Author ORCID Identifier

https://orcid.org/0000-0002-1888-3396

Semester

Fall

Date of Graduation

2022

Document Type

Dissertation

Degree Type

PhD

College

Statler College of Engineering and Mineral Resources

Department

Mechanical and Aerospace Engineering

Committee Chair

Arvind Thiruvengadam

Committee Co-Chair

Gregory Thompson

Committee Member

Gregory Thompson

Committee Member

Derek Johnson

Committee Member

V’yacheslav Akkerman

Committee Member

Berk Demirgok

Abstract

The process of on-road, heavy-duty engine family certification is regulated by the United States Environmental Protection Agency (US EPA). Currently, the US EPA 2010 emissions standards require the threshold from the Federal Testing Procedure (FTP) engine dynamometer cycle to be at or below a brake-specific NOx (bs-NOx) value of 0.20 g/bhp-hr for heavy-duty (HD) engines. The engine manufacturers are also required to conduct in-use portable emission measurement system (PEMS) testing to prove their products' compliance. The selected vehicles are required to satisfy not-to-exceed (NTE) analysis under normal driving conditions in the heavy-duty in-use testing (HDIUT) program. California Air Resources Board (CARB) also independently performs PEMS testing in the heavy-duty in-use compliance (HDIUC) program.

The regulatory standards are becoming more stringent on certification level and on-road requirements. Heavy-duty engine manufacturers are also required to satisfy regulatory agencies' current compliance standards for engine certification. However, real-world driving conditions differ from controlled testing environments; hence, the NTE evaluation protocol has been developed to verify emissions compliance under real-world driving conditions. However, while regulatory effort has been made, studies are implying that regulatory measurements are not achieving the desired low emissions levels. The studies show that on-road measurements are higher than the NOx certification and in-use standards. In-use emissions depend highly on the duty-cycle, which dominates the results, especially if the vehicle has a higher idle and low-load operation fraction.

The aim of this study was to develop a model structure and to train a model based on chassis dynamometer datasets and subsequently use the trained model in conjunction with PEMS datasets in order to identify vehicles as possible high-NOx emitting vehicles. The long-short term memory (LSTM) model developed based on a single reference vehicle (i.e., Vehicle A) dataset was applied to the entire 12 diesel vehicle PEMS datasets in order to identify high-NOx emitters. The results showed that the vehicles that were manually identified as high emitting vehicles (i.e., control subjects) were also identified by the LSTM model to exceed real-world NOx emissions. The prediction results show that high NOx emitting vehicles exhibited a ratio of predicted-to-measured NOx emissions that were lower than one (1). Similarly, a random forest (RF) model was developed for a reference CNG vehicle (i.e., Vehicle N) and subsequently applied to 11 CNG vehicles with a 0.2 g/bhp-hr NOx regulation limit using PEMS data in order to identify any possible high NOx emitting vehicles. The results showed that the vehicles that were manually labeled as high emitters were also identified by the RF model to exhibit high real-world NOx emissions beyond any properly working vehicle. The prediction results show that high NOx emitting vehicles had a ratio of predicted versus measured NOx emissions that were lower than unity.

In addition, the model's high accuracy during the evaluation of the test datasets indicated that the models could potentially be used for predicting the NOx emissions for any random selected vehicle during chassis dynamometer testing for both diesel and 0.2 g/bhp-hr NOx emissions limit CNG vehicles.

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