Skip to main content
Have a personal or library account? Click to login
Adaptive Hybrid LSTM-EKF Model for Reliable State of Charge Estimation in Lithium-Ion Batteries Under Noisy Conditions Cover

Adaptive Hybrid LSTM-EKF Model for Reliable State of Charge Estimation in Lithium-Ion Batteries Under Noisy Conditions

Acta Mechanica et Automatica
Volume 19 (2025): Issue 4 (December 2025)
By: Karim KHEMIRI and  Ridha DJEBALI  
Open Access
|Dec 2025
Authors

Authors

Karim KHEMIRI

karim.khemiri@islaib.u-jendouba.tn

UR22ES12: Modeling, Optimization and Augmented Engineering, ISLAIB, University of Jendouba, Beja, Tunisia

Ridha DJEBALI

ridha.djebali@ipein.rnu.tn

UR22ES12: Modeling, Optimization and Augmented Engineering, ISLAIB, University of Jendouba, Beja, Tunisia
DOI: https://doi.org/10.2478/ama-2025-0085 | Journal eISSN: 2300-5319 | Journal ISSN: 1898-4088
Journal RSS Feed
Language: English
Page range: 761 - 767
Submitted on: Jun 26, 2025
Accepted on: Nov 9, 2025
Published on: Dec 31, 2025
Published by: Bialystok University of Technology
In partnership with: Paradigm Publishing Services
Publication frequency: 4 issues per year
Keywords:
lithiumion batteries,
state of charge estimation,
long short-term memory,
extended Kalman filter,
adaptive fusion,
hybrid modeling,
noisy environment
Related subjects:
Engineering,
Electrical engineering,
Electronics,
Mechanical engineering,
Mechanics,
Bioengineering and biomedical engineering,
Biomechanics,
Civil engineering,
Environmental engineering

© 2025 Karim KHEMIRI, Ridha DJEBALI, published by Bialystok University of Technology
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Volume 19 (2025): Issue 4 (December 2025)
Previous article
Next article