Ellie completed a BSc in Environmental Sciences from the University of Leeds in 2020, gaining an understanding and appreciation for the complexity of environmental, social, and technological systems. From this, an overarching goal of climate change mitigation led her to complete an MSc in Energy Systems and Data Analytics at UCL, within which the connectivity of transport, built environment, and social and political sectors were explored using machine learning techniques on big data, to identify plausible solutions for decarbonisation. Projects completed included investigating the driving factors of micromobility demand, and carrying out an uncertainty analysis on global net zero pathways using clustering for her thesis. Outside of her university work Ellie was also involved in additional research roles, including looking into public responses to carbon taxes, and how demographics can impact this, and also the applicability of digital platforms to inform and engage the public in local policy changes.
It is due to the dynamism of the transportation sector, and the challenge that it poses to reaching net zero targets by 2050, that Ellie chose to pursue this area of study. Her specific research interests revolve around sustainability and efficiency; by utilising machine learning techniques she hopes to identify novel approaches that unlock the potential of technological innovation, policy measures, and behavioural change for large scale decarbonisation.
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.