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：汽车的可用性高度取决于其能量消耗。特别是，防止电动车（EV）、混合动力车（HEV）和插电式混合动力车（PHEV）的大规模采用的一个主要因素是范围焦虑，即当驾驶员对某次旅行的能源供应不确定时。为解决这个问题，我们提出了一种机器学习方法来建模电池的能量消耗。通过减少预测的不确定性，这种方法可以帮助增加对汽车性能的信任，从而提高其可用性。大多数相关工作专注于影响能量消耗的电池的物理和/或化学模型。我们提出了一种数据驱动方法，依赖于包括电池相关属性在内的真实世界数据集。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都显示了改进。