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Development of prediction methodology for CO2 emissions and fuel economy of light duty vehicle

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  • Song, Jingeun
  • Cha, Junepyo

Abstract

Fuel economy prediction models usually require vehicle specifications such as a fuel consumption map which are not publicly available. Therefore, the present study proposed a new data analyzing procedure to predict CO2 emissions and fuel economy using on-road driving data without confidential specifications. Vehicle specifications such as gear ratios and vehicle mass which are provided in a service manual and driving data such as vehicle speed and CO2 emission were used to develop the prediction model. Instead of the fuel consumption map, linear equations for each gear between wheel power and CO2 emissions were used to predict CO2 emissions for various driving modes. Since higher gears exhaust less CO2 than lower gears (the seventh gear exhausted 24.4% less CO2 than the first gear), the accuracy of fuel economy prediction was improved by applying the equations for each gear stage. The accuracy of the prediction was verified by comparing it with measurement data. The comparisons showed that the equations for each gear can predict the fuel economy more accurately than one equation representing the entire gear. In worldwide harmonized light vehicles test cycle (WLTC) mode, the former had a maximum error of 6.1%, but the latter showed an error of 17.9%.

Suggested Citation

  • Song, Jingeun & Cha, Junepyo, 2022. "Development of prediction methodology for CO2 emissions and fuel economy of light duty vehicle," Energy, Elsevier, vol. 244(PB).
    • Handle: RePEc:eee:energy:v:244:y:2022:i:pb:s036054422200069x
    DOI: 10.1016/j.energy.2022.123166
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    1. Tsiakmakis, Stefanos & Fontaras, Georgios & Dornoff, Jan & Valverde, Victor & Komnos, Dimitrios & Ciuffo, Biagio & Mock, Peter & Samaras, Zissis, 2019. "From lab-to-road & vice-versa: Using a simulation-based approach for predicting real-world CO2 emissions," Energy, Elsevier, vol. 169(C), pages 1153-1165.
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    5. Sarvaiya, Shradhdha & Ganesh, Sachin & Xu, Bin, 2021. "Comparative analysis of hybrid vehicle energy management strategies with optimization of fuel economy and battery life," Energy, Elsevier, vol. 228(C).
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    Cited by:

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    3. Qingbo Tan & Zhuning Wang & Wei Fan & Xudong Li & Xiangguang Li & Fanqi Li & Zihao Zhao, 2022. "Development Path and Model Design of a New Energy Vehicle in China," Energies, MDPI, vol. 16(1), pages 1-15, December.
    4. Huang, Junfeng & Gao, Jianbing & Wang, Yufeng & Yang, Ce & Ma, Chaochen & Tian, Guohong, 2023. "Effect of asymmetric fuel injection on combustion characteristics and NOx emissions of a hydrogen opposed rotary piston engine," Energy, Elsevier, vol. 262(PB).
    5. Choi, Mingi & Cha, Junepyo & Song, Jingeun, 2024. "Analysis of fuel economy reduction factors of hybrid electric vehicles in winter using on-road driving data," Energy, Elsevier, vol. 289(C).

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