Research on Status Assessment and Operation and Maintenance of Electric Vehicle DC Charging Stations Based on XGboost

Abstract

Highlights: What are the main findings? Taking into account factors such as electric vehicle users’ driving and charging habits, road traffic conditions, and charging station equipment, a training dataset was created using historical data, online monitoring data, and external environmental data. An XGBoost algorithm was employed to develop a charging station state assessment model. Based on the assessment results and integrating fault parameters, a risk assessment model was established. This model aims to optimize the maintenance of DC charging stations by balancing economic efficiency and reliability, determining the maintenance duration for each station under different conditions. What is the implications of the main findings? The model comprehensively evaluates the operational status of charging stations, identifies potential issues, and ranks them by severity to enable differentiated intelligent maintenance, overcoming the limitations of traditional scheduled maintenance. This approach significantly improves system reliability and overall efficiency, ensuring that charging stations at different locations receive appropriate maintenance resources. As a result, it effectively reduces the overall fault rate of charging stations and ensures the normal operation of both urban traffic networks and power grids. With the rapid development of electric vehicles, the infrastructure for charging stations is also expanding quickly, and the failure rate of charging piles is increasing. To address the effective operation and maintenance of charging stations, a method based on the XGBoost algorithm for electric vehicle DC charging stations is proposed. An operation and maintenance system is constructed based on state analysis, considering the operational status of the charging stations and users’ charging habits. Factors such as driving and charging habits, road traffic, and charging station equipment are taken into account. The training sample data are established using historical data, online monitoring data, and external environmental data, and the charging station status evaluation model is trained using the XGBoost algorithm. Based on the condition assessment results, a risk assessment model is established in combination with fault parameters. Risk tracking of the charging stations is conducted using the energy not charged (ENC), evaluating the risk level of each station and determining the operation and maintenance order. The optimal operation and maintenance model for DC charging stations, aimed at achieving both economic and reliability goals, is constructed to determine the operation and maintenance schedule for each station. The results of the case study demonstrate that the state evaluation and operation and maintenance strategy can significantly improve the reliability of the system and the overall benefits of operation and maintenance while meeting the required standards.