Descripción del proyecto
"The realisation of the EU industrial and political ambition of promoting transportation electrification to reduce carbon emissions depends heavily on the continued advancement of battery technologies. However, rapid battery pack degradation caused by improper charging behaviours is one of the most crucial factors restricting the wide usage of electric vehicles. This project aims to make step changes in research and innovation of battery management by developing 1) a user charging preference learning and demand prediction model methodology, 2) a user demand-informed optimisation methodology of charging trajectory, and 3) a distributed trajectory tracking-based battery pack charging control, and realise an advanced career development for the experienced researcher. As an MSCA-PF fellow, the experienced researcher, Dr. Quan Ouyang, will receive crucial career development at the Chalmers University of Technology and engage in detailed battery charging control works which span the areas of automatic control and artificial intelligence using a clearly defined training-through-research approach. The project results will include a generic smart charging system to prolong battery life and improve the vehicle’s resource efficiency and convenience. The incorporated battery pack charging strategies will benefit from revolutionarily considering the user preferences and demand, where the designed charging currents can be intelligently adjusted according to different user demands that can effectively restrict battery degradation. The hosting group at the Chalmers University of Technology has a tradition of projects in close cooperation with the vehicle industry (e.g., Volvo Cars, Scania, and CEVT), making it highly likely that the results of this project will be taken up by the industry. Ultimately, this project will contribute to the EU's carbon neutrality goal and the UN Sustainable Development Goals in ""affordable and clean energy"" and ""sustainable cities""."