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The Li-ion Battery State of Charge Prediction of Electric Vehicle Using Deep Neural Network

Aiming to achieving safe and efficient energy utilization for electric vehicles, research into the monitoring of lithium-ion batteries (LIBs) has become increasingly important. However, various estimation strategies are proposed at the cost of the higher design complexity and the poorer model performance, which are hard to be implemented. Complementarily, in this paper, we propose an Deep Neural Networks (DNNs)-based State of Charge (SOC) observer design for LIBs to ensure safe and reliable battery operations, which avoiding overcharging or over-discharging of the battery. More specifically, a Recursive Neural Networks (RNNs)-based feature extraction model is proposed to obtain sufficient feature information. Then, the well-trained feature vector is integrated into Convolutional Neural Networks (CNNs) to predict the LIBs SOC. In other words, the output of the RNNs are used as the input of the CNNs, which this practice can improve the model performance obviously. Furthermore, the extensive real-world experiments demonstrate that Neural Network-based SOC prediction model can provide faster convergence speed and higher precision in contrast to the optimal method to achieve SOC estimation over regular model.