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.
Highlights
In a world where environment protection and energy conservation are receiving extensive concerns, the development of electric vehicles has taken on an accelerated pace [1, 2]
An update mechanism for the normal set and border set is built, which will help us to deal with concept drift problem
In order to compare the efficiency and effectiveness of the algorithm, angle-based outlier detection (ABOD) [20] algorithm and the Simple VOA [17] algorithm were selected as the comparison algorithms, because both of them apply the ideas based on angle distribution
Summary
In a world where environment protection and energy conservation are receiving extensive concerns, the development of electric vehicles has taken on an accelerated pace [1, 2]. There are some additional characteristics in the battery system data, like high dimension, concept drift, and so on [5] This makes it more difficult to evaluate the safety performance of the battery system and disturbs the normal running of electric vehicles. Based on Breunig’s, a new abnormality judging standard to each object called MDEF is proposed by Papadimitriou et al [12] Most of these methods implicitly rely on finding nearest neighbors for every object and typically use indexing data structures to improve the performance. In order to reduce the security risk of the battery system and efficiently detect the hidden local outliers in massive data stream, a new outlier detection approach based on the angle distribution for highdimensional data stream is proposed in this paper. The computational complexity of the algorithm is obviously reduced
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