Artificial neural networks for fuel consumption and emissions modeling in light duty vehicles
Date
2019
Authors
Chenna, Shiva Tarun, author
Jathar, Shantanu, advisor
Bradley, Thomas, committee member
Anderson, Chuck, committee member
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Abstract
There is growing evidence that real world, on-road emissions from mobile sources exceed emissions determined during laboratory tests and that the air quality, climate, and human health impacts from mobile sources might be substantially different than initially thought. Hence, there is an immediate need to measure and model these exceedances if we are to better understand and mitigate the environmental impacts of mobile sources. In this work, we used a portable emissions monitoring system (PEMS) and artificial neural networks (ANNs) to measure and model on-road fuel consumption and tailpipe emissions from Tier-2 light-duty gasoline and diesel vehicle. Tests were performed on at least five separate days for each vehicle and each test included a cold start and operation over a hot phase. Routes were deliberately picked to mimic certain features (e.g., distance, time duration) of driving cycles used for emissions certification (e.g., FTP-75). Data were gathered for a total of 49 miles and 145 minutes for the gasoline vehicle and 52 miles and 165 minutes for the diesel vehicle. Fuel consumption and emissions data were calculated at 1 Hz using information gathered from the vehicle using the onboard diagnostics port and the PEMS measurements. Route-integrated tailpipe emissions did not exceed the Tier-2 emissions standard for CO, NOX, and non-methane organic gases (NMOG) for either vehicle but did exceed so for PM for the diesel vehicle. We trained ANN models on part of the data to predict fuel consumption and tailpipe emissions at 1 Hz for both vehicles and evaluated these models against the rest of the data. The ANN models performed best when the training iterations (or epochs) were set to larger than 25 and the number of neurons in the hidden layer was between 7 and 9, although we did not see any specific advantage in increasing the number of hidden layers beyond 1. The trained ANN model predicted the fuel consumption over test routes within 5.5% of the measured value for both gasoline and diesel vehicles. The ANN performance varied significantly with pollutant type for the two vehicles and we were able to develop satisfactory models only for unburned hydrocarbons (HC) and NOX for diesel vehicles. Over independent test routes, the trained ANN models predicted HC within 12.5% of the measured value for the gasoline vehicle and predicted NOX emissions within 3% of the measured values for the diesel vehicle. The ANN performed better than, and hence could be used in lieu of, multivariable regression models such as those used in mobile source emissions models (e.g., EMFAC). In an 'environmental-routing' case study performed over three origin-destination pairs, the ANNs were able to successfully pick routes that minimized fuel consumption. Our work demonstrates the use of artificial neural networks to model fuel consumption and tailpipe emissions from light-duty passenger vehicles, with applications ranging from environmental routing to emissions inventory modeling.
Description
Rights Access
Subject
emissions modeling
NOx emissions
PEMS
neural networks
chassis dynamometer
on road emissions