Mobility and Energy Improvements Realized Through Prediction-Based Vehicle Powertrain Control and Traffic Management

Principal Investigator:
Thomas H. Bradley, Colorado State University

Project Impact/Takeaway:

  • Enable mobility and energy efficiency improvements from a synergistic combination of infrastructure-level and vehicle-level control
  • Deep collaboration with municipalities, NREL and Clean Cities Coalitions to develop novel metrics, technologies, extension case studies, and outreach. 
  • Solve real-world transportation problems in local municipalities

Relevance:

To reduce travel time and travel time variance while minimizing energy consumption and human health-specific emissions exposure due to transportation.

Objectives:

A data driven approach requires development of a set of coordinated traffic, vehicle (FE and emissions), and infrastructure data using connected vehicle probe data collection techniques.

Optimize traffic management systems and connected/automated vehicle powertrain control to test scenarios demonstrating the synergistic benefits of system-level data sharing, infrastructure management and CAV controls optimization.

Measure and evaluate the multi-objective results using a transportation system level metric of Mobility Energy Productivity.  The results of these studies are then tested for their extensibility through a partnership with cities and counties in the Denver metro area.

This project will address these problems:

  • The problem of traffic congestion along major transportation corridors of the municipality (College Ave. Fort Collins, Speer Blvd. Denver) and the potential to use TMS and CAVs to improve throughput on these corridors without modification of the physical roadway.
  • The problem of the interface between bus rapid transit (BRT) and traffic at intersections (Mason St. Ft Collins, Colfax Ave. Denver). BRT uses dedicated lanes to skip queues and congestion along major transportation corridors, but BRT must still participate in signalized intersections at cross streets. Enabling prioritization and vehicle-level energy management control of these BRT vehicles is hypothesized to improve metrics of mobility energy productivity.
  • The problem of through-town Class 8 freight truck transport (Shields Ave., Fort Collins, I-70/Colorado Blvd, Denver). Due to the growth of Class 8 truck transport, these municipalities face noise pollution, emissions, human health, and safety considerations due to a high volume of Class 8 trucks moving through town on surface streets. Enabling signal prioritization (which also enables platooning) and vehicle-level energy management control for these high-energy consumption, high emissions vehicles is hypothesized to improve metrics of mobility energy productivity.

Technical Accomplishments and Progress:

  • Data collection and synthesis along key routes and for samples of all vehicle types (LD Diesel, LD Gasoline, BRT Diesel, Class 8 Diesel)
  • Multi-channel data is collected and processed to make coherent datasets.

    These datasets are then used as inputs to the model validation and testing processes.

  • Microscopic Traffic Simulation modeling of two artery networks (Harmony Road, College Avenue & Mason Street, Fort Collins) validated to real-world datasets across multiple metrics of performance.

  • Vehicle-level optimal energy management is performed using predictive powertrain control. Fuel economy and emissions assessments are performed using ANN fits to PEMS datasets (n=13). Prediction of future vehicle operation is used to optimize vehicle powertrain control for FE and emissions. Various optimization schemes realize a 2% – 4% improvement in FE.

  • Costs and benefits are traded off in a metric at transportation system level (Mobility Energy Productivity). MEP is a spatially realized metric of accessibility, assessing the number of jobs, goods, and service opportunities which are available within prescribed travel times from a location. 

DMCC is looking for metro-area partners to test this application. If you’re interested in learning more, please contact us.