A data-driven based adaptive state of charge estimator of lithium-ion polymer battery used in electric vehicles

• Published in: Applied energy 2014-01, Vol.113, p.1421-1433
• Main Authors: Xiong, Rui, Sun, Fengchun, Gong, Xianzhi, Gao, Chenchen
• Format: Article
• Language: English
• Subjects: Adaptive extended Kalman filter; Applied sciences; Data-driven; Direct energy conversion and energy accumulation; Electric charge; Electric potential; Electric vehicles; Electrical engineering. Electrical power engineering; Electrical power engineering; Electrochemical conversion: primary and secondary batteries, fuel cells; Errors; Estimators; Exact sciences and technology; Lithium batteries; Lithium-ion polymer battery; Mathematical models; Recursive least square; State of charge; Voltage
• ISSN: 0306-2619; 1872-9118
• DOI: 10.1016/j.apenergy.2013.09.006

•A lumped parameter battery model against different battery aging levels is proposed.•The RLS based method is used to identify the parameter of battery model in real-time.•A data-driven based adaptive SoC estimator is developed by RLS and AEKF algorithm.•The robustness of the SoC estimator against varying loading profiles is evaluated.•The robustness of the SoC estimator against different aging levels is evaluated. An accurate State of Charge (SoC) estimation method is one of the most significant and difficult techniques to promote the commercialization of electric vehicles. The paper attempts to make three contributions. (1) Through the recursive least square algorithm based identification method, the parameter of the lumped parameter battery model can be updated at each sampling interval with the real-time measurement of battery current and voltage, which is called the data-driven method. Note that the battery model has been improved with a simple electrochemical equation for describing the open circuit voltage against different aging levels and SoC. (2) Through the real-time updating technique of model parameter, a data-driven based adaptive SoC estimator is established with an adaptive extended Kalman filter. It has the potential to overcome the estimation error against battery degradation and varied operating environments. (3) The approach has been verified by different loading profiles of various health states of Lithium-ion polymer battery (LiPB) cells. The results indicate that the maximum estimation errors of voltage and SoC are less than 1% and 1.5% respectively.