Abstract
Aiming at the problem that the State of charge (SOC) can not be directly measured, the SOC soft sensing method based on subtractive clustering and adaptive fuzzy neural network is introduced. Firstly, the structure of AFNN was decided by SC; Secondly, by adopting the back-propagation algorithm and least square method respectively, the front and back parameters of AFNN were optimized, and the study efficiency of the parameters was raised. Finally, the fuzzy membership functions and rules which generated automatically by AFNN are applied to the soft measurement of battery SOC for HEV. Under the CYC-HWFET working conditions, the working voltages, currents and surface temperature of battery are used to predict the value of SOC, and the results indicate that the prediction model possesses higher predicted accuracy, and the errors between the real value and the soft measurement value are small.
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