Deployment of Machine Learning Algorithm to Predict Battery Behavior

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dc.contributor.advisorGarabito-Siordia, Luis E.
dc.contributor.authorFlores-Triana, Jorge A.
dc.contributor.authorCinco-Ahumada, Jesus A.
dc.date.accessioned2023-10-16T17:11:51Z
dc.date.available2023-10-16T17:11:51Z
dc.date.issued2023-07
dc.descriptionThe growth of the electric car industry has increased in recent years, along with the trend of green energy around the world. For this reason, automotive companies have invested in finding different solutions to monitor lithium batteries that power vehicles. These applications include State of Charge (SoC) and State of Health (SoH) analysis of the battery cells by monitoring key variables such as temperature, current, and voltage to predict the behavior of the system and apply preventive maintenance. In this paper, a deep neural network using the Deep Learning MATLAB Toolbox was designed to predict the SoC from an emulated battery in Simulink. The model was then compiled and deployed in an NXP S32K344 microcontroller using the NXP Model-Based Design Toolbox. The results obtained showed a network with up to 90% accuracy and an execution time of 2.6 ms when running the core at 160 MHz.es_MX
dc.description.sponsorshipITESO, A. C.es
dc.identifier.citationCinco-Ahumada, J. A.; Flores-Triana, J. A. (2023). Deployment of Machine Learning Algorithm to Predict Battery Behavior. Trabajo de obtención de grado, Especialidad en Sistemas Embebidos. Tlaquepaque, Jalisco: ITESO.
dc.identifier.urihttps://hdl.handle.net/11117/10264
dc.language.isoenges_MX
dc.publisherITESOes_MX
dc.rights.urihttp://quijote.biblio.iteso.mx/licencias/CC-BY-NC-2.5-MX.pdfes_MX
dc.subjectBattery Predictioneres_MX
dc.subjectEmbedded Systemses_MX
dc.subjectMachine Learninges_MX
dc.subjectDeploymentes_MX
dc.subjectEmbedded Coderes_MX
dc.titleDeployment of Machine Learning Algorithm to Predict Battery Behaviores_MX
dc.typeinfo:eu-repo/semantics/academicSpecializationes_MX
dc.type.versioninfo:eu-repo/semantics/acceptedVersiones_MX

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