IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v13y2020i9p2380-d356008.html
   My bibliography  Save this article

A LSTM-STW and GS-LM Fusion Method for Lithium-Ion Battery RUL Prediction Based on EEMD

Author

Listed:
  • Ling Mao

    (School of Electrical Engineering, Shanghai University of Electric Power, No. 2588, Changyang Road, Yangpu District, Shanghai 200090, China)

  • Jie Xu

    (School of Electrical Engineering, Shanghai University of Electric Power, No. 2588, Changyang Road, Yangpu District, Shanghai 200090, China)

  • Jiajun Chen

    (Pegasus Power Energy Co., Ltd., Hangzhou 310019, China)

  • Jinbin Zhao

    (School of Electrical Engineering, Shanghai University of Electric Power, No. 2588, Changyang Road, Yangpu District, Shanghai 200090, China)

  • Yuebao Wu

    (School of Electrical Engineering, Shanghai University of Electric Power, No. 2588, Changyang Road, Yangpu District, Shanghai 200090, China)

  • Fengjun Yao

    (School of Automation, Shanghai University of Electric Power, No.2588, Changyang Road, Yangpu District, Shanghai 200090, China)

Abstract

To address inaccurate prediction in remaining useful life (RUL) in current Lithium-ion batteries, this paper develops a Long Short-Term Memory Network, Sliding Time Window (LSTM-STW) and Gaussian or Sine function, Levenberg-Marquardt algorithm (GS-LM) fusion batteries RUL prediction method based on ensemble empirical mode decomposition (EEMD). Firstly, EEMD is used to decompose the original data into high-frequency and low-frequency components. Secondly, LSTM-STW and GS-LM are used to predict the high-frequency and low-frequency components, respectively. Finally, the LSTM-STW and GS-LM prediction results are effectively integrated in order to obtain the final prediction of the lithium-ion battery RUL results. This article takes the lithium-ion battery data published by NASA as input. The experimental results show that the method has higher accuracy, including the phenomenon of sudden capacity increase, and is less affected by the prediction starting point. The performance of the proposed method is better than other typical battery RUL prediction methods.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:9:p:2380-:d:356008
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/13/9/2380/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/13/9/2380/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. 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.
    2. Zheng, Xiujuan & Fang, Huajing, 2015. "An integrated unscented kalman filter and relevance vector regression approach for lithium-ion battery remaining useful life and short-term capacity prediction," Reliability Engineering and System Safety, Elsevier, vol. 144(C), pages 74-82.
    3. Xiaodong Xu & Chuanqiang Yu & Shengjin Tang & Xiaoyan Sun & Xiaosheng Si & Lifeng Wu, 2019. "Remaining Useful Life Prediction of Lithium-Ion Batteries Based on Wiener Processes with Considering the Relaxation Effect," Energies, MDPI, vol. 12(9), pages 1-17, May.
    4. Pei Wang & Xue Dan & Yong Yang, 2019. "A multi-scale fusion prediction method for lithium-ion battery capacity based on ensemble empirical mode decomposition and nonlinear autoregressive neural networks," International Journal of Distributed Sensor Networks, , vol. 15(3), pages 15501477198, March.
    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. Can Ding & Yiyuan Zhou & Qingchang Ding & Kaiming Li, 2022. "Integrated Carbon-Capture-Based Low-Carbon Economic Dispatch of Power Systems Based on EEMD-LSTM-SVR Wind Power Forecasting," Energies, MDPI, vol. 15(5), pages 1-27, February.
    2. Yao Ahoutou & Adrian Ilinca & Mohamad Issa, 2022. "Electrochemical Cells and Storage Technologies to Increase Renewable Energy Share in Cold Climate Conditions—A Critical Assessment," Energies, MDPI, vol. 15(4), pages 1-30, February.
    3. Konstantin Zadiran & Maxim Shcherbakov, 2023. "New Method of Degradation Process Identification for Reliability-Centered Maintenance of Energy Equipment," Energies, MDPI, vol. 16(2), pages 1-21, January.
    4. Jikai Bi & Jae-Cheon Lee & Hao Liu, 2022. "Performance Comparison of Long Short-Term Memory and a Temporal Convolutional Network for State of Health Estimation of a Lithium-Ion Battery using Its Charging Characteristics," Energies, MDPI, vol. 15(7), pages 1-24, March.
    5. Zhonghua Yun & Wenhu Qin & Weipeng Shi & Peng Ping, 2020. "State-of-Health Prediction for Lithium-Ion Batteries Based on a Novel Hybrid Approach," Energies, MDPI, vol. 13(18), pages 1-22, September.
    6. 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).

    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. 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).
    2. 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).
    3. 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.
    4. 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.
    5. Yang, Yixin, 2021. "A machine-learning prediction method of lithium-ion battery life based on charge process for different applications," Applied Energy, Elsevier, vol. 292(C).
    6. Felipe Salinas & Julia Kowal, 2020. "Classifying Aged Li-Ion Cells from Notebook Batteries," Sustainability, MDPI, vol. 12(9), pages 1-17, April.
    7. 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.
    8. 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.
    9. Xu, Xiaodong & Tang, Shengjin & Yu, Chuanqiang & Xie, Jian & Han, Xuebing & Ouyang, Minggao, 2021. "Remaining Useful Life Prediction of Lithium-ion Batteries Based on Wiener Process Under Time-Varying Temperature Condition," Reliability Engineering and System Safety, Elsevier, vol. 214(C).
    10. Cai, Yishan & Yang, Lin & Deng, Zhongwei & Zhao, Xiaowei & Deng, Hao, 2018. "Online identification of lithium-ion battery state-of-health based on fast wavelet transform and cross D-Markov machine," Energy, Elsevier, vol. 147(C), pages 621-635.
    11. 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).
    12. E. Skordilis & R. Moghaddass, 2017. "A condition monitoring approach for real-time monitoring of degrading systems using Kalman filter and logistic regression," International Journal of Production Research, Taylor & Francis Journals, vol. 55(19), pages 5579-5596, October.
    13. Xu, Xin & Chen, Nan, 2017. "A state-space-based prognostics model for lithium-ion battery degradation," Reliability Engineering and System Safety, Elsevier, vol. 159(C), pages 47-57.
    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. 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.
    16. 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.
    17. 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).
    18. Haipeng Pan & Chengte Chen & Minming Gu, 2022. "A Method for Predicting the Remaining Useful Life of Lithium Batteries Considering Capacity Regeneration and Random Fluctuations," Energies, MDPI, vol. 15(7), pages 1-15, March.
    19. Xiaodong Xu & Chuanqiang Yu & Shengjin Tang & Xiaoyan Sun & Xiaosheng Si & Lifeng Wu, 2019. "Remaining Useful Life Prediction of Lithium-Ion Batteries Based on Wiener Processes with Considering the Relaxation Effect," Energies, MDPI, vol. 12(9), pages 1-17, May.
    20. Wang, Xiaofei & Wang, Bing Xing & Jiang, Pei Hua & Hong, Yili, 2020. "Accurate reliability inference based on Wiener process with random effects for degradation data," Reliability Engineering and System Safety, Elsevier, vol. 193(C).

    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:13:y:2020:i:9:p:2380-:d:356008. 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.