Please use this identifier to cite or link to this item: https://scholarhub.balamand.edu.lb/handle/uob/1673
Title: Battery Modeling and Lifetime Prediction
Authors: Salameh, Jack
Ghossein, Nagham El
Hassan, Moustapha El 
Karami, Nabil
Najjar, Maged B. 
Affiliations: Department of Electrical Engineering 
Department of Computer Engineering 
Keywords: Equivalent circuit model
Battery
Parameters identification techniques
Bayesian classifier
Lead-acid
Issue Date: 2017
Part of: Modern environmental science and engineering
Volume: 3
Issue: 4
Start page: 278
End page: 290
Abstract: 
Battery Management Systems (BMS) are gaining greater interest by researchers due to the excessive increase of battery dependent electrical/electronic systems. Batteries are becoming more abundantly used worldwide, mainly in wireless mobile electrical devices, as well as Hybrid Electric Vehicles (HEVs) and Electric Vehicles (EVs). Moreover, batteries emerged as the only device capable of storing transformed energy, henceforth they formed the power banks of all renewable energy systems extending from solar panels, wind turbines, etc. This paper targets modeling various types of batteries, which implemented into the BMS can give an insight on their performance. Parameters of three common models for various types of batteries were identified. Moreover, a common method that gives an insight on the lifespan of any battery under examination was found. This technique was based on several measurements taken at the laboratory and relies on using the Bayesian classifier for finding the state of health of a tested battery. Fast methods are introduced starting from modeling batteries to knowing their lifespan.
URI: https://scholarhub.balamand.edu.lb/handle/uob/1673
Open URL: Link to full text
Type: Journal Article
Appears in Collections:Department of Electrical Engineering

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