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Minimizing Fuel Use During Power Transients for Naturally Aspirated and Turbo Charged Diesel Engines
Linköping University, Department of Electrical Engineering, Vehicular Systems. Linköping University, The Institute of Technology.
Linköping University, Department of Electrical Engineering, Vehicular Systems. Linköping University, The Institute of Technology. Volvo Construction Equipment.
Linköping University, Department of Electrical Engineering, Vehicular Systems. Linköping University, The Institute of Technology.
2014 (English)Report (Other academic)
Abstract [en]

Recent development has renewed the interest in drivetrain concepts which gives a higher degree offreedom by disconnecting the engine and vehicle speeds. This freedom raises the demand for activecontrol, which especially during transients is not trivial, but of which the quality is crucial for the successof the drivetrain concept. In this work the fuel optimal engine operating point trajectories for a naturallyaspirated and a turbocharged diesel engine, connected to a load which does not restrict the engine speed,is derived, analysed and utilized for finding a suboptimal operating point trajectory. The analysis andoptimization is made with dynamic programming, Pontryagin’s maximum principle and a suboptimalstrategy based on the static optimal operating points. Methods are derived for using Pontryagin’smaximum principle for finding the optimal operating point trajectories, for simple load cases. The timeneeded for computation is reduced a factor 1000−100, depending on engine layout, compared to dynamicprogramming. These methods are only applicable to very simple load cases though. Finally, a suboptimalcalculation method which reduce the time needed for computation a factor > 1000 compared to dynamicprogramming, while showing a < 5% increase in fuel consumption compared to the optimal, is presented.

Place, publisher, year, edition, pages
2014. , 13 p.
Series
LiTH-ISY-R, ISSN 1400-3902 ; 3077
National Category
Control Engineering
Identifiers
URN: urn:nbn:se:liu:diva-112720ISRN: LiTH-ISY-R-3077OAI: oai:DiVA.org:liu-112720DiVA: diva2:770141
Available from: 2014-12-09 Created: 2014-12-09 Last updated: 2015-01-19Bibliographically approved
In thesis
1. Optimal Predictive Control of Wheel Loader Transmissions
Open this publication in new window or tab >>Optimal Predictive Control of Wheel Loader Transmissions
2015 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

The transmissions of present heavy wheel loaders are in general based on torque converters. The characteristics of this component suits these machines, especially in that it enables thrust from zero vehicle speed without risk of stalling the engine, without active control. Unfortunately, the component also causes losses which might become large compared to the transmitted power. One approach for mitigating these losses is to switch to a continuously variable transmission. Changing to such a system greatly increases the possibility, and the need, for actively selecting the engine speed, and here a conflict emerges. A low engine speed is desired for high efficiency but a high speed is required for high power.

Heavy wheel loaders often operate according to a common repeating pattern known as the short loading cycle. This cycle is extremely transient, which makes the choice of engine operating point both important and difficult. At the same time, the repeating pattern in the operation enables a rough prediction of the future operation. One way to use the uncertain prediction is to use optimization techniques for selecting the best control actions. This requires a method for detecting the operational pattern and producing a prediction from this, to formulate a manageable optimization problem, and for solving this, and finally to actually control the machine according to the optimization results. This problem is treated in the four papers that are included in this dissertation.

The first paper describes a method for automatically detecting when the machine is operating according to any of several predefined patterns. The detector uses events and automata descriptions of the cycles, which makes the method simple yet powerful. In the evaluations over 90% of the actual cycles are detected and correctly identified. The detector also enables a quick analysis of large datasets. In several of the following papers this is used to condense measured data sequences into statistical cycles for the control optimization.

In the second paper dynamic programming and Pontryagin’s maximum principle is applied to a simplified system consisting of a diesel engine and a generator. Methods are developed based on the maximum principle analysis, for finding the fuel optimal trajectories at output power steps, and the simplicity of the system enables a deeper analysis of these solutions. The methods are used to examine and visualize the mechanisms behind the solutions at power transients, and the models form the basis for the models in the following papers.

The third paper describes two different concepts for implementing dynamic programming based optimal control of a hydrostatic transmission. In this system one load component forms a stochastic state constraint, and the concepts present two different strategies for handling this constraint. The controller concepts are evaluated through simulations, in terms of implementability, robustness against uncertainties in the prediction and fuel savings.

The fourth paper describes the implementation and testing of a predictive controller, based on stochastic dynamic programming, for the engine and generator in a diesel electric powertrain. The controller is evaluated through both simulations and field tests, with several drivers, at a realistic work site, thus including all relevant disturbances and uncertainties. The evaluations indicate a ∼ 5% fuel benefit of utilizing a cycle prediction in the controller.

Place, publisher, year, edition, pages
Linköping: Linköping University Electronic Press, 2015. 27 p.
Series
Linköping Studies in Science and Technology. Dissertations, ISSN 0345-7524 ; 1636
National Category
Mechanical Engineering
Identifiers
urn:nbn:se:liu:diva-112722 (URN)10.3384/diss.diva-112722 (DOI)978-91-7519-171-3 (ISBN)
Public defence
2015-03-20, Visionen, Hus B, Campus Valla, Linköpings universitet, Linköpiong, 14:24 (English)
Supervisors
Available from: 2015-01-12 Created: 2015-01-12 Last updated: 2015-01-12Bibliographically approved

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Nilsson, TomasFröberg, AndersÅslund, Jan

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