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<p>Integrating artificial intelligence (AI) with solar-powered electric vehicle (EV) charging systems plays a critical role in reducing greenhouse gas emissions, accelerating renewable energy (RE) adoption, and enabling sustainable mobility. This review systematically examines advanced AI-enabled energy management strategies aimed at addressing the core challenges of integration, reliability, and scalability in solar-based EV infrastructure. Key obstacles include limitations in data acquisition, performance bottlenecks, and elevated infrastructure costs. A structured literature screening of studies published between 2007 and 2025 was conducted to ensure thematic and methodological relevance. AI-driven techniques, such as predictive maintenance and demand forecasting, substantially improve system reliability and operational continuity. Deep learning (DL) models further enhance energy conversion efficiency and real-time decision-making. The adoption of user-centric platforms, agile system architectures, and decentralized control mechanisms helps reduce deployment costs and increase scalability. These developments underscore AI’s central role in advancing energy performance, operational resilience, and decarbonization goals. This review provides actionable insights for policymakers, industry practitioners, and academic researchers, emphasizing the need for refined AI algorithms, secure data governance, and robust regulatory frameworks. Future research should prioritize the development of smart grid technologies, edge computing, and real-time analytics to further optimize system responsiveness. Continued innovation in metering infrastructure will be essential for seamless integration into diverse energy distribution networks, driving the global transition towards a sustainable energy future.</p>

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Artificial intelligence integration in solar-powered EV charging systems: challenges, opportunities, and future perspectives

  • 期刊论文

  • 发表日期：

    2025年7月22日

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  • Anuj Prajapati,
  • Prashant Paraye,
  • Brajesh Kumar Ahirwar,
  • Chi Fung Tam

摘要

Integrating artificial intelligence (AI) with solar-powered electric vehicle (EV) charging systems plays a critical role in reducing greenhouse gas emissions, accelerating renewable energy (RE) adoption, and enabling sustainable mobility. This review systematically examines advanced AI-enabled energy management strategies aimed at addressing the core challenges of integration, reliability, and scalability in solar-based EV infrastructure. Key obstacles include limitations in data acquisition, performance bottlenecks, and elevated infrastructure costs. A structured literature screening of studies published between 2007 and 2025 was conducted to ensure thematic and methodological relevance. AI-driven techniques, such as predictive maintenance and demand forecasting, substantially improve system reliability and operational continuity. Deep learning (DL) models further enhance energy conversion efficiency and real-time decision-making. The adoption of user-centric platforms, agile system architectures, and decentralized control mechanisms helps reduce deployment costs and increase scalability. These developments underscore AI’s central role in advancing energy performance, operational resilience, and decarbonization goals. This review provides actionable insights for policymakers, industry practitioners, and academic researchers, emphasizing the need for refined AI algorithms, secure data governance, and robust regulatory frameworks. Future research should prioritize the development of smart grid technologies, edge computing, and real-time analytics to further optimize system responsiveness. Continued innovation in metering infrastructure will be essential for seamless integration into diverse energy distribution networks, driving the global transition towards a sustainable energy future.