Abstract
Fuel cells (FCs), one of the renewable energy sources, have started to be preferred as a power source in electric vehicles in recent years and research studies are continuing on designs in this direction. Although their efficiency is low <50%, they produce Direct Current (DC) electrical energy by electrochemical conversion without requiring battery systems, which can be used in electric vehicle drive systems. There are zero-emission effects such as water and temperature rise with waste and environmental aspects. One of the major disadvantages is the DC voltage amplitude they produce is inversely proportional to the temperature increase. In this context, parameter estimation is required to adapt the fluctuating FC voltage to a certain value adaptively with the DC-DC boost converter circuit. In this study, parametric simulation studies were carried out with Ansys-Electronics 2019-R3 software to determine the DC voltage level of a certain number of series and parallel connected FC cells depending on different temperature values. Duty ratio values of two-phase interleaved dual cascaded DC-DC boost converter circuit for desired output voltage were determined by using Adaptive Nero-Fuzzy Inference System (ANFIS) modeling of 1300 data determined by simulation studies. Thus, the output voltage of the converter is adaptively fixed at a certain value.
Highlights
FUEL CELLS (FCs) are electrochemical structures that use a chemical reaction like a battery in the production of Direct Current (DC) electrical energy
To keep the output voltage constant at the desired level, the output voltage level estimation according to the input variables such as the number of cells connected in series and parallel and the temperature was tested with Adaptive Nero-Fuzzy Inference System (ANFIS) and a certain number of data were received from the 1300 data obtained and trained in the ANFIS network for verification
ANFIS is a system in which artificial neural networks of artificial intelligence methods and fuzzy logic are applied together as an inference system derived from mathematical expressions
Summary
FUEL CELLS (FCs) are electrochemical structures that use a chemical reaction like a battery in the production of DC electrical energy. The use of ANFIS for parameter estimation in the design of power electronic circuits is very useful for predicting the performance of complex systems and offers a very low error rate modeling In this context, a PEMFC system with a rated power of 5 kW was modeled with an extremely high accuracy performance with ANFIS. It is recommended to make parameter estimation with ANFIS to use the electrical energy obtained from FCs effectively and to make an adaptive voltage controller in the two-phase IDCB converter circuit. The most important contribution of this study to the previous literature is an adaptive controlled two-phase dual boost converter circuit for duty control in DC voltage value fluctuations provided from fuel cells used in electric vehicle power systems. It is shown that all parameters can be monitored with parametric simulation supported artificial intelligence techniques before the prototype power electronics are installed
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