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

Matthieu Matignon, Mehdi Mcharek, Toufik Azib () and Ahmed Chaibet
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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

Energies, 2025, vol. 18, issue 16, 1-16

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.

Keywords: LSTM; iEMS; FCHEV; fuel cell systems; hybrid vehicles; speed prediction; driving prediction techniques; real-time prediction; fuel efficiency; data-driven AI models (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
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