电池组预测性能源管理

该项目利用固态佩尔蒂埃装置与混合人工智能控制器，开发了一种用于锂离子电池的预测性热管理系统。该系统通过优化热负荷管理，旨在提升安全性、使用寿命及效率，其效果已通过 MATLAB/Simulink 仿真与物理实验得到验证。申请者应具备电池技术及人工智能应用方面的专业知识。

About the project This project develops a predictive battery thermal management system for lithium-ion batteries, combining solid-state Peltier elements with a hybrid AI controller. A predictive model forecasts thermal loads; a decision algorithm optimises Peltier operation, minimising parasitic energy. Validated via MATLAB/Simulink simulation and experiments, it targets safer, longer-lasting, more efficient energy storage. The performance, longevity, and safety of lithium-ion batteries, the cornerstone of electric vehicles and grid-scale energy storage, are critically dependent on maintaining a narrow optimal operating temperature range. Conventional Battery Thermal Management Systems (BTMS), typically based on complex liquid cooling, are reactive and consume significant parasitic energy, reducing overall system efficiency. This research project shifts thermal management from reactive to predictive to provide a more efficient and reliable solution. This project will investigate a novel BTMS architecture that pairs solid-state thermoelectric Peltier elements with an advanced artificial intelligence (AI) control system. Peltier elements offer significant advantages, including high reliability, silent operation, and precise, bidirectional (heating and cooling) temperature control. Critically, they also offer rapid response rates compared to traditional fluid-based cooling systems. The core of this project is the development of a hybrid AI controller designed to minimise the parasitic energy consumption of the Peltier-based BTMS, while also giving more accurate, real-time, thermal control of the battery cells. This controller will consist of two synergistic components: predictive, to forecast the battery's future thermal load based on real-time operational and environmental data decision, to proactively control the Peltier element. By using the predictive algorithms’ anticipated thermal needs, the decision algorithm will operate the Peltier element in its most efficient profile to pre-emptively manage temperature fluctuations of the battery cells. The methodology involves developing a high-fidelity, multi-physics simulation environment in MATLAB/Simulink to model the coupled electrochemical-thermal dynamics of the battery cells and the thermoelectric properties of the Peltier element. This virtual testbed will be used to train and validate the AI controller across a range of dynamic drive cycles and environmental conditions. The performance of the final AI-driven system will be experimentally verified. The anticipated contribution of this research is a validated framework for an intelligent, energy-efficient, and highly reliable solid-state BTMS. By demonstrating the viability of AI-controlled thermoelectric devices, this work aims to advance battery management technology, leading to safer, longer-lasting, and more efficient electrical energy storage systems. You will be trained in Lithium ion Batteries and Thermoelectric devices as well as Matlab and Komsol simulations. The School of Engineering is committed to promoting equality, diversity inclusivity as demonstrated by our Athena SWAN award. We welcome all applicants regardless of their gender, ethnicity, disability, sexual orientation or age, and will give full consideration to applicants seeking flexible working patterns and those who have taken a career break. The University has a generous maternity policy, onsite childcare facilities, and offers a range of benefits to help ensure employees’ well-being and work-life balance. The University of Southampton is committed to sustainability and has been awarded the Platinum EcoAward.

入学要求 英国2:1荣誉学位，或其国际等效学位。

资助：竞争性资助 学费与资助 我们为英国本土学生和国际学生提供多种资助机会。符合条件的学生可通过“地平线欧洲”学费减免，自动免除国际学生学费与英国本土学生学费之间的差额。 大学通过竞争性遴选授予的校长助学金，为遴选排名靠前的申请人支付国际学生学费与英国本土学生学费之间的差额。 本校各学院通过竞争性遴选提供的博士奖助金，通常为遴选排名靠前的申请人支付按英国本土学生标准收取的学费，并提供生活津贴。 资助采用滚动授予方式，因此请尽早申请，以尽可能增加进入资助评选的机会。 如需了解更多信息，请访问我们的研究生研究资助页面。

如何申请 您需： 选择课程类型（研究型）、2027/28学年、工程与物理科学学院 选择全日制或非全日制 搜索工程与环境博士项目（7175） 在申请表第2部分填写导师姓名 申请材料应包括： 您的CV（个人简历） 2份学术推荐信 截至目前的学位课程成绩单和证书 英语语言资格证明（如适用）