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
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