Advanced battery modeling and State-of-Charge estimation of lithium-ion batteries: A comprehensive review of modeling approaches, parameterization, and operational challenges

Lithium-ion batteries, being the core power sources in electric vehicles and energy storage systems, require accurate State-of-Charge (SOC) estimation to ensure optimized performance and extended lifespan. The implementation of accurate SOC estimation algorithms in battery management systems (BMS) not only ensures safety and reliability but also helps in monitoring and mitigating the adverse effects of overcharging and deep discharging. Although numerous review articles have addressed model-based and data-driven SOC estimation techniques, limited attention has been given to battery model parameterization, testing methodologies, and the practical challenges associated with joint and dual model-based estimation frameworks. This review provides a comprehensive discussion of battery testing methods and critically examines the advantages and limitations of various parameterization techniques used in battery modeling. Furthermore, the operational challenges encountered in real-time SOC estimation under dynamic operating conditions are systematically analyzed. Advanced estimation strategies, including joint and dual estimation frameworks, with particular emphasis on adaptive Kalman filter-based approaches, are reviewed for their potential to enhance estimation accuracy, robustness, and adaptability. Finally, a comparative assessment of the reviewed methods is presented, highlighting their suitability for real-time implementation. The insights provided in this review are intended to support researchers and industry practitioners in selecting and developing advanced SOC estimation techniques to improve battery performance and extend operational lifespan.