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Diagnostic Method Combining Map and Fault Models Applied on a Hybrid Electric Vehicle
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.
2014 (English)Manuscript (preprint) (Other academic)
Abstract [en]

A common situation in the automotive industry is that map based models are available. In general these models accurately describe the fault free system, and are therefore suited for fault detectability in a diagnosis system. However, one drawback using such a model is that fault isolation then requires that measurements of the faulty system is done, which is costly. Another approach is to use a model of the system where the faults are explicitly included. To directly achieve good diagnostic performance such a model needs to be accurate, which also is costly. Therefore, in the new approach taken here, two models are used in combination to achieve both good fault detectability and isolability in a diagnosis system; one is a map based model, and one is describing how the faults affect the system. The approach is exemplified by designing a diagnosis system monitoring the power electronics and the electric machine in a hybrid electric vehicle. In an extensive simulation study it is shown that the approach works well and is a promising path to achieve both good fault detectability and isolability performance, without the need for neither measurements of a faulty system nor detailed physical modeling. In the designed diagnosis system all faults are fully isolated, and the size of the faults are accurately estimated.

Place, publisher, year, edition, pages
2014.
National Category
Vehicle Engineering
Identifiers
URN: urn:nbn:se:liu:diva-105486OAI: oai:DiVA.org:liu-105486DiVA: diva2:707683
Available from: 2014-03-25 Created: 2014-03-25 Last updated: 2014-03-25Bibliographically approved
In thesis
1. Model Based Vehicle Level Diagnosis for Hybrid Electric Vehicles
Open this publication in new window or tab >>Model Based Vehicle Level Diagnosis for Hybrid Electric Vehicles
2014 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

When hybridizing a vehicle, new components are added that need to be monitored due to safety and legislative demands. Diagnostic aspects due to powertrain hybridization are investigated, such as that there are more mode switches in the hybrid powertrain compared to a conventional powertrain, and that there is a freedom in choosing operating points of the components in the powertrain via the overall energy management and still fulfill the driver torque request. A model of a long haulage truck is developed, and a contribution is a new electric machine model. The machine model is of low complexity, and treats the machine constants in a different way compared to a standard model. It is shown that this model describes the power losses significantly better when adopted to real data, and that this modeling improvement leads to better signal separation between the non-faulty and faulty cases compared to the standard model.

To investigate the influence of the energy management design and sensor configuration on the diagnostic performance, two vehicle level diagnosis systems based on different sensor configurations are designed and implemented. It is found that there is a connection between the operating modes of the vehicle and the diagnostic performance, and that this interplay is of special relevance in the system based on few sensors.

In consistency based diagnosis it is investigated if there exists a solution to a set of equations with analytical redundancy, i.e. there are more equations than unknown variables. The selection of sets of equations to be included in the diagnosis system and in what order to compute the unknown variables in the used equations affect the diagnostic performance. A systematic method that finds properties and constructs residual generator candidates based on a model has been developed. Methods are also devised for utilization of the residual generators, such as initialization of dynamic residual generators, and for consideration of the fault excitation in the residuals using the internal form of the residual generators. For demonstration, the model of the hybridized truck is used in a simulation study, and it is shown that the methods significantly increase the diagnostic performance.

The models used in a diagnosis system need to be accurate for fault detection. Map based models describe the fault free behavior accurately, but fault isolability is often difficult to achieve using this kind of model. To achieve also good fault isolability performance without extensive modeling, a new diagnostic approach is presented. A map based model describes the nominal behavior, and another model, that is less accurate but in which the faults are explicitly included, is used to model how the faults affect the output signals. The approach is exemplified by designing a diagnosis system monitoring the power electronics and the electric machine in a hybrid vehicle, and simulations show that the approach works well.

Place, publisher, year, edition, pages
Linköping: Linköping University Electronic Press, 2014. 10 p.
Series
Linköping Studies in Science and Technology. Dissertations, ISSN 0345-7524 ; 1589
National Category
Engineering and Technology
Identifiers
urn:nbn:se:liu:diva-105487 (URN)10.3384/diss.diva-105487 (DOI)978-91-7519-356-4 (ISBN)
Public defence
2014-04-25, Visionen, Hus B, Campus Valla, Linköpings universitet, Linköping, 10:15 (English)
Supervisors
Available from: 2014-03-25 Created: 2014-03-25 Last updated: 2014-04-08Bibliographically approved

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Sundström, ChristoferFrisk, ErikNielsen, Lars

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