基于EIS和神经网络的退役电池SOH快速估计

doi:10.19799/j.cnki.2095-4239.2021.0503

储能科学与技术 ›› 2022, Vol. 11 ›› Issue (2): 673-678.doi: 10.19799/j.cnki.2095-4239.2021.0503

基于EIS和神经网络的退役电池SOH快速估计

耿萌萌¹(), 范茂松¹, 杨凯¹(), 赵光金², 谭震¹, 高飞¹, 张明杰¹

^1.中国电力科学研究院有限公司，北京 100192
^2.国网河南省电力公司电力科学研究院，河南郑州 450052

收稿日期:2021-09-27 修回日期:2021-10-14 出版日期:2022-02-05 发布日期:2022-02-08
通讯作者: 杨凯 E-mail:gengmengmeng@epri.sgcc.com.cn;ykbit@126.com
作者简介:耿萌萌（1989—），女，硕士，工程师，主要研究方向为储能技术，E-mail：gengmengmeng@epri.sgcc.com.cn；
基金资助:
国家电网公司总部科技项目“梯次利用动力电池快速分选重组技术和标准体系框架研究”(5419-201955214A-0-0-00)

Fast estimation method for state-of-health of retired batteries based on electrochemical impedance spectroscopy and neural network

Mengmeng GENG¹(), Maosong FAN¹, Kai YANG¹(), Guangjin ZHAO², Zhen TAN¹, Fei GAO¹, Mingjie ZHANG¹

^1.China Electric Power Research Institute, Beijing 100192, China
^2.State Grid Henan Electric Power Research Institute, Zhengzhou 450052, Henan, China

Received:2021-09-27 Revised:2021-10-14 Online:2022-02-05 Published:2022-02-08
Contact: Kai YANG E-mail:gengmengmeng@epri.sgcc.com.cn;ykbit@126.com

摘要/Abstract

摘要：

为了提高退役电池健康状态估计的速度和精度，针对某电动大巴车退役的方形磷酸铁锂电池，选取其中8只电池继续进行循环老化实验，并在不同循环周期后进行电化学阻抗测试。根据锂离子电池阻抗特性，提取300 Hz、60 Hz以及1 Hz下的实部、虚部和模值为特征参量，将测试时间由十几分钟缩短至几秒钟。以特征参量为输入参数，结合BP神经网络算法，搭建了基于电化学阻抗和BP神经网络的退役电池健康状态快速估计模型，采用19组未参与模型训练的数据对模型进行验证，验证样本的健康状态估计值的平均绝对百分误差(MAPE)为1.46%，均方根误差(RMSE)为1.60%，结果表明整体误差较低。该方法估测精度高，测试时间短，实现了退役电池健康状态快速估计，更有利于实际应用。

关键词: 交流阻抗谱, BP神经网络, 退役电池, 健康状态

Abstract:

To improve the speed and accuracy of estimating the state-of-health (SOH) of decommissioned batteries, for retired prismatic lithium-iron-phosphate batteries of certain electric buses, eight batteries were selected to continue the cyclic aging experiment, and electrochemical impedance tests were conducted for different cycles. According to the impedance characteristics of lithium-ion batteries, the real part, imaginary part, and modulus at 300 Hz, 60 Hz, and 1 Hz were extracted as characteristic parameters, which reduced the test time from 10 min to a few seconds. Using characteristic parameters as input parameters, combined with BP neural network algorithm, we developed a fast estimation model of retired battery health based on electrochemical impedance and BP neural network and verified the model using 19 datasets that did not participate in model training. The average absolute percentage error of the verified sample's SOH estimate was 1.46%, and the root-mean-square error was 1.60%. The results show that the overall error is low. The method has high estimation accuracy and short test time, realizes rapid estimation of the health status of retired batteries, and is conducive for practical applications.

Key words: electrochemical impedance spectroscopy, BP neural network, retired battery, state-of-health

中图分类号:

TM 912

耿萌萌, 范茂松, 杨凯, 赵光金, 谭震, 高飞, 张明杰. 基于EIS和神经网络的退役电池SOH快速估计[J]. 储能科学与技术, 2022, 11(2): 673-678.

Mengmeng GENG, Maosong FAN, Kai YANG, Guangjin ZHAO, Zhen TAN, Fei GAO, Mingjie ZHANG. Fast estimation method for state-of-health of retired batteries based on electrochemical impedance spectroscopy and neural network[J]. Energy Storage Science and Technology, 2022, 11(2): 673-678.

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频率	实部	虚部	模值
300 Hz	Z′₃₀₀	Z″₃₀₀	\|Z\|₃₀₀
60 Hz	Z′₆₀	Z″₆₀	\|Z\|₆₀
1 Hz	Z′₁	Z″₁	\|Z\|₁

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基于EIS和神经网络的退役电池SOH快速估计

Fast estimation method for state-of-health of retired batteries based on electrochemical impedance spectroscopy and neural network

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