# Profiling Electric Vehicles via Early Charging Voltage Patterns

arXiv: https://arxiv.org/abs/2506.07714 · HTML: https://mlsanity.com/paper/2506.07714/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 42 references that are in the map.
- First posted on arXiv 9 Jun 2025.

## Builds on (most cited first, 9 of 9)

- [A Unified Approach to Interpreting Model Predictions](https://mlsanity.com/paper/1705.07874/) (2017)
- [EVBattery: A Large-Scale Electric Vehicle Dataset for Battery Health and Capacity Estimation](https://mlsanity.com/paper/2201.12358/) (2022)
- [Impact of Electric Vehicles Botnets on the Power Grid](https://mlsanity.com/paper/2103.09153/) (2021)
- [EVScout2.0: Electric Vehicle Profiling Through Charging Profile](https://mlsanity.com/paper/2106.16016/) (2021)
- [EVExchange: A Relay Attack on Electric Vehicle Charging System](https://mlsanity.com/paper/2203.05266/) (2022)
- [On the Feasibility of Profiling Electric Vehicles through Charging Data](https://mlsanity.com/paper/2210.05433/) (2022)
- [Electric Vehicles Security and Privacy: Challenges, Solutions, and Future Needs](https://mlsanity.com/paper/2301.04587/) (2023)
- [Your Battery Is a Blast! Safeguarding Against Counterfeit Batteries with Authentication](https://mlsanity.com/paper/2309.03607/) (2023)
- [Leaky Batteries: A Novel Set of Side-Channel Attacks on Electric Vehicles](https://mlsanity.com/paper/2503.08956/) (2025)

## Abstract (arXiv)

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.