Lithium-Ion Batteries State of Charge Prediction of Electric Vehicles Using RNNs-CNNs Neural Networks
Fen Zhao, Yinguo Li, Xinheng Wang, Ling Bai, Tailin Liu
Abstract
In order to achieve the safe and efficient energy use in the electric vehicle, the continuous and accurate monitoring of lithium-ion batteries (LIBs) has become a long-standing research hot spot. However, existing researches of LIBs state of charge (SOC) prediction are at the cost of unrefined vector representation and inadequate feature extraction, which have been unable to meet prediction requirements of LIBs SOC. Complementarily, in this study, a deep learning-based SOC prediction model is proposed to ensure reliable vector representation and sufficient feature extraction. In order to improve battery data representation, a recursive neural networks (RNNs)-based method is proposed. Then, aiming to fully extract feature information, a multi-channel extended convolutional neural networks (CNNs)-based method, which is fed with the well-trained vector representation, is proposed to accurately predict LIBs SOC. Based on the reliable vector representation and sufficient feature extraction, the proposed method can provide improved SOC prediction performance. Merits of the proposed method are verified using simulation test, which shows that the proposed method gives improved prediction performance of 4.3% and 11.3% compared with recurrent neural networks and Ah counting method, respectively.