2026-07-09T04:20:40Zhttps://ebiltegia.mondragon.edu/oai/request

oai:ebiltegia.mondragon.edu:20.500.11984/145232026-06-11T06:15:40Zcom_20.500.11984_473com_20.500.11984_14090col_20.500.11984_478

Lizaso Eguileta, Olatz Gil, Endika Martinez Laserna, Egoitz RIVAS GUTIERREZ, MIKEL Miguel, Eduardo IRAOLA, UNAI 2026-06-10T11:04:04Z 2026-06-10T11:04:04Z 2024 0018-9545 https://katalogoa.mondragon.edu/janium-bin/janium_login_opac.pl?find&ficha_no=172000 https://hdl.handle.net/20.500.11984/14523 The monitoring and modelling of the Li-Ion Batteries behaviour is still a major technical challenge due to the non-linearities and coupled phenomena that determine their operation. These Li-Ion Batteries can consist of thousands of cells with series/parallel connections, which suffer from operating and degradation deviations. The pursuit of new, increasingly intelligent and heavier state estimation algorithms requires a significant amount of data and computational power, which can be challenging to deploy in current Battery Management System solutions. To solve this problem, this paper proposes a Digital Twin Simulation Platform that considers all the individual cells based on the Cloud to extend the computational power and data storage capacity. This work presents validated cell models, a module-level modelling approach, and an experimental validation platform is suggested. In addition, the first results obtained when implementing the Digital Twin Simulation Platform in the Cloud are presented. eng © 2024 IEEE Batteries Computational modeling Integrated circuit modeling Data models Cloud computing Hysteresis Voltage Digital twin cloud computing battery models state of charge Validation and Implementation in the Cloud of Cell-Level Models of a Digital Twin Simulation Platform