A Neural Network Model to Calculate the Energy Demand of the Vehicle Based on Traffic Features

Hybrid electric vehicles (HEVs) and plug-in hybrid electric vehicles (PHEVs) can achieve high fuel economy and low emissions. And the optimization-based energy management strategies can fully exploits the potential of HEVs to reduce the fuel consumption. As a premise, necessary information about the driving cycles must be known prior. This paper proposes a model to obtain the energy demand of the vehicle, which is pretty useful in the energy management of the HEVs. It uses a radial basis function (RBF) neural network (NN) to process the characteristic parameters of a driving cycle and then outputs the predicted energy demand of the vehicle. The intrinsic parameters of the established NN are optimized using a genetic algorithm (GA). Through tests of real-world driving cycles and standard cycles, the accuracy of the model is verified.