State estimation applied for SOC monitoring of batteries for electric vehicles

• Grant number: 25/12417-0
• Support Opportunities: Scholarships in Brazil - Scientific Initiation
• Start date: September 01, 2025
• End date: August 31, 2026
• Field of knowledge: Engineering - Electrical Engineering - Industrial Electronics, Electronic Systems and Controls
• Principal Investigator: Marcos Rogério Fernandes
• Grantee: Davi de Almeida Cordeiro
• Host Institution: Escola de Engenharia de São Carlos (EESC). Universidade de São Paulo (USP). São Carlos , SP, Brazil

Abstract

This undergraduate research proposal aims to develop a study on the state of charge (SOC) estimation problem for batteries used in electric vehicles, with a particular focus on predicting estimation error. Specifically, the Extended Kalman Filter (EKF), the Unscented Kalman Filter (UKF), and the Bootstrap Particle Filter will be implemented for state of charge (SOC) estimation. A performance comparison among the three filter types will then be conducted under different noise scenarios to evaluate the advantages and disadvantages of each estimation strategy and the consistency of the error prediction. To support this study, a battery simulator will be developed in the MATLAB/Simulink environment to generate synthetic signals for testing and validating the state estimation algorithms. Modern applications often involve the use of batteries either directly or indirectly, such as in electric vehicles and renewable energy storage systems. Therefore, accurate battery charge information is essential for proper battery management in these systems, as well as for preventing failures and accidents. This project aims to contribute to the advancement of battery charge monitoring technologies that can be applied in various contexts, especially in electric vehicles. Moreover, it will enable the student to work with signal processing, dynamic system simulation, and estimation theory, and develop and enhance their practical programming skills. (AU)