## 1 Introduction  
Electric vehicles (EVs) are becoming increasingly common on our roads, bringing with them the potential to revolutionize transportation and energy systems [1]. In addition to being a mode of transportation, EVs can also act as mobile energy storage units, connecting to the power grid to supply electricity when needed and store excess energy when it is available. This two-way flow of energy between EVs and the grid, known as vehicle-to-grid (V2G) systems [2], holds great promise for addressing some of the key challenges facing our energy infrastructure. They can help balance the supply and demand of electricity, which is particularly important as we transition to more renewable energy sources like wind and solar power [3]. By allowing EVs to discharge electricity back to the grid during times of peak demand or when renewable energy generation is low, V2G systems can help stabilize the grid and reduce the need for expensive infrastructure upgrades [4]. Furthermore, V2G technology has the potential to improve the resilience and reliability of our energy systems [5]. During emergencies or blackouts, EVs equipped with a V2G framework can serve as backup power to homes, businesses, and critical infrastructure, enhancing the overall resilience of existing energy infrastructure and helping communities better withstand and recover from disruptions [6]. However, realizing the full potential of V2G systems requires overcoming several optimization challenges [7]. One of the key challenges is managing the dynamic nature of energy demand and supply [8]. Unlike traditional power plants, which can adjust their output to match changes in demand, EVs connected to the grid have varying levels of charge and availability, which complicates the scheduling and coordination of EV battery charging and discharging to ensure optimal grid operation [9]. Another challenge is optimizing the use of EV batteries while considering user preferences, grid constraints, and environmental factors [10]. For example, EV owners may have specific preferences regarding when and how their vehicles are charged, such as prioritizing charging during off-peak hours or maximizing the use of renewable energy. Balancing these preferences with the need to maintain grid stability and minimize costs requires advanced optimization techniques. In recent years, there has been growing interest in using artificial intelligence (AI) and machine learning techniques to address these challenges in V2G systems. These advanced methods can analyze large volumes of data, learn from past experiences, and adapt to changing conditions in real time, enabling more efficient and effective management of V2G operations [11]. In this paper, we analyze the main optimization difficulties facing these systems and explore the potential of AI-driven solutions to overcome them. Through a review of the existing literature, case studies, and examples, we show how AI-driven algorithms, machine learning, and other advanced techniques can improve grid stability, reduce costs, and enhance the overall performance of V2G frameworks. Finally, we discuss the opportunities and future directions for integrating AI-driven optimization into these systems. The main contribution of this paper is the introduction of several novel AI-based methodologies for solving various V2G optimization problems. Key methodologies highlighted include simheuristics, learnheuristics, and agile-optimization heuristics. The rest of the article is structured as follows: Section 2 discusses the specific challenges faced in optimizing V2G systems, including managing energy demand, ensuring grid stability, and minimizing costs. Section 3 provides a review of the existing research on optimization in this field, highlighting gaps in the current approaches and the need for more advanced techniques. Section 4 analyzes the application of AI solving methods for V2G optimization, including AI-driven algorithms, machine learning, and metaheuristic extensions.