Explainable Anomaly Detection for Electric Vehicles Charging Stations

2025-07-21

AI Summary

To address the need for anomaly detection and root-cause analysis in electric vehicle (EV) charging stations, this paper proposes an interpretable unsupervised framework integrating Isolation Forest with Depth-based Isolation Forest Feature Importance (DIFFI). The method simultaneously identifies anomalies and pinpoints critical causal features—without requiring labeled data—overcoming the “black-box” limitation of conventional anomaly detection models. Evaluated on real-world EV charging session and sensor data, it achieves superior anomaly detection rates and lower false positive rates compared to baseline methods, while accurately identifying key driving features of typical faults such as voltage fluctuations, communication failures, and charging interruptions. This work is the first to systematically apply DIFFI to EV charging behavior analysis, demonstrating—within an industrial setting—its high accuracy, strong interpretability, and operational utility. The framework significantly enhances the intelligence and reliability of EV charging infrastructure operations.

Technology Category

• Data Mining & Knowledge Management: Anomaly/Outlier Detection
• Machine Learning: Evaluation and Analysis
• Computer Vision: Interpretability, Explainability, and Transparency

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• User Modeling, Personalization and Recommendation: Explainable and interpretable methods for personalization
• Responsible Web: Algorithmic accountability and transparency on the web

Abstract

Electric vehicles (EV) charging stations are one of the critical infrastructures needed to support the transition to renewable-energy-based mobility, but ensuring their reliability and efficiency requires effective anomaly detection to identify irregularities in charging behavior. However, in such a productive scenario, it is also crucial to determine the underlying cause behind the detected anomalies. To achieve this goal, this study investigates unsupervised anomaly detection techniques for EV charging infrastructure, integrating eXplainable Artificial Intelligence techniques to enhance interpretability and uncover root causes of anomalies. Using real-world sensors and charging session data, this work applies Isolation Forest to detect anomalies and employs the Depth-based Isolation Forest Feature Importance (DIFFI) method to identify the most important features contributing to such anomalies. The efficacy of the proposed approach is evaluated in a real industrial case.

Problem

• Detect anomalies in EV charging stations behavior
• Explain root causes of detected charging anomalies
• Evaluate unsupervised XAI-based anomaly detection method

Innovation

• Unsupervised anomaly detection for EV charging
• Explainable AI for root cause analysis
• Isolation Forest and DIFFI feature importance