论文标题
电池模型的空间填充子集选择
Space-Filling Subset Selection for an Electric Battery Model
论文作者
论文摘要
电池性能的动态模型是在汽车驱动列车的整个开发过程中的重要工具。本研究介绍了一种方法，使大型数据集适用于对电阻抗进行建模。在获得数据驱动的模型时，通常的假设是更多的观察结果会产生更好的模型。但是，电池行为上的真实驱动数据代表了系统的强烈不均匀激发，这对建模产生了负面影响。因此，开发了可用数据的子集选择。它旨在更有效地构建准确的非线性自回归外源性（NARX）模型。该算法选择那些更均匀地填充非线性模型的输入空间的动态数据点。结果表明，与使用所有数据点相比，与随机子集相比，训练数据的这种减少与随机子集相比和更快的训练相比，导致更高的模型质量。
Dynamic models of the battery performance are an essential tool throughout the development process of automotive drive trains. The present study introduces a method making a large data set suitable for modeling the electrical impedance. When obtaining data-driven models, a usual assumption is that more observations produce better models. However, real driving data on the battery's behavior represent a strongly non-uniform excitation of the system, which negatively affects the modeling. For that reason, a subset selection of the available data was developed. It aims at building accurate nonlinear autoregressive exogenous (NARX) models more efficiently. The algorithm selects those dynamic data points that fill the input space of the nonlinear model more homogeneously. It is shown, that this reduction of the training data leads to a higher model quality in comparison to a random subset and a faster training compared to modeling using all data points.
