State-of-health estimation of Lithium-ion battery based on back-propagation neural network with adaptive hidden layer

被引：0
作者：
Liping Chen
Changcheng Xu
Xinyuan Bao
António Lopes
Penghua Li
Chaolong Zhang
机构：
[1] Hefei University of Technology,School of Electrical Engineering and Automation
[2] University of Porto,LAETA/INEGI, Faculty of Engineering
[3] Chongqing University of Posts and Telecommunications,Automotive Electronics Engineering Research Center, College of Automation
[4] Jinling Institute of Technology,College of Intelligent Science and Control Engineering
来源：
Neural Computing and Applications | 2023年 / 35卷
关键词：
Back-propagation neural network; Adaptive hidden layer; Lithium-ion battery; State-of-health;
D O I：
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中图分类号：
学科分类号：
摘要：
The reliability and safety of lithium-ion batteries (LIBs) are key issues in battery applications. Accurate prediction of the state-of-health (SOH) of LIBs can reduce or even avoid battery-related accidents. In this paper, a new back-propagation neural network (BPNN) is proposed to predict the SOH of LIBs. The BPNN uses as input the LIB voltage, current and temperature, as well as the charging time, since it is strongly correlated with the SOH. The number of hidden layer nodes is adaptively set based on the training data in order to improve the generalization capability of the BPNN. The effectiveness and robustness of the proposed scheme is verified using four distinct battery datasets and different training data. Experimental results show that the new BPNN is able to accurately predict the SOH of LIBs, revealing superiority when compared to other alternatives.
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页码：14169 / 14182
页数：13
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  • [11] Manzie C(2013)Model based identification of aging parameters in lithium ion batteries J Power Sources 232 51-63
  • [12] Nešić D(2017)Battery state of health monitoring by estimation of the number of cyclable Li-ions Control Eng Pract 66 119502-4017
  • [13] Shang Y(2022)A comparative study of different features extracted from electrochemical impedance spectroscopy in state of health estimation for lithium-ion batteries Appl Energy 322 4006-525
  • [14] Liu K(2014)Accelerated lifetime testing methodology for lifetime estimation of lithium-ion batteries used in augmented wind power plants IEEE Trans Ind Appl 50 517-1141
  • [15] Cui N(2017)Degradation behavior of lithium-ion batteries during calendar ageing-The case of the internal resistance increase IEEE Trans Ind Appl 54 1136-436
  • [16] Wang N(2017)Lithium-ion battery power degradation modelling by electrochemical impedance spectroscopy IET Renew Power Gener 11 3404-399
  • [17] Li K(2020)A review of the state of health for lithium-ion batteries: research status and suggestions J Clean Prod 261 106563-538
  • [18] Zhang C(2022)Genetically optimized extended Kalman filter for state of health estimation based on Li-ion batteries parameters Energies 15 426-1881
  • [19] Xu Z(2023)A novel back propagation neural network-dual extended Kalman filter method for state-of-charge and state-of-health co-estimation of lithium-ion batteries based on limited memory least square algorithm J Energy Storage 59 384-11769
  • [20] Guo Y(2022)A method for state of charge and state of health estimation of lithium-ion battery based on adaptive unscented Kalman filter Energy Rep 8 527-4593
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