Professor Kang Li

Profile

Kang has over thirty years of research experience working on a wide range of control engineering applications in energy, transport and manufacturing systems. He currently holds the Chair of Smart Energy Systems in the School of Electronic and Electrical Engineering and is the Director of Institute of Communication and Power Networks. Prior to joining the University of Leeds, Kang was a Professor of Intelligent Systems and Control at the Queen’s University of Belfast from 2011-2018. Kang received his PhD in Control Theory and Applications from Shanghai Jiaotong University in 1995, and he had various research experiences in Shanghai Jiaotong University, Delft University of Technology, and the Queen’s University Belfast before he started his academic career in 2002. 

Kang was the Secretary of IEEE UK and Ireland Section (2008-2011), Chair of the IEEE UK and Ireland Control and Communication (Ireland) Joint Chapter (2009-2021), a member  of Executive Committee of UK Automatic Control Council (2011-2014), and a Vice President of The International Scientific Research Alliance on Intelligent Measurement Control and Applications of Complex Networked Systems (2022 - present). He was the executive editor of Transactions of Institute of Measurement and Control (2019-2022), and is currently an Associate Editor of several journals in his reseach areas, including IFAC Journal Control Engineering Practice, Neurocomputing, Cognitive Computation, Journal of Modern Power Systems and Clean Energy, Transportation Safety and Environment, Electricity Generation Technology, and Integrated Intelligent Energy. Kang is the founder and general co-chair of ICSEE conference series, committee chair or co-chair of over 20 conferences and workshops, and was invited to give over 80 plenaries and seminars worldwide.

Responsibilities

• Director, Institute of Communication and Power Networks

Research interests

A control engineer by training, Kang’s work spans many research topics (nonlinear system modelling and identification, control theory, human machine systems, AI and machine learning), but his greatest interest is in the development of holistic sensing, modelling, control, and optimization systems to support low carbon transition of different sectors. 

Kang’s work on energy management and control of energy intensive manufacturing processes funded by EPSRC (e.g. EP/P004636/1, EP/G059489/1, EP/F021070/1) has led to the development of a minimal-invasive edge-cloud based energy monitoring and analytic platform (Point Energy Technology) which has been successfully used in polymer processing and food processing to support process monitoring, control and energy management, winning 2015 Institute of Measurement and Control ICI prize for the best application paper, 2016 Northern Ireland Science Park INVENT award, the finalist of 2016 Sustainable Energy Awards by Ireland Sustainable Energy Authority, and 2015 Outstanding Award for Knowledge Transfer Partnership.

Kang’s research on energy and power started in 1998 when he joined the Queen’s University Belfast as a research fellow, first researching NOx emission modelling and control for coal-fired power plants, and he proposed physics-informed artificial neural network models, namely engineering-genes for modelling and simulating NOx formation in complex combustion processes, late funded by EPSRC (GR/S85191/01) and other funding sources. He has further developed fast and effective two-stage framework and algorithms for constructing artificial neural networks and regression models which have been widely cited and used in the research community. 

After gaining more insights in the harmful environmental impacts of coal-fired power plants, from 2008, Kang’s research interest has shifted to electrical vehicles and integration of renewable generation into the power grid, with funding from EPSRC (e.g.  EP/G042594/1, EP/L001063/1) and other funding resources. Kang’s research in early years covers a broad range of research topics, including power unit commitment and economic load dispatch considering renewable generations and EV charging, wide area power system monitoring and fault detection using PMUs, stochastic modelling and forecasting of wind power, control of multi-terminal HVDC systems for integrating offshore wind farms, multi-vector energy systems and energy market, microgrid and district energy management, and wireless EV charging. He then gradually focused more on battery energy management, including electro-thermal modelling of batteries for State of Charge (SoC) estimation, model predictive control (MPC) framework for battery charging and discharging considering multiple objectives and constraints, machine-learning based state of health (SoH) estimation, and development of FBG optic fibre sensing system and associated AI orchestrated data driven analytic platform for battery design, degradation analysis, and real-time key state monitoring and thermal management. These research have been highly cited, including 5 ESI highly cited papers, winning 4 best paper awards. These advanced SoC estimation methods, MPC battery control technology and fibre optic sensors have been adopted in engineering applications either through innovation projects or through research students working in the industry after graduation.  

Kang’s current biggest interest lies in the development of microgrid technology for railway and heavy-duty vehicle electrification, and farm decarbonization in collaboration with industrial partners. For example, funded by Ofgem Strategic Innovation Fund as well as by key industry sectors, he is leading the technical development and implementation of Railway Energy Hubs, a new microgrid technology to supply railway traction power from local renewables and from curtailed wind power from onshore wind farms, transforming the inflexible load (railway is the single largest electricity consumer in the UK) to flexible load, and hence support the future power grid running almost all on clean and renewable energy sources. Funded by DESNZ/Innovate UK, he is working with industrial partners to demonstrate vehicle-to-X (V2X) technology through multiple use cases to show the benefits of V2X and energy flexibility to reduce carbon footprint and improve energy efficiency, whilst addressing the limitations of smart charging for EV fleet operators. Funded by the key industry sector, he has also investigated different power supply modes and safety and maintenance issues for electric road systems. He has also been collaborating with academics and partners to design low-cost microgrid technology to support low carbon transition of developing countries (e.g. EP/R030243/1).

Kang’s current research challenge is to develop a new generation of physics-informed AI and vision-based framework and methodologies to transform the way we model and control engineering systems. 

Qualifications

• DSc, Queen’s University Belfast, 2015
• MSc in Informatics, Ulster University, 2002
• PGCert in Higher Education Teaching, Queen’s University Belfast, 2000
• PhD in Control Theory and Applications, Shanghai Jiaotong University, 1995

Professional memberships

• Senior Member, IEEE
• Fellow, HEA

Research groups and institutes

• Smart energy systems

Current postgraduate researchers

• Jialei Su
• Jiayang Zhang
• Wei Zuo
• Amir Aghazadeh
• Yejing Fan
• ‪Amin Nassaj
• Xiaofei Han
• Shihao Zhao
• Yifei Zhang
• Zhengrun Zhao
• Yingqi Zhu
• Shiyun Liu
• Renwentai Zhou
• Muhammad Afnan Habibi

Projects

<li><a href="//phd.leeds.ac.uk/project/2093-digital-twining-of-energy-hubs-to-accelerate-railway-and-power-grid-decarbonization-">Digital twining of energy hubs to accelerate railway and power grid decarbonization </a></li> <li><a href="//phd.leeds.ac.uk/project/2092-hierarchical-control-of-energy-hubs-to-deliver-services-to-both-power-and-transport-sectors">Hierarchical Control of Energy Hubs to Deliver Services to Both Power and Transport Sectors</a></li>