State-of-Charge Estimation and Health Prognosis for Lithium-Ion Batteries Based on Temperature-Compensated Bi-LSTM Network and Integrated Attention Mechanism-Reference-Cited by-同舟云学术

State-of-Charge Estimation and Health Prognosis for Lithium-Ion Batteries Based on Temperature-Compensated Bi-LSTM Network and Integrated Attention Mechanism

Published:2024-06 Issue:6 Volume:71 Page:5586-5596
ISSN:0278-0046
Container-title:IEEE Transactions on Industrial Electronics
language:
Short-container-title:IEEE Trans. Ind. Electron.

Author:

Xu Peihang¹,Wang Chengchao¹,Ye Jinlu¹,Ouyang Tiancheng²^ORCID

Affiliation:

1. School of Mechanical Engineering, Guangxi University, Nanning, China

2. Department of Energy and Power Engineering, School of Mechanical Engineering, Guangxi University, Nanning, China

Funder

National Natural Science Foundation of China

Liuzhou Science Research and Planning Development

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Control and Systems Engineering

Link

http://xplorestaging.ieee.org/ielx7/41/10416960/10199138.pdf?arnumber=10199138

Reference43 articles.

1. Battery Health Prognosis for Electric Vehicles Using Sample Entropy and Sparse Bayesian Predictive Modeling

2. Health Prognosis for Electric Vehicle Battery Packs: A Data-Driven Approach

3. Load-responsive model switching estimation for state of charge of lithium-ion batteries

4. General Discharge Voltage Information Enabled Health Evaluation for Lithium-Ion Batteries

5. A Robust Algorithm for State-of-Charge Estimation With Gain Optimization

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