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Validation of a data-driven fast numerical model to simulate the Immersion Cooling of a Lithium-ion Battery Pack

hal.structure.identifierCertified Adaptive discRete moDels for robust simulAtions of CoMplex flOws with Moving fronts [CARDAMOM]
dc.contributor.authorSOLAI, Elie
hal.structure.identifierEXOES
dc.contributor.authorGUADAGNINI, Maxime
hal.structure.identifierCertified Adaptive discRete moDels for robust simulAtions of CoMplex flOws with Moving fronts [CARDAMOM]
dc.contributor.authorBEAUGENDRE, Heloise
hal.structure.identifierEXOES
dc.contributor.authorDACCORD, Rémi
hal.structure.identifierUncertainty Quantification in Scientific Computing and Engineering [PLATON]
dc.contributor.authorCONGEDO, Pietro Marco
dc.date.accessioned2024-04-04T02:41:03Z
dc.date.available2024-04-04T02:41:03Z
dc.date.issued2022-05-06
dc.identifier.issn0360-5442
dc.identifier.urihttps://oskar-bordeaux.fr/handle/20.500.12278/191118
dc.description.abstractEnThermal management of Lithium-ion batteries is a key element to the widespread of elec- tric vehicles. In this study, we illustrate the validation of a data-driven numerical method permitting to evaluate fast the behavior of the Immersion Cooling of a Lithium-ion Battery Pack. First, we illustrate an experiment using a set up of immersion cooling battery pack, where the temperatures, voltage and electrical current evolution of the Li-ion batteries are monitored. The impact of different charging/discharging cycles on the thermal behavior of the battery pack is investigated. Secondly, we introduce a numerical model, that simulates the heat transfer and electrical behavior of an immersion cooling Battery Thermal Management System. The deterministic numerical model is compared against the experimental measure- ments of temperatures. Then, we perform a Bayesian calibration of the multi-physics input parameters using the experimental measurements directly. The informative distributions outcoming of this process are used to validate the model in different experimental conditions and reduce the uncertainty in the model’s temperatures predictions. Finally, the learned distributions of inputs and the numerical model are used to design the system under realistic conditions representing a realistic racing car operation. A Sobol indices based sensitivity analysis is performed to get further analysis elements on the behavior of the BTMS.
dc.language.isoen
dc.publisherElsevier
dc.subject.enLithium-ion batteries
dc.subject.enImmersion cooling
dc.subject.enExperimental dataset
dc.subject.enNumerical simulation
dc.subject.enUncertainty quantification
dc.subject.enSensitivity analysis
dc.subject.enSurrogate model
dc.subject.enBayesian calibration
dc.title.enValidation of a data-driven fast numerical model to simulate the Immersion Cooling of a Lithium-ion Battery Pack
dc.typeArticle de revue
dc.identifier.doi10.1016/j.energy.2022.123633
dc.subject.halPhysique [physics]/Physique [physics]/Dynamique des Fluides [physics.flu-dyn]
bordeaux.journalEnergy
bordeaux.page123633
bordeaux.volume249
bordeaux.hal.laboratoriesInstitut de Mathématiques de Bordeaux (IMB) - UMR 5251*
bordeaux.institutionUniversité de Bordeaux
bordeaux.institutionBordeaux INP
bordeaux.institutionCNRS
bordeaux.peerReviewedoui
hal.identifierhal-03707343
hal.version1
hal.popularnon
hal.audienceInternationale
hal.origin.linkhttps://hal.archives-ouvertes.fr//hal-03707343v1
bordeaux.COinSctx_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.jtitle=Energy&rft.date=2022-05-06&rft.volume=249&rft.spage=123633&rft.epage=123633&rft.eissn=0360-5442&rft.issn=0360-5442&rft.au=SOLAI,%20Elie&GUADAGNINI,%20Maxime&BEAUGENDRE,%20Heloise&DACCORD,%20R%C3%A9mi&CONGEDO,%20Pietro%20Marco&rft.genre=article


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