基于冲击响应特性的锂电池SOH估计  

SOH Estimation of Lithium Batteries Based on Shock Response Characteristics

作　　者：李华森 林琼斌[1] 詹银 代妍妍 LI Hua-sen;LIN Qiong-bin;ZHAN Yin;DAI Yan-yan(School of Electrical Engineering and Automation Fuzhou University Fuzhou 350108,China;Fujian Electric Power Survey Design Institute Co.Ltd.,Fuzhou 350108,China)

机构地区：[1]福州大学电气工程与自动化学院,福建福州350108 [2]中国电建集团福建省电力勘测设计院有限公司,福建福州350108

出　　处：《电气开关》2022年第6期68-73,共6页Electric Switchgear

摘　　要：随着电动汽车快速增长和智能电网飞速发展的时代的到来,人们对电池的需求与日俱增。健康状态(SOH)是监测电池状态的关键参数,SOH决定了电池能否安全、稳定地运行。本文提出一种基于冲击响应特性的锂电池SOH快速估计方法。首先,本文提出了多种特征提取方法对冲击响应曲线进行特征提取,分别为基于小波变换方法、基于差分电压方法和基于数值微分方法。其次,引入灰色关联度分析(GRA)方法对特征进行相关性分析,并利用改进型模糊小脑模型神经网络(IFCMNN)估计SOH。最后,实验结果表明了所提方法能在较高的SOH分辨精度上实现对处在任意SOC的锂电池进行快速估计SOH,验证了所提方法的有效性。With the advent of the era of the rapid growth of electric vehicles and the rapid development of smart grids, the demand for batteries is increasing day by day.State of Health(SOH)is a key parameter for monitoring battery status, and SOH determines whether the battery can operate safely and stably.In this paper, a fast estimation method of lithium battery SOH based on shock response characteristics is proposed.Firstly, this paper proposes a variety of feature extraction methods to extract features of shock response curves, which are based on wavelet transform method, based on differential voltage method and based on numerical differentiation method.Secondly, the gray correlation analysis(GRA)method is introduced to analyze the correlation of the features, and the SOH is estimated by using the improved fuzzy cerebellar model neural network(IFCMNN).Finally, the experimental results show that the proposed method can quickly estimate the SOH of lithium batteries at any SOC with high SOH resolution accuracy, which verifies the effectiveness of the proposed method.

关 键 词：冲击响应特性 健康状态 改进型模糊小脑模型神经网络 特征提取 

分 类 号：TM91[电气工程—电力电子与电力传动]