IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v12y2019i12p2247-d239228.html
   My bibliography  Save this article

A Lithium-ion Battery RUL Prediction Method Considering the Capacity Regeneration Phenomenon

Author

Listed:
  • Xiaoqiong Pang

    (School of Data Science and Technology, North University of China, No.3, XueYuan Road, JianCaoPing District, Taiyuan 030051, China)

  • Rui Huang

    (School of Data Science and Technology, North University of China, No.3, XueYuan Road, JianCaoPing District, Taiyuan 030051, China)

  • Jie Wen

    (School of Electrical and Control Engineering, North University of China, No.3, XueYuan Road, JianCaoPing District, Taiyuan 030051, China)

  • Yuanhao Shi

    (School of Electrical and Control Engineering, North University of China, No.3, XueYuan Road, JianCaoPing District, Taiyuan 030051, China)

  • Jianfang Jia

    (School of Electrical and Control Engineering, North University of China, No.3, XueYuan Road, JianCaoPing District, Taiyuan 030051, China)

  • Jianchao Zeng

    (School of Data Science and Technology, North University of China, No.3, XueYuan Road, JianCaoPing District, Taiyuan 030051, China)

Abstract

Prediction of Remaining Useful Life (RUL) of lithium-ion batteries plays a significant role in battery health management. Battery capacity is often chosen as the Health Indicator (HI) in research on lithium-ion battery RUL prediction. In the rest time of batteries, capacity will produce a certain degree of regeneration phenomenon, which exists in the use of each battery. Therefore, considering the capacity regeneration phenomenon in RUL prediction of lithium-ion batteries is helpful to improve the prediction performance of the model. In this paper, a novel method fusing the wavelet decomposition technology (WDT) and the Nonlinear Auto Regressive neural network (NARNN) model for predicting the RUL of a lithium-ion battery is proposed. Firstly, the multi-scale WDT is used to separate the global degradation and local regeneration of a battery capacity series. Then, the RUL prediction framework based on the NARNN model is constructed for the extracted global degradation and local regeneration. Finally, the two parts of the prediction results are combined to obtain the final RUL prediction result. Experiments show that the proposed method can not only effectively capture the capacity regeneration phenomenon, but also has high prediction accuracy and is less affected by different prediction starting points.

Suggested Citation

  • Xiaoqiong Pang & Rui Huang & Jie Wen & Yuanhao Shi & Jianfang Jia & Jianchao Zeng, 2019. "A Lithium-ion Battery RUL Prediction Method Considering the Capacity Regeneration Phenomenon," Energies, MDPI, vol. 12(12), pages 1-14, June.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:12:p:2247-:d:239228
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/12/12/2247/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/12/12/2247/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Nicholas Williard & Wei He & Christopher Hendricks & Michael Pecht, 2013. "Lessons Learned from the 787 Dreamliner Issue on Lithium-Ion Battery Reliability," Energies, MDPI, vol. 6(9), pages 1-14, September.
    2. Yu, Jianbo, 2018. "State of health prediction of lithium-ion batteries: Multiscale logic regression and Gaussian process regression ensemble," Reliability Engineering and System Safety, Elsevier, vol. 174(C), pages 82-95.
    3. Wu, Ji & Zhang, Chenbin & Chen, Zonghai, 2016. "An online method for lithium-ion battery remaining useful life estimation using importance sampling and neural networks," Applied Energy, Elsevier, vol. 173(C), pages 134-140.
    4. Yu Peng & Yandong Hou & Yuchen Song & Jingyue Pang & Datong Liu, 2018. "Lithium-Ion Battery Prognostics with Hybrid Gaussian Process Function Regression," Energies, MDPI, vol. 11(6), pages 1-20, June.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Ma, Qiuhui & Zheng, Ying & Yang, Weidong & Zhang, Yong & Zhang, Hong, 2021. "Remaining useful life prediction of lithium battery based on capacity regeneration point detection," Energy, Elsevier, vol. 234(C).
    2. Jingxi Yang & Matthew Beatty & Dani Strickland & Mina Abedi-Varnosfaderani & Joe Warren, 2023. "Second-Life Battery Capacity Estimation and Method Comparison," Energies, MDPI, vol. 16(7), pages 1-17, April.
    3. Chuang Sun & An Qu & Jun Zhang & Qiyang Shi & Zhenhong Jia, 2022. "Remaining Useful Life Prediction for Lithium-Ion Batteries Based on Improved Variational Mode Decomposition and Machine Learning Algorithm," Energies, MDPI, vol. 16(1), pages 1-15, December.
    4. Felipe Salinas & Julia Kowal, 2020. "Classifying Aged Li-Ion Cells from Notebook Batteries," Sustainability, MDPI, vol. 12(9), pages 1-17, April.
    5. Tang, Ting & Yuan, Huimei, 2022. "A hybrid approach based on decomposition algorithm and neural network for remaining useful life prediction of lithium-ion battery," Reliability Engineering and System Safety, Elsevier, vol. 217(C).
    6. Ling Mao & Jie Xu & Jiajun Chen & Jinbin Zhao & Yuebao Wu & Fengjun Yao, 2020. "A LSTM-STW and GS-LM Fusion Method for Lithium-Ion Battery RUL Prediction Based on EEMD," Energies, MDPI, vol. 13(9), pages 1-13, May.
    7. Bing Long & Xiangnan Li & Xiaoyu Gao & Zhen Liu, 2019. "Prognostics Comparison of Lithium-Ion Battery Based on the Shallow and Deep Neural Networks Model," Energies, MDPI, vol. 12(17), pages 1-13, August.
    8. Yongsheng Shi & Tailin Li & Leicheng Wang & Hongzhou Lu & Yujun Hu & Beichen He & Xinran Zhai, 2023. "A Method for Predicting the Life of Lithium-Ion Batteries Based on Successive Variational Mode Decomposition and Optimized Long Short-Term Memory," Energies, MDPI, vol. 16(16), pages 1-16, August.
    9. Ma, Guijun & Zhang, Yong & Cheng, Cheng & Zhou, Beitong & Hu, Pengchao & Yuan, Ye, 2019. "Remaining useful life prediction of lithium-ion batteries based on false nearest neighbors and a hybrid neural network," Applied Energy, Elsevier, vol. 253(C), pages 1-1.
    10. Chen, Dan & Meng, Jinhao & Huang, Huanyang & Wu, Ji & Liu, Ping & Lu, Jiwu & Liu, Tianqi, 2022. "An Empirical-Data Hybrid Driven Approach for Remaining Useful Life prediction of lithium-ion batteries considering capacity diving," Energy, Elsevier, vol. 245(C).
    11. Xingxing Wang & Peilin Ye & Shengren Liu & Yu Zhu & Yelin Deng & Yinnan Yuan & Hongjun Ni, 2023. "Research Progress of Battery Life Prediction Methods Based on Physical Model," Energies, MDPI, vol. 16(9), pages 1-20, April.
    12. Jianfang Jia & Jianyu Liang & Yuanhao Shi & Jie Wen & Xiaoqiong Pang & Jianchao Zeng, 2020. "SOH and RUL Prediction of Lithium-Ion Batteries Based on Gaussian Process Regression with Indirect Health Indicators," Energies, MDPI, vol. 13(2), pages 1-20, January.
    13. Rauf, Huzaifa & Khalid, Muhammad & Arshad, Naveed, 2022. "Machine learning in state of health and remaining useful life estimation: Theoretical and technological development in battery degradation modelling," Renewable and Sustainable Energy Reviews, Elsevier, vol. 156(C).
    14. Marcin Witczak & Marcin Mrugalski & Bogdan Lipiec, 2021. "Remaining Useful Life Prediction of MOSFETs via the Takagi–Sugeno Framework," Energies, MDPI, vol. 14(8), pages 1-23, April.
    15. Shaheer Ansari & Afida Ayob & Molla Shahadat Hossain Lipu & Aini Hussain & Mohamad Hanif Md Saad, 2021. "Multi-Channel Profile Based Artificial Neural Network Approach for Remaining Useful Life Prediction of Electric Vehicle Lithium-Ion Batteries," Energies, MDPI, vol. 14(22), pages 1-22, November.
    16. Shaheer Ansari & Afida Ayob & Molla Shahadat Hossain Lipu & Aini Hussain & Mohamad Hanif Md Saad, 2021. "Data-Driven Remaining Useful Life Prediction for Lithium-Ion Batteries Using Multi-Charging Profile Framework: A Recurrent Neural Network Approach," Sustainability, MDPI, vol. 13(23), pages 1-25, December.
    17. Martín Antonio Rodríguez Licea & Francisco Javier Pérez Pinal & Allan Giovanni Soriano Sánchez, 2021. "An Overview on Electric-Stress Degradation Empirical Models for Electrochemical Devices in Smart Grids," Energies, MDPI, vol. 14(8), pages 1-23, April.
    18. Hong, Joonki & Lee, Dongheon & Jeong, Eui-Rim & Yi, Yung, 2020. "Towards the swift prediction of the remaining useful life of lithium-ion batteries with end-to-end deep learning," Applied Energy, Elsevier, vol. 278(C).
    19. Tianfei Sun & Bizhong Xia & Yifan Liu & Yongzhi Lai & Weiwei Zheng & Huawen Wang & Wei Wang & Mingwang Wang, 2019. "A Novel Hybrid Prognostic Approach for Remaining Useful Life Estimation of Lithium-Ion Batteries," Energies, MDPI, vol. 12(19), pages 1-22, September.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Rauf, Huzaifa & Khalid, Muhammad & Arshad, Naveed, 2022. "Machine learning in state of health and remaining useful life estimation: Theoretical and technological development in battery degradation modelling," Renewable and Sustainable Energy Reviews, Elsevier, vol. 156(C).
    2. Sui, Xin & He, Shan & Vilsen, Søren B. & Meng, Jinhao & Teodorescu, Remus & Stroe, Daniel-Ioan, 2021. "A review of non-probabilistic machine learning-based state of health estimation techniques for Lithium-ion battery," Applied Energy, Elsevier, vol. 300(C).
    3. Chen, Dinghong & Zhang, Weige & Zhang, Caiping & Sun, Bingxiang & Cong, XinWei & Wei, Shaoyuan & Jiang, Jiuchun, 2022. "A novel deep learning-based life prediction method for lithium-ion batteries with strong generalization capability under multiple cycle profiles," Applied Energy, Elsevier, vol. 327(C).
    4. Liu, Kailong & Ashwin, T.R. & Hu, Xiaosong & Lucu, Mattin & Widanage, W. Dhammika, 2020. "An evaluation study of different modelling techniques for calendar ageing prediction of lithium-ion batteries," Renewable and Sustainable Energy Reviews, Elsevier, vol. 131(C).
    5. Jianfang Jia & Jianyu Liang & Yuanhao Shi & Jie Wen & Xiaoqiong Pang & Jianchao Zeng, 2020. "SOH and RUL Prediction of Lithium-Ion Batteries Based on Gaussian Process Regression with Indirect Health Indicators," Energies, MDPI, vol. 13(2), pages 1-20, January.
    6. Gui, Yonghao & Wei, Baoze & Li, Mingshen & Guerrero, Josep M. & Vasquez, Juan C., 2018. "Passivity-based coordinated control for islanded AC microgrid," Applied Energy, Elsevier, vol. 229(C), pages 551-561.
    7. Wang, Fu-Kwun & Amogne, Zemenu Endalamaw & Chou, Jia-Hong & Tseng, Cheng, 2022. "Online remaining useful life prediction of lithium-ion batteries using bidirectional long short-term memory with attention mechanism," Energy, Elsevier, vol. 254(PB).
    8. Li, Yi & Liu, Kailong & Foley, Aoife M. & Zülke, Alana & Berecibar, Maitane & Nanini-Maury, Elise & Van Mierlo, Joeri & Hoster, Harry E., 2019. "Data-driven health estimation and lifetime prediction of lithium-ion batteries: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 113(C), pages 1-1.
    9. Wei, Yupeng & Wu, Dazhong & Terpenny, Janis, 2024. "Remaining useful life prediction using graph convolutional attention networks with temporal convolution-aware nested residual connections," Reliability Engineering and System Safety, Elsevier, vol. 242(C).
    10. Zhang, Jiusi & Jiang, Yuchen & Li, Xiang & Huo, Mingyi & Luo, Hao & Yin, Shen, 2022. "An adaptive remaining useful life prediction approach for single battery with unlabeled small sample data and parameter uncertainty," Reliability Engineering and System Safety, Elsevier, vol. 222(C).
    11. Jun Peng & Zhiyong Zheng & Xiaoyong Zhang & Kunyuan Deng & Kai Gao & Heng Li & Bin Chen & Yingze Yang & Zhiwu Huang, 2020. "A Data-Driven Method with Feature Enhancement and Adaptive Optimization for Lithium-Ion Battery Remaining Useful Life Prediction," Energies, MDPI, vol. 13(3), pages 1-20, February.
    12. Shahjalal, Mohammad & Roy, Probir Kumar & Shams, Tamanna & Fly, Ashley & Chowdhury, Jahedul Islam & Ahmed, Md. Rishad & Liu, Kailong, 2022. "A review on second-life of Li-ion batteries: prospects, challenges, and issues," Energy, Elsevier, vol. 241(C).
    13. Wang, Yixiu & Zhu, Jiangong & Cao, Liang & Gopaluni, Bhushan & Cao, Yankai, 2023. "Long Short-Term Memory Network with Transfer Learning for Lithium-ion Battery Capacity Fade and Cycle Life Prediction," Applied Energy, Elsevier, vol. 350(C).
    14. Cao, Mengda & Zhang, Tao & Liu, Yajie & Zhang, Yajun & Wang, Yu & Li, Kaiwen, 2022. "An ensemble learning prognostic method for capacity estimation of lithium-ion batteries based on the V-IOWGA operator," Energy, Elsevier, vol. 257(C).
    15. Suchandra Paul, 2018. "Crisis in Boeing 787 Dreamliner: An Investigation from Project Management Control Perspective," International Journal of Human Resource Studies, Macrothink Institute, vol. 8(4), pages 242251-2422, December.
    16. Jin, Changyong & Sun, Yuedong & Wang, Huaibin & Zheng, Yuejiu & Wang, Shuyu & Rui, Xinyu & Xu, Chengshan & Feng, Xuning & Wang, Hewu & Ouyang, Minggao, 2022. "Heating power and heating energy effect on the thermal runaway propagation characteristics of lithium-ion battery module: Experiments and modeling," Applied Energy, Elsevier, vol. 312(C).
    17. Wei, Yupeng & Wu, Dazhong, 2023. "Prediction of state of health and remaining useful life of lithium-ion battery using graph convolutional network with dual attention mechanisms," Reliability Engineering and System Safety, Elsevier, vol. 230(C).
    18. Yong Tian & Qianyuan Dong & Jindong Tian & Xiaoyu Li, 2023. "Capacity Estimation of Lithium-Ion Batteries Based on Multiple Small Voltage Sections and BP Neural Networks," Energies, MDPI, vol. 16(2), pages 1-18, January.
    19. Li, Penghua & Zhang, Zijian & Grosu, Radu & Deng, Zhongwei & Hou, Jie & Rong, Yujun & Wu, Rui, 2022. "An end-to-end neural network framework for state-of-health estimation and remaining useful life prediction of electric vehicle lithium batteries," Renewable and Sustainable Energy Reviews, Elsevier, vol. 156(C).
    20. Yi Wu & Saurabh Saxena & Yinjiao Xing & Youren Wang & Chuan Li & Winco K. C. Yung & Michael Pecht, 2018. "Analysis of Manufacturing-Induced Defects and Structural Deformations in Lithium-Ion Batteries Using Computed Tomography," Energies, MDPI, vol. 11(4), pages 1-22, April.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:12:y:2019:i:12:p:2247-:d:239228. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.