重复通勤驾驶周期数据集及其在短期车速预测中的应用

Yuan Liu, J. Zhang
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引用次数: 2

摘要

车速预测在客车多系统设备的运行调度中起着至关重要的作用。通过预测控制算法或车辆生态系统控制协同设计,预测信息是车辆能量管理不可或缺的先决条件。本文首先生成了达拉斯地区固定路线上的重复城市驾驶循环数据集,旨在模拟日常通勤路线,为进一步的能量管理研究提供基础。为了研究其动态特性,通过交叉口/停车识别将这些行驶周期分段划分。在此基础上,建立了基于隐马尔可夫链模型、长短期记忆网络、人工神经网络、支持向量回归和相似度方法的车辆速度预测模型池。为了进一步提高预测性能,对局部模型选择、集成方法以及它们的组合等更高层次的算法进行了研究和比较。结果表明:(1)与全周期预测相比,分段预测的预测精度提高了20.1%;(ii)结合动态模型选择和集成方法的混合局部模型框架可以进一步提高9.7%的精度。此外,本研究还评估了利用交叉口停车位置来估计等待时间的潜力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Repeated Commuting Driving Cycle Dataset with Application to Short-term Vehicle Velocity Forecasting
Vehicle velocity forecasting plays a critical role in operation scheduling of varying systems and devices for a passenger vehicle. The forecasted information serves as an indispensable prerequisite for vehicle energy management via predictive control algorithms or vehicle ecosystem control Co-design. This paper first generates a repeated urban driving cycle dataset at a fixed route in the Dallas area, aiming to simulate a daily commuting route and serves as a base for further energy management study. To explore the dynamic properties, these driving cycles are piecewise divided into cycle segments via intersection/stop identification. A vehicle velocity forecasting model pool is then developed for each segment, including the hidden Markov chain model, long short-term memory network, artificial neural network, support vector regression, and similarity methods. To further improve the forecasting performance, higher-level algorithms like localized model selection, ensemble approaches, and a combination of them are investigated and compared. Results show that (i) the segment-based forecast improves the forecasting accuracy by up to 20.1%, compared to the whole cycle-based forecast; and (ii) the hybrid localized model framework that combines dynamic model selection and an ensemble approach could further improve the accuracy by 9.7%. Moreover, the potential of leveraging the stopping location at an intersection to estimate the waiting time is also evaluated in this study.
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