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 ...


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    Titel :

    Model Based Vehicle Level Diagnosis for Hybrid Electric Vehicles


    Beteiligte:

    Erscheinungsdatum :

    2014-01-01


    Anmerkungen:

    doi:10.3384/diss.diva-105487



    Medientyp :

    Hochschulschrift


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Klassifikation :

    DDC:    629



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