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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 LiFeO 4 and LiMnO 2 batteries, respectively. Results indicate that when a voltage drift of +3 mV was applied on the LiFeO 4 battery under UDDS condition and an initial SOC error was applied on LiMnO 2 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. [ABSTRACT FROM AUTHOR]

An accurate battery State of Charge estimation plays an important role in battery electric vehicles. This paper makes two contributions to the existing literature. (1) A recursive least squares method with fuzzy adaptive forgetting factor has been presented to update the model parameters close to the real value more quickly. (2) The statistical information of the innovation sequence obeying chi-square distribution has been introduced to identify model uncertainty, and a novel combination algorithm of strong tracking unscented Kalman filter and adaptive unscented Kalman filter has been developed to estimate SOC (State of Charge). Experimental results indicate that the novel algorithm has a good performance in estimating the battery SOC against initial SOC errors and voltage sensor drift. A comparison with the unscented Kalman filter-based algorithms and adaptive unscented Kalman filter-based algorithms shows that the proposed SOC estimation method has better accuracy, robustness and convergence behavior. [ABSTRACT FROM AUTHOR]