Vehicle Level Diagnosis for Hybrid Powertrains

There are possibilities to reduce the fuel consumption in trucks using hybrid technology. New components are added when hybridizing a vehicle, and these need to be monitored due to safety and legislative demands. Diagnosis aspects due to hybridization of the powertrain are investigated using a model of a long haulage truck. Such aspects are for example that there are more mode switches in the hybrid powertrain compared to a conventional vehicle, and there is a freedom in choosing operating points of the components in the powertrain via the energy management and still fulfill the torque request of the driver. To investigate the influence of energy management and sensor configuration on the performance of the diagnosis system, three diagnosis systems on vehicle level are designed and implemented. The systems are based on different sensor configurations; one with a fairly typical sensor configuration, one with the same number of sensors but in model sense placed more closely to the components to be monitored, and one with the minimal number of sensors to ideally achieve full fault isolability. It is found that there is a connection between the design of the energy management and the diagnosis systems, and that this connection is of special relevance when the model used in the diagnosis is valid only for some operating modes of the powertrain. In consistency based diagnosis it is investigated if there exists a solution to a set of equations with analytical redundancy, where the redundancy is obtained using measurements. The selection of sets of equations to be included in the diagnosis and how and in what order the unknown variables are to be computed affect the diagnosis performance. A simplified vehicle model is used to exemplify how an algebraic loop can be avoided for one computational sequence of the unknowns, but can not be avoided for a different computational sequence given the same overdetermined set of model equations. A vehicle level diagnosis system is designed using a systematic method to obtain unique residuals and that no signal is differentiated. The performance of the designed system is evaluated in a simulation study, and compared to a diagnosis system based on the same sets of equations, but where the residual generators are selected ad hoc. The results of the comparison are positive, which reinforces the idea of considering the properties of the residual generators in a systematic way. A diagnosis system using a map based model of the electric machine is designed. The benefits of using map based models are that it is easy to construct the models if measurements are available, and that such models in general are accurate. As a consequence of the structure of the model, full fault isolability is not possible to achieve using only the model for fault free behavior of the machine. To achieve full fault isolability, fault models are added to the diagnosis system using a model with a different model structure. The system isolates the faults, even though the induced faults are small in the simulation study, and the size of the faults are accurately estimated using observers.