AbstractThe article contains an analysis of charging electric cars as a way to increase the use of energy produced from a photovoltaic system. A photovoltaic system is described, consisting of two micro installations, with a power output of up to 40 kWp each, supplying two sectors of the building of the Lublin Science and Technology Park. An internet platform for monitoring the operation of a photovoltaic system is presented. Next, an innovative system for monitoring, consumption and production analysis of electric energy in individual building sectors is described. A surplus of energy produced in one of the sectors was found. It was proposed that this excess energy would be used to charge electric vehicles. An analysis of the surplus power generated by the photovoltaic system in excess of the energy consumed by the building sector was used to determine the power available to the wall charger. The surplus of energy produced was used for calculations related to the amount of charging the electric vehicle required and the cost of traveling 100 km. Charging an electric car not only provides a faster return on investment in the installation, but also presents drivers with a very ecological and economical transport solution.

Electric vehicles are fully ecological means of transport only when the electricity required to charge them comes from Renewable Energy Sources (RES). When building a photovoltaic carport, the complex of its functions must consider the power consumption necessary to charge an electric vehicle. The performance of the photovoltaic system depends on the season and on the intensity of the sunlight, which in turn depends on the geographical conditions and the current weather. This means that even a large photovoltaic system is not always able to generate the amount of energy required to charge an electric vehicle. The problem discussed in the article is maximization of the share of renewable energy in the process of charging of electric vehicle batteries. Deep recurrent neural networks (RNN) trained on the past data collected by performance monitoring system can be applied to predict the future performance of the photovoltaic system. The accuracy of the presented forecast is sufficient to manage the process of the distribution of energy produced from renewable energy sources. The purpose of the numerical calculations is to maximize the use of the energy produced by the photovoltaic system for charging electric cars.

This article presents a method for assessing the selection of carport power for an electric vehicle using the Metalog probability distribution family. Carports are used to generate electricity and provide shade for vehicles parked underneath them. On the roof of the carport, there is a photovoltaic system consisting of photovoltaic panels and an inverter. An inverter with Internet of Things functions generates data packets which describe the operation of the entire system at certain intervals and sends them via wireless transmission to a cloud server. The transmitted data can be processed offline and used to determine the charging capacity of individual electric vehicles. This article presents the use of the Metalog family of distributions to predict the production of electricity by a photovoltaic carport with the accuracy of the probability distribution. Based on the calculations, an electric vehicle was selected that can be charged from the carport.

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The article describes the design and optimization of operation of an electric bus powered by the hydrogen fuel cells. At the beginning, an approach to design of a 12-meter urban bus, powered by hydrogen, is presented, as well as examples of components for its construction. Next, the problem of selecting the size of traction batteries and stacks of the Proton Exchange Membrane (PEM) hydrogen fuel cells was discussed. These are the key components affecting the price of the bus and should be subject to optimization. The results of optimization of the size of traction batteries and the fuel cell system for a bus traveling in inter-city traffic are presented. The optimization was based on data from the literature and data from the monitoring system of actual hydrogen powered buses located on the Internet platform. The main purpose of the research, which was to determine the total costs of ownership (TCO), is presented as well.