The Roles of Generative Artificial Intelligence in Internet of Electric Vehicles

TL;DR

This survey categorizes GenAI for IoEV into four different layers namely, EV's battery layer, individual EV layer, smart grid layer, and security layer, and provides a roadmap for future research directions, enabling the development of more robust and efficient IoEV systems through the integration of advanced GenAI techniques.

Abstract

With the advancements of generative artificial intelligence (GenAI) models, their capabilities are expanding significantly beyond content generation and the models are increasingly being used across diverse applications. Particularly, GenAI shows great potential in addressing challenges in the electric vehicle (EV) ecosystem ranging from charging management to cyber-attack prevention. In this paper, we specifically consider Internet of electric vehicles (IoEV) and we categorize GenAI for IoEV into four different layers namely, EV's battery layer, individual EV layer, smart grid layer, and security layer. We introduce various GenAI techniques used in each layer of IoEV applications. Subsequently, public datasets available for training the GenAI models are summarized. Finally, we provide recommendations for future directions. This survey not only categorizes the applications of GenAI in IoEV across different layers but also serves as a valuable resource for researchers and practitioners by highlighting the design and implementation challenges within each layer. Furthermore, it provides a roadmap for future research directions, enabling the development of more robust and efficient IoEV systems through the integration of advanced GenAI techniques.

Figures (5)

• Figure 1: GenAI for IoEV applications can be categorized into four layers: Layer 1's problems are anomaly detection, SoC estimation, and SoH evaluation. Layer 2 primarily focuses on data augmentation in EV charging behaviors, prediction of an EV load at home, and solving the optimal EV routing problem. Layer 3 concentrates on: Forecasting and augmenting the EV charging load profiles; Optimizing EV charging schedules based on the constraints from either EV charging stations or the smart grid; Predicting the electricity price is necessary for EV charging station operators. Layer 4 studies various attacks which may be harmful to the EV and the charging system. Both cyber and physical attacks need to be studied and detected such as adversarial attacks, false data injection attacks, denial of service attacks, fuzzy attacks, and impersonation attacks.
• Figure 2: Concept of EV charging system in an electrical distribution network: the network consists of charging stations, smart home, distributed resources, and load. DSO and CSOs manage the distribution network and the charging stations respectively. Smart homes and charging stations with PV and ESS can coordinate with DSO to ensure a stable and robust smart grid operation.
• Figure 3: Concept of basic GenAI models — Transformer, GAN, AE, VAE, and GDM: (a) shows a single-layer Transformer process where the output vectors are achieved by passing the input vectors through self-attention and feedforward layers with residual connections; (b) illustrates principles of GAN where the generator competes with the discriminator by producing increasingly realistic samples to "fool" the discriminator, while the discriminator attempts to differentiate between real and fake data; (c) depicts principles of AE: the left side shows the training process of AE, while the right side depicts its usage once the model is completely trained; VAE is a subcategory of AE, but it differs slightly in that VAE uses the mean and diagonal covariance to generate samples in both the encoder and decoder; (d) displays the processes of GDM consisting of forward diffusion and reverse diffusion.
• Figure 4: The flowchart of proposed framework to address the cold-start forecasting problem in predicting the EV charging behaviors such as plug-out hour and required energy for newly committed EVs Ref27.
• Figure 5: Framework of DRL with Transformer for EV routing problem Ref24.