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All Journal International Journal of Robotics and Control Systems
Ali Rezaei
Quchan University of Technology
Control & Systems Engineering Electrical & Electronics Engineering

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Design a New Multiport DC-DC Converter to Charge an Electric Car Amin Abedini Rizi; Ali Rezaei; Mohammadreza Ghorbani Rizi; Mohammadmahdi Aliakbari Rizi
International Journal of Robotics and Control Systems Vol 2, No 1 (2022)
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/ijrcs.v2i1.566

Abstract

Due to the lack of oil and gas, electric cars have been in high demand in recent years. There are three kinds of electric vehicles, including Hybrid Electric Vehicles (HEV), Battery Electric Vehicle (BEV), and Plug-in Hybrid Electric Vehicle (PHEV).  There is no charging portion for the batteries in the HEV where the batteries are not connected to the power grid, but BEV and PHEV can be charged by a power outlet, and the number of batteries is increased. In order to charge the battery of the Electric Vehicles (EVs), there are two ways, including the power grid and renewable energies. There are already quite a few outages in many countries, and using a power grid for charging the batteries is not suitable. Therefore, the only choice is renewable energy sources such as photovoltaic (PV), fuel-cell (FC), and so on. Furthermore, to use the DC voltage of the renewable sources, two conventional DC-DC converters are required to deliver the energy of the sources to the bank of batteries. To feed the batteries, this paper proposes a two-input one output topology that contains PV, FC, and other components. Simulation results demonstrate that the presented system is improving the system because it is able to feed the batteries with low power losses and low ripples.
Techno-Economic Analysis of a 12-kW Photovoltaic System Using an Efficient Multiple Linear Regression Model Prediction Pouya Pourmaleki; Willis Agutu; Ali Rezaei; Nima Pourmaleki
International Journal of Robotics and Control Systems Vol 2, No 2 (2022)
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/ijrcs.v2i2.702

Abstract

Renewable energy sources are expected to replace traditional energy sources such as oil and gas in the future. It goes without saying that solar energy has been demonstrated to be a key source of green energy. Solar energy is used because it is abundant, pollution-free, and easily available. However, the power utility market requires highly exact solar energy forecasts. These challenges need the creation of a device that can precisely predict solar energy output via processing the location's weather data, which is accomplished through the use of machine learning and multiple linear regression (MLR). Some elements, such as the number of cloudy days, humidity, temperature, wind condition, and precipitation, should be addressed while simulating solar power output. In this paper, a 12-kW photovoltaic (PV) system on the rooftop of a house in Isfahan was studied using the System Advisor Model (SAM). The most significant research contribution of the proposed paper is to predict the output power of a solar system with the lowest possible error. According to the simulation results, by using the MLR model, the predicted power has an error of 6 % with the actual power, which is a very good estimation. In addition, this system meets each household's energy needs plus an additional 8430 kWh per year, resulting in being paid by utility companies, a fewer number of outages, and lower air pollution levels.
Co-Authors Amin Abedini Rizi Mohammadmahdi Aliakbari Rizi Mohammadreza Ghorbani Rizi Nima Pourmaleki Pouya Pourmaleki Willis Agutu