Uluslararası İleri Doğa Bilimleri ve Mühendislik Araştırmaları Dergisi Sayı 7, S. 1-6, 5, 2023 © Telif hakkı IJANSER’e aittir Araştırma Makalesi https://as-proceeding.com/index.php/ijanser ISSN: 2980-0811 International Journal of Advanced Natural Sciences and Engineering Researches Volume 7, pp. 1-6, 5, 2023 Copyright © 2023 IJANSER Research Article 1 A Novel Method for SoC Estimation of Lithium-Ion Batteries Based on Kalman Filter in Electric Vehicle Mehmet ŞEN *1 , Muciz ÖZCAN 2 1,2 Necmettin Erbakan University, Engineering Faculty, Department of Electrical and Electronics Engineering, Konya, Turkey * mehmet.sen@asbu.edu.tr Email of the corresponding author (Received: 29 May 2023, Accepted: 20 June 2023) (1st International Conference on Pioneer and Innovative Studies ICPIS 2023, June 5-7, 2023) ATIF/REFERENCE: Şen, M. & Özcan, M. (2023). A Novel Method for SoC Estimation of Lithium-Ion Batteries Based on Kalman Filter in Electric Vehicle. International Journal of Advanced Natural Sciences and Engineering Researches, 7(5), 1- 6. Abstract – In recent years, the energy crisis has become more and more serious. Li-ion batteries are used in grids because of their benefits such as contributing to the intermittent generation of renewable energy sources and stabilizing the grid. In addition, li-ion batteries are widely used in electric vehicles due to their long cycle life and high energy density. Li-ion battery state of charge (SoC) is an important indicator for safety. Therefore, the SoC estimation of li-ion batteries is important. Today, there are different methods to determine the state of the SoC in many applications. The traditional estimation method, the ampere-hour integration method and the coulomb counting method, has a cumulative error and cannot achieve good results in a working environment with Gaussian noise. For this purpose, in this study, firstly, the Thevenin equivalent model was created for battery SOC estimation, and then the Kalman filter algorithm was applied. Thus, the estimation error caused by Gaussian noise is eliminated. SoC estimation was simulated for the battery model created in the MATLAB/Simulink program using this method. Using these simulation results, the charge/discharge characteristics of the battery were obtained. However, the SoC estimation has been made for the charging and discharging processes of the battery. In the simulation, the charge value was recorded for 6 hours. The data recorded every 10 minutes gave results very close to the true value. Keywords – Electric Vehicle, Estimation, Li-Ion Battery, Kalman Filter, SoC I. INTRODUCTION The world is threatened by the emission of greenhouse gases from the use of fossil fuels [1]. One of the main causes of these pollutants is the widespread use of automobiles with internal combustion engines. The use of electric vehicles is seen as a method to reduce these environmental impacts [2-3]. For this solution to be effectively implemented, electric vehicle prices must be competitive with conventional vehicle prices. The battery has the largest share in the costs of electric vehicles [4]. In addition to electric vehicles, batteries have recently been used worldwide to store electrical energy for reasons such as grid stability and energy crisis [5-7]. Since electrical energy cannot be stored directly, batteries store electrical energy by converting it into chemical energy. With the development of technology, interest in battery systems is increasing day by day [8]. Battery types have also started to be produced in different types to meet the needs of