Kriging and local climate zones – key to higher accuracy in range prediction?

Thomas Herzlieb, Johannes H. L. Sturm
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Abstract

Range prediction of electric vehicles requires knowledge of many parameters, including information about the vehicle environment. As an alternative to onboard measurement, external data sources (secondary data) can be used. The presented methods are suitable for estimating climatic data for vehicle application including urban areas and areas of complex topography. Compared to an inverse distance weighting approach for ambient temperature values, Kriging yields better overall accuracy especially for areas of complex topography, improving accuracy in urban regions, however, remains a challenge. An approach to improve accuracy in urban areas by considering local climate zones in the selection of secondary data sources produces mixed results.
克里格和当地气候带——提高距离预测精度的关键?
电动汽车的里程预测需要了解许多参数,包括车辆环境信息。作为机载测量的替代方案,可以使用外部数据源(辅助数据)。本文提出的方法适用于城市地区和地形复杂地区的车辆气候数据估计。与环境温度值的逆距离加权方法相比,Kriging方法的总体精度更高,特别是在复杂地形地区,然而,提高城市地区的精度仍然是一个挑战。在选择次要数据源时考虑当地气候带以提高城市地区精度的方法产生了不同的结果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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