A distributed optimization approach for complete vehicle energy management

T.C.J. Romijn (Corresponding author), M.C.F. Donkers, J.T.B.A. Kessels, S. Weiland

Research output: Contribution to journalArticleAcademicpeer-review

Abstract

In this paper, a distributed optimization approach is proposed to solve the complete vehicle energy management (CVEM) problem of a hybrid truck with several controllable auxiliaries. The first part of the approach is a dual decomposition, which allows the underlying optimal control problem to be solved for every subsystem separately. For the second part of the approach, the optimal control problem for every subsystem is further decomposed by splitting the control horizon into several smaller horizons. Two methods for splitting the control horizon are used; the first method uses alternating direction method of multipliers and divides the horizon a priori, while the second method divides the horizon iteratively by solving unconstrained optimization problems analytically. We demonstrate the approach by solving the CVEM problem of a hybrid truck with a refrigerated semitrailer, an air supply system, an alternator, a dc-dc converter, a low-voltage battery, and a climate control system. Simulation results show that the fuel consumption can be reduced up to 0.52% by including smart auxiliaries in the energy management problem. More interestingly, the computation time is reduced by a factor of 64 up to 1825, compared with solving a centralized convex optimization problem.
LanguageEnglish
Pages964-980
JournalIEEE Transactions on Control Systems Technology
Volume27
Issue number3
DOIs
StatePublished - May 2019

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Energy management
Trucks
Climate control
Convex optimization
Fuel consumption
Decomposition
Control systems
Electric potential
Air

Cite this

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title = "A distributed optimization approach for complete vehicle energy management",
abstract = "In this paper, a distributed optimization approach is proposed to solve the complete vehicle energy management (CVEM) problem of a hybrid truck with several controllable auxiliaries. The first part of the approach is a dual decomposition, which allows the underlying optimal control problem to be solved for every subsystem separately. For the second part of the approach, the optimal control problem for every subsystem is further decomposed by splitting the control horizon into several smaller horizons. Two methods for splitting the control horizon are used; the first method uses alternating direction method of multipliers and divides the horizon a priori, while the second method divides the horizon iteratively by solving unconstrained optimization problems analytically. We demonstrate the approach by solving the CVEM problem of a hybrid truck with a refrigerated semitrailer, an air supply system, an alternator, a dc-dc converter, a low-voltage battery, and a climate control system. Simulation results show that the fuel consumption can be reduced up to 0.52{\%} by including smart auxiliaries in the energy management problem. More interestingly, the computation time is reduced by a factor of 64 up to 1825, compared with solving a centralized convex optimization problem.",
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A distributed optimization approach for complete vehicle energy management. / Romijn, T.C.J. (Corresponding author); Donkers, M.C.F.; Kessels, J.T.B.A.; Weiland, S.

In: IEEE Transactions on Control Systems Technology, Vol. 27, No. 3, 05.2019, p. 964-980.

Research output: Contribution to journalArticleAcademicpeer-review

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AB - In this paper, a distributed optimization approach is proposed to solve the complete vehicle energy management (CVEM) problem of a hybrid truck with several controllable auxiliaries. The first part of the approach is a dual decomposition, which allows the underlying optimal control problem to be solved for every subsystem separately. For the second part of the approach, the optimal control problem for every subsystem is further decomposed by splitting the control horizon into several smaller horizons. Two methods for splitting the control horizon are used; the first method uses alternating direction method of multipliers and divides the horizon a priori, while the second method divides the horizon iteratively by solving unconstrained optimization problems analytically. We demonstrate the approach by solving the CVEM problem of a hybrid truck with a refrigerated semitrailer, an air supply system, an alternator, a dc-dc converter, a low-voltage battery, and a climate control system. Simulation results show that the fuel consumption can be reduced up to 0.52% by including smart auxiliaries in the energy management problem. More interestingly, the computation time is reduced by a factor of 64 up to 1825, compared with solving a centralized convex optimization problem.

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