OPTIMIZATION OF THE ELECTRIC VEHICLE CHARGING SYSTEM BASED ON OPERATIONS RESEARCH METHODS

DOI: 10.31673/2412-4338.2024.038693

Abstract

In the context of global urbanization and the growing popularity of electric vehicles, there is a need to develop effective charging infrastructure systems to ensure sustainable development of urban and remote regions. The article investigates methods for optimizing electric vehicle charging systems based on operations research to ensure efficient resource management, minimize costs, and increase the productivity of charging stations. Particular attention is paid to the development of mathematical models for the optimal placement of charging stations and management of charging processes, taking into account factors such as electricity demand, peak loads, and the integration of renewable energy sources.
The article analyzes current scientific research in the field, including mathematical programming, stochastic modeling, and fast charging algorithms that minimize driver waiting time. The presented approaches are aimed at improving the efficiency of charging stations in urban areas and optimizing the use of infrastructure in real time. In addition, the paper considers the issues of forecasting the demand for charging stations, which is important to avoid overloading the power grid during peak periods. The results of the study demonstrate that the integration of renewable energy sources and the use of machine learning methods for adaptive control can significantly improve the stability of power grids and reduce dependence on traditional energy sources.
The purpose of the article is to develop new approaches to the planning and management of charging infrastructure, which will contribute to environmentally friendly transportation, the development of a sustainable urban environment, and more efficient use of resources in the face of growing demand for electric vehicles.

Keywords: optimization, electric vehicles, charging infrastructure, operations research, renewable energy sources, mathematical modeling, energy management, machine learning, urban infrastructure, sustainable development.

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