期刊
ENERGY
卷 247, 期 -, 页码 -出版社
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.energy.2022.123455
关键词
Driving cycle; MMACO; Markov chain; Fuel consumption estimation
资金
- National Natural Science Foundation of China [52008110]
- Natural Science Foundation of Fujian Province [2020J05195]
The lack of representativeness in existing driving cycles has led to concerns about the increasing gap between real-world fuel consumption and type-approval. This study proposes a novel data-driven driving cycle development method to improve the representativeness of driving cycles. The research finds significant differences in cycle parameters under different driving conditions, resulting in a large deviation in fuel consumption rate estimation.
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.(c) 2022 Elsevier Ltd. All rights reserved.
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