Route-optimized Energy Management of Connected and Automated Multi-mode Plug-in Hybrid Electric Vehicle using Reduced-order Powertrain Modeling and Dynamic Programming

This thesis details the development of a methodology to blend charge-depleting (CD) and charge-sustaining (CS) modes in a multi-mode plug-in hybrid electric vehicle (PHEV) to minimize energy consumption when the planned drive route cannot be completely executed in all-electric mode. This methodology enables efficient utilization of onboard energy resources by using increased awareness of driving conditions facilitated by Vehicle-to-Vehicle (V2V), Vehicle-to-Infrastructure (V2I), Vehicle-to-Everything (V2X) connectivity, and onboard perception technologies of Connected and Automated Vehicles (CAVs). With such application demanding a real-time update of optimal mode scheme to dynamic traffic conditions, the emphasis of this study is to develop a quick and computationally inexpensive blended mode optimizer by reduced-order modeling of Chevrolet Volt. On-road validation of the developed optimizer on a fleet of 4 instrumented vehicles revealed energy savings in the range of 2 to 12% and an initial optimization time less than 7 seconds for a 24-mile drive cycle