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Energies 2017, 10(8), 1096; doi:10.3390/en10081096

Real-Time Distributed Economic Model Predictive Control for Complete Vehicle Energy Management

1,†,* , 1,†
,
2,†
and
1,†
1
Department of Electrical Engineering, Eindhoven University of Technology, 5612 AZ Eindhoven, The Netherlands
2
DAF Trucks NV, Vehicle Control Group, 5643 TW Eindhoven, The Netherlands
All authors contributed equally to this work.
*
Author to whom correspondence should be addressed.
Received: 15 June 2017 / Revised: 19 July 2017 / Accepted: 20 July 2017 / Published: 27 July 2017
(This article belongs to the Special Issue Energy Management Control)
View Full-Text   |   Download PDF [1115 KB, uploaded 31 July 2017]   |  

Abstract

In this paper, a real-time distributed economic model predictive control approach for complete vehicle energy management (CVEM) is presented using a receding control horizon in combination with a dual decomposition. The dual decomposition allows the CVEM optimization problem to be solved by solving several smaller optimization problems. The receding horizon control problem is formulated with variable sample intervals, allowing for large prediction horizons with only a limited number of decision variables and constraints in the optimization problem. Furthermore, a novel on/off control concept for the control of the refrigerated semi-trailer, the air supply system and the climate control system is introduced. Simulation results on a low-fidelity vehicle model show that close to optimal fuel reduction performance can be achieved. The fuel reduction for the on/off controlled subsystems strongly depends on the number of switches allowed. By allowing up to 15-times more switches, a fuel reduction of 1.3% can be achieved. The approach is also validated on a high-fidelity vehicle model, for which the road slope is predicted by an e-horizon sensor, leading to a prediction of the propulsion power and engine speed. The prediction algorithm is demonstrated with measured ADASIS information on a public road around Eindhoven, which shows that accurate prediction of the propulsion power and engine speed is feasible when the vehicle follows the most probable path. A fuel reduction of up to 0.63% is achieved for the high-fidelity vehicle model. View Full-Text
Keywords: energy management; hybrid vehicles; distributed model predictive control; dual decomposition; auxiliaries energy management; hybrid vehicles; distributed model predictive control; dual decomposition; auxiliaries
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Romijn, C.; Donkers, T.; Kessels, J.; Weiland, S. Real-Time Distributed Economic Model Predictive Control for Complete Vehicle Energy Management. Energies 2017, 10, 1096.

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