PhD Studentship: AI-Enhanced Battery State of Health Estimation Using Ring Probabilistic Logic Neural Networks

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: 20th April 2026

Interviews: TBC (online)

Start date:  September 2026

Project Title: AI-Enhanced Battery State of Health Estimation Using Ring Probabilistic Logic Neural Networks

Director of Studies: Prof Shahab Resalati

Supervisors: Dr Aydin Azizi

Contact: Prof Shahab Resalati (sresalati@brookes.ac.uk)

Entry requirements: 

Essential Criteria

• A Master’s degree (or equivalent) in Electrical Engineering, Control Engineering, Mechatronics, or Robotics, with a heavy emphasis on dynamic system theory, or a closely related discipline.
• Strong academic background in applied intelligent control techniques, machine learning, or artificial intelligence.
• Knowledge of control systems, system modelling, and data-driven modelling approaches.
• Proven ability to discretise continuous-time models and implement real-time estimation algorithms
• Experience with MATLAB/Simulink, including Control System Toolbox, System Identification Toolbox, or Deep Learning Toolbox.
• Understanding of battery systems, electrochemical energy storage, or battery management systems (BMS).
• Ability to develop and implement algorithms for modelling, estimation, or control applications.
• Strong analytical thinking, problem-solving ability, and capability to conduct independent research.
• Excellent written and verbal communication skills in English.
• Motivation to conduct high-quality research leading to publications in international peer-reviewed journals and conferences.

Desirable Criteria

• Experience implementing machine learning or deep learning models (e.g., neural networks, probabilistic learning methods).
• Knowledge of state estimation techniques, such as Kalman filters, extended Kalman filters, or particle filters.
• Prior experience with battery modelling, battery management systems, or battery state estimation (SOC/SOH/SOP).
• Experience with battery testing, experimental data acquisition, or lithium-ion battery degradation analysis.
• Familiarity with EV, energy storage systems, or smart energy technologies.
• Experience working with large datasets, data-driven modelling, or uncertainty-aware modelling techniques.
• Evidence of research activity, such as a Master’s thesis, research project, conference paper, or journal publication.

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 State of Health (SOH) estimation is vital for electric vehicle safety and longevity. Current models often fail to balance accuracy with computational efficiency. Collaborating with Jaguar Land Rover, this research proposes the Ring Probabilistic Logic Neural Network (RPLNN).

Unlike opaque deep learning models, the RPLNN fuses neural computation with probabilistic logic rules within a ring-based structure. This framework enhances interpretability, data efficiency, and resistance to drift, addressing critical limitations in existing AI-based SOH estimation methods.

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)

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

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