A New Calibration Approach for Charging Facilities for Electric Vehicles via Machine Learning

The traditional calibration method for charging piles which used for electric vehicles relies on the field inspection of standard devices of metrology agency. However, with the continuous expansion of electric vehicles' scale in the future, the calibration workload of charging piles will become larger and larger, calibration method that relies solely on the standard devices cannot hold so heavy workload. In this paper, we propose a new calibration method, which can reduce the calibration workload of standard devices fundamentally. Firstly, we train a model which called virtual standard device for the metrology standard through machine learning, and when calibrating the charging pile, we just need to input the charging message into the model, and then we can get the calibration result which includes normal pile, abnormal pile and uncertain pile. Once the model training is completed, the entire calibrating process including the collection of message data, the import of data into the model and the calibration result requires no human intervention unless the result is uncertain. Moreover, it is found that the higher the model accuracy is, the smaller the uncertainty interval is and the less the manual calibration work is. The method of simulated calibration plus field calibration proposed in this paper can help reduce the calibration workload fundamentally and greatly reduce the calibrating pressure of metrology agency.