Estimação do Estado de Carga (SoC) e do Estado de Saúde (SoH) por filtro de Kalman estendido considerando a perda de capacidade em baterias de veículos elétricos e híbridos

• Ano de defesa: 2025
• Autor(a) principal: Corrêa, Paulo Henrique Strauss
• Tipo de documento: Dissertação
• Tipo de acesso: Acesso aberto
• Idioma: por
• Instituição de defesa: Universidade Tecnológica Federal do Paraná; Ponta Grossa; Brasil; Programa de Pós-Graduação em Engenharia Elétrica; UTFPR
• Palavras-chave em Português: Veículos elétricos; Baterias elétricas; Lítio; Controle de perdas; Kalman, Filtragem de; Electric vehicles; Electric batteries; Lithium; Loss control; Kalman filtering; CNPQ::ENGENHARIAS::ENGENHARIA ELETRICA; Engenharia/Tecnologia/Gestão
• Link de acesso: http://repositorio.utfpr.edu.br/jspui/handle/1/36910

This thesis presents a simple methodology for estimation of the state of charge and state of health of a lithium-iron-phosphate (LFP) cell used in the development of a hybrid-flex urban light vehicle (VHF-Urbano) as part of the ROTA 2030 Program. This methodology is tested and validated in this work for a lithium-nickel-cobalt-aluminum-oxide (NCA) battery and then applied to the VHF-Urbano cell. In this thesis, the batteries are modeled as equivalent electrical circuits based on the Thévenin model with two RC pairs to obtain a computational model of the batteries. This model represents the dynamic behavior of the batteries being equivalent to real batteries. The methodology includes the use of extended Kalman filters and the proposed electrical model for the estimation of the state of charge and state of health of the batteries in a computational environment considering the loss of battery capacity (capacity fade). Initially, this thesis presents a theoretical review on lithium-ion batteries and electrical modeling techniques. The Thévenin model is then developed and validated through simulations in MATLAB and experiments with real batteries, demonstrating high accuracy and computational efficiency. The results highlight the ability of the proposed electrical model to represent the dynamic behavior of the battery and the effectiveness of the extended Kalman filter in estimating the state of charge and state of health under variable conditions. The results obtained in a computational environment guide the methodology for real-time estimation of the state of charge and state of health of batteries. The application of this approach covers sectors such as electric vehicles, electronic devices and energy storage systems. This thesis contributes significantly to the improvement of the battery management system, offering a robust basis for future research and industrial applications.