A novel approach for brake emission estimation based on traffic microsimulation, vehicle system dynamics, and machine learning modeling
Rahimi, Mostafa LU ; Candeo, Stefano ; Da Lio, Mauro ; Biral, Francesco ; Wahlström, Jens LU
and Bortoluzzi, Daniele
(2023)
In Atmospheric Pollution Research
14(10).
- Abstract
Brake wear is known as the primary source of traffic-related non-exhaust particle generation. Its generation rate is influenced by parameters at different levels: subsystem (type of brakes, pads, materials, etc.), system (vehicles' dynamics, driving style etc.) and suprasystem (road geometries, traffic parameters, etc.). At the subsystem level, we proposed a neural network brake emission modeling, trained and validated through emission data collected from a reduced-scale dynamometer. At the system level, a model of a car dynamics was developed to calculate the wheels’ brake torques and angular velocities. At the suprasystem level, the traffic behavior in a sensitive urban area was characterized experimentally and simulated in a traffic... (More)
Brake wear is known as the primary source of traffic-related non-exhaust particle generation. Its generation rate is influenced by parameters at different levels: subsystem (type of brakes, pads, materials, etc.), system (vehicles' dynamics, driving style etc.) and suprasystem (road geometries, traffic parameters, etc.). At the subsystem level, we proposed a neural network brake emission modeling, trained and validated through emission data collected from a reduced-scale dynamometer. At the system level, a model of a car dynamics was developed to calculate the wheels’ brake torques and angular velocities. At the suprasystem level, the traffic behavior in a sensitive urban area was characterized experimentally and simulated in a traffic microsimulation software. The vehicle traffic-based records were used to calculate the vehicle dynamic quantities, converted into brake emission through the neural network. To examine the overall traffic impacts on brake emission, the total particle number (PN) and total particle mass were estimated regarding the route choice in the sensitive area and in the whole transportation network. The findings of this study showed significant generation rate of brake emissions (in terms of mass and number) around congested areas (in the order of 10e9 #/s). The brake emission estimation in a real area provides fundamental information to the decision-makers to better insight into the rate of non-exhaust emissions generation.
(Less)
- author
- Rahimi, Mostafa
LU
; Candeo, Stefano
; Da Lio, Mauro
; Biral, Francesco
; Wahlström, Jens
LU
and Bortoluzzi, Daniele
- organization
- publishing date
- 2023-10
- type
- Contribution to journal
- publication status
- published
- subject
- keywords
- Artificial neural network, Brake emission, Brake wear, Simulation, Vehicle dynamics, VISSIM
- in
- Atmospheric Pollution Research
- volume
- 14
- issue
- 10
- article number
- 101872
- publisher
- Elsevier
- external identifiers
-
- scopus:85166913357
- ISSN
- 1309-1042
- DOI
- 10.1016/j.apr.2023.101872
- language
- English
- LU publication?
- yes
- additional info
- Publisher Copyright: © 2023 Turkish National Committee for Air Pollution Research and Control
- id
- dc071bab-c8ae-43cc-8bc2-ee37f577668f
- date added to LUP
- 2023-08-21 20:10:30
- date last changed
- 2023-08-29 10:52:42
@article{dc071bab-c8ae-43cc-8bc2-ee37f577668f,
abstract = {{<p>Brake wear is known as the primary source of traffic-related non-exhaust particle generation. Its generation rate is influenced by parameters at different levels: subsystem (type of brakes, pads, materials, etc.), system (vehicles' dynamics, driving style etc.) and suprasystem (road geometries, traffic parameters, etc.). At the subsystem level, we proposed a neural network brake emission modeling, trained and validated through emission data collected from a reduced-scale dynamometer. At the system level, a model of a car dynamics was developed to calculate the wheels’ brake torques and angular velocities. At the suprasystem level, the traffic behavior in a sensitive urban area was characterized experimentally and simulated in a traffic microsimulation software. The vehicle traffic-based records were used to calculate the vehicle dynamic quantities, converted into brake emission through the neural network. To examine the overall traffic impacts on brake emission, the total particle number (PN) and total particle mass were estimated regarding the route choice in the sensitive area and in the whole transportation network. The findings of this study showed significant generation rate of brake emissions (in terms of mass and number) around congested areas (in the order of 10e9 #/s). The brake emission estimation in a real area provides fundamental information to the decision-makers to better insight into the rate of non-exhaust emissions generation.</p>}},
author = {{Rahimi, Mostafa and Candeo, Stefano and Da Lio, Mauro and Biral, Francesco and Wahlström, Jens and Bortoluzzi, Daniele}},
issn = {{1309-1042}},
keywords = {{Artificial neural network; Brake emission; Brake wear; Simulation; Vehicle dynamics; VISSIM}},
language = {{eng}},
number = {{10}},
publisher = {{Elsevier}},
series = {{Atmospheric Pollution Research}},
title = {{A novel approach for brake emission estimation based on traffic microsimulation, vehicle system dynamics, and machine learning modeling}},
url = {{http://dx.doi.org/10.1016/j.apr.2023.101872}},
doi = {{10.1016/j.apr.2023.101872}},
volume = {{14}},
year = {{2023}},
}