关键词: 锂离子电池, 健康状态, 改进鹈鹕优化算法, ＢＰ神经网络

Abstract: Lithium battery health status (SOH) is the basis for stable battery operation.Improving the accuracy of SOH estimation of lithium batteries can effectively improve their operational reliability.In order to improve the accuracy of SOH estimation of lithium batteries,an estimation model based on improved Pelican optimization algorithm (POA) combined with back propagation (BP) neural network is built.Firstly,several groups of health factors related to lithium battery SOH are extracted through NASA public data set,and a correlation analysis is made,and health factors with good correlation are selected as model inputs.Then the weights and thresholds of BP neural network are optimized by the improved POA algorithm.Compared with BP neural network,particle swarm optimization algorithm (PSO) combined with BP neural network and POA algorithm combined with BP neural network,the proposed method has a lower root-mean-square error and a higher determination coefficient,and thus possesses more practical application values.