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Enhancing Fuel Cell Hybrid Electric Vehicle Energy Management with Real-Time LSTM Speed Prediction

Author

Listed:
  • Matthieu Matignon

    (ESTACA, ESTACA’Lab–Paris-Saclay, 78180 Montigny-le-Bretonneux, France)

  • Mehdi Mcharek

    (ESTACA, ESTACA’Lab–Paris-Saclay, 78180 Montigny-le-Bretonneux, France)

  • Toufik Azib

    (ESTACA, ESTACA’Lab–Paris-Saclay, 78180 Montigny-le-Bretonneux, France)

  • Ahmed Chaibet

    (DRIVE Nevers, Université de Bourgogne, 58027 Nevers, France)

Abstract

This paper presents an innovative approach to optimize real-time energy management in fuel cell electric vehicles (FCEVs) through an integrated EMS (iEMS) framework based on a nested concept. Central to our method are two LSTM-based speed prediction models, trained and validated on open-source datasets to enhance adaptability and efficiency. The first model, trained on a 27 h real-time database, is embedded within the iEMS for dynamic real-time operation. The second model assesses the impact of incorporating external traffic data on the prediction accuracy, offering a systematic approach to refining speed prediction models. The results demonstrate significant improvements in fuel efficiency and overall performance compared to existing models. This study highlights the promise of data-driven AI models in next-generation FCEV energy management, contributing to smarter and more sustainable mobility solutions.

Suggested Citation

  • Matthieu Matignon & Mehdi Mcharek & Toufik Azib & Ahmed Chaibet, 2025. "Enhancing Fuel Cell Hybrid Electric Vehicle Energy Management with Real-Time LSTM Speed Prediction," Energies, MDPI, vol. 18(16), pages 1-16, August.
    • Handle: RePEc:gam:jeners:v:18:y:2025:i:16:p:4340-:d:1724726
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