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dc.contributor.authorMian Qaisar, Saeed
dc.contributor.authorAlboody, Ahed
dc.contributor.authorAldossary, Shahad
dc.contributor.authorAlhamdan, Alhanoof
dc.contributor.authorMoahammad, Nouf
dc.contributor.authorTurki Almaktoom, Abdulaziz
dc.date.accessioned2024-01-18T05:25:53Z
dc.date.available2024-01-18T05:25:53Z
dc.date.issued2023-11-10
dc.identifier.doihttps://doi.org/10.1109/ELIT61488.2023.10310833en_US
dc.identifier.urihttp://hdl.handle.net/20.500.14131/1346
dc.description.abstractFor an effective and economical deployment of battery-powered electric vehicles, mobile phones, laptops, and medical gadgets, the State of Charge (SoC) of the batteries must be properly assessed. It permits a safe operation, have a longer usable battery life, and prevent malfunctions. In this context, the battery management systems provide diverse SoC estimation solutions. However, the Machine Learning (ML) based SoC estimation mechanisms are becoming popular because of their robustness and higher precision. In this study, the features set is prepared using the intended battery cell charge/discharge curves for voltage, current, and temperature. Utilizing statistical analysis and the shape context, the attributes are extracted. Following that, three credible machine learning (ML) algorithms-decision trees, random forests, and linear regression-process the set of mined attributes. The applicability is tested using the Panasonic Lithium-Ion (Li-Ion) battery cells, publicly provided by the McMaster University. The feature extraction and the ML based SoC prediction modules are implemented in MATLAB. The “correlation coefficient”, “mean absolute error”, and “root mean square error” are used to assess the prediction performance. The results show an outperformance of the random forest regressor among the intended ones by attaining the correlation coefficient value of 0.9988.en_US
dc.description.sponsorshipEffat Universityen_US
dc.publisherIEEEen_US
dc.subjectLi-Ion Batteryen_US
dc.subjectEvaluation Measureen_US
dc.subjectMachine learningen_US
dc.subjectMATLABen_US
dc.subjectRechargeable Batteryen_US
dc.subjectState of Chargeen_US
dc.titleMachine Learning Assistive State of Charge Estimation of Li-Ion Batteryen_US
dc.contributor.researcherDepartment Collaborationen_US
dc.contributor.researcherUniversity Collaborationen_US
dc.contributor.researcherExternal Collaborationen_US
dc.contributor.labEnergy Laben_US
dc.subject.KSAENERGYen_US
dc.contributor.ugstudent3en_US
dc.contributor.alumnae0en_US
dc.source.indexScopusen_US
dc.contributor.departmentElectrical and Computer Engineeringen_US
dc.contributor.pgstudent0en_US
dc.contributor.firstauthorMian Qaisar, Saeed
dc.conference.locationLviv, Ukraineen_US
dc.conference.name2023 IEEE 13th International Conference on Electronics and Information Technologies (ELIT)en_US
dc.conference.date2023-09-26


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