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With increasing energy demands and environmental challenges, city centers worldwide are confronting difficulties. Smart mobility solutions, particularly electric cars (EVs) integrated with renewable power, are gaining popularity. One of the major technological advancements in the sector is the Vehicle-to-Grid (V2G) technology, facilitating bidirectional energy transfer between EVs and the grid. The revolutionary role that computational intelligence (CI) plays in optimizing V2G systems to manage sustainable energy in cities is covered in this chapter. The optimal energy scheduling for a fleet of 20 electric vehicles in Ludhiana, India, is simulated and modeled in this study using fuzzy logic controllers, evolutionary algorithms, and artificial neural networks (ANN). According to the findings, intelligent coordination may reduce peak grid loads by 8–12%, boost the usage of renewable energy by up to 18%, and benefit EV owners financially. The chapter demonstrates the financial and technological viability of CI-based V2G systems using scenario-based simulations. The findings support the capacity to combine renewable energy sources, smart grid infrastructure, and AI-based decision-making to create more robust and sustainable energy systems for metropolitan areas.

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Smart Mobility for a Greener Future: Computational Intelligence and Vehicle-to-Grid Integration in Energy Management

  • 章节

  • 发表日期：

    2026年

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  • Harpreet Kaur Channi,
  • Surinder Kaur,
  • Jeyagopi Raman,
  • Sudesh Nair Baskara

摘要

With increasing energy demands and environmental challenges, city centers worldwide are confronting difficulties. Smart mobility solutions, particularly electric cars (EVs) integrated with renewable power, are gaining popularity. One of the major technological advancements in the sector is the Vehicle-to-Grid (V2G) technology, facilitating bidirectional energy transfer between EVs and the grid. The revolutionary role that computational intelligence (CI) plays in optimizing V2G systems to manage sustainable energy in cities is covered in this chapter. The optimal energy scheduling for a fleet of 20 electric vehicles in Ludhiana, India, is simulated and modeled in this study using fuzzy logic controllers, evolutionary algorithms, and artificial neural networks (ANN). According to the findings, intelligent coordination may reduce peak grid loads by 8–12%, boost the usage of renewable energy by up to 18%, and benefit EV owners financially. The chapter demonstrates the financial and technological viability of CI-based V2G systems using scenario-based simulations. The findings support the capacity to combine renewable energy sources, smart grid infrastructure, and AI-based decision-making to create more robust and sustainable energy systems for metropolitan areas.