Prognostics of Remaining Useful Life for Lithium-Ion Batteries Based on Hybrid Approach of Linear Pattern Extraction and Nonlinear Relationship Mining-Reference-Cited by-同舟云学术

Prognostics of Remaining Useful Life for Lithium-Ion Batteries Based on Hybrid Approach of Linear Pattern Extraction and Nonlinear Relationship Mining

Published:2023-01 Issue:1 Volume:38 Page:1054-1063
ISSN:0885-8993
Container-title:IEEE Transactions on Power Electronics
language:
Short-container-title:IEEE Trans. Power Electron.

Author:

Wang Yingzhou¹^ORCID,Hei Chenyang¹,Liu Hui²,Zhang Shude¹^ORCID,Wang Jianguo¹^ORCID

Affiliation:

1. Jilin Province International Research Center of Precision Drive and Intelligent Control, Northeast Electric Power University, Jilin, China

2. State Grid Jibei Electric Power Research Institute (North China Electric Power Research Institute Co. Ltd), Beijing, China

Funder

Science and Technology Project of Jilin Province

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering

Link

http://xplorestaging.ieee.org/ielx7/63/9911205/09795116.pdf?arnumber=9795116

Reference23 articles.

1. A Data-Driven Approach With Uncertainty Quantification for Predicting Future Capacities and Remaining Useful Life of Lithium-ion Battery

2. Battery Health Prognosis Using Brownian Motion Modeling and Particle Filtering

3. Prognostics of lithium-ion batteries based on flexible support vector regression

4. Lithium-ion batteries remaining useful life prediction based on a mixture of empirical mode decomposition and ARIMA model

5. Early prediction of remaining useful life for Lithium-ion batteries based on a hybrid machine learning method

Cited by 21 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Towards enhanced remaining useful life prediction of lithium-ion batteries with uncertainty using optimized deep learning algorithm;Journal of Energy Storage;2024-09

2. Capacity Estimation for Lithium-Ion Batteries Based on Impedance Spectral Dynamics and Deep Gaussian Process;IEEE Transactions on Power Electronics;2024-08

3. Lithium-ion battery health state and remaining useful life prediction based on hybrid model MFE-GRU-TCA;Journal of Energy Storage;2024-08

4. A novel remaining useful life prediction method based on gated recurrent unit network optimized by tunicate swarm algorithm for lithium-ion batteries;Transactions of the Institute of Measurement and Control;2024-07-24

5. Remaining useful life prediction of lithium-ion batteries based on performance degradation mechanism analysis and improved Deep Extreme Learning Machine model;Ionics;2024-07-06