论文标题
使用深卷积神经网络估算电动汽车的能源消耗，以减少驾驶员范围焦虑
Estimation of energy consumption of electric vehicles using Deep Convolutional Neural Network to reduce driver's range anxiety
论文作者
论文摘要
这项工作的目的是通过使用深层卷积神经网络估算电动汽车的实时能源消耗来减少驾驶员的焦虑。实时估计值可用于准确预测车辆的剩余范围，因此可以减轻驾驶员的焦虑范围。与现有技术相反，影响因素的组合引起的非线性和复杂性使问题更适合于深度学习方法。所提出的方法需要三个参数，即车速，拖流努力和道路高程。进行多个具有不同变体的实验，以探索层数和输入特征描述符的影响。拟议方法和五种现有技术的比较表明，所提出的模型的执行始终比最低误差的现有技术更好。
The goal of this work is to reduce driver's range anxiety by estimating the real-time energy consumption of electric vehicles using deep convolutional neural network. The real-time estimate can be used to accurately predict the remaining range for the vehicle and hence, can reduce driver's range anxiety. In contrast to existing techniques, the non-linearity and complexity induced by the combination of influencing factors make the problem more suitable for a deep learning approach. The proposed approach requires three parameters namely, vehicle speed, tractive effort and road elevation. Multiple experiments with different variants are performed to explore the impact of number of layers and input feature descriptors. The comparison of proposed approach and five of the existing techniques show that the proposed model performed consistently better than existing techniques with lowest error.
