Accelerated Battery Aging for Rapid Lifetime Prediction
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: Cheng Zhang
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
Reliable prediction of battery lifetime normally requires months or years of ageing tests, creating a major bottleneck in battery development, qualification and management. Conventional accelerated ageing approaches expose cells to elevated temperatures, high currents or extreme states of charge to shorten testing time. However, aggressive acceleration can alter the underlying degradation modes and mechanisms, meaning that ageing observed under accelerated conditions may not accurately represent degradation during normal operation.
This PhD project will develop a new closed-loop design-of-experiments (DoE) framework for accelerated battery ageing and early-life prognostics. The central research question is: how can battery degradation be accelerated while preserving the degradation modes and mechanisms observed under normal ageing conditions?
The project will combine battery experiments, advanced diagnostics, degradation modelling, optimal experimental design and machine learning. Initial experiments will establish reference ageing behaviour under representative operating conditions and identify measurable signatures associated with important degradation mechanisms. A closed-loop experimental framework will then iteratively select informative ageing conditions based on results from previous experiments, balancing the competing objectives of accelerating degradation, maximising information gain and maintaining similarity to normal-ageing mechanisms.
The ultimate aim is to substantially reduce the time and experimental cost required for battery lifetime characterisation while retaining physically meaningful degradation behaviour, enabling faster battery development and more reliable lifetime prediction.
Research may include
• Design and implementation of battery ageing experiments under normal and accelerated conditions.
• Development of closed-loop and optimal DoE algorithms to autonomously select subsequent experimental conditions.
• Identification of acceleration factors that reduce testing time without changing dominant degradation mechanisms.
• Battery diagnostic techniques such as capacity analysis, incremental/differential capacity analysis, pulse testing and electrochemical impedance spectroscopy.
• Identification and quantification of degradation modes, including loss of lithium inventory (LLI), loss of active material (LAM) and resistance growth.
• Development of quantitative metrics to assess degradation-mechanism similarity between accelerated and normal ageing.
• Early-life feature extraction and identification of prognostic indicators from electrical, thermal and electrochemical measurements.
• Machine-learning, Bayesian and physics-informed approaches for early prediction of SoH, degradation trajectory and remaining useful life.
• Validation across different cells, operating conditions and, where appropriate, battery chemistries.
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 College and Centre for Research Capability and Development, 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
- 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.
PLUS
- The potential to engage in innovative research and to complete the PhD within 3.5 years.
- A minimum of English language proficiency (IELTS academic overall minimum score of 7.0 with a minimum of 6.5 in each component).
Additional requirements
Applicants should have, or expect to obtain, a good undergraduate or Master's degree in a relevant discipline such as electrical/electronic engineering, control engineering, computer science, or a closely related subject.
A strong interest in batteries, experimental research and data analysis is essential. Experience in one or more of the following areas would be advantageous: battery testing, electrochemistry, battery modelling, optimisation, design of experiments, machine learning, statistics, signal processing, MATLAB or Python.
The successful candidate should be comfortable working across both experimental and computational research, including conducting laboratory battery tests, analysing large experimental datasets and developing modelling/optimisation algorithms.
Good analytical and problem-solving skills, ability to work independently, and effective written and verbal communication skills are expected. Previous battery research experience is desirable but not essential, provided that the applicant has a strong quantitative background and motivation to develop expertise in battery ageing and prognostics.
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, A*STAR and the Singapore Research Attachment Programme.
How to apply
To find out more about the project, please contact Cheng Zhang
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.
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