The ever-increasing concerns over urban air quality, noise pollution, and considerable savings in total cost of ownership encouraged more and more cities to introduce battery electric buses (e-bus). Based on the sensor records of 99 e-buses that included over 250,000 h across 4.7 million kilometers, this paper unveiled the relationship between driving behaviors and e-bus battery energy consumption under various environments. Battery efficiency was evaluated by the distance traveled per unit battery energy (1% SoC, State of Charge). Mix effect regression was applied to quantify the magnitude and correlation between multiple factors; and 13 machine learning methods were adopted for enhanced prediction and optimization. Although regenerative braking could make a positive contribution to e-bus battery energy recovery, unstable driving styles with greater speed variation or acceleration would consume more energy, hence reduce the battery efficiency. The timing window is another significant factor and the result showed higher efficiency at night, over weekends, or during cooler seasons. Assuming a normal driving behavior, this paper investigated the most economical driving speed in order to maximize battery efficiency. An average of 19% improvement could be achieved, and the optimal driving speed is time-dependent, ranging from 11 to 18 km/h.

中文翻译：

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了解城市公共交通系统中纯电动公交车的能耗

对城市空气质量、噪音污染和总体拥有成本大幅节省的日益关注促使越来越多的城市引入电池电动公交车（e-bus）。本文基于 99 辆电动公交车的传感器记录，包括 470 万公里超过 250,000 小时的传感器记录，揭示了不同环境下驾驶行为与电动公交车电池能量消耗之间的关系。电池效率通过每单位电池能量行驶的距离（1% SoC，充电状态）进行评估。应用混合效应回归来量化多因素之间的大小和相关性；并采用了 13 种机器学习方法来增强预测和优化。虽然再生制动可以为电动巴士电池能量回收做出积极贡献，速度变化或加速度较大的不稳定驾驶方式会消耗更多能量，从而降低电池效率。时间窗口是另一个重要因素，结果显示夜间、周末或凉爽季节的效率更高。假设正常驾驶行为，本文研究了最经济的驾驶速度，以最大限度地提高电池效率。平均可实现 19% 的改进，最佳驾驶速度与时间有关，范围从 11 到 18 公里/小时。本文研究了最经济的行驶速度，以最大限度地提高电池效率。平均可实现 19% 的改进，最佳驾驶速度与时间有关，范围从 11 到 18 公里/小时。本文研究了最经济的行驶速度，以最大限度地提高电池效率。平均可实现 19% 的改进，最佳驾驶速度与时间有关，范围从 11 到 18 公里/小时。