N. Kempema, Conner Sharpe, Xiao Wu, Merhdad Shahabi, D. Kubinski
{"title":"Development of a Machine-Learning Classification Model for an\u0000 Electrochemical Nitrogen Oxides Sensor in Gasoline Powertrains","authors":"N. Kempema, Conner Sharpe, Xiao Wu, Merhdad Shahabi, D. Kubinski","doi":"10.4271/03-16-04-0031","DOIUrl":"https://doi.org/10.4271/03-16-04-0031","url":null,"abstract":"Future automotive emission regulations are becoming increasingly dependent on\u0000 off-cycle (acquired on road and referred to as “real-world”) driving and\u0000 testing. This was driven in part by the often-observed fact that laboratory\u0000 emission drive cycles (developed to evaluate a vehicle’s emissions on a chassis\u0000 dynamometer) may not fully capture the nature of real-world driving. As a\u0000 result, portable emission measurement systems were developed that could be fit\u0000 in the trunk of a vehicle, but were relatively large, expensive, and complex to\u0000 operate. It would be advantageous to have low-cost and simple to operate\u0000 on-board sensors that could be used in a gasoline powertrain to monitor\u0000 important criteria emission species, such as NOx. The electrochemical\u0000 NOx sensor is often used for emissions control systems in diesel\u0000 powertrains and a proven technology for application to the relatively harsh\u0000 environment of automotive exhaust. However, electrochemical NOx\u0000 sensors are nearly equally sensitive to both NOx and NH3,\u0000 setting up an implicit classification problem that must be solved before they\u0000 can accurately measure NOx. In this work, we develop a\u0000 machine-learning model to classify the output of a NOx sensor in a\u0000 gasoline powertrain. A model generalization study is conducted, and the model is\u0000 found to be ~96% accurate and able to predict NOx mass emitted over a\u0000 drive cycle within ~9% of a perfectly classified NOx sensor.","PeriodicalId":47948,"journal":{"name":"SAE International Journal of Engines","volume":"14 1","pages":""},"PeriodicalIF":1.2,"publicationDate":"2022-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88832372","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}