Brake wear induced PM10 emissions during the world harmonised light-duty vehicle test procedure-brake cycle

被引:15
|
作者
Liu, Ye [1 ]
Wu, Sijin [1 ]
Chen, Haibo [1 ]
Federici, Matteo [2 ]
Perricone, Guido [2 ]
Li, Ying [3 ]
Lv, Gang [4 ]
Munir, Said [1 ]
Luo, Zhiwen [5 ]
Mao, Baohua [6 ]
机构
[1] Univ Leeds, Inst Transport Studies, Leeds LS2 9JT, England
[2] Brembo SpA, Via Europa 2, BG, I-24020 Stezzano, Italy
[3] Dynnoteq, 61 Bridge St, Kington HR5 3DJ, England
[4] Tianjin Univ, State Key Lab Engines, Tianjin 300072, Peoples R China
[5] Univ Reading, Sch Built Environm, Reading RG6 6AW, England
[6] Beijing Jiaotong Univ, Integrated Transport Res Ctr China, Beijing 100044, Peoples R China
基金
欧盟地平线“2020”;
关键词
Non-exhaust emissions; Brake wear PM10 emissions; WLTP-B cycle; FEA; Machine learning; PARTICULATE MATTER; ULTRAFINE PARTICLES; FRICTION MATERIAL; DYNAMOMETER;
D O I
10.1016/j.jclepro.2022.132278
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
In this work, the particulate matter less than 10 mu m (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 PM(10)emissions from brake wear.
引用
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页数:9
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