A multi-model probability SOC fusion estimation approach using an improved adaptive unscented Kalman filter technique

Yanwen Li, Chao Wang and Jinfeng Gong

Energy, 2017, vol. 141, issue C, 1402-1415

Abstract: Battery model is crucial for the accurate estimation of the state of charge (SOC) in a battery management system of electric vehicles. However, differences exist within optimal battery models corresponding to different types of batteries. Even for the same type of battery, the corresponding optimal battery model may vary with the change of the battery status. To solve the problem, this paper proposes a multi-model probability fusion estimation (MMPFE) method to realize an accurate description of battery characteristics and a precise SOC estimation. An improved adaptive unscented Kalman filter (AUKF) approach is developed for measurement noise variance online update based on the idea of orthogonality between residual and innovation during the SOC estimation. Finally, the proposed MMPFE method was verified by experiments using LiFeO4 and LiMnO2 batteries, respectively. Results indicate that when a voltage drift of +3 mV was applied on the LiFeO4 battery under UDDS condition and an initial SOC error was applied on LiMnO2 battery under FUDS condition at different temperatures, the proposed method still can estimated the precise SOC. Comparing with the results obtained by the other methods under the same conditions, the method presented in the paper shows a higher SOC estimation accuracy and better robustness.

Keywords: Electric vehicles; Lithium-ion battery; Multi-model probability; State of charge; Dual scale; Adaptive unscented Kalman filter (search for similar items in EconPapers)
Date: 2017
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (20)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:141:y:2017:i:c:p:1402-1415

DOI: 10.1016/j.energy.2017.11.079

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