Dr Quan Zhou PhD, MEng, BEng

Dr Quan Zhou

Department of Mechanical Engineering
Honorary Assistant Professor in Automotive Engineering

Contact details

Email
q.zhou@bham.ac.uk
View my research portal
Address
Vehicle and Engine Research Centre
University of Birmingham
Edgbaston
Birmingham
B15 2TT
UK

Qualifications

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

Biography

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 of Birmingham (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., EP/J00930X/1, EP/N021746/1, Innovate UK 102253) 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.

Postgraduate supervision

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.

Research

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 ‘dedicated 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 
  • Lv.3 Vehicle-level driver-machine interaction
  • Lv.4 Fleet-level collaborative energy management with vehicle-to-everything (V2X) network. 

Dr Zhou's work is available with open access at https://www.researchgate.net/profile/Quan_Zhou16

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.

Publications

Highlight publications

Zhou, Q, Zhao, D, Shuai, B, Li, Y, Williams, H & Xu, H 2021, 'Knowledge implementation and transfer with an adaptive learning network for real-time power management of the plug-in hybrid vehicle', IEEE Transactions on Neural Networks and Learning Systems, vol. 32, no. 12, pp. 5298-5308. https://doi.org/10.1109/TNNLS.2021.3093429

Zhou, Q, Li, Y, Zhao, D, Li, J, Williams, H, Xu, H & Yan, F 2022, 'Transferable representation modelling for real-time energy management of the plug-in hybrid vehicle based on k-fold fuzzy learning and Gaussian process regression', Applied Energy, vol. 305, 117853. https://doi.org/10.1016/j.apenergy.2021.117853

Zhou, Q, Li, J, Shuai, B, Williams, H, He, Y, Li, Z, Xu, H & Yan, F 2019, 'Multi-step reinforcement learning for model-free predictive energy management of an electrified off-highway vehicle', Applied Energy, vol. 255, 113755. https://doi.org/10.1016/j.apenergy.2019.113755

Zhou, Q, Zhang, Y, Li, Z, Li, J, Xu, H & Olatunbosun, O 2018, 'Cyber-physical energy-saving control for hybrid aircraft-towing tractor based on online swarm intelligent programming', IEEE Transactions on Industrial Informatics, vol. 14, no. 9, pp. 4149-4158. https://doi.org/10.1109/TII.2017.2781230

Zhou, Q, Zhang, W, Cash, S, Olatunbosun, O, Xu, H & Lu, G 2017, 'Intelligent sizing of a series hybrid electric power-train system based on Chaos-enhanced accelerated particle swarm optimization', Applied Energy, vol. 189, pp. 588-601. https://doi.org/10.1016/j.apenergy.2016.12.074

Recent publications

Article

Guo, S, Zheng, S, Li, J, Zhou, Q & Xu, H 2025, 'A lightweight social cognitive risk potential field model for path planning with dedicated dynamic and static traffic factors', IET Intelligent Transport Systems, vol. 19, no. 1, e12595. https://doi.org/10.1049/itr2.12595

Wang, J, Du, C, Yan, F, Hua, M, Gongye, X, Yuan, Q, Xu, H & Zhou, Q 2025, 'Bayesian optimization for hyper-parameter tuning of an improved twin delayed deep deterministic policy gradients based energy management strategy for plug-in hybrid electric vehicles', Applied Energy, vol. 381, 125171. https://doi.org/10.1016/j.apenergy.2024.125171

Hua, M, Chen, D, Jiang, K, Zhang, F, Wang, J, Wang, B, Zhou, Q & Xu, H 2025, 'Communication-Efficient MARL for Platoon Stability and Energy-Efficiency Co-Optimization in Cooperative Adaptive Cruise Control of CAVs', IEEE Transactions on Vehicular Technology, vol. 74, no. 4, pp. 6076-6087. https://doi.org/10.1109/TVT.2024.3511091

Wu, Y, Zuo, Z, Wang, Y, He, D, Li, J, Zhou, Q & Xu, H 2025, 'Dynamic Source-Aware Transfer Learning for Vehicular Platoon Predictive Control', IEEE Transactions on Industrial Electronics. https://doi.org/10.1109/TIE.2025.3639826

Hua, M, Shuai, B, Zhang, F, Wang, J, Zhang, C, Zhou, Q & Xu, H 2025, 'Efficient Energy Management of Plug-In Hybrid Electric Vehicles Through Ensemble with In-Target Minimization Q-Learning', IEEE Transactions on Transportation Electrification, vol. 11, no. 5, pp. 11570-11581. https://doi.org/10.1109/TTE.2025.3579614

Wang, J, Du, C, Yan, F, Duan, X, Hua, M, Xu, H & Zhou, Q 2025, 'Energy Management of a Plug-In Hybrid Electric Vehicle Using Bayesian Optimization and Soft Actor-Critic Algorithm', IEEE Transactions on Transportation Electrification, vol. 11, no. 1, pp. 912-921. https://doi.org/10.1109/TTE.2024.3398046

Jiang, K, Hua, M, He, X, Dong, L, Zhou, Q, Xu, H & Sun, C 2025, 'Improving String Stability in Cooperative Adaptive Cruise Control Through Multiagent Reinforcement Learning With Potential-Driven Motivation', IEEE Transactions on Artificial Intelligence, vol. 6, no. 5, pp. 1114-1127. https://doi.org/10.1109/TAI.2024.3511513

Zhang, F, Zhou, Q, Zhang, C, Hua, M, Du, S, Duan, Y, Li, J, Williams, H & Xu, H 2025, 'Meta-Heuristic Adaptive Equivalent Consumption Minimization of a Fuel Cell Vehicle Incorporating Fuzzy Inference and Particle Swarm Optimization', IEEE Transactions on Transportation Electrification, vol. 11, no. 2, pp. 5237-5248. https://doi.org/10.1109/TTE.2024.3478187

Shuai, B, Hua, M, Li, Y, Shuai, S, Xu, H & Zhou, Q 2025, 'Optimal Energy Management of Plug-In Hybrid Electric Vehicles Through Ensemble Reinforcement Learning With Exploration-to-Exploitation Ratio Control', IEEE Transactions on Intelligent Vehicles, vol. 10, no. 4, pp. 2479-2489. https://doi.org/10.1109/TIV.2024.3377215

Zhang, F, Yan, Y, Wang, S, Hua, M, He, X, Zhang, C, Li, J, Zhou, Q, Duan, Y, Williams, H, Du, S & Xu, H 2025, 'Optimal sizing and energy management for fuel cell electric vehicles with 3D-ordered MEAs: A pareto frontier study', International Journal of Hydrogen Energy, vol. 189, 152206. https://doi.org/10.1016/j.ijhydene.2025.152206

Liu, ZE, Li, Y, Zhou, Q, Shuai, B, Hua, M, Xu, H, Xu, L, Tan, G & Li, Y 2025, 'Real-time energy management for HEV combining naturalistic driving data and deep reinforcement learning with high generalization', Applied Energy, vol. 377, no. Part A, 124350. https://doi.org/10.1016/j.apenergy.2024.124350

Dong, L, Li, X, He, X, Hua, M, Zhou, Q, Sun, C & Jiang, K 2026, 'Robustness-enhanced cooperative adaptive cruise control for multi-task scenarios via generalised joint multi-agent reinforcement learning', Neurocomputing, vol. 664, 132036. https://doi.org/10.1016/j.neucom.2025.132036

Zhang, F, Hua, M, Zhou, Q, Wang, S, Zhang, C, Du, S, Duan, Y, Williams, H & Xu, H 2025, 'Robustness Optimization of the Energy Management Strategy for a Fuel Cell Vehicle Using Adversary Evolutionary Learning', IEEE Transactions on Transportation Electrification, vol. 11, no. 4, pp. 8729-8741. https://doi.org/10.1109/TTE.2025.3549857

Preprint

Hua, M, Chen, D, Jiang, K, Zhang, F, Wang, J, Wang, B, Zhou, Q & Xu, H 2024 'Communication-Efficient MARL for Platoon Stability and Energy-efficiency Co-optimization in Cooperative Adaptive Cruise Control of CAVs' arXiv. https://doi.org/10.48550/arXiv.2406.11653

Review article

Hua, M, Qi, X, Chen, D, Jiang, K, Liu, ZE, Sun, H, Zhou, Q & Xu, H 2025, 'Multi-Agent Reinforcement Learning for Connected and Automated Vehicles Control: Recent Advancements and Future Prospects', IEEE Transactions on Automation Science and Engineering, vol. 22, pp. 16266-16286. https://doi.org/10.1109/TASE.2025.3574280

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