Dr Quan Zhou PhD, MEng, BEng

Dr Quan Zhou

Department of Mechanical Engineering
Assistant Professor in Automotive Engineering

Contact details

Address
Vehicle and Engine Research Centre
University of Birmingham
Edgbaston
Birmingham
B15 2TT
UK

Dr Quan Zhou is Assistant Professor in Automotive Engineering at the University of Birmingham and leads the research on Connected and Autonomous Systems for Electrified Vehicles (CASE-V). He obtained a PhD in Mechanical Engineering from the University of Birmingham, 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. His work has received an award from Innovate UK ICURe programme. PhD position applications are welcome.

Dr Zhou aspires to harness the emerging power of AI to reshape the design and control of vehicles, helping to attain a more sustainable society. His research interests include fuzzy inferences, evolutionary computation, deep and reinforcement learning, and their applications in automotive engineering. With a track record of 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 has gained recognition from industry and academia. He has close collaboration 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.

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/1EP/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.

Teaching

Co-supervisor of final year projects.

Teaching assistant:

  • Powertrain and Vehicle Engineering (Simulink);

Past teaching assistant:

  • Software and systems;
  • Computing for Engineers;
  • Advanced Mechanics;
  • Engineering Materials;
  • Sustainable Engineering;

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.

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

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

Excellent PhD applicants will have the opportunity to be sponsored by the university’s PhD scholarship. We can support application on external funding (e.g. Newton/EPSRC Fellowship, EPSRC Studentship, CSC PhD Scholarship).

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.

Other activities

  • Member of the Institute of Electrical and Electronical Engineers (IEEE)
  • Member of the Association of Self-Financed Outstanding Scholarship Awardees-UK (ASOSA-UK)
  • Member of the UK Society of Chinese Automotive Engineers (UKCSAE)
  • Reviewer for Applied Energy, IEEE Transactions on {Industrial Electronics (TIE), Industrial Informatics (TII), Mechatronics (TMECH), Vehicle Technology (TVT)}, Proceedings of IMechE Part D-Journal of Automotive Engineering, SAE conference/journals.

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. 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

Li, J, Liu, K, Zhou, Q, Meng, J, Ge, Y & Xu, H 2022, 'Electrothermal dynamics-conscious many-objective modular design for power-split plug-in hybrid electric vehicles', IEEE/ASME Transactions on Mechatronics. https://doi.org/10.1109/TMECH.2022.3156535

Li, J, Zhou, Q, Williams, H, Xu, P, Xu, H & Lu, G 2022, 'Fuzzy-tree-constructed data-efficient modelling methodology for volumetric efficiency of dedicated hybrid engines', Applied Energy, vol. 310, 118534. https://doi.org/10.1016/j.apenergy.2022.118534

Xu, B, Zhou, Q, Shi, J & Li, S 2022, 'Hierarchical Q-learning network for online simultaneous optimization of energy efficiency and battery life of the battery/ultracapacitor electric vehicle', Journal of Energy Storage, vol. 46, 103925. https://doi.org/10.1016/j.est.2021.103925

Li, J, Zhou, Q, Williams, H, Xu, H & Du, C 2021, 'Cyber-physical data fusion in surrogate-assisted strength pareto evolutionary algorithm for PHEV energy management optimization', IEEE Transactions on Industrial Informatics. https://doi.org/10.1109/TII.2021.3121287

Li, J, Zhou, Q, He, Y, Williams, H, Xu, H & Lu, G 2021, 'Distributed cooperative energy management system of connected hybrid electric vehicles with personalized non-stationary inference', IEEE Transactions on Transportation Electrification. https://doi.org/10.1109/TTE.2021.3127142

Zhou, Q, Guo, S, Xu, L, Guo, X, Williams, H, Xu, H & Yan, F 2021, 'Global optimization of the Hydraulic-electromagnetic Energy-harvesting Shock Absorber for road vehicles with Human-knowledge-integrated Particle Swarm Optimization scheme', IEEE/ASME Transactions on Mechatronics. https://doi.org/10.1109/TMECH.2021.3055815

Zhou, Q, Wang, C, Sun, Z, Li, J, Williams, H & Xu, H 2021, 'Human-knowledge-augmented Gaussian process regression for state-of-health prediction of lithium-ion batteries with charging curves', Journal of Electrochemical Energy Conversion and Storage, vol. 18, no. 3, 030907 . https://doi.org/10.1115/1.4050798

Li, J, Gu, Y, Wang, C, Liu, M, Zhou, Q, Lu, G, Pham, DT & Xu, H 2021, 'Pedestrian-aware supervisory control system interactive optimization of connected hybrid electric vehicles via fuzzy adaptive cost map and Bees Algorithm', IEEE Transactions on Transportation Electrification. https://doi.org/10.1109/TTE.2021.3124606

Xu, H & Zhou, Q 2021, '人工智能在发动机控制开发中的应用及前景', Journal of Automotive Safety and Energy, vol. 12, no. 2, pp. 150-162. https://doi.org/10.3969/j.issn.1674-8484.2021.02.002

Wang, J, Hou, X, Du, C, Xu, H & Zhou, Q 2020, 'A Moment-of-Inertia-Driven Engine Start-Up Method Based on Adaptive Model Predictive Control for Hybrid Electric Vehicles With Drivability Optimization', IEEE Access, vol. 8, 9144597, pp. 133063-133075. https://doi.org/10.1109/ACCESS.2020.3010528

Li, J, Zhou, Q, He, Y, Williams, H & Xu, H 2020, 'Driver-identified supervisory control system of hybrid electric vehicles based on spectrum-guided fuzzy feature extraction', IEEE Transactions on Fuzzy Systems. https://doi.org/10.1109/TFUZZ.2020.2972843

Li, Z, Zhou, Q, Zhang, Y, Li, J & Xu, H 2020, 'Enhanced intelligent proportional-integral-like fuzzy knowledge–based controller using chaos-enhanced accelerated particle swarm optimization algorithm for transient calibration of air–fuel ratio control system', Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering, vol. 234, no. 1, pp. 39-55. https://doi.org/10.1177/0954407019862079

Shuai, B, Zhou, Q, Li, J, He, Y, Li, Z, Williams, H, Xu, H & Shuai, S 2020, 'Heuristic action execution for energy efficient charge-sustaining control of connected hybrid vehicles with model-free double Q-learning', Applied Energy, vol. 267, 114900, pp. 1-11. https://doi.org/10.1016/j.apenergy.2020.114900

Zhou, Q, He, Y, Zhao, D, Li, J, Li, Y, Williams, H & Xu, H 2020, 'Modified particle swarm optimization with chaotic attraction strategy for modular design of hybrid powertrains', IEEE Transactions on Transportation Electrification. https://doi.org/10.1109/TTE.2020.3014688

He, Y, Zhou, Q, Makridis, M, Mattas, K, Li, J, Williams, H & Xu, H 2020, 'Multiobjective co-optimization of cooperative adaptive cruise control and energy management strategy for PHEVs', IEEE Transactions on Transportation Electrification, vol. 6, no. 1, 9000888, pp. 346 - 355. https://doi.org/10.1109/TTE.2020.2974588

View all publications in research portal