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Optimal Predictive Control of Wheel Loader Transmissions
Linköping University, Department of Electrical Engineering, Vehicular Systems. Linköping University, The Institute of Technology.
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: urn:nbn:se:liu:diva-112722DOI: 10.3384/diss.diva-112722ISBN: 978-91-7519-171-3 (print)OAI: oai:DiVA.org:liu-112722DiVA: diva2:779181
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
List of papers
1. Robust Driving Pattern Detection and Identification with a Wheel Loader Application
Open this publication in new window or tab >>Robust Driving Pattern Detection and Identification with a Wheel Loader Application
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2014 (English)In: International journal of vehicle systems modelling and testing, ISSN 1745-6436, Vol. 9, no 1, 56-76 p.Article in journal (Refereed) Published
Abstract [en]

Information about wheel loader usage can be used in several ways to optimize customer adaption. First, optimizing the configuration and component sizing of a wheel loader to customer needs can lead to a significant improvement in e.g. fuel efficiency and cost. Second, relevant driving cycles to be used in the development of wheel loaders can be extracted from usage data. Third, on-line usage identification opens up for the possibility of implementing advanced look-ahead control strategies for wheel loader operation. The main objective here is to develop an on-line algorithm that automatically, using production sensors only, can extract information about the usage of a machine. Two main challenges are that sensors are not located with respect to this task and that significant usage disturbances typically occur during operation. The proposed method is based on a combination of several individually simple techniques using signal processing, state automaton techniques, and parameter estimation algorithms. The approach is found to berobust when evaluated on measured data of wheel loaders loading gravel and shot rock.

Place, publisher, year, edition, pages
InderScience Publishers, 2014
Keyword
Driving cycle; Driving cycle identification; Driving pattern; Pattern identification; Robust detection; State automaton; Usage classification; Usage detection; Wheel loader
National Category
Engineering and Technology
Identifiers
urn:nbn:se:liu:diva-92222 (URN)10.1504/IJVSMT.2014.059156 (DOI)2-s2.0-84893958574 (Scopus ID)
Available from: 2013-05-08 Created: 2013-05-08 Last updated: 2015-04-01Bibliographically approved
2. Minimizing Fuel Use During Power Transients for Naturally Aspirated and Turbo Charged Diesel Engines
Open this publication in new window or tab >>Minimizing Fuel Use During Power Transients for Naturally Aspirated and Turbo Charged Diesel Engines
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.

Publisher
13 p.
Series
LiTH-ISY-R, ISSN 1400-3902 ; 3077
National Category
Control Engineering
Identifiers
urn:nbn:se:liu:diva-112720 (URN)LiTH-ISY-R-3077 (ISRN)
Available from: 2014-12-09 Created: 2014-12-09 Last updated: 2015-01-19Bibliographically approved
3. Development of look-ahead controller concepts for a wheel loader application: [Développement de concepts d’une commande prédictive, destinée à une application pour chargeur sur pneus]
Open this publication in new window or tab >>Development of look-ahead controller concepts for a wheel loader application: [Développement de concepts d’une commande prédictive, destinée à une application pour chargeur sur pneus]
2015 (English)In: Oil & gas science and technology, ISSN 1294-4475, E-ISSN 1953-8189, Vol. 70, no 1, 159-178 p.Article in journal (Refereed) Published
Abstract [en]

This paper presents two conceptual methods, based on dynamic programming, for one-step look-ahead control of a Continuously Variable Transmission (CVT) in a wheel loader. The first method developed, designated Stochastic Dynamic Programming (SDP), uses a statistical load prediction and stochastic dynamic programming for minimizing fuel use. The second method developed, designated Free-Time Dynamic Programming (FTDP), has vehicle speed as a state and introduces a fixed 0.1 s delay in the bucket controls in a combined minimization of fuel and time. The methods are evaluated using a set of 34 measured loading cycles, used in a ‘leave one out’ manner.

The evaluation shows that the SDP method requires about 1/10th of the computational effort of FTDP and has a more transparent impact of differences in the cycle prediction. The FTDP method, on the other hand, shows a 10% lower fuel consumption, which is close to the actual optimum, at the same cycle times, and is able to complete a much larger part of the evaluation cycles.

Abstract [fr]

Ce document présente deux méthodes de conception, basées sur la programmation dynamique, pour la commande à un pas de prédiction d’une transmission continûment variable (Continuously Variable Transmission, CVT) d’un chargeur sur pneus. La première méthode développée, appelée programmation dynamique stochastique (Stochastic Dynamic Programming, SDP) utilise une prédiction statistique de la charge et la programmation dynamique stochastique pour minimiser l’utilisation de carburant. La seconde méthode développée, appelée programmation dynamique à temps libre (Free-Time Dynamic Programming, FTDP), établit la vitesse du véhicule en tant qu’état et introduit un retard de 0:1 s dans les commandes du godet pour minimiser à la fois l’utilisation de carburant et le temps nécessaire à l’opération.

Les méthodes sont évaluées en s’appuyant sur 34 cycles de chargement mesurés, utilisés selon la méthode de validation croisée « leave-one-out ».

L’évaluation montre que la méthode SDP requiert environ 1 dixième de l’effort de calcul de la méthode FTDP, et qu’elle a un impact plus transparent sur les écarts dans la prédiction du cycle. D’un autre côté, avec la méthode FTDP on obtient une réduction de 10% de la consommation de carburant, ce qui est proche de l’optimum réel, pour les mêmes durées de cycle, et elle permet de réaliser une plus grande partie des cycles d’évaluation.

Place, publisher, year, edition, pages
EDITIONS TECHNIP, 2015
National Category
Control Engineering
Identifiers
urn:nbn:se:liu:diva-112719 (URN)10.2516/ogst/2014022 (DOI)000351444400011 ()
Available from: 2014-12-09 Created: 2014-12-09 Last updated: 2017-12-05
4. Predictive control of a diesel electric wheel loader powertrain
Open this publication in new window or tab >>Predictive control of a diesel electric wheel loader powertrain
2015 (English)In: Control Engineering Practice, ISSN 0967-0661, E-ISSN 1873-6939, Vol. 41, 47-56 p.Article in journal (Refereed) Published
Abstract [en]

Wheel loaders often have a highly repetitive pattern of operation, which can be used for creating a rough prediction of future operation. As the present torque converter based transmission is replaced with an infinitely variable device, such as an electric or hydraulic transmission, a freedom in the choice of engine speed is introduced. This choice is far from trivial in the extremely transient operation of these machines, but the availability of a load prediction should be utilized.

In this paper, a predictive engine and generator controller, based on stochastic dynamic programming, is described, implemented and evaluated. The evaluation is performed against non-predictive controllers in the same system, to lift out any possible benefits of utilizing the repetition based prediction. Simulations and field tests show that the controllers are able to handle disturbances introduced from model errors, the machine environment and the human operator, and that the predictive controller gives around 5% lower fuel consumption than the non-predictive reference controllers.

Place, publisher, year, edition, pages
Elsevier, 2015
Keyword
Diesel engine; Continuously variable transmission; Predictive control; Stochastic dynamic programming
National Category
Mechanical Engineering
Identifiers
urn:nbn:se:liu:diva-112891 (URN)10.1016/j.conengprac.2015.04.008 (DOI)000357546200005 ()
Available from: 2014-12-19 Created: 2014-12-19 Last updated: 2017-12-05Bibliographically approved

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