考虑性能偏差的基于DBSCAN和PCA的电动汽车退役电池二次使用最优聚类算法

Jeyeong Lim, Eui-Seong Han, Dong Hwan Kim, B. Lee
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引用次数: 0

摘要

针对退役电池的再利用问题,提出了一种考虑参数性能偏差的最优聚类算法和数据预处理方法。该方法利用基于密度的带噪声应用空间聚类算法(DBSCAN),考虑退役电池数据集的密度和性能偏差,对电池进行重新分组。此外,采用主成分分析(PCA)对数据进行预处理,避免了聚类算法的计算复杂度和过拟合问题,提高了算法的性能。通过与k-均值聚类和高斯混合模型等一般聚类算法的比较,验证了该算法的可行性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
An Optimal Clustering Algorithm for Second Use of Retired EV Batteries Using DBSCAN and PCA Schemes Considering Performance Deviation
This paper proposes an optimal clustering algorithm considering performance deviation of parameters and data preprocessing method for reusing retired batteries. The proposed method regroups batteries by considering the density and performance deviation of the retired battery dataset through a clustering algorithm using density-based spatial clustering of applications with noise (DBSCAN). Additionally, the performance of the algorithm was improved through data preprocessing using a principal component analysis (PCA) that prevents the computational complexity and overfitting of clustering algorithm. The feasibility of the proposed algorithm is verified by comparing with general clustering algorithms such as the k-means clustering and Gaussian mixture model.
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