基于LSTM&GRU-Attention多联合模型的锂离子电池SOH估计  

SOH estimation of lithium-ion batteries based on LSTM&GRU-Attention multijoint model

作　　者：毛百海 覃吴[1] 肖显斌[1] 郑宗明[1] MAO Baihai;QIN Wu;XIAO Xianbin;ZHENG Zongming(School of New Energy,North China Electric Power University,Beijing 102206,China)

机构地区：[1]华北电力大学新能源学院,北京102206

出　　处：《储能科学与技术》2023年第11期3519-3527,共9页Energy Storage Science and Technology

基　　金：国家科工技术基础科研项目(JSZL2022204B003)。

摘　　要：锂离子电池的健康状态(state of health,SOH)准确估计对于储能电站的稳定高效运行至关重要。为了进一步提高数据驱动方法对SOH估计的精度,本团队提出了一种利用交叉验证训练的线性回归加权融合模型的方法。首先,从放电电压曲线、充电和放电温度曲线中提取了健康特征,并使用Pearson相关系数对所选特征进行了相关性分析,确定了网络模型输入的健康因子参数。随后,通过在LSTM与GRU中加入注意力机制,建立了LSTM-Attention与GRU-Attention模型,分别以NASA电池老化数据集B0005、B0006、B0007和B0018电池的前50%作为模型训练集,用剩余数据对模型进行验证,分别得到了模型对应的ŷ_(L-A)与ŷ_(G-A)估计值,然后使用所提融合模型方法对两个估计值进行线性回归加权,结果显示该方法的最大均方根误差和平均绝对误差分别为0.00291和0.00200。最后,为验证所提模型的抗干扰能力,在输入模型的健康因子中加入不同比例的高斯白噪声,实验结果显示融合模型的抗干扰能力较强,最大均方根误差和平均绝对误差仅为0.03562和0.02889。The accurate estimation of the state-of-health(SOH)of lithium-ion batteries(LiBs)plays a critical role in ensuring the stable and efficient operation of energy storage systems.This study proposes a fusion model based on cross-validation-trained linear regression weighting to enhance the precision of data-driven methods for SOH estimation.First,health features are extracted from the discharge voltage curve as well the as charging and discharging temperature curves.Second,Pearson correlation coefficients are used to analyze the selected features,determining the healthindicator parameters for the network model inputs.Finally,attention mechanisms were incorporated into the long short-term memory(LSTM)and gated recurrent unit(GRU)to establish the LSTM-Attention and GRU-Attention models,respectively.These models are trained using the first 50%of data from NASA's battery aging datasets,B0005,B0006,B0007,and B0018,with the remaining 50%used for validation.The LSTM-and GRU-Attention models produce SOH estimates of ŷ_(L-A)and ŷ_(G-A),respectively.Then,the fusion model proposed in this study performs linear regression weighting on these two estimates,yielding a maximum root mean square error(RMSE)and mean absolute error(MAE)of 0.00291 and 0.00200,respectively.Furthermore,the robustness of the proposed model is demonstrated by subjecting the health factors input to various proportions of Gaussian white noise.The results indicate that the fusion model exhibits strong resistance to interference,with a maximum RMSE and MAE of only 0.03562 and 0.02889,respectively.

关 键 词：锂离子电池 健康状态 健康因子 LSTM-Attention GRU-Attention 线性回归加权法 

分 类 号：TM911[电气工程—电力电子与电力传动]