PhD Studentship: Battery degradation modelling and SOX estimation EV

3 Year, full-time PhD studentship

Eligibility: Open to home, EU and international students

Bursary p.a: £21,805

University fees and bench fees: This studentship will cover university fees at the HOME RATE ONLY. International students and EU students without Settled Status will need to cover the difference between the home and the international fee rates. Visas and associated costs are not covered.

Closing date: 23rd 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 ()

Requirements: Entry requirements:

• 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.

• 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.

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.

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.

For any queries, please contact tde-t