Model Based Vehicle Level Diagnosis for Hybrid Electric Vehicles
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.
Christofer Sundström
2014
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Last updated: 2021-11-10
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