Hybrid optimized remaining useful life prediction framework for lithium-ion batteries with limited data samples

Abstract This study introduces a Jellyfish optimization technique integrated with a Multi-Layer Perceptron, specifically a Feedforward Neural Network (FNN) model, for remaining useful life (RUL) prediction of lithium-ion batteries (LIBs). A multiple battery with multi-input (MBMI) profile is utilize...

Täydet tiedot

Bibliografiset tiedot

Päätekijät:	Md Ibrahim, Shaheer Ansari, Afida Ayob, M. S. Hossain Lipu, Maher G. M. Abdolrasol, Abdul Waheed Khawaja, Muhammad Amir Khalil, Daniel Ioan Stroe
Aineistotyyppi:	Artigo
Kieli:	Inglês
Julkaistu:	Nature Portfolio 2025-11-01
Sarja:	Scientific Reports
Aiheet:	Lithium-ion battery Remaining useful life Optimization Data-driven
Linkit:	https://doi.org/10.1038/s41598-025-26743-1
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