【摘 要】
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The paper proposes an adoption of slope, elevation, speed and route distance preview to achieve optimal energy management of plug-in hybrid electric vehicles (PHEVs). The approach is to identify route features from historical and real-time traffic data, i
【机 构】
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Department of Mechanical and Electrical Engineering,Guangdong University of Science and Technology,D
论文部分内容阅读
The paper proposes an adoption of slope, elevation, speed and route distance preview to achieve optimal energy management of plug-in hybrid electric vehicles (PHEVs). The approach is to identify route features from historical and real-time traffic data, in which information fusion model and traffic prediction model are used to improve the information accuracy. Then, dynamic programming combined with equivalent con-sumption minimization strategy is used to compute an optimal solution for real-time energy management. The solution is the reference for PHEV energy management control along the route. To improve the system\'s ability of handling changing situation, the study further explores predictive control model in the real-time control of the energy. A simulation is performed to model PHEV under above energy control strategy with route preview. The results show that the average fuel consumption of PHEV along the previewed route with model predictive control (MPC) strategy can be reduced compared with optimal strategy and base control strategy.
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