Abstract
The accurate residual range estimation of a battery electric vehicle (BEV) can alleviate the driver’s range anxiety and improve driving safety. In this study, the residual range estimation is considered a nonlinear system involving battery factors and a variety of vehicle working status factors. Given that the radial basis function neural network (RBF NN) performs well in approximating nonlinear systems, this study proposes a RBF NN method to estimate the residual range of BEV. The contribution analysis method is used to simplify the input layer of RBF NN and enhance the real-time performance of estimation. Then, the residual range is estimated by RBF NN using historical data newly collected at a fixed time in current discharge process. Some experimental data are from operational BEVs in Beijing, China. Experimental results of different types demonstrate that all errors are comparatively small and within the engineering limit.
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