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An event-triggered and dimension learning scheme WOA for PEMFC modeling and parameter identification

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  • Sun, Zhe
  • Wang, Yiwen
  • Xie, Xiangpeng
  • Yang, Qiming
  • Bi, Yunrui
  • Sun, Zhixin

Abstract

Aiming at the high dimension and complexity of parameters identification problem, a whale optimization algorithm based on event-triggered and dimension learning scheme (EDWOA) is presented, which is specifically designed for the proton exchange membrane fuel cell (PEMFC) model. Drawing inspiration from group optimization strategies, a novel dimensional learning method is introduced to enhance the dynamic search capabilities of the algorithm. To assess the efficacy of the proposed algorithm, benchmark function testing was conducted, and its fitness surpassed common heuristic algorithms on over 15 objective functions. The results clearly indicate that the EDWOA algorithm outperforms its counterparts in terms of global search performance. Its ability to navigate complex search spaces sets it apart from other algorithms. Finally, the proposed EDWOA algorithm is successfully applied to parameter identification in the PEMFC model. Through a comparative analysis with existing research findings, it was found that the identified PEMFC model exhibited a notable enhancement in fitness, ranging from 0.02 to 0.93. This underscores the effectiveness of the EDWOA algorithm in improving the performance and dynamic output of PEMFC models.

Suggested Citation

  • Sun, Zhe & Wang, Yiwen & Xie, Xiangpeng & Yang, Qiming & Bi, Yunrui & Sun, Zhixin, 2024. "An event-triggered and dimension learning scheme WOA for PEMFC modeling and parameter identification," Energy, Elsevier, vol. 305(C).
    • Handle: RePEc:eee:energy:v:305:y:2024:i:c:s0360544224021261
    DOI: 10.1016/j.energy.2024.132352
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    References listed on IDEAS

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    1. Sun, Zhe & Cao, Dan & Ling, Yawen & Xiang, Feng & Sun, Zhixin & Wu, Fan, 2021. "Proton exchange membrane fuel cell model parameter identification based on dynamic differential evolution with collective guidance factor algorithm," Energy, Elsevier, vol. 216(C).
    2. Lei, Gang & Zheng, Hualin & Zhang, Jun & Siong Chin, Cheng & Xu, Xinhai & Zhou, Weijiang & Zhang, Caizhi, 2023. "Analyzing characteristic and modeling of high-temperature proton exchange membrane fuel cells with CO poisoning effect," Energy, Elsevier, vol. 282(C).
    3. Yuan, Yi & Chen, Li & Lyu, Xingbao & Ning, Wenjing & Liu, Wenqi & Tao, Wen-Quan, 2024. "Modeling and optimization of a residential PEMFC-based CHP system under different operating modes," Applied Energy, Elsevier, vol. 353(PA).
    4. Zhang, Bo & Wang, Rongjie & Jiang, Desong & Wang, Yichun & lin, Anhui & Wang, Jianfeng & Ruan, Bingcong, 2023. "Parameter identification of proton exchange membrane fuel cell based on swarm intelligence algorithm," Energy, Elsevier, vol. 283(C).
    5. Dimitrova, Zlatina & Nader, Wissam Bou, 2022. "PEM fuel cell as an auxiliary power unit for range extended hybrid electric vehicles," Energy, Elsevier, vol. 239(PA).
    6. Cui, Binghan & Wang, Han & Li, Renlong & Xiang, Lizhi & Zhao, Huaian & Xiao, Rang & Li, Sai & Liu, Zheng & Yin, Geping & Cheng, Xinqun & Ma, Yulin & Huo, Hua & Zuo, Pengjian & Lu, Taolin & Xie, Jingyi, 2024. "Ultra-early prediction of lithium-ion battery performance using mechanism and data-driven fusion model," Applied Energy, Elsevier, vol. 353(PA).
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