A Pattern-Driven Stochastic Degradation Model for the Prediction of Remaining Useful Life of Rechargeable Batteries-Reference-Cited by-同舟云学术

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A Pattern-Driven Stochastic Degradation Model for the Prediction of Remaining Useful Life of Rechargeable Batteries

Published:2022-12 Issue:12 Volume:18 Page:8586-8594
ISSN:1551-3203
Container-title:IEEE Transactions on Industrial Informatics
language:
Short-container-title:IEEE Trans. Ind. Inf.

Author:

Zhang Zihan¹^ORCID,Jeong Yeonjeong²^ORCID,Jang Jongseong²^ORCID,Lee Chi-Guhn³^ORCID

Affiliation:

1. H. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, GA, USA

2. LG AI Research, Toronto, ON, Canada

3. Department of Mechanical and Industrial Engineering, University of Toronto, Toronto, ON, Canada

Funder

Natural Sciences and Engineering Research Council of Canada

Alliance Grant

LG AI Research

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Computer Science Applications,Information Systems,Control and Systems Engineering

Link

http://xplorestaging.ieee.org/ielx7/9424/9906876/09726903.pdf?arnumber=9726903

Reference33 articles.

1. Model Migration Neural Network for Predicting Battery Aging Trajectories

2. Battery data set;saha,2007

3. An ensemble model for predicting the remaining useful performance of lithium-ion batteries

4. An Adaptive Prognostic Approach via Nonlinear Degradation Modeling: Application to Battery Data

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