Dr Ning Zhao FHEA, CEng, PhD

Department of Electronic, Electrical and Systems Engineering
Assistant Professor in Railway Systems Engineering

Contact details

Telephone: +44 (0) 121 414 3093
Fax: +44 (0) 121 414 4291
Email: n.zhao@bham.ac.uk

Address: School of Engineering
University of Birmingham
Edgbaston
Birmingham
B15 2TT
UK

Dr Ning Zhao is an Assistant Professor specialising in autonomous vehicle operations, perception systems, obstacle detection, vehicle control and digital systems. He has been awarded several grants funded by EPSRC, Innovate UK, Royal Society, and industry partners. Ning has published more than 50 papers in high-impact journals and conferences including IEEE ICRA, IEEE Transactions on ITS, Transportation Research Part C. He has a wide teaching portfolio, which includes leading the Railway Control and Digital System module of University of Birmingham’s Railway MSc programme at both Edgbaston and Dubai campuses.

Qualifications

Chartered Engineer (CEng), 2020
Fellow of Higher Education Academy (FHEA), 2019
PhD in Electronic, Electrical and Computer Engineering, University of Birmingham, 2013
MSc in Communication Engineering, University of Birmingham, 2009

Biography

Dr Ning Zhao‘s research focuses on autonomous vehicle systems, divided into two core areas: perception systems and autonomous vehicle control. By integrating advanced obstacle detection with intelligent vehicle control, his work aims to significantly enhance vehicle safety, reduce energy consumption, and increase system capacity. Moreover, his research into perception systems extends to the detection and monitoring of transport infrastructure, such as bridges and junctions. These technologies have been validated or are in long-term applications across various transportation systems, including Edinburgh Tram, Nottingham Tram (NET), Manchester Tram (Metrolink), Very Light Rail, SMRT (Singapore), Network Rail, etc., receiving a few awards such as ‘Highly Recommended Commendation’ at the Global Light Rail Awards. Ning was registered as a Chartered Engineer (CEng) in 2020, and has published more than 50 papers in high-impact journals and conferences including IEEE International Conference on Robotics and Automation (ICRA), IEEE Transactions on Intelligent Transportation Systems and Transportation Research Part C: Emerging Technologies.

Dr Ning Zhao delivers a number of teaching modules for the university, both in Birmingham and overseas (e.g., Dubai and Singapore). He is the module lead for several MSc modules, including Railway Control and Digital Systems, and Principles of Railway Control Systems. He was awarded the Fellowship of the Higher Education Academy (FHEA) in 2019. Ning has also provided supervision and support to more than 100 MSc, PhD, and UG students. Furthermore, he manages the relationship between University of Birmingham and several overseas institutions regarding undergraduate and postgraduate exchange programmes and research cooperation.

Teaching

Module lead of MSc Railway Control Systems Engineering
Module lead of MSc Railway Control Systems Engineering (Dubai)
Module lead of MSc Principles of Railway Control Systems
Lecturer of Sensing and Control for Autonomous Systems
Lecturer of MSc (Singapore) Railway Technology I: Command, Control, and Communications
Lecturer of MSc (Singapore) Railway Technology II: Command, Control, and Communications
Lecturer of MSc (Singapore) Railway Technology II: Infrastructure and Permanent Way
Lecturer of MSc (Singapore) Railway Technology II: Traction and Power Systems

Postgraduate supervision

As an academic member, Ning supervises PhD, MRes, MSc, and UG projects that fall within his research area and expertise. Please contact Ning if you would be interested in studying for a PhD or MRes in autonomous vehicle, especially in the areas shown below:

Environment cooperative perception and infrastructure-assisted sensing for vehicle operation or asset management
Eco-driving and optimal control for energy-efficient vehicle operation
Autonomous vehicle decision-making and trajectory planning
Multi-sensor calibration and real-time sensor fusion
AI-driven modelling and intelligent control in transport environments
Railway control and digital systems, including the study on ETCS, CBTC, and ATO, timetable, etc
Railway hydrogen systems, including fuel cell and battery control

Research

Ning’s main research focuses on the development of next-generation autonomous vehicle technologies that combine energy-efficient control with advanced environmental perception. The student will explore how real-time sensing, multi-sensor fusion, and intelligent algorithms can jointly enable safer, greener, and smarter vehicle operations or asset management.

Key research topics include eco-driving, environment cooperative perception, multi-sensor calibration, and AI-based vehicle control. By leveraging onboard and infrastructure-supported sensors (such as camera, radar, and LiDAR), the student’s work will enhance a vehicle’s ability to perceive its environment and respond optimally in dynamic operating conditions. Meanwhile, the student will also develop intelligent control strategies that minimise energy use while ensuring punctuality and safety.

Alternatively, the student could investigate how perception systems can be used to detect transport infrastructure (such as junctions), enabling more intelligent asset management, reducing maintenance costs, and improving overall system safety.

Core research themes include:

Environment cooperative perception and infrastructure-assisted sensing for vehicle operation or asset management
Eco-driving and optimal control for energy-efficient vehicle operation
Autonomous vehicle decision-making and trajectory planning
Multi-sensor calibration and real-time sensor fusion
AI-driven modelling and intelligent control in transport environments
Railway control and digital systems, including the study on ETCS, CBTC, and ATO, timetable, etc
Railway hydrogen systems, including fuel cell and battery control

Ning’s research team already has perception system developed and a large amount of first-hand, self-collected datasets available for use. The student will work closely with industrial partners, gaining access to experimental platforms, real-world data, and test environments.

Please contact Ning if you would be interested in studying for a PhD or MRes in the above areas.

Publications

Selected journal publications

[1]. Si, W., Gao, S., Yan, F., Zhao, N., Zhang, H., and Dong, H., Linkage-constraint criteria for robust exponential stability of nonlinear BAM system with derivative contraction coefficients and piecewise constant arguments. Information Sciences, 2022. 612, pp. 926-941.

[2]. Xu, Z., Zhao, N., Hillmansen, S., Roberts, C., and Yan, Y., Techno-Economic Analysis of Hydrogen Storage Technologies for Railway Engineering: A Review. Energies, 2022. 15(17), pp. 6467.

[3]. Zhao, N., Tian, Z., Hillmansen, S., Chen, L., Roberts, C., and Gao, S., Timetable Optimization and Trial Test for Regenerative Braking Energy Utilization in Rapid Transit Systems. Energies, 2022. 15(13), pp. 4879.

[4]. Dai, X., Chen, M., Lai, J., Chen, Y., Chen, T., and Zhao, N., Negative Sequence Compensation Method for High-Speed Railway with Integrated Photovoltaic Generation System. CPSS Transactions on Power Electronics and Applications, 2022. 7(2), pp. 130-138.

[5]. Lai, J., Chen, M., Dai, X., and Zhao, N., Energy Management Strategy Adopting Power Transfer Device Considering Power Quality Improvement and Regenerative Braking Energy Utilization for Double-Modes Traction Systems. CPSS Transactions on Power Electronics and Applications, 2022. 7(1).

[6]. Gao, S., Li, M., Zheng, Y., Zhao, N., and Dong, H., Fuzzy Adaptive Protective Control for High-Speed Trains: An Outstretched Error Feedback Approach. IEEE Transactions on Intelligent Transportation Systems, 2022, pp. 1-10.

[7]. Chen, M., Liu, S., Zhao, N., Fu, H., and Lv, Y., Fault diagnosis and location of electrified railway grounding grids based on intelligent algorithm. International Transactions on Electrical Energy Systems, 2021. 31(2), pp. e12719.

[8]. Chen, L., Chen, M., Chen, Y., Chen, Y., Cheng, Y., and Zhao, N., Modelling and control of a novel AT-fed co-phase traction power supply system for electrified railway. International Journal of Electrical Power & Energy Systems, 2021. 125, pp. 106405.

[9]. Zhao, N., Z. Tian, L. Chen, C. Roberts, and S. Hillmansen, Driving Strategy Optimization and Field Test on an Urban Rail Transit System. IEEE Intelligent Transportation Systems Magazine, 2021. 13(3): p. 34-44.

[10]. Tian, Z., Zhao, N., Hillmansen, S., Su, S., and Wen, C., Traction Power Substation Load Analysis with Various Train Operating Styles and Substation Fault Modes. Energies, 2020. 13(11).

[11]. Tian, Z., Zhao, N., Hillmansen, S., Roberts, C., Dowens, T., Kerr, C.: 'SmartDrive: Traction Energy Optimization and Applications in Rail Systems', IEEE Transactions on Intelligent Transportation Systems, 2019, pp. 1-10.

[12]. Li, Z., Chen, L., Roberts, C., Zhao, N.: 'Dynamic Trajectory Optimization Design for Railway Driver Advisory System', IEEE Intelligent Transportation Systems Magazine, 2018. 10, (1), pp. 121-132.

[13]. Wang, H., Zhao, N., Ning, B., Tang, T., Chai, M.: 'Safety monitor for train-centric CBTC system', IET Intelligent Transport Systems, 2018. 12, (8), pp. 931-938.

[14]. Dunbar, R., Roberts, C., Zhao, N.: 'A tool for the rapid selection of a railway signalling strategy to implement train control optimisation for energy saving', Journal of Rail Transport Planning & Management, 2017. 7, (4), pp. 224-244.

[15]. Zhao, N., Chen, L., Tian, Z., Roberts, C., Hillmansen, S., Lv, J.: 'Field test of train trajectory optimisation on a metro line', IET Intelligent Transport Systems, 2017. 11, (5), pp. 273-281.

[16]. Zhao, N., Roberts, C., Hillmansen, S., Tian, Z., Weston, P., Chen, L.: 'An integrated metro operation optimization to minimize energy consumption', Transportation Research Part C: Emerging Technologies, 2017. 75, pp. 168-182.

[17]. Tian, Z., Weston, P., Zhao, N., Hillmansen, S., Roberts, C., Chen, L.: 'System energy optimisation strategies for metros with regeneration', Transportation Research Part C: Emerging Technologies, 2017. 75, pp. 120-135.

[18]. Chen, M., Roberts, C., Weston, P., Hillmansen, S., Zhao, N., Han, X.: 'Harmonic modelling and prediction of high-speed electric train based on non-parametric confidence interval estimation method', International Journal of Electrical Power & Energy Systems, 2017. 87, pp. 176-186.

[19]. Chen, M., Li, Q., Roberts, C., Hillmansen, S., Tricoli, P., Zhao, N., Krastev, I.: 'Modelling and performance analysis of advanced combined co-phase traction power supply system in electrified railway', IET Generation, Transmission & Distribution, 2016. 10, (4), pp. 906-916.

[20]. Tian, Z., Hillmansen, S., Roberts, C., Weston, P., Zhao, N., Chen, L., Chen, M.: 'Energy evaluation of the power network of a DC railway system with regenerating trains', IET Electrical Systems in Transportation, 2016. 6, (2), pp. 41-49.

[21]. Umiliacchi, S., Nicholson, G., Zhao, N., Schmid, F., Roberts, C.: 'Delay management and energy consumption minimisation on a single-track railway', IET Intelligent Transport Systems, 2016. 10, (1), pp. 50-57.

[22]. Zhao, N., Roberts, C., Hillmansen, S., Nicholson, G.: 'A Multiple Train Trajectory Optimization to Minimize Energy Consumption and Delay', Intelligent Transportation Systems, IEEE Transactions on, 2015. 16, (5), pp. 2363 - 2372.

[23]. Wang, H., Zhao, N., Chen, L.: 'An integrated capacity evaluation method for CBTC- system-equipped urban rail lines', Proceedings of the Institution of Mechanical Engineers, Part F: Journal of Rail and Rapid Transit 2015. 229, (3), pp. 291-302.

[24]. Zhao, N., Roberts, C., Hillmansen, S.: 'The application of an enhanced Brute Force Algorithm to minimise energy costs and train delays for differing railway train control systems', Proceedings of the Institution of Mechanical Engineers, Part F: Journal of Rail and Rapid Transit, 2014. 228, (2), pp. 158-168.

Selected conference publications

[1]. Jibrin, R., Hillmansen, S., Roberts, C., Zhao, N., and Tian, Z. Convex Optimization of Speed and Energy Management System for Fuel Cell Hybrid Trains. in 2021 IEEE Vehicle Power and Propulsion Conference (VPPC). 2021.

[2]. Tian, Z., Zhang, G., Zhao, N., Hillmansen, S., Tricoli, P., Roberts, C.: 'Energy Evaluation for DC Railway Systems with Inverting Substations'. in 2018 IEEE International Conference on Electrical Systems for Aircraft, Railway, Ship Propulsion and Road Vehicles & International Transportation Electrification Conference (ESARS-ITEC). 2018.

[3]. Zhao, N., Saade, L., Chen, Z., Stewart, E., Roberts, C., Yuan, M., Zhang, T., Cai, J.: 'Fault Analysis of the Switch and Point Machine to Improve Railway Reliability', in The Stephenson Conference: Research for Railway. 2017, London.

[4]. Tian, Z., Weston, P., Hillmansen, S., Roberts, C., Zhao, N.: 'System energy optimisation of metro-transit system using Monte Carlo Algorithm'. in 2016 IEEE International Conference on Intelligent Rail Transportation (ICIRT). 2016.

[5]. Hamid, H.A., Nicholson, G.L., Douglas, H., Zhao, N., Roberts, C.: 'Investigation into train positioning systems for saving energy with optimised train trajectories'. in 2016 IEEE International Conference on Intelligent Rail Transportation (ICIRT). 2016.

[6]. Zhao, N., Tian, Z., Hillmansen, S., Roberts, C., Yuan, M., Li, J., Shi, H., Li, K.: 'Metro Traction and Power System Energy Optimisation', in The Stephenson Conference. 2015, London. pp. 191-198.

[7]. Tian, Z., Hillmansen, S., Roberts, C., Western, P., Chen, L., Zhao, N., Su, S., Xin, T.: 'Modeling and Simulation of DC Rail Traction Systems for Energy Saving', in The 17th International IEEE Conference on Intelligent Transportation Systems. 2014, IEEE, Qingdao. pp. 2354-2359.

[8]. Zhao, N., Roberts, C., Hillmansen, S., Western, P., Chen, L., Tian, Z., Xin, T., Su, S.: 'Train Trajectory Optimisation of ATO Systems for Metro Lines', in The 17th International IEEE Conference on Intelligent Transportation Systems. 2014, IEEE, Qingdao. pp. 1796-1801.

[9]. Xin, T., Roberts, C., Hillmansen, S., Western, P., Zhao, N., Chen, L., Tian, Z., Su, S.: 'Railway Vertical Alignment Optimisation at Stations to Minimize Energy', in The 17th International IEEE Conference on Intelligent Transportation Systems. 2014, IEEE, Qingdao. pp. 2119-2124.

[10]. Lu, S., Western, P., Zhao, N.: 'Maximise the Regenerative Braking Energy using Linear Programming'. in 17th International IEEE Conference on Intelligent Transportation Systems (ITSC). 2014.

[11]. Zhao, N., Roberts, C., Hillmansen, S.: 'An approach for optimising railway traffic flow on high speed lines with differing signalling systems', in Comprail 2012, C.A. Brebbia, N. Tomii, J.M. Mera, B. Ning, and P. Tzieropoulos, Editors. 2012, WIT Press, New Forest. pp. 11.