Lithium-Ion Battery State of Health Prediction With a Robust Collaborative Augmented Hidden Layer Feedforward Neural Network Approach-Reference-Cited by-同舟云学术

Lithium-Ion Battery State of Health Prediction With a Robust Collaborative Augmented Hidden Layer Feedforward Neural Network Approach

Published:2023-09 Issue:3 Volume:9 Page:4492-4502
ISSN:2332-7782
Container-title:IEEE Transactions on Transportation Electrification
language:
Short-container-title:IEEE Trans. Transp. Electrific.

Author:

Berghout Tarek¹^ORCID,Benbouzid Mohamed²^ORCID,Amirat Yassine³^ORCID,Yao Gang⁴^ORCID

Affiliation:

1. Laboratory of Automation and Manufacturing Engineering, University of Batna 2, Batna, Algeria

2. UMR CNRS 6027 IRDL, University of Brest, Brest, France

3. ISEN Yncréa Ouest, L@bISEN, Brest, France

4. College of Logistics Engineering, Shanghai Maritime University, Shanghai, China

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Energy Engineering and Power Technology,Transportation,Automotive Engineering

Link

http://xplorestaging.ieee.org/ielx7/6687316/10251026/10018362.pdf?arnumber=10018362

Reference35 articles.

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2. Real-Time Prediction of Capacity Fade and Remaining Useful Life of Lithium-Ion Batteries Based on Charge/Discharge Characteristics

3. An adaptive remaining useful life prediction approach for single battery with unlabeled small sample data and parameter uncertainty

4. An iterative model of the generalized Cauchy process for predicting the remaining useful life of lithium-ion batteries

5. Battery Health Prediction Using Fusion-Based Feature Selection and Machine Learning

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