Driver- and situation-specific impact factors for the energy prediction of EVs based on crowd-sourced speed profiles
Driver- and situation-specific impact factors for the energy prediction of EVs based on crowd-sourced speed profiles
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基于众包速度曲线的电动汽车能量预测的特定于驾驶员和特定情况的影响因素
DOI：
10.1109/ivs.2014.6856501
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发表时间：
2014
期刊：
影响因子：
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通讯作者：
M. Lienkamp
中科院分区：
文献类型：
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作者：
S. Grubwinkler;Martin Hirschvogel;M. Lienkamp
本文提出了一种预测电动汽车选择行程所需能量的系统，该系统可用于各种电动汽车辅助，如里程估计。我们使用从人群源速度曲线中提取的统计特征进行能量预测，因为它们考虑了个人驾驶风格和当前交通状况的不同影响因素。统计预测模型利用这些特征来预测电动汽车的平均能耗偏差。因此，该模型预测了个体驾驶行为等引起的能源消耗方差。结果表明，如果考虑统计特征，能量预测提高了5.4个百分点。在给定路线出发前对电动汽车推进能量的预测相对平均误差为6.8%。
This paper presents a system for the prediction of the necessary energy for selected trips of electric vehicles (EVs), which can be used for various EV assistants like range estimation. We use statistical features extracted from crowd-sourced speed profiles for the energy prediction, since they consider the varying impact factors of the individual driving style and the prevailing traffic condition. A statistical prediction model uses these features in order to predict the deviation from the mean energy consumption of the EV. Hence, the model predicts the variance of energy consumption caused for example by individual driving behavior. The results show an improvement of the energy prediction by 5.4 percentage points if the statistical features are considered. The prediction of the propulsion energy for EVs before the start of a given route has a relative mean error of 6.8%.