PhD Studentship in Battery Degradation Modelling and State Estimation for Electric Vehicles
Develop advanced state estimation algorithms for lithium-ion battery systems to enhance EV performance, safety, and longevity.
Best for:Best for candidates with a master’s degree in Electrical Engineering, Control Engineering, or related fields, and expertise in state-space modelling, estimation theory, and MATLAB/Simulink.
Overview
This three-year PhD studentship at Oxford Brookes University focuses on advancing battery degradation modelling and state estimation for electric vehicle applications. Collaborating with Jaguar Land Rover, the project aims to create physics-informed, state-based estimation algorithms for lithium-ion battery systems. The research will integrate electrochemical degradation models with advanced estimation techniques, such as Kalman filtering and observer-based methods, to enable real-time prediction of internal battery states and ageing mechanisms. The successful candidate will contribute to next-generation battery management systems (BMS) with improved diagnostics, prognostics, and control for automotive applications. The position is ideal for applicants with a strong background in electrical engineering, control systems, and battery modelling.
What you'll do
- Develop and implement advanced state estimation algorithms, including Kalman filters and observers, for real-time battery state prediction.
- Integrate electrochemical degradation models with estimation techniques to predict internal battery states and ageing mechanisms.
- Conduct research to balance model fidelity, computational efficiency, and robustness under varying operating conditions.
- Collaborate with Jaguar Land Rover to ensure practical applicability of research outcomes in automotive BMS.
- Publish findings in leading academic journals and conferences.
- Present research results at conferences and workshops.
- Work independently and collaboratively to advance battery technology for electric vehicles.
Requirements
Degree required: Master’s degree (or equivalent) in Electrical Engineering, Control Engineering, Mechatronics, Robotics, or a related discipline with a strong focus on dynamic systems.
Degree offered: PhD
Language: English; IELTS Academic score of 6.0 overall with no band below 5.5 (or equivalent) for international/EU applicants.
Instruction: English
Publications: Motivation to publish in leading journals and conferences.
- Strong background in state-space modelling and estimation theory.
- Proven ability to develop and implement state estimation algorithms (e.g., Kalman filters, observers).
- Good understanding of lithium-ion battery systems, battery management systems (BMS), and battery models (equivalent circuit and electrochemical models).
- Proficiency in MATLAB and Simulink for modelling, simulation, and validation.
- Strong analytical, independent research, and communication skills.
- Open to home, EU, and international students.
- No age restrictions mentioned.
Funding & terms
How to apply
Deadline: 2026-10-23
Applicants must first contact the provided email address for guidance before applying. Applications are submitted directly via the university portal. Include a cover letter, CV, details of two referees (at least one academic), degree certificates and transcripts, a passport scan, and evidence of English language proficiency (IELTS or equivalent). International/EU candidates must also provide evidence of funding.
- Cover letter
- CV
- Details of two referees (at least one academic)
- Copies of previous degree certificates and transcripts
- Scan of passport
- Evidence of valid IELTS or other English language qualification (international/EU candidates only)
- Evidence of funding (international/EU candidates only)
Contact
Shahab Resalati· Director of Studies
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