A fast classification method based on factor analysis and K-means clustering forretired electric vehicle batteries
DOI：DOI: 10.19783/j.cnki.pspc.201413
Key Words:battery management system (BMS)  hybrid pulse power characteristic (HPPC)  factor analysis  clustering algorithm  fast classification and regroup
Author NameAffiliation
ZHANG Chaolong School of Electronic Engineering and Intelligent Manufacturing, Anqing Normal Univessity, Anqing 246011, China 
ZHAO Shaishai School of Electronic Engineering and Intelligent Manufacturing, Anqing Normal Univessity, Anqing 246011, China 
ZHANG Bo School of Electronic Engineering and Intelligent Manufacturing, Anqing Normal Univessity, Anqing 246011, China 
Hits: 4075
Download times: 1109
Abstract:In order to solve the problem of a large number of retired electric vehicle batteries with a lack of quick sorting means, a fast classification and regroup approach of batteries is presented based on Hybrid Pulse Power Characteristic (HPPC), factor analysis and a clustering algorithm. The maximum available capacity of the battery is calculated by the voltage data of the battery cell in the Battery Management System (BMS). The primary discharge pulse of the HPPC is used to extract the open-circuit voltage, ohmic, polarization and concentration resistances of the battery as characteristic variables. After the characteristic variable data is processed by the normalization algorithm and factor analysis, the battery sorting and recombination are completed by the clustering algorithm. Experimental results show that the average separation and recombination time of a single battery is compressed within 30 min using the proposed method, which has practical significance for classification and regrouping of retired electric vehicle batteries. This work is supported by the National Natural Science Foundation of China (No. 51607004), the Collaborative Innovation Project of Anhui Universities (No. GXXT-2019-002) and the Natural Science Research Project of Anhui University (No. KJ2020A0509).
View Full Text  View/Add Comment  Download reader