Battery state-of-charge estimation based on chaos immune evolutionary neural network

被引：0
|
作者：
Cheng, Bo
Han, Lin
Guo, Zhen-Yu
Wang, Jun-Ping
Cao, Bing-Gang
机构：
[1] School of Construction Machinery, Chang'an University, Xi'an 710061, China
[2] School of Mechanical Engineering, Xi'an Jiaotong University, Xi'an 710049, China
[3] Department of Mathematics, Xi'an Polytechnic University, Xi'an 710048, China
来源：
Xitong Fangzhen Xuebao / Journal of System Simulation | 2008年 / 20卷 / 11期
关键词：
D O I：
暂无
中图分类号：
学科分类号：
摘要：
It is difficult for conventional method to correctly predict battery state of charge (SOC). Partial least square regression is used to select input variables. Battery terminal voltage, voltage derivative, voltage second derivative, discharge current and battery temperature are inputs of neural network and state of charge is output. In order to avoid defect of BP algorithm, the adaptive parallel chaos immune evolutionary programming was adopted to train weights. Finally, under the state of dynamic power cycle, predicted SOC and actual SOC were compared to verify the proposed neural network within 5 percent error.
引用
收藏
页码：2889 / 2892
相关论文
共 50 条
  • [31] Artificial neural network in estimation of battery state-of-charge (SOC) with nonconventional input variables selected by correlation analysis
    Cai, CH
    Du, D
    Liu, ZY
    Zhang, H
    [J]. 2002 INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND CYBERNETICS, VOLS 1-4, PROCEEDINGS, 2002, : 1619 - 1625
  • [32] State-of-Charge Estimation of Lithium-Ion Battery Pack Based on Improved RBF Neural Networks
    Zhang, Li
    Zheng, Min
    Du, Dajun
    Li, Yihuan
    Fei, Minrui
    Guo, Yuanjun
    Li, Kang
    [J]. COMPLEXITY, 2020, 2020
  • [33] Lithium Iron Phosphate Battery Electric Vehicle State-of-Charge Estimation Based on Evolutionary Gaussian Mixture Regression
    Sheng, Hanmin
    Xiao, Jian
    Wang, Peng
    [J]. IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS, 2017, 64 (01) : 544 - 551
  • [34] State-of-charge estimation of lithium-ion batteries based on gated recurrent neural network
    Yang, Fangfang
    Li, Weihua
    Li, Chuan
    Miao, Qiang
    [J]. ENERGY, 2019, 175 : 66 - 75
  • [35] BATTERY STATE OF CHARGE ESTIMATION USING AN ARTIFICIAL NEURAL NETWORK
    Ismail, Mahmoud
    Dlyma, Rioch
    Elrakaybi, Ahmed
    Ahmed, Ryan
    Habibi, Saeid
    [J]. 2017 IEEE TRANSPORTATION ELECTRIFICATION CONFERENCE AND EXPO (ITEC), 2017, : 342 - 349
  • [36] State-of-Charge Estimation of Lithium Battery Based on Deep Residual Shrinkage Networks and a Variant Long Short Term Memory Neural Network
    Li, Penghua
    Zhang, Yihui
    [J]. 2023 35TH CHINESE CONTROL AND DECISION CONFERENCE, CCDC, 2023, : 1343 - 1347
  • [37] A Novel Neural Network With Gaussian Process Feedback for Modeling the State-of-Charge of Battery Cells
    Savargaonkar, Mayuresh
    Chehade, Abdallah
    Hussein, Ala A.
    [J]. IEEE TRANSACTIONS ON INDUSTRY APPLICATIONS, 2022, 58 (04) : 5340 - 5352
  • [38] An Adaptive Deep Neural Network with Transfer Learning for State-of-Charge Estimations of Battery Cells
    Savargaonkar, Mayuresh
    Chehade, Abdallah
    [J]. 2020 IEEE TRANSPORTATION ELECTRIFICATION CONFERENCE & EXPO (ITEC), 2020, : 598 - 602
  • [39] A Novel Neural Network with Gaussian Process Feedback for Modeling the State-of-Charge of Battery Cells
    Chehade, Abdallah
    Hussein, Ala A.
    [J]. 2020 IEEE ENERGY CONVERSION CONGRESS AND EXPOSITION (ECCE), 2020, : 1524 - 1527
  • [40] A Novel State-of-Charge Estimation Algorithm of EV Battery Based on Bilinear Interpolation
    Wang Liye
    Wang Lifang
    Li Yong
    [J]. 2013 9TH IEEE VEHICLE POWER AND PROPULSION CONFERENCE (VPPC), 2013, : 26 - 29
12345