HanZongtao / Shenzhen Academy of Metrology & Quality Inspection
ZhengHuiZheng / Shenzhen Academy of Metrology & Quality Inspection
YuanRuiming / Metrology Center, State Grid Jibei Electric Power Company Limited
Compared to traditional fuel vehicles, new energy vehicles offer advantages such as no exhaust emissions and lower carbon footprint. In recent years, the number of electric vehicles has grown exponentially, and the number of charging infrastructure has also increased rapidly. Electric vehicle charging stations are measurement instruments used for trade settlement, and regulations require these instruments to be periodically verified. Traditional measurement work for charging stations involves on-site verification based on physical standards, which is limited by physical space and cannot meet the growing demand efficiently. This paper analyzes and discusses the issue based on Bayesian theory, designing a system for verifying electric vehicle charging stations that improves efficiency. The algorithm explores remote measurement error assessment and uncertainty evaluation methods, using the tree-like structure of the charging process to estimate measurement errors, achieving an accuracy rate of over 85% for identifying out-of-tolerance charging stations, demonstrating the algorithm's effectiveness.