Detection of Lying Electrical Vehicles in Charging Coordination Application Using Deep Learning

TL;DR

This paper addresses the problem of detecting dishonest electric vehicles in charging coordination using deep learning, demonstrating that the proposed neural network models can effectively identify lying EVs and improve grid stability.

Contribution

It introduces a deep learning-based anomaly detection method for identifying dishonest EVs in charging coordination, utilizing real driving data and simulated attacks.

Findings

Lying EVs gain higher charging priority than honest ones.

The GRU model outperforms MLP in detection accuracy.

High detection accuracy with low false positive rate.

Abstract

The simultaneous charging of many electric vehicles (EVs) stresses the distribution system and may cause grid instability in severe cases. The best way to avoid this problem is by charging coordination. The idea is that the EVs should report data (such as state-of-charge (SoC) of the battery) to run a mechanism to prioritize the charging requests and select the EVs that should charge during this time slot and defer other requests to future time slots. However, EVs may lie and send false data to receive high charging priority illegally. In this paper, we first study this attack to evaluate the gains of the lying EVs and how their behavior impacts the honest EVs and the performance of charging coordination mechanism. Our evaluations indicate that lying EVs have a greater chance to get charged comparing to honest EVs and they degrade the performance of the charging coordination mechanism. Then, an anomaly based detector that is using deep neural networks (DNN) is devised to identify the lying EVs. To do that, we first create an honest dataset for charging coordination application using real driving traces and information revealed by EV manufacturers, and then we also propose a number of attacks to create malicious data. We trained and evaluated two models, which are the multi-layer perceptron (MLP) and the gated recurrent unit (GRU) using this dataset and the GRU detector gives better results. Our evaluations indicate that our detector can detect lying EVs with high accuracy and low false positive rate.