Date of Award
2026
Document Type
Campus Access Dissertation
Degree Name
Doctor of Philosophy in Mechanical Engineering-Engineering Mechanics (PhD)
Administrative Home Department
Department of Mechanical and Aerospace Engineering
Advisor 1
Jeffrey D. Naber
Advisor 2
Bo Chen
Committee Member 1
Darrell L. Robinette
Committee Member 2
Anthony J. Pinar
Abstract
This dissertation focuses on the development and validation of on-vehicle, predictive energy estimation algorithms and the investigation of energy-saving strategies for modern PHEV and EV light-duty vehicles, including connected and automated vehicles (CAVs). The access to detailed information about the vehicle and the planned route allows for the optimization of vehicle operations to reduce energy consumption.
The first technology detailed is a methodology to optimize the blending of charge-depleting (CD) and charge-sustaining (CS) modes in a multi-mode plug-in hybrid electric vehicle (PHEV). The objective of the optimization is to best utilize onboard energy for minimum overall energy consumption based on speed and elevation profile. The optimization reduces overall energy consumption when the selected route cannot be completely driven in all-electric mode. The optimization method splits drive cycles into constant distance segments and then uses a reduced-order energy model to sort the segments by the best use of battery energy vs. fuel energy.
The second technology detailed is a methodology to optimally select between routes proposed by mapping software. The objective of the optimization is to make the best trade-off between travel time and energy consumption when deciding between different routes. The method uses an intelligent driver model to convert the data from the mapping software into a vehicle speed $\&$ torque profile, then uses a reduced order energy model to find the vehicle energy consumption for each route. Weightings are applied to the difference in energy and travel time for each route compared to the primary route. The vehicle used in this investigation is the Stellantis Pacifica PHEV.
Finally, a methodology is presented for an EV to optimize the charging profile for long-distance travel and the effects on route selection. The objective of the optimization is to choose the optimum route based on the EV properties and the available chargers. The method uses the Eco-routing technologies of the previous technology, combined with a vehicle-specific charge planning algorithm to decide the optimum charging location and duration. The routing is informed by the real location and capability of deployed EV fast chargers.
Collectively, this dissertation presents a group of methodologies to utilize the enhanced vehicle awareness and routing information on CAVs to optimize the energy consumption of PHEV and EV light-duty vehicles.
Recommended Citation
Robare, Andrew L., "CONNECTED AND AUTONOMOUS VEHICLE ENERGY SAVINGS BY MODE BLENDING, ROUTE PLANNING, AND CHARGE PLANNING", Campus Access Dissertation, Michigan Technological University, 2026.