Hybrid electric vehicles are a compromise between traditional vehicles and pure electric\nvehicles and can be part of the solution to the energy shortage problem. Energy management strategies\n(EMSs) are highly related to energy utilization in HEVs� fuel economy. In this research, we have\nemployed a neuro-dynamic programming (NDP) method to simultaneously optimize fuel economy\nand battery state of charge (SOC). In this NDP method, the critic network is a multi-resolution wavelet\nneural network based on the Meyer wavelet function, and the action network is a conventional\nwavelet neural network based on the Morlet function. The weights and parameters of both networks\nare obtained by an algorithm of backpropagation type. The NDP-based EMS has been applied\nto a parallel HEV and compared with a previously reported NDP EMS and a stochastic dynamic\nprograming-based method. Simulation results under ADVISOR2002 have shown that the proposed\nNDP approach achieves better performance than both the methods. These indicate that the proposed\nNDP EMS, and the CWNN and MRWNN, are effective in approximating a nonlinear system.
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