用于延长电池寿命的闭环自适应电池管理技术

该项目旨在开发一种闭环自适应电池管理系统，该系统能够持续根据电池状态的变化进行学习，从而优化性能与使用寿命。通过整合实时数据及自适应控制策略，本研究旨在提高电池监测的精度，并通过更有效的管理措施增强安全性与电池寿命。申请者需具备扎实的电气或电子工程背景，并对电池系统有浓厚的兴趣。

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: Conventional battery management systems are typically designed and calibrated using battery characteristics measured at the beginning of life. However, batteries continuously evolve during operation due to ageing, temperature variations and changing usage conditions. Capacity, resistance, electrochemical characteristics and degradation mechanisms can change significantly over lifetime. As a result, fixed BMS models and control strategies may become increasingly inaccurate and suboptimal as the battery ages, limiting performance, lifetime and safety. This PhD project will develop a closed-loop adaptive battery management framework that continuously learns the evolving condition of the battery and adapts its estimation and control strategies to maximise useful lifetime. The proposed framework will integrate battery sensing, state and parameter estimation, ageing diagnosis, model adaptation and optimal control within a closed loop. Operational battery data will be used to identify changes in battery behaviour and update models and parameters throughout life. Particular attention will be given to the challenge of extracting reliable ageing information from limited and noisy field measurements, where battery parameters and degradation modes may be weakly observable. Based on the estimated battery condition, adaptive control strategies will be developed to modify operating limits, charging profiles, thermal management or power constraints. The objective is to balance immediate performance requirements against long-term degradation, enabling the BMS to actively manage the battery towards improved lifetime rather than simply monitoring its condition. The research will combine battery experiments, physics-based and data-driven modelling, estimation, optimisation and control. The ultimate aim is to establish the foundations of a self-adapting battery management system capable of learning from battery behaviour and continuously optimising its operation throughout its lifetime. Research may include • Experimental characterisation of battery behaviour and degradation under different operating conditions. • Development of ageing-adaptive battery models and digital twins. • Online estimation of battery states, health indicators and ageing-dependent parameters. • Investigation of parameter identifiability and observability using realistic BMS measurements. • Selection of informative operational data for reliable online model and parameter recalibration. • Diagnosis of battery degradation modes, such as loss of lithium inventory (LLI), loss of active material (LAM) and resistance growth. • Development of robust state and parameter estimation methods considering measurement noise, model mismatch and uncertainty. • Development of closed-loop adaptive charging, power and thermal management strategies. • Multi-objective optimal control balancing performance, charging time, safety and degradation. • Ageing-adaptive, plating-aware fast-charging strategies. • Physics-informed and machine-learning approaches for adaptive battery management. • Uncertainty quantification and confidence-aware adaptation to prevent unsafe or unreliable model updates. • Experimental validation of lifetime benefits compared with conventional non-adaptive BMS approaches.

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 4 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, automotive engineering, mechanical engineering, or a closely related subject. A strong interest in battery management, modelling, control and experimental research is essential. Experience in one or more of the following areas would be advantageous: battery testing, battery modelling, state estimation, system identification, optimisation, control, machine learning, signal processing, MATLAB/Simulink, Python or embedded systems. The successful candidate should be comfortable working across experimental and computational research, including conducting battery tests, analysing experimental datasets, developing estimation and control algorithms, and validating algorithms using battery test facilities. 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 adaptive battery management. 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.

资助来源： 学费与奖学金 优势： 成功候选人将获得全面的研究培训，包括技术、个人及职业技能方面的培养。考文垂大学的所有研究人员（从博士到教授）均属于博士学院与研究能力与发展中心，该机构将提供高质量的培训与职业发展支持。 您被邀请加入考文垂大学人工智能博士培训中心（C-CDT AI）：这是一个充满活力的合作平台，汇集了大学各处的人工智能研究人员与PGR研究人员。作为C-CDT成员，您将受益于系统的人工智能培训（涵盖核心研究方法至伦理与创新领域）、跨学科同行网络、专业指导、共享GPU/云资源、研讨会、讲座以及以行业为导向的挑战项目。这种额外的培训与社区支持可补充您的本研究所提供的课程，加速您的技术与职业发展，并让您融入一个广泛的人工智能研究生态系统，该体系致力于解决各领域的关键问题。

申请方式： 关于该项目的更多信息，请联系**[会员信息]** 所有申请者必须提供完整的辅助文件、推荐信，以及一篇2000字的说明文，以证明申请者的专业能力与兴趣与该项目具有相关性。 申请考文垂大学 申请流程：