摘要
为了提高储能电池健康状态(state of health,SOH)精度及工程适应性,首先,分析某光储电站的运行数据,设计模拟工况实验,结合数据特点和老化机理,选取了3.41 V前30 min内充电电压差(时间间隔分别为1 min、3 min及5 min)作为储能电池健康状态评价的特征参量,并利用20 Ah磷酸铁锂电池的循环数据,结合遗传算法(genetic algorithm,GA)改进的反向传播(back propagation,BP)神经网络进行建模。利用1200次未参与模型训练的数据对模型的精度进行验证,其中1 min间隔电压差为特征参量的模型精度最高,平均绝对百分误差(mean absolute percentage error,MAPE)为0.37%,均方根误差(root mean square error,RMSE)为0.456 5。其次,通过模型迁移,将260 Ah磷酸铁锂电池SOH估计最大误差由5.52%降低到1.89%。最后,利用该模型对光储电站中一簇储能电池单体进行了批量SOH估计,工程适应性良好。
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 0.4565.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.
作者
耿萌萌
范茂松
魏斌
张明杰
胡晨
GENG Mengmeng;FAN Maosong;WEI Bin;ZHANG Mingjie;HU Chen(China Electric Power Research Institute Co.,Ltd.,Haidian District,Beijing 100192,China)
出处
《全球能源互联网》
北大核心
2025年第1期57-66,共10页
Journal of Global Energy Interconnection
基金
国家电网有限公司科技项目(5100-202155307A-0-0-00)。
