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vafaie-souraki--roghaye
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Predicting SOC Level of Electric Vehicles Battery based on Machine Algorithms in Cloud Environment
, M.Sc. Thesis Sharif University of Technology ; Moeini Aghtaei, Moein (Supervisor)
Abstract
With the increasing adoption of electric vehicles and the growing need for improved energy management, accurate state-of-charge estimation for lithium-ion batteries has become a critical parameter in energy storage systems. Traditional methods, such as Coulomb counting and Kalman filters, lack sufficient accuracy when dealing with variable operational conditions and the nonlinear behavior of batteries. This study aims to present a comprehensive and precise approach for state-of-charge estimation. A fractional-order model was first developed to simulate the dynamic behavior of batteries, and initial state-of-charge and parameter estimation were achieved using a fractional-order unscented...
Prediction of asphaltene deposition during turbulent flow using heat transfer approach
, Article Petroleum Science and Technology ; Volume 36, Issue 9-10 , February , 2018 , Pages 632-639 ; 10916466 (ISSN) ; Ayatollahi, S ; Vafaie Seftie, M ; Sharif University of Technology
Taylor and Francis Inc
2018
Abstract
In this study, asphaltene deposition from crude oil has been studied experimentally using a test loop and prediction using theoretical study under turbulent flow (Reynolds numbers below 5000). The effects of many parameters such as oil velocity, surface temperature and concentration of flocculated asphaltene on the asphaltene deposition were investigated. The results showed that asphaltene deposition thickness increases with increasing both surface temperature and concentration of flocculated asphaltene and decreasing oil velocity. Thermal approach was used to describe the mechanisms involved in this process and the results of data fitting showed that there are good agreements between the...