Combining Multi-Scale Convolutional Neural Network with Long Short-Term Memory Neural Network for State of Charge Estimation of Lithium-ion Batteries

Abstract

To develop safe and intelligent battery management systems for electric vehicles, it is necessary to accurately estimate the state of charge (SOC) of lithium-ion batteries. At present, deep learning methods have been broadly applied in the field of SOC estimation of lithium-ion batteries. However, existing deep SOC estimators are difficult to capture global trends due to being too sensitive to the changes of continuous time data points. In addition, a single non-linear neural network tends to ignore the linear features of the data, which makes the robustness of the estimation not good enough. To address these two issues, this paper proposes a SOC estimation method by combining multi-scale convolutional neural network with long short-term memory neural network (called MCNN-LSTM). Specifically, on the one hand, multiple one-dimension convolutions with different dilation rates are used to extract features at different time scales, and LSTM neural networks are used to acquire the long-term dependencies of the data. On the other hand, an additional fully connected layer is used to extract the linear features, which reduces the volatility of the estimation and enhances the robustness of the estimation. The results suggest that MCNN-LSTM has higher estimation accuracies and better robustness than DNN, LSTM, and CNN-LSTM.