Metering Error Estimation of Fast-Charging Stations Using Charging Data Analytics

Accurate electric energy metering (EEM) of fast charging stations (FCSs) is the cornerstone for ensuring fair electric energy transactions in electric vehicle (EV) industry. Hence, monitoring EEM errors of FCSs is of significant practical importance. However, traditional field verification methods are constrained by high costs and low efficiency, while existing data-driven approaches struggle to achieve highly reliable and accurate estimation of EEM errors. In response, a novel estimation method, i.e. metering performance comparison (MPC) method, is proposed. In MPC method, by utilizing the measurement data from EVs’ battery management system (BMS) as medium, comparison chains of EEM errors of multiple FCSs are established. Combining with big data analytics, high accuracy estimation of EEM errors is achieved. According to on-site charging data, the simulation results indicate that for FCSs with an accuracy grade of 2%, the discriminative accuracy of MPC method exceeds 95%. In the future, with continuous increase of FCSs, MPC method is expected to enhance the foundation for a fair electricity trading market with low cost. Moreover, MPC method is anticipated to accelerate the digital transformation of EEM errors verification for FCSs.