Plug-In Hybrid Vehicle Optimization Using Vehicle-to-Cloud Connectivity

Plug-in hybrid electric vehicles (PHEVs) represent a growing segment of the U.S. passenger vehicle market. An increasing number of PHEV models are available to consumers. PHEVs are similar to parallel hybrid vehicles like the Toyota Prius in that they have an electric motor and internal combustion engine that are used synergistically to significantly reduce fuel consumption while maintaining a near constant battery state of charge (i.e. charge sustaining). However, they are different from parallel hybrid vehicles in that they allow external charging when the vehicle is stationary to enable electric-only operation. Electric-only operation (EOO) allows PHEVs to achieve significantly higher fuel economy on a mile per gallon of gasoline equivalent (MPGe) basis over conventional powertrain vehicles. One limitation for consumers choosing to purchase PHEVs is the uncertainty associated with EOO as it strongly depends on route, driving style and ambient temperature. The primary goal of this project is to extend EOO range, and therefore improve fuel economy by using a connected vehicle powertrain and routing co-optimization (VPRO) strategy as an aftermarket solution for PHEV owners. The proposed project combines the Macchina team’s expertise in vehicle closed area network (CAN) interpretation and vehicle communication device (VCD) hardware development with the University of Minnesota’s (UMN) research expertise in vehicle powertrain and routing co- optimization (VPRO) technology. VPRO technology uses historical data and exogenous data like weather and traffic and machine learning algorithms in concert with low-order physics-based models to determine when a PHEV should be in EOO mode for a given pre-programmed route. The primary technical objectives of this Phase I project are to: 1) Design a prototype vehicle communication device (VCD) based our previously developed M2 automotive interface to collect necessary vehicle data and enable EV mode in a chosen PHEV remotely; 2) Determine the technical feasibility and robustness of the hardware; and 3) Quantify potential vehicle fuel economy improvements through computer simulation of a chosen PHEV using real-world vehicle data recorded using the VCD to improve accuracy.