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State of Charge Estimation for Lithi...

Lotfivand, Azizeh.

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State of Charge Estimation for Lithium-Ion Batteries with Model-Based Algorithms.

Record Type:	Electronic resources : Monograph/item
Title/Author:	State of Charge Estimation for Lithium-Ion Batteries with Model-Based Algorithms./
Author:	Lotfivand, Azizeh.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2023,
Description:	116 p.
Notes:	Source: Masters Abstracts International, Volume: 85-06.
Contained By:	Masters Abstracts International85-06.
Subject:	Disadvantages. -
Online resource:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=30736354
ISBN:	9798381025491

State of Charge Estimation for Lithium-Ion Batteries with Model-Based Algorithms.
Lotfivand, Azizeh.

State of Charge Estimation for Lithium-Ion Batteries with Model-Based Algorithms. - Ann Arbor : ProQuest Dissertations & Theses, 2023 - 116 p.

Source: Masters Abstracts International, Volume: 85-06.

Thesis (M.Phil.)--Liverpool John Moores University (United Kingdom), 2023.

This item must not be sold to any third party vendors.

Electric vehicles have revolutionized automotive manufacturing in recent years. However, they are faced with some challenges that are essential to overcome to have an acceptable performance. Therefore, these kinds of vehicles need a safe, fast charging, and extended life cycle battery. Lithium-ion batteries have these characteristics and are used in different state-of-the-art industries. Having reliable data for the Lithium-ion batteries Battery Management System (BMS) is critical. They are required to monitor and control all parameters such as State of Charge and State of Health. These parameters cannot be measured directly, and the system should estimate them accurately and reliably. This study consists of 5 main parts: literature review, modelling, research methodology, data collection, and data analysis and interpretation. Firstly, the recent papers related to methods of SOC (State of Charge) estimation were reviewed to find out the existing algorithms' productivity and deeply realized in literature reviewing step.Because of their inherent safety, fast charging capacity, and extended cycle life, lithium-ion batteries are preferred over other types of batteries in electric vehicle applications. It's critical to be able to determine state factors like state of charge and state of health to generate an accurate battery model. The state of charge estimation algorithms for generic Lithium-ion batteries were enhanced using LA92 drive cycle experiment data. To begin, a mathematical model for an analogous circuit battery was created with the goal of accurately imitating the behaviour of a lithium-ion battery. The Thevenin model is created by 2 RC branches and identifies the model parameters with the Coulomb Counting, Extended Kalman Filter (EKF) and Unscented Kalman Filter (UKF). The Hybrid Pulse Power Characterization (HPPC) test data obtained at 40°C, 25°C, 10°C, 0°C, and -10°C are used to calculate the OCV 3-dimensional curve as a function of SOC and T (Temperature). A comparison of the three methods is shown, indicating that the UKF method of battery SOC evaluation is more accurate than the Coulomb Counting method and EKF.

ISBN: 9798381025491Subjects--Topical Terms:

3684692
Disadvantages.

State of Charge Estimation for Lithium-Ion Batteries with Model-Based Algorithms.
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