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Development and optimization of a lightweight LSTM model for battery state-of-charge estimation

descriptionPublicationkeyboard_double_arrow_right Conference object Under curation English Publisher:ZenodoFunded by:EC | PowerizeD
Authors: Paktitis, Thalis; Georgiadis, Dimitris; Karathanasopoulou, Konstantina; Panagiotopoulos, Ilias; Dimitrakopoulos, George;

doi: 10.5281/zenodo.19094355

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.

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