Scholarlink

Purpose of Review <p>The rapid adoption of Electric Vehicles (EVs) is expected to increase the load on existing power grids due to rising charging demands, but bidirectional energy flow in vehicle-to-grid (V2G) systems allows EVs to function as mobile energy storage units within the energy market. Grid resilience depends on effective EV charging and discharging coordination through price incentives, demand forecasting, load balancing, decentralized control, and adaptive peer-to-peer energy trading – primarily enabled by artificial intelligence. This paper aims to systematically summarize AI approaches for energy management in V2G systems, review their advantages and challenges, analyze existing case studies, and propose strategic recommendations for overcoming identified barriers.</p> Recent Findings <p>Deep learning-based load forecasting and fault detection improve accuracy, scalability, and responsiveness by accounting for weather, social events, and user behavior. Predictive algorithms enable dynamic pricing and coordinated EV charging, optimizing timing, balancing grid load, and preventing overload. Bidirectional energy flow allows peer-to-peer trading via bidirectional chargers, with AI optimizing schedules and pricing for efficient prosumer-consumer matching. AI and blockchain-based federated learning, combined with encryption, privacy systems, and performance monitoring, create a secure feedback loop for continuous improvement. AI also enhances battery longevity and EV performance through state-of-charge prediction and energy optimization, while game theory and multi-agent systems enable decentralized scheduling for smart grid–V2G integration.</p> Summary <p>AI plays a vital role in V2G systems through demand forecasting, load balancing, decentralized EV charging control, and cybersecurity, using techniques like load prediction and fault detection for effective load management. AI-driven dynamic pricing and smart charge/discharge scheduling enable real-time V2G optimization, while multi-agent frameworks support decentralized control, adaptive peer-to-peer energy trading, and SoC estimation. Blockchain and federated learning further strengthen cybersecurity, encouraging greater EV participation in energy management services.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Artificial Intelligence Approaches in Electric Vehicle-to-Grid (V2G) Systems: A Review of Mechanisms and Challenges

  • 期刊论文

  • 发表日期：

    2026年3月6日

查看全文
  • Arijit Goswami

摘要

Purpose of Review

The rapid adoption of Electric Vehicles (EVs) is expected to increase the load on existing power grids due to rising charging demands, but bidirectional energy flow in vehicle-to-grid (V2G) systems allows EVs to function as mobile energy storage units within the energy market. Grid resilience depends on effective EV charging and discharging coordination through price incentives, demand forecasting, load balancing, decentralized control, and adaptive peer-to-peer energy trading – primarily enabled by artificial intelligence. This paper aims to systematically summarize AI approaches for energy management in V2G systems, review their advantages and challenges, analyze existing case studies, and propose strategic recommendations for overcoming identified barriers.

Recent Findings

Deep learning-based load forecasting and fault detection improve accuracy, scalability, and responsiveness by accounting for weather, social events, and user behavior. Predictive algorithms enable dynamic pricing and coordinated EV charging, optimizing timing, balancing grid load, and preventing overload. Bidirectional energy flow allows peer-to-peer trading via bidirectional chargers, with AI optimizing schedules and pricing for efficient prosumer-consumer matching. AI and blockchain-based federated learning, combined with encryption, privacy systems, and performance monitoring, create a secure feedback loop for continuous improvement. AI also enhances battery longevity and EV performance through state-of-charge prediction and energy optimization, while game theory and multi-agent systems enable decentralized scheduling for smart grid–V2G integration.

Summary

AI plays a vital role in V2G systems through demand forecasting, load balancing, decentralized EV charging control, and cybersecurity, using techniques like load prediction and fault detection for effective load management. AI-driven dynamic pricing and smart charge/discharge scheduling enable real-time V2G optimization, while multi-agent frameworks support decentralized control, adaptive peer-to-peer energy trading, and SoC estimation. Blockchain and federated learning further strengthen cybersecurity, encouraging greater EV participation in energy management services.