摘要
针对目前电化学储能电池的健康状态(state of health,SOH)评估精度有限的问题,首先通过分析数据相关性,选取充电过程中电压达到3.25 V后的10 min、达到3.35 V前的20 min以及达到3.40 V前30 min的电压变化值作为健康因子,作为模型的输入参量,输入到遗传算法优化的长短期记忆神经网络中以实现储能电池的SOH评估。为了验证评估方法的有效性,选取5支软包磷酸铁锂电池进行循环老化实验,得到健康因子和实际的SOH值用于建模,将评估结果与实际SOH值进行对比。用均方误差(mean square error,MSE)与平均绝对百分比误差(mean absolute percentage error,MAPE)对评估结果进行量化,结果表明,模型的SOH评估准确,相较于未被优化的长短期记忆神经网络,MSE和MAPE分别降低了48.3%与74.1%。
To address the issue of limited accuracy of state of health(SOH)of electrochemical energy storage batteries evaluation,firstly,through the analysis of data correlation,the voltage variation values in 10 minutes after the voltage reaches 3.25 V,20 minutes before reaching 3.35 V and 30 minutes before reaching 3.4 V during the charging process are selected as health factors.These factors are utilized as input parameters of the model in a long short-term memory neural network optimized through genetic algorithms to facilitate SOH assessment for energy storage batteries.To validate the effectiveness of this evaluation method,five soft pack LiFePO4 batteries are subjected to cyclic aging experiments,obtaining health factors alongside actual SOH values for model development.The evaluation results are then compared with the actual SOH values obtained from testing.The evaluation results are quantified using mean square error(MSE)and mean absolute percentage error(MAPE),which shows that the SOH evaluation of the model is accurate,and MSE and MAPE are reduced by 48.3%and 74.1%respectively,compared with the unoptimized long short-term memory neural network.
作者
耿萌萌
范茂松
官亦标
魏斌
GENG Mengmeng;FAN Maosong;GUAN Yibiao;WEI Bin(China Electric Power Research Institute,Beijing 100192,China)
出处
《湖南电力》
2025年第1期8-13,共6页
Hunan Electric Power
基金
国家电网有限公司科技项目(5419-202323785A-3-8-KJ)。
关键词
储能电池
健康状态
片段数据
长短期记忆神经网络
energy storage batteries
state of health
fragment data
long short-term memory
