Lithium-Ion Battery Degradation and Capacity Prediction Model Considering Causal Feature-Reference-Cited by-同舟云学术

Lithium-Ion Battery Degradation and Capacity Prediction Model Considering Causal Feature

Published:2022-09 Issue:3 Volume:8 Page:3630-3647
ISSN:2332-7782
Container-title:IEEE Transactions on Transportation Electrification
language:
Short-container-title:IEEE Trans. Transp. Electrific.

Author:

Tian Yi¹^ORCID,He Jiabei¹,Peng Zhen²,Guan Yong¹,Wu Lifeng¹^ORCID

Affiliation:

1. Information Engineering College and the Beijing Key Laboratory of Electronic System Reliability Technology, Capital Normal University, Beijing, China

2. Information Management Department, Beijing Institute of Petrochemical Technology, Beijing, China

Funder

National Natural Science Foundation of China

Key Projects of Science and Technology Program of the Beijing Municipal Education Commission

Youth Innovative Research Team

Academy for Multidisciplinary Studies of Capital Normal University

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/ielx7/6687316/9847565/09755928.pdf?arnumber=9755928

Reference41 articles.

1. Capacity estimation of batteries: Influence of training dataset size and diversity on data driven prognostic models

2. A cloud-edge collaborative strategy for capacity prognostic of lithium-ion batteries based on dynamic weight allocation and machine learning

3. Investigating Causal Relations by Econometric Models and Cross-spectral Methods

4. A study of systemic risk of global stock markets under COVID-19 based on complex financial networks

5. Electromagnetic source imaging using simultaneous scalp EEG and intracranial EEG: An emerging tool for interacting with pathological brain networks

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