Various energy management strategies for hybrid electric vehicles have been published in recent years. The controller setup of all of these approaches is optimized for a single drive cycle based on the average usage of the vehicle. The weakness of this approach lies in the deviations of the duty cycles. A controller optimized for just one average-assumed drive cycle is not necessarily an optimal controller for the entire driving profile of the vehicle. This paper presents a stochastic energy management controller that is optimized for an entire driving profile. The probabilistic driving profile is implemented in a "drive cycle generator tool". This software tool is used to train a radial-basis neural network controller. The created network predictively determines the optimal energy management strategy (EMS) configuration for the expected drive cycle using GPS and traffic information. It is shown that the proposed controller considerably improves the overall fuel efficiency over the entire life time of the vehicle by up to 2%.
Stochastic optimization of an energy management controller for hybrid electric vehicles
2010
4 Seiten, 5 Bilder, 1 Tabelle, 11 Quellen
(nicht paginiert)
Aufsatz (Konferenz)
Datenträger
Englisch
Energy Management Control and Optimization for Hybrid Electric Vehicles
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