The usability of vehicles is highly dependent on their energy consumption. In particular, one of the main factors hindering the mass adoption of electric (EV), hybrid (HEV), and plug-in hybrid (PHEV) vehicles is range anxiety, which occurs when a driver is uncertain about the availability of energy for a given trip. To tackle this problem, we propose a machine learning approach for modeling the battery energy consumption. By reducing predictive uncertainty, this method can help increase trust in the vehicle's performance and thus boost its usability. Most related work focuses on physical and/or chemical models of the battery that affect the energy consumption. We propose a data-driven approach which relies on real-world datasets including battery related attributes. Our approach showed an improvement in terms of predictive uncertainty as well as in accuracy compared to traditional methods.

翻译：车辆的可用性高度依赖于其能源消耗。特别是，阻碍电动汽车（EV）、混合动力电动汽车（HEV）及插电式混合动力电动汽车（PHEV）大规模普及的主要因素之一是里程焦虑，即当驾驶员无法确定特定行程中能源是否充足时产生的担忧。为解决这一问题，我们提出了一种基于机器学习建模电池能耗的方法。通过降低预测不确定性，该方法有助于增强对车辆性能的信任，从而提升其可用性。大多数相关研究侧重于影响能耗的电池物理和/或化学模型，而我们提出了一种数据驱动的方法，该依赖包含电池相关属性的真实世界数据集。与传统方法相比，我们的方法在预测不确定性和准确性方面均表现出改进。