Connected and Autonomous Vehicles

Collaborative and Predictive Routing, Speed, and Powertrain Control for Fuel Economy Optimization:

Together with collaborators at Penn State, MIT, and Volvo Group North America, our team at UNC-Charlotte is developing predictive control algorithms that use real-time data from the target vehicle and surrounding vehicles to optimize the route (subject to start and end points), vehicle speed (subject to arrival time constraint windows), and powertrain control strategies. The goal of this optimization is to achieve 20% total fuel economy improvement through the combined optimization at four levels – route control, vehicle control, transmission control, and engine/accessory control. The investigated control strategies are applicable to model year 2016 internal combustion vehicles; through its collaboration with Volvo Group North America, the research team will be performing simulation and experimental validation studies of its proposed solutions on a VNL 300 heavy diesel truck.

The diagram below shows the interaction between the multiple layers of the hierarchical control structure. The UNC-Charlotte team is focusing largely on the development of control algorithms at the vehicle control level. In particular, UNC-Charlotte is taking a lead role on vehicle speed trajectory optimization.


ARPA-E (Lead Institution – Penn State): Maximizing Vehicle Fuel Economy Through the Real-Time, Collaborative, and Predictive Co-Optimization of Routing, Speed, and Powertrain Control


1) Penn State University

2) Massachusetts Institute of Technology

3) University of North Carolina at Charlotte

4) Volvo Group North America