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- W2990759177 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." @default.
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- W2990759177 date "2019-09-01" @default.
- W2990759177 modified "2023-09-26" @default.
- W2990759177 title "High-dimensional data abnormity detection based on improved Variance-of-Angle (VOA) algorithm for electric vehicles battery" @default.
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- W2990759177 doi "https://doi.org/10.1109/ecce.2019.8912777" @default.
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