Using trip information for PHEV fuel consumption minimization

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.