KEYWORDS: Roads, Resistance, Networks, Computer simulations, Neural networks, Monte Carlo methods, Error analysis, Telecommunications, System on a chip, Solids
Electric vehicle load forecasting is the basis for the safe and stable operation of the distribution network, and it is also a prerequisite for the planning and layout of electric vehicle infrastructure. Firstly, considering the characteristics of electric vehicles as participants in the transportation network and the characteristics and mobile load characteristics of electric vehicles as vehicles, a method for forecasting the temporal and spatial distribution of electric vehicle charging load considering traffic flow is proposed. This method first establishes a road network model that considers the flow-density-speed model and the road section impedance and node impedance based on the traffic flow of the road section based on the characteristics of the urban road network multiple intersections and the traffic flow of each section. Secondly, introduce the time function of charging probability and Freud's path search algorithm to assign start and end nodes and plan the driving path for electric vehicles to simulate its dynamic driving process and charging behavior. Finally, a simulation experiment of charging load prediction is carried out with a typical regional road network. The result shows that the distribution of electric vehicle charging load in different functional areas is different and the time distribution is uneven, which verifies the effectiveness and feasibility of the proposed method.
With the rapid development of new energy vehicles, problems such as “charging difficulty” are becoming increasingly prominent. The charging space is occupied by fuel vehicles, resulting in the inability of new energy vehicles to charge after they arrive at the charging station. This paper proposes a license plate recognition algorithm model which combines the three steps of vehicle detection, license plate detection, and license plate recognition, which improves the robustness of license plate recognition in complex scenes. In view of the lack of Chinese license plate data, the data enhancement method is adopted to improve the recognition accuracy of Chinese provincial abbreviations and common color license plates such as blue, green, and yellow. According to the characteristics of a small license plate target and easy rotation in the license plate recognition task, the WPOD-NET network is selected for license plate detection. Finally, the license plates of new energy vehicles and fuel vehicles are distinguished by recognizing the color and law of license plates, so as to provide supporting information for the generation of fuel vehicle occupancy alarm. In this paper, the data collected by the cameras of some charging stations in a city are used for verification. The verification results show that the accuracy of oil vehicle occupancy recognition is as high as 97%.
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