The particle swarm optimization multi-kernel relevance vector machine for remaining useful life prediction of lithium-ion batteries-Reference-Cited by-同舟云学术

The particle swarm optimization multi-kernel relevance vector machine for remaining useful life prediction of lithium-ion batteries

Published:2022-10-03 Issue: Volume: Page:
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Container-title:2022 IEEE 6th Advanced Information Technology, Electronic and Automation Control Conference (IAEAC )
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Author:

Tian Qiancheng¹,Liu Xiping²,Ding Shuai¹,Chen Haitao¹,Huang Jun¹,Yang Ziwei¹

Affiliation:

1. State Key Laboratory of Space Power-Sources Technology,Shangha,China

2. Shanghai Institute of Space Power-Sources,Shanghai,China

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/9929429/9929329/09929806.pdf?arnumber=9929806

Reference18 articles.

1. Remaining Useful Life Prediction of Lithium-Ion Batteries Based on the Wiener Process with Measurement Error

2. Health diagnosis and remaining useful life prognostics of lithium-ion batteries using data-driven methods

3. Prediction of Remaining Useful Life of Lithium-ion Battery based on Multi-kernel Support Vector Machine with Particle Swarm Optimization;gao;Journal of Power Electronics,2017

4. An integrated unscented kalman filter and relevance vector regression approach for lithium-ion battery remaining useful life and short-term capacity prediction

5. Satellite lithium-ion battery remaining useful life estimation with an iterative updated RVM fused with the KF algorithm

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1. Prediction Model of Capacity Degradation in Lithium-Ion Batteries Based on Electrochemical Impedance Spectroscopy;2024

2. State-of-health and remaining-useful-life estimations of lithium-ion battery based on temporal convolutional network-long short-term memory;Journal of Energy Storage;2023-12

3. Scalability, Explainability and Performance of Data-Driven Algorithms in Predicting the Remaining Useful Life: A Comprehensive Review;IEEE Access;2023