NOX emissions are one of the major limiting factors of modern
diesel engine technology; they heavily influence, directly or
indirectly, both engine and after-treatment design, cost,
complexity and reliability; they are also linked in an important
trade-off with CO₂ emissions and therefore fuel consumption. It is
paramount for OEMs (Original Equipment Manufacturers) to exploit
more sophisticated techniques for modeling the formation of NOX
to reduce costs and increase their ability to meet the legislative
requirements for both CO₂ and NOX. Many existing simulation
models predict NOX simply by interpolating steady state engine
maps with limited ability to efficiently capture the effects of
engine warm up, speed-load transients and air system dynamics. For
conventional powertrains running on light cycles this might still
be acceptable, but it becomes inadequate when applied to fast and
deep transients across unconventional speed and load patterns. On
the other side of the spectrum, extremely sophisticated, fully
predictive combustion models are just too complex to be attractive
as a development tool.
The objective of this paper is to describe a semi-empiric model
based approach for dynamic NOX emission modeling that is being
developed by Prodrive as part of the FHSPV (Flywheel Hybrid System
for Premium Vehicle - www.FHSPV.org) consortium. The required
measured data are kept to a minimum and are still primarily based
on stationary engine maps recorded on test bed. These maps
determine the steady state component of the NOX prediction, the
dynamic part being calculated based on key engine parameters. From
onboard sensor data the model calculates in-cylinder conditions at
Inlet Valve Closing (IVC); based on an average wall temperature it
then calculates the conditions at the start of the compression
stroke and, based on a Wiebe heat release model, it determines the
degree-by-degree profiles for pressure and temperature. The model
uses a simplified Zeldovich mechanism to calculate NOX. The
result is used to validate the model against the measured
steady-state engine maps. Attention will be given to the
correlation process that enables the shift from an angle-based to a
time-based domain as this is a key aspect of this approach and one
which can be finely tuned to vary the fidelity of the model based
on the requirements of the end-user.
In conclusion the paper demonstrates the predictive performance
of the model in relation to transient events within the NEDC (New
European Drive Cycle). It also lists key advantages and suggests
the next steps to address its limitations.