Artificial Intelligence in Energy Systems: Applications, Challenges, and Future Directions

Artificial Intelligence (AI) is transforming energy conversion and storage technologies by enhancing efficiency, scalability, and reliability. This chapter examines AI-driven innovations in electrochemical processes, including battery design, fuel cell optimization, and supercapacitor operation. Advanced algorithms, such as Machine Learning (ML) and Deep Learning (DL), enable precise modeling, real-time diagnostics, and predictive analytics, thereby accelerating material discovery and improving energy storage efficiency. Key developments are highlighted in chargedischarge optimization, energy loss reduction, and lifecycle extension for lithium-ion, solid-state, and flow batteries. The chapter also examines AI’s impact on dynamic energy management, fault detection, and predictive maintenance in large-scale energy systems, such as solar and wind. Case studies demonstrate the transition from trial-an- -error to data-driven approaches, resulting in reduced costs and enhanced sustainability. Further discussions include algorithmic frameworks, digital twins, and hybrid modeling for enhanced energy conversion. Challenges such as data scarcity, model generalization, and ethical considerations are addressed, along with insights into future trends. This work envisions a sustainable energy landscape driven by AI and advanced computational intelligence.