基于BP神经网络的车辆碳排放测算研究

彭美春; 阳晨; 李君平; 叶伟斌; 黄文伟

doi:10.12052/gdutxb.220103

欢迎来到能源电力期刊网！

English Version 期刊登录

您当前的位置：

首页 >

文章列表页 >

基于BP神经网络的车辆碳排放测算研究

更新时间：2026-05-27

- 基于BP神经网络的车辆碳排放测算研究
- A Research on Vehicle Carbon Emission Calculating Method Based on BP Neural Network
- 广东工业大学学报 2023年40卷第1期页码：107-112
- 作者机构：
  
  1. 广东工业大学机电工程学院,广东,广州,510006
  2. 深圳职业技术学院汽车与交通学院,广东,深圳,518055
- 作者简介：
  
  彭美春(1963–)，女，教授，博士，主要研究方向为机动车排放污染等，E-mail：mcpeng@gdut.edu.cn
- 基金信息：
  
  深圳市环境科研课题项目(2019XY1368STZF)
- DOI：10.12052/gdutxb.220103
  中图分类号： U467.1+1
- 网络首发：2023-01-25，
  
  纸质出版：2023
- 稿件说明：
移动端阅览
彭美春, 阳晨, 李君平, 叶伟斌, 黄文伟. 基于BP神经网络的车辆碳排放测算研究[J]. 广东工业大学学报, 2023, 40(1): 107-112.

Peng Mei-chun, Yang Chen, Li Jun-ping, et al. A Research on Vehicle Carbon Emission Calculating Method Based on BP Neural Network[J]. 2023, 40(1): 107-112.
彭美春, 阳晨, 李君平, 叶伟斌, 黄文伟. 基于BP神经网络的车辆碳排放测算研究[J]. 广东工业大学学报, 2023, 40(1): 107-112. DOI： 10.12052/gdutxb.220103.

Peng Mei-chun, Yang Chen, Li Jun-ping, et al. A Research on Vehicle Carbon Emission Calculating Method Based on BP Neural Network[J]. 2023, 40(1): 107-112. DOI： 10.12052/gdutxb.220103.

摘要

研究轻型车辆碳排放测算方法，分析车辆碳排放与运行工况关系。基于车辆实际行驶污染物排放(Real Drive Emission

RDE)车载测试数据，以CO

当量CO

代表碳排放，分析得出碳排放速率随车速、比功率(Vehicle Specific Power

VSP)增大而上升；运用BP (Back Propagation) 神经网络算法建立车辆碳排放与车速、加速度、比功率多参数间非线性关系测算模型，计算得出世界轻型车测试循环(World Light Vehicle Test Cycle，WLTC)、新欧洲行驶循环(New European Driving Cycle

NEDC)和中国轻型商用车行驶工况(China Light-duty Vehicle Test Cycle-commercial Car

CLTC-C)3种台架测试循环工况下的碳排放因子。比较发现3种台架测试循环工况下的碳排放因子均高于实际道路行驶碳排放因子，其中WLTC下碳排放因子最高，其次是NEDC，再是CLTC-C，原因是加速度越大、车速越高的测试工况导致碳排放增加。

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 CO

equivalent CO

. 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 calcu

late 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.

关键词

Keywords

references

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.

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.

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.

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.

BOWYER D P, AKCELIK R, BIGGS D C. Guide to fuel consumption analyses for urban traffic management[M]. Melbourne: Australian Road Research Board, 1985.

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.

LIU S H, WANG H, ZHENG T L, et al . Research on the revised draft of CO 2 emission regulations for light-duty vehicles in Europe and USA [J ] . China Auto, 2021(9): 10-16.

CUI Y, ZHANG M S. Comparative study of CO 2 emission level between the passenger cars from China and European Union [J ] . The Administration and Technique of Environmental Monitoring, 2018, 30(6): 6-9.

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.

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.

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.

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.

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.

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.

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.

浏览量

下载量

CSCD

文章被引用时，请邮件提醒。

提交

工具集

关联资源

城市社区尺度碳排放时变计算方法及应用——以广州某三个高密度住区为例

针对忆阻神经网络加速器的混合粒度剪枝方法研究

广东省农业碳氮磷排放的时空演变特征与驱动力

基于神经网络的HEVC帧内预测组合快速算法

基于分离结果信噪比估计与自适应调频网络的单通道语音分离技术