The growth and adoption of EVs is driving advancements in renewable energy integration and charging infrastructure, fostering a cleaner, greener, more sustainable, and resilient future. Battery management systems (BMS) is used in electric vehicle to monitor and control the charging and discharging of rechargeable batteries which makes the operation more economical. Therefore, accurate estimation of the state of charge (SOC) in batteries is crucial for optimizing performance, extending battery life, and ensuring safe operation. The performance evaluation of the battery is based on the degradation and non-degradation characteristics. This research focuses on evaluating the efficacy of extended Kalman filter (EKF)-based SOC estimation techniques for both degrading and non-degrading two-resistor-capacitor (2RC) EV battery models. The study employs a comprehensive analysis that considers the impact of battery degradation on SOC estimation accuracy, offering valuable insights for real-world applications. In order to maintain the state of the battery, voltage, current, and ambient temperature, different monitoring techniques are used. This paper addresses state of charge, state of health, and state of life and also maximum capacity of a battery.