Evaluation on State of Charge Estimation of Batteries With Adaptive Extended Kalman Filter by Experiment Approach

• Published in: IEEE transactions on vehicular technology 2013-01, Vol.62 (1), p.108-117
• Main Authors: Xiong, Rui, He, Hongwen, Sun, Fengchun, Zhao, Kai
• Format: Article
• Language: English
• Subjects: Adaptation models; Adaptive extended Kalman filter (AEKF); Applied sciences; Batteries; battery management system; Capacitors. Resistors. Filters; Circuit properties; Detection, estimation, filtering, equalization, prediction; Direct energy conversion and energy accumulation; Discharges (electric); electric vehicles (EVs); Electric, optical and optoelectronic circuits; Electrical engineering. Electrical power engineering; Electrical power engineering; Electrochemical conversion: primary and secondary batteries, fuel cells; Electronic circuits; Electronics; Estimation; Exact sciences and technology; Frequency filters; Information, signal and communications theory; Kalman filters; lithium-ion battery; Signal and communications theory; Signal, noise; state of charge (SoC); System-on-a-chip; Telecommunications and information theory; Various equipment and components; Voltage measurement
• ISSN: 0018-9545; 1939-9359
• DOI: 10.1109/TVT.2012.2222684

An accurate State-of-Charge (SoC) estimation plays a significant role in battery systems used in electric vehicles due to the arduous operation environments and the requirement of ensuring safe and reliable operations of batteries. Among the conventional methods to estimate SoC, the Coulomb counting method is widely used, but its accuracy is limited due to the accumulated error. Another commonly used method is model-based online iterative estimation with the Kalman filters, which improves the estimation accuracy in some extent. To improve the performance of Kalman filters in SoC estimation, the adaptive extended Kalman filter (AEKF), which employs the covariance matching approach, is applied in this paper. First, we built an implementation flowchart of the AEKF for a general system. Second, we built an online open-circuit voltage (OCV) estimation approach with the AEKF algorithm so that we can then get the SoC estimate by looking up the OCV-SoC table. Third, we proposed a robust online model-based SoC estimation approach with the AEKF algorithm. Finally, an evaluation on the SoC estimation approaches is performed by the experiment approach from the aspects of SoC estimation accuracy and robustness. The results indicate that the proposed online SoC estimation with the AEKF algorithm performs optimally, and for different error initial values, the maximum SoC estimation error is less than 2% with close-loop state estimation characteristics.