Commuter Route Optimized Energy Management of Hybrid Electric Vehicles
Artikel i vetenskaplig tidskrift, 2014

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%.

intelligent vehicles

dynamic programming

data mining

hybrid electric vehicles

energy management

Clustering algorithms


Viktor Larsson

Chalmers, Signaler och system, System- och reglerteknik

Lars Johannesson

Chalmers, Signaler och system, System- och reglerteknik

Bo Egardt

Chalmers, Signaler och system, System- och reglerteknik

Sten Karlsson

Chalmers, Energi och miljö, Fysisk resursteori

IEEE Transactions on Intelligent Transportation Systems

1524-9050 (ISSN) 1558-0016 (eISSN)

Vol. 15 3 1145-1154 6719539


Hållbar utveckling








Mer information

Senast uppdaterat