ZHAO Xianhe;GENG Guangchao;LIN Da;LI Zhihao;ZHANG Yang;

• 1:College of Electrical Engineering
• 2:Zhejiang University
• 3:State Grid Zhejiang Electric Power Research Institute
• 4:State Grid Zhejiang Electric Power Co.

Abstract：

As lithium-ion batteries are widely applied in various energy storage systems, health management and degradation analysis now have become hot issues in many fields including operation and maintenance of energy storage power stations, the safety monitoring for electric vehicles and cascade utilization of decommissioned power lithium battery. At the same time, the development of big data and machine learning techniques have broken the constraints of the difficulty in modeling complex nonlinear systems, making it possible to estimate battery health based on data-driven methods. This paper provides a detailed overview of the current research status of data-driven lithium-ion battery health estimation, analyzes the influencing factors of battery degradation, summarizes and compares modeling methods of battery health state estimation and residual life prediction. Finally, it summarizes the current challenges and development trend in this field.

Key Words： lithium-ion battery;degradation;state of health estimation;residual life prediction;data-driven

Abstract:

Keywords:

Foundation: 国网浙江省电力有限公司科技项目（5211DS180037）

Authors: ZHAO Xianhe;GENG Guangchao;LIN Da;LI Zhihao;ZHANG Yang;

DOI: 10.19585/j.zjdl.202107011

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