Bridging data-driven and physics-based models for lithium-ion battery state analysis and future performance assessment via active material volume fraction

Accurately estimating the current state and predicting the future performance of lithium-ion batteries are essential for their optimal operation and lifetime extension. However, the limited availability of internal degradation metrics and reliable historical data often constrains effective battery m...

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Detalhes bibliográficos

Principais autores:	Andrés Bernabeu-Santisteban, Alejandro Clemente, Bernhard C. Geiger, Franz M. Rohrhofer, Francisco Díaz-González, Lluís Trilla
Formato:	Artigo
Idioma:	Inglês
Publicado em:	Elsevier 2026-05-01
coleção:	Energy and AI
Assuntos:	Lithium-ion batteries State estimation Performance forecasting Hybrid modeling Non-invasive technique
Acesso em linha:	http://www.sciencedirect.com/science/article/pii/S2666546826000753
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