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 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 estimationunder 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.