Impacto da tecnologia HCCI no desempenho e emissões de gases poluentes em veículos híbridos

• Ano de defesa: 2024
• Autor(a) principal: Moura Júnior, Claudecir Fernandes de Freitas
• Tipo de documento: Dissertação
• Tipo de acesso: Acesso aberto
• Idioma: por
• Palavras-chave em Português: Biocombustíveis; Python (Linguagem de programação de computador); Catalisadores; Motores de combustão interna; Biomass energy; Python (Computer program language); Catalysts; Internal combustion engines; CNPQ::ENGENHARIAS::ENGENHARIA MECANICA
• Link de acesso: http://repositorio.ufc.br/handle/riufc/80910

This study aimed to analyze pollutant gas emissions by evaluating different vehicle configurations using two types of powertrains. For each powertrain configuration, both a conventional internal combustion engine and a HCCI engine were tested. The simulations were performed using IGNITE software, and the exhaust gases were treated by a catalyst modeled in Python. The computational model of the Renault Sandero vehicle demonstrated good accuracy, with validation errors below 1%, while the catalyst model showed a maximum emission error below 3%. The results indicated that the conventional powertrain configuration with the HCCI engine performed better compared to the conventional engine. In contrast, the hybrid powertrain model showed unsatisfactory results, suggesting the need for further optimizations. Therefore, an in-depth energy management analysis was conducted to maximize the utilization of the HCCI engine in the hybrid configuration, with the expectation of improving efficiency and reducing emissions.