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Title: Fused Global-Local Economic Model Predictive Control for Real-Time Eco-Optimal Control of a Heavy Duty Truck

Abstract

This paper introduces a fused global-local economic model predictive control (MPC) with varying time steps for maximizing the fuel economy of a heavy-duty truck, where global MPC is performed over a long horizon using cloud-based dynamic programming (DP), and local MPC is performed on-board the vehicle using sequential quadratic programming (SQP). Because of the computational complexity of DP, an in-depth analysis of how the discretization levels, time constant, and horizon length influence the energy savings and computational complexity of online DP has been performed. The fused optimization technique has been evaluated over different route profiles, accounting for traffic, using a medium-fidelity proprietary Volvo model. Results show 4-7% fuel savings, as compared to a baseline model.

Authors:
 [1];  [1];  [1];  [2];  [3]
  1. Univ. of North Carolina, Charlotte, NC (United States)
  2. Volvo Group Trucks, Greencastle, PA (United States)
  3. Univ. of North Carolina, Charlotte, NC (United States). Dept. of Mechanical Engineering
Publication Date:
Research Org.:
North Carolina State Univ., Raleigh, NC (United States)
Sponsoring Org.:
USDOE Advanced Research Projects Agency - Energy (ARPA-E)
Contributing Org.:
Volvo Group Trucks
OSTI Identifier:
1557257
DOE Contract Number:  
AR0000801
Resource Type:
Conference
Resource Relation:
Conference: 14. International Symposium on Advanced Vehicle Control, Beijing (China), 16-20, Jul 2018
Country of Publication:
United States
Language:
English
Subject:
33 ADVANCED PROPULSION SYSTEMS

Citation Formats

Earnhardt, Christian, Borek, John, Groelke, Ben, Geyer, Steve, and Vermillion, Chris. Fused Global-Local Economic Model Predictive Control for Real-Time Eco-Optimal Control of a Heavy Duty Truck. United States: N. p., 2019. Web.
Earnhardt, Christian, Borek, John, Groelke, Ben, Geyer, Steve, & Vermillion, Chris. Fused Global-Local Economic Model Predictive Control for Real-Time Eco-Optimal Control of a Heavy Duty Truck. United States.
Earnhardt, Christian, Borek, John, Groelke, Ben, Geyer, Steve, and Vermillion, Chris. Fri . "Fused Global-Local Economic Model Predictive Control for Real-Time Eco-Optimal Control of a Heavy Duty Truck". United States.
@article{osti_1557257,
title = {Fused Global-Local Economic Model Predictive Control for Real-Time Eco-Optimal Control of a Heavy Duty Truck},
author = {Earnhardt, Christian and Borek, John and Groelke, Ben and Geyer, Steve and Vermillion, Chris},
abstractNote = {This paper introduces a fused global-local economic model predictive control (MPC) with varying time steps for maximizing the fuel economy of a heavy-duty truck, where global MPC is performed over a long horizon using cloud-based dynamic programming (DP), and local MPC is performed on-board the vehicle using sequential quadratic programming (SQP). Because of the computational complexity of DP, an in-depth analysis of how the discretization levels, time constant, and horizon length influence the energy savings and computational complexity of online DP has been performed. The fused optimization technique has been evaluated over different route profiles, accounting for traffic, using a medium-fidelity proprietary Volvo model. Results show 4-7% fuel savings, as compared to a baseline model.},
doi = {},
journal = {},
number = ,
volume = ,
place = {United States},
year = {2019},
month = {8}
}

Conference:
Other availability
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