About this role
3 Year, full-time PhD studentship
Eligibility: Open to home, EU and international students
University fees and bench fees: This studentship will cover university fees at the home rate. However, international students and EU students without Settled Status will need to cover the difference between the home rate and the international. Visas and associated costs are not covered.
Closing date: 5th June 2026
Interviews: TBC (online)
Start date: September 2026
Project Title: Battery degradation modelling and SOX estimation for EV applications
Director of Studies: Prof Shahab Resalati
Supervisors: Dr Aydin Azizi
Contact: Prof Shahab Resalati ([email protected])
Requirements: Entry requirements:
Essential Criteria
Master’s degree (or equivalent) in Electrical Engineering, Control Engineering, Mechatronics, Robotics, or a related discipline with a strong focus on dynamic systems. Strong background in state-space modelling, estimation theory, and control systems. Good understanding of Lithium-ion battery systems, BMS, and battery models, including equivalent circuit and electrochemical models. Proven ability to develop and implement state estimation algorithms, such as Kalman filters and observers, for real-time applications. Proficiency in MATLAB and Simulink for modelling, simulation, and validation using experimental or real-world data. Strong analytical, independent research, and communication skills, with motivation to publish in leading journals and conferences.
Desirable Criteria
Experience with advanced state estimation methods, including Extended/Unscented Kalman Filters and particle filters. Knowledge of battery degradation, ageing, and state estimation (SOC, SOH, SOP), including diagnostics and prognostics. Familiarity with reduced-order electrochemical models and hybrid physics-based/data-driven approaches. Practical experience in battery testing, parameter identification, and data acquisition. Familiarity with automotive systems, electric vehicles, and embedded BMS constraints. Experience with system identification, uncertainty-aware modelling, large datasets, and machine learning. Evidence of research capability through a thesis, publications, conference presentations, or relevant industrial experience.
English language requirements:
International/EU applicants must have a valid IELTS Academic test certificate (or equivalent) with an overall minimum score of 6.0 and no score below 5.5 issued in the last 2 years by an approved test centre.
Project Description:
Accurate battery degradation modelling and estimation of electrochemical states are essential for improving electric vehicle performance, safety, and lifetime. This PhD, in collaboration with Jaguar Land Rover, will develop physics-informed, state-based estimation algorithms for Lithium-ion battery systems. The project will integrate electrochemical degradation models with advanced estimation methods, including Kalman filtering and observer-based techniques, to enable real-time prediction of internal states and ageing mechanisms. Emphasis will be placed on balancing model fidelity, computational efficiency, and robustness under varying operating conditions. The outcomes will support next-generation BMS with improved diagnostics, prognostics, and control for automotive applications.
Application process
Please contact us directly at [email protected] before applying. Apply directly via the university portal (via the above 'Apply' button). Please include the following in your application:
A cover letter A CV Details of two referees, at least one from an academic background Copies of your previous degree certificates and transcripts A scan of your passport Evidence of a valid IELTS or other valid English language qualification, in line with Oxford Brookes’ requirements (international and EU candidates only)
For any queries, please contact [email protected]