基于神经网络和电化学阻抗谱的锂电池重要频率分析及电池健康状态预测

电化学阻抗图谱是电池的一种非侵入性的信息，与电池的状态、剩余寿命以及健康度之间存在内在联系。本文将电化学阻抗谱(electrochemical impedance spectroscopy, EIS)所有频率对应的实部和虚部数据依次排列作为频率特征，基于神经网络模型，将EIS的频率特征作为输入特征，构建了EIS与电池健康状态(state of health, SOH)之间的拟合关系，结果表明均方根误差可以达到0.778 9。通过对EIS中各个频率特征的重要性进行计算，得到重要频率的赫兹取值范围，研究发现EIS高频和低频频率比较重要。通过对重要频率特征进行相关性分析，进一步得到重要频率特征为f₆₁、f₆₅和f₉₁,对应的频率分别是20 004.453 Hz、7 835.48 Hz和17.796 13 Hz。模型拟合结果表明，在预测电池SOH的模型中，基于EIS的16个最重要频率特征的模型拟合效果与全部120个频率特征的模型拟合效果相当甚至有所提升，均方根误差为0.624,为预测电池SOH时缩小EIS检测范围提供了参考。

Important Frequency Analysis and Battery Health Prediction of Lithium Batteries Based on Neural Network and Electrochemical Impedance Spectroscopy

Electrochemical impedance spectroscopy (EIT) is a kind of non-invasive information of the battery, which is intrinsically related to the state, remaining life and health of the battery. In this paper, the real and imaginary data corresponding to all frequencies of EIS were arranged in sequence as frequency features. Based on the neural network model, the frequency features of EIS were taken as input features, and the fitting relationship between battery EIS and battery SOH was constructed. The results show that the root-mean-square error can reach 0.7789. By calculating the importance of each frequency feature in EIS, the Hertz value range of important frequencies is obtained. It is found that high frequency and low frequency of EIS are more important. Through the correlation analysis of important frequency features, it is further obtained that the important frequency features are f61, f65 and f91, and the corresponding frequencies are 20004.453HZ, 7835.48HZ and 17.79613HZ, respectively. The model fitting results show that the fitting effect of the 16 most important frequency features based on EIS is equivalent to or even improved with the fitting effect of all 120 frequency features, and the root-mean-square error is 0.624, which provides a reference for narrowing the detection range of EIS when predicting battery SOH.