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

Amina Yahia, Djafar Chabane, Salah Laghrouche, Abdoul N’Diaye and Abdesslem Djerdir ()
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Amina Yahia: Université Marie et Louis Pasteur, UTBM, CNRS, Institut FEMTO-ST, FCLAB, F-90000 Belfort, France
Djafar Chabane: Université Marie et Louis Pasteur, UTBM, CNRS, Institut FEMTO-ST, FCLAB, F-90000 Belfort, France
Salah Laghrouche: Université Marie et Louis Pasteur, UTBM, CNRS, Institut FEMTO-ST, FCLAB, F-90000 Belfort, France
Abdoul N’Diaye: Université Marie et Louis Pasteur, UTBM, CNRS, Institut FEMTO-ST, FCLAB, F-90000 Belfort, France
Abdesslem Djerdir: Université Marie et Louis Pasteur, UTBM, CNRS, Institut FEMTO-ST, FCLAB, F-90000 Belfort, France

Energies, 2025, vol. 18, issue 22, 1-18

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 (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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