Remaining useful life prediction of lithium-ion batteries via an EIS based deep learning approach-Reference-Cited by-同舟云学术

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Remaining useful life prediction of lithium-ion batteries via an EIS based deep learning approach

Published:2023-11 Issue: Volume:10 Page:3629-3638
ISSN:2352-4847
Container-title:Energy Reports
language:en
Short-container-title:Energy Reports

Author:

Li Jie,Zhao Shiming,Miah Md Sipon,Niu Mingbo^ORCID

Funder

Chang'an University

State Administration for Market Regulation

Shaanxi Province Department of Science and Technology

Shaanxi Province Key Research and Development Projects

Publisher

Elsevier BV

Subject

General Energy

Reference37 articles.

1. Identification of battery circuit model from EIS data;Agudelo;JCGE-Congrès Des. Jeunes Cherch. En. Génie Électr.,2019

2. State of charge prediction of EV Li-ion batteries using EIS: a machine learning approach;Babaeiyazdi;Energy,2021

3. Degradation diagnostics for lithium ion cells;Birkl;J. Power Sources,2017

4. Ageing behaviour of electrochemical double layer capacitors: Part I. Experimental study and ageing model;Bohlen;J. Power Sources,2007

5. Main aging mechanisms in Li ion batteries;Broussely;J. Power Sources,2005

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1. Advanced data-driven techniques in AI for predicting lithium-ion battery remaining useful life: a comprehensive review;Green Chemical Engineering;2024-09

2. High precision estimation of remaining useful life of lithium-ion batteries based on strongly correlated aging feature factors and AdaBoost framework;Ionics;2024-08-03

3. Online multi-scenario impedance spectra generation for batteries based on small-sample learning;Cell Reports Physical Science;2024-08

4. An efficient electrochemical optimizer for the distribution of relaxation times of lithium-ion batteries;Journal of Power Sources;2024-06

5. Fast Impedance Spectrum Construction for Lithium-Ion Batteries Using a Multi-Density Clustering Algorithm;Batteries;2024-03-20

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