Abstract:

Electric vehicles (EVs) are becoming more popular, which has resulted in considerable advancements in battery technology. State of charge (SOC) estimation is a fundamental component of the battery management system—EVs' heart. Kalman filtering is a standard SOC estimating approach. It is difficult to measure the performance of SOC estimate algorithms due to non-uniformities in tuning and testing conditions. A SOC estimation algorithm is developed in this work, extended Kalman filter (EKF), and tested for variable scenarios like adding sensor noise and bias to terminal voltage and current and varying state and parameter initializations. Also, a dual EKF is implemented to estimate the sensor voltage and current bias and compared against the state EKF to estimate SOC. Finally, a comparative study has been introduced to decide which algorithm represents the most accurate estimation for the battery parameters, and it was found that the dual EKF gave the best results.