A Novel State Estimation Approach Based on Adaptive Unscented Kalman Filter for Electric Vehicles

Abstract

Accurately estimating the state-of-charge (SOC) of battery is of particular importance for real-time monitoring and safety control in electric vehicles. To obtain better SOC estimation accuracy, a joint modeling method based on adaptive unscented Kalman filter(AUKF) and least-squares support vector machine(LSSVM) is proposed. This article improves the accuracy of SOC estimation from four aspects. Firstly, the nonlinear relationship between SOC, current, and voltage is established by LSSVM. Secondly, a novel voltage estimation method based on improved LSSVM is proposed. Thirdly, the measurement equation of the novel AUKF is created by the improved LSSVM. Finally, the effectiveness of the proposed model is verified under different driving conditions. The comparison results show that the model can improve the accuracy of voltage and SOC estimation, and the SOC estimation error is controlled within 2%.

Keywords

• State-of-charge (SOC)
• adaptive unscented Kalman filter (AUKF)
• terminal voltage
• least squares support vector machine (LSSVM)