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Design of Engine-Out Virtual NO x Sensor Using Neural Networks and Dynamic System Identification
Journal Article
2011-01-0694
ISSN: 1946-3936, e-ISSN: 1946-3944
Sector:
Topic:
Citation:
Wang, Y., He, Y., and Rajagopalan, S., "Design of Engine-Out Virtual NOx Sensor Using Neural Networks and Dynamic System Identification," SAE Int. J. Engines 4(1):837-849, 2011, https://doi.org/10.4271/2011-01-0694.
Language:
English
Abstract:
Fuel economy improvement and stringent emission regulations
worldwide require advanced air charging and combustion
technologies, such as low temperature combustion, PCCI or HCCI
combustion. Furthermore, NOx aftertreatment systems,
like Selective Catalyst Reduction (SCR) or lean NOx trap
(LNT), are needed to reduce vehicle tailpipe emissions. The
information on engine-out NOx emissions is essential for
engine combustion optimization, for engine and aftertreatment
system development, especially for those involving combustion
optimization, system integration, control strategies, and for
on-board diagnosis (OBD).
A physical NOx sensor involves additional cost and
requires on-board diagnostic algorithms to monitor the performance
of the NOx sensor. A control-oriented engine-out
NOx model that meets throughput constraints is desirable
for on-board applications as a virtual NOx sensor, which
could be used in replacement of the physical sensor or run parallel
as an analytical redundancy to detect potential in-range failure
for the physical sensor.
This paper presents the design of a virtual NOx
sensor using neural networks combined with nonlinear dynamic
system. NOx emission estimation with or without cylinder
pressure information is investigated, and a correction to a
baseline NOx model is proposed in order to compensate
transient EGR responses. The developed virtual NOx
sensor demonstrates a higher level of fidelity estimation when
compared with actual NOx measurements.