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Commuter Route Optimized Energy Management of Hybrid Electric Vehicles

Viktor Larsson (Institutionen för signaler och system, Reglerteknik) ; Lars Johannesson (Institutionen för signaler och system, Reglerteknik) ; Bo Egardt (Institutionen för signaler och system, Reglerteknik) ; Sten Karlsson (Institutionen för energi och miljö, Fysisk resursteori)
IEEE transactions on intelligent transportation systems (1524-9050). Vol. 15 (2014), 3, p. 1145-1154.
[Artikel, refereegranskad vetenskaplig]

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 precompute 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%.

Nyckelord: Clustering algorithms; data mining; dynamic programming; energy management; hybrid electric vehicles; intelligent vehicles



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Denna post skapades 2014-01-22. Senast ändrad 2015-05-08.
CPL Pubid: 192983

 

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