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

Accurate electric energy metering (EEM) of fast charging stations (FCSs), serving as critical infrastructure in the electric vehicle (EV) industry and as significant carriers of vehicle-to-grid (V2G) technology, is the cornerstone for ensuring fair electric energy transactions. Traditional on-site verification methods, constrained by their high costs and low efficiency, struggle to keep pace with the rapid global expansion of FCSs. In response, this paper adopts a data-driven approach and proposes the measuring performance comparison (MPC) method. By utilizing the estimation value of state-of-charge (SOC) as a medium, MPC establishes comparison chains of EEM performance of multiple FCSs. Therefore, the estimation of EEM errors for FCSs with high efficiency is enabled. Moreover, this paper summarizes the interfering factors of estimation results and establishes corresponding error models and uncertainty models. Also, a method for discriminating whether there are EEM performance defects in FCSs is proposed. Finally, the feasibility of MPC method is validated, with results indicating that for FCSs with an accuracy grade of 2\%, the discriminative accuracy exceeds 95\%. The MPC provides a viable approach for the online monitoring of EEM performance for FCSs, laying a foundation for a fair and just electricity trading market.

A Self-Healing Magnetic-Array-Type Current Sensor with Data-Driven Identification of Abnormal Magnetic Measurement Units

Magnetic-array-type current sensors have garnered increasing popularity owing to their notable advantages, including broadband functionality, a large dynamic range, cost-effectiveness, and compact dimensions. However, the susceptibility of the measurement error of one or more magnetic measurement units (MMUs) within the current sensor to drift significantly from the nominal value due to environmental factors poses a potential threat to the measurement accuracy of the current sensor.In light of the need to ensure sustained measurement accuracy over the long term, this paper proposes an innovative self-healing approach rooted in cyber-physics correlation. This approach aims to identify MMUs exhibiting abnormal measurement errors, allowing for the exclusive utilization of the remaining unaffected MMUs in the current measurement process. To achieve this, principal component analysis (PCA) is employed to discern the primary component, arising from fluctuations of the measured current, from the residual component, attributed to the drift in measurement error. This analysis is conducted by scrutinizing the measured data obtained from the MMUs. Subsequently, the squared prediction error (SPE) statistic (also called $Q$ statistic) is deployed to individually identify any MMU displaying abnormal behavior. The experimental results demonstrate the successful online identification of abnormal MMUs without the need for a standard magnetic field sensor. By eliminating the contributions from the identified abnormal MMUs, the accuracy of the current measurement is effectively preserved.