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A novel optimization-based method to develop representative driving cycle in various driving conditions

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  • Cui, Yuepeng
  • Zou, Fumin
  • Xu, Hao
  • Chen, Zhihui
  • Gong, Kuangmin

Abstract

The lack representativeness of in-used driving cycles has raised substantial concerns regarding the enlarging gap between real-world fuel consumption and type-approval. Considering the high randomness of existing driving cycle development methods, the developed cycle still has low representativeness in capturing the patterns in the real-world. In this study, a novel data-driven driving cycle development method MMACO-MC based on Min-Max Ant Colony Optimization (MMACO) and Markov Chain is proposed to improve the representativeness of driving cycles. The proposed MMACO-MC is then applied to develop driving cycles in Fuzhou city under various driving conditions. Significant differences in cycle parameters have been observed in different driving conditions, which further lead to a 15% deviation on the FCR estimation (Fuel Consumption Rate). Meanwhile, the FCR estimation in the whole region of Fuzhou also deviates from the standard cycles from 22.8% to 29.4%. Lastly, the optimal cycle length is explored to ensure the stability of FCR estimation under various traffic scenarios. This study highlighted the necessity of optimization-based driving cycle development in the accuracy of fuel consumption estimation. The proposed method and the conclusions could be applied as a reference by the authorities to establish fuel consumption standards in the future.

Suggested Citation

  • Cui, Yuepeng & Zou, Fumin & Xu, Hao & Chen, Zhihui & Gong, Kuangmin, 2022. "A novel optimization-based method to develop representative driving cycle in various driving conditions," Energy, Elsevier, vol. 247(C).
    • Handle: RePEc:eee:energy:v:247:y:2022:i:c:s0360544222003589
    DOI: 10.1016/j.energy.2022.123455
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