Online Estimation of State-of-charge Based on the H infinity and Unscented Kalman Filters for Lithium Ion Batteries. (May 2017)

• Record Type: Journal Article
• Authors: Yu, Quanqing; Xiong, Rui; Lin, Cheng
• Is Part Of: Energy procedia. Volume 105(2017)
• Page Start: 2791
• Page End: 2796
• Publication Date: 2017-05
• Subjects: state of charge -- H infinity filter -- unscented Kalman filter -- lithium-ion battery; Power resources -- Congresses; Power resources -- Periodicals; Power resources; Conference proceedings; Periodicals; 333.7905
• DOI: 10.1016/j.egypro.2017.03.600
• Languages: English
• ISSNs: 1876-6102

Abstract

Abstract: The state of charge (SOC) is a key indicator for the battery management system (BMS) of electric vehicles. A SOC joint estimation method based on the H infinity filter (HF) and unscented Kalman filter (UKF) algorithms is proposed in this paper, HF based parameters identification can trace the parameters online according to the working conditions while he UKF based state estimation method does not require the jacobian matrix derivation and the linearization for nonlinear model. The HF-UKF SOC joint estimation method has been experimentally validated at different temperatures. The results show that this method is robust to the inaccurate initial SOC value and the different working temperatures.