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<p>Energy management in fuel cell vehicles (FCVs) remains a major challenge, affecting hydrogen utilization, system lifespan, and overall efficiency. Traditional FCVs require additional batteries or ultra-capacitors to stabilize voltage during dynamic load conditions, which increases cost and environmental burden. This study aims to evaluate a battery-free FCV architecture that eliminates the need for auxiliary energy storage by optimizing DC-DC converter and integrating energy management to improve voltage regulation, reduce system cost, and enhance hydrogen utilization. The proposed system integrates a DC-DC converter, whose control factors are optimized utilizing Gazelle Optimization Algorithm (GOA) to achieve cost minimization and voltage stability. A Hybrid Graph Convolutional Neural Network (HGCNN) is also employed to enhance predictive energy management, improving hydrogen economy and extending fuel cell life. This combined approach is named as GOA-HGCNN. The approach is simulated using MATLAB and findings demonstrate that proposed GOA-HGCNN technique achieves substantial reduction in FC cost compared to various existing methods. The method also ensures stable voltage regulation under varying operating conditions, reduces torque ripple and improves efficiency without requiring lithium-ion batteries or super capacitors. The proposed GOA-HGCNN hybrid control strategy delivers a scalable, cost-effective, and sustainable solution for battery-free FCVs. By eliminating battery dependency and enhancing hydrogen utilization, the study supports long-term system reliability, reduced environmental impact, and alignment with clean mobility objectives.</p>

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Innovative hybrid approach for clean, battery-free fuel cell vehicles with optimized DC-DC conversion

  • 期刊论文

  • 发表日期：

    2026年2月19日

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  • Rajesh G. Bodkhe,
  • Priyesh Kumar Pandey,
  • Anupam Agrawal,
  • Sarath Chandran M. C

摘要

Energy management in fuel cell vehicles (FCVs) remains a major challenge, affecting hydrogen utilization, system lifespan, and overall efficiency. Traditional FCVs require additional batteries or ultra-capacitors to stabilize voltage during dynamic load conditions, which increases cost and environmental burden. This study aims to evaluate a battery-free FCV architecture that eliminates the need for auxiliary energy storage by optimizing DC-DC converter and integrating energy management to improve voltage regulation, reduce system cost, and enhance hydrogen utilization. The proposed system integrates a DC-DC converter, whose control factors are optimized utilizing Gazelle Optimization Algorithm (GOA) to achieve cost minimization and voltage stability. A Hybrid Graph Convolutional Neural Network (HGCNN) is also employed to enhance predictive energy management, improving hydrogen economy and extending fuel cell life. This combined approach is named as GOA-HGCNN. The approach is simulated using MATLAB and findings demonstrate that proposed GOA-HGCNN technique achieves substantial reduction in FC cost compared to various existing methods. The method also ensures stable voltage regulation under varying operating conditions, reduces torque ripple and improves efficiency without requiring lithium-ion batteries or super capacitors. The proposed GOA-HGCNN hybrid control strategy delivers a scalable, cost-effective, and sustainable solution for battery-free FCVs. By eliminating battery dependency and enhancing hydrogen utilization, the study supports long-term system reliability, reduced environmental impact, and alignment with clean mobility objectives.