Drive Mode Optimization and Path Planning for Plug-in Hybrid Electric Vehicles

TL;DR

This paper presents optimization algorithms for drive mode selection and path planning in plug-in hybrid electric vehicles to minimize fuel consumption, incorporating trip data and station locations, with empirical validation on a Chevrolet Volt.

Contribution

It introduces novel optimization algorithms for drive mode and path planning in PHEVs, integrating trip and station data for fuel efficiency improvements.

Findings

Significant fuel savings demonstrated on Chevrolet Volt.

Effective online algorithm based on trip information.

Integrated drive mode and path planning approach.

Abstract

Drive modes are driver-selectable pre-set configurations of powertrain and certain vehicle parameters. Plug-in hybrid electric vehicles (PHEVs) typically feature special options of drive modes that can affect the hybrid energy source management system, for example, electric vehicle (EV) mode (that draws fully on battery) and charge sustaining (CS) mode (that utilizes internal combustion engine to charge battery while propelling the vehicle). This paper studies an optimization problem to enable the driver to select the appropriate drive modes for fuel minimization. We develop optimization algorithms that optimize the decisions of drive modes based on trip information, and integrated with path planning to find an optimal path, considering intermediate filling and charging stations. We further provide an online algorithm that is based on the revealed trip information. We evaluate our algorithms empirically on a Chevrolet Volt, which shows significant fuel savings.