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Robust Driving Pattern Detection and Identification with a Wheel Loader Application
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.
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.
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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. Vol. 9, no 1, 56-76 p.
Keyword [en]
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: urn:nbn:se:liu:diva-92222DOI: 10.1504/IJVSMT.2014.059156Scopus ID: 2-s2.0-84893958574OAI: oai:DiVA.org:liu-92222DiVA: diva2:620352
Available from: 2013-05-08 Created: 2013-05-08 Last updated: 2015-04-01Bibliographically approved
In thesis
1. Evaluation, Transformation, and Extraction of Driving Cycles and Vehicle Operations
Open this publication in new window or tab >>Evaluation, Transformation, and Extraction of Driving Cycles and Vehicle Operations
2013 (English)Licentiate thesis, comprehensive summary (Other academic)
Abstract [en]

A driving cycle is a representation of how vehicles are driven and  is usually represented by a set of data points of vehicle speed  versus time.  Driving cycles have been used to evaluate vehicles for  a long time. A traditional usage of driving cycles have been in  certification test procedures where the exhaust gas emissions from  the vehicles need to comply with legislation. Driving cycles are now  also used in product development for example to size components or  to evaluate different technologies.  Driving cycles can be just a  repetition of measured data, be synthetically designed from  engineering standpoints, be a statistically equivalent  transformation of either of the two previous, or be obtained as an  inverse problem e.g. obtaining driving/operation patterns.  New  methods that generate driving cycles and extract typical behavior  from large amounts of operational data have recently been proposed.  Other methods can be used for comparison of driving cycles, or to  get realistic operations from measured data. 

This work addresses evaluation, transformation and extraction of  driving cycles and vehicle operations.  To be able to test a vehicle  in a controlled environment, a chassis dynamometer is an  option. When the vehicle is mounted, the chassis dynamometer  simulates the road forces that the vehicle would experience if it  would be driven on a real road. A moving base simulator is a  well-established technique to evaluate driver perception of e.g. the  powertrain in a vehicle, and by connecting these two simulators the  fidelity can be enhanced in the moving base simulator and at the  same time the mounted vehicle in the chassis dynamometer is  experiencing more realistic loads. This is due to the driver's  perception in the moving base simulator is close to reality. 

If only a driving cycle is considered in the optimization of a  controller there is a risk that the controllers of vehicles are  tailored to perform well in that specific driving cycle and not  during real-world driving. To avoid the sub-optimization issues, the  operating regions of the engine need to be excited differently. This  can be attained by using a novel algorithm, which is proposed in  this thesis, that alters the driving cycle while maintaining that  the driving cycle tests vehicles in a similar way. This is achieved  by keeping the mean tractive force constant during the process. 

From a manufacturers standpoint it is vital to understand how your  vehicles are being used by the customers. Knowledge about the usage  can be used for design of driving cycles, component sizing and  configuration, during the product development process, and in  control algorithms.  To get a clearer picture of the usage of wheel  loaders, a novel algorithm that automatically, using existing  sensors only, extracts information of the customers usage, is  suggested. The approach is found to be robust when evaluated on  measured data from wheel loaders loading gravel and shot rock.

Place, publisher, year, edition, pages
Linköping: Linköping University Electronic Press, 2013. 103 p.
Series
Linköping Studies in Science and Technology. Thesis, ISSN 0280-7971 ; 1596
National Category
Engineering and Technology
Identifiers
urn:nbn:se:liu:diva-92180 (URN)LIU-TEK-LIC-2013:30 (Local ID)978-91-7519-597-1 (ISBN)LIU-TEK-LIC-2013:30 (Archive number)LIU-TEK-LIC-2013:30 (OAI)
Presentation
2013-05-30, Visionen, Hus B, Campus Valla, Linköpings universitet, Linköping, 10:15 (Swedish)
Opponent
Supervisors
Available from: 2013-05-08 Created: 2013-05-08 Last updated: 2013-05-08Bibliographically approved
2. 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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