2026-04-11T01:18:57Zhttps://ebiltegia.mondragon.edu/oai/request

oai:ebiltegia.mondragon.edu:20.500.11984/53672024-03-04T10:31:32Zcom_20.500.11984_473col_20.500.11984_478

Azkue, Markel Aizpuru, Iosu 2021-09-06T14:36:54Z 2021-09-06T14:36:54Z 2021 2032-6653 https://katalogoa.mondragon.edu/janium-bin/janium_login_opac.pl?find&ficha_no=164554 https://hdl.handle.net/20.500.11984/5367 Getting accurate lifetime predictions for a particular cell chemistry remains a challenging process, largely dependent on time and cost-intensive experimental battery testing. This paper proposes a transfer learning (TL) method to develop LIB ageing models, which allow for the leveraging of experimental laboratory testing data previously obtained for a different cell technology. The TL method is implemented through Neural Networks models, using LiNiMnCoO2/C laboratory ageing data as a baseline model. The obtained TL model achieves an 1.01% overall error for a broad range of operating conditions, using for retraining only two experimental ageing tests of LiFePO4/C cells. eng Attribution 4.0 International http://creativecommons.org/licenses/by/4.0/ © 2021 by the authors. Licensee MDPI machine learning transfer learning lithium-ion batteries calendar ageing artificial neural network Calendar Ageing Model for Li-Ion Batteries Using Transfer Learning Methods http://purl.org/coar/resource_type/c_6501