• Chandula Wanasinghe

  • Theme:Digital Systems, Optimisation and Integration
  • Project:Autonomous Parameter Estimation for Electric Machines
  • Supervisor: Xiaoze Pei ,Chris Brace ,Sam Akehurst
  • Industry Partner: AVL
  • The Gorgon's Head - Bath University Logo


Chandula joined the AAPS CDT having recently graduated from his BEng in Automotive Engineering at Brunel University London with First Class honours. In his dissertation, Chandula focussed on building and evaluating 3 active cooling solutions for a first-generation Nissan Leaf battery pack. Using computational fluid dynamics and electrochemical models, he ascertained the behaviour of fluid flows and battery cell temperature distributions. He was able to conclude that the implementation of a modular active thermal management system would be beneficial to both the longevity and performance of the battery pack. Stemming from an early age, Chandula had a fondness of cars which pushed him, in his final year at the Royal Grammar School High Wycombe, to build a Caterham kit car from the ground up with a team of like-minded individuals.

Chandula’s industry experience covers both hybrid and pure internal combustion engine powertrains. His first placement at JD Auto Care he covered the maintenance of tradition ICE systems, culminating in a final project of rebuilding and rewiring a Toyota 4E-FE engine on his own accord. In years to come, he would join Jaguar Land Rover Sri Lanka as a hybrid vehicle diagnostics intern. This role expanded his knowledge of automotive powertrains to those that included part electrification, and the challenges faced by such vehicles when acclimatising to warmer climates.

Chandula is seeking to continue his fascination in the battery thermal management system path he forged through his BEng. He intends on focussing on creating high accuracy simulation models of thermal management systems; utilising more complex computational analysis based upon real world data. Striving to create a more sustainable tomorrow, Chandula is focussing on making vehicle electrification a more attractive and viable solution for both personal mobility and mass transport.


  • I was chairman of Brunel University Cricket Club for the 2020/21 season
  • I freelance as an automotive photographer in my free time
  • I can recite all of the Clarkson era TopGear episodes off by heart
  • I have a beagle called Rusty who has her own instagram page with more followers than me.

Autonomous Parameter Estimation for Electric Machines

Electric machines are becoming more prevalent in the automotive industry as they become the main propulsion system in road vehicles with the industry’s shift towards emissions free mobility. With over 15% of new car sales being electric, being able to accurately characterise electric machines virtually is imperative for maximising their performance and efficiency. A key predictor of a model’s ability to replicate transient behaviour is the accuracy of the parameters used to characterise the motor. Relying solely on the information and specifications provided by the manufacturer to create a robust model is impractical as they often only include information required for the machine’s operation. The overarching aim of this work is to develop a procedure to automate the parameterisation of electric motor models for later use in the vehicle development process.

There are many potential use cases for motor models, and many motor architectures of interest. In each combination of use case and motor architecture, the appropriate motor model structure is expected to differ. Typically, the level of spatial and temporal resolution will increase when more insight into detailed motor performance is needed. Once a model structure is defined, the datarequired to parameterise and validate this model can be defined. Then, the experiments necessary to generate this data, along with the instrumentation required can be defined. Focusing on the model development of electric machines, this project aims to create an end to end workflow between model and data to increase model accuracy and adaptability to new units under test. The work will explore the potential for a general motor model and parameterisation procedure that is compatible with all the likely motor topologies of interest: flux switching, induction, and synchronous motor architectures. It will focus on implementing an autonomous parameter characterisation process, and on streamlining the experimental procedure behind the collection of data required for the parameterisation of an electric machine.

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