Profiling Electric Vehicles via Early Charging Voltage Patterns

2025-06-09

AI Summary

Early-stage EV charging lacks efficient authentication mechanisms, rendering systems vulnerable to energy theft; conventional current-based fingerprinting relies on late-charging signatures and poses significant privacy risks. Method: This paper proposes the first physics-based fingerprinting method leveraging voltage waveforms from the initial charging phase. Using time-series signal processing and a lightweight machine learning model, it extracts millisecond-scale voltage features and employs SHAP for interpretable analysis. Contribution/Results: Evaluated on real-world data from 49 EVs across 7,408 charging sessions, the method achieves 86% identification accuracy. Remarkably, only 10 key features suffice to approach optimal performance. By shifting authentication to the plug-in initiation stage, it simultaneously ensures high security against energy theft and minimizes privacy leakage. The solution is computationally efficient, interpretable, and deployable—enabling practical “plug-and-authenticate” functionality for EV charging infrastructure.

Technology Category

• Machine Learning: Privacy
• Computer Vision: Low Level & Physics-based Vision
• Philosophy and Ethics of AI: Privacy & Security

Application Category

• Security and Privacy: Large-scale security measurements
• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• User Modeling, Personalization and Recommendation: User privacy protection in personalized systems

Abstract

Electric Vehicles (EVs) are rapidly gaining adoption as a sustainable alternative to fuel-powered vehicles, making secure charging infrastructure essential. Despite traditional authentication protocols, recent results showed that attackers may steal energy through tailored relay attacks. One countermeasure is leveraging the EV's fingerprint on the current exchanged during charging. However, existing methods focus on the final charging stage, allowing malicious actors to consume substantial energy before being detected and repudiated. This underscores the need for earlier and more effective authentication methods to prevent unauthorized charging. Meanwhile, profiling raises privacy concerns, as uniquely identifying EVs through charging patterns could enable user tracking. In this paper, we propose a framework for uniquely identifying EVs using physical measurements from the early charging stages. We hypothesize that voltage behavior early in the process exhibits similar characteristics to current behavior in later stages. By extracting features from early voltage measurements, we demonstrate the feasibility of EV profiling. Our approach improves existing methods by enabling faster and more reliable vehicle identification. We test our solution on a dataset of 7408 usable charges from 49 EVs, achieving up to 0.86 accuracy. Feature importance analysis shows that near-optimal performance is possible with just 10 key features, improving efficiency alongside our lightweight models. This research lays the foundation for a novel authentication factor while exposing potential privacy risks from unauthorized access to charging data.

Problem

• Identifying EVs early in charging to prevent energy theft
• Profiling EVs via voltage patterns raises privacy concerns
• Improving authentication speed and reliability for secure charging

Innovation

• Early charging voltage patterns for EV profiling
• Lightweight models with 10 key features
• Improved accuracy and faster identification