A comprehensive framework for estimating the remaining useful life of Li-ion batteries under limited data conditions with no temporal identifier
Authors:
- Camilo Lopez-SalazarCLCamilo Lopez-Salazar
- Stephen Ekwaro-OsireStephen Ekwaro-Osire
- Shweta DabetwarSDShweta Dabetwar
- Fisseha AlemayehuFAFisseha Alemayehu
- 5 October 2024
- journal article
- research article
- Published by Elsevier in Reliability Engineering & System Safety
- Vol. 253
Abstract
No abstract availableKeywords
This publication has 47 references indexed in Scilit:
- Degradation trajectory prediction of lithium‐ion batteries based on charging‐discharging health features extraction and integrated data‐driven modelsQuality and Reliability Engineering International, 2024
- A Physics-Constrained Bayesian neural network for battery remaining useful life predictionApplied Mathematical Modelling, 2023
- An Empirical-Data Hybrid Driven Approach for Remaining Useful Life prediction of lithium-ion batteries considering capacity divingEnergy, 2022
- Prognostics of battery cycle life in the early-cycle stage based on hybrid modelEnergy, 2021
- A hybrid transfer learning scheme for remaining useful life prediction and cycle life test optimization of different formulation Li-ion power batteriesApplied Energy, 2021
- Deep convolutional neural networks with ensemble learning and transfer learning for capacity estimation of lithium-ion batteriesApplied Energy, 2020
- Remaining useful life prediction of lithium-ion batteries based on false nearest neighbors and a hybrid neural networkApplied Energy, 2019
- Lithium-ion battery charging management considering economic costs of electrical energy loss and battery degradationEnergy Conversion and Management, 2019
- A single particle model with chemical/mechanical degradation physics for lithium ion battery State of Health (SOH) estimationApplied Energy, 2018
- Statistical aspects in neural network for the purpose of prognosticsJournal of Mechanical Science and Technology, 2015