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Development of a Machine-Learning Classification Model for an Electrochemical Nitrogen Oxides Sensor in Gasoline Powertrains
- Nathan J. Kempema - Ford Motor Company, Research & Advanced Engineering, USA ,
- Conner Sharpe - Ford Motor Company, Global Data, Insight & Analytics, USA ,
- Xiao Wu - Ford Motor Company, Global Data, Insight & Analytics, USA ,
- Merhdad Shahabi - Ford Motor Company, Global Data, Insight & Analytics, USA ,
- David Kubinski - Ford Motor Company, Research & Advanced Engineering, USA
Journal Article
03-16-04-0031
ISSN: 1946-3936, e-ISSN: 1946-3944
Sector:
Topic:
Citation:
Kempema, N., Sharpe, C., Wu, X., Shahabi, M. et al., "Development of a Machine-Learning Classification Model for an Electrochemical Nitrogen Oxides Sensor in Gasoline Powertrains," SAE Int. J. Engines 16(4):529-538, 2023.
Language:
English
Abstract:
Future automotive emission regulations are becoming increasingly dependent on
off-cycle (acquired on road and referred to as “real-world”) driving and
testing. This was driven in part by the often-observed fact that laboratory
emission drive cycles (developed to evaluate a vehicle’s emissions on a chassis
dynamometer) may not fully capture the nature of real-world driving. As a
result, portable emission measurement systems were developed that could be fit
in the trunk of a vehicle, but were relatively large, expensive, and complex to
operate. It would be advantageous to have low-cost and simple to operate
on-board sensors that could be used in a gasoline powertrain to monitor
important criteria emission species, such as NOx. The electrochemical
NOx sensor is often used for emissions control systems in diesel
powertrains and a proven technology for application to the relatively harsh
environment of automotive exhaust. However, electrochemical NOx
sensors are nearly equally sensitive to both NOx and NH3,
setting up an implicit classification problem that must be solved before they
can accurately measure NOx. In this work, we develop a
machine-learning model to classify the output of a NOx sensor in a
gasoline powertrain. A model generalization study is conducted, and the model is
found to be ~96% accurate and able to predict NOx mass emitted over a
drive cycle within ~9% of a perfectly classified NOx sensor.