Lithium-ion Battery SoC Estimation with UKF and RTOS μCOS-II Platform

Abstract

To develop a battery management system for lithium-Ion batteries used in electric vehicles, the state of charge (SoC) estimation is improved with an unscented Kalman filter (UKF) and realized with the RTOS μCOS-II platform. The Kalman filters approach is broadly used for the battery SOC estimation recently. Here, we select the UKF algorithm, for which utilizes the unsented transform to solve filtering problems and is able to accurately capture the posterior mean and covariance to 3rd order of Taylor series expansion, as a result, the SoC estimation accuracy is improved with a faster convergence ability. To further evaluate the real-time performance of the SoC estimation, 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 can apply for electric vehicles.