A method for state-of-charge estimation of lithium-ion batteries based on PSO-LSTM

作     者：Ren, Xiaoqing Liu, Shulin Yu, Xiaodong Dong, Xia 

作者机构：Qilu Univ Technol Shandong Acad Sci Dept Elect Engn & Automat Jinan 250353 Peoples R China 

出 版 物：《ENERGY》 (能)

年 卷 期：2021年第234卷

页      面：121236-121236页

核心收录：

学科分类：0820[工学-石油与天然气工程] 08[工学] 0807[工学-动力工程及工程热物理] 

基　　金：Natural Science Foundation of Shandong Province [ZR2020QF064  ZR2020ME206] 

主　　题：Lithium-ion battery SOC estimation Particle swarm optimization algorithm Long short-term memory neural network 

摘      要：State-of-charge (SOC) estimation of lithium-ion battery is one of the core functions of battery management system. In order to improve the estimation accuracy of SOC, this paper proposes a long shortterm memory neural network based on particle swarm optimization (PSO-LSTM). Firstly, the key parameters of LSTM are optimized by PSO algorithm, so that the data characteristics of lithium-ion battery can match the network topology. In addition, random noise is added to the input layer of PSO-LSTM neural network to improve the anti-interference ability of the network. Finally, experiments show that the proposed method can achieve accurate estimation under different conditions. The estimates based on PSO-LSTM converge to the real state-of-charge within an error of 0.5%. (c) 2021 Published by Elsevier Ltd.