Data-Driven State Estimation of Li-ion Batteries Assisted with Machine Learning and FBG Sensors
The global economy will be greatly shaped by the transformed energy landscapes. Battery storage systems play an important role in decarbonizing the whole energy chain from accepting renewable generations to electrification of transport and other sectors. The talk presents some recent studies in the data-driven state estimation of Li-ion battery systems assisted with machine learning and novel fibre optical sensors.
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m.garcia-constantino@ulster.ac.uk
- Co-sponsored by Ulster University
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Kang of University of Leeds, UK
Data-Driven State Estimation of Li-ion Batteries Assisted with Machine Learning and FBG Sensors
The global economy will be greatly shaped by the transformed energy landscapes. Battery storage systems play an important role in decarbonizing the whole energy chain from accepting renewable generations to electrification of transport and other sectors. The talk presents some recent studies in the data-driven state estimation of Li-ion battery systems assisted with machine learning and novel fibre optical sensors.
Biography:
Prof Kang Li holds the Chair of Smart Energy Systems and the Director of the Institute of Communication and Power Networks at School of Electronic and Electrical Engineering, University of Leeds. A control engineer by training, Kang's work spans many research topics (non-linear 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. His work on the development of minimal-invasive cloud-based energy and condition monitoring platform (Point Energy Technology) has been successfully trialled in food processing and polymer processing industries, winning InstMC ICI prize 2015 and Northern Ireland INVENT 2016 award, and was included in the finalist of the Sustainable Energy Awards 2016 from Sustainable Energy Authority of Ireland. In battery research, his team is among the first to develop constrained multi-objective battery charging control strategies based on coupled thermoelectric battery models and has successfully adopted Fibre Bragg Grating sensor technologies for novel battery state monitoring and estimation to improve the battery "transparency". His most research interest is around the development of microgrid technology for transport and farm decarbonization in collaboration with industrial partners. In partnership with leading industrial partners in the power and transport sectors, Kang is currently leading the technological development and demonstration of railway Energy Hubs, a novel microgrid solution to revolutionize railway decarbonization, transforming the inflexible rail demand to flexible load, and providing flexibility and ancillary services to the power grid, funded by Ofgem SIF, EPSRC and industry, totalling over £12M. Kang has published over 200 journal papers and edited 18 international conference proceedings in his area, winning over 10 national and international prizes and awards, including 2019 Springer Nature's China New Development Award in recognition of the exceptional contributions to the delivery of the UN Sustainable Development Goals.
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