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

Chunxi LI; Yingying FU; Xiangke CUI; Quanbo GE

doi:10.1631/FITEE.2200212

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

Regular Papers | Updated：2023-02-27

- Dynamic time prediction for electric vehicle charging based on charging pattern recognition
- 一种基于充电模式识别的电动汽车充电时间预测方法
- Frontiers of Information Technology & Electronic Engineering Vol. 24, Issue 2, Pages: 299-313(2023)
- Affiliations：
  
  1.Logistics Engineering College, Shanghai Maritime University, Shanghai 200135, China
  2.School of Economics and Management, Beijing Jiaotong University, Beijing 102603, China
  3.School of Automation, Nanjing University of Information Science and Technology, Nanjing 210044, China
  4.Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology (CICAEET), Nanjing University of Information Science and Technology, Nanjing 210044, China
  5.Jiangsu Key Laboratory of Big Data Analysis Technology (B-DAT), Nanjing University of Information Science and Technology, Nanjing 210044, China
- Author bio：
  
  E-mail: lcx46@163.com;
  Fuyy8652@163.com;
  18458320@qq.com;
  ‡Corresponding author
- Funds：
- DOI：10.1631/FITEE.2200212
  CLC： U469.72; TP391.4
- Received：16 May 2022，
  
  Accepted：23 August 2022，
  
  Published：0 February 2023
- Accepted：
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Chunxi LI, Yingying FU, Xiangke CUI, et al. Dynamic time prediction for electric vehicle charging based on charging pattern recognition[J]. Frontiers of Information Technology & Electronic Engineering, 2023, 24(2): 299-313.
DOI：

Chunxi LI, Yingying FU, Xiangke CUI, et al. Dynamic time prediction for electric vehicle charging based on charging pattern recognition[J]. Frontiers of Information Technology & Electronic Engineering, 2023, 24(2): 299-313. DOI： 10.1631/FITEE.2200212.

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。

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.

关键词

Keywords

references

Altché F , de la Fortelle A , 2017 . An LSTM network for highway trajectory prediction . Proc IEEE 20 th Int Conf on Intelligent Transportation Systems , p. 353 - 359 . doi: 10.1109/ITSC.2017.8317913 http://doi.org/10.1109/ITSC.2017.8317913

Chang XQ , Song ZX , Wang JH , 2020 . Electric vehicle charging load prediction and system development based on Monte Carlo algorithm . High Volt Appar , 56 ( 8 ): 1 - 5 (in Chinese) . doi: 10.13296/j.1001-1609.hva.2020.08.001 http://doi.org/10.13296/j.1001-1609.hva.2020.08.001

Cheng SY , Lin PC , Lin PJ , 2019 . New method of predict remaining charging time for lithium-ion batteries . Chin J Power Sources , 43 ( 1 ): 99-102, 135 (in Chinese) . doi: 10.3969/j.issn.1002-087X.2019.01.028 http://doi.org/10.3969/j.issn.1002-087X.2019.01.028

Du MJ , Ding SF , Jia HJ , 2016 . Study on density peaks clustering based on k -nearest neighbors and principal component analysis . Knowl-Based Syst , 99 : 135 - 145 . doi: 10.1016/j.knosys.2016.02.001 http://doi.org/10.1016/j.knosys.2016.02.001

Frendo O , Graf J , Gaertner N , et al. , 2020 . Data-driven smart charging for heterogeneous electric vehicle fleets . Energy AI , 1 : 100007 . doi: 10.1016/j.egyai.2020.100007 http://doi.org/10.1016/j.egyai.2020.100007

Ge QB , Guo C , Jiang HY , et al. , 2022 . Industrial power load forecasting method based on reinforcement learning and PSO-LSSVM . IEEE Trans Cybern , 52 ( 2 ): 1112 - 1124 . doi: 10.1109/TCYB.2020.2983871 http://doi.org/10.1109/TCYB.2020.2983871

Han T , Xu M , Lu YF , 2014 . Charging Remaining Time Estimation Method, Device and Mobile Equipment. CN Patent 201210297383.4 (in Chinese) .

Hu W , Li ZS , 2007 . A simpler and more effective particle swarm optimization algorithm . J Softw , 18 ( 4 ): 861 - 868 (in Chinese) . doi: 10.1360/jos180861 http://doi.org/10.1360/jos180861

Huang TS , Wu SY , 2005 . Enhanced particle swarm optimization algorithm based on Kalman filter principle . Comput Eng Appl , 41 ( 35 ): 56 - 58 (in Chinese) . doi: 10.3321/j.issn:1002-8331.2005.35.019 http://doi.org/10.3321/j.issn:1002-8331.2005.35.019

Li B , Han R , He YG , et al. , 2020 . Applications of the improved random forest algorithm in fault diagnosis of motor bearings . Proc CSEE , 40 ( 4 ): 1310 - 1319 (in Chinese) . doi: https://doi.org/10.13334/j.0258-8013.pcsee.190501 https://doi.org/10.13334/j.0258-8013.pcsee.190501

Li CX , Fu YY , Zhang JM , et al. , 2020 . Evaluation methods on charging safety for EV power battery . Proc 35 th Youth Academic Annual Conf of Chinese Association of Automation , p. 318 - 323 . doi: 10.1109/YAC51587.2020.9337499 http://doi.org/10.1109/YAC51587.2020.9337499

Li YY , 2020 . Engineering Intelligent Kalman Filtering Method . MS Thesis, Hangzhou Dianzi University , Hangzhou, China (in Chinese) . doi: https://doi.org/10.27075/d.cnki.ghzdc.2020.000227 https://doi.org/10.27075/d.cnki.ghzdc.2020.000227

Lin PC , 2018 . Tri-section SVR model for predicting charging time of lithium-ion batteries . Chin J Power Sources , 42 ( 8 ): 1155-1157, 1232 (in Chinese) . doi: 10.3969/j.issn.1002-087X.2018.08.019 http://doi.org/10.3969/j.issn.1002-087X.2018.08.019

Liu B , 2020 . Research of Short-Term Power Load Forecasting Based on PSO-LSTM Algorithm . MS Thesis, Jilin University , Jilin, China (in Chinese) . doi: https://doi.org/10.27162/d.cnki.gjlin.2020.003369 https://doi.org/10.27162/d.cnki.gjlin.2020.003369

Liu WW , Sang SB , Zhang HP , 2022 . Study on improved heart sound classification model based on CNN+LSTM . Electron Des Eng , 30 ( 2 ): 38 - 42 (in Chinese) . doi: 10.14022/j.issn1674-6236.2022.02.009 http://doi.org/10.14022/j.issn1674-6236.2022.02.009

Liu X , 2020 . Prediction of Lithium Battery's Remaining Charging Time Based on IndyLSTM . MS Thesis, Xi’an University of Science and Technology , Xi’an, China (in Chinese) . doi: https://doi.org/10.27397/d.cnki.gxaku.2020.000677 https://doi.org/10.27397/d.cnki.gxaku.2020.000677

Liu YY , 2018 . Research on modeling and simulation of detailed model of photovoltaic power generation system . Comput Knowl Technol , 14 ( 10 ): 221-224, 228 (in Chinese) . doi: 10.14004/j.cnki.ckt.2018.1153 http://doi.org/10.14004/j.cnki.ckt.2018.1153

Rodriguez A , Laio A , 2014 . Clustering by fast search and find of density peaks . Science , 344 ( 6191 ): 1492 - 1496 . doi: 10.1126/science.1242072 http://doi.org/10.1126/science.1242072

Roondiwala M , Patel H , Varma S , 2017 . Predicting stock prices using LSTM . Int J Sci Res , 6 ( 4 ): 1754 - 1756 .

Sun J , Chen SB , You PC , et al. , 2021 . Battery-assisted online operation of distributed data centers with uncertain workload and electricity prices . IEEE Trans Cloud Comput , early access . doi: 10.1109/TCC.2021.3132174 http://doi.org/10.1109/TCC.2021.3132174

Wang ZL , Li J , Song YF , 2020 . Improved K -means algorithm based on distance and weight . Comput Eng Appl , 56 ( 23 ): 87 - 94 (in Chinese) . doi: 10.3778/j.issn.1002-8331.2009-0103 http://doi.org/10.3778/j.issn.1002-8331.2009-0103

Xu ZY , Kang Y , Cao Y , et al. , 2019 . Man-machine verification of mouse trajectory based on the random forest model . Front Inform Technol Electron Eng , 20 ( 7 ): 925 - 929 . doi: 10.1631/FITEE.1700442 http://doi.org/10.1631/FITEE.1700442

Yang QM , Liu GL , Bao YN , et al. , 2022 . Fault detection of wind turbine generator bearing using attention-based neural networks and voting-based strategy . IEEE/ASME Trans Mechatron , 27 ( 5 ): 3008 - 3018 . doi: 10.1109/TMECH.2021.3127213 http://doi.org/10.1109/TMECH.2021.3127213

Zhang QS , Zhao QC , 2020 . Effects of overcharge cycling on the aging and safety of lithium ion batteries . High Volt Eng , 46 ( 10 ): 3390 - 3397 (in Chinese) . doi: 10.13336/j.1003-6520.hve.20200361 http://doi.org/10.13336/j.1003-6520.hve.20200361

Zhang YF , Zhao ZD , Deng YJ , et al. , 2021 . ECGID: a human identification method based on adaptive particle swarm optimization and the bidirectional LSTM model . Front Inform Technol Electron Eng , 22 ( 12 ): 1641 - 1654 . doi: 10.1631/FITEE.2000511 http://doi.org/10.1631/FITEE.2000511

Zhou D , Song XH , Lu WB , et al. , 2019 . Real-time SOH estimation algorithm for lithium-ion batteries based on daily segment charging data . Proc CSEE , 39 ( 1 ): 105 - 111 (in Chinese) . doi: https://doi.org/10.13334/j.0258-8013.pcsee.181026 https://doi.org/10.13334/j.0258-8013.pcsee.181026

Zhu XQ , Wang ZP , Wang C , et al. , 2019 . An experimental study on overcharge behaviors of lithium-ion power battery with LiNi 0.6 Co 0.2 Mn 0.2 O 2 cathode . Automot Eng , 41 ( 5 ): 582 - 589 (in Chinese) . doi: 10.19562/j.chinasae.qcgc.2019.05.015 http://doi.org/10.19562/j.chinasae.qcgc.2019.05.015

Zhu ZC , Zheng YJ , 2017 . Prediction method of rechargeable electricity of vehicle battery at different temperature . Agric Equip Veh Eng , 55 ( 8 ): 1 - 5 . doi: 10.3969/j.issn.1673-3142.2017.08.001 http://doi.org/10.3969/j.issn.1673-3142.2017.08.001

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