Controle preditivo não linear para o gerenciamento de energia de um veículo elétrico híbrido a célula a combustível

• Ano de defesa: 2020
• Autor(a) principal: Pereira, Derick Furquim
• Tipo de documento: Tese
• Tipo de acesso: Acesso aberto
• Idioma: por
• Instituição de defesa: Universidade Federal do Rio de Janeiro; Brasil; Instituto Alberto Luiz Coimbra de Pós-Graduação e Pesquisa de Engenharia; Programa de Pós-Graduação em Engenharia Elétrica; UFRJ
• Palavras-chave em Português: Controle preditivo não linear; Veículos elétricos híbridos; Células a combustível; Engenharia Elétrica
• Link de acesso: http://hdl.handle.net/11422/23223

This work proposes an energy management system (EMS) for a fuel cell hybrid electric vehicle (FCHEV). This system is based on nonlinear model predictive control (NMPC) and employs a recurrent neural network (RNN) to model a proton exchange membrane (PEM) fuel cell (FC). With NMPC, it is possible to formulate control objectives that would not be possible with linear model predictive control (MPC), such as maximum e ciency point tracking (MEPT) of the FC. In addition, compared to traditional electrochemical models, the RNN can predict the FC's nonlinear dynamics with better accuracy. The EMS was implemented on a low-cost single board computer, and the experiments for controller validation were performed on a hardware-in-the-loop (HiL) test bench equipped with a real 3 kW FC stack. The experimental results demonstrate that the proposed EMS can meet the vehicle's energy demand, where it performs the battery's charge sustaining battery, and can operate the FC in its most e cient region. In addition, a comparative study was also carried out between the proposed NMPC, a linear MPC and a hysteresis band control. The results of this comparative study demonstrate that the NMPC provides a better fuel economy and can reduce the FC degradation.