School of Engineering and Materials Science Research Studentships
Prediction of battery cooling processes in electric vehicles by Physics-Informed Neural Networks
Supervisors: Nader KARIMI
Application Deadline: 30-01-2022
Battery thermal management (BTM) is an ongoing major challenge hindering the wide application of electric vehicles (EV). It is well established that batteries can operate optimally only in a small temperature range, while there are a number of time-dependent factors that can heavily influence battery temperature. For example, the drive cycles and thus battery discharge scenarios are highly unsteady, turning BTM to a dynamic problem. Nonetheless, a vast majority of the existing models of battery cooling are limited to steady cases. More importantly, they are predominantly focused on a single or a small number of battery cells. Yet, in practice, an EV often uses thousands of cells. These shortcomings have contributed to the formation of a major gap between capabilities of the existing models and the practical needs of industry.
Performance prediction of BTM systems requires careful consideration of electrochemistry, fluid dynamics and heat transfer. Addition of unsteadiness and complex configuration of a battery packs renders the task quite complicated. This makes the conventional modelling approaches expensive and therefore impractical. To resolve this issue, this project exploits the latest developments in the field of machine learning and computational modelling and uses the so-called ‘Physics-Informed Neural Network’ (PNN) to model the time-dependent cooling processes in real battery packs. This leads to substantial reduction in the computational cost and therefore allows for utilisation of the method as a predictive design tool in industry.
The project includes generation of limited training and testing data by using CFD integrated with the existing electrochemical models. In parallel, PNN models are developed through combining deep learning techniques with the governing physical equations of the system. This builds upon a recently developed PNN-based flow simulator in our group and advances that to include electrochemistry. The PNN will be validated and refined using the data generated by conventional modelling.
This studentship is fully funded via the UKRI EPSRC Doctoral Training Programme for 3.5 years and includes a stipend (currently £17,609 in 2021/2022) and Fees.
This year UKRI announced that there will be a limited number of studentships for international students available. International applicants are encouraged to apply but should note that studentship awards will be subject to eligibility and the availability of funding.
To be classed as a home student, applicants must meet the following criteria:
- Be a UK National (meeting residency requirements), or
- Have settled status, or
- Have pre-settled status (meeting residency requirements), or
- Have indefinite leave to remain or enter
If a candidate does not meet the criteria above, they would be classified as an international student.
- The minimum requirement for this studentship opportunity is a good Honours degree (minimum 2(i) honours or equivalent) or MSc/MRes in a relevant discipline.
- If English is not your first language, you will require a valid English certificate equivalent to IELTS 6.5+ overall with a minimum score of 6.0 in Writing and 5.5 in all sections (Reading, Listening, Speaking).
- Candidates are expected to start from September 2022
Supervisor Contact Details:
For informal enquiries about this position, please contact Dr Nader Karimi, E-mail: email@example.com
To apply for this studentship and for entry on to the PhD Full-time Mechanical Engineering - Semester 1 (September Start) please follow the instructions detailed on the following webpage:
Research degrees in Engineering: http://www.qmul.ac.uk/postgraduate/research/subjects/engineering.html
Further Guidance: http://www.qmul.ac.uk/postgraduate/research/
Please be sure to include a reference to ‘2022 EPSRC DTP NK’ to associate your application with this studentship opportunity.