Full Text:   <1730>

Summary:  <252>

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: 1263

Citations:  Bibtex RefMan EndNote GB/T7714

 ORCID:

Chunxi LI

https://orcid.org/0000-0003-1055-4755

Quanbo GE

https://orcid.org/0000-0002-6907-7837

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

http://doi.org/10.1631/FITEE.2200212


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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pages="299-313",
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publisher="Zhejiang University Press & Springer",
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T1 - Dynamic time prediction for electric vehicle charging based on charging pattern recognition
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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. 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.

一种基于充电模式识别的电动汽车充电时间预测方法

李春喜1,傅莹颖1,崔向科2,葛泉波3,4,5
1上海海事大学物流工程学院,中国上海市,200135
2北京交通大学经济管理学院,中国北京市,102603
3南京信息工程大学自动化学院,中国南京市,210044
4南京信息工程大学江苏省大气环境与装备技术协同创新中心,中国南京市,210044
5南京信息工程大学江苏省大数据分析技术重点实验室,中国南京市,210044
摘要:电动汽车动力电池过度充电容易导致电池加速老化和严重的安全事故。因此,准确预测车辆充电时间对充电安全防护意义重大。由于电池组结构复杂,充电方式多样,传统方法因缺乏充电模式识别而预测精度不高。本文应用数据驱动和机器学习理论,提出一种新的基于充电模式识别的充电时间预测方法。首先,基于动态加权密度峰值聚类(DWDPC)和随机森林融合的智能算法对车辆充电模式进行分类;然后,采用改进的简化粒子群优化算法(ISPSO)和强跟踪滤波器(STF),对LSTM神经网络进行优化,构建了一种高性能的充电时间预测方法;最后,通过实际工程数据对所提出的ISPSO-LSTM-STF方法进行了验证。实验结果表明,该方法能够有效区分充电模式,提高了充电时间预测精度,具有实际工程意义。

关键词:充电模式;充电时长;随机森林;长短期记忆网络(LSTM);简化粒子群优化算法(SPSO)

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