The increasing complexity and scale of Internet of Vehicles (IoV) networks pose significant security challenges, necessitating the development of advanced intrusion detection systems (IDS). Traditional IDS approaches, such as rule-based and signature-based methods, are often inadequate in detecting novel and sophisticated attacks due to their limited adaptability and dependency on predefined patterns. To overcome these limitations, machine learning (ML) and deep learning (DL)-based IDS have been introduced, offering better generalization and the ability to learn from data. However, these models can still struggle with zero-day attacks, require large volumes of labeled data, and may be vulnerable to adversarial examples. In response to these challenges, Generative AI-based IDS—leveraging models such as Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), and Transformers—have emerged as promising solutions that offer enhanced adaptability, synthetic data generation for training, and improved detection capabilities for evolving threats. This survey provides an overview of IoV architecture, vulnerabilities, and classical IDS techniques while focusing on the growing role of Generative AI in strengthening IoV security. It discusses the current landscape, highlights the key challenges, and outlines future research directions aimed at building more resilient and intelligent IDS for the IoV ecosystem.

Distributed power generation systems (DPGSs) integrate power sources that tend to be smaller than the typical utility scale, such as for renewable energy sources and other applications. Storage systems that incorporate supercapacitors (SCs) have been proposed to extend the life of batteries and to increase the power capacity of the DPGSs, guaranteeing maximum efficiency. The extraction of energy in SCs is more demanding than in the case of batteries; when SCs have delivered only 75% of their energy, their voltage has already decreased to 50%. Beyond this value, the banks fail to meet the requirements demanded by loads that require a minimum voltage to operate correctly, leaving 25% of the energy unused, thereby limiting the deep charge/discharge cycles that occur. This paper presents a model of a switching matrix applied in a bank of SCs. The model allows the use of a simpler circuit to achieve a large number of serial/parallel-configuration connections (levels), improving the utilization of energy to obtain deep discharge cycles in each SC; therefore, by increasing the average energy extracted from each SC, it extends the power delivery time in the storage bank. The efficiency was verified by experimental results obtained using a bank of six SCs.

This paper presents a novel multi-loop control strategy for Vienna rectifiers that eliminates coordinate transformations while achieving superior performance under adverse grid conditions. Unlike conventional $dq$-frame controllers that suffer from computational complexity and degraded performance during unbalanced conditions, the proposed $abc$-frame scheme achieves a power factor of $98\%$ with total harmonic distortion (THD) below $5\%$ across all operating conditions. The system exhibits a settling time under 120 μs for $90\%$ load transients and ensures robust operation during Type A voltage sags while maintaining a $94\%$ power factor. Furthermore, it guarantees zero steady-state neutral point deviation. The controller employs a dual-loop architecture with high-gain current tracking and PI-based voltage regulation, validated through extensive PSIM/C++ co-simulations at 120 kw. Comparative analysis demonstrates a $35\%$ reduction in computational burden relative to $dq$-frame alternatives, while fully complying with IEEE-519:2022 standards. These results highlight the proposed method as a practical and robust solution for industrial rectification applications requiring grid-fault tolerance.