A Comparative Assessment of Economic Model Predictive Control Strategies for Fuel Economy Optimization of Heavy-Duty Trucks
- Univ. of North Carolina, Charlotte, NC (United States)
- Volvo Group Trucks, Washington, DC (United States)
- Volvo Group Trucks, Greencastle, PA (United States)
- 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:
- IEEECopyright Statement
- Country of Publication:
- United States
- Language:
- English
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