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首页> 外文期刊>Transportation Research >Hybrid electric buses fuel consumption prediction based on real-world driving data
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Hybrid electric buses fuel consumption prediction based on real-world driving data

机译：基于现实世界驾驶数据的混合动力电动总线燃料消耗预测

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Estimating fuel consumption by hybrid diesel buses is challenging due to its diversified operations and driving cycles. In this study, long-term transit bus monitoring data were utilized to empirically compare fuel consumption of diesel and hybrid buses under various driving conditions. Artificial neural network (ANN) based high-fidelity microscopic (1 Hz) and mesoscopic (5-60 min) fuel consumption models were developed for hybrid buses. The microscopic model contained 1 Hz driving, grade, and environment variables. The mesoscopic model aggregated 1 Hz data into 5 to 60-minute traffic pattern factors and predicted average fuel consumption over its duration. The prediction results show mean absolute percentage errors of 1-2% for microscopic models and 5-8% for mesoscopic models. The data were partitioned by different driving speeds, vehicle engine demand, and road grade to investigate their impacts on prediction performance.
机译：估计混合柴油总线的燃料消耗由于其多元化的运营和驾驶循环导致挑战。在这项研究中，利用长期运输总线监测数据来在各种驾驶条件下经验在柴油和混合总线的燃料消耗。为混合公共汽车开发了基于人工神经网络（ANN）的高保真微观（1 Hz）和介于介质（5-60分钟）燃料消耗模型。显微模型包含1 Hz驾驶，等级和环境变量。介观模型将1 Hz数据汇总为5至60分钟的交通模式因子，并在其持续时间内预测平均燃料消耗。预测结果显示微观模型的1-2％的平均绝对百分比，介面模型为5-8％。数据通过不同的驾驶速度，车辆发动机需求和道路等级来划分，以研究它们对预测性能的影响。

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