Kang Li

Clean Growth Committee Member

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

A control engineer by training, his work spans many research topics, covering nonlinear system modelling and identification, intelligent control, and AI and machine learning, but his greatest interest is on the development of holistic sensing, modelling, control, and optimization techniques to support low carbon transition of different sectors. His work on energy management and control of energy intensive manufacturing processes funded by EPSRC 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 has made systematic contributions to battery energy management, covering electro-thermal modelling of batteries for State of Charge estimation, model predictive control framework for battery charging and discharging considering multiple objectives and constraints, machine-learning based state of health estimation, and development of FBG optic fibre sensing system and associated AI orchestrated data driven analytic platform for battery management. These research works have been highly cited and adopted in engineering applications either through innovation demonstration projects or through research students working in the industry after graduation. Funded by Ofgem, EPSRC and industry, he is currently leading the development of a new microgrid technology, namely railway energy hubs to accelerate railway decarbonization, with the first live demonstrator to be built in Scotland, with the purpose to support the roll-out of the energy hub technology to as many of 2500 railway stations across UK, providing services of substantial scale to both railway and power grids.