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.
