Journal of Guangdong University of Technology ›› 2023, Vol. 40 ›› Issue (01): 107-112.doi: 10.12052/gdutxb.220103

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A Research on Vehicle Carbon Emission Calculating Method Based on BP Neural Network

Peng Mei-chun1, Yang Chen1, Li Jun-ping1, Ye Wei-bin1, Huang Wen-wei2   

  1. 1. School of Electromechanical Engineering, Guangdong University of Technology, Guangzhou 510006, China;
    2. School of Automotive and Transportation Engineering, Shenzhen Polytechnic, Shenzhen 518055, China
  • Received:2022-06-08 Online:2023-01-25 Published:2023-01-12

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Abstract: The carbon emission calculating method of light vehicle is studied, and the relationship between vehicle carbon emission and operating conditions analyzed. Based on the on-board test data of real drive emission (RDE) of vehicles, the carbon emission is represented by CO2 equivalent CO2e. It is indicated that the carbon emission rate increases with the increase of vehicle speed and specific power. The Back Propagation (BP) neural network algorithm is used to establish the nonlinear relationship between vehicle carbon emission and speed, acceleration and vehicle specific power (VSP), and calculate the carbon emission factors under three bench test cycle conditions of World Light Vehicle Test Cycle (WLTC), New European Driving Cycle (NEDC) and China Light-duty Vehicle Test Cycle-commercial Car (CLTC-C). It is found that the carbon emission factors under the three bench test cycle conditions are higher than the actual road driving carbon emission factors, among which the carbon emission factor under WLTC is the highest, followed by NEDC and CLTC-C. The reason is that the test conditions with higher acceleration and speed lead to increased carbon emissions.

Key words: light-duty gasoline vehicle, carbon emissions, neural network, calculation method, real drive emission (RDE), test cycles

CLC Number: 

  • U467.1+1
[1] 周菊. 汽车业碳中和: “倒逼”还未开始[EB/OL]. (2021-06-18) . http://www.eeo.com.cn/2021/0618/492132.shtml.
[2] 国家市场监督管理总局, 中国国家标准化管理委员会. GB/T 38146.1-2019中国汽车行驶工况 第1部分: 轻型汽车[S]. 北京: 中国标准出版社, 2019.
[3] 彭美春, 朱兵禄, 胡红斐, 等. 重型货运车辆碳排放特性研究[J]. 安全与环境学报, 2016, 16(1): 269-272.
PENG M C, ZHU B L, HU H F, et al. Study on the carbon emission characteristics of the heavy duty freight trucks [J]. Journal of Safety and Environment, 2016, 16(1): 269-272.
[4] 刘明, 何超, 李加强, 等. 基于比功率与发动机功率的重型柴油车实际道路排特性[J]. 科学技术与工程, 2018, 18(34): 64-70.
LIU M, HE C, LI J Q, et al. Actual road emission characteristics of heavy-duty diesel vehicle based on vehicle specific power and engine power [J]. Science Technology and Engineering, 2018, 18(34): 64-70.
[5] DEMIR E, BEKTAS T, LAPORTE G. A review of recent research on green road freight transportation [J]. European Journal of Operational Research, 2014, 237(3): 775-793.
[6] BOWYER D P, AKCELIK R, BIGGS D C. Guide to fuel consumption analyses for urban traffic management[M]. Melbourne: Australian Road Research Board, 1985.
[7] 彭美春, 廖清睿, 曾隆隆, 等. 道路营运新能源汽车减碳测算[J]. 广东工业大学学报, 2020, 37(2): 39-44.
PENG M C, LIAO Q R, ZENG L L, et al. Calculation of carbon reduction for new energy vehicles in road transportation [J]. Journal of Guangdong University of Technology, 2020, 37(2): 39-44.
[8] 柳邵辉, 王昊, 郑天雷, 等. 欧美轻型汽车二氧化碳排放法规修订草案研究[J]. 中国汽车, 2021(9): 10-16.
LIU S H, WANG H, ZHENG T L, et al. Research on the revised draft of CO2 emission regulations for light-duty vehicles in Europe and USA [J]. China Auto, 2021(9): 10-16.
[9] 崔莹, 张明顺. 中欧乘用车碳排放水平对比研究[J]. 环境监测管理与技术, 2018, 30(6): 6-9.
CUI Y, ZHANG M S. Comparative study of CO2 emission level between the passenger cars from China and European Union [J]. The Administration and Technique of Environmental Monitoring, 2018, 30(6): 6-9.
[10] 陈盛樑, 罗宇, 黄川, 等. 汽车尾气三效催化转化器及其最新进展[J]. 重庆大学学报(自然科学版) , 2002(9): 90-93.
CHEN S L, LUO Y, HUANG C, et al. Three-effect catalytic converter of vehicle exhaust and the latest development [J]. Journal of Chongqing University (Natural Science Edition) , 2002(9): 90-93.
[11] 曾恩山, 熊锐, 吴坚, 等. 车用催化剂的台架快速老化研究[J]. 广东工业大学学报, 2016, 33(6): 49-52,56.
ZENG E S, XIONG R, WU J, et al. A study of rapid aging of automotive catalyst on the bench [J]. Journal of Guangdong University of Technology, 2016, 33(6): 49-52,56.
[12] 何立强. 中国机动车温室效应污染物排放清单及其削减潜力研究[D]. 北京: 中国环境科学研究院, 2014.
[13] 王一帆. 成都市机动车碳排放量计算探析[D]. 成都: 西南交通大学, 2011.
[14] 孙凤. 面向交通排放测算的轻重型车比功率分布特性与模型研究[D]. 北京: 北京交通大学, 2012.
[15] 赵金元, 马振, 唐海亮. BP神经网络和多元线性回归模型对碳排放预测的比较[J]. 科技和产业, 2020, 20(11): 172-176.
ZHAO J Y, MA Z, TANG H L. Comparison of BP neural network and multiple linear regression models for carbon emissions prediction [J]. Science Technology and Industry, 2020, 20(11): 172-176.
[16] 刘天舒. BP神经网络的改进研究及应用[D]. 哈尔滨: 东北农业大学, 2011.
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