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Atmosphere
Volume 13
Issue 9
10.3390/atmos13091466
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Article

A Deep Learning Micro-Scale Model to Estimate the CO2 Emissions from Light-Duty Diesel Trucks Based on Real-World Driving

by
Rongshuo Zhang
1,
Yange Wang
2,
Yujie Pang
1,
Bowen Zhang
1,
Yangbing Wei
1,
Menglei Wang
1 and
Rencheng Zhu
1,3,*
1
School of Ecology and Environment, Zhengzhou University, Zhengzhou 450001, China
2
School of Computer and Artificial Intelligence, Zhengzhou University of Economics and Business, Zhengzhou 450099, China
3
Research Centre of Engineering and Technology for Synergetic Control of Environmental Pollution and Carbon Emissions of Henan Province, Zhengzhou University, Zhengzhou 450001, China
*
Author to whom correspondence should be addressed.
Atmosphere 2022, 13(9), 1466; https://doi.org/10.3390/atmos13091466
Submission received: 22 August 2022 / Revised: 3 September 2022 / Accepted: 5 September 2022 / Published: 9 September 2022
(This article belongs to the Special Issue Characteristics and Control of Traffic-Related Emissions)
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Abstract

On-road carbon dioxide (CO2) emissions from light-duty diesel trucks (LDDTs) are greatly affected by driving conditions, which may be better predicted with the sequence deep learning model as compared to traditional models. In this study, two typical LDDTs were selected to investigate the on-road CO2 emission characteristics with a portable emission measurement system (PEMS) and a global position system (GPS). A deep learning-based LDDT CO2 emission model (DL-DTCEM) was developed based on the long short-term memory network (LSTM) and trained by the measured data with the PEMS. Results show that the vehicle speed, acceleration, VSP, and road slope had obvious impacts on the transient CO2 emission rates. There was a rough positive correlation between the vehicle speed, road slope, and CO2 emission rates. The CO2 emission rate increased significantly when the speed was >5 m/s, especially at high acceleration. The correlation coefficient (R2) and the root mean square error (RMSE) between the monitored CO2 emissions with PEMS and the predicted values with the DL-DTCEM were 0.986–0.990 and 0.165–0.167, respectively. The results proved that the model proposed in this study can predict very well the on-road CO2 emissions from LDDTs.
Keywords: vehicle emission model; deep learning; light-duty diesel truck (LDDT); CO2 emissions; portable emission measurement system (PEMS); long short-term memory network (LSTM) vehicle emission model; deep learning; light-duty diesel truck (LDDT); CO2 emissions; portable emission measurement system (PEMS); long short-term memory network (LSTM)

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MDPI and ACS Style

Zhang, R.; Wang, Y.; Pang, Y.; Zhang, B.; Wei, Y.; Wang, M.; Zhu, R. A Deep Learning Micro-Scale Model to Estimate the CO2 Emissions from Light-Duty Diesel Trucks Based on Real-World Driving. Atmosphere 2022, 13, 1466. https://doi.org/10.3390/atmos13091466

AMA Style

Zhang R, Wang Y, Pang Y, Zhang B, Wei Y, Wang M, Zhu R. A Deep Learning Micro-Scale Model to Estimate the CO2 Emissions from Light-Duty Diesel Trucks Based on Real-World Driving. Atmosphere. 2022; 13(9):1466. https://doi.org/10.3390/atmos13091466

Chicago/Turabian Style

Zhang, Rongshuo, Yange Wang, Yujie Pang, Bowen Zhang, Yangbing Wei, Menglei Wang, and Rencheng Zhu. 2022. "A Deep Learning Micro-Scale Model to Estimate the CO2 Emissions from Light-Duty Diesel Trucks Based on Real-World Driving" Atmosphere 13, no. 9: 1466. https://doi.org/10.3390/atmos13091466

APA Style

Zhang, R., Wang, Y., Pang, Y., Zhang, B., Wei, Y., Wang, M., & Zhu, R. (2022). A Deep Learning Micro-Scale Model to Estimate the CO2 Emissions from Light-Duty Diesel Trucks Based on Real-World Driving. Atmosphere, 13(9), 1466. https://doi.org/10.3390/atmos13091466

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