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

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Tác giả:

Ngôn ngữ: eng

Ký hiệu phân loại: 629.28 Tests, driving, maintenance, repair

Thông tin xuất bản: Oak Ridge, Tenn. : Distributed by the Office of Scientific and Technical Information, U.S. Dept. of Energy, 2018

Mô tả vật lý: Size: p. 834-839 : , digital, PDF file.

Bộ sưu tập: Metadata

ID: 266452

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.
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