Holdings: Towards enhanced remaining useful life prediction of lithium-ion batteries with uncertainty using optimized deep learning algorithm

Towards enhanced remaining useful life prediction of lithium-ion batteries with uncertainty using optimized deep learning algorithm

An essential component of assessing lithium-ion battery (LIB) performance, reliability, and administration in the application of battery health monitoring and management is determining the battery's Remaining Useful Life (RUL). However, existing RUL prediction approaches have difficulties with...

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Bibliographic Details
Main Authors:	Reza M.S., Hannan M.A., Mansor M., Ker P.J., Rahman S.A., Jang G., Mahlia T.M.I.
Other Authors:	59055914200
Format:	Article
Published:	Elsevier Ltd 2025
Subjects:	Battery management systems Drops Extraction Forecasting Ions Learning algorithms Learning systems Long short-term memory Mean square error NASA Parameter estimation Particle swarm optimization (PSO) Uncertainty analysis Data feature Deep learning Learn+ Memory modeling Optimization algorithms Remaining useful life predictions Search Algorithms Systematic sampling Uncertainty Uncertainty parameters Lithium-ion batteries
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Towards enhanced remaining useful life prediction of lithium-ion batteries with uncertainty using optimized deep learning algorithm

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