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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
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    References listed on IDEAS

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    1. 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).
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    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.
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    Cited by:

    1. Tamilarasan, Sathesh & Wang, Chong-Kai & Kuan, Yean-Der & Shih, Yang-Cheng & Stachiv, Ivo, 2026. "Machine learning as a catalyst for PEMFC optimization: A comprehensive review from flow fields to system integration with a multiscale perspective on research and applications," Renewable and Sustainable Energy Reviews, Elsevier, vol. 226(PA).
    2. Elkholy, Mohamed & Boureima, Abdoulaye & Kim, Jaeyeon & Aziz, Muhammad, 2025. "Data-driven modeling and prediction of PEM fuel cell voltage response to load transients for energy applications," Energy, Elsevier, vol. 335(C).
    3. Deng, Zhihua & Miao, Bin & Chen, Qihong & Chen, Jian & Tong, Chengguang & Liu, Hao & Deendarlianto, & Suwarno, & Wang, Haijiang & Chan, Siew Hwa, 2026. "Degradation prediction and remaining useful life estimation of PEMFCs: Mechanisms, methods, datasets, and challenges," Renewable and Sustainable Energy Reviews, Elsevier, vol. 229(C).

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