Fast charging shock-nonlinear degradation coupled model for battery remaining useful life prediction
Authors:
- Fei FengFFFei Feng
- Haoyang LiHLHaoyang Li
- Jiale XieJiale Xie
- Shiyu WuSWShiyu Wu
- Bin ChenBCBin Chen
- Min ChenMCMin Chen
- Hongpeng YinHYHongpeng Yin
- 21 March 2026
- journal article
- Published by Elsevier in Reliability Engineering & System Safety
- Vol. 272
Abstract
No abstract availableFunding Information
- National Natural Science Foundation of China (52577227)
- Natural Science Foundation of Chongqing Municipality (CSTB2023NSCQ-MSX0359)
- National University's Basic Research Foundation of China
- Fundamental Research Funds for the Central Universities (2024CDJGF-056)
This publication has 28 references indexed in Scilit:
- A review of thermal management systems for extreme fast charging Li-ion batteriesApplied Thermal Engineering, 2025
- Defects in lithium-ion batteries: From origins to safety risksGreen Energy and Intelligent Transportation, 2024
- Online battery model parameters identification approach based on bias-compensated forgetting factor recursive least squaresGreen Energy and Intelligent Transportation, 2024
- SEI/dead Li-turning capacity loss for high-performance anode-free solid-state lithium batteriesJournal of Energy Chemistry, 2024
- Electrochemical Modeling of Fast Charging in BatteriesAdvanced Energy Materials, 2024
- Efficient stochastic parametric estimation for lithium-ion battery performance degradation tracking and prognosisJournal of Manufacturing Systems, 2024
- Early remaining-useful-life prediction applying discrete wavelet transform combined with improved semi-empirical model for high-fidelity in battery energy storage systemEnergy, 2024
- Joint nonlinear-drift-driven Wiener process-Markov chain degradation switching model for adaptive online predicting lithium-ion battery remaining useful lifeApplied Energy, 2023
- Prognostics of battery capacity based on charging data and data-driven methods for on-road vehiclesApplied Energy, 2023
- Practical options for selecting data-driven or physics-based prognostics algorithms with reviewsReliability Engineering & System Safety, 2015