基于CNN-BiLSTM网络的锂离子电池健康状态检测方法

锂离子电池健康状态(SOH)是锂离子电池可靠运行的重要参考指标，为提高电池健康状态检测的精确性，提出一种基于CNN-BiLSTM网络的锂电池健康状态检测方法。该方法使用CALCE锂离子电池容量衰减数据集，提取电池健康因子(HI)作为模型输入数据，同时利用灰色关联分析法(GRA)验证HI选取的合理性，采用卷积神经网络(CNN)、双向长短期记忆神经网络(BiLSTM)构建网络模型，对电池容量进行预测，实现锂离子电池健康状态检测。实验结果表明，该方法SOH检测的平均绝对误差为1.3%,均方根误差为1.78%,精确度和可靠性较高。

Lithium-ion batteries state of health detection method based on CNN-BiLSTM network

The state of health (SOH) of lithium-ion batteries is an important reference indicator for the reliable operation of lithium-ion batteries. To improve the accuracy of the battery state of health detection, a method for the lithium batteries state of health detection based on the CNN-BiLSTM network is proposed. This method uses CALCE lithium-ion battery capacity decay data set, extracts battery health indicator (HI) as the model input data, and uses grey relational analysis (GRA) to verify the rationality of HI selection. Convolutional neural networks (CNN) and bi-directional long short-term memory (BiLSTM) are used to construct network models to predict battery capacity and to detect the health status of lithium-ion batteries. The results show that the method has 1.79% RMSE and 1.3% MAE for SOH detection, with high accuracy and reliability.