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For power batteries used in the electric vehicle, accurate state-of-charge estimation is important. However, as one of the most commonly used estimation method, the least square support vector regression is hard to balance the accuracy and efficiency. To solve this problem, the fuzzy information granulation based on asymmetric Gaussian membership function is proposed to improve the utilization efficiency of the effective data and enhance the prediction accuracy of battery states. In addition, the performance of the proposed method is compared with that of other commonly used member -ship functions. In experiments, the dynamic stress test condition and the urban dynamometer driving schedule condition are used to verify the effectiveness. Compared with the most commonly used triangle membership function, the proposed method improves the accuracy of estimation by 6.47% and 2.18% under two current conditions, respectively.

Accurate estimation of state-of-charge (SOC) and state-of-health (SOH) is extremely important for the state diagnosis of power batteries, which is related to the energy efficiency and safety of electric vehicles. However, in order to represent the signal noises of sensors, the most commonly used method based on Kalman filter introduces the random Gaussian noise into the estimation, which causes the uncertainty of the estimation results. In this article, the multithread dynamic optimization method is proposed to solve the problem. In addition, the fractional-order model and the unscented Kalman filter are used in SOC estimation. The Gaussian linear models based on parameters of six commonly used open-circuit-voltage models are proposed to estimate SOH. Finally, the dynamic stress test current condition and four lithium-ion batteries are implemented to verify the effectiveness of the proposed method in the experiment. For SOC estimation, root-mean-square error (RMSE) of the proposed method is 0.098 and the average value of the six models is 0.123, thus the proposed method improves the estimation accuracy by 20.32%. For SOH estimation, we compare the smallest RMSE of the six models and that of the proposed method for four experimental batteries, thus the average improvement of accuracy is 25.44%.