Interpretable machine learning prediction for li-ion battery's state of health based on electrochemical impedance spectroscopy and temporal features-Reference-Cited by-同舟云学术

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Interpretable machine learning prediction for li-ion battery's state of health based on electrochemical impedance spectroscopy and temporal features

Published:2024-08 Issue: Volume:494 Page:144449
ISSN:0013-4686
Container-title:Electrochimica Acta
language:en
Short-container-title:Electrochimica Acta

Author:

Bao Minghan,Liu Dexun,Wu Yuyao^ORCID,Wang Zhengying,Yang Jing,Lan Lin^ORCID,Ru Qiang

Publisher

Elsevier BV

Reference59 articles.

1. Performance and cost of materials for lithium-based rechargeable automotive batteries;Schmuch;Nat. Energy,2018

2. Application of energy storage in integrated energy systems — A solution to fluctuation and uncertainty of renewable energy;Wang;J. Energy Storage,2022

3. Hybrid electric vehicles and their challenges: a review;Hannan;Renew. Sustain. Energy Rev.,2014

4. Review of energy storage systems for electric vehicle applications: issues and challenges;Hannan;Renew. Sustain. Energy Rev.,2017

5. Investigation of battery end-of-life conditions for plug-in hybrid electric vehicles;Wood;J. Power. Sources.,2011

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1. Multi-scenario failure diagnosis for lithium-ion battery based on coupling PSO-SA-DBSCAN algorithm;Journal of Energy Storage;2024-10

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