Enhance Parameter Identification Accuracy for State-of-Charge Optimization in Lithium-Ion Batteries

Abstract

For optimizing the performance, lifespan, and safety of lithium-ion batteries (LIBs), Accurate State-of-charge (SOC) estimation is important because it is a cornerstone technology used in electric vehicles and renewable energy systems. Based on these points, the study presents a novel approach for enhancing parameter identification accuracy that is a main challenge in SOC optimization. Through combining advanced mathematical modeling, hybrid optimization framework, and adaptive parameter identification techniques, the proposed method is showing superior performance across various scenarios. Secondly, the experimental results are showing that the proposed method achieves lower root mean square error (RMSE), and SOC estimation error is compared with conventional methods, including Extended Kalman Filter, Coulomb Counting, and hybrid data-driven models. Moreover, when dynamic driving cycles are used, then, SOC error was minimized to 1.94% that significantly enhance accuracy level. Also, robustness level under changing noise levels, temperature profiles, and aging effects was validated that shows the reliability of method in real-world applications. This study contributes to battery management system development through providing an effective and robust framework for SOC estimation and pave the way for next-generation battery technologies. However, future work is necessary to resolve computational optimization, scalability, and applicability across diverse chemistries to enhance its practical implementation.