Reliable and accurate estimation of an electric bus’s instantaneous energy consumption is critical in evaluating energy impacts of planning and control of electric bus operations. In this study, we developed machine learning-based long short-term memory (LSTM) and artificial neural network (ANN) models to estimate 1 Hz energy consumption of electric buses based on continuous monitoring data of electric buses in Chattanooga, Tennessee, in 2019 and 2020. We propose a data-partitioning algorithm to separate energy charging and discharging modes before applying data-driven estimation models. A K-fold cross-validation-based model selection process was conducted to identify the optimal model structure and input variables in terms of prediction accuracy. The estimation results show the predicted mean absolute percentage error rates of LSTM and ANN models were 3% and 5%, respectively. We compared the proposed models with existing models in the literature based on the same testing data to demonstrate the predictability of our models.

对电动公交车的瞬时能耗进行可靠和准确的估算对于评估电动公交车运营规划和控制的能源影响至关重要。在这项研究中，我们开发了基于机器学习的长短期记忆 (LSTM) 和人工神经网络 (ANN) 模型，基于 2019 年田纳西州查塔努加市电动公交车的连续监测数据来估算电动公交车的 1 Hz 能耗和 2020 年。我们提出了一种数据分区算法，用于在应用数据驱动的估计模型之前分离能量充电和放电模式。一个ķ进行了基于 -fold 交叉验证的模型选择过程，以根据预测精度确定最佳模型结构和输入变量。估计结果表明，LSTM 和 ANN 模型的预测平均绝对百分比错误率分别为 3% 和 5%。我们基于相同的测试数据将所提出的模型与文献中的现有模型进行了比较，以证明我们模型的可预测性。