Smart Data Selection and Reduction for Electric Vehicle Service Analytics

Abstract

Battery electric vehicles (BEV) are increasingly used in mobility services such as car-sharing. A severe problem with BEV is battery degradation, leading to a reduction of the already very limited range of a BEV. Analytic models are required to determine the impact of service usage to provide guidance on how to drive and charge and also to support service tasks such as predictive maintenance. However, while the increasing number of sensor data in automotive applications allows for more fine-grained model parameterization and better predictive outcomes, in practical settings the amount of storage and transmission bandwidth is limited by technical and economical considerations. By means of a simulation-based analysis, dynamic user behavior is simulated based on real-world driving profiles parameterized by different driver characteristics and ambient conditions. We find that by using a shrinked subset of variables the required storage can be reduced considerably at low costs in terms of only slightly decreased predictive accuracy.

Loss-Aware Histogram Binning and Principal Component Analysis for Customer Fleet Analytics

“…Bartlomiejczyk [21] analyzed the driving behavior of bus drivers by aggregating the measurement signals followed by PCA. Schoch et al [22] implemented PCA for binned sensor data to reduce the features for electric vehicle service analytics. Ling et al [23] applied PCA to improve the representativeness of customer sampling based on aggregated usage data and thus identified fringe customers.…”

“…Given the scale and structure parameters, we estimate the average information loss per measurement variables per vehicle (shortly "information loss" in the following) using our empirical loss model according to ( 19)- (22) and their parameters.…”