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Engine-in-the-Loop Study of a Hierarchical Predictive Online Controller for Connected and Automated Heavy-Duty Vehicles
ISSN: 0148-7191, e-ISSN: 2688-3627
Published April 14, 2020 by SAE International in United States
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This paper presents a cohesive set of engine-in-the-loop (EIL) studies examining the use of hierarchical model-predictive control for fuel consumption minimization in a class-8 heavy-duty truck intended to be equipped with Level-1 connectivity/automation. This work is motivated by the potential of connected/automated vehicle technologies to reduce fuel consumption in both urban/suburban and highway scenarios. The authors begin by presenting a hierarchical model-predictive control scheme that optimizes multiple chassis and powertrain functionalities for fuel consumption. These functionalities include: vehicle routing, arrival/departure at signalized intersections, speed trajectory optimization, platooning, predictive optimal gear shifting, and engine demand torque shaping. The primary optimization goal is to minimize fuel consumption, but the hierarchical controller explicitly accounts for other key objectives/constraints, including operator comfort and safe inter-vehicle spacing. This work is experimentally experimentally validated via a sequence of EIL studies intended for evaluating the computational costs and fuel savings associated with these algorithms. These EIL studies involve the closed-loop validation of the proposed control strategies, both individually and combined. These studies show that this hierarchy of algorithms is capable of running online, with the round-trip communication delay inherent in EIL simulation being one of the key factors affecting the EIL results. Moreover, the EIL studies are encouraging, both in terms of the successful hierarchical integration of the underlying algorithms and also in the resulting fuel savings seen in the EIL tests. In particular, the EIL results suggest that an aggressive overall goal of reducing vehicle fuel consumption by 15-20% or more is potentially achievable, especially in urban/suburban scenarios.
- Chu Xu - The University of Maryland
- Ben Groelke - North Carolina State University
- Miguel Alvarez Tiburcio - The University of Maryland
- Christian Earnhardt - North Carolina State University
- John Borek - The University of North Carolina
- Evan Pelletier - Penn State University
- Stephen Boyle - Ohio State University
- Brian Huynh - Penn State University
- Mohamed Wahba - Penn State University
- Stephen Geyer - Volvo Group North America
- Christopher Graham - Volvo Group North America
- Mark Magee - Volvo Group North America
- Kyle Palmeter - Volvo Group North America
- Mohammad Naghnaeian - Clemson University
- Sean Brennan - Penn State University
- Stephanie Stockar - Ohio State University
- Christopher Vermillion - North Carolina State University
- Hosam Fathy - The University of Maryland
CitationXu, C., Groelke, B., Alvarez Tiburcio, M., Earnhardt, C. et al., "Engine-in-the-Loop Study of a Hierarchical Predictive Online Controller for Connected and Automated Heavy-Duty Vehicles," SAE Technical Paper 2020-01-0592, 2020, https://doi.org/10.4271/2020-01-0592.
Data Sets - Support Documents
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