UC Irvine

In hopes of lessening the reliance on fossil fuels, Plug-in Hybrid Electric Vehicles (PHEVs) have become an attractive option as an alternative fuel vehicle due to their larger electric motors and energy storage systems (ESS). PHEVs can propel themself relying solely on their internal combustion engine (ICE), electric motor (EM), and or a hybrid of both. To improve their fuel efficiency, many studies have been done to investigate the use of a priori route information to optimize the use of a PHEV’s ICE and EM. This study introduces a real-time machine learning application of a control strategy known as Trajectory Forecasting (TF). TF takes a priori knowledge of a PHEV’s pre-planned route to determine when the vehicle will use its different forms of propulsion in the form of propulsion mode scheduling. However, it assumes constant route data such as traffic and resulting driving speed for its scheduling to be applicable. To automatically account for changing traffic as well as choose better alternative routes, this study looks at the use of a Convolutional Neural Network (CNN) to simulate a PHEV’s operation along available routes beforehand according to the rules of TF to choose a route that best satisfies a driver’s want, better fuel efficiency and possibly lower emissions. This new real-time TF-based machine learning control strategy is evaluated and compared to common PHEV control strategies such as Charge Sustaining (CS) and Charge Depletion (CD) using National Renewable Energy Laboratory’s vehicle simulator ADVISOR. Results show possible increases in Mpgge from 1.72%-130%, decreases in emitted hydrocarbons (HC), carbon monoxide (CO), and nitrous oxides (NOx) from 0.05%-70%, and 0.05%-90.35% reduction in gasoline consumption depending on overall route length and PHEV configuration.