Convolutional Neural Network-Long Short-Term Memory-Based State of Health Estimation for Li-Ion Batteries under Multiple Working Conditions

The state of health (SOH) for lithium-ion batteries is an important indicator to ensure the safety and reliability of battery energy storage systems. Aiming at the difficulty of accurately estimating the SOH of lithium-ion batteries under different working conditions, this article proposes a method based on a hybrid convolutional neural network-long short-term memory (CNN-LSTM) model. First, the battery health indicators and capacity data under different operating conditions are extracted from the public dataset to form a new dataset. Second, the CNN has multiple one-dimensional convolutional layers to improve the efficiency of feature extraction from new datasets, and the resulting features are used as inputs to the LSTM to predict SOH. Finally, the CNN-LSTM model integrates a fully connected layer that outputs the estimation of SOH for different operating conditions. The results show that the mean absolute error of the SOH estimation results is within 2.33% and 3.01% for the same and different working conditions, respectively. Reorganization of the Center for Advanced Life Cycle Engineering public dataset battery health features and capacity data. A hybrid convolutional neural network-long short-term memory (CNN-LSTM) model combining a one-dimensional CNN and LSTM is proposed. The state of health estimation for multiple working conditions of lithium-ion batteries is realized.image (c) 2023 WILEY-VCH GmbH