基于多模型组合和EIS的锂电池SOH和URL预测

电池健康状态（state of health, SOH）和剩余使用寿命（remaining useful life, RUL）是评价电池健康程度和剩余寿命的重要技术指标。SOH和RUL的估计是电池管理系统的重要组成部分，是实现电池管理系统智能监控和科学运营的基础。电池电化学阻抗谱（electrochemical impedance spectroscopy，EIS）是一种用于表征电池内部电化学过程的测试方法，它具备精度高和非侵入性损害等优点。多种研究表明，电池阻抗谱EIS与电池的SOH和RUL存在一些内在的联系，因此成为电化学领域的研究热点。基于EIS预测SOH和RUL，传统机器学习方法比较成熟，但预测精度和稳定性仍有局限，难以完全挖掘电池衰减规律。因此，需要与深度学习等方法相结合才能提高预测性能。将降维模型和多种深度学习模型引入SOH和RUL预测领域，并对模型进行有效组合，取得了很好的效果。将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，首先使用主成分分析（principle component analysis，PCA）模型对EIS值进行降维，提炼出10个精炼的主成分，然后使用卷积神经网络（convolution neural network，CNN）模型提取EIS的空间特征，使用双向长短期记忆网络(bidirectional long short-term memory，BiLSTM）模型提取EIS时间序列变化规律，使用注意力（attention）机制进一步选取EIS数据的时空特征中的重要部分，共同预测SOH和RUL。在测试数据上进行实验表明，SOH预测的均方误差（root mean square error, RMSE）达到0.146 8，RUL预测的均方误差达到2.614 5，效果均好于传统的方法。

Prediction of SOH and URL for Lithium Battery Based on Multiple Model Combination and EIS

The State of Health (SOH) and Remaining Useful Life (RUL) of batteries are important technical indicators for evaluating the health level and remaining lifespan of batteries. The estimation of SOH and RUL is an important component of the battery management system and the foundation for achieving intelligent monitoring and scientific operation of the battery management system.Electrochemical Impedance Spectroscopy (EIS) is a testing method used to characterize the internal electrochemical processes of batteries, which has the advantages of high accuracy and non-invasive damage. Various studies have shown that there are some inherent connections between battery impedance spectroscopy (EIS) and the SOH and RUL of batteries, making it a research hotspot in the field of electrochemistry. Based on EIS to predict SOH and RUL,traditional machine learning methods are relatively mature, but there are still limitations in prediction accuracy and stability, making it difficult to fully explore the decay patterns of batteries. Therefore, it is necessary to combine with methods such as deep learning to improve prediction performance. Dimensionality reduction models and various deep learning models were introduced into the fields of SOH and RUL prediction, and the models were effectively combined, achieving good results. The real and imaginary data was arranged corresponding to all frequencies of the EIS as frequency features. Firstly, the Principal Component Analysis (PCA) model was used to reduce the dimensionality of the EIS values,10 refined principal components were extracted, and then the Convolutional Neural Network (CNN) model was used to extract the spatial features of the EIS, Using the Bidirectional Long Short Term Memory (BiLSTM) model to extract the variation patterns of EIS time series, using the Attention mechanism to further select important parts of the spatiotemporal features of EIS data, and SOH and RUL were jointly predicted. Experiments on test data show that the Root Mean Square Error (RMSE) of SOH prediction reaches 0.1468, and the mean square error of RUL prediction reaches 2.6145, both of which are better than traditional methods.