Causal Physics-Infused Hybrid Learning (CPIHL) Framework for Next-Gen Battery Health Forecasting
A novel hybrid model, denoted by Causal Physics-Informed Hybrid Learning Neural Networks (CPIHL), is developed in this study to significantly enhance the accuracy, interoperability, and real-time feasibility of battery health predictions. The model incorporates the effects of temperature and voltage...
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| Autori principali: | , , , , |
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| Natura: | Artigo |
| Lingua: | Inglês |
| Pubblicazione: |
IEEE
2025-01-01
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| Serie: | IEEE Access |
| Soggetti: | |
| Accesso online: | https://ieeexplore.ieee.org/document/10945873/ |
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