Abstract: The state of charge (SOC) of a battery is a key parameter for the safe and efficient operation of electric vehicles (EVs), as it directly affects driving range estimation, energy management, and battery protection. Accurate SOC estimation is challenging due to the nonlinear behavior of batteries, variations in operating conditions, temperature effects, and aging phenomena. This paper presents a comprehensive review of SOC monitoring and estimation techniques for electric vehicle applications. First, the fundamental concepts of battery SOC, key battery characteristics, and the role of SOC in battery management systems are discussed. Subsequently, a detailed review of conventional, model-based, and data-driven SOC estimation methods is provided, highlighting their underlying principles and practical applications. The performance of existing approaches is then compared in terms of estimation accuracy, robustness, computational complexity, and suitability under real-world driving and charging conditions. Key challenges and limitations associated with current SOC estimation techniques are identified. Finally, emerging research trends and future directions toward intelligent, adaptive, and real-time SOC estimation frameworks are outlined, followed by concluding remarks on the development of reliable SOC estimation strategies for next-generation electric vehicles.

Keywords: State of Charge (SOC), Electric Vehicles, Battery Management System (BMS), SOC Estimation, Lithium-Ion Batteries, Model-Based Estimation, Data-Driven Methods, Machine Learning, Battery Monitoring.

Downloads: | DOI: 10.17148/IARJSET.2025.121254