Remote Metrological Verification Method for Electric Vehicle Off-Board Chargers Based on the SIS Infectious Disease Model in a Vehicle-Pile Interaction Network

To address the inefficiency and inaccuracy of traditional offline sampling calibration and online calibration technologies for large-scale verification of electric vehicle off-board chargers (EVObCs), this article proposes a remote verification method for EVObC measurement errors (MEs) based on the charging interactions between electric vehicles (EVs) and EVObCs. First, by leveraging the principles of infectious disease transmission mode, a progressive metrological verification strategy for massive EVObCs is introduced. In addition, a method for calculating benchmark accumulated charging energy, based on the temporal convolutional neural network-attention mechanism (TCN-Attention), is proposed to enhance the accuracy of EVObC ME identification. Furthermore, based on the energy decay model, a method for evaluating the intensity degradation of EVObC metrological performance is presented to objectively reflect the degradation of EVObC metrological accuracy and the dynamic variability of metering states in the proposed model. Finally, an experiment based on real data from 192 off-board chargers distributed over an area of $40^{2}$ km was conducted. The results demonstrate that the proposed method can accurately identify standard noncompliant metering states of EVObCs, demonstrating its speed, accuracy, and strong generalization capabilities for large-scale verification and error identification across different types of EVObCs.