An accurate prediction of energy consumption in electric buses (EBs) can effectively reduce driving range anxiety and facilitate bus scheduling. Existing studies have not provided real-time predictions based on distance traveled using integrated machine learning methods. This study proposes a framework for predicting EB energy consumption, which is primarily divided into energy consumption estimation, kinematic feature prediction, and energy consumption prediction. The framework begins by fusing high-resolution real-world EB data with weather and road information, from which five types of influencing factors are extracted for different driving distances. An eXtreme Gradient Boosting (XGBoost) model is developed to evaluate feature importance and estimate the energy consumption rate (ECR). The SHapley Additive explanation (SHAP) method is then used to analyze the factors affecting the ECR. To predict important kinematic characteristics, spatial and temporal characteristics are captured using Long Short-Term Memory (LSTM) and a fully connected neural network. Finally, the predicted kinematic characteristics and the XGBoost model are combined to enable real-time prediction of the ECR. The results indicate that estimation and prediction accuracies gradually improve with increased driving distance. The mean absolute error of average ECR decreases from 43.9 % for 100 m to 7.5 % for 16 km. Temperature, bus stop density, and peak periods emerge as the most significant external factors after 8 km. This framework shows an improvement of over 10 % in most scenarios compared with other models in the literature, enabling individual forecasts of energy consumption currently in transit and aiding in the calculation of remaining battery-supported distance.

中文翻译：

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使用集成机器学习算法的电动公交车能耗实时预测框架


准确预测电动公交车 （EB） 的能耗可以有效降低行驶里程焦虑并促进公交调度。现有研究尚未使用集成机器学习方法提供基于行驶距离的实时预测。该文提出了一个预测EB能耗的框架，主要分为能耗估计、运动学特征预测和能耗预测。该框架首先将高分辨率的真实世界 EB 数据与天气和道路信息融合，从中提取不同驾驶距离的五种影响因素。开发了 eXtreme Gradient Boosting （XGBoost） 模型来评估特征重要性并估计能耗率 （ECR）。然后使用 SHapley 加法解释 （SHAP） 方法分析影响 ECR 的因素。为了预测重要的运动学特征，使用长短期记忆 （LSTM） 和全连接神经网络捕获空间和时间特征。最后，将预测的运动特性与 XGBoost 模型相结合，以实现 ECR 的实时预测。结果表明，随着行驶距离的增加，估计和预测精度逐渐提高。平均 ECR 的平均绝对误差从 100 m 的 43.9 % 下降到 16 km 的 7.5 %。温度、公交站密度和高峰期成为 8 公里后最重要的外部因素。与文献中的其他模型相比，该框架在大多数情况下显示提高了 10% 以上，从而能够单独预测当前运输中的能源消耗，并有助于计算剩余电池支持距离。