IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v12y2024i19p2959-d1484097.html
   My bibliography  Save this article

Machine Learning Prediction of Fuel Cell Remaining Life Enhanced by Variational Mode Decomposition and Improved Whale Optimization Algorithm

Author

Listed:
  • Zerong Huang

    (Huizhou Power Supply Bureau, Guangdong Power Grid Corporation, Huizhou 516000, China)

  • Daxing Zhang

    (Huizhou Power Supply Bureau, Guangdong Power Grid Corporation, Huizhou 516000, China)

  • Xiangdong Wang

    (Huizhou Power Supply Bureau, Guangdong Power Grid Corporation, Huizhou 516000, China)

  • Xiaolong Huang

    (Huizhou Power Supply Bureau, Guangdong Power Grid Corporation, Huizhou 516000, China)

  • Chunsheng Wang

    (School of Automation, Central South University, Changsha 410083, China)

  • Liqing Liao

    (School of Automation, Central South University, Changsha 410083, China)

  • Yaolin Dong

    (School of Automation, Central South University, Changsha 410083, China)

  • Xiaoshuang Hou

    (School of Automation, Central South University, Changsha 410083, China)

  • Yuan Cao

    (School of Automation, Central South University, Changsha 410083, China)

  • Xinyao Zhou

    (School of Automation, Central South University, Changsha 410083, China)

Abstract

In predicting the remaining lifespan of Proton Exchange Membrane Fuel Cells (PEMFC), it is crucial to accurately capture the multi-scale variations in cell performance. This study employs Variational Mode Decomposition (VMD) to decompose performance data into intrinsic modes, elucidating critical multi-scale dynamics vital for understanding the complex degradation processes in fuel cells. In addition to VMD, this research utilizes an Improved Whale Optimization Algorithm (IWOA) to optimize a Back Propagation (BP) Neural Network. The IWOA focuses on precise adjustments of weights and biases, enabling the BP network to effectively interpret complex nonlinear relationships within the dataset. This optimization enhances the predictive model’s reliability and stability. Extensive experimental evaluations demonstrate that the integration of VMD, and the learning capabilities of the IWOA-optimized BP network significantly improves the model’s accuracy and stability across multiple predictions, thereby increasing the reliability of lifespan predictions for PEMFCs. This methodology offers a robust framework for extending the operational life and efficiency of fuel cells.

Suggested Citation

  • Zerong Huang & Daxing Zhang & Xiangdong Wang & Xiaolong Huang & Chunsheng Wang & Liqing Liao & Yaolin Dong & Xiaoshuang Hou & Yuan Cao & Xinyao Zhou, 2024. "Machine Learning Prediction of Fuel Cell Remaining Life Enhanced by Variational Mode Decomposition and Improved Whale Optimization Algorithm," Mathematics, MDPI, vol. 12(19), pages 1-16, September.
    • Handle: RePEc:gam:jmathe:v:12:y:2024:i:19:p:2959-:d:1484097
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/12/19/2959/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2227-7390/12/19/2959/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Ruifeng Guo & Dongfang Chen & Yuehua Li & Wenlong Wu & Song Hu & Xiaoming Xu, 2023. "Anode Nitrogen Concentration Estimation Based on Voltage Variation Characteristics for Proton Exchange Membrane Fuel Cell Stacks," Energies, MDPI, vol. 16(5), pages 1-16, February.
    2. Zhang, Caizhi & Zhang, Yuqi & Wang, Lei & Deng, Xiaozhi & Liu, Yang & Zhang, Jiujun, 2023. "A health management review of proton exchange membrane fuel cell for electric vehicles: Failure mechanisms, diagnosis techniques and mitigation measures," Renewable and Sustainable Energy Reviews, Elsevier, vol. 182(C).
    3. Liu, Ze & Xu, Sichuan & Zhao, Honghui & Wang, Yupeng, 2022. "Durability estimation and short-term voltage degradation forecasting of vehicle PEMFC system: Development and evaluation of machine learning models," Applied Energy, Elsevier, vol. 326(C).
    4. Li, Bing & Wan, Kechuang & Xie, Meng & Chu, Tiankuo & Wang, Xiaolei & Li, Xiang & Yang, Daijun & Ming, Pingwen & Zhang, Cunman, 2022. "Durability degradation mechanism and consistency analysis for proton exchange membrane fuel cell stack," Applied Energy, Elsevier, vol. 314(C).
    5. Xueshuang Ren & Xin Zhang & Teng Teng & Congxin Li, 2020. "Research on Estimation Method of Fuel Cell Health State Based on Lumped Parameter Model," Energies, MDPI, vol. 13(23), pages 1-13, December.
    Full references (including those not matched with items on IDEAS)

    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. Cabello González, G.M. & Toharias, Baltasar & Iranzo, Alfredo & Suárez, Christian & Rosa, Felipe, 2023. "Voltage distribution analysis and non-uniformity assessment in a 100 cm2 PEM fuel cell stack," Energy, Elsevier, vol. 282(C).
    2. Yang, Yue & Yuan, Songmei & Liu, Jieyuan & Zhang, Zikang & Lu, Tie, 2024. "Effect of catalyst ink particle size on the structure of the catalyst layer and electrical performance in the process of ultrasonic spray manufacturing PEMFCs," Energy, Elsevier, vol. 294(C).
    3. Zenan Shen & Shaoquan Liu & Wei Zhu & Daoyuan Ren & Qiang Xu & Yu Feng, 2024. "A Review on Key Technologies and Developments of Hydrogen Fuel Cell Multi-Rotor Drones," Energies, MDPI, vol. 17(16), pages 1-36, August.
    4. Tao, Zihan & Zhang, Chu & Xiong, Jinlin & Hu, Haowen & Ji, Jie & Peng, Tian & Nazir, Muhammad Shahzad, 2023. "Evolutionary gate recurrent unit coupling convolutional neural network and improved manta ray foraging optimization algorithm for performance degradation prediction of PEMFC," Applied Energy, Elsevier, vol. 336(C).
    5. Jia, Fei & Tian, Xiaodi & Liu, Fengfeng & Ye, Junjie & Yang, Chengpeng, 2023. "Oxidant starvation under various operating conditions on local and transient performance of proton exchange membrane fuel cells," Applied Energy, Elsevier, vol. 331(C).
    6. Chuang Sheng & Yi Zheng & Rui Tian & Qian Xiang & Zhonghua Deng & Xiaowei Fu & Xi Li, 2023. "A Comparative Study of the Kalman Filter and the LSTM Network for the Remaining Useful Life Prediction of SOFC," Energies, MDPI, vol. 16(9), pages 1-16, April.
    7. Lopez-Juarez, M. & Rockstroh, T. & Novella, R. & Vijayagopal, R., 2024. "A methodology to develop multi-physics dynamic fuel cell system models validated with vehicle realistic drive cycle data," Applied Energy, Elsevier, vol. 358(C).
    8. Zhao, Lei & Hong, Jichao & Xie, Jiaping & Jiang, Shangfeng & Wei, Xuezhe & Ming, Pingwen & Dai, Haifeng, 2023. "Investigation of local sensitivity for vehicle-oriented fuel cell stacks based on electrochemical impedance spectroscopy," Energy, Elsevier, vol. 262(PA).
    9. Liu, Yang & Zhao, Junjie & Tu, Zhengkai, 2024. "Detecting performance degradation in a dead-ended hydrogen-oxygen proton exchange membrane fuel cell used for an unmanned underwater vehicle," Renewable Energy, Elsevier, vol. 222(C).
    10. Lu, Guolong & Liu, Mingxin & Su, Xunkang & Zheng, Tongxi & Luan, Yang & Fan, Wenxuan & Cui, Hao & Liu, Zhenning, 2024. "Study on counter-flow mass transfer characteristics and performance optimization of commercial large-scale proton exchange membrane fuel cells," Applied Energy, Elsevier, vol. 359(C).
    11. Zuo, Jian & Steiner, Nadia Yousfi & Li, Zhongliang & Hissel, Daniel, 2024. "Health management review for fuel cells: Focus on action phase," Renewable and Sustainable Energy Reviews, Elsevier, vol. 201(C).
    12. Ying Tian & Qiang Zou & Jin Han, 2021. "Data-Driven Fault Diagnosis for Automotive PEMFC Systems Based on the Steady-State Identification," Energies, MDPI, vol. 14(7), pages 1-17, March.
    13. Zhang, Yuqi & Li, Yu & Zhang, Caizhi & Yang, Yunzi & Yu, Xingzi & Niu, Tong & Wang, Lei & Wang, Gucheng, 2024. "Intelligent diagnosis of proton exchange membrane fuel cell water states based on flooding-specificity experiment and deep learning method," Renewable Energy, Elsevier, vol. 222(C).
    14. Aihua Tang & Yuanhang Yang & Quanqing Yu & Zhigang Zhang & Lin Yang, 2022. "A Review of Life Prediction Methods for PEMFCs in Electric Vehicles," Sustainability, MDPI, vol. 14(16), pages 1-18, August.
    15. Gong, Zhichao & Wang, Bowen & Xu, Yifan & Ni, Meng & Gao, Qingchen & Hou, Zhongjun & Cai, Jun & Gu, Xin & Yuan, Xinjie & Jiao, Kui, 2022. "Adaptive optimization strategy of air supply for automotive polymer electrolyte membrane fuel cell in life cycle," Applied Energy, Elsevier, vol. 325(C).
    16. Chen, Dongfang & Wu, Wenlong & Chang, Kuanyu & Li, Yuehua & Pei, Pucheng & Xu, Xiaoming, 2023. "Performance degradation prediction method of PEM fuel cells using bidirectional long short-term memory neural network based on Bayesian optimization," Energy, Elsevier, vol. 285(C).
    17. Brindha Ramasubramanian & Rayavarapu Prasada Rao & Vijila Chellappan & Seeram Ramakrishna, 2022. "Towards Sustainable Fuel Cells and Batteries with an AI Perspective," Sustainability, MDPI, vol. 14(23), pages 1-27, November.
    18. Pei, Houchang & Xiao, Chenguang & Tu, Zhengkai, 2022. "Experimental study on liquid water formation characteristics in a novel transparent proton exchange membrane fuel cell," Applied Energy, Elsevier, vol. 321(C).
    19. Jeon, Kyung-Won & Gong, Ji-Hyeon & Kim, Min-Ju & Shim, Jae-Oh & Jang, Won-Jun & Roh, Hyun-Seog, 2024. "Review on the production of renewable biofuel: Solvent-free deoxygenation," Renewable and Sustainable Energy Reviews, Elsevier, vol. 195(C).
    20. Dan Wang & Haitao Min & Weiyi Sun & Bin Zeng & Haiwen Wu, 2023. "Durability Study of Frequent Dry–Wet Cycle on Proton Exchange Membrane Fuel Cell," Energies, MDPI, vol. 16(11), pages 1-10, May.

    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:jmathe:v:12:y:2024:i:19:p:2959-:d:1484097. 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.