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Development and Optimization of a Lightweight LSTM Model for Battery State-of-Charge Estimation

descriptionPublicationkeyboard_double_arrow_right Article , Conference object 05 Feb 2026Publisher:IEEEJournal:2026 International Conference on Artificial Intelligence, Computer, Data Sciences and Applications (ACDSA)Funded by:EC | PowerizeD
Authors: Paktitis, Thalis; Georgiadis, Dimitris; Karathanasopoulou, Konstantina; Panagiotopoulos, Ilias; Dimitrakopoulos, George;

doi: 10.1109/acdsa67686.2026.11468004 , 10.5281/zenodo.19094355 , 10.5281/zenodo.19094354

Development and Optimization of a Lightweight LSTM Model for Battery State-of-Charge Estimation

Abstract

Accepted version of the paper "Development and optimization of a lightweight LSTM model for battery state-of-charge estimation" presented at the 3rd International Conference on Artificial Intelligence, Computer, Data Sciences and Applications (ACDSA 2026) on February 7, 2026 by Thalis Paktitis. A final, appropriately paginated version will appear in ieeexplore.com in due course.

Related Organizations
Keywords

Machine learning, Battery State of Charge, Long Short Term Memory

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average
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