Liu, Y orcid.org/0000-0002-9367-3532, Wu, S, Chen, H orcid.org/0000-0003-0753-7735 et al. (7 more authors) (2022) Brake wear induced PM10 emissions during the world harmonised light-duty vehicle test procedure-brake cycle. Journal of Cleaner Production, 361. 132278. ISSN 0959-6526
Abstract
In this work, the particulate matter less than 10 μm (PM10) emissions from a medium-sized passenger vehicle's front brake wear were studied using a finite element analysis (FEA) and experimental approaches. The world harmonised light-duty vehicle test procedure-brake (WLTP-B) cycle was chosen to simulate real-world driving. An electrical low-pressure impactor (ELPI+) was used to count the brake wear particles on a brake dynamometer sealed in a chamber. In addition, a machine learning method, namely, extreme gradient boosting (XGBoost), was employed to capture the feature importance rankings of braking conditions contributing to brake wear PM10 emissions. The simulated PM10 emissions were quite consistent with the measured ones, with an overall relative error of 9%, indicating that the proposed simulation approach is promising to predict brake wear PM10 during the WLTP-B cycle. The simulated and experimental PM10 emission factors during the WLTP-B cycle were 6.4 mg km−1 veh−1 and 7.0 mg km−1 veh−1, respectively. Among the 10 trips of the WLTP-B cycle, the measured PM10 of trip #10 was the largest contributor, accounting for 49% of total PM10 emissions. On the other hand, the XGBoost results revealed that the top five most important factors governing brake wear PM10 emissions were dissipation energy, initial braking speed, final rotor temperature, braking power, and deceleration rate. From the perspective of friendly driving behaviour and regulation, limiting severe braking and high-speed braking has the potential to reduce PM10 emissions from brake wear.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2022 The Authors. This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC BY-NC-ND 4.0) (https://creativecommons.org/licenses/by-nc-nd/4.0/) |
Keywords: | Non-exhaust emissions; Brake wear PM10 emissions; WLTP-B cycle; FEA; Machine learning |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Environment (Leeds) > Institute for Transport Studies (Leeds) > ITS: Spatial Modelling and Dynamics (Leeds) |
Funding Information: | Funder Grant number EU - European Union 815189 |
Depositing User: | Symplectic Publications |
Date Deposited: | 05 Sep 2022 13:15 |
Last Modified: | 05 Sep 2022 13:15 |
Status: | Published |
Publisher: | Elsevier |
Identification Number: | 10.1016/j.jclepro.2022.132278 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:189013 |