电力系统自动化2024，Vol.48Issue(4)：160-168,9.DOI:10.7500/AEPS20230627011
基于双向长短期记忆网络含间接健康指标的锂电池SOH估计
State-of-health Estimation for Lithium-ion Batteries Incorporating Indirect Health Indicators Based on Bi-directional Long Short-term Memory Networks
摘要
Abstract
Rapid and accurate estimation of the state of health(SOH)of lithium-ion batteries throughout their entire life cycle can help improve the safety and reliability of energy storage equipment.An SOH estimation model is proposed,which combines indirect health indicators(IHIs)with bi-directional long short-term memory(BiLSTM)network optimized by the whale optimization algorithm(WOA).The model takes into account the influence of future states on the current SOH.First,the constant current-constant voltage charging and discharging process of lithium-ion battery is analyzed,multiple time characteristics of voltage,current,and temperature that dynamically change with charging and discharging cycles are extracted as IHIs,and the indicator of discharging load voltage drop time is added.Then,through correlation analysis,selected IHIs with high correlation to capacity are set as input features.Finally,a BiLSTM network optimized by WOA is established as the battery SOH estimation model,and the NASA lithium-ion battery dataset is used to estimate the battery SOH under two different operating conditions.The results indicate that the proposed method can effectively improve the estimation accuracy of SOH.关键词
健康状态/锂离子电池/间接健康指标/鲸鱼优化算法/双向长短期记忆网络Key words
state of health/lithium-ion battery/indirect health indicator/whale optimization algorithm/bi-directional long short-term memory network引用本文复制引用
方斯顿,刘龙真,孔赖强,牛涛,陈冠宏,廖瑞金..基于双向长短期记忆网络含间接健康指标的锂电池SOH估计[J].电力系统自动化,2024,48(4):160-168,9.基金项目
国家电网公司科技项目(5108-202218280A-2-314-XG). This work is supported by State Grid Corporation of China(No.5108-202218280A-2-314-XG). （5108-202218280A-2-314-XG）
