Comparative Analysis of State of Charge Estimation Methods for Lithium-Ion Batteries in a 4S3P Configuration

This paper presents a comparative analysis of three State of Charge (SoC) estimation methods for lithium-ion batteries: Coulomb Counting, Voltage-based, and Deep Learning. The study evaluates their performance in a simulated environment with varying temperature profiles, discharge conditions, and capacity adjustments. Metrics such as RMSE and MAE were used to assess accuracy. Results show that Voltage-based estimation achieves the highest accuracy in the simulated scenario (RMSE: 0.4159, MAE: 0.3744), while Deep Learning demonstrates superior adaptability to non-linear conditions. Coulomb Counting, though simple, suffers from significant error accumulation (RMSE: 187.7519, MAE: 163.0211). These findings highlight the strengths and limitations of each method, offering insights for their application in battery management systems.