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Look-ahead control for heavy trucks to minimize trip time and fuel consumption
Linköping University, Department of Electrical Engineering, Vehicular Systems. Linköping University, The Institute of Technology.
Linköping University, Department of Electrical Engineering. Linköping University, The Institute of Technology. (Scania CV AB)
Linköping University, Department of Electrical Engineering, Vehicular Systems. Linköping University, The Institute of Technology.
Linköping University, Department of Electrical Engineering. Linköping University, The Institute of Technology.
2009 (English)In: Control Engineering Practice, ISSN 0967-0661, E-ISSN 1873-6939, Vol. 17, no 2, 245-254 p.Article in journal (Refereed) Published
Abstract [en]

The scenario studied is a drive mission for a heavy diesel truck. With aid of an on board road slope database in combination with a GPS unit, information about the road geometry ahead is extracted. This look-ahead information is used in an optimization of the velocity trajectory with respect to a criterion formulation that weighs trip time and fuel consumption. A dynamic programming algorithm is devised and used in a predictive control scheme by constantly feeding the conventional cruise controller with new set points. The algorithm is evaluated with a real truck on a highway, and the experimental results show that the fuel consumption is significantly reduced.

Place, publisher, year, edition, pages
2009. Vol. 17, no 2, 245-254 p.
Keyword [en]
predictive control, dynamic programming, fuel-optimal control
National Category
Engineering and Technology Computer and Information Science Control Engineering
Identifiers
URN: urn:nbn:se:liu:diva-16629DOI: 10.1016/j.conengprac.2008.07.005OAI: oai:DiVA.org:liu-16629DiVA: diva2:159541
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Note

Original Publication: Erik Hellström, Maria Ivarsson, Jan Åslund and Lars Nielsen, Look-ahead control for heavy trucks to minimize trip time and fuel consumption, 2009, Control Engineering Practice, (17), 2, 245-254. http://dx.doi.org/10.1016/j.conengprac.2008.07.005 Copyright: Elsevier Science B.V., Amsterdam. http://www.elsevier.com/

Available from: 2009-02-08 Created: 2009-02-06 Last updated: 2017-12-14
In thesis
1. Fuel Optimal Powertrain Control for Heavy Trucks Utilizing Look Ahead
Open this publication in new window or tab >>Fuel Optimal Powertrain Control for Heavy Trucks Utilizing Look Ahead
2009 (English)Licentiate thesis, comprehensive summary (Other academic)
Abstract [en]

The road topography in highways affects the powertrain control of a heavy truck substantially since the engine power is low in relation to the vehicle weight. In large road gradients constant speed is not possible to keep, which would have been beneficial otherwise, and in some uphills shifting gears becomes inevitable. If information about the road ahead, i.e. look ahead information, is available, then the powertrain can be controlled in a more fuel efficient way. Trial runs are performed, where the velocity trajectory that minimizes energy consumption, is calculated and communicated in real time as set points to the conventional cruise control. This look ahead control gives significant fuel consumption reductions compared to a standard cruise control, while keeping to the same mean speed. The results are the inspiration to further studies in how powertrain control can benefit from look ahead information. An engine with a non-linear fuel map is studied to understand its impact on fuel optimal speed. It is shown that for a significant fuel map non-linearity, quantified by a threshold value, constant speed in small road gradients is no longer optimal. Further, an automated manual transmission (AMT) optimal gear control is studied. It is shown that the reduced propulsion of a typical AMT gear-shifting process must be considered when choosing when to shift gears. Thus, additional reductions of fuel consumption are obtained with a look ahead control based on knowledge of engine and transmission characteristics.

Place, publisher, year, edition, pages
Linköping: Linköping University Electronic Press, 2009. 14 p.
Series
Linköping Studies in Science and Technology. Thesis, ISSN 0280-7971 ; 1400
National Category
Engineering and Technology
Identifiers
urn:nbn:se:liu:diva-18077 (URN)978-91-7393-637-8 (ISBN)
Presentation
2009-04-28, Glashuset, B-Huset, Campus Valla, Linköpings universitet, Linköping, 10:00 (Swedish)
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Supervisors
Available from: 2009-05-11 Created: 2009-05-05 Last updated: 2009-06-12Bibliographically approved
2. Look-ahead Control of Heavy Vehicles
Open this publication in new window or tab >>Look-ahead Control of Heavy Vehicles
2010 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Trucks are responsible for the major part of inland freight and so, they are a backbone of the modern economy but they are also a large consumer of energy. In this context, a dominating vehicle is a truck with heavy load on a long trip. The aim with look-ahead control is to reduce the energy consumption of heavy vehicles by utilizing information about future conditions focusing on the road topography ahead of the vehicle.

The possible gains with look-ahead control are evaluated by performing experiments with a truck on highway. A real-time control system based on receding horizon control (RHC) is set up where the optimization problem is solved repeatedly on-line for a certain horizon ahead of the vehicle. The experimental results show that significant reductions of the fuel consumption are achieved, and that the controller structure, where the algorithm calculates set points fed to lower level controllers, has satisfactory robustness to perform well on-board in a real environment. Moreover, the controller behavior has the preferred property of being intuitive, and the behavior is perceived as comfortable and natural by participating drivers and passengers.

A well-behaved and efficient algorithm is developed, based on dynamic programing, for the mixed-integer nonlinear minimum-fuel problem. A modeling framework is formulated where special attention is given to properly include gear shifting with physical models. Fuel equivalents are used to reformulate the problem into a tractable form and to construct a residual cost enabling the use of a shorter horizon ahead of the vehicle. Analysis of errors due to discretization of the continuous dynamics and due to interpolation shows that an energy formulation is beneficial for reducing both error sources. The result is an algorithm giving accurate solutions with low computational effort for use in an on-board controller for a fuel-optimal velocity profile and gear selection.

The prevailing approach for the look-ahead problem is RHC where main topics are the approximation of the residual cost and the choice of the horizon length. These two topics are given a thorough investigation independent of the method of solving the optimal control problem in each time step. The basis for the fuel equivalents and the residual cost is formed from physical intuition as well as mathematical interpretations in terms of the Lagrange multipliers used in optimization theory. Measures for suboptimality are introduced that enables choosing horizon length with the appropriate compromise between fuel consumption and trip time.

Control of a hybrid electric powertrain is put in the framework together with control of velocity and gear. For an efficient solution of the minimum-fuel problem in this case, more fuel equivalence factors and an energy formulation are employed. An application is demonstrated in a design study where it is shown how the optimal trade-off between size and capacity of the electrical system depends on road characteristics, and also that a modestly sized electrical system achieves most of the gain.

The contributions develop algorithms, create associated design tools, and carry out experiments. Altogether, a feasible framework is achieved that pave the way for on-board fuel-optimal look-ahead control.

Place, publisher, year, edition, pages
Linköping: Linköping University, 2010. 16 p.
Series
Linköping Studies in Science and Technology. Dissertations, ISSN 0345-7524 ; 1315
Keyword
automotive control, dynamic programming, predictive control, fuel-optimal control, long haulage truck
National Category
Engineering and Technology Computer and Information Science Control Engineering
Identifiers
urn:nbn:se:liu:diva-54922 (URN)978-91-7393-389-6 (ISBN)
Public defence
2010-05-26, Visionen, Hus B, Campus Valla, Linköpings universitet, Linköping, 10:15 (English)
Opponent
Supervisors
Available from: 2010-04-26 Created: 2010-04-22 Last updated: 2010-04-26Bibliographically approved

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Hellström, ErikIvarsson, MariaÅslund, JanNielsen, Lars

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