麻豆精选

• PhD in Mechanical Engineering, The University 麻豆精选, 2019
• MEng (by research) in Vehicle Engineering, Wuhan University of Technology, 2015
• BEng in Vehicle Engineering, Wuhan University of Technology, 2012

Dr Quan Zhou received BEng and MEng. degrees in Automotive Engineering from Wuhan University of Technology, China, in 2012 and 2015, respectively and obtained a PhD in Mechanical Engineering from the University 麻豆精选 (UoB), UK, in 2019. He is the sole recipient of the Ratcliffe Prize in 2019 which is awarded by UoB for the best postgraduate research in the Science.

Before his appointment as Assistant Professor at UoB, he was a full-time Research Fellow (2019-2022) at the Engine and Vehicle Research Centre and a part-time Research Associate (2016-2019) and Teaching Fellow (2015-2018) at UoB.

Dr Zhou is the co-founder and leader of the CASE-V research group, which plays a significant role in the Birmingham C.A.S.E. Automotive Research and Education Centre. He has been instrumental in the successful delivery of several government and industry research projects (e.g., ,听, ) and established expertise in dedicated AI systems for automotive engineering. He has more than 50 research papers published in international journals (e.g., IEEE Transactions on Neural Networks and Learning Systems, IEEE Transactions on Industrial Informatics, Applied Energy) and conference proceedings and 9 patent inventions. Dr Zhou is working closely with several world-leading research institutes, e.g., EU Joint Research Centre, Nanyang Technological University, Tsinghua, RTWH Aachen.

Dr Zhou serves several editorial roles for SCI/EI journals including Automotive Innovation (academic editor), eTransport (guest editor), IET Intelligent Transport Systems (associate editor), and International Journal of Powertrains (editorial assistant). He actively reviews papers for more than 20 journals including IEEE Transactions and Applied Energy. He has successfully contributed to the organization of international conferences including the IEEE/CAA International Conference on Vehicular Control and Intelligence, IFAC Conference on Engine and Powertrain Control Simulation and Modelling, International Conference on Applied Energy, Applied Energy Symposium on Low Carbon Cities & Urban Energy Systems, and IFAC Symposium on Advances in Automotive Control.

Full-time PhD applicants and visiting scholars/students are welcome in the following areas:

1) Evolutionary multi-objective optimisation for online/offline optimisation of vehicle systems;
2) Reinforcement Learning for real-time advanced decision making in the vehicle systems;
3) Model-based predictive control for energy management in hybrid/electric vehicles;
4) Human factors for driving safety and economy;
5) Information fusion and global energy efficiency optimisation of connected autonomous vehicles.

Autonomous and electrified vehicles will be in a key position for future transport to achieve ultra-low emissions, and he is working towards a new area of 鈥榙edicated artificial intelligence (DAI) for e-mobility that incorporates AI with advanced electrified propulsion technologies. His research develops AI-based control/optimisation methods at four different vehicle operating levels for CO2 emission mitigation:

• Lv.1 Engine/motor level transient control/calibration
• Lv.2 Powertrain-level component sizing and energy management聽
• Lv.3 Vehicle-level driver-machine interaction
• Lv.4 Fleet-level collaborative energy management with vehicle-to-everything (V2X) network.聽

Dr Zhou's work is available with open access at聽

Full-time PhD applicants and visiting scholars/students are welcome in the following areas:

• Evolutionary multi-objective optimisation for online/offline optimisation of vehicle systems;
• Reinforcement Learning for real-time advanced decision making in the vehicle systems;
• Model-based predictive control for energy management in hybrid/electric vehicles;
• Human factors for driving safety and economy;
• Information fusion and global energy efficiency optimisation of connected autonomous vehicles.