State of Health Estimation of Lithium Ion Battery Based on Denoising Autoencoder-Gated Recurrent Unit
Chen Yuan1, Duan Wenxian2, He Yigang3, Huang Xiaohe1
1. College of Artificial Intelligence Anhui University Hefei 230601 China; 2. State Key Laboratory of Automotive Simulation and Control Jilin University Changchun 130022 China; 3. School of Electrical Engineering and Automation Wuhan University Wuhan 430000 China
Abstract：The state-of-health (SOH) estimation of lithium-ion batteries is a key technology in battery management systems, which can ensure the safe operation of the battery system. In practical applications, sensors for voltage and current collection are susceptible to external environmental interference, and the collected data often contains a large amount of noise. The estimation accuracy of the battery SOH model largely depends on the parameters in the algorithm, and some methods are usually not universal. In addition, the charging and discharging conditions of the battery are not complete, so the extracted feature information can easily deviate from the degradation trajectory. Such issues can lead to inaccurate estimation of SOH. This paper proposes a new framework based on the denoising autoencoder (DAE) and gated recurrent unit recurrent neural network (GRU-RNN) with the gated recurrent unit. Firstly, the voltage-capacity (VC) model is used to reconstruct the voltage curve during the constant current charging stage, and the least squares method is used for model parameter identification. The incremental capacity (IC) curve is obtained from the reconstructed voltage curve. Subsequently, relevant features are extracted from the reconstructed voltage and IC curves as inputs of the SOH estimation model. The reconstructed voltage curve can easily obtain the IC curve of the battery and identify its feature information. Even when the noise ratio is 0.03, the reconstructed voltage curve is consistent with the real voltage curve, which shows that this method has strong noise anti-jamming ability. Then, the reconstructed voltage curve is divided into different voltage segments, and relevant features are extracted from different voltage segments and IC curves. The Pearson correlation coefficient method is used, and features with high correlation are selected as inputs for the estimation model. Subsequently, a denoising autoencoder is used to train input features with noise in an unsupervised manner and learn more robust and valuable feature information from the damaged data. Finally, two experiments are performed to verify the effectiveness of the proposed method. When the features contain noise, the proposed algorithm battery has high SOH prediction accuracy, and the relative error is 6.39% to 23.23% lower than that of the GRU-RNN and deep neural network (DNN) models. When the battery SOH is predicted using all the features, the battery’s MAE and RMSE errors of the two models are less than 2%, and the IA is more than 99.4%. When the features are obtained from part of the voltage and IC curve, the prediction error of the battery SOH increases. Two experiments show that the proposed method can learn more robust feature information from noisy data to improve the model’s prediction accuracy and has strong generalization ability. A model can be trained on multiple test sets without adjusting the model’s parameters.
陈媛, 段文献, 何怡刚, 黄小贺. 带降噪自编码器和门控递归混合神经网络的电池健康状态估算[J]. 电工技术学报, 2024, 39(24): 7933-7949.
Chen Yuan, Duan Wenxian, He Yigang, Huang Xiaohe. State of Health Estimation of Lithium Ion Battery Based on Denoising Autoencoder-Gated Recurrent Unit. Transactions of China Electrotechnical Society, 2024, 39(24): 7933-7949.
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2024, Vol. 39 
