Data-driven Thermal Management of Electric/Hybrid Vehicles for Optimum Energy Consumption

Data-driven modelling and analysis has become a revolutionary concept and modern scientific and engineering tool to model, predict and control complex systems, which are typically nonlinear, dynamic, multi-scale, and high-dimensional. With the increasing degree of electrification in automotive industry and stringent legislative requirements, the management of energy flow poses a significant challenge during the development of electric/hybrid vehicles (EV/HV). Thermal analysis and management is an integral part of modern EV/HV design and delivery. With modern mathematical methods and artificial intelligence approaches, data-driven intelligent thermal management and optimization allow researchers and engineers to provide control strategies for electric/hybrid vehicles to achieve improved performance due to optimized heat balance of the engine, transmission, battery and motor temperatures, while maintaining fast full-climate control of the cabin to deliver driver comfort.
This PhD programme will focus on data-driven modelling and conversion of thermal data into predictive control algorithm using data analytics. This is critical to obtain optimum energy consumption
solutions for future electric/hybrid vehicles. The PhD will work closely with groups in JLR, taking part in the development of reduced-order EV/HV thermal energy and control models optimised for industrial solutions.