Journal of Electrical Engineering ›› 2022, Vol. 17 ›› Issue (4): 2-10.doi: 10.11985/2022.04.002
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YANG Songyuan1(), TIAN Yong1(), TIAN Jindong1,2()
Received:
2022-08-25
Revised:
2022-10-24
Online:
2022-12-25
Published:
2023-02-03
Contact:
TIAN Yong, E-mail:ytian@szu.edu.cn
CLC Number:
YANG Songyuan, TIAN Yong, TIAN Jindong. State of Health Estimation of Lithium-ion Batteries Based on iCEEMDAN and Transfer Learning[J]. Journal of Electrical Engineering, 2022, 17(4): 2-10.
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[1] | 骆凡, 黄海宏, 王海欣. 基于电化学阻抗谱的退役动力电池荷电状态和健康状态快速预测[J]. 仪器仪表学报, 2021, 42(9):172-180. |
LUO Fan, HUANG Haihong, WANG Haixin. Rapid prediction of the state of charge and state of health of decommissioned power batteries based on electrochemical impedance spectroscopy[J]. Chinese Journal of Scientific Instrument, 2021, 42(9):172-180. | |
[2] |
LI J, ADEWUYI K, LOTFI N, et al. A single particle model with chemical/mechanical degradation physics for lithium-ion battery state of health (SOH) estimation[J]. Applied Energy, 2018, 212:1178-1190.
doi: 10.1016/j.apenergy.2018.01.011 |
[3] |
匡柯, 孙跃东, 任东生, 等. 车用锂离子电池电化学-热耦合高效建模方法[J]. 机械工程学报, 2021, 57(14):10-22.
doi: 10.3901/JME.2021.14.010 |
KUANG Ke, SUN Yuedong, REN Dongsheng, et al. Efficient approach for electrochemical-thermal coupled modeling of large-format lithium-ion power battery[J]. Journal of Mechanical Engineering, 2021, 57(14):10-22.
doi: 10.3901/JME.2021.14.010 |
|
[4] |
贾俊, 胡晓松, 邓忠伟, 等. 数据驱动的锂离子电池健康状态综合评分及异常电池筛选[J]. 机械工程学报, 2021, 57(14):141-149,159.
doi: 10.3901/JME.2021.14.141 |
JIA Jun, HU Xiaosong, DENG Zhongwei, et al. Data-driven comprehensive evaluation of lithium-ion battery state of health and abnormal battery screening[J]. Journal of Mechanical Engineering, 2021, 57(14):141-149,159.
doi: 10.3901/JME.2021.14.141 |
|
[5] |
LI Xiaoyu, YUAN Changgui, LI Xiaohui, et al. State of health estimation for Li-ion battery using incremental capacity analysis and Gaussian process regression[J]. Energy, 2020, 190:116467.
doi: 10.1016/j.energy.2019.116467 |
[6] | 吴铁洲, 刘思哲, 张晓星, 等. 基于FA-BP神经网络的锂离子电池SOH估算[J]. 电池, 2021, 51(1):21-25. |
WU Tiezhou, LIU Sizhe, ZHANG Xiaoxing, et al. SOH estimation of Li-ion battery based on FA-BP neural network[J]. Battery Bimonthly, 2021, 51(1):21-25. | |
[7] | WU Yitao, XUE Qiaoshen, JIANG Wei, et al. State of health estimation for lithium-ion batteries based on healthy features and long short-term memory[J]. IEEE Access, 2020:28533-28547. |
[8] |
WANG Jiwei, DENG Zhongwei, YU Tao, et al. State of health estimation based on modified Gaussian process regression for lithium-ion batteries[J]. Journal of Energy Storage, 2022, 51:104512.
doi: 10.1016/j.est.2022.104512 |
[9] | MA Yan, YAO Meihao, LIU Hongcheng, et al. State of health estimation and remaining useful life prediction for lithium-ion batteries by improved particle swarm optimization-back propagation neural network[J]. Journal of Energy Storage, 2022,52,104750. |
[10] |
GOH H H, LAN Zhentao, ZHANG Dongdong, et al. Estimation of the state of health (SOH) of batteries using discrete curvature feature extraction[J]. Journal of Energy Storage, 2022, 50:104646.
doi: 10.1016/j.est.2022.104646 |
[11] |
MA Yan, SHAN Ce, GAO Jinwu, et al. A novel method for state of health estimation of lithium-ion batteries based on improved LSTM and health indicators extraction[J]. Energy, 2022, 251:123973.
doi: 10.1016/j.energy.2022.123973 |
[12] |
WEN Jianping, CHEN Xing, LI Xianghe, et al. SOH prediction of lithium battery based on IC curve feature and BP neural network[J]. Energy, 2022, 261:125234.
doi: 10.1016/j.energy.2022.125234 |
[13] |
YANG Duo, ZHANG Xu, PAN Rui, et al. A novel gaussian process regression model for state-of-health estimation of lithium-ion battery using charging curve[J]. Journal of Power Sources, 2018, 384:387-395.
doi: 10.1016/j.jpowsour.2018.03.015 |
[14] |
TAN Yandan, ZHAO Guangcai. Transfer learning with long short-term memory network for state-of-health prediction of lithium-ion batteries[J]. IEEE Transactions on Industrial Electronics, 2020, 67(10):8723-8731.
doi: 10.1109/TIE.2019.2946551 |
[15] | National Aeronautics and Space Administration Prognostics Center of Excellence. PCoE datasets[EB/OL]. [2022-10-25]. https://ti.arc.nasa.gov/tech/dash/groups/pcoe/prognostic-data-repository/. |
[16] |
FAN Lingfeng, WANG Ping, CHENG Ze. A remaining capacity estimation approach of lithium-ion batteries based on partial charging curve and health feature fusion[J]. Journal of Energy Storage, 2021, 43:103115.
doi: 10.1016/j.est.2021.103115 |
[17] |
COLOMINAS M A, SCHLOTTHAUER G, TORRES M E. Improved complete ensemble EMD:A suitable tool for biomedical signal processing[J]. Biomedical Signal Processing and Control, 2014, 14:19-29.
doi: 10.1016/j.bspc.2014.06.009 |
[18] |
LIANG Yanhui, LIN Yu, LU Qin. Forecasting gold price using a novel hybrid model with ICEEMDAN and LSTM-CNN-CBAM[J]. Expert Systems with Applications, 2022, 206:117847.
doi: 10.1016/j.eswa.2022.117847 |
[19] |
HE Y, KIM F T. Universities power energy management:A novel hybrid model based on iCEEMDAN and Bayesian optimized LSTM[J]. Energy Reports, 2021, 7:6473-6488.
doi: 10.1016/j.egyr.2021.09.115 |
[20] |
ZHANG Yongzhi, XIONG Rui, HE Hongwen, et al. Long short-term memory recurrent neural network for remaining useful life prediction of lithium-ion batteries[J]. IEEE Transactions on Vehicular Technology, 2018, 67(7):5695-5705.
doi: 10.1109/TVT.2018.2805189 |
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