2026-04-07T18:47:12Zhttps://ebiltegia.mondragon.edu/oai/request

oai:ebiltegia.mondragon.edu:20.500.11984/63602024-04-19T10:43:41Zcom_20.500.11984_1143col_20.500.11984_1148

Azkue, Markel Oca, Laura IRAOLA, UNAI 2024-04-19T09:32:55Z 2024-04-19T09:32:55Z 2022 https://katalogoa.mondragon.edu/janium-bin/janium_login_opac.pl?find&ficha_no=170385 https://hdl.handle.net/20.500.11984/6360 The development of State-of-Charge (SoC) algorithms for Li-ion batteries involves carrying out different laboratory tests with the money and time that this entails. Furthermore, such laboratory labours must typically be repeated for each new Li-ion cell reference. In order to minimise this issue, this work proposes a new approach for developing SoC algorithms, using a Recurrent Neural Network in combination with a Transfer Learning method. The latter technique will make possible to take advantage of the data generated for previously tested cell references and use it for the development of a SoC estimation algorithm for a new cell reference. This work provides a proof-of-concept for the proposed approach, using synthetic data generated from electrochemical models, which describes the behaviour of different Li-ion cell references. eng © 2022 The Authors Machine Learning Transfer Learning lithium-ion batteries State-of-Charge Artificial Neural Network Li-ion Battery State-of-Charge estimation algorithm with CNN-LSTM and Transfer Learning using synthetic training data http://purl.org/coar/resource_type/c_c94f