2026-04-11T06:58:04Zhttps://ebiltegia.mondragon.edu/oai/request

oai:ebiltegia.mondragon.edu:20.500.11984/56052024-03-05T11:29:52Zcom_20.500.11984_1143col_20.500.11984_1148

Miguel, Eduardo IRAOLA, UNAI 2022-06-16T08:58:34Z 2022-06-16T08:58:34Z 2022 978-1-6654-0528-7 https://katalogoa.mondragon.edu/janium-bin/janium_login_opac.pl?find&ficha_no=167812 https://hdl.handle.net/20.500.11984/5605 The pursue of the new increasingly intelligent, and heavier state estimation algorithms requires a significant amount of data and computing power, which may challenge their deployment in current BMS solutions. To address that issue, this paper proposes a cloud-based Digital Twin Platform to extend computing power and data storage capacity. This tool aims to contain the integration of models to analyse thermoelectricand ageing aspects of a LIB, based on experimental operation data by comparative analysis. Based on well-known cell-level modelling techniques, a module-level modelling approach is proposed and an experimental validation platform is suggested. eng © 2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. digital twin Cloud computing Battery models State of Charge Module-Level Modelling Approach for a Cloudbased Digital Twin Platform for Li-Ion Batteries http://purl.org/coar/resource_type/c_c94f