ARPA-E

Simultaneous Optimization of Vehicle and Powertrain Operation Using Connectivity and Automation


Sponsor:
ARPAE NEXTCAR Program


Principal Investigator (PI):

Prof. Andreas Malikopoulos, University of Delaware


Co-PIs:

  • Christos Cassandras, Boston University
  • Huei Peng, University of Michigan
  • Jackeline Rios-Torres, Oak Ridge National Laboratory
  • Shyam Jade, Bosch

Project Description

The overarching goal of this project is to develop and implement the control technologies aimed at maximizing the energy efficiency of a 2016 Audi A3 e-tron plug-in hybrid electric vehicle (PHEV) without degradation in tailpipe out exhaust emission levels, and without sacrificing the vehicle’s drivability, performance, and safety. These technologies will exploit connectivity between vehicles and the infrastructure to optimize concurrently vehicle-level and powertrain-level operations. We develop a two-level control architecture to: (1) optimize the vehicle’s speed profile aimed at minimizing (ideally, eliminating) stop-and-go driving, and (2) optimize the powertrain of the vehicle for this optimal speed profile obtained under (1). These control technologies will consist of a vehicle dynamic (VD) controller, a powertrain (PT) controller, and a supervisory controller. The supervisory controller will (1) oversee the VD and PT controllers, (2) communicate the endogenous and exogenous information appropriately, (3) compute the optimal routing for any desired origin-destination, (4) determine the regions where electric driving will have the major impact and derive a desired battery state-of-charge trajectory, and (5) synthesize a description of the upcoming road segment from the exogenous information and communicate it to the VD controller. The VD controller will optimize online the acceleration/deceleration and speed profile of the vehicle, and thus, torque demand. The PT controller will compute the optimal nominal operation (“set-points”) for the engine, motor, battery, and transmission corresponding to the optimal solution of the VD controller. We will manage the complexity of the problem dimensionality by establishing two parallel and appropriately interacting computational levels: 1) a cloud-based, and 2) a vehicle-based level. The proposed control technologies will be applied to the operation of the vehicle over a range of real-world driving scenarios deemed characteristic of typical commute. At the end of the project, we will deliver the technologies that will revolutionize the way that a vehicle is optimized today and simultaneously achieve the following: 1) compute optimal routing for any desired origin-destination to bypass bottlenecks, accidents, special events, and other conditions that affect traffic flow, 2) accelerate/decelerate optimally based on traffic conditions and the state of the surrounding roads to avoid getting into congestion so that the vehicle will not have to come to a full stop, thereby conserving momentum and improving efficiency, and 3) realize online the optimal efficiency of the powertrain. The long-term potential impact of these technologies is substantial. Energy efficiency will be increased by more than 20%, and thus the market penetration of hybrid electric vehicles and PHEVs will be boosted dramatically with significant implications in reducing oil displacement and greenhouse gas emissions. The expected outcome of this project will promote scientific and technological innovations that will eventually advance the economic and energy security of the U.S, and decrease the dependence on foreign energy sources.