A Comparative Assessment of Economic Model Predictive Control Strategies for Fuel Economy Optimization of Heavy-Duty Trucks

Journal Article · Fri Jun 01 00:00:00 EDT 2018 · Proceedings of the American Control Conference (ACC)

DOI:https://doi.org/10.23919/ACC.2018.8431050· OSTI ID:1557256

Groelke, Ben ^[1]; Borek, John ^[1]; Earnhardt, Christian ^[1]; Li, Jian ^[2]; Geyer, Stephen ^[3]; Vermillion, Chris ^[4]

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1. Univ. of North Carolina, Charlotte, NC (United States)
2. Volvo Group Trucks, Washington, DC (United States)
3. Volvo Group Trucks, Greencastle, PA (United States)
4. Univ. of North Carolina, Charlotte, NC (United States). Dept. of Mechanical Engineering

This paper provides a comparative assessment of three control strategies that fuse a global, offline dynamic programming (DP) optimization with online model predictive control (MPC) in an effort to minimize fuel consumption for a heavy-duty truck. The online MPC optimization, which is local in nature, makes refinements to a coarsely (but globally, subject to grid resolution) optimized target velocity profile from the DP optimization. Three candidate economic MPC formulations are evaluated: a time-based formulation that directly penalizes predicted fuel consumption, a simplified time-based formulation that penalizes braking effort in place of fuel consumption, and a distance-based convex formulation that maintains a tradeoff between energy expenditure and tracking of the coarsely optimized velocity based on DP. The performance of each approach is presented for three representative route profiles, using a medium-fidelity proprietary Volvo model of the heavy-duty truck’s longitudinal dynamics. Results demonstrate 4-7% fuel economy improvement across all three formulations, when compared to a baseline strategy. Furthermore, we present a detailed analysis of energy usage by “type” (aerodynamic losses, braking losses, and comparison of brake-specific fuel consumption), under each candidate control approach.

• Research Organization: North Carolina State University, Raleigh, NC (United States)
• Sponsoring Organization: USDOE Advanced Research Projects Agency - Energy (ARPA-E)
• Grant/Contract Number: AR0000801
• OSTI ID: 1557256
• Journal Information: Proceedings of the American Control Conference (ACC), Vol. 2018; Conference: 2018 Annual American Control Conference (ACC), Milwaukee, WI (United States), 27-29 Jun 2018; ISSN 2378-5861
• Publisher: IEEE
• Country of Publication: United States
• Language: English

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