Full Text:   <6909>

Summary:  <299>

CLC number: U469.72; TP391.4

On-line Access: 2023-02-27

Received: 2022-05-16

Revision Accepted: 2023-02-27

Crosschecked: 2022-08-23

Cited: 0

Clicked: 1542

Citations:  Bibtex RefMan EndNote GB/T7714


Chunxi LI


Quanbo GE


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Frontiers of Information Technology & Electronic Engineering  2023 Vol.24 No.2 P.299-313


Dynamic time prediction for electric vehicle charging based on charging pattern recognition

Author(s):  Chunxi LI, Yingying FU, Xiangke CUI, Quanbo GE

Affiliation(s):  Logistics Engineering College, Shanghai Maritime University, Shanghai 200135, China; more

Corresponding email(s):   lcx46@163.com, Fuyy8652@163.com, 18458320@qq.com, QuanboGe@163.com

Key Words:  Charging mode, Charging time, Random forest, Long short-term memory (LSTM), Simplified particle swarm optimization (SPSO)

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, 2023, 24(2): 299-313.

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author="Chunxi LI, Yingying FU, Xiangke CUI, Quanbo GE",
journal="Frontiers of Information Technology & Electronic Engineering",
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%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
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%I Zhejiang University Press & Springer
%DOI 10.1631/FITEE.2200212

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 - 24
IS - 2
SP - 299
EP - 313
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PB - Zhejiang University Press & Springer
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DOI - 10.1631/FITEE.2200212

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. It is necessary to accurately predict the vehicle's charging time to effectively prevent the battery from overcharging. Due to the complex structure of the battery pack and various charging modes, the traditional 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 run by the actual engineering system are verified for the proposed charging time prediction algorithm. 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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