考虑前车动态的网联电动汽车自适应生态巡航控制

IF 15 1区 工程技术 Q1 ENERGY & FUELS Etransportation Pub Date : 2023-12-03 DOI:10.1016/j.etran.2023.100299
Yichen Liang, Haoxuan Dong, Dongjun Li, Ziyou Song
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引用次数: 0

摘要

前车(也称为前车)的驾驶行为对车辆的能耗和行驶安全影响很大。对前车未预料到的变化作出不适当的反应可能导致能源效率下降,并增加追尾碰撞的风险。为了解决这一问题,本研究提出了一种基于前车动态行为预测的自适应生态巡航控制策略(AECS)。AECS采用了两阶段地平线后退控制框架,与传统的生态巡航策略相比,AECS能够以更安全、更节能的方式适应前车切入或驶出的情况,而传统的生态巡航策略只关注恒速前车。首先,利用贝叶斯网络建立前车动态行为预测模型;该模型使用真实车辆驾驶数据进行训练,使其能够预测前方车辆变道的驾驶轨迹。第二阶段,将以节能、安全、驾驶舒适性为导向的优化问题以二次规划形式表述。然后对生态巡航速度进行优化,以适应动态交通环境,特别是当前车随时间变化时。最后通过仿真验证了AECS的有效性。结果表明,与现有巡航控制策略相比,AECS可将自动驾驶汽车的能源效率平均提高11.80%和19.53%,并在不影响行驶时间的前提下确保车辆的驾驶安全性和舒适性。此外,车辆入路位置、入路车速和自我车速对AECS的能效提升性能也有影响。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

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Adaptive eco-cruising control for connected electric vehicles considering a dynamic preceding vehicle

Energy consumption and driving safety of a vehicle are greatly influenced by the driving behaviors of the vehicle in front (also termed the preceding vehicle). Inappropriate responses to unanticipated changes in the preceding vehicle can lead to decreased energy efficiency and an increased risk of rear-end collisions. To address this issue, this study proposes an innovative Adaptive Eco-cruising Control Strategy (AECS) for connected electric vehicles (CEVs) considering the dynamic behavior prediction of the preceding vehicle. The AECS, which is designed with a two-stage receding horizon control framework, can adapt to scenarios where the preceding vehicle cuts in or moves out in a safer and energy-efficient manner compared to traditional eco-cruising strategies, which merely focus on a constant preceding vehicle. In the first stage, a prediction model for characterizing the dynamic behavior of preceding vehicles is developed using the Bayesian network. This model is trained using real-world vehicle driving data, allowing it to anticipate the driving trajectories of vehicles changing lanes in front. In the second stage, an energy-saving, safety, and driving comfort-oriented optimization problem is formulated as a quadratic programming form. The eco-cruising speed is then optimized to adapt to the dynamic traffic environment, especially when the preceding vehicle changes over time. Finally, several simulations are conducted to validate the AECS. The results demonstrate that the AECS can improve the energy efficiency of CEVs by up to 11.80% and 19.53% on average compared to the existing cruise control strategies and ensure vehicle driving safety and comfort, without compromising travel time. Additionally, the vehicle cut-in position, the cut-in vehicle speed, and the ego vehicle speed affect the energy efficiency improvement performance of the AECS.

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来源期刊
Etransportation
Etransportation Engineering-Automotive Engineering
CiteScore
19.80
自引率
12.60%
发文量
57
审稿时长
39 days
期刊介绍: eTransportation is a scholarly journal that aims to advance knowledge in the field of electric transportation. It focuses on all modes of transportation that utilize electricity as their primary source of energy, including electric vehicles, trains, ships, and aircraft. The journal covers all stages of research, development, and testing of new technologies, systems, and devices related to electrical transportation. The journal welcomes the use of simulation and analysis tools at the system, transport, or device level. Its primary emphasis is on the study of the electrical and electronic aspects of transportation systems. However, it also considers research on mechanical parts or subsystems of vehicles if there is a clear interaction with electrical or electronic equipment. Please note that this journal excludes other aspects such as sociological, political, regulatory, or environmental factors from its scope.
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