Information and Collaboration Levels in Vehicular Strings: A Comparative Study

Conference · Wed Jan 01 04:00:00 EST 2020 · IFAC-PapersOnLine

DOI:https://doi.org/10.1016/j.ifacol.2020.12.892· OSTI ID:1863225

Dollar, R. Austin ^[1]; Sciarretta, Antonio; Vahidi, Ardalan

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1. General Motors

The potential safety, productivity, and energy benefits of automated vehicles have driven a surge of research interest in their algorithms. Even within single-lane driving, control engineers now have a profusion of approaches available to them. Algorithm classes include classical controllers, receding horizon controllers, and constrained eco-driving formulae based on Pontryagin’s Minimum Principle. Differing connectivity architectures and collaboration levels further differentiate algorithms from one another. This study evaluated six controllers in two drive cycle-based scenarios using an electric powertrain model for energy analysis. Individual-vehicle and string performance were examined, including string stability and length. Algorithms with greater access to information generally performed best. Although collaboration affected energy use only slightly, it made a greater impact on string length.

• Research Organization: Clemson University
• Sponsoring Organization: USDOE Office of Energy Efficiency and Renewable Energy (EERE), Transportation Office. Vehicle Technologies Office
• DOE Contract Number: EE0008232
• OSTI ID: 1863225
• Conference Information: Journal Name: IFAC-PapersOnLine Journal Issue: 2 Journal Volume: 53
• Country of Publication: United States
• Language: English