State of charge estimation of a Li-ion battery based on extended Kalman filtering and sensor bias

The growing usage of electric vehicles (EVs) has led to significant advancements in batteries' technology. State of charge (SOC) estimation is an essential function of the battery management system-the heart of EVs and Kalman filtering is a standard SOC estimation method. Because of the non-uniformities in tuning and testing scenarios, it is challenging to quantify SOC estimation algorithms' performance. 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 it 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.

Automated summary

A study developed a SOC estimation algorithm using extended Kalman filter (EKF) and found that the dual EKF algorithm provided the most accurate estimation for battery parameters through comparative analysis.