امير اعلائي بهرام

The SoC of batteries is an important indicator for eva‎luating their future performance and optimizing the operation of the system in which the battery is used. The SoC of batteries has a non-linear relationship with terminal voltage, current, battery temperature and other factors affecting the battery, which is not easily possible to obtain this non-linear relationship. For this reason, the accurate and instantaneous estimation of the charge level of batteries has been a constant challenge and issue. Therefore, a reliable and adaptive estimation method is needed to estimate the charge level of batteries. In this thesis, the common types of batteries have been introduced first. Then the classification of methods, types of battery equivalent circuits and the explanation of the common methods of estimating the charge level of batteries are presented, and the weaknesses and strengths of each of them are investigated in the battery management system. In general, the focus of this thesis is on the simulation part of the battery model using the electrical equivalent circuit and the electrochemical-thermal model. Also, in the discussion of battery charge level estimation, neural network and Kalman Filter methods are used in simulation. A brief summary of the research conducted in the articles has been included in this thesis for more familiarity, and at the end, recommendations have been presented that can make the way to choose the appropriate and accurate method smoother and be useful for future works.

سيستم مديريت باتري , تخمين سطح شارژ , شبكه عصبي مصنوعي , فيلتركالمن , مدلسازي باتري

Battery Management System , SoC Estimation , Artificial Neural Network , Kalman Filter , Battery Modeling

Amir Alaei Bahram

Mohhamad-Hasan ShojaeFard/ Abdollah Amirkhani