On behalf of the standing committees of the TRB Technical Activities Division, I have been informed that my work on “Energy-Optimal Operation of Connected Automated Plug-In Hybrid Electric Vehicles” has been selected for a poster presentation session in the “Energy Ideathon” session of the TRB 99th Annual Meeting, to be held January 12-16, 2020, in Washington, D.C. This particular work has been sponsored by ARPA-E NEXTCAR Program. Details regarding the schedule of the presentation is noted below:

Details:

Presentation Number: P20-21198
Presentation Title: Energy-Optimal Operation of Connected Automated Plug-In Hybrid Electric Vehicles

Session: 1479, Energy Ideathon
Session Location: Hall A / Convention Center
Session Time: Tuesday, Jan 14, 2020 10:15AM 12:00PM
Poster-board Location Number: B373

Abstract:

The implementation of connected and automated vehicle (CAV) technologies enables a novel computational framework to deliver real-time control actions that optimize travel time, energy consumption, and passenger safety. In this work, we exploit the “vehicle-to-everything” (V2X) communication framework in a plug-in hybrid electric vehicle (PHEV) to develop and implement an energy efficient controller. We propose a two-level supervisory control architecture that concurrently optimizes (1) the speed profile of the PHEV to reduce stop-and-go driving and travel time, and (2) the powertrain operation to ensure the most efficient torque set-points of the PHEV. We bound the complexity of the problem dimensionality by establishing simultaneous and interactive computational levels between the cloud and vehicle onboard. The proposed supervisory controller is applicable to the connected and automated PHEVs over a corridor including real-world driving scenarios (highway on-ramp, speed reduction zone, and roundabout) representative of the characteristics of typical commute. To validate and evaluate the performance of the proposed supervisory controller, we present an extensive study in a commercial simulation environment. We observe complete elimination of stop-and-go driving along with significant improvement in vehicle travel time and energy consumption. Partial aspects of the proposed supervisory controller has already been integrated into a 2016 Audi A3 e-tron PHEV and tested in the Mcity with the help of augmented reality.