An Online Energy Management Control for Hybrid Electric Vehicles Based on Neuro-Dynamic Programming
AbstractHybrid electric vehicles are a compromise between traditional vehicles and pure electric vehicles and can be part of the solution to the energy shortage problem. Energy management strategies (EMSs) are highly related to energy utilization in HEVs’ fuel economy. In this research, we have employed a neuro-dynamic programming (NDP) method to simultaneously optimize fuel economy and battery state of charge (SOC). In this NDP method, the critic network is a multi-resolution wavelet neural network based on the Meyer wavelet function, and the action network is a conventional wavelet neural network based on the Morlet function. The weights and parameters of both networks are obtained by an algorithm of backpropagation type. The NDP-based EMS has been applied to a parallel HEV and compared with a previously reported NDP EMS and a stochastic dynamic programing-based method. Simulation results under ADVISOR2002 have shown that the proposed NDP approach achieves better performance than both the methods. These indicate that the proposed NDP EMS, and the CWNN and MRWNN, are effective in approximating a nonlinear system. View Full-Text
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Qin, F.; Li, W.; Hu, Y.; Xu, G. An Online Energy Management Control for Hybrid Electric Vehicles Based on Neuro-Dynamic Programming. Algorithms 2018, 11, 33.
Qin F, Li W, Hu Y, Xu G. An Online Energy Management Control for Hybrid Electric Vehicles Based on Neuro-Dynamic Programming. Algorithms. 2018; 11(3):33.Chicago/Turabian Style
Qin, Feiyan; Li, Weimin; Hu, Yue; Xu, Guoqing. 2018. "An Online Energy Management Control for Hybrid Electric Vehicles Based on Neuro-Dynamic Programming." Algorithms 11, no. 3: 33.
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