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Fuel cell energy management strategies (FCEMS): a Word2Vec-driven bibliometric framework for trend mapping and algorithmic advancements

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  • Liu, Kunxiang
  • Liu, Bo
  • Wang, Yu
  • Wang, Haijiang
  • Yang, Jun
  • Zhao, Chen

Abstract

In the global energy transition, hydrogen fuel cells have drawn a lot of attention as a clean energy source. Developing new energy vehicles that rely on hydrogen fuel cells as their primary power source is crucial to reaching net-zero carbon emissions. As the central component and key to the overall operation of new energy vehicles, fuel cell energy management (FCEM) is crucial, particularly for enhancing durability and fuel economy. However, the literature screening process in existing bibliometric studies is often opaque and lacks publicly available criteria, leading to irreproducible findings. To address this, we propose a transparent and reproducible bibliometric framework that integrates an enhanced Word2Vec model for systematic literature screening. Our AI-driven screening method, based on calculating the similarity of titles, abstracts, and keywords, is validated by achieving 91.4751% alignment with the Web of Science (WOS) relevance ranking, offering a quantifiable and automated alternative to opaque screening processes. Using this framework, we systematically analyze the characteristics of FCEMS-related scholarship in terms of publication journals, country geographic distribution, institutional collaborations, author collaborations, and keyword co-occurrence frequencies. The analysis reveals a pattern of policy-associated growth: post-2015, China contributes to 45% of the global FCEM literature, likely benefiting from the national hydrogen energy strategy. Furthermore, we detail FCEMS strategies including rule-based, optimization-based, and learning-based approaches, summarize their research progress in applications such as vehicles, aircraft, and ships, and analyze future research trends from multiple perspectives. This work represents the first integration of bibliometrics with natural language processing (NLP) for algorithmic literature screening, and its inaugural application in the FCEMS domain.

Suggested Citation

  • Liu, Kunxiang & Liu, Bo & Wang, Yu & Wang, Haijiang & Yang, Jun & Zhao, Chen, 2026. "Fuel cell energy management strategies (FCEMS): a Word2Vec-driven bibliometric framework for trend mapping and algorithmic advancements," Applied Energy, Elsevier, vol. 409(C).
    • Handle: RePEc:eee:appene:v:409:y:2026:i:c:s030626192600084x
    DOI: 10.1016/j.apenergy.2026.127432
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