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This dissertation presents the development of a modeling and simulation framework
for diesel engine vehicles to enable soot emissions as a constraint in powertrain design
and control. To this end, numerically efficient models for predicting temporallyresolved
transient soot emissions are identified in the form of a third-order dual-input
single-output (DISO) Volterra series from transient soot data recorded by integrating
real-time (RT) vehicle level models in Engine-in-the-loop (EIL) experiments. It is
shown that the prediction accuracy of transient soot significantly improves over the
steady-state maps, while the model remains computationally efficient for systemslevel
work.
The evaluation of powertrain design also requires a systematic procedure for dealing
with the issue that drivers potentially adapt their driving styles to a given design. In
order to evaluate the implications of different powertrain design changes on transient
soot production it is essential to compare these design changes on a consistent basis.
This problem is explored in the context of longitudinal motion of a vehicle following a standard drive-cycle repeatedly. This dissertation develops a proportional-derivative
(PD) type iterative learning based algorithm to synthesize driver actuator inputs that
seek to minimize soot emissions using the Volterra series based transient soot models.
The solution is compared to the one obtained using linear programming. Results
show that about 19% reduction in total soot can be achieved for the powertrain design
considered in about 40 iterations.
The two contributions of this dissertation: development of computationally efficient
system level transient soot models and the synthesis of driver inputs via iterative
learning for reducing soot, both contribute to improving the art of modeling and
simulation for diesel powertrain design and control.PhDMechanical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/86386/1/ahlawatr_1.pd
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