基于ISSA-CNN-BiGRU-Attention的锂电池健康状态评估  

State of health assessment of lithium batteries based on ISSA-CNN-BiGRU-Attention

作　　者：陈新岗 赵龙 马志鹏 李松 张知先 Chen Xingang;Zhao Long;Ma Zhipeng;Li Song;Zhang Zhixian(School of Electrical and Electronic Engineering,Chongqing University of Technology,Chongqing 400054,China;Chongqing Engineering Research Center of Energy Interconnection,Chongqing 400054,China)

机构地区：[1]重庆理工大学电气与电子工程学院,重庆400054 [2]重庆市能源互联网工程技术研究中心,重庆400054

出　　处：《电子测量技术》2024年第8期45-52,共8页Electronic Measurement Technology

基　　金：重庆市自然科学基金(CSTB2023NSCQ-MSX0337);重庆市教育委员会科学技术研究项目(KJZD-K202101103);重庆理工大学研究生教育高质量发展项目(gzlcx20233151)资助。

摘　　要：健康状态(SOH)预测对于电池管理系统至关重要。针对电池健康状态评估建模复杂、预测误差大等问题,准确的SOH预测仍需要改进。本文结合容量增量分析(ICA)和差分电压分析(DVA)方法,提出了一种改进麻雀优化算法(ISSA)-卷积神经网络(CNN)-双向门控递归单元(BiGRU)-注意力机制(Attention)的锂电池健康状态评估方法。通过对容量增量(IC)曲线和差分电压(DV)曲线进行高斯滤波处理,避免了噪声的影响。通过马里兰大学先进的生命周期工程中心(CALCE)数据进行处理,从滤波后的IC和DV曲线上提取一组新的电池老化特征,所提4个老化特征与SOH之间的Pearson相关系数在0.9以上。使用ISSA-CNN-BiGRU-Attention方法来构建电池SOH的预测模型,将所提方法与CNN、BiGRU、CNN-BiGRU等方法进行比较,实验结果表明,该方法的MAE与RMSE误差最大值分别为0.00544和0.00717,对比其他模型,具有优秀的鲁棒性和准确性,具有更好的实际使用价值。State of health(SOH) predictions are critical for battery management systems.Due to the complexity of battery health assessment modeling and large prediction errors,accurate SOH prediction still needs to be improved.In this paper,Improved sparrow search algorithm(ISSA)-Convolutional neural network(CNN)-Bidirectional Gated Recurrent Unit(BiGRU)-Attention mechanism for lithium battery health status assessment is proposed by combining capacity increment analysis(ICA) and differential voltage analysis(DVA) methods.Firstly,the capacity increment(IC) curve and differential voltage(DV) curve are processed by Gaussian filtering to avoid the influence of noise.A set of new battery aging features were extracted from the filtered IC and DV curves through the center for advanced life cycle engineering Advanced Life Cycle Engineering(CALCE) data processing.The Pearson correlation coefficient between the four aging features and SOH was above 0.9.ISSA-CNN-BiGRU-Attention method was used to construct a prediction model of battery SOH,and the proposed method was compared with CNN,BiGRU,CNN-BIGRU and other methods.Experimental results showed that the maximum MAE and RMSE errors of the proposed method were 0.005 44 and 0.007 17,respectively.Compared with other models,it has excellent robustness and accuracy,and has better practical use value.

关 键 词：锂离子电池 SOH 容量增量分析 差分电压分析 CNN BiGRU 

分 类 号：TM912[电气工程—电力电子与电力传动] TN98[电子电信—信息与通信工程]