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To overcome range anxiety problem of Electric Vehicles (EVs), an accuratereal-time energy consumption estimation is necessary, which can be used toprovide the EV's driver with information about the remaining range inreal-time. A hybrid CNN-BDT approach has been developed, in which ConvolutionalNeural Network (CNN) is used to provide an energy consumption estimateconsidering the effect of temperature, wind speed, battery's SOC, auxiliaryloads, road elevation, vehicle speed and acceleration. Further, Bagged DecisionTree (BDT) is used to fine tune the estimate. Unlike existing techniques, theproposed approach doesn't require internal vehicle parameters from manufacturerand can easily learn complex patterns even from noisy data. Comparison resultswith existing techniques show that the developed approach provides betterestimates with least mean absolute energy deviation of 0.14.

soft computing

Convolutional Neural Network-Bagged Decision Tree: A hybrid approach to  reduce electric vehicle's driver's range anxiety by estimating energy  consumption in real-time