Fast data based identification of thermal vehicle models for integrated powertrain control
Sprache des Vortragstitels:
Englisch
Original Tagungtitel:
American Control Conference, ACC 2021
Sprache des Tagungstitel:
Englisch
Original Kurzfassung:
In vehicles without additional heat sources but
the combustion engine, fast cabin heating tends to delay engine
heating. This negatively effects consumption so that a trade-off
is necessary between windscreen heating, cabin temperature
and fuel consumption. This is even more the case for hybrid
electric vehicles (HEVs), as they may have to use the thermal
mode even if an electrical operation would be preferable, for
instance in city traffic conditions. A fixed strategy may not be
optimal, as the actual heating behavior will depend on several
environmental factors, like wind, presence of snow on the roof
or sun radiation. In order to optimize the heating strategy
in real time, computationally efficient ? whilst still accurate
? models of the different thermal systems are required. This
paper presents a fast data based approach to model the heat
flows based on first principles, but using only easily accessible
data from real drives. The chosen model structure enables the
possibility of online identification in case of parameter changes
during a drive.
Forschungs-