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

Zhang, Rongshuo; Wang, Yange; Pang, Yujie; Zhang, Bowen; Wei, Yangbing; Wang, Menglei; Zhu, Rencheng

doi:10.3390/atmos13091466

Cited by 19 publications

(6 citation statements)

References 30 publications

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“…The output gate, similar to the preceding two gates, utilizes the sigmoid function to ascertain the degree to which the information residing within the memory cell should be incorporated into the final output. More detail can be found in our previous study [36].…”

Section: The Model Of Lstmmentioning

confidence: 98%

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Exhaust Emissions from Gasoline Vehicles with Different Fuel Detergency and the Prediction Model Using Deep Learning

Zhang,

Chen,

Xie

et al. 2023

Sensors

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Enhancing gasoline detergency is pivotal for enhancing fuel efficiency and mitigating exhaust emissions in gasoline vehicles. This study investigated gasoline vehicle emission characteristics with different gasoline detergency, explored synergistic emission reduction potentials, and developed versatile emission prediction models. The results indicate that improved fuel detergency leads to a reduction of 5.1% in fuel consumption, along with decreases of 3.2% in total CO2, 55.4% in CO, and 15.4% in HC emissions. However, during low-speed driving, CO2 and CO emissions reductions are limited, and HC emissions worsen. A synergistic emission reduction was observed, particularly with CO exhibiting a pronounced reduction compared to HC. The developed deep-learning-based vehicle emission model for different gasoline detergency (DPVEM-DGD) enables accurate emission predictions under various fuel detergency conditions. The Pearson correlation coefficients (Pearson’s r) between predicted and measured values of CO2, CO, and HC emissions before and after adding detergency agents are 0.913 and 0.934, 0.895 and 0.915, and 0.931 and 0.969, respectively. The predictive performance improves due to reduced peak emissions resulting from improved fuel detergency. Elevated gasoline detergency not only reduces exhaust emissions but also facilitates more refined emission management to a certain extent.

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Section: The Model Of Lstmmentioning

confidence: 98%

“…Previous studies have concluded that the motor vehicles' instantaneous exhaust emission rate not only depends on the current driving conditions but also the long-term conditions [26,30,36]. Recurrent neural networks (RNNs) are ones of neural networks that are specialized for processing a sequence of values.…”

Section: The Model Of Lstmmentioning

confidence: 99%

Exhaust Emissions from Gasoline Vehicles with Different Fuel Detergency and the Prediction Model Using Deep Learning

Zhang,

Chen,

Xie

et al. 2023

Sensors

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“…Emission models are divided according to their precision scale into macro (regional, national area), meso (local area) and micro (areas of a dedicated part of a city, intersection road sections) [59].…”

Section: Overview Of Selected Exhaust Emission Modelsmentioning

confidence: 99%

Vehicle Emission Models and Traffic Simulators: A Review

Mądziel¹

2023

Preprint

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Accurate estimation of vehicle emissions is essential and can be helpful in the decision-making process. Thanks to calculation methods, it is possible to accurately estimate the emissions that result from driving a car and to determine their impact on, for example, the health of pedestrians who are on the pavement near an arterial road. Recent years have seen an increase in the use and importance of emission models, as well as vehicle traffic simulation models. Increasingly, emission models are being combined with traffic simulation models, as it is not practical to actually measure the on-road emissions of an entire fleet of vehicles over a given stretch of road. This paper provides an overview of the selected work on the topic of emission modelling and vehicle traffic simulation models. The models are distinguished according to their respective scales of accuracy, i.e., micro, meso, and macro. Selected work combining emission and traffic simulation models is also presented, as well as development trends in the field. In particular, the paper also highlights the fact of proper calibration of vehicle traffic simulation models in order to obtain the most reliable vehicle emissions results from computational models. The review of the works carried out may be helpful for future projects concerning the use of simulation tools for transport and environmental decision-makers in the area of arterial roads.

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“…The errors quantify the model precision in predicting outcomes. The MAPE decreased from 12.54 to 8.32%, and the MRE decreased from 56.6 to 25.89% Zhang et al ( 2022a ) Zhang et al ( 2022b ) Local distance-based decision trees (LDDTs) and deep learning decision tree CO 2 emission model (DL-DTCEM) Two LDDTs were evaluated for on-road emissions using a portable emission measuring system (PEMS) and a global positioning system (GPS). The DL-DTCEM emissions were developed using deep learning to accurately predict emissions from LDDTs.…”

Section: Introductionmentioning

confidence: 99%

Optimizing soybean biofuel blends for sustainable urban medium-duty commercial vehicles in India: an AI-driven approach

Rajak,

Chaurasiya,

Verma

et al. 2024

Environ Sci Pollut Res

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This article presents the outcomes of a research study focused on optimizing the performance of soybean biofuel blends derived from soybean seeds specifically for urban medium-duty commercial vehicles. The study took into consideration elements such as production capacity, economics and assumed engine characteristics. For the purpose of predicting performance, combustion and emission characteristics, an artificial intelligence approach that has been trained using experimental data is used. At full load, the brake thermal efficiency (BTE) dropped as engine speed increased for biofuel and diesel fuel mixes, but brake-specific fuel consumption (BSFC) increased. The BSFC increased by 11.9% when diesel compared to using biofuel with diesel blends. The mixes cut both maximum cylinder pressure and $${{\text{NO}}}_{{\text{x}}}$$ NO x emissions. The biofuel-diesel fuel proved more successful, with maximum reduction of 9.8% and 22.2 at rpm, respectively. The biofuel and diesel blend significantly improved carbon dioxide ($${{\text{CO}}}_{2}$$ CO 2 ) and smoke emissions. The biofuel blends offer significant advantages by decreeing exhaust pollutants and enhancing engine performance. Graphical Abstract

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

Cited by 19 publications

References 30 publications

Exhaust Emissions from Gasoline Vehicles with Different Fuel Detergency and the Prediction Model Using Deep Learning

Exhaust Emissions from Gasoline Vehicles with Different Fuel Detergency and the Prediction Model Using Deep Learning

Vehicle Emission Models and Traffic Simulators: A Review

Optimizing soybean biofuel blends for sustainable urban medium-duty commercial vehicles in India: an AI-driven approach

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