摘要
The optimal energy management for a plug-in hybrid electric bus(PHEB)running along the fixed city bus route is an important technique to improve the vehicles’fuel economy and reduce the bus emission.Considering the inherently high regularities of the fixed bus routes,the continuous state Markov decision process(MDP)is adopted to describe a cost function as total gas and electric consumption fee.Then a learning algorithm is proposed to construct such a MDP model without knowing the all parameters of the MDP.Next,fitted value iteration algorithm is given to approximate the cost function,and linear regression is used in this fitted value iteration.Simulation results show that this approach is feasible in searching for the control strategy of PHEB.Simultaneously this method has its own advantage comparing with the CDCS mode.Furthermore,a test based on a real PHEB was carried out to verify the applicable of the proposed method.
The optimal energy management for a plug-in hybrid electric bus （PHEB） running along the fixed city bus route is an im- portant technique to improve the vehicles＇ fuel economy and reduce the bus emission. Considering the inherently high regular-ities of the fixed bus routes, the continuous state Markov decision process （MDP） is adopted to describe a cost function as total gas and electric consumption fee. Then a learning algorithm is proposed to construct such a MDP model without knowing the all parameters of the MDP. Next, fitted value iteration algorithm is given to approximate the cost function, and linear regres- sion is used in this fitted value iteration. Simulation results show that this approach is feasible in searching for the control strategy of PHEB. Simultaneously this method has its own advantage comparing with the CDCS mode. Furthermore, a test based on a real PHEB was carried out to verify the applicable of the proposed method.
基金
supported by the National Natural Science Foundation of China(Grant No.51275557)
the National Science-technology Support Plan Projects of China(Grant No.2013BAG14B01)
