Autoencoder-Based Sensor Drift Detection and Mitigation for Resilient Charging Systems

Abstract

This work presents an autoencoder-based approach for sensor signal reconstruction and drift detection for charging systems. The proposed strategy is implemented within a Simulink-based system framework and evaluated under multiple operating conditions. An autoencoder with 8 neurons in the bottleneck layer is adopted, achieving accurate reconstruction across 10 variables and strong agreement with the physical sensor readings under normal conditions. In the case of a sensor fault, the autoencoder reconstruction remains closer to the expected true value compared to the corrupted measurement. Furthermore, feeding the autoencoder-reconstructed signal value back into the control framework in place of the faulty sensor signal leads to improved power monitoring. These results highlight the potential of autoencoder-based virtual sensing to extend the concept of resiliency to all components of the charging system, including sensors.