Estimation of state of health based on charging characteristics and back-propagation neural networks with improved atom search optimization algorithm

作     者：Yu Zhang Yuhang Zhang Tiezhou Wu Yu Zhang;Yuhang Zhang;Tiezhou Wu

作者机构：Hubei University of TechnologyHubei Key Laboratory of Solar Energy Efficient Utilization and Energy Storage Operation ControlHubeiWuhan 430068P.R.China 

出 版 物：《Global Energy Interconnection》 (全球能源互联网（英文版）)

年 卷 期：2023年第6卷第2期

页      面：228-237页

核心收录：

学科分类：12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 0808[工学-电气工程] 081104[工学-模式识别与智能系统] 08[工学] 0835[工学-软件工程] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：supported by National Natural Science Foundation of China (Grant No. 51677058) 

主　　题：State of health Lithium-ion battery Dt_DT Improved atom search optimization algorithm 

摘      要：With the rapid development of new energy technologies, lithium batteries are widely used in the field of energy storage systems and electric vehicles. The accurate prediction for the state of health(SOH) has an important role in maintaining a safe and stable operation of lithium-ion batteries. To address the problems of uncertain battery discharge conditions and low SOH estimation accuracy in practical applications, this paper proposes a SOH estimation method based on constant-current battery charging section characteristics with a back-propagation neural network with an improved atom search optimization algorithm. A temperature characteristic, equal-time temperature variation(Dt_DT), is proposed by analyzing the temperature data of the battery charging section with the incremental capacity(IC) characteristics obtained from an IC analysis as an input to the data-driven prediction model. Testing and analysis of the proposed prediction model are carried out using publicly available datasets. Experimental results show that the maximum error of SOH estimation results for the proposed method in this paper is below 1.5%.