A data-driven adaptive state of charge and power capability joint estimator of lithium-ion polymer battery used in electric vehicles

• Published in: Energy (Oxford) Vol. 63; pp. 295 - 308
• Main Authors: Xiong, Rui, Sun, Fengchun, He, Hongwen, Nguyen, Trong Duy
• Format: Journal Article
• Language: English
• Published: Kidlington Elsevier Ltd 15.12.2013; Elsevier
• Subjects: Adaptive extended Kalman filter; Algorithms; Applied sciences; batteries; Data-driven; Direct energy conversion and energy accumulation; Electric batteries; Electric charge; Electric potential; Electric power generation; Electric vehicles; Electrical engineering. Electrical power engineering; Electrical power engineering; Electrochemical conversion: primary and secondary batteries, fuel cells; Energy; Estimators; Exact sciences and technology; Lithium batteries; Lithium-ion polymer battery; Parameter identification; polymers; State of charge (SoC); State of power capability (SoP); vehicles (equipment); Electric vehicles; Lithium-ion polymer battery; Adaptive extended Kalman filter; Data-driven; State of power capability (SoP); State of charge (SoC); Lithium; Polymer; Electric vehicle; Battery
• ISSN: 0360-5442
• DOI: 10.1016/j.energy.2013.10.027

An accurate SoC (state of charge) and SoP (state of power capability) joint estimator is the most significant techniques for electric vehicles. This paper makes two contributions to the existing literature. (1) A data-driven parameter identification method has been proposed for accurately capturing the real-time characteristic of the battery through the recursive least square algorithm, where the parameter of the battery model is updated with the real-time measurements of battery current and voltage at each sampling interval. (2) An adaptive extended Kalman filter algorithm based multi-state joint estimator has been developed in accordance with the relationship of the battery SoC and its power capability. Note that the SoC and SoP can be predicted accurately against the degradation and various operating environments of the battery through the data-driven parameter identification method. The robustness of the proposed data-driven joint estimator has been verified by different degradation states of lithium-ion polymer battery cells. The result indicates that the estimation errors of voltage and SoC are less than 1% even if given a large erroneous initial state of joint estimator, which makes the SoP estimate more accurate and reliable for the electric vehicles application. •A data-driven parameter identification method is developed by RLS algorithm.•An adaptive multi-state joint estimator of the battery is developed by AEKF algorithm.•A data-driven SoC and SoP joint estimator is developed with the real-time measurement.•Robustness of the joint estimator is verified by different aging states of LiPB cells.