全球能源互联网2025，Vol.8Issue(1)：57-66,10.DOI:10.19705/j.cnki.issn2096-5125.2025.01.007
基于片段数据的储能电池SOH估计
SOH Estimation of Energy Storage Batteries Based on Fragmented Data
摘要
Abstract
To improve the accuracy and engineering adaptability of State of Health(SOH)for energy storage batteries,firstly,by analysing the operating data of a certain solar energy storage power station,design simulated operating conditions experiments.Based on the characteristics of the data and aging mechanism,the charging voltage difference within 30 minutes before 3.41 V(with time intervals of 1 minute,3 minutes,and 5 minutes respectively)was selected as the characteristic parameter for evaluating the SOH of energy storage batteries.Using the cyclic data of a 20 Ah lithium iron phosphate battery and combining it with a genetic algorithm(GA)improved the back propagation(BP)neural network for modelling,the accuracy of the model was verified using 1200 sessions of data that were not involved in model training.Among them,the model with a 1-minute interval voltage difference as the characteristic parameter had the highest accuracy,and the mean absolute percentage error(MAPE)was 0.37%,the root mean square error(RMSE)is 0.456 5.Secondly,model transfer reduced the maximum error in estimating SOH for 260 Ah lithium iron phosphate batteries from 5.52%to 1.89%.Finally,the model was used to perform batch SOH estimation on a cluster of energy storage battery cells in a photovoltaic power station,with good engineering adaptability.关键词
储能锂离子电池/片段数据/GA-BP神经网络/健康状态估计/模型迁移Key words
energy storage lithium-ion batteries/fragmented data/GA-BP neural network/estimation of SOH/model migration分类
动力与电气工程引用本文复制引用
耿萌萌,范茂松,魏斌,张明杰,胡晨..基于片段数据的储能电池SOH估计[J].全球能源互联网,2025,8(1):57-66,10.基金项目
国家电网有限公司科技项目(5100-202155307A-0-0-00).Science and Technology Foundation of SGCC(5100-202155307A-0-0-00). （5100-202155307A-0-0-00）
