Estratégia de gerenciamento e controle visando o aumento da vida útil de baterias utilizadas em inversores fotovoltaicos híbridos off-grid

• Ano de defesa: 2025
• Autor(a) principal: Gonschorowski, Ezequiel
• Tipo de documento: Dissertação
• Tipo de acesso: Acesso aberto
• Idioma: por
• Instituição de defesa: Universidade Tecnológica Federal do Paraná; Pato Branco; Brasil; Programa de Pós-Graduação em Engenharia Elétrica e de Computação; UTFPR
• Palavras-chave em Português: Inversores elétricos; Baterias elétricas; Capacitadores; Vida útil (Engenharia); Electric inverters; Electric batteries; Capacitors; Service life (Engineering); CNPQ::ENGENHARIAS::ENGENHARIA ELETRICA; Engenharia/Tecnologia/Gestão
• Link de acesso: http://repositorio.utfpr.edu.br/jspui/handle/1/36905

Li-ion batteries are an efficient solution for energy storage in photovoltaic hybrid inverters due to their high energy and power density. To use them efficiently, estimating their state of charge () and state of health () is necessary. For this purpose, estimators that depend on the parameters of the electrical equivalent circuit of the battery, which vary with the operating point, are used. Prior tests are usually performed to obtain these parameters. To avoid dependence on these tests, this work presents a real-time estimation of the parameters of the electrical equivalent circuit of the battery. To improve the accuracy of the estimates, a calibration strategy for the maximum battery capacity is also presented, together with the characterization of the open circuit voltage ( ) curve as a function of the state of charge. Additionally, to ensure proper battery utilization in the off-grid hybrid photovoltaic inverter, aiming to extend battery life, this work proposes using model-based predictive control ( ) as a system power management strategy. The entire study was conducted through hardware-in-the-loop () simulation, using the Typhoon platform to simulate the inverter and the battery. The estimation, calibration, and power management strategy was implemented on the TMS320F28379D microcontroller. The estimation and calibration results demonstrated average percentage errors lower than 1.881% for the estimation and a maximum percentage error of 1.8% for the estimation. The power management performance was compared with two other strategies: rule-based and frequency-based. For all the evaluated indicators, such as the battery life cycle usage, the effective current ( ), and the energy dissipation in the storage system, the proposed strategy presented better results when compared to the rule-based and frequency-based techniques.