Closing date: 23^rd October 2026

Interviews: TBC (online)

Start date:  January 2027

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 (sresalati@brookes.ac.uk)

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 tde-tdestudentships@brookes.ac.uk before applying. Apply directly via the university portal here: https://www.brookes.ac.uk/study/how-to-apply/applying-directly. 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)
• Evidence of funding (international and EU candidates only)

For any queries, please contact tde-tdestudentships@brookes.ac.uk

Closing date: 23^rd October 2026

Interviews: TBC (online)

Start date:  January 2027

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 (sresalati@brookes.ac.uk)

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 tde-tdestudentships@brookes.ac.uk before applying. Apply directly via the university portal here: https://www.brookes.ac.uk/study/how-to-apply/applying-directly. 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)
• Evidence of funding (international and EU candidates only)

For any queries, please contact tde-tdestudentships@brookes.ac.uk