Research on Outlier Detection Algorithm for Evaluation of Battery System Safety

Research on Outlier Detection Algorithm for Evaluation of Battery System Safety
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电池系统安全评价异常值检测算法研究

DOI:
10.1155/2014/830402
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发表时间:
2014-01
影响因子:
2.1
通讯作者:
Sheng Lu
Sheng Lu
中科院分区:
工程技术4区
文献类型:
--
作者:
Changhao Piao;Zhi Huang;Ling Su;Sheng Lu

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电池系统是电动汽车的关键部件。为了有效地实现电池系统运行过程中的离群点检测,提出了一种新的基于角度分布的高维数据流离群点检测算法。首先,为了提高算法在高维空间的稳定性,采用了基于角度分布的离群点检测算法。其次,为了降低计算复杂度,建立了一个由正规集和边界集组成的小规模数据流计算集。为了解决概念漂移问题,本文提出了一种规范集和边界集的更新机制。通过这种方式,这些隐藏的异常点将被快速检测到。在真实的数据集和电池系统仿真数据集上的实验结果表明,DSOD比简单角度方差(Simple VOA)和基于角度的离群点检测(ABOD)更有效,非常适合于电池系统安全性的评估。
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.
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