Estimação do estado de carga de baterias de lítio de segunda vida com observador não linear

• Ano de defesa: 2025
• Autor(a) principal: Mota, Ana Carolina da Silva
• Orientador(a): Reginato, Romeu
• Banca de defesa: Cantante, Daniel Augusto , Battistella, Sandro
• Tipo de documento: Dissertação
• Tipo de acesso: Acesso aberto
• Idioma: por
• Instituição de defesa: Universidade Estadual do Oeste do Paraná; Foz do Iguaçu
• Programa de Pós-Graduação: Programa de Pós-Graduação em Engenharia Elétrica e Computação
• Departamento: Centro de Engenharias e Ciências Exatas
• País: Brasil
• Palavras-chave em Português: Baterias de lítio; Segunda vida; Observador não-linear; SOC; SOH; BMS
• Palavras-chave em Inglês: Lithium battery; Second-life; Non-linear observer; SOC; SOH; BMS
• Área do conhecimento CNPq: ENGENHARIAS::ENGENHARIA ELETRICA
• Link de acesso: https://tede.unioeste.br/handle/tede/8018

Batteries in electric vehicles (EVs) are replaced when they reach approximately 80% of their nominal capacity. Avoiding disposal and making use of the remaining potential in these batteries is the primary motivation behind research on second-life battery (B2U) applications. The state of charge (SOC), although it informs the system of the available energy in the batteries for use, cannot be directly measured by sensors. It is the responsibility of the battery management system (BMS) to have an algorithm that estimates it with the necessary accuracy. Thus, this work addresses the development and tuning of a nonlinear observer to estimate the SOC of second-life batteries. The tuning method uses the linearized observer model and root locus analysis, aiming to reduce the observation error convergence time. Additionally, the influence of the battery’s State of Health (SOH) on SOC estimation is studied to assess the robustness and performance of the NLO method. Besides the simplicity of the tuning method, simulation results show that for the proposed design, convergence occurs more quickly, and for the SOH at the beginning of the second life, estimation errors remained below 2%. Moreover, even with battery degradation, the maximum error for a 200% increase in internal resistance was less than 9%.