An innovative multitask learning - Long short-term memory neural network for the online anti-aging state of charge estimation of lithium-ion batteries adaptive to varying temperature and current conditions

Junjie Tao, Shunli Wang, Wen Cao, Carlos Fernandez, Frede Blaabjerg and Liangwei Cheng

Energy, 2025, vol. 314, issue C

Abstract: As the new industrial revolution accelerates, new energy storage systems are becoming increasingly vital to the industrial chain. The overall performance of the battery management system can be improved by using a long short-term memory neural network online multi-task learning model based on physical model constraints to estimate the state of charge of the lithium-ion battery accurately. This paper uses a model reference adaptive system to perform parameter identification on the equivalent circuit model to avoid inaccurate SOC-OCV voltage curves caused by battery aging. At the same time, drawing on the ideas of PINN neural networks, the equivalent circuit model is used as the physical information constraint of the long short-term memory neural network, which improves the model's generalization ability. Finally, the Mahalanobis distance is used for offset determination, and an online learning method is used to improve model robustness. A multi-condition aging experiment on a lithium-ion battery showed that the proposed model improved the accuracy of equivalent circuit model parameter estimation by up to 67.38 % and state of charge estimation by an average of 59.73 %. This work introduces a new model design to drive innovation in battery management systems.

Keywords: Physical model constraints long short-term memory neural network; Model reference adaptive system; Multi-task online learning; Wide temperature range adaptability; Anti-aging ability (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0360544224040507
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:314:y:2025:i:c:s0360544224040507

DOI: 10.1016/j.energy.2024.134272

Access Statistics for this article

Energy is currently edited by Henrik Lund and Mark J. Kaiser

More articles in Energy from Elsevier
Bibliographic data for series maintained by Catherine Liu ().