论文标题
卷卷神经网络袋决策树：一种通过实时估算能源消耗来减少电动汽车驾驶员焦虑焦虑的混合方法
Convolutional Neural Network-Bagged Decision Tree: A hybrid approach to reduce electric vehicle's driver's range anxiety by estimating energy consumption in real-time
论文作者
论文摘要
为了克服电动汽车（EV）的范围焦虑问题，必须进行准确的实时能耗估计，可用于为电动汽车的驾驶员实时提供有关其余范围的信息。考虑到温度，风速，电池SOC，辅助负载，道路高程，车速和加速度，使用了混合CNN-BDT方法，其中使用卷积神经网络（CNN）来提供能源消耗估算。此外，袋式决策树（BDT）用于微调估计值。与现有技术不同，所提出的方法不需要制造商的内部车辆参数，即使从嘈杂的数据中也可以轻松学习复杂的模式。与现有技术的比较结果表明，开发的方法提供了更好的估计值，而平均绝对能量偏差为0.14。
To overcome range anxiety problem of Electric Vehicles (EVs), an accurate real-time energy consumption estimation is necessary, which can be used to provide the EV's driver with information about the remaining range in real-time. A hybrid CNN-BDT approach has been developed, in which Convolutional Neural Network (CNN) is used to provide an energy consumption estimate considering the effect of temperature, wind speed, battery's SOC, auxiliary loads, road elevation, vehicle speed and acceleration. Further, Bagged Decision Tree (BDT) is used to fine tune the estimate. Unlike existing techniques, the proposed approach doesn't require internal vehicle parameters from manufacturer and can easily learn complex patterns even from noisy data. Comparison results with existing techniques show that the developed approach provides better estimates with least mean absolute energy deviation of 0.14.
