulation behavior that causes the system to stray into unrealistic operating states. For example – the engine simulation could simply crash (due to a bug) or it could execute correctly but give dangerously large output predictions which then cause damage to the UUT.

This project seeks to consider all appropriate techniques that can speed up and improve the setup and verification of such complex test scenarios. It is likely that some measure of expert knowledge or ‘big data’ approaches would be useful, along with some procedures to ensure that all possible test conditions are anticipated and verified before the test program is scheduled on the real test room.

Some likely research objectives are to:

  • Produce all possible errors on a running system
  • Identify and enumerate (cluster) all possible error states
  • Classify error (Error by software or simulation).
  • Identify a recommendation to solve the error.

The successful project will develop techniques that offer new scientific approaches in areas such as:

  • Test Mutation methods to generate scenarios of interest
  • Recommender Systems
  • Property based testing
  • Model based testing
  • Increasing Quality of testing
  • System Transparency for complex hybrid test systems

The successful candidates will be working with Engineers form the project partner, AVL, a world class test and simulation techniques developer at their global headquarters in Graz and with teams at the new state of the art IAAPS laboratory complex on the Bristol bath Science Park (web link).AVL List GmbH is the world's largest independent company for the development, simulation and testing of all types of powertrain systems (hybrid, combustion engine, transmission, electric drive, batteries, fuel cell and control technology), their integration into the vehicle and is increasingly taking on new tasks in the field of assisted and autonomous driving as well as data intelligence.

Digital Systems, Optimisation and Integration
Automated Configuration of Simulation Parameters
Supervisor:
Chris Brace
Industry Partner: AVL

Automotive propulsion system development processes have advanced greatly over the last years. State of the art techniques use a blend of physical experiments and high fidelity simulation to provide a highly realistic environment in which to study system behavior.

Practically, this means that the simulated parts of the system need to behave just as if they were physically present in the test room. The test system needs to allow the Unit Under Test (UUT) to interact with the simulated components just as they would in a vehicle. Both requirements lead to significant complexity when compared with traditional scenarios. Take the example of a hybrid vehicle transmission undergoing physical test. The simulation of the remaining parts of the system – engine, vehicle, battery etc. need to cater for conditions such as start, warm up, shut down and error states in a way that is rarely called for in a pure simulation environment.

The hybrid test field is a complex combination of physical hardware (UUT) and software simulations of other aspects of the system. Both sides of the system are underpinned by complex computation and test automation platforms. All of these elements need to work together in harmony and make the UUT believe it is operating in a real vehicle in the real world. To achieve this, the simulated aspects of the system must be carefully calibrated to operate in the desired manner. This is a significant task and this PhD aims to develop techniques to speed up and simplify this process for the test engineer. Such a system will need to identify:

  • The best simulation parameters (in terms of Robustness and fewest simulation errors)
  • Identifying the minimum set of simulation parameters that meet this requirement to reduce complexity
  • Generate user friendly information to allow users to understand the system
  • Automatic and guided parameterization with minimum number of experimental runs (Preferably 0, all parameterisation is performed and verified ahead of the physical test programme)
  • Strategies to allow self healing in the case that an error state is encountered.

The successful project will develop techniques that offer new scientific approaches in areas such as:

  • Recommender System
  • Co-Simulation
  • Optimization Techniques
  • Usability in Simulation (e.g. Robust System Stability)

The successful candidates will be working with Engineers form the project partner, AVL, a world class test and simulation techniques developer at their global headquarters in Graz and with teams at the new state of the art IAAPS laboratory complex on the Bristol bath Science Park.

AVL List GmbH is the world's largest independent company for the development, simulation and testing of all types of powertrain systems (hybrid, combustion engine, transmission, electric drive, batteries, fuel cell and control technology), their integration into the vehicle and is increasingly taking on new tasks in the field of assisted and autonomous driving as well as data intelligence.

As a CDT AAPS student sponsored by AVL, you will also benefit from the peer support and professional development offered by AVL’s Systems Engineering Lab.

In 2014 AVL’s “SE-Lab” was founded as an interdisciplinary communication & collaboration platform for systems engineering. It comprises around 60 students (part-time, Master/Bachelor/PhD thesis) from various studies, ranging from computer sciences and engineering to psychology, economics and law.

Propulsion Electrification
Ultrasonic sensors for battery SOC measurement
Supervisor:
Chris Vagg

Batteries change in density as they are charged and discharged, and recent research has shown that ultrasound can be used to detect these changes, allowing the charge level of the battery to be detected.  Mac Geoffrey's project aims to take this new technique and apply it to automotive batteries to allow continuous in-service charge and health monitoring.

Batteries are key to the sustainability of future transportation solutions, and ultrasonic charge monitoring has several advantages which make it interesting.  Presently, battery state of charge (SOC) is 'tracked' by charging the battery to its maximum voltage and setting SOC to 100% (full), and then integrating the charge (or energy) taken from it.  The ultrasound method allows the battery SOC to be directly measured at any time, even while being used, and without needing to know the usage history since the last charge.  This will allow for better estimation of SOC and maximum power limits, improved battery range estimation (in Electric Vehicles) and accelerated battery testing.

Ultrasonic charge monitoring has been demonstrated in laboratory tests at an individual cell level.  The changes in the mechanical properties of the battery change the sound speed of an ultrasound pulse travelling through the battery cell.  The sound speed is determined by measuring the time taken for a pulse to traverse the cell and is used to infer the SOC.  Automotive batteries consist of many cells, and we need your help to explore how this method can be applied to multiple cells stacked in a module, and how this might constrain module design.  You will design and build a test system, collect data from it, use analysis and numerical modelling to interpret the data, and test the effects of different sensor and module designs.

Low Carbon Fuels
Crude Sulfate Turpentine as a new source of biorenewable fuel
Supervisor:
Steve Bull

Dwindling fossil fuel supplies and global warming mean there is an urgent need to develop biorenewable replacements for the petrochemical based fuels and lubricants consumed by the automotive industry. Bioethanol from fermentation of lignocellulose biomass and other high energy biofuel replacements derived from hydrogenated vegetable oils have been developed, however many of these biofuels have low densities and volumetric heating values.

Terpenes represent an alternative class of naturally occurring hydrocarbons which have comparatively higher densities that make them ideal-fuel replacements/additives. Nature produces an estimated one billion tonnes of terpene annually, which is a sufficient volume to consider using these hydrocarbons as replacement biofuels. Recent developments in industrial biotechnology have also demonstrated the potential of engineering metabolic pathways into microbes for the industrial production of economically important higher terpenes. The cheapest commercial sources of terpene currently available is Crude Sulfate Turpentine (CST) which is produced as a waste by-product of the Kraft paper pulp process (approx. 240,000 tonnes p/a) and gum turpentine (110,000 tonnes p/a) that is available from sustainable tapping of pine trees. Both turpentine sources are comprised of a mixture of cyclic monoterpenes (-pinene, -pinene, 3-carene and limonene), that are currently used as chemical feedstocks by the flavour/fragrance industries or burnt on-site to provide a cheap energy source for the pulping plant.

We have recently developed a catalytic two-step ring fragmentation/hydrogenation protocol to convert CST into a ‘sulfur free’ p-menthane biofuel containing controllable amounts of aromatic p-cymene. The monocyclic saturated branched ring structure of p-menthane means that it should exhibiy excellent automotive biofuel properties (high energy, branched, resistant to oxidation, low freezing point, non-carcinogenic). Aaron's project will optimise the chemical route from untreated industrial CST (e.g. desulfurisation technology, catalyst recycling) obtained from a Swedish paper mill (Södra) to produce p-menthane/p-cymene blends (ratio dependent on partial hydrogen pressure) whose combustion performance (e.g. melting point, cloud point, cetane level, temperature performance, combustion kinetics/pathways) will then be optimised to enable field tests to be carried out in different types of combustion engine.

Digital Systems, Optimisation and Integration
Driving Simulator for Connected Autonomous Vehicles
Supervisor:
Nic Zhang

Dmitry's research project aims to build a high-fidelity immersive driving simulator at IAAPS to enable research on topics such as autonomous driving, vehicle driver pedestrian interactions, real-world efficiency and emissions.

The planned driving simulator will be software and hardware agnostic, with the ability to easily upgrade the hardware setup to support motion platforms and 360° projection screens. A framework for processing and fusing multi-sensor data will be created to convert map and real-world captured data into virtual validation routes and scenarios. Uniquely it will be possible to connect the driving simulator to a powertrain dynamometer to allow hardware-in-the-loop control of a physical vehicle or subsystems thereof as an alternative to full vehicle simulation.

The simulator is expected to include the following critical components and systems:

  • Model interface system
    • Allowing seamless transfer of data between a variety of different software and hardware platforms that might be linked together as part of the driving simulator.
    • The model interface layer is the most critical component of the simulator, allowing different components to easily communicate with one another and increasing the ease of commercial collaboration
  • Vehicle model system
    • Plug-in based system allowing full vehicle simulation (e.g. via Simulink or CarMaker) through to physical vehicle interface on powertrain-dyno (via AVL Pump)
  • Driving route and scenario generation pipeline
    • Using a combination of map and in-vehicle captured data sources
    • Generation of roads and surrounding scenery, including road markings
    • Traffic characterisation data to be fed to traffic model (see below)
    • Weather effects
  • Traffic modelling and traffic signal simulation
    • Plugin-based to allow use of existing tools (e.g. SUMO) as well as development of agent-based systems and combinations of micro and macro simulations
  • Display and rendering system
    • Enable use of different display/rendering packages (e.g. CarMaker, OpenDS, rfPro)
    • Scalable display output system (e.g. single monitor to multiple projector systems)
  • User input and human-machine interface system
    • Allowing use of different user input devices (from keyboard to gaming steering wheel and pedals to full vehicle CAN-based input systems)
    • Allowing use of different HMI systems for dashboard and in-vehicle systems (from on-screen generated through to CAN-interfaced physical dashboard)

The project will interact with other research centres (CAMERA CENTAUR) and CDTs (ART-AI) within the university.

Propulsion Electrification
Electric Motor Torque Estimation
Supervisor:
Chris Vagg
Industry Partner: AVL

Knowing the torque produced by an electric motor is essential in a wide range of applications. In electric vehicles it is important to deliver the torque requested by the driver, and it becomes very important for good drivability when blending regenerative braking torque with the hydraulic brakes. It is a crucial information for the ECU control systems that ensure the drive unit does not exceed battery power limits. It is also a key information when testing and evaluating new motor developments on a dynamometer.

The torque produced by an electric motor is a function of the phase current, and this is commonly used to estimate the motor torque. Unfortunately this simplistic estimation is prone to significant error, which compromises the usefulness of the information. Drivability may be adversely affected, additional safety margin may need to be taken to protect the battery (reducing its effective power density), and in dynamometer applications use of a torquemeter is invariably necessary to ensure the accuracy of the test data. For the dynamometer case, high-accuracy torquemeters are extremely expensive, especially as motor speeds increase beyond 30,000rpm, and also introduce mechanical installation problems due to rotordynamics. The inaccuracy of the estimated torque is due to a wide range of factors including electrical losses, mechanical losses and varying magnet temperature, for example.

This PhD will aim to understand in detail the torque generation (electromagnetic and reluctance torques) as well as all of the loss mechanisms in an electric motor, and to design a reliable and accurate estimate of motor torque. It will be supported by AVL GmbH, the world's largest independent company for the development, simulation and testing of all types of powertrain systems. The initial case study will be a highly instrumented prototype of one of AVL’s next-generation PMSM dynamometer motors, though other applications may also be considered. The research has a strong application focus, and you will have the opportunity to direct the experimental work needed to understand the motor torque. This may take place at IAAPS in Bristol, at AVL in Graz (Austria), or a combination of both.

Outcomes from this PhD will directly contribute to new AVL software upgrades, and their aim is to develop a system which is accurate enough that a torquemeter is no longer essential for dynamometer testing. The findings will be directly applicable to other applications, including control of electric vehicle traction machines.

In undertaking this PhD you will join a team of researchers within the Institute for Advanced Automotive Propulsion Systems (IAAPS), including over 60 other PhD students from a range of disciplines all working in automotive research. You will have direct links with engineers from the project partner, AVL, and will benefit from working alongside another PhD student working on a sister AVL project focussing on thermal modelling of electric machines.

This project would suit a student with a background in Electrical or Mechanical Engineering.

Propulsion Electrification
Energy-Optimal Testing
Supervisor:
Chris Vagg
Industry Partner: AVL

Development of propulsion systems for any type of vehicle involves enormous amounts of testing at a range of levels, from components through to whole vehicles. The electrical energy consumption of these test systems is not negligible and can become very significant for long-term experimental campaigns, such as battery degradation or powertrain durability studies. In addition, when there are multiple Units Under Test (UUT) being tested at the same time the peak power requirements can become very high (the sum of all the UUTs), which forces a need for a very large power supply. These two issues lead to increased capital investment costs, higher running costs and larger CO2 footprint.

A great deal of work has been done to optimise component sizing and energy management in hybrid-electric vehicles, but these techniques have never been used to optimise the testbed, which is what this research will explore.

This PhD seeks to understand to what extent energy management strategies can be used to optimise the simultaneous testing of multiple components. It will be supported by AVL GmbH, the world's largest independent company for the development, simulation and testing of all types of powertrain systems. The initial case study will be AVL’s next-generation 36-channel battery cell tester, and other applications may also be considered. Within this battery cell tester an Active Front End is used to maintain a shared DC-link at an elevated voltage, and each channel uses a step-down DC-DC converter to reach a voltage close to the cell voltage. By optimising the power scheduling of the 36 channels, and by recirculating power between channels, the objective is to reduce the system complexity and cost whilst simultaneously improving the energy efficiency of its testing.

The research has a strong application focus and the opportunity to evaluate solutions in hardware with the support of AVL. The outcomes will directly contribute to new AVL software upgrades and inform future generations of powertrain test systems, reducing the cost and CO2 footprint of developing new powertrains.

In undertaking this PhD, you will join a team of researchers within the Institute for Advanced Automotive Propulsion Systems (IAAPS), including over 60 other PhD students from a range of disciplines all working in automotive research. You will also have direct links with engineers from the project partner, AVL.

This project would suit a student with a background in Electrical Engineering; candidates with a background in Mechanical Engineering or IMEE with appropriate experience would also be considered.

Digital Systems, Optimisation and Integration
Autonomous Anomaly Detection and Self-healing in a smart test environment
Supervisor:
Chris Brace
Nic Zhang
Industry Partner: AVL

The importance of simulation is continuously increasing in the world of vehicle powertrain development. A lot of tasks are being moved into the virtual world allowing them to be completed earlier in the development process and allowing more use-cases to be considered.

Nevertheless, final hardware testing on a physical test environment is still required as product variance, unknown effects and simulation inaccuracies needs to be compensated before heading into the market. Additionally, real hardware testing is critical to ensure a good simulation quality because test data is used to adapt and optimize the virtual models during the development process. This is true for all kind of powertrain units, no matter if it is an internal combustion engine, a pure electrical drive, a fuel cell or a hybrid setup.

For all test environments, the essential requirement to reduce the development costs and the time spent on the testbed, is reliable data. Additionally, with increasing virtualization, the physical testbeds have an important role to support the model development. As a consequence, the gathered data needs to be as accurate as possible because every single measurement point is influencing the model behavior and any error will propagate in the complete simulation chain. Thus, things like reproducibility, signal to noise ratio, measurement errors, etc. need to be considered. By the general change of the powertrain development process, it becomes harder to compensate poor measurement quality by engineering experience.

What does this mean? It means that the data that is gained on any kind of testbed must be 100% correct – in terms of test bed equipment but also related to the UUT (“unit under test”).

In this research project, the first step is to work out answers to the following questions which form the base to analyze the entire system regarding its actual status.

  • What is data quality?
  • What does 100% correct data mean?
  • What are we comparing with? What is the reference?
  • How can the test environment be monitored to the full extend?
  • What kind of anomalies may happen? How can they be detected?
  • Can they be categorized? Can the root cause be determined?
  • How can we identify that we have a problem in or with the test environment?

Answering these questions will require Ellie to acquire a detailed understanding of the test environment (with training, support and experience provided by the team). Some ideas to be considered include – can machine learning or other types of AI help us to identify, classify and diagnose anomalies in the data? Can we incorporate the expert knowledge of experienced engineers into a software tool that helps to automate the process, using AI and/or a rule based approach? If we can, how much confidence can we have that the AI is understandable, reliable and traceable?

The final goal is to ensure that the end user can rely on the output of the test environment. It must be guaranteed that all users – even the most skeptical ones – are convinced and can understand the complete process.

In the next step of the project, Ellie will investigate what can be done with the analysis result:

  • How can the information be displayed to the user? What is actually necessary to display?
  • What kind of report is necessary to ensure a consistent documentation?
  • Is it possible to judge automatically if an anomaly is relevant for the final output or is a human interaction always necessary?
Digital Systems, Optimisation and Integration
AI approaches to automate Bill of Materials Validation
Supervisor:
Chris Brace
Student: James Angus
Industry Partner: Quick Release

A Bill of Materials (BoM) is a document that lists all of the components and resources needed to build a product, in this case a vehicle. Each car has around 15,000 components. If any of these components are missing or incompatible the factory cannot build the vehicle. The problem is made more complex because vehicle makers offer an almost infinite variety of model variations and customisation options. Each of these needs a complete and accurate BoM if the manufacturing process is to succeed. Therefore, each BoM must be validated to ensure it is correct before the vehicle can be built. Techniques exist to automate this validation process, but there is still a heavy reliance on expert knowledge to ensure that nothing is missed or duplicated.

Using AI techniques, it may be possible to understand the variant configuration of each buildable combination and thus eradicate miss-builds and provide vehicle makers with the good information across the whole product line-up which will allow for more accurate planning in terms of assembly as well as financial control. There is a rich dataset of historical BoMs available which can be used to help with this process, as well as access to human experts whose knowledge may able to be represented in an automated procedure. It is likely that the most effective approach will combine these two approaches.

Transport, Behaviour and Society
Psycho(patho)logical predictors of (automotive related) pro-environmental attitudes and behaviours
Supervisor:
Punit Shah
Student: Lois Player

The supervisory team are currently involved in research providing crucial data to inform the upcoming implementation of Clean Air Zones in the UK. CAZs aim to significantly reduce the use of older vehicles, and equally encourage UK residents to upgrade to, and use, more environmentally friendly forms of transport including new hybrid vehicles. To this end, we are currently funded to conduct basic research on understanding the psychological processes underlying support and opposition to the CAZ. This will focus on qualitative data (e.g., following interviews) and cursory experiments.

Lois will complete her PhD, though publications, starting with an in-depth review of the relationships between normal and psychopathological (i.e., abnormal) psychological traits, environmental attitudes, and pro-environmental behaviours (Article 1). For example, building on some of our ongoing work (Taylor…Shah, under review), this will follow a line of theoretical enquiry on the links between autism, mental health conditions, and pro-environmental (automotive) behaviours.

Based on the review of the literature, partly using data collected as part of the CAZs implementation, Lois will complete a series of empirical studies (Articles 2-5) to assess the various psycho(patho)logical predictors of pro-environmental behaviours, accounting for climate change beliefs and environmental attitudes, with a focus on understanding any potential barriers to engaging in pro-environmental behaviours.

Overall, the proposed work will feed into existing CAZ projects, and thereby governmental policy, but also generate new findings that will be important for understanding and changing, both attitudes and behaviours, towards the use of more environmentally friendly vehicles and public transport.

Chemical Energy Converters
Leidenfrost propulsion for cooling flows in AM parts
Supervisor:
Andrew Rhead
Student: Onur Tokkan
Industry Partner: GKN

Water droplets placed on a surface heated to a sufficient temperature (the Leidenfrost temperature) levitate on a film of their own vapour. A recent Nature article showed that adding a sawtooth pattern to the heated surface causes these levitating droplets to be propelled (even uphill!). Very recent work at Bath has shown that this propulsion can be achieved in mm diameter enclosed pipes. This opens the way to exploitation of Leidenfrost propulsion for active cooling systems that operate without moving parts and use waste heat energy for the thermal pumping effect. Onur's project will seek to extend this work by using AM techniques to created 3D printed cooling systems with internal ratchet microstructures. Work will focus on optimum fluid/surface choices (experiments have previously been confined to water) and ensuring AM quality is sufficient to reliably allow propulsion as surface roughness can cause an increase in the Leidenfrost temperature. The effect of pressure on heat transfer will be studied both experimentally and numerically and set in context against current systems.

Digital Systems, Optimisation and Integration
Closed cycle water injection for internal combustion engines
Supervisor:
Sam Akehurst
Industry Partner: VM Technologies & Engineering GmbH

Immanuel's project investigates a pathway to making water injection for combustion engines mass market proof. Water injection for combustion engines has been implemented a number of times into limited production motor vehicles to enable higher engine performance, mainly with forced induction. In these cases, the technology was used to lift the knock limit by decreasing combustion temperatures. However, water injection enables better thermal efficiency with lower combustion temperatures which can decrease particulate, CO, CO2 and HC emissions together with fuel consumption. Nowadays, a large portion of engines utilise forced induction which at certain times requires extra cooling where the engine is made to run rich. Having lower combustion temperatures removes this need. Furthermore, as mentioned, with lower combustion temperatures, engines could run higher compression ratios which would make engines smaller, hence reducing rotational masses and improving fuel consumption. These potential benefits reflect the current drive toward more efficient and cleaner combustion engines. Electrification is one of the pathways being implemented to fight global warming but combustion engines are set to be part of the majority of drivetrains available in the next few decades. One of the reasons why water injection has not made its way into mass production vehicles is the need for the consumer to refill the water tank after relatively short intervals with distilled water from the grocery store. This is impractical and not acceptable for consumer satisfaction. This project aims to produce a solution where the water vapour in the exhaust gas is condensed to liquid form, cleaned and stored to then be injected back into the engine to result in a closed cycle. There are several issues that need to be addressed when proposing such a solution. Among those are water pH values, moulding, freezing and impurities within the condensed water.

Through the sponsoring company, a natural non-toxic additive is in development and may be used for the purpose of the project to potentially eliminate some of the issues with closed cycle water injection. The proposed solution should then be capable to run several thousand kilometres without a refill of the additive, similar to the AdBlue principle in diesel engines. Depending on the progress of the research, a prototype of the whole system is possible where a side effect of the water injection may be that the currently imposed GPF filter for gasoline engines could be removed due to water injection reducing the particulate emissions. This would be a positive side effect since less aftertreatment would result in a weight and efficiency benefit.

Chemical Energy Converters
Design and validation methods for additively manufactured heat exchangers
Supervisor:
Joseph Flynn
Industry Partner: GKN

With electrification being one of the biggest potential disruptors in modern transport, there is a growing need for lightweight, reliable and efficient thermal management systems. Conventional solutions will struggle to keep future powertrain and propulsion systems cool without incurring significant mass penalties. In addition, future systems are likely to be highly complex, which may also negate the performance benefits. Recovering waste heat will also be a priority to reach desired overall system efficiencies – affecting both the environment as well as operational economics. Advancements in additive manufacturing technologies combined with mathematical/parametric modelling enable the construction of radically different heat exchangers. These units can be designed to reflect their function and application in particular environments. In this PhD, Edgar will focus on engineering methods development to design and validate high-effectiveness, additively manufactured heat exchangers. This research will consider both analytical and experimental techniques. A modular approach is proposed with a focus on heat transfer through thin-walled components and balancing the trade-off between effectiveness and pressure drop across the unit. Finally, Edgar's project will consider further integration with computational intelligence or optimisation techniques in order to radically accelerate the development of complex heat exchangers.

Propulsion Electrification
Structural Batteries Project B: Fibre matrix interface scale - Battery concepts and fibre electrolyte electrical connectivity
Supervisor:
Andrew Rhead
Student: Rob Gray
Industry Partner: GKN

Batteries based on carbon fibre reinforced plastic (CFRP) have the potential to supply power with an improved overall efficiency (vehicle power to weight rather than battery power to weight) compared to current battery technologies. By integrating batteries into the structure in the form of CFRP, lightweighting is not only achieved from the change in material but also from the removal of the non-structural dead weight of conventional batteries and their casements. For example, in automotive applications, structural batteries achieve a 26% theoretical mass saving over use of separate systems for energy storage and load carrying. The current state-of-the-art in structural batteries is a half-cell based on a structural cathode. Significant work is required before a full cell can be manufactured and expected to sustain loading for multiple discharge and mechanical load cycles. Three projects are suggested which focus on challenges at different length scales this is project A:

Fibre matrix interface scale - Battery concepts and fibre electrolyte electrical connectivity: Rob's PhD will focus on both creation and development of new structural battery concepts and on the dual role of the supporting matrix. The matrix must both mechanically support the fibre, for which complete wetting of the fibre by the matrix is optimal, and allow ion migration, for which partial wetting of the fibre, in some battery constructions, is optimal as it allows liquid electrolyte to be in contact with the electrically conductive carbon fibre electrodes. The interface between the fibre and resin is subject to interface chemistry including sizing/functionalisation of the carbon fibres and processes which can control the wetting of the fibres by the matrix. Exploration of both will be key to Rob's PhD.

Propulsion Electrification
Structural Batteries Project A: Atom-scale modelling, anode development and charging rates/battery cycling
Supervisor:
Andrew Rhead
Industry Partner: GKN

Batteries based on carbon fibre reinforced plastic (CFRP) have the potential to supply power with an improved overall efficiency (vehicle power to weight rather than battery power to weight) compared to current battery technologies. By integrating batteries into the structure in the form of CFRP, lightweighting is not only achieved from the change in material but also from the removal of the non-structural dead weight of conventional batteries and their casements. For example, in automotive applications, structural batteries achieve a 26% theoretical mass saving over use of separate systems for energy storage and load carrying. The current state-of-the-art in structural batteries is a half-cell based on a structural cathode. Significant work is required before a full cell can be manufactured and expected to sustain loading for multiple discharge and mechanical load cycles. Three projects are suggested which focus on challenges at different length scales this is project A:

Atom-scale modelling, anode development and charging rates/battery cycling: Thomas' PhD will focus on optimising the construction of the anode of a CFRP structural battery (the cathode being investigated at Chalmers University in Sweden) and assessing its performance under load. Work will be undertaken in atomic modelling of the anode and the change in ion migration pathways as the anode is stretched by intercalation (absorption) of ions and mechanical loading. Work will focus on the electrochemical aspects of anode development and leave mechanical aspects to the other projects.

Chemical Energy Converters
Thermodynamic and kinematic analysis and modelling of the ISOTOPE-X cross-linked opposed-piston free-piston engine
Supervisor:
Aaron Costall
Student: Alex Young

Alex's research will investigate a new concept of free-piston engine for which a patent has been applied for by the University. This concept (known as “ISOTOPE-X”) is mainly intended to function as a range extender for a battery electric vehicle. The project will investigate the performance of the machine firstly as a combustion engine, including possibilities afforded by the flexibility of the piston motion, to in turn establish the requirements placed on the electrical components, and then secondly model these to gauge the feasibility of the whole device. Control requirements will also be studied, including the potential benefit of using “bounce chambers” for instantaneous energy requirement reduction, and the use of pumping chambers for scavenging air supply the cylinders.

The combustion modelling will include the possibility to vary the compression ratio and so investigate the feasibility of using some form of compression ignition to further improve efficiency and reduce emissions. Heat transfer effects will be quantified as part of establishing the losses and also the magnitude of the thermal challenge passed to the magnets of the electrical machine.

It is anticipated that an initial conceptual design will be modelled and that as the analysis progresses and new insights are gained that updates to the design will be incorporated at various stages.

A significant potential avenue is the investigation of hydrogen as a fuel for the machine, since with advanced combustion modes enabled by variable compression ratio it is possible that this combination could be effectively zero emission and more efficient than a fuel cell for heavy duty applications, while being cheaper too.

Chemical Energy Converters
Solid Oxide Fuel Cell for Small/Medium Aerospace Applications
Supervisor:
Alfred Hill
Industry Partner: GKN

Elisabetta's PhD will explore the use of solid oxide fuel cells (SOFCs) for the direct conversion of hydrogen storage vectors such as ammonia to electrical energy. SOFCs have several advantages over PEM, including, multi-fuel capability, resilience to poisoning from fuel impurities and lower use of precious metal catalysts.

Chemical molecules such as ammonia have the potential to be excellent hydrogen storage vectors for aviation fuels. They do not require high pressure containment but still achieve very high hydrogen storage densities arising from the hydrogen stored within their chemical structure. However release of the hydrogen requires a catalytic conversion and ppm levels of ammonia are a poison for PEM fuel cells so a SOFC is required.

The project proposes the (1) characterisation and (2) optimisation of SOFCs for usage in aerospace electric propulsion applications. Characterisation of the cells will focus on cycle efficiency of different fuels (Ammonia, hydrocarbons, H2) and the internal chemistry/catalysers used. Optimisation will be on structural weight reduction, power transfer efficiency, and thermal management of waste heat using AM.

WP1: Exploration of Fuel Cell topology / architecture / fuel: To investigate the most suitable fuel, catalyst and electrolyte performance and topology for aerospace applications. Bath already has equipment required to quantify the conversion efficiencies of the cells while some will be purchased directly as part of the project.

WP2: Integrated thermal management: This will investigate different materials and 3D geometries to highly integrate cells and their thermal management making use of extensive simulations. AM heat exchangers and cell components will be prototyped and tested. This will include consideration of fast start-up capability by application of direct radio-frequency heating.

WP3: Technology demonstrator: a prototype SOFC stack to be produced to demonstrate feasibility.

Transport, Behaviour and Society
Interaction between different categories of road user
Supervisor:
Janet Bultitude
Ian Walker

Our streets are shared by multiple types of user. At the extremes, vulnerable pedestrians might occupy urban spaces with vastly more dangerous heavy goods vehicles. Such interactions introduce many disparities and asymmetries in terms of the ability of one party to harm another, and the extent to which each party is legally and physically regulated. A key issue is how these different classes of road user can effectively communicate with one another. This becomes more pressing as we consider the possibility of future autonomous vehicles, which entirely lack a human component and so might communicate very differently (e.g., they are unlikely to interpret informal signals the way a human driver would). All this takes place within a built environment which is regulated by a legal system and surrounded by cultural influences such as news and mass media. Catherine's PhD will look at how communication between road users currently takes place and how people, policy and engineering might be changed to facilitate and improve this.

Low Carbon Fuels
New Materials for Automotive Tribo-chemistry
Supervisor:
Andrew Johnson
Industry Partner: Infineum

Optimum lubrication and low-friction in automotive applications represents a potential for reduced energy consumption and emissions in engines. Moving engine components operate under high-temperature and high-pressure conditions where oil additives activate to form sacrificial protective tribo-films, which in turn reduce friction and wear. This synthetic tribo-chemistry based PhD will focus on the design, synthesis, characterisation and tribo-testing of new inorganic molecules designed to form wear resistant and low-friction films at points within the engine where friction and wear are present.

Digital Systems, Optimisation and Integration
Use-case Improved System Identification for Battery Systems
Supervisor:
Chris Brace
Industry Partner: AVL

Virtualisation of complex automotive propulsions systems represents an indispensable requirement for effectively overcoming engineering challenges encountered in their development cycle. State of the art models enable engineers to build high fidelity virtual prototypes as well as simulations capable of offering a realistic operating environment, leading to effective investigations into system behaviour and reducing the efforts associated with physical experiments.

Depending on the structure and complexity, robust effective models require a significant amount of resources during their development to maximise the amount of information describing the physical system. One vital step during this process is the parametrisation of the model selected which may present itself as costly and time-consuming due to the computational power and expert knowledge required. This type of process is also often devised only for a specific application by incorporating assumptions shaping a readily available dataset or acquisition routine, limiting the repeatability of the process relative to measurement data. These shortcomings increased the demand for automated procedures effective in creating virtual representations.

The main aim of this PhD project is to develop robust parametrisation procedures maximising model accuracy while reducing efforts by efficiently using information specific to the system and model structure employed. The initial use-case targeted is represented by battery systems, while others are set to follow and benefit from techniques that have already been employed by the project. The ideal outcome is represented by a demonstrated automated methodology capable to produce optimally parametrised models for given requirements, recognized by a set of Key Performance Indicators (KPIs) targeting the validity and quality of the parametrisation, while reducing testbed as well as computational effort.

It is expected that Vicentiu will produce a prototype implementation of the final methodology and demonstrate this in the prototype factory at the new Institute for Advanced Propulsion Systems (IAAPS). The outputs of this PhD project are also anticipated for integration as part of a new generation of engineering software tools aimed to assist engineers in the creation of mathematical models used to support further powertrain development tasks.

Transport, Behaviour and Society
Non-contact driver attentiveness detection system
Supervisor:
Benjamin Metcalfe
Industry Partner: Infineon Technologies AG

The primary objective of this Infineon Technologies AG sponsored project is the development of novel Radar based systems for detecting driver attentiveness via non-contact vital sign monitoring and gaze detection. For level 3 autonomous driving, the human must be attentive in order to take over from the autonomous systems in the vehicle if required. Recent research has shown that it is possible (in a lab setting) to measure vital signs such as heart rate, respiratory rate, blood pressure, and blood oxygenation using non-contact methods such as Radar. These vital signs can be combined (via sensor fusion methods) with movement detection to develop new models of driver attentiveness. For example, low variation in heart rate and a reduction in body temperature are good indicators of drowsiness.

Infineon Technologies AG are specialists in developing automotive Radar systems that are currently used predominantly for collision avoidance and external sensing. Gengqian's project represents an exciting opportunity to use this same low-cost automotive technology for driver attentiveness monitoring.

Transport, Behaviour and Society
Getting the timing right: using ‘moments of change’ to promote sustainable travel behaviour
Supervisor:
Lorraine Whitmarsh

Addressing climate change requires profound behavioural changes (CCC, 2019), including within transport. Indeed, reducing car use, switching to electric vehicles, and avoiding flying are the most impactful mitigation behaviour changes that individuals can make (Wynes & Nicholas, 2017). Yet, travel behaviours are amongst the most difficult to change (Whitmarsh, 2009). This is partly because they are strongly habitual – unconscious routines triggered by contextual cues (e.g., ‘it’s 8am, time to drive to work’) rather than the product of conscious deliberation of alternatives (e.g., ‘which mode of transport would be best today?’; Kurz et al., 2015). Many interventions (e.g., information campaigns) are ineffective because they are not strong enough to disrupt habits (Verplanken et al., 1997). But since habits are cued by stable contexts (i.e., the same time, place and/or social group; Wood et al., 2005), change in context disrupts habits (Verplanken et al., 2008). Consistent with this, ‘moments of change’ – where the circumstances of an individual’s life change considerably within a short timeframe – are one of the most important levers for lifestyle change (Capstick et al., 2014). Research shows that disruptions – whether concerning a person’s life-course (e.g. moving home) or structural (e.g. pandemic, flooding) – can provide opportunities to reshape behaviours in new directions (Marsden et al., 2020).

Interventions targeted to moments of change are thus more effective than at other times (Verplanken et al., 2019). Several studies show that mobility interventions are more effective when targeted to relocation (Thøgersen, 2012; Ralph & Brown, 2017). For example, one German study found that tailored public transport information and a one-day free transit pass was only effective when given to people who had recently moved house (increasing bus use from 18% to 47%), and did not change behaviour for those not relocating (Bamberg, 2006). Other such opportunities to intervene include temporal milestones (e.g., New Year, becoming an adult), having a child, retiring, infrastructure disruption (e.g., road closures), and COVID-19 (Verplanken et al., 2018; Burningham & Venn, 2020; Whitmarsh et al., 2020).

Working with a local authority partner (North Somerset Council) who are committed to ambitious climate change action, Tara's PhD project will design and evaluate interventions to promote sustainable travel behaviours (e.g., walking/cycling) targeted to moments of change. Tara will focus on three possible moments of change: (a) residential relocation to a new housing development near Weston-Super-Mare, (b) opening of new schools in the region, and (b) infrastructure changes to promote active travel using COVID-19 emergency access funds. The PhD project will apply a mixed-methods approach, comprising an initial scoping study (e.g., evaluating the impact of the COVID-19 funding on commuters’ travel habits), followed by a field experiment to shape travel habits of new residents and/or school children to be low-carbon and sustainable.

Chemical Energy Converters
Air handling system optimisation for Fuel cell applications
Supervisor:
Richard Burke
Industry Partner: Cummins Turbo Technologies

Delivering zero emission heavy duty automotive applications is expected to involve the extensive use of fuel cells. Like traditional combustion engines, fuel cells require an air handling system to provide a pressurised flow of oxygen to react with the hydrogen fuel. However, the challenges presented by a fuel cell differ to those presented by traditional Diesel engines. On the one hand, the problem is simplified because there are no longer pulsating flows to deal with. However, there are new challenges caused by the need for oil free air and demanding temperature constraints. The fuel cell has a relatively high inlet pressure requirement combined with a relatively low inlet temperature requirement, which puts a high emphasis on the isentropic efficiency of the compressor. The exhaust from the fuel cell has much lower temperature than a Diesel engine, meaning there is not enough energy to drive the compressor alone, meaning there is a need to electrify the air handling system. These challenges present a number of new design variables which are yet to be fully understood by turbomachinery manufacturers.

Matthew's PhD, will be to establish a deep understanding of the air handling requirements for fuel cells in heavy duty applications. He will undertake a theoretical exercise into the fuel cell itself to fully understand its operating principles and air flow requirements and constraints and then look to match these constraints to air handling solutions, considering the breadth of possible configurations, including proposing his own novel configurations where appropriate.

Transport Policy & Economics
Transitioning to a Low Carbon Bus Transportation Solution
Supervisor:
Charles Larkin

Jac will look at the development of a public policy framework to transition mass transit fleets in the UK from existing diesel propulsion to net-zero or near-net-zero propulsion systems. The analysis will look at the financing requirements and the design of battery-electric, hybrid-electric and hydrogen fuel cell fleets in terms of costs and subsidization requirements for transition and medium-term operations.

The research work will deliver the design of a covered bond (green bond) to finance the roll out of such fleets in the absence of the EIB lending facility in a post-Brexit UK. This will include the full ratings agency data analysis, yield structure, stress indices and “road show” materials. It would also include an outline of the required legislative and statutory instrument changes and potential public procurement/state aid regulatory changes that would be required to make the project viable under different population, topographical and propulsion scenarios.

Sustainability and Low Carbon Transition
Life cycle assessment of current and future passenger transport technologies in the UK
Supervisor:
Stephen Allen

In the coming years, the electric powertrain is expected to replace fossil fuelled vehicles. From an environmental perspective, there are still many uncertainties when assessing the potential life cycle impacts of future powertrain technologies. Many factors effect electric vehicle environmental performance - from electricity grid mix used to driver behaviour. A detailed evaluation of these sensitivities is required in order to understand when and under what driving conditions the electric powertrain is desirable. This is not only important to informing future vehicle LCA methodology and approaches, but is also important to policy-making – ensuring transportation in the future really does enable the UK Government to reduce net carbon emissions to zero by 2050.

This research is expected to be highly multidisciplinary, allowing the candidate to get to grips with a range of transport technologies, where they will be using life cycle assessment and driver behaviour data to perform a detailed analysis of the sensitivities and uncertainties relating to electric vehicles. The project will involve programming in advanced LCA software using Python.

Low Carbon Fuels
Sun + CO2: a match to drive a more sustainable future
Supervisor:
Antonio Exposito

The UK is moving towards zero carbon emissions by 2030. To reach this objective, it is necessary to develop technologies to capture and transform CO2. Artificial photosynthesis, a carbon-negative process that mimics the reaction in plants, is capable of transforming CO2, using energy from the Sun, into different molecules such as methanol which can then be used as liquid fuel. However, poor conversion and selectivity of current artificial photosynthesis mechanisms are the main barriers to the commercialisation of the technology.

Solution: We will investigate the role of continuous microreactors, devices with channel dimensions below 1 mm, which can provide a step-change needed in the technology because of their exceptional mass transfer, offering better conversion and control in the selectivity of the process.

Most of the work to boost the process has focused on the reaction chemistry (e.g. improving the catalyst), but reactor engineering and process intensification can be parallel research lines to improve the technology. Only a few groups have reported the use of microreactors for continuous CO2 reduction and they stated that the better mass transfer in these devices enhanced the selectivity towards liquid hydrocarbons. Mass transfer is essential for reagents to move across the boundary layer adjacent to the surface of the catalyst. It is especially crucial in multi-phase reactions, such as in the case of artificial photosynthesis. Despite the promising results using continuous microreactors, the reactor role in the process has not been deeply understood and further investigation and optimisation of the channel designs could lead to a superior control in the process.

The reactors will be 3D-printed with metallic materials (copper or aluminium) in a laser melting printer at the University of Bath. 3D printing provides flexibility to try different channel designs. The internal surface of the channels will be coated with carbon nitride-based photocatalyst and irradiated with an artificial sunlight lamp. Water saturated with CO2 will be circulated through the reactor by a HPLC pump. At the end of the experiment, the products collected will be analysed with Raman Spectroscopy and Gas Chromatography (GC). The catalyst will be characterised by Scanning Electron Microscopy (SEM), Transmission Electron Microscope (TEM), X-Ray Diffraction (XRD), X-Ray Photoelectron Spectroscopy (XPS) and photoelectrochemical techniques such as transient photocurrent responses. The optimisation will also be augmented by computational studies, which aim to establish a relationship between the products obtained, the flow dynamics, and the mass transfer effects.

Propulsion Electrification
Structural batteries mechanical resilience
Supervisor:
Alex Lunt
Andrew Rhead
Industry Partner: GKN

Batteries based on carbon fibre reinforced plastic (CFRP) have the potential to supply power with an improved overall efficiency (vehicle power to weight rather than battery power to weight) compared to current battery technologies. By integrating batteries into the structure in the form of CFRP, lightweighting is not only achieved from the change in material but also from the removal of the non-structural dead weight of conventional batteries and their casements. For example, in automotive applications, structural batteries achieve a 26% theoretical mass saving over use of separate systems for energy storage and load carrying.

The current state-of-the-art in structural batteries is a half-cell based on a structural cathode. Significant work is required before a full cell can be manufactured and expected to sustain loading for multiple discharge and mechanical load cycles. Three projects are suggested which focus on challenges at different length scales this is project A:

Micromechanical scale - Mechanical resilience: During charging, ions are absorbed into the fibre (intercalation) which causes the anode to swell. Swelling impacts the residual stress state, mechanical properties and microstructure of the composite material, and may result in microscale fracture. Such physical changes will critically influence the ability of the material to hold charge and carry structural load. In this PhD, Paloma will focus on use of synchrotron techniques to measure fibre scale mechanical properties of both anodes and cathodes during charge cycling, accumulation of microscale damage and understanding of ion intercalation patterns within the anode. Work will progress to understand similar properties under axial fatigue loading. A proposal for synchrotron time has already been made.

Application of Mathematics
Reduced order models for battery management systems
Supervisor:
Tristan Pryer
Student: Alex Trenam

Lithium-ion batteries are prevalent sources of electric energy for a variety of applications, ranging from portable electronic devices like mobile phones, tablets and laptops to Electric Vehicles and Hybrid EVs. Compared to alternative energy storage technologies, Li-ion batteries have excellent energy-to-weight ratio, no memory effect and very low self-discharge rate in idle state. These favourable properties together with the continuously decreasing production costs have established Li-ion batteries as the unique contender for automotive as well as aviation applications. In the automotive sector, the increasing demand for EVs and HEVs is pushing manufacturers to the limits of contemporary automotive battery technology. These applications form a very challenging task since operating of EVs and HEVs demands large amounts of energy and power to ensure long range and high performance, whilst the battery cells must operate safely, reliably, and durably for a time scale of the order of a decade or more.

Typically, a battery pack for an electric vehicle consists of a large number of the battery cells, physical packaging (including bus bars, casing and connectors), and Battery Management System (BMS). A BMS is composed of hardware and software controlling the charging-discharging states, guaranteeing reliable and safe operation. The BMS also handles additional operations, such as cell balancing and thermal management of the pack. The design of a sophisticated BMS is necessary to ensure long life and high performance because battery behaviour varies in time. Additionally, the BMS is crucial for safe usage because Li-ion batteries may explode or ignite if overcharged.

The goal of Alex's project is to develop mathematical techniques manifesting themselves through efficient numerical algorithms for the PDE/variational model that are able to inform the design of appropriate reduced complexity models that can be incorporated into the software of the Li-ion cell’s BMS, allowing the adjustment of operating conditions accordingly to ensure maximal calendar battery life, bypassing premature ageing and the dangers of thermal run-away.

Propulsion Electrification
Advanced system engineering processes for electrified powertrains
Supervisor:
Chris Brace
Chris Vagg
Student: Lukas Macha
Industry Partner: AVL

The development of a modern powertrain system is a complex task that starts with the definition of the system level requirements. Once these requirements have been defined the physical design and manufacture of the system can begin. Today the system is then evaluated experimentally to verify that it meets all the design targets. This is a slow and laborious process, with only a limited capacity to study all the important use cases. In future, verification needs to be much faster and more robust. This PhD is focussed on the development of digital tools to speed up the process, combined with the intelligent use of experiments where these are necessary to give confidence that the system is fit for purpose.

An important focus of the research is the need for a better and faster way to verify our designs early in the development process, building on existing system modelling capability. AVL have significant expertise in the functional representation of the powertrain system in a systems modelling environment – SysML.SysML can be used to capture the capture functional, performance, and interface requirements of the powertrain as a way to evaluate system level interactions before detailed physical models of the components are available.

Lukas' research aims to use this expertise to accelerate the verification phase of the process, performing elements of the system verification in software that today are performed experimentally. Clearly not all of the system behaviours can be verified in this way, experiments will still be essential. Advanced experimental techniques developed by AVL will be used to allow a mix of physical and simulated components to be tested together in real time, bridging the gap between model based and experimental processes in a way that offers a highly robust and rapid verification process.

The methods developed during the PhD will be demonstrated by applying them to a battery electric vehicle concept – incorporating the system simulation, automated generation of test sequences and execution of these sequences on the state of the art experimental platforms with the new IAAPS research facility.

Digital Systems, Optimisation and Integration
Beyond Predictive Energy Management
Supervisor:
Chris Brace
Industry Partner: AVL

The aim of this research will be to investigate the benefits and trade-offs from the use of predictive, multi-objective control strategies for X-EV connected hybrid vehicles. Charlie will be applying significant rigour to identifying beneficial combinations of mobility system attributes and technologies to carry into more detailed problem definition, simulation and control function development culminating in the practical demonstration of one or more predictive control strategy. The project will be conducted in collaboration with IAAPS partner AVL.

Background

As of the present, a limited but increasing number of automotive companies are bringing to market some form of look ahead, predictive functionality for powertrain management. The scope of these systems is generally somewhat limited, although broader, multi-objective approaches are beginning to emerge in research. There is, at present, an open research question around how the ideas of predictive controls, combined with the emergence of effective high bandwidth communication for vehicles , may be used to best effect in a real-world, multi-objective system.

Vehicle attributes for optimisation may include, without being restricted to: emissions, driving range and energy consumption, performance availability, system lifetime and health or financial costs. It is inevitable that the performance of a system with respect to one attribute will not be independent of others, resulting in a complex optimisation task. In addition, it is possible to assess the performance of a vehicle system over a variety of time horizons, from instantaneous through to whole-lifecycle performance. The advent of advanced connectivity in automotive and mobility as a whole will also increase the potential for impact of an individual vehicle on the wider fleet or infrastructure and vice-versa when control decisions are being made.

Propulsion Electrification
Understanding the influence of battery current ripple
Supervisor:
Chris Vagg
Industry Partner: AVL

The battery cell is probably the most critical component of an EV, and key to the sustainability of future transportation solutions. Currently most battery testing is performed using very “clean” DC test currents whereas in reality, when used in a vehicle, the battery is subjected to current profiles with a lot of high-frequency (AC) components owing to the commutation (switching) of the inverter power electronics. Paramount in understanding how this affects its performance as part of a real powertrain system is the understanding of its electrochemical behaviour and processes.

The goal of this study is to investigate the influence that current ripple has on a Lithium-ion battery cell when it is applied on top of the DC current used to charge/discharge the cell.

Several studies have demonstrated that current ripples applied to cells can impact their performance (capacity, internal resistance, aging, etc) either positively or negatively. This PhD seeks to understand these phenomena in detail through experimentation and thermal-electro-chemical modelling of the cell behaviour, to predict the impact that any profile of current ripple might have on a particular type of battery. The research will have a strong experimental aspect to collect data from a range of battery cells, which in turn will directly support the theoretical investigations.

Howard's outcomes will inform best-practice for powertrain hardware design (inverters and filter capacitors) and software strategies implemented in the Battery Management System, as well as contribute to the understanding of how other techniques, such as battery self-heating using AC, might be applied in the future.

Propulsion Electrification
Thermal Modelling of Electric Machines
Supervisor:
Chris Vagg
Student: Ryan Hughes
Industry Partner: AVL

Ryan will develop a reduced order thermal model of a high-speed permanent magnet machine, forming a key part of a future digital twin. The model will allow for a variety of cooling methods such as air, water, and oil to be simulated. The proposed model will enable predictive and real-time estimation of the electric machine’s thermal behavior. Specifically, enabling the temperatures of physically difficult to measure components to be found, such as the rotor or magnets. By better understanding the temperature of key components within the machine in real-time and into the future the machine can be overloaded more often without damage. This will improve the power density of the machines and enable special test cycles to be performed that may otherwise have been thought to cause overheating.

Digital Systems, Optimisation and Integration
Machine Learning Algorithms for Freevalve Optimization
Supervisor:
Nic Zhang
Industry Partner: Koeniggsegg

Abdelrahman's PhD will address the shortcomings associated with the conventional map-based controller design and calibration practices used in powertrain development. It will provide novel, futuristic, non-linear physical causality predictive modelling and experimental approaches for system identification to conclude a real-time capable control system. The project will be undertaken in collaboration with Koenigsegg Automotive AB and Freevalve AB on the novel cam-less engine technology, Freevalve, facilitating major efficiency and power improvements for future powertrains. It will enable the full exploitation of the technical potential of a camless engine and the reduction of harmful gaseous and particulate matter emissions, putting the technology in a market-leading position ready for large-scale implementation.

Propulsion Electrification
Quantifying the value of EV flexibility through spatiotemporal modelling and optimisation at grid-scale
Supervisor:
Furong Li

The supply of low carbon energy to a rapidly growing fleet of electric vehicles (EVs) presents major network constraint and energy supply challenges. From a network perspective, peak load increases resulting from uncontrolled EV charging could surpass the capacity at vulnerable points in the power network, thus requiring expensive grid upgrades. From an energy perspective, variable renewable energy generation does not always align with EV charging demand. As such, any excess renewable energy currently requires storage or curtailment which are both expensive for suppliers.


The smart charging of EVs can help to form a synergistic relationship between the transport and energy sectors, thus accelerating their decarbonisation. Smart charging exploits EV demand-side flexibility by shifting charging time or modulating charging power, subject to grid constraints and the vehicle owner’s needs. Efficient and practical smart charging algorithms require accurate quantification of EV flexibility at different scales to maximise the whole-system benefits.


Current attempts to model and optimise EV charging tend to make large and unrealistic assumptions about consumer travel and charging behaviour. In addition, the modelling of charging at a range of locations is understudied (e.g. domestic vs commercial setting). Studying the relationships between driving behaviour, charging behaviour and the energy system, over a range of spatial and temporal scales can reveal the value of flexibility. Furthermore, understanding how changes in EV charging behaviour and flexibility affect the energy system at local and regional level is critical for energy suppliers and network operators to allow fast and cheap integration of EVs and renewables into the grid.


Isaac's PhD will investigate the definition, quantification, aggregation and optimisation of EV flexibility, and their consequential system values to appropriately reward EV drivers, based on their level of flexibility. Spatiotemporal analysis of charging behaviour will be used to model charging demand in a granular detail with realistic assumptions, identify potential vulnerabilities in the distribution network, and assess the degree of misalignment with renewable energy. To fully realise the potential financial and environmental benefits, EV charging will be optimised over a range of spatial (e.g. single EV, EV cluster, street level, town/city level, regional level and national level) and temporal scales (e.g. hour, day, week, month), and against different weather conditions and local demographics. The outcome of this research will inform how charging optimisation and behaviour should evolve with increasing renewable penetration and changing mobility patterns, and the required upgrading in charging and electrical infrastructure.

Digital Systems, Optimisation and Integration
Computational Modelling of Hydrogen Combustion in Internal Combustion Engines
Supervisor:
Sam Akehurst
Industry Partner: Jaguar Land Rover

Kacper’s PhD is intended to advance the field of computational hydrogen combustion modelling in internal combustion engines (ICE), of which the main focus is the modelling of combustion in predictive scenario, in order to accelerate the development of sustainable (people, profit, planet) powertrains by brining new tools and expertise to the industry.

Transport Policy & Economics
Improving intrapreneurial behaviour
Supervisor:
Dimo Dimov

The Automotive industry is currently undergoing a period of transformation. For more than 100 years, vehicle manufacturers have delivered incremental innovation in internal combustion engine vehicle technologies. However, radical innovation in Autonomous, Connectivity, Electrification and Shared Mobility technologies is beginning to change this traditional way of working. Vehicle manufacturers are therefore expected to successfully alter one or more aspects of their strategy and organisation in order to maintain their value as incumbent players.


One method that vehicle manufacturers use to achieve this process of strategic renewal is through Intrapreneurship, which is employee behaviour that is innovative. However, a key issue is that Intrapreneurship is inherently uncertain, the innovative idea has not yet been tried and tested, and has a relatively high likelihood of failure. To address this uncertainty, companies can invest in the thinking and practices of their intrapreneurs, to ensure calculated innovative action. This would involve improving the way intrapreneurs frame their innovative objectives, how they plan to achieve these objectives, and how they perform the necessary actions.


Patrick's PhD will seek to engage with the realities of intrapreneurs by analysing how intrapreneurial behaviour currently unfolds within the organization of a vehicle manufacturer. In addition, it seeks to understand how a company can develop the art and skills of their intrapreneurs.

Low Carbon Fuels
Sun + CO2: designing microreactors for solar fuel production
Supervisor:
Antonio Exposito

The UK is moving towards zero carbon emissions by 2030. To reach this objective, it is necessary to develop technologies to capture and transform CO2. Artificial photosynthesis, a carbon-negative process that mimics the reaction in plants, is capable of transforming CO2, using energy from the Sun, into different molecules such as methanol which can then be used as liquid fuel. However, poor conversion and selectivity of current artificial photosynthesis mechanisms are the main barriers to the commercialisation of the technology.

Solution: We will investigate the role of continuous microreactors, devices with channel dimensions below 1 mm, which can provide a step-change needed in the technology because of their exceptional mass transfer, offering better conversion and control in the selectivity of the process.

Most of the work to boost the process has focused on the reaction chemistry (e.g. improving the catalyst), but reactor engineering and process intensification can be parallel research lines to improve the technology. Only a few groups have reported the use of microreactors for continuous CO2 reduction and they stated that the better mass transfer in these devices enhanced the selectivity towards liquid hydrocarbons. Mass transfer is essential for reagents to move across the boundary layer adjacent to the surface of the catalyst. It is especially crucial in multi-phase reactions, such as in the case of artificial photosynthesis. Despite the promising results using continuous microreactors, the reactor role in the process has not been deeply understood and further investigation and optimisation of the channel designs could lead to a superior control in the process.

The reactors will be 3D-printed with metallic materials (copper or aluminium) in a laser melting printer at the University of Bath. 3D printing provides flexibility to try different channel designs. The internal surface of the channels will be coated with carbon nitride-based photocatalyst and irradiated with a artificial sunlight lamp. Water saturated with CO2 will be circulated through the reactor by a HPLC pump. At the end of the experiment, the products collected will be analysed with Raman Spectroscopy and Gas Chromatography (GC). The catalyst will be characterised by Scanning Electron Microscopy (SEM), Transmission Electron Microscope (TEM), X-Ray Diffraction (XRD), X-Ray Photoelectron Spectroscopy (XPS) and photoelectrochemical techniques such as transient photocurrent responses. The optimisation will also be augmented by computational studies, which aim to establish a relationship between the products obtained, the flow dynamics, and the mass transfer effects.

Propulsion Electrification
Bi-directional high power density on-board charger using WBG devices
Supervisor:
Vincent Zeng

A smart and high power density charger is the key power electronics converter to overcome challenges such as range anxiety, slow charging for battery electric vehicles and plug-in hybrid electric vehicles. Wide bandgap (WBG) semiconductor devices, such as SiC and GaN, with fast switching transitions provide a solution to meet stringent automotive requirements for high power on-board chargers, while maintaining a compact size and lightweight design.

Constantinos' PhD will be investigating an innovative multi-level topology tailored for WBG devices. Multi-level topologies offer many advantages such as modularity, scalability, lower losses, limited voltage gradients, and higher AC voltage quality. The modularity of a multi-level topology also lends itself to higher fault tolerance, which is attractive in safety-critical applications. It is widely-accepted as the most promising topology for high voltage and high power applications. In automotive applications, this topology will enable the use of higher voltage DC bus systems and also help facilitate the penetration of low voltage WBG devices in these applications. 

Sustainability and Low Carbon Transition
Feasibility of implementing Mobility as a Service in a regional context
Supervisor:
Andrew Heath

In an era where technology and transportation are so interlinked, new mobility concepts arise such as Mobility as a Service (MaaS). MaaS is expected to produce significant improvements in mobility such as the increase in the modal share of more environmentally friendly and efficient mobility options, the reduction in private car use/ownership, improving accessibility and frequency of the transportation network and the strengthening of cooperation and collaboration between public and private entities in order to reinforce the integration of transport modes in one platform accessible to everyone.

Despite being a well-known concept its implementation and subsequent effects have been not been widely explored when it comes to the connection between urban areas or even the linkage between urban and rural areas. Rita's research will be focused on those aspects of MaaS in order to assess its feasibility in these environments and making sure that the concept is design to respond to the citizen's needs while corresponding to the expectations of its implementation.

Sustainability and Low Carbon Transition
A consequential Life Cycle Assessment and decision support model for hydrogen production, storage and distribution to refilling stations for large road vehicles
Supervisor:
Marcelle McManus

The context of this research is the urgent global climate challenge of preventing a global mean surface temperature increase of more than 1.5 °C compared to the pre-industrial average. We are already 80% of the way to this threshold (Morice et al 2021, Met Office 2021). In the UK, road transport has reduced its carbon footprint less than other sectors since 1990, and larger vehicles are particularly problematic to decarbonise due to the huge infrastructure requirements for electrification, and the limited range provided by battery traction. Hydrogen fuel cells are a possible solution for powering larger road vehicles cleanly, as outlined in the Hydrogen Strategy of the UK Government (2021). However, about 95% of hydrogen is currently produced by steam methane reforming, which has significant carbon emissions even when carbon capture is implemented (Howarth and Jacobson 2021). Most research on the environmental impacts of hydrogen production, storage and delivery has focused on a narrow subset of hydrogen technologies or a narrow range of environmental indicators. There is also a need to consider the intersections between decisions made for road transport and competing uses of hydrogen for ammonia production and industrial processes, and domestic heating and cooking. This project is intended to fill these gaps and to assess the potential of hydrogen technologies to sustainably decarbonise large road vehicles. The methodologies will encompass:

  • consideration and inclusion of a broad range of new hydrogen technologies as they mature;
  • a wide range of environmental indicators;
  • real-world performance data rather than simulated or modelled data where possible, including analysis of purification requirements and minimising fugitive greenhouse gas emissions;
  • consequential Life Cycle Assessment (LCA) with an integrated tool to assist decision makers, such as multi-criteria decision making (MCDM), which considers competing uses of hydrogen in its analysis.


Julian's research project will produce as its outputs: a review of recent LCAs of hydrogen; a review of the most promising hydrogen technologies; a detailed consequential LCA of hydrogen production, storage and delivery (cradle to station); and an online decision support tool that shows costs and benefits (financial and environmental) for a range of hydrogen pathways under user-selected economic and technological scenarios.

Transport, Behaviour and Society
Sustainable travel in Bath
Supervisor:
Lorraine Whitmarsh

The citizens jury being co-organised by the University of Bath (UoB) and the local Council (BANES) in late 2021 will address sustainable travel from the city to the University and surrounding area. It seeks to promote collaboration between the University and local stakeholders to identify acceptable technological and behavioural options for safe, sustainable, cost-effective, and healthy travel by staff, students, and residents in the local area. The proposed PhD research will build on this deliberative activity by developing and evaluating one or more modal shift and/or reduced demand behaviour change interventions to achieve BANES’ and the University’s goals (including UoB’s Climate Action Framework and the Council’s sustainable communities agenda).

The project will draw on current work being undertaken within the AAPS Transportation & Society theme on consumer decision-making in relation to low-carbon transport, including timing modal shift interventions to ‘moments of change’ (periods of disruption or transition, such as the start of the academic year, when new students have not yet developed travel habits). It will also seek to incorporate engineering innovations in AAPS, for example exploring how digital technologies and sustainable vehicle/fuel technologies may form part of the final intervention packages, along with social/behavioural elements.

Low Carbon Fuels
Next generation nanomaterials for high performance fuel cell electrodes
Supervisor:
Adam Squires

This project will harness new topology electrode nanomaterials developed in our laboratory, for applications in fuel cells used in transportation. Their unique nanostructures give enhanced reactivity and stability compared with nanoparticles currently used. The technology is “platform agnostic” in terms of fuel, with properties and reactions common to a range of fuel cells. This project will explore their use in fuel cell reactions and devices, bridging the gap from preliminary data to real world applications and commercialisation.

Electric Vehicles are key to reducing carbon emissions. While rechargeable batteries are likely to be the main technology for cars, there are long-distance applications (boats, planes, lorries, trains) for which the energy density by weight of batteries is too low, and alternatives are required. Fuel cells overcome this problem. In a fuel cell, electricity is generated by an electrochemical reaction between a fuel and oxygen. Powering vehicles in this way uses 50% less fuel than a combustion engine, and the energy density of typical fuels is tens of times greater than that of lithium ion batteries, whether by weight or volume. [1,2] However, wider commercialisation of fuel cells is currently limited by catalyst performance, cost and stability.

Our team has recently developed a route to new nanostructure topologies for high performance electrodes in fuel cells. The process is green, mild, and industrially scalable, and can be used to grow a range of different metals. The electrodes comprise 3D nanowire networks, which give ultra-high surface areas; high stability, avoiding the use of nanoparticles, which present a major limitation on current device lifetimes; and high reactivity. The technology has been adopted widely, and superior reactivity and stability have been demonstrated in the oxidation of alcohols, glycerol [3] and formic acid [4].

Our electrode materials are “platform agnostic” in terms of fuel. There are potential advantages and disadvantages to each of hydrogen, alcohol, and formic acid, and future adoption depends on advances in green methods of production – respectively through water electrolysis, biofuel, and CO2 reduction. Underpinning all of these fuel cell types is the counterpart oxygen reduction reaction, for which superior activity and stability have also been reported for electrode materials similar to ours.[5] Whichever technology wins out, our materials can therefore play a part.

This project will extend the previous work in three directions:

  1. New reactions: characterise our materials’ performance towards the hydrogen oxidation and oxygen reduction reactions
  2. New devices: incorporate our electrode materials into membrane electrode assemblies and evaluate their performance in fuel cells acting under “real” conditions
  3. New metals: our method has so far been applied to platinum and palladium.

[1]https://core.ac.uk/download/pdf/34994335.pdf

[2]https://www.energy.gov/sites/prod/files/2015/11/f27/fcto_fuel_cells_fact_sheet.pdf

[3]https://pubs.acs.org/doi/abs/10.1021/acsami.8b13230

[4]https://onlinelibrary.wiley.com/doi/full/10.1002/ange.201914649

[5]https://pubs.rsc.org/en/content/articlehtml/2021/ta/d1ta01950c

Chemical Energy Converters
Lifetime modelling of PEM fuel cell stacks
Supervisor:
Tom Fletcher

The development of a hydrogen economy is a key part of the UK’s commitment to net zero as recommended by the Climate Change Committee. Whereas battery electric vehicles are expected to satisfy the vast majority of light duty vehicle applications, their limited energy density means that they are unsuitable for many energy-dense applications such as long-distance haulage, shipping, rail and aviation. Equally, long charging times significantly affect their suitability for high availability applications.

Fuel cells overcome these issues by separating the energy storage from the energy conversion, enabling refuelling times similar to those of conventionally fuelled vehicles (<5 minutes) while maintaining zero emissions at the point of use. However, durability is key challenge for fuel cells in these markets where the system lifetime target is 25,000 hours (by 2030) and 1 million miles for Class 8 truck applications according to the US DoE.

This research aims to tackle this challenge by producing predictive models of the causes of PEM fuel cell degradation, seeking to understand not just individual degradation modes, but the interactions between them and their development over the lifetime of the stack. Current techniques are usually split into theoretical and empirical models. Whereas theoretical models are predictive; for electro-chemical devices they tend to be highly complex, slow to simulate and contain many parameters which are difficult to determine. Conversely, empirical models are fast running, but tend to be highly simplistic have low generality. The aim of this project will be to bridge this gap to enable investigation into how design and control strategy changes will affect the long-term fuel cell durability.

In addition to the main project aim, several secondary objectives are proposed. These will form a series of interim milestones for the project and include development of standardised test procedures, accelerated aging methods, condition monitoring techniques, requirement specification for future cell development and recommendations for control strategy targets. 

Application of Mathematics
Quantifying failure of non-contacting gas lubricated mechanical face seals
Supervisor:
Nicola Bailey
Tristan Pryer

Turbochargers are increasingly used to improve engine performance and power output, while downsizing. They will be key in helping to reduce fuel consumption and emissions through increasing engine efficiency. For next generation technology, turbocharger speeds and pressure ratios will increase, causing blow-by to increase and potentially drastic oil leakages/insufficient lubrication due to inadequate sealing. Incorporating novel sealing technology has the potential to increase turbocharger operating ranges, maximise efficiency and improve reliability. One approach is to use non-contacting mechanical face seals, which employ a very thin fluid film between a rotating face (rotor) and a stationary face (stator) to maintain a clearance. This allows operation at much smaller clearances increasing efficiency, reducing wear and having an improved dynamic response.

This project focuses on investigating the dynamics and suitability of a non-contacting mechanical face seals for operation in high performance turbocharger applications. A mathematical model will be developed for this fluid-structure-interaction problem, based on thin film flow (lubricating approximation) and a spring-mass-damper model for the faces. Key factors to incorporate include thermal effects, high speed operation and the effect of external disturbances due to components surrounding the seal interacting with it. A robust numerical technique will be formulated that is computationally efficient and produces sufficiently accurate results. A numerical study will allow safe operating conditions to be identified, and which factors play a significant role in potential destabilising behaviour. The outcomes of this work will inform designers of seals efficiency and reliability under different geometric and operating conditions.

Low Carbon Fuels
Chemical vapor deposition for advanced lithium ion batteries and supercapacitors
Supervisor:
Andrew Johnson

The age of the electric car is here. Advanced electrochemical energy storage is internationally considered as one of the disruptive technologies of the future. Early in 2021, the US automobile giant General Motors announced that it aims to stop selling petrol-powered and diesel models by 2035. Audi, based in Germany, plans to stop producing such vehicles by 2033. Many other automotive multinationals have issued similar road maps. Suddenly, the prevaricating and foot-dragging displayed by major carmakers on the electrifying their fleets is turning into a rush for the exit. Anticipating a world dominated by electric vehicles, chemists and materials scientists are working toward the development of new materials which will provide the structural frameworks of better electrodes and electrolytes for the next generation of batteries and supercapacitors.

The application of non-line-of-sight deposition techniques, such as chemical vapor deposition (CVD) and atomic layer deposition (ALD), offer unique opportunities to produce well-defined high surface area current collectors, thin films, or various nanostructures of active (ion-storage) materials. Low electrical and ionic conductivities, high volume changes during charging, various types of mechanical, electrochemical or chemical degradation of electrodes and the resulting degradation are among the most frequently faced problems. To overcome these challenges, the formation of nanostructured composites with finely tuned microstructure, morphology and chemistry is required.

The research proposed here will focus on the development of advanced nanostructured 3D solid host networks for the formation of lithium chalcogenide-based intercalation cathode materials of the form MXn, including M-oxides, sulfides or selenides such as SnO SnO2, SnS2 SnSe2, SnSeS, SnSe0.5S0.5, MoSe2, GeS, MoS2 and WS2, all of which show an excellent ability to intercalate metals such as lithium or sodium.

By focusing on the development of these materials by CVD and ALD we hope to maximise and improve cell performance by virtue of the advantages that these processes provide, specifically (I) the ability to deposit conformally active materials onto highly structured scaffolds such as nano-carbon rods, tubes and flakes, (II) the ability to deposit dense uniform coatings onto electrode materials as protective shells (e.g. against chemical degradation) (III) the ability to form new materials, with a high degree of control over stoichiometry, new phases of materials by virtue of the judicious choice of CVD/ALD precursor, deposition parameters and through chemical doping (IV) the applicability of these deposition techniques to an industrial high throughput scale.

Throughout this research, particular attention will be paid to the application of sustainable materials avoiding metals in batteries that are scarce, expensive, problematic, or difficult to recycle.

Low Carbon Fuels
Innovation in Ultrathin Layer Manufacturing: Electrospraying for SOFC Electrolyte/Electrode Energy Converters
Supervisor:
Bernardo Castro Dominguez

Thin ceramic films are hard to manufacture, but very important in energy conversion. Electrospraying (ES) is a versatile technique which has been used to dry, crystallize, and fabricate ultrathin layers of various materials. In ES, a high voltage is applied to a liquid precursor flowing through a nozzle, to create an aerosol of charged monodispersed nanodroplets. Drying air is fed into the drying chamber vaporising the droplets and forming solid particles with crystalline structures. ES allows (i) control over the degree of atomization of the feed, thus increasing the droplet surface area and extent of drying, (ii) control of the direction of the aerosolization jet and particle size deposition; (iii) ultrathin layer formation, controlled by the throughput of the aerosolization jet and voltage; and (iv) film self-healing behaviour when exposed to moisture.

In this project, we will exploit ES to manufacture ultrathin electrolyte and electrode layers for used in solid oxide fuel cells (SOFCs). SOFCs exhibit greater energy efficiency and can tolerate a far wider range of fuel materials compared to PEM. For this reason, they are increasingly being proposed as aviation and marine propulsion devices using zero carbon fuels such as ammonia. SOFCs are presently limited in performance by the ion conductivity of the solid electrolyte, and this would be much improved if a thinner electrolyte could be created. They are also challenging to manufacture due to sequential processing including multiple thermal steps.

Here, we will assess the benefits of electro-confined particle deposition at the fundamental level, as well as explore how the ES process affects the overall performance of SOFCs. At the University of Bath, we have demonstrated the capabilities of ES for polymorph control, and crystal formation in organic molecules; therefore, building on this established framework, this interdisciplinary PhD project - containing aspects of Chemical Engineering, Chemistry and Manufacturing - will further develop this technique to provide insights into the effects of electrical charges and confinement on the formation of ultrathin ceramic layers. We envision, that this PhD project will encompass the following activities:

  • Explore different ES formulations to generate ultrathin electrolyte/electrode layers.
  • Benchmark ES against conventional processes such as tape casting and screen printing.
  • Optimize ES process parameters for film formation
  • Develop methods for sequential deposition of electrode, electrolyte and support layers to create an SOFC cell.
  • Assess the performance of the whole fuel cell in SOFC energy converter applications to eliminate fossil fuel technologies.
Low Carbon Fuels
Simply the best? Rapid AI-driven screening of porous materials for hydrogen purification and low carbon fuels

Nanoporous materials used in adsorption applications play an important role in hydrogen purification and the storage and processing of low carbon fuels. However, numbering in the 100,000s, the enormous range of existing and hypothetical materials to be considered for a specific application makes standard, experimental and simulated screenings prohibitively expensive. Machine learning is emerging in materials screening but often the focus is on traditional machine-learning prediction, where a model is first trained using a very large number of simulations of the application of interest and then used to estimate properties of interest for all structures in the data set to identify the top performing new materials.

We have developed a new approach combining Bayesian optimisation/ active machine learning and molecular simulation, which allows us to identify the top performing materials of a database without having to calculate the performance of 100,000s of individual structures. One of the core attractions of this new methodology is that our model can make recommendations based on limited information, updating itself in-situ from molecular simulation of performance for a given application. We have successfully applied this approach to simple performance targets such as the uptake of hydrogen at a particular storage pressure or the separation of two simple gases. While impressive, this type of screening does not include important process parameters including the presence of impurities or kinetic separation effects which might mean that in practice a promising material identified through computational screening might not be as promising as thought. Another area that is beyond the scope of current screening approaches due to the computational effort required is optimising process conditions such as temperature or pressure ranges. 

In this PhD project you will combine active machine learning techniques/Bayesian optimisation with molecular and process simulations to extend our screening approach to more realistic conditions for applications in the areas of hydrogen purification and low carbon fuels, the exact nature of which will be determined in discussions with an industrial partner. As our approach allows identifying promising materials out of a database of 100,000s materials by just conducting a few 100s – 1000s simulations, screening using more expensive simulations such as process simulations becomes more tractable. The overall aim of the PhD project is to integrate multi-scale modelling from the molecular scale to the process scale into the screening of porous materials for processing low carbon fuels, and to develop methods that combine the type of cheap molecular simulation that we already conduct with more complex, targeted simulations (or even experiments) to identify promising porous materials. This will include developing algorithms capable of choosing which simulations / experiments to run in order to discover the best material with the least effort. The project is suitable for engineers and scientists who are interested in modelling and machine learning and have good maths skills.

Propulsion Electrification
Structural Health Monitoring of Li-ion batteries using ultrasonics techniques
Supervisor:
Michele Meo

Lithium-ion batteries are widely used as the power supply for transportation power sources. However, they are prone to experience gradual degradation (capacity-fade and resistance-increase) or catastrophic failures. The gradual degradation is driven by complex electrochemical side reactions (e.g., active material dissolution, electrode particle cracking, and deterioration of electrode adhesion) over the long period of normal cycling. The catastrophic failures of a battery include a sudden drop in battery capacity, a sudden increase in battery temperature, swelling due to gas generation, and even fire/explosion. A battery can also fail catastrophically due to manufacturing defects, mechanical abuse (e.g., shock,), electrical abuse (e.g., overcharge/over-discharge), or thermal abuse (e.g., external heating). To optimize battery performance, lifespan, and most importantly, safety, a battery management system (BMS) is needed to perform battery condition monitoring and management. To facilitate these functions, the BMS must first and foremost be able to accurately monitor the batteries’ critical internal states. These primarily include state of charge (SoC) (the useful charge remaining in the battery with respect to its fully charged capacity, or the equivalent of a fuel gauge) and state of health (SoH) (the degree of degradation in battery health which usually manifests as a reduction in the fully charged capacity over time). It is a true detriment to the field that the practical implementation of high-energy battery systems is still extremely challenging due to the lack of a field-deployable, yet affordable, BMS that can reliably and accurately monitor SoH. Most in-operando techniques are electrically-based and rely on measurement of the cell terminal voltage from a remote, centralized data acquisition unit. These methods are simple and work reasonably well for SoC estimation because of the highly-correlated SoC-voltage relationship, but they can be inaccurate and unreliable, particularly for SoH estimation.

This project aims to propose stress elastic wave excitation method for probing SoC/SoH of Li-ion batteries coupled to cell impedance measurements method. The proposed approach exploits the fact that mechano-electrochemical coupling is present when batteries undergo charging, discharging, and aging, which is detectable via variation of material properties using ultrasound. The technique can be made scalable and deployable in practical battery applications. We plan to provide a formulation of a systematic framework for estimating and providing prognosis of battery health. The project will explore how features extracted from stress waves and impedance measurement may be used in a systematic framework to assess the internal state variables of a battery system as well as to perform prognostics of the remaining useful life (RUL). The material state serves as indirect measurements where, together with the knowledge of a system’s anticipated transition and historical data, inference and state estimation techniques may also be applied to predict its state and remaining life.

Propulsion Electrification
Thermal Management and Degradation Pathways of Sustainable Batteries
Supervisor:
Chris Vagg
Alex Lunt
Frank Marken

This research will focus on understanding the link between battery degradation and methods of battery thermal management, especially with respect to cells of different sizes.

Exposure to high temperatures and temperature cycling are two of the most significant aggravating factors for battery aging. The trend in automotive applications is for ever increasing cell sizes, with some vehicles now featuring cells of several hundred amp-hours and up to 1m long. As cells sizes increase achieving a uniform temperature across and through the cell is increasingly difficult because only the cell surface is cooled, and because the cooling fluid (air/water/oil) will typically reach some parts of the cell before others. This non-uniform temperature distribution will very likely lead to non-uniform aging of the cells, which this PhD aims to investigate, quantify, understand, and propose mitigation mechanisms against. This is an important topic not only for maximising the lifetime of the cells in the vehicle, but also when considering the potential value of the cells in second life, or how they might be recycled.

Work will focus initially on immersive cooling, where battery cells are directly immersed in a dielectric oil. This is because immersive cooling is considered the most advanced and high-performance approach to thermal management and is a current focus for research. Problems with this include the cost and weight added to the system by the fluid. This trade-off will be examined by considering the possibility of partially filling the battery with fluid so that cells are only partially submerged, reducing fluid weight at the expense of some thermal homogeneity.

Opportunities may exist for synergy with the group working on Structural Batteries, depending on the size scale of the batteries which that group have succeeded in producing by this time. These opportunities will be explored as appropriate, as the relevance of this proposed doctoral research is particularly relevant to structural batteries owing to their increased value and added difficulty in recycling them. The work of the existing group to date has focussed primarily on producing working batteries. Degradation has not yet been investigated, and whilst recyclability has been embedded in materials selection no analysis has been performed in this space.

Electrochemical testing of cells will be possible with charging/discharging experiments and electrochemical impedance monitoring. Microstructural characterisation of the impact of degradation will form a key aspect of the doctoral study. This will involve the use of nanoindentation, electron and atomic force microscopy and/or Focused Ion Beam (FIB) to study internal changes to the microstructure through the preparation of microscale cross-sections and lamella. Synchrotron work (microtomography, X-ray diffraction, and/or spectroscopy) with in-situ electrochemical testing will reveal regions of heating/degradation, formation of stresses locally at anode or cathode, and opportunities for retaining battery performance. These insights will be used to generate enhanced models of degradation, providing crucial insights into predicted lifetimes and potential recycling opportunities associated with these systems at end of life.

Application of Mathematics
Modelling and thermal management of next generation power batteries
Supervisor:
John Chew

The promotion and application of electric vehicles (EVs) is a vital strategy for many countries to achieve reduced carbon emissions and realise carbon neutrality by 2050 (Global EV Outlook 2021). As the power source of electric automotive, power batteries play a decisive role in the performance, driving range and lifespan of EVs. At present, lithium-ion (Li-ion) batteries are the most promising candidate to propel usage of EVs due to their high energy/power density, long cycle life, high stability and high energy efficiency. However, Li-ion batteries are sensitive to the operating temperatures. For instance, at temperatures > 35oC, side reactions inside the batteries are intensified, causing capacity fading and battery ageing. More seriously, thermal runaway incidents of EVs due to overheating of batteries are frequently reported, raising questions and attention in EVs’ safety. On the other hand, when the temperature is low, typically < 15oC, the discharge capacity is largely reduced due to the increased internal resistance and depressed reaction kinetics, leading to a much shorter driving range. In addition, the non-uniform temperature distribution will cause inconsistent electrochemical process and further reduce the battery pack capacity and cycle life. Therefore, an   efficient battery thermal management system is essential to ensure the safety and performance of Li-ion batteries in EVs.

The main aim of this research is to design high-performing and safer Li-ion battery designs by using numerical modelling that can fully characterise the interactions between chemical reaction and thermal transport mechanisms of current and next-generation battery designs. The numerical models will be used to provide insights into thermal propagation, possible overpressure due to runaway chemical reaction and other associated risks in the enclosed systems. In addition, we will use the validated models to predict performances of new battery configurations and propose adequate safety measures to prevent disasters, with reduced physical testing. Such an approach is imperative for the design of safer and high capacity Li-ion batteries for EVs.

To achieve our aims, we proposed the following specific objectives:

  1. Develop a series of robust and adaptable numerical models to assess the thermal and chemical stability of current and next-generation Li-ion batteries;
  2. Assess the wide applicability of lumped heat transfer correlations to model thermal propagation of Li-on batteries;
  3. Identify and design new approaches to maintain a stable and uniform temperature of the battery pack;
  4. Design a unified thermal management system (air conditioning and battery) by energy integration;
  5. Design safety measures that can offer prompt and powerful responses to critical thermal issues.
Low Carbon Fuels
Modular hydrogen storage for low-carbon road transport
Supervisor:
Tim Mays

Green molecular or di-hydrogen (H2) is an exciting and interesting option as a low-carbon fuel for road transport, including light- and heavy-duty vehicles, with the added benefit of lower negative air quality impacts compared with fossil fuels whether used in ICEs or fuel cells. 

However, a major challenge with on-board storage is refuelling of fixed tanks which requires a distributed, publicly accessible infrastructure incorporating the safe management of high-pressures (up to 70 MPa for compressed gas) or low temperatures (below 30 K for liquid H2).  An alternative option is a modular approach where empty tanks are replaced with tanks that have been filled separately by well-controlled and ultra-safe agents. 

This transfers refuelling risks (and skills) to these agents from the public and does not necessarily require extensive infrastructure.  Refuelling (or “retanking”) may also be quicker than currently envisaged at a H2 station. However, there are issues that need to be understood to determine whether this modular approach is technically feasible, economically viable, would be acceptable to the public and would lead to lower carbon emissions over the vehicle life cycle compared with all the major options including batteries. 

The proposed PhD would identify and assess these issues lading to the conceptual design of a prototype modular refuelling system (or systems) for the road vehicle fleet.  An important outcome will be CAD and possibly also physical desk-top models to demonstrate the principles and operation of these systems. 

Chemical Energy Converters
CFD Modelling of Fuel Cell Lifetime
Supervisor:
Tom Fletcher

The development of a hydrogen economy is a key part of the UK’s commitment to net zero as recommended by the Climate Change Committee. Whereas battery electric vehicles are expected to satisfy the vast majority of light duty vehicle applications, their limited gravimetric energy density means that they are unsuitable for many energy-dense applications such as long-distance haulage, shipping, rail and aviation. Equally, long charging times significantly affect their suitability for high availability applications.

Fuel cells overcome this issue by separating the energy storage from the energy conversion, enabling refuelling times similar to those of conventionally fuelled vehicles (<5 minutes) while maintaining zero emissions at the point of use. However, durability is key challenge for fuel cells in these markets where the system lifetime target is 25,000 hours (by 2030) and 1 million miles for Class 8 truck applications according to the US DoE.

The aim of this project is to investigate the links between fuel cell design and aging mechanisms in order to allow comparison between different component technologies and manufacturing methods. The project will use transient three-dimensional CFD techniques, at the cell level, over a range of membrane, catalyst and support structure types in order to estimate the rate of MEA performance loss under realistic loading conditions. In particular, this work will extend previous work in this area [1] by investigating the effect of development of fuel cell degradation mechanisms over extended periods of time.

This work will help inform decision making processes regarding not only fuel cell design, but also future research requirements, accelerated testing procedures and control strategies.

Propulsion Electrification
High-efficiency Power-dense Electric Motors Using Reconfigurable Windings
Supervisor:
Chris Vagg
Student: Joshua Best

Whilst most people consider the efficiency of electric motors to be very good (above 90% or even 95% for much of their operating envelope) there is actually a lot of scope for improvement. Firstly, these ‘headline’ efficiencies are at high-load operating areas which is not where it counts most; low-load operating areas (e.g. used in cruising) may often be <90% efficient, and worse at high speeds. In addition, the power density of motors could benefit from being improved, especially for environmentally friendly motors which do not use rare-earth magnets (e.g. synchronous reluctance machines).

One technology which has strong potential to deliver a step change in efficiency and power density is reconfigurable windings. This concept uses multiple sets of windings in the stator which can be configured in different electrical configurations, for example in series or in parallel. This offers a much greater speed-torque operating range from the same motor, and allows for the most efficient configuration to be used at any time. In many ways it is directly analogous to adding a 2-speed gearbox. The disadvantage is increased complexity, however it seems likely the switching mechanism could be considerably simpler, lighter and cheaper than an equivalent 2-speed mechanical gearbox.

In this PhD Josh will aim to determine the feasibility of a “reconfigurable” automotive traction motor. This will principally involve investigating the feasibility of a mechanical switching system using a single actuator, rather than using semiconductor switches which have been used in previous research on the topic. This would significantly reduce the cost of such a system and render it commercially attractive.

Chemical Energy Converters
Combustion Mixture Preparation in a Direct Injection Hydrogen Internal Combustion Engine
Supervisor:
Sam Akehurst

The internal combustion engine (ICE) has been the ‘silver bullet’ in powering machinery for the transportation, mining and construction industries. However, with existing and upcoming regulations on CO2 emissions, the industry is exploring the viability of fuelling ICEs with hydrogen as a carbon neutral alternative – notable examples include BMW, Toyota, Yamaha and JCB.

Current hydrogen combustion research focuses on achieving high brake thermal efficiency (≥45%) while keeping NOx emissions levels low by utilising direct injection fuelling strategies. This results in increased volumetric efficiency and allows for a more precise control of abnormal combustion events compared to port fuel injection. Nevertheless, topics such as combustion irregularities, turbocharger design for hydrogen-specific operation and injection strategy optimisation for efficiency, emissions and power density remain under-researched.

This research project will explore air-fuel mixture preparation techniques in a reciprocating hydrogen internal combustion engine utilising 3D CFD models validated using empirical data. These models will be used to study different hydrogen direct-injection strategies under lean operating conditions (λ < 0.6) and to investigate advanced ignition and combustion modes under distinct intake conditions (i.e., intake charge temperature, pressure, humidity, etc.). The aim is to identify injection and mixing strategies that promote efficient mixing of the air-fuel mixture and result in lean hydrogen flames with suppressed emissions, improved combustion stability and increased thermal efficiency. This work will provide more accurate requirements for turbochargers for hydrogen engines and a better picture of the in-cylinder effects at diverse engine operating points. Ultimately, it will fulfil gaps in the understanding of fundamental hydrogen combustion and identify regimes for high efficiency, zero-carbon zero-emission operation.

As an extension, the project activities will be preceded by a detailed one-dimensional simulation study in GT-POWER of a hydrogen ICE operating under lean conditions as part of the MRes Summer Project activities. The aim of this study will be to compare the 1D simulation findings and assess their accuracy and robustness against experimental data and previous 3D simulation studies. The variables of interest are brake thermal efficiency, NOx emissions, in-cylinder pressure and temperature.

Propulsion Electrification
Development of manufacturing methods for scale up of novel structural battery architectures
Supervisor:
Andrew Rhead

Current structural batteries research, as part of ongoing AAPS/GKN PhD projects, has identified that current state of the art carbon fibre structural battery architectures need to be revised as using a layered approach places too much physical distance between anode and cathode which slows ion transfer and reduces battery performance. To overcome this issue two novel architectures are being considered. One seeks to intermingle anodes and cathodes (based on carbon fibres coated with battery materials) by decomposing tows of carbon fibres into thinner layers (tens of fibres thick) and the second looks to create ultrathin layers of electrospun batteries to act as veils in manufacture of non-crimp fabrics. Each requires a combined mechanical and chemical manufacturing process to be established together with prototype/proof of principle manufacturing systems. The full PhD will look to prototype the manufacturing process and use a combination of electrochemical and mechanical tests to demonstrate a working product.

© Copyright 2021 AAPS CDT, Centre for Doctoral Training in Advanced Automotive Propulsion Systems at the University of Bath