@inproceedings{78a48956d3ca40f8856194e4b7cfa320,
title = "High-dimensional data abnormity detection based on improved Variance-of-Angle (VOA) algorithm for electric vehicles battery",
abstract = "As the battery is grouped by many cells, the inconsistency of battery is unavoidable. If the battery is frequently charged or discharged without a balancer, the battery cells with the lowest capacity may be overcharged or over-discharged, which is one of the major reasons for battery thermal runaway which can cause a fire. This paper proposes a cloud data based electric vehicle battery voltage consistency evaluation method for vehicles in service. The density-based spatial clustering of applications with noise (DBSCAN) method is employed to improve the computational efficiency of the variance-of-angle (VOA) which is a widely used outlier detection method. The DBSCAN-VOA results are compared with VOA results and this shows that the distinction capability of VOA is kept while the computational complexity is significantly reduced.",
keywords = "Battery, Cloud computation, Computational complexity, DBSCAN-VOA, Inconsistency",
author = "Peng Liu and Jin Wang and Zhenpo Wang and Zhaosheng Zhang and Shuo Wang and Dorrell, {David G.}",
note = "Publisher Copyright: {\textcopyright} 2019 IEEE.; 11th Annual IEEE Energy Conversion Congress and Exposition, ECCE 2019 ; Conference date: 29-09-2019 Through 03-10-2019",
year = "2019",
month = sep,
doi = "10.1109/ECCE.2019.8912777",
language = "English",
series = "2019 IEEE Energy Conversion Congress and Exposition, ECCE 2019",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "5072--5077",
booktitle = "2019 IEEE Energy Conversion Congress and Exposition, ECCE 2019",
address = "United States",
}