GPS Data and Machine Learning Tools, a Practical and Cost-Effective Combination for Estimating Light Vehicle Emissions

This paper focuses on the emissions of the three most sold categories of light vehicles: sedans, SUVs, and pickups. The research is carried out through an innovative methodology based on GPS and machine learning in real driving conditions. For this purpose, driving data from the three best-selling v...

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Bibliographic Details
Published in:Sensors (Basel, Switzerland) Switzerland), 2024-04, Vol.24 (7), p.2304
Main Authors: Rivera-Campoverde, Néstor Diego, Arenas-Ramírez, Blanca, Muñoz Sanz, José Luis, Jiménez, Edisson
Format: Article
Language:English
Subjects:
Air pollution
Air quality
Automotive emissions
Combustion
Data collection
Datasets
emission parametric model
Emissions
Energy consumption
Environmental conditions
Internal combustion engines
low-cost emission model
Machine learning
machine learning model
Neural networks
Pollutants
portable emissions measurement system
real driving emissions
Traffic
Variables
Vehicles
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Summary:This paper focuses on the emissions of the three most sold categories of light vehicles: sedans, SUVs, and pickups. The research is carried out through an innovative methodology based on GPS and machine learning in real driving conditions. For this purpose, driving data from the three best-selling vehicles in Ecuador are acquired using a data logger with GPS included, and emissions are measured using a PEMS in six RDE tests with two standardized routes for each vehicle. The data obtained on Route 1 are used to estimate the gears used during driving using the K-means algorithm and classification trees. Then, the relative importance of driving variables is estimated using random forest techniques, followed by the training of ANNs to estimate CO , CO, NO , and HC. The data generated on Route 2 are used to validate the obtained ANNs. These models are fed with a dataset generated from 324, 300, and 316 km of random driving for each type of vehicle. The results of the model were compared with the IVE model and an OBD-based model, showing similar results without the need to mount the PEMS on the vehicles for long test drives. The generated model is robust to different traffic conditions as a result of its training and validation using a large amount of data obtained under completely random driving conditions.
ISSN:1424-8220
1424-8220
DOI:10.3390/s24072304