Estimating State-of-Charge in Lithium-Ion Batteries Through Deep Learning Techniques: A Comparative Evaluation

Abstract

Accurate estimation of the state of charge (SoC) is crucial for optimizing battery performance, battery health estimation, and ensuring reliable operation. In recent years, deep learning techniques have shown promising results in capturing complex non-linear relationships between input features and estimated the SoC as they do not rely on battery models. However, a detailed comparison of these techniques is not available. Therefore, this paper presents a comprehensive comparison of SoC estimation using ANN (artificial neural network), LSTM (long short-term memory), BiLSTM (bidirectional LSTM), and GRU (gated recurrent unit) models applied to lithium-ion batteries. These models are trained using five distinct drive cycles and tested against four additional drive cycles from a publicly available dataset. The effectiveness is evaluated with RMSE (root mean squared error) and R2 score (coefficient of determination). The results provide insights into the applicability of ANN, LSTM, BiLSTM, and GRU for SoC estimation in lithium-ion battery systems, highlighting the advantages and limitations of each architecture. Additionally, the neuron effect is considered to examine the impact of the number of neurons in the hidden layer on the prediction of SoC. Comparing all models, it is observed that the BiLSTM network shows promising results, showing RMSE of 0.0097 and R2 value of 0.9987 when evaluated on one of the testing datasets, surpassing the performance of the other models. Further, the addition of temperature as a feature and the effect of external noise on the SoC estimation using the BiLSTM network are analyzed, highlighting the significance of accurate SoC estimation.

Keywords

• Deep learning
• electric vehicles
• lithium-ion battery
• neural networks
• state of charge