Development of a Machine-Learning Classification Model for an Electrochemical Nitrogen Oxides Sensor in Gasoline Powertrains

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 NO_x. The electrochemical NO_x 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 NO_x sensors are nearly equally sensitive to both NO_x and NH₃, setting up an implicit classification problem that must be solved before they can accurately measure NO_x. In this work, we develop a machine-learning model to classify the output of a NO_x sensor in a gasoline powertrain. A model generalization study is conducted, and the model is found to be ~96% accurate and able to predict NO_x mass emitted over a drive cycle within ~9% of a perfectly classified NO_x sensor.