PREDICTIVE MAXIMUM POWER POINT TRACKING (MPPT) ALGORITHM FOR PERMANENT EXCHANGE MEMBRANE FUEL CELL (PEMFC)

Renewable energy is increasingly being used as a backup energy source by a wide range of sectors due to concerns about negative impacts of continuous consumption of fossil fuel. Fuel cell power generation technology is gaining importance in its own right in the current global landscape of electricity generation, distribution, and satisfying consumer demand since it has numerous advantages such as environmentally friendly, high efficiency, noise-free, and safe operation. In this project, a PEMFC has been chosen due to its striking features that is suitable for stationary applications like residential and transportation uses. Generally, the output characteristics of fuel cells are non-linear and influenced by parameters such as the cell temperature, oxygen partial pressure, hydrogen partial pressure, and membrane water content. In each particular condition, there is only one unique operating point for a fuel cell system with the maximum output. Therefore, it is important to find the optimal operating voltage or current of fuel cell systems in order to increase the efficiency of fuel cells. This maximum output can be determined by using MPPT method such as P&O, INC, FLC, PSO, MPC and more. The milestone of this project is to develop a fully functional PEMFC system based on the mathematical modelling presented in the literature with predictive MPPT control. This algorithm is said to be able to enhance the performance of the PEMFC system due to high in stability and low in complexity. This project introduces a PEMFC system with model predictive control (MPC). In this project, a DC-DC boost converter is used to regulate the output voltage of the fuel cell in order to extract the maximum output power where the switch of this boost converter is controlled by the MPC. The focus of this project is to show the power characteristics extracted from the PEMFC system by using predictive control. A comparison of PEMFC performances based on the proposed technique with other existing MPPT algorithms will be done to validate the algorithm performance. As a result, model predictive control (MPC) exhibits the fast tracking of MPP locus, outstanding accuracy, and robustness with respect to environmental changes