Online Adaptive Optimisation of Thermal Controls for Improving Battery Electric Vehicle Efficiency at Loughborough University

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Please note the deadline has been extended to 12 November for October 2023 or January 2024 start date

This project is an exciting opportunity to work in Loughborough University with JLR as the industrial partners as they move towards full vehicle electrification.

The focus of the project is on improving the energy efficiency of battery electric vehicles using advanced optimal control methods that incorporate machine learning technologies. The machine learning algorithms adapt to changes in the vehicle performance and the many boundary conditions that influence the efficient and effective thermal management of the battery electric vehicle.

Work will begin in developing an understanding of how conventional battery, Electric Drive Unit (EDU), Autonomous Driver Assistance System (ADAS), cabin, and holistic thermal management systems, etc function to achieve various engineering targets including the level of refinement required of such systems in a premium vehicle brand. Following from this a new approach to more efficient management of the vehicle as an energy system will be created with help from engineers working in relevant areas within the company. Developing the concept and moving to a greater level of technology maturity will require the use of state-of-the-art rapid control prototyping technology and industry leading thermal energy and advanced control system vehicle experimental rig supported by JLR engineering teams.

The aim of the project is to validate improvement in vehicle efficiency achieved through the advanced control and machine learning technologies employed.

International students may apply however the total value of the studentship will cover the international tuition fee only.

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