Affiliation(s):
Logistics Engineering College, Shanghai Maritime University, Shanghai 200135, China;
moreAffiliation(s): Logistics Engineering College, Shanghai Maritime University, Shanghai 200135, China; School of Economics and Management, Beijing Jiaotong University, Beijing 102603, China; School of Automation, Nanjing University of Information Science and Technology, Nanjing 210044, China;
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Chunxi LI, Yingying FU, Xiangke CUI, Quanbo GE. Dynamic time prediction for electric vehicle charging based on charging pattern recognition[J]. Frontiers of Information Technology & Electronic Engineering , 1998, -1(5): .
@article{title="Dynamic time prediction for electric vehicle charging based on charging pattern recognition", author="Chunxi LI, Yingying FU, Xiangke CUI, Quanbo GE", journal="Frontiers of Information Technology & Electronic Engineering", volume="-1", number="-1", pages="", year="1998", publisher="Zhejiang University Press & Springer", doi="10.1631/FITEE.2200212" }
%0 Journal Article %T Dynamic time prediction for electric vehicle charging based on charging pattern recognition %A Chunxi LI %A Yingying FU %A Xiangke CUI %A Quanbo GE %J Frontiers of Information Technology & Electronic Engineering %V -1 %N -1 %P %@ 1869-1951 %D 1998 %I Zhejiang University Press & Springer
TY - JOUR T1 - Dynamic time prediction for electric vehicle charging based on charging pattern recognition A1 - Chunxi LI A1 - Yingying FU A1 - Xiangke CUI A1 - Quanbo GE J0 - Frontiers of Information Technology & Electronic Engineering VL - -1 IS - -1 SP - EP - %@ 1869-1951 Y1 - 1998 PB - Zhejiang University Press & Springer ER -
Abstract: Overcharging is an important safety issue in the charging process of electric vehicle power batteries, and can easily lead to accelerated battery aging and serious safety accidents. Therefore, it is necessary to accurately predict the vehicleâĂŹs charging time to effectively prevent the battery from overcharging. Due to the complex structure of the battery pack and various charging modes, the charging time prediction method often encounters modeling difficulties and low accuracy. In response to the above problems, data drivers and machine learning theories are applied. On the basis of fully considering the different electric vehicle battery management system (BMS) charging modes, a charging time prediction method with charging mode recognition is proposed. First, an intelligent algorithm based on dynamic weighted density peak clustering (DWDPC) and random forest fusion is proposed to classify vehicle charging modes. Then, on the basis of an improved simplified particle swarm optimization (ISPSO) algorithm, a high-performance charging time prediction method is constructed by fully integrating Long Short-Term Memory (LSTM) and a strong tracking filter. Finally, the data that is run by the actual engineering system is verified for the proposed charging time prediction algorithm. The experimental results show that the new method can effectively distinguish the charging modes of different vehicles, identify the charging characteristics of different electric vehicles, and achieve high prediction accuracy.
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