Economic Optimal Control for Minimizing Fuel Consumption of Heavy-Duty Trucks in a Highway Environment

Borek, John; Groelke, Ben; Earnhardt, Christian; Vermillion, Chris

doi:10.1109/TCST.2019.2918472

Title: Economic Optimal Control for Minimizing Fuel Consumption of Heavy-Duty Trucks in a Highway Environment

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Abstract

This paper provides a comparative assessment of three economic optimal control strategies, aimed at minimizing the fuel consumption of heavy-duty trucks in a highway environment, under a representative lead vehicle model informed by traffic data. These strategies fuse a global, offline dynamic programming (DP) optimization with online model predictive control (MPC). We then show how two of the three strategies can be adapted to accommodate the presence of traffic and optimally navigate signalized intersections using infrastructure-to-vehicular (I2V) communication. The 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. The three candidate economic MPC formulations that are evaluated include: a nonlinear time-based formulation that directly penalizes predicted fuel consumption, a nonlinear time-based formulation that penalizes braking effort as a surrogate for fuel consumption, and a linear distance-based convex formulation that maintains a tradeoff between energy expenditure and tracking of the coarsely optimized velocity profile obtained from DP. Using a medium-fidelity Simulink model, based on a Volvo truck's longitudinal and engine dynamics, we analyze the optimization's performance on four highway routes under various traffic scenarios. Results demonstrate 3.7-8.3% fuel economy improvement on highway routes withoutmore »« less

Authors:

^[1];

^[2];

^[2]; Vermillion, Chris ^[2]

Univ. of North Carolina, Charlotte, NC (United States)
North Carolina State Univ., Raleigh, NC (United States)

Publication Date:: Mon Jun 17 00:00:00 EDT 2019

Research Org.:: Univ. of North Carolina, Charlotte, NC (United States)

Sponsoring Org.:: USDOE Advanced Research Projects Agency - Energy (ARPA-E)

OSTI Identifier:: 1557265

Grant/Contract Number:: AR0000801

Resource Type:: Accepted Manuscript

Journal Name:: IEEE Transactions on Control Systems Technology

Additional Journal Information:: Journal Volume: 28; Journal Issue: 5; Journal ID: ISSN 1063-6536

Publisher:: IEEE

Country of Publication:: United States

Language:: English

Subject:: 33 ADVANCED PROPULSION SYSTEMS

Citation Formats


                    Borek, John, Groelke, Ben, Earnhardt, Christian, and Vermillion, Chris. Economic Optimal Control for Minimizing Fuel Consumption of Heavy-Duty Trucks in a Highway Environment.  United States: N. p., 2019. 
Web.  doi:10.1109/TCST.2019.2918472.

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                    Borek, John, Groelke, Ben, Earnhardt, Christian, & Vermillion, Chris. Economic Optimal Control for Minimizing Fuel Consumption of Heavy-Duty Trucks in a Highway Environment.  United States.  https://doi.org/10.1109/TCST.2019.2918472

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                    Borek, John, Groelke, Ben, Earnhardt, Christian, and Vermillion, Chris. Mon .  
"Economic Optimal Control for Minimizing Fuel Consumption of Heavy-Duty Trucks in a Highway Environment".  United States.  https://doi.org/10.1109/TCST.2019.2918472.  https://www.osti.gov/servlets/purl/1557265.

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@article{osti_1557265,

  title        = {Economic Optimal Control for Minimizing Fuel Consumption of Heavy-Duty Trucks in a Highway Environment},

  author       = {Borek, John and Groelke, Ben and Earnhardt, Christian and Vermillion, Chris},

  abstractNote = {This paper provides a comparative assessment of three economic optimal control strategies, aimed at minimizing the fuel consumption of heavy-duty trucks in a highway environment, under a representative lead vehicle model informed by traffic data. These strategies fuse a global, offline dynamic programming (DP) optimization with online model predictive control (MPC). We then show how two of the three strategies can be adapted to accommodate the presence of traffic and optimally navigate signalized intersections using infrastructure-to-vehicular (I2V) communication. The 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. The three candidate economic MPC formulations that are evaluated include: a nonlinear time-based formulation that directly penalizes predicted fuel consumption, a nonlinear time-based formulation that penalizes braking effort as a surrogate for fuel consumption, and a linear distance-based convex formulation that maintains a tradeoff between energy expenditure and tracking of the coarsely optimized velocity profile obtained from DP. Using a medium-fidelity Simulink model, based on a Volvo truck's longitudinal and engine dynamics, we analyze the optimization's performance on four highway routes under various traffic scenarios. Results demonstrate 3.7-8.3% fuel economy improvement on highway routes without traffic and 6.5-10% on the same routes with traffic included. 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 strategy.},

  doi          = {10.1109/TCST.2019.2918472},

  journal      = {IEEE Transactions on Control Systems Technology},

  number       = 5,

  volume       = 28,

  place        = {United States},

  year         = {Mon Jun 17 00:00:00 EDT 2019},

  month        = {Mon Jun 17 00:00:00 EDT 2019}

}

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