健康状况
计算机科学
电池(电)
估计
锂离子电池
可靠性工程
功率(物理)
工程类
系统工程
量子力学
物理
作者
Lei Yao,Shiming Xu,Aihua Tang,Fang Zhou,Junjian Hou,Yanqiu Xiao,Zhijun Fu
摘要
Lithium-ion power batteries have been widely used in transportation due to their advantages of long life, high specific power, and energy. However, the safety problems caused by the inaccurate estimation and prediction of battery health state have attracted wide attention in academic circles. In this paper, the degradation mechanism and main definitions of state of health (SOH) were described by summarizing domestic and foreign literatures. The estimation and prediction methods of lithium-ion power battery SOH were discussed from three aspects: model-based methods, data-driven methods, and fusion technology methods. This review summarizes the advantages and disadvantages of the current mainstream SOH estimation and prediction methods. This paper believes that more innovative feature parameter extraction methods, multi-algorithm coupling, combined with cloud platform and other technologies will be the development trend of SOH estimation and prediction in the future, which provides a reference for health state estimation and prediction of lithium-ion power battery.
科研通智能强力驱动
Strongly Powered by AbleSci AI