Title :
Using trip information for PHEV fuel consumption minimization
Author :
Karbowski, Dominik ; Smis-Michel, Vivien ; Vermeulen, Valentin
Author_Institution :
Argonne Nat. Lab., Argonne, IL, USA
Abstract :
When driven past their all-electric range, plug-in hybrid vehicles (PHEVs) must use their engines. Numerous theoretical studies showed that the conventional control strategy, i.e. all-electric mode followed by a charge-sustaining mode, is not the most energy-efficient control strategy. Better strategies require knowledge of the trip ahead. In this paper, we present a method of predicting a trip for a given itinerary (vehicle speed, stop time, and grade) defined by using a geographical information system (GIS). For each segment of the itinerary, a vehicle speed profile is generated through a Markov process, defined by transition probabilities extracted from a large database of real-world trip records. Ten trip predictions are then generated from a single itinerary for evaluation of an optimal control strategy for a short-range power-split PHEV by using Autonomie, a powertrain modeling environment. The baseline controller uses rules and optimal operating point look-up tables when in charge-sustaining mode. The optimal controller uses the Pontryagin´s Minimization Principle (PMP), the performance of which heavily depends on the choice of one scalar parameter, the equivalence factor. Finally, we demonstrate the fuel-saving potential of the PMP controller, using the aforementioned trip predictions.
Keywords :
electric vehicles; geographic information systems; optimal control; GIS; Markov process; PHEV fuel consumption minimization; PMP controller; Pontryagin minimization principle; all-electric mode; baseline controller; charge-sustaining mode; control strategy; energy-efficient control strategy; geographical information system; optimal control strategy; plug-in hybrid vehicles; point look-up tables; powertrain modeling environment; short-range power-split PHEV; transition probabilities; trip information; Batteries; Engines; Markov processes; Optimization; Torque; Vehicles; GPS; PHEV; control; optimization; prediction;
Conference_Titel :
Electric Vehicle Symposium and Exhibition (EVS27), 2013 World
Conference_Location :
Barcelona
DOI :
10.1109/EVS.2013.6914710