Accurate State of Charge Estimation for Real-World Battery Systems Using a Novel Grid Search and Cross Validated Optimised LSTM Neural Network

Abstract

State of charge (SOC) is one of the most important parameters in battery management systems, and the accurate and stable estimation of battery SOC for real-world electric vehicles remains a great challenge. This paper proposes a long short-term memory network based on grid search and cross-validation optimisation to estimate the SOC of real-world battery systems. The real-world electric vehicle data are divided into parking charging, travel charging, and finish charging cases. Meanwhile, the parameters associated with the SOC estimation under each operating condition are extracted by the Pearson correlation analysis. Moreover, the hyperparameters of the long short-term memory network are optimised by grid search and cross-validation to improve the accuracy of the model estimation. Moreover, the gaussian noise algorithm is used for data augmentation to improve the accuracy and robustness of SOC estimation under the working conditions of the small dataset. The results indicate that the absolute error of SOC estimation is within 4% for the small dataset and within 2% for the large dataset. More importantly, the robustness and effectiveness of the proposed method are validated based on operational data from three different real-world electric vehicles, and the mean square error of SOC estimation does not exceed 0.006. This paper aims to provide guidance for the SOC estimation of real-world electric vehicles.