Predictive Maintenance of DC Fast-Charging Stations Using Unsupervised Anomaly Detection
The reliability of electric vehicle fast-charging infrastructure is becoming increasingly critical as deployment accelerates and the number of unavailable charging points grows. This work presents an unsupervised anomaly detection framework aimed at supporting predictive maintenance in DC fast-charging stations. The approach uses real minute-resolution operational data from a real charging station, including active, reactive and apparent power, power factor, phase power measurements and charger-side power measurements. Three complementary anomaly detection models were designed to capture different abnormal operating conditions: deviations in consumption patterns, efficiency losses between charger and grid analyser measurements, and phase imbalance in three-phase operation. Local Outlier Factor and Isolation Forest algorithms were integrated into an automated monitoring pipeline. Since labelled fault data were not available, validation was based on controlled injection of synthetic anomalies into real test signals, including physically coherent power disturbances, sensor or communication inconsistencies, progressive efficiency degradation and phase imbalance events. The results show that the framework is effective for detecting anomaly families that produce clear or sustained deviations, while more subtle temporal behaviours remain more challenging. Overall, the proposed framework provides a practical condition monitoring and early-warning approach that can support predictive maintenance decisions in DC charging infrastructure, while further temporal modelling is required for explicit degradation forecasting.