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

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.