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A Versatile Prognostics Approach for Batteries and Hydrogen Storage Systems Health Management
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| Author(s) |
Théo LENOIR, Daniela CHRENKO, Robin ROCHE, Samir JEMEI, Mickaël HILAIRET |
| Abstract |
The degradation of energy storage systems, including batteries, proton exchange membrane fuel cells, and electrolyzers, presents a critical challenge to their long-term reliability and efficiency. This study addresses the prediction of Remaining Useful Life by selecting a robust state-of-health indicator tailored to each system. A novel data-driven prognostic approach is proposed, leveraging Long Short-Term Memory neural networks to capture temporal dependencies and nonlinear degradation trends. The recurrent neural network model demonstrates versatility, adapting to both electrochemical storage and hydrogen-based systems by effectively learning from diverse datasets. Results highlight the method's capability to generalize across technologies, enabling accurate degradation predictions and offering significant insights into performance management. |
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| Filename: | 0178-epe2025-full-15143195.pdf |
| Filesize: | 741.1 KB |
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| Type |
Members Only |
| Date |
Last modified 2025-08-31 by System |
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