STATE OF CHARGE (SOC) AND STATE OF HEALTH (SOH) ESTIMATION TECHNIQUES FOR EV BATTERIES

– The growth of electric vehicles (EVs) has revolutionized the automotive industry, emphasizing the need for accurate monitoring of battery performance. Two critical parameters determining the reliability and efficiency of EV batteries are the State of Charge (SOC) and the State of Health (SOH). SOC provides information on the remaining energy available in the battery, while SOH estimates the degradation and aging of the battery over time. Various estimation techniques have been developed to improve the accuracy and reliability of SOC and SOH, including Coulomb counting, Kalman filtering, neural networks, and machine learning approaches. Despite advancements, challenges such as model complexity, environmental variations, and real-time computation persist. This article explores the latest techniques for SOC and SOH estimation, analyzing their strengths and limitations. The study highlights gaps in current methodologies and proposes a hybrid approach that integrates advanced machine learning models with robust statistical techniques to enhance accuracy and adaptability. This comprehensive analysis provides a foundation for future research, addressing the challenges of battery management systems (BMS) and ensuring safer, more efficient EV battery operation.