Abstract
In this paper, we evaluate the fuel savings of a plug-in hybrid electric vehicle (PHEV) that uses an optimal controller, itself based on the Pontryagin Minimum Principle (PMP). A process was developed to synthesize speed profiles through a combination of Markov chains and information from a digital map about the future route. In a potential real-world scenario, the future trip (speed, grade, stops, etc.) can be estimated, but not deterministically known. The stochastic trip prediction process models such uncertainty. A PMP strategy was implemented in a Simulink controller for a model of Prius-like PHEV and compared to a baseline strategy using Autonomie, an automotive modeling environment. Multiple real-world itineraries were defined in urban areas with various environments, and for each of them multiple speed profiles were synthesized so as to provide a statistically representative dataset, and finally fuel savings were evaluated with the optimal control.
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