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dc.contributor.advisorKittaneh, Omar
dc.contributor.advisorAbdulmajid, Mohammed
dc.contributor.authorMouais, Talal Ali
dc.date.accessioned2022-10-06T07:34:57Z
dc.date.available2022-10-06T07:34:57Z
dc.date.issued2021
dc.date.submitted2021
dc.identifier.urihttp://hdl.handle.net/20.500.14131/82
dc.description.abstractOne of the significant drawbacks of renewable energy is that renewable energy sources such as solar and wind are intermittent and operate with different degrees of intermittency. In other words, they only generate power when the sun is shining or when the wind is blowing. One of the most promising methods of overcoming the problem intermittent renewable energy supplies is the use batteries that can store renewable energy until it is needed. Batteries are known for their high commercial potential, fast response time, modularity, flexible installation, and short construction cycles. Consequently, the battery is an attractive option for storing renewable energy and peak shaving during intensive grid loads, and it can serve as a back-up system to control voltage drops in the energy grid. The lithium-ion battery is regarded as one of the most promising battery technologies because it has a high specific energy density, a high volumetric energy density, a low and falling cost, and a long lifetime. The failure mechanism of the Li-ion battery is a highly complex phenomenon produced by a complex interplay of many physical and chemical mechanisms. Three main approaches are used to modeling the lifetime of batteries: the physics-based model (Electrochemical modeling); the half empirical model (statistical methods), which is based on conducting and analyzing battery aging experiments; and the data-driven model, which is based on numerous battery aging experiments that require data analysis and machine learning. This study employs the statistical method to understand and uncover hidden failure interactions inside the cell. It is used here because of its relative simplicity. Based on Accelerated lifetime data of Li-ion adapted from [10], we examine the fitting of three lifetime distributions; the Weibull, lognormal and normal distributions. We conclude that the lognormal distribution is the best lifetime model for Li-ion batteries. Also, this study shows that the electrode physical parameters, such as thickness, play an important role in the lifetime model of Li-ion battery.
dc.language.isoen_US
dc.publisherEffat University
dc.subjectRenewable Energy
dc.subjectEnergy storage
dc.subjectLi-ion
dc.subjectLifetime
dc.subjectReliability
dc.subjectWeibull distribution
dc.titleInvestigating The Best Statistical Lifetime Model for Commercial Lithium-Ion Batteries
dc.typeThesis
refterms.dateFOA2022-10-06T07:34:57Z
dc.contributor.researcherGraduate Studies and Research


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