Research on Outlier Detection Algorithm for Evaluation of Battery System Safety

Abstract

Battery system is the key part of the electric vehicle. To realize outlier detection in the running process of battery system effectively, a new high-dimensional data stream outlier detection algorithm (DSOD) based on angle distribution is proposed. First, in order to improve the algorithm stability in high-dimensional space, the method of angle distribution-based outlier detection algorithm is employed. Second, to reduce the computational complexity, a small-scale calculation set of data stream is established, which is composed of normal set and border set. For the purpose of solving the problem of concept drift, an update mechanism for the normal set and border set is developed in this paper. By this way, these hidden abnormal points will be rapidly detected. The experimental results on real data sets and battery system simulation data sets demonstrate that DSOD is more efficient than Simple variance of angles (Simple VOA) and angle-based outlier detection (ABOD) and is very suitable for the evaluation of battery system safety.