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Shunli Wang, Speaker at Catalysis Conferences
Smart Energy Storage Institute, China
Title : Large-scale energy storage safety warning and risk monitoring


As an important component of the smart grid energy storage system, high-precision state of health  estimation of lithium-ion batteries is crucial for ensuring the power quality and supply capacity of the smart grid. To achieve this goal, an improved integrated algorithm based on multiple layer kernel extreme learning machine and genetic particle swarm optimization algorithm is proposed to estimate the SOH of Lithium-ion batteries. Kernel function parameters are used to simulate the update of particle position and speed, and genetic algorithm is introduced to select, cross and mutate particles. The improved particle swarm optimization is used to optimize the extreme value to improve prediction accuracy and model stability. The cycle data of different specifications of LIB units are processed to construct the traditional high-dimensional health feature dataset and the low-dimensional fusion feature dataset, and each version of ML-ELM network is trained and tested separately. The numerical analysis of the prediction results shows that the root mean square error  of the comprehensive algorithm for SOH estimation is controlled within 0.66%. The results of the multi-indicator comparison show that the proposed algorithm can track the true value stably and accurately with satisfactory high accuracy and strong robustness, providing guarantees for the efficient and stable operation of the smart grid.


Prof. Shunli Wang is a Doctoral Supervisor, Academic Dean, Academic Leader of the National Electrical Safety and Quality Testing Center, Academician of the Russian Academy of Natural Sciences, Provincial Senior Overseas Talent, Provincial level scientific and technological talents, Academic and Technical Leader of China Science and Technology City, and top 2% of top scientists in the world, who is an authoritative expert in renewable energy research. His research interests include modeling, state estimation, and safety management for energy storage systems. 56 projects have been undertaken, including the projects from the National Natural Science Foundation of China and the Provincial Science and Technology Department. 258 research papers have been published (Research Interest Score: 11547; Citations: 3167; h-index: 29). 53 intellectual property rights have been applied, of which 23 authorizations have been approved. 9 books have been published by famous publishers such as Elsevier and IET. 23 honorary titles or awards have been achieved, such as young scholars and leading experts of innovative talent teams. Also, the team has been continuously supported by the "University & Enterprise Innovative Talent Team Support Plan" and unanimously praised by employers and peer experts.