A Hybrid Data-Driven Method for State-of-Charge Estimation of Lithium-Ion Batteries-Reference-Cited by-同舟云学术

A Hybrid Data-Driven Method for State-of-Charge Estimation of Lithium-Ion Batteries

Published:2022-08-15 Issue:16 Volume:22 Page:16263-16275
ISSN:1530-437X
Container-title:IEEE Sensors Journal
language:
Short-container-title:IEEE Sensors J.

Author:

Yan Xiaodong¹^ORCID,Zhou Gongbo¹^ORCID,Wang Wei¹^ORCID,Zhou Ping¹^ORCID,He Zhenzhi²^ORCID

Affiliation:

1. Jiangsu Key Laboratory of Mine Mechanical and Electrical Equipment, School ofMechatronic Engineering, China University of Mining and Technology, Xuzhou, Jiangsu, China

2. School of Mechatronic Engineering, Jiangsu Normal University, Xuzhou, Jiangsu, China

Funder

National Natural Science Foundation of China

Fundamental Research Funds for the Central Universities

Open Fund Project of State Key Laboratory of Mining Response and Disaster Prevention and Control in Deep Coal Mines

Priority Academic Program Development of Jiangsu Higher Education Institutions

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Instrumentation

Link

http://xplorestaging.ieee.org/ielx7/7361/9857501/09830531.pdf?arnumber=9830531

Reference38 articles.

1. A combined method for state-of-charge estimation for lithium-ion batteries using a long short-term memory network and an adaptive cubature Kalman filter

2. State-of-charge estimation of lithium-ion batteries using LSTM and UKF

3. State of charge estimation for Li-ion batteries using neural network modeling and unscented Kalman filter-based error cancellation

4. Long Short-Term Memory With Attention Mechanism for State of Charge Estimation of Lithium-Ion Batteries

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