Deviations between system current measurements and real values in the power train of Electric Vehicles (EVs) can cause severe problems. Among others, these are restricted performance and cruising range. In this work, we propose a fleet-based framework to correct such deviations. We assume that the real value is the mean of all identically constructed EVs’ measurements for the same input. Under this assumption, we decide for each vehicle whether it displays hardware errors with the help of a binary classifier. Depending on the classification, if no hardware errors are detected, we recover the parameters of an assumed measurement error model via Linear Regression. Otherwise, we combine the regression with a convex optimization problem and sparsity constraints. We achieve an overall recovery rate of up to 90%, allowing the full automation of the measurement correction procedure with no need to add more sensors, or computational units on-board of the EV.

中文翻译：

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基于压缩感知的电动汽车测量校正

电动汽车（EV）的动力总成系统电流测量值与实际值之间的偏差会导致严重的问题。其中，这些是受限制的性能和巡航范围。在这项工作中，我们提出了一个基于舰队的框架来纠正这种偏差。我们假设实际值是相同输入下所有相同构造的电动汽车测量值的平均值。在此假设下，我们通过二进制分类器来确定每辆车是否显示硬件错误。根据分类，如果未检测到硬件错误，我们将通过线性回归来恢复假定的测量错误模型的参数。否则，我们将回归与凸优化问题和稀疏约束相结合。我们的整体恢复率高达90％，