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

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...

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Bibliographic Details
Published in:Energy (Oxford) 2022-05, Vol.247, p.123455, Article 123455
Main Authors: Cui, Yuepeng, Zou, Fumin, Xu, Hao, Chen, Zhihui, Gong, Kuangmin
Format: Article
Language:English
Subjects:
Driving cycle
Fuel consumption estimation
Markov chain
MMACO
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Summary: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. •A high-performance driving cycle development method is proposed.•Improve representativeness in various driving conditions in a heuristic trend.•Fuel consumption rates for developed and standard cycles are studied.•The impact of cycle length on the fuel consumption estimation is analyzed.
ISSN:0360-5442
DOI:10.1016/j.energy.2022.123455