中国电力2024，Vol.57Issue(6)：37-44,8.DOI:10.11930/j.issn.1004-9649.202312106
基于LWOA-LSTM的大容量锂电池SOC估计
SOC Estimation of Large Capacity Lithium Batteries Based on LWOA-LSTM
摘要
Abstract
Accurate prediction of the state of charge(SOC)of lithium batteries is crucial for their safe operation,and analyzing the SOC in different power grid modes is the basis for the comprehensive promotion of lithium batteries.This paper proposes a whale optimization algorithm based on Levy flight(LWOA)to optimize long short-term memory neural network(LSTM)for estimating the SOC of large capacity lithium-ion batteries in frequency modulation mode.Firstly,the LSTM neural network and LWOA algorithm are analyzed,and the LWOA-LSTM model is constructed to optimize the parameters.Then,the experimental data of the large capacity lithium-ion battery pack in frequency modulation mode are selected for data preprocessing and model training.Finally,SOC estimation of lithium batteries in frequency modulation mode is achieved.The experimental results show that the constructed model can accurately predict the SOC of lithium batteries.Compared with the WOA-LSTM model,the evaluation indicators RMSE and MAE are reduced by 25.55% and 28.71%,respectively,while R2 increases by 0.76%.关键词
荷电状态/锂电池/鲸鱼优化算法/长短时记忆网络/调频模式Key words
state of charge/lithium batteries/whale optimization algorithm/LSTM/frequency modulation mode引用本文复制引用
马宏忠,宣文婧,朱沐雨,陈悦林..基于LWOA-LSTM的大容量锂电池SOC估计[J].中国电力,2024,57(6):37-44,8.基金项目
国家自然科学基金资助项目(双馈异步发电机内部故障的振动(学)机理分析与机电(声)融合诊断研究,51577050) （双馈异步发电机内部故障的振动(学）
国家电网有限公司科技项目(不同网储互动模式下储能电池安全性能研究,J2022158). This work is supported by National Natural Science Foundation of China(Vibration Mechanism Analysis and Electromechanical(Acoustic)Fusion Diagnosis Research on Internal Faults of Doubly Fed Asynchronous Generators,No.51577050)and Science&Technology Project of SGCC(Research on Safety Performance of Energy Storage Batteries under Different Network Storage Interaction Modes,No.J2022158). （不同网储互动模式下储能电池安全性能研究,J2022158）
