A data-driven multi-scale extended Kalman filtering based parameter and state estimation approach of lithium-ion polymer battery in electric vehicles

• Published in: Applied energy 2014-01, Vol.113, p.463-476
• Main Authors: Xiong, Rui, Sun, Fengchun, Chen, Zheng, He, Hongwen
• Format: Article
• Language: English
• Subjects: Algorithms; Applied sciences; Battery capacity; Data-driven; Direct energy conversion and energy accumulation; Electric potential; Electric vehicles; Electrical engineering. Electrical power engineering; Electrical power engineering; Electrochemical conversion: primary and secondary batteries, fuel cells; Estimates; Exact sciences and technology; Kalman filtering; Lithium batteries; Lithium-ion polymer battery; Mathematical models; Multi-scale; State-of-Charge; Voltage
• ISSN: 0306-2619; 1872-9118
• DOI: 10.1016/j.apenergy.2013.07.061

•A data-driven multi-scale extended Kalman filtering is developed for battery system.•A lumped parameter battery model against different aging levels has been proposed.•The proposed approach has less computation efficiency but higher estimation accuracy.•The proposed approach can estimate battery parameter, capacity and SoC concurrently.•The robustness of the proposed approach against different aging levels is evaluated. Accurate estimations of battery parameter and state play an important role in promoting the commercialization of electric vehicles. This paper tries to make three contributions to the existing literatures through advanced time scale separation algorithm. (1) A lumped parameter battery model was improved for achieving accurate voltage estimate against different battery aging levels through an electrochemical equation, which has enhanced the relationship of battery voltage to its State-of-Charge (SoC) and capacity. (2) A multi-scale extended Kalman filtering was proposed and employed to execute the online measured data driven-based battery parameter and SoC estimation with dual time scales in regarding that the slow-varying characteristic on battery parameter and fast-varying characteristic on battery SoC, thus the battery parameter was estimated with macro scale and battery SoC was estimated with micro scale. (3) The accurate estimate of battery capacity and SoC were obtained in real-time through a data-driven multi-scale extended Kalman filtering algorithm. Experimental results on various degradation states of lithium-ion polymer battery cells further verified the feasibility of the proposed approach.