Integrating physics and artificial intelligence for advanced battery prognostics

About the project

Eligibility: UK/International (including EU) graduates with the required entry requirements

Duration: Full-Time – Four years fixed term

Application deadline: 15 November 2026

Interview date: Will be confirmed to shortlisted candidates

Start date: May 2027

For further details contact: Haijun Ruan

This PhD project forms part of an ASTAR Singapore Research Attachment Programme (SRAP), in collaboration with Coventry University (UK) and the ASTAR's Advanced Remanufacturing and Technology Centre (ARTC), Singapore. The project includes between 1-2 years mobility to Singapore.

Introduction

Through SRAP, Coventry-based PhD researchers undertake one to two years of research training in Singapore under the joint supervision of researchers from both Coventry and ARTC. The programme offers PhD researchers an exceptional opportunity to gain international research experience while contributing to ambitious, multidisciplinary research addressing global technological and sustainability challenges.

This collaborative PhD programme enables researchers to work at the interface of academic research, advanced engineering and industrial innovation. Researchers will engage with expertise and facilities across both institutions, build international research networks, and develop experience in translating advanced research into practical solutions for future mobility and remanufacturing systems.

The programme is particularly suited to ambitious researchers seeking to shape next-generation electric mobility systems and circular manufacturing technologies, while gaining valuable research experience across the UK and Singapore.

Project details

This particular PhD research project will develop an advanced physics-informed artificial intelligence (AI) framework for battery prognostics, enabling accurate prediction of battery degradation trajectory, and remaining useful life (RUL) under dynamic operating conditions. The research will integrate electrochemical and degradation physics with machine learning and data-driven modelling to understand and predict battery ageing trajectories. A key objective will be to develop models capable of predicting future degradation trajectories together with quantified confidence levels, providing reliable uncertainty estimates for decision-making rather than relying solely on point predictions.

The project will further investigate second-life battery applications, forecasting the RUL of batteries when they are repurposed for different operating profiles and applications. This will involve considering variations in duty cycles, temperature, charging strategies, load profiles, and application requirements.

The research might include:

• Investigate battery degradation under a range of operating conditions relevant to electrified propulsion and second-life applications;
• Implement the battery prognostics through machine learning and deep learning techniques;
• Analyse how historical physical mechanisms such as capacity loss, resistance growth, loss of lithium inventory, loss of active materials, and other relevant ageing processes can be represented in the prognostics;
• Integrate physics-based battery models/constraints and AI effectively to improve long-term degradation prediction;
• Forecast battery degradation trajectories with reliable confidence and uncertainty estimates;
• Predict the RUL when batteries transition from their primary application to different second-life applications.
• Investigate how the developed approach can be transferred across battery chemistries, and operating conditions.

Funding

Tuition fees and bursary

Benefits

The successful candidate will receive comprehensive research training including technical, personal and professional skills. All researchers at Coventry University (from PhD to Professor) are part of the Doctoral and Researcher College, which provides support with high-quality training and career development activities.

You’re invited to become part of the Coventry Centre for Doctoral Training in AI (C-CDT AI ): a dynamic, collaborative hub uniting AI staff and PGR researchers from across the University.

As a C-CDT member you’ll benefit from structured AI training (from core research methods to ethics and innovation), interdisciplinary peer networks, specialist supervision, shared GPU/cloud resources, workshops, seminars and industry-focused challenge projects. This additional training and community, complements your home research centre programme, accelerates your technical and professional development, and connects you with a broad AI research ecosystem tackling impactful problems across sectors.

Entry requirements

PLUS

• A minimum of a 2:1 first degree in a relevant discipline/subject area with a minimum 60% mark in the project element or equivalent with a minimum 60% overall module average.
• The potential to engage in innovative research and to complete the PhD within 4 years.
• A minimum of English language proficiency (IELTS academic overall minimum score of 6.5 with a minimum of 6.0 in each component).

Additional requirements

Candidates should normally hold, or expect to obtain, a strong first degree and/or master's qualification in a relevant STEM discipline.

Suitable backgrounds may include:

Depending on the specific PhD project, desirable technical experience may include one or more of the following:

Candidates should demonstrate a strong interest in interdisciplinary research and an enthusiasm for addressing complex engineering and sustainability challenges.

Candidates should be capable of working independently while also contributing effectively to a multidisciplinary research team. Strong analytical, problem-solving, communication and scientific writing skills are essential.

As the programme involves international collaborative research and an extended research attachment in Singapore, candidates should also demonstrate adaptability, cultural awareness, effective communication and a willingness to work in an international research environment.

Candidates may be required to satisfy the academic, English-language, immigration, security and other eligibility requirements of Coventry University, ASTAR and the Singapore Research Attachment Programme.

• Computer Science and Engineering
• Mathematics
• Control Science and Engineering
• Electrochemical Engineering
• Mechanical Engineering
• Electrical and Electronic Engineering, or a closely related discipline.
• Battery technologies and battery management systems
• Battery testing and characterisation
• Battery degradation
• Artificial Intelligence
• Modelling
• Electric vehicle or electric propulsion systems
• Control
• Forecasting

How to apply

To find out more about the project, please contact Haijun Ruan

All applications require full supporting documentation, a covering letter, plus a 2000-word supporting statement showing how the applicant’s expertise and interests are relevant to the project.

Making an application

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