Implementation of Deep Learning Algorithm to Estimate the State of Charge (SOC) of Lithium Ion Battery

In electric vehicles (EVs), the need for superior battery management systems (BMS) to confirm efficiency, maximum performance of lithium-ion batteries (LIBs) and safety is increasing. Accurate evaluation of state of charge (SOC) is essential to avoid overcharge and over discharge, which can degrade the battery health and performance. Conventional techniques for estimating SOC often struggle with the nonlinearities and temporal complexities of LIB dynamics, make necessary adoption of advanced machine learning techniques. To enhance the SOC estimation, this analysis suggests a combined Convolutional Neural Network (CNN) and Gated Recurrent Unit (GRU) model to ensure the accuracy under dynamic operating conditions. CNNs are adapt at extracting spatial features from battery data, while GRUs excel in modeling sequential dependencies. The combined CNN-GRU architecture leverages the complementary strengths of both models, providing robust predictions that address the limitations of standalone frameworks. The performance of this model is measured using dataset based on the widely used model like Long Short-Term Memory (LSTM) in data network. The CNN-GRU model performs far better than the LSTM model, based on the experimental data, with lower Mean Absolute Error (MAE) and Root Mean Square Error (RMSE). The CNN-GRU's capability to record both local as well as global temporal patterns results in the reduced prediction deviations, particularly during rapid SOC transitions. Analysis of training and validation losses further highlights the CNN-GRU's generalization capability, with stable convergence and minimal overfitting. This analysis underscores the potential of hybrid architectures in advancing intelligent BMS design. By integrating efficient feature extraction and sequence modeling, the CNN-GRU framework ensures accurate and robust SOC estimation and real-time performance.