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Look-ahead control of heavy vehicles / Erik Hellström.

Hellström, Erik, 1979- (författare)
Nielsen, Lars (preses)
Åslund, Jan (preses)
Guzzella, Lino (opponent)
Vehicular Systems (medarbetare)
Linköpings universitet Institutionen för systemteknik (utgivare)
Linköpings universitet Tekniska högskolan (utgivare)
Publicerad: Linköping : Linköping University, 2010
Engelska 1 onlineresurs (16 sidor)
Serie: Linköping Studies in Science and Technology. Dissertations, 0345-7524 0345-7524
Läs hela texten (Sammanfattning och ramberättelse från Linköping University Electronic Press)
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  • E-bokAvhandling(Diss. (sammanfattning) Linköping : Linköpings universitet, 2010)
Sammanfattning Ämnesord
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  • 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. 

Ämnesord

Bränsleförbrukning  (sao)
Lastbilar  (barn)
Engineering and Technology  (ssif)
Teknik  (ssif)
Natural Sciences  (ssif)
Computer and Information Sciences  (ssif)
Naturvetenskap  (ssif)
Data- och informationsvetenskap (Datateknik)  (ssif)
Engineering and Technology  (ssif)
Electrical Engineering, Electronic Engineering, Information Engineering  (ssif)
Control Engineering  (ssif)
Teknik  (ssif)
Elektroteknik och elektronik  (ssif)
Reglerteknik  (ssif)
TECHNOLOGY  (svep)
TEKNIKVETENSKAP  (svep)
TECHNOLOGY  (svep)
Information technology  (svep)
TEKNIKVETENSKAP  (svep)
Informationsteknik  (svep)
TECHNOLOGY  (svep)
Information technology  (svep)
Automatic control  (svep)
TEKNIKVETENSKAP  (svep)
Informationsteknik  (svep)
Reglerteknik  (svep)
Energy consumption  (lcsh)

Genre

government publication  (marcgt)

Indexterm och SAB-rubrik

automotive control
dynamic programming
predictive control
fuel-optimal control
long haulage truck
Automotive control
Framförhållningsregler
Prabd Lastbilar

Klassifikation

629.224 (DDC)
Prabd (kssb/8)
Inställningar Hjälp

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