Route-Based Online Energy Management of a PHEV and Sensitivity to Trip Prediction

Abstract

In this paper, we present a method of optimizing the energy management of a plug-in hybrid electric vehicle (PHEV) using GIS-assisted stochastic trip prediction. A process was developed to synthesize speed profiles through a combination of Markov chains and information from a geographical information system (GIS) 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. The route-based energy management presented in this paper uses the Pontryagin Minimum Principle (PMP). 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. An itinerary was defined, and several speed profiles were synthesized. It was then possible to evaluate the sensitivity of PMP tuning to the speed profile, providing insights about the applicability of PMP control in real-world situations.