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 Vol. 113; pp. 463 - 476
• Main Authors: Xiong, Rui, Sun, Fengchun, Chen, Zheng, He, Hongwen
• Format: Journal Article
• Language: English
• Published: Kidlington Elsevier Ltd 01.01.2014; Elsevier
• Subjects: Algorithms; Applied sciences; batteries; Battery capacity; commercialization; Data-driven; Direct energy conversion and energy accumulation; Electric potential; electric power; Electric vehicles; Electrical engineering. Electrical power engineering; Electrical power engineering; Electrochemical conversion: primary and secondary batteries, fuel cells; electrochemistry; equations; Estimates; Exact sciences and technology; Kalman filtering; Lithium batteries; Lithium-ion polymer battery; Mathematical models; Multi-scale; polymers; State-of-Charge; Voltage; Electric vehicles; Lithium-ion polymer battery; Battery capacity; Data-driven; Multi-scale; State-of-Charge; Parameter estimation; Battery; Lithium; Polymer; Kalman filtering; Electric vehicle

•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 pa...