Commuter Route Optimized Energy Management of Hybrid Electric Vehicles
Journal article, 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, Signals and Systems, Systems and control

Lars Johannesson

Chalmers, Signals and Systems, Systems and control

Bo Egardt

Chalmers, Signals and Systems, Systems and control

Sten Karlsson

Chalmers, Energy and Environment, Physical Resource Theory

IEEE Transactions on Intelligent Transportation Systems

1524-9050 (ISSN)

Vol. 15 3 1145-1154 6719539

Driving Forces

Sustainable development

Areas of Advance



Subject Categories

Control Engineering



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