Real-time estimation of battery state-of-charge with unscented Kalman filter and RTOS μCOS-II platform

To develop an advanced battery estimation unit for electric vehicles application, the state-of-charge (SoC) estimation is proposed with an unscented Kalman filter (UKF) and realized with the RTOS μCOS-II platform. Kalman filters are broadly used to deploy various battery SoC estimators recently. Herein, an UKF algorithm has been employed to develop a systematic adaptive SoC estimation framework. Compared with traditional used extended Kalman filter, it uses an unscented transform to deal with the state estimation problem, thus it has the potential to achieve third order accuracy of the Taylor expansion for tracking posterior estimate of the inner inhabited state. Beneficial from it, the SoC estimation accuracy has been improved with higher tracking accuracy and faster convergence ability. To further evaluate and verify the performance of the proposed online SoC estimation approach, a battery-in-loop platform is built and the SoC estimation is calculated with a RTOS μCOS-II platform. The analog acquisition, communication system and SoC estimation algorithms were programmed, the performance of the proposed SoC estimation with UKF algorithm was finally investigated. The battery management system with UKF algorithm and RTOS μCOS-II platform has good performance and it can apply for electric vehicles.