基于TCT-PSA模型的锂离子电池健康状态评估  

Lithium-ion battery state of health estimation based on TCT-PSA modeling

作　　者：李红磊 刘勋川 高强 贺国刚 韩松[1] Li Honglei;Liu Xunchuan;Gao Qiang;He Guogang;Han Song(School of Electrical Engineering,Guizhou University,Guiyang 550025,China;Guizhou Jinyuan Green Chain Logistics Development Co.,Ltd.,Guiyang 550081,China;State Grid Liaoning Electric Power Company Limited Dandong Power Supply Company,Dandong 118000,China)

机构地区：[1]贵州大学电气工程学院,贵阳550025 [2]贵州金元绿链物流开发有限公司,贵阳550081 [3]国网辽宁省电力有限公司丹东供电公司,丹东118000

出　　处：《电子测量技术》2024年第12期122-131,共10页Electronic Measurement Technology

基　　金：贵州省优秀青年科技人才项目(黔科合平台人才[2021]5645);贵州省科技支撑计划项目([2023]329);贵州省科技支撑计划项目([2023]290)资助。

摘　　要：针对传统估计方法估计精度低的问题,本文提出了一种基于时空卷积网络与金字塔分割注意力融合Transformer(TCT-PSA)模型框架的新型健康状态(SOH)估计方法。首先对NASA电池数据集进行预处理,然后从锂离子电池充电阶段提取健康因子(HF),采用Pearson相关系数和灰色关联分析法量化HF与锂离子电池SOH的相关性,并将相关性高的HF输入到TCT-PSA模型中,SOH为模型输出。为了验证模型的有效性,利用TCT-PSA模型估计各组电池容量衰减;使用不同模型估计各组电池的SOH并进行比较,并利用分位数估计,验证TCT-PSA模型的准确性和鲁棒性。实验结果表明,通过对各组电池测试集和训练集容量衰减的平均绝对估计误差的验证,所提模型的误差均在2%以内;所提模型估计各组电池SOH的平均绝对误差(MAE)、平均绝对百分比误差(MAPE)和均方根误差(RMSE)均在0.035以内;在锂离子电池SOH分位数估计中最高精度达到99.82%。Aiming at the problem of low estimation accuracy of traditional estimation methods,this paper proposes a novel state of health(SOH)estimation method based on the framework of spatio-temporal convolutional network and Pyramid Squeeze Attention Fusion(TCT-PSA)model.Convolutional Transformer-Pyramid Squeeze Attention(TCT-PSA)modeling framework for a novel state of health(SOH)estimation method.The NASA battery dataset was first preprocessed,and then the health factor(HF)was extracted from the charging stage of lithium-ion batteries,and the correlation between HF and SOH of lithium-ion batteries was quantified by using Pearson′s correlation coefficient and grey correlation analysis,and the HF with high correlation was inputted into the TCT-PSA model,and SOH was the model output.In order to verify the validity of the model,the TCT-PSA model was used to estimate the capacity degradation of each group of batteries;the SOH of each group of batteries was estimated using different models and compared,and the quantile estimation was used to verify the accuracy and robustness of the TCT-PSA model.The experimental results show that the errors of the proposed models are within 2%by validating the average absolute estimation errors of the capacity decays in the test and training sets of each group of batteries;the average absolute error(MAE),the average absolute percentage error(MAPE),and the root mean square error(RMSE)of the proposed models for estimating the SOH of each group of batteries are within 0.035;and the highest accuracy of the quartile estimation of the SOH of lithium-ion batteries is 99.82%.The highest accuracy reaches 99.82%.

关 键 词：锂离子电池 TCT-PSA SOH估计 HF Pearson相关系数 灰色关联分析法 

分 类 号：TN-9[电子电信]