论文标题
混合动力汽车电池能耗的不确定性预测
Uncertainty-Aware Prediction of Battery Energy Consumption for Hybrid Electric Vehicles
论文作者
论文摘要
车辆的可用性高度取决于它们的能耗。特别是，阻碍电动（EV），混合动力车（HEV）和插电式混合动力车（PHEV）车辆的大量采用的主要因素之一是范围焦虑，这是当驾驶员不确定给定旅行的能量可用性时发生。为了解决这个问题，我们提出了一种用于建模电池能耗的机器学习方法。通过降低预测性不确定性，此方法可以帮助提高对车辆性能的信任，从而提高其可用性。大多数相关的工作着重于影响能耗的电池的物理和/或化学模型。我们提出了一种数据驱动的方法，该方法依赖于包括电池相关属性在内的实际数据集。与传统方法相比，我们的方法在预测不确定性以及准确性方面有所改善。
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.
