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Energies
Volume 18
Issue 22
10.3390/en18225969
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Article

Performance Analysis of Offline Data-Driven Methods for Estimating the State of Charge of Metal Hydride Tanks

by
Amina Yahia
,
Djafar Chabane
,
Salah Laghrouche
,
Abdoul N’Diaye
and
Abdesslem Djerdir
*
Université Marie et Louis Pasteur, UTBM, CNRS, Institut FEMTO-ST, FCLAB, F-90000 Belfort, France
*
Author to whom correspondence should be addressed.
Energies 2025, 18(22), 5969; https://doi.org/10.3390/en18225969
Submission received: 10 October 2025 / Revised: 2 November 2025 / Accepted: 10 November 2025 / Published: 13 November 2025
(This article belongs to the Special Issue Hydrogen Energy Generation, Storage, Transportation and Utilization)
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Abstract

This paper aims to propose an accurate method for estimating the state of charge (SoC) in metal hydride tanks (MHT) to enhance the energy management of hydrogen-powered fuel cell systems. Two data-driven prediction methods, Long Short-Term Memory (LSTM) networks and Support Vector Regression (SVR), are developed and tested on experimental charge/discharge data from a dedicated MHT test bench. Three distinct LSTM architectures are evaluated alongside an SVR model to compare both generalization performance and computational overhead. Results demonstrate that the SVR approach achieves the lowest root mean square error (RMSE) of 0.0233% during discharge and 0.0283% during charge, while also requiring only 164 ms per inference step for both cycles. However, LSTM variants have a higher RMSE and significantly higher computational cost, which highlights the superiority of the SVR method.
Keywords: metal hydride tanks; state of charge estimation; long short-term memory; support vector regression metal hydride tanks; state of charge estimation; long short-term memory; support vector regression

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MDPI and ACS Style

Yahia, A.; Chabane, D.; Laghrouche, S.; N’Diaye, A.; Djerdir, A. Performance Analysis of Offline Data-Driven Methods for Estimating the State of Charge of Metal Hydride Tanks. Energies 2025, 18, 5969. https://doi.org/10.3390/en18225969

AMA Style

Yahia A, Chabane D, Laghrouche S, N’Diaye A, Djerdir A. Performance Analysis of Offline Data-Driven Methods for Estimating the State of Charge of Metal Hydride Tanks. Energies. 2025; 18(22):5969. https://doi.org/10.3390/en18225969

Chicago/Turabian Style

Yahia, Amina, Djafar Chabane, Salah Laghrouche, Abdoul N’Diaye, and Abdesslem Djerdir. 2025. "Performance Analysis of Offline Data-Driven Methods for Estimating the State of Charge of Metal Hydride Tanks" Energies 18, no. 22: 5969. https://doi.org/10.3390/en18225969

APA Style

Yahia, A., Chabane, D., Laghrouche, S., N’Diaye, A., & Djerdir, A. (2025). Performance Analysis of Offline Data-Driven Methods for Estimating the State of Charge of Metal Hydride Tanks. Energies, 18(22), 5969. https://doi.org/10.3390/en18225969

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