期刊
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
卷 15, 期 3, 页码 1145-1154出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TITS.2013.2294723
关键词
Clustering algorithms; data mining; dynamic programming; energy management; hybrid electric vehicles; intelligent vehicles
资金
- Swedish Hybrid Vehicle Centre
- Chalmers Energy Initiative
Optimal energy management of hybrid electric vehicles requires a priori information regarding future driving conditions; the acquisition and processing of this information is nevertheless often neglected in academic research. This paper introduces a commuter route optimized energy management system, where the bulk of the computations are performed on a server. The idea is to identify commuter routes from historical driving data, using hierarchical agglomerative clustering, and then pre-compute an optimal solution to the energy management control problem with dynamic programming; the obtained solution can then be transmitted to the vehicle in the form of a lookup table. To investigate the potential of such a system, a simulation study is performed using a detailed vehicle model implemented in the Autonomie simulation environment for MATLAB/Simulink. The simulation results for a plug-in hybrid electric vehicle indicate that the average fuel consumption along the commuter route(s) can be reduced by 4%-9% and battery usage by 10%-15%.
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