A Data-driven Approach for Charging Characteristic Parameter Identification Method of Electric Vehicles. Issue 1 (August 2021)

• Record Type: Journal Article
• Authors: Gao, Zhenyu; Chen, Zhonghua; Hu, Qian; Xue, Hua; Ling, Chen; Zhang, Xiaowen
• Is Part Of: Journal of physics. Volume 1993:Issue 1(2021)
• Publication Date: 2021-08
• Subjects: Physics -- Congresses; 530.5
• DOI: 10.1088/1742-6596/1993/1/012017
• Languages: English
• ISSNs: 1742-6588

Abstract

Abstract: In order to accurately identify new energy electric vehicles charging behaviour characteristic parameter, the theory of consistency is put forward based on the k-means clustering method. The complex coupling network including consistency control is introduced into the data clustering analysis to accurately describe the consistency characteristics of the electric vehicles charging load data in different periods, and the dissimilarity measure with the state update of the adjacent cluster data is used to quickly calculate the initial cluster center. The k-means method is used to quickly identify the expected value of EV initial charging time in typical scenes, and to accurately extract the probability distribution function of EV charging probability and charging initial time. Combined with a practical case, it is verified that the proposed method has the advantages of simple calculation, fast clustering.