Intelligent Renewable Energy System: A Focus on Hydrogen Fuel Cells and Battery Storage with AI

Dear Colleagues,

Renewable energy generation and storage are key to carbon neutrality. Hydrogen fuel cells and battery storage, as representatives of renewable energy carriers, have the advantages of high-energy conversion efficiency, simple structure and well flexibility, and can be widely used as a mobility and stationary power source, such as the electrification of transportation and households in remote areas. At present, the efficient designs and utilizations of the renewable energy system are still an urgent matter in the global new energy research field. Fortunately, inspired by advanced multi-disciplinary integration with artificial intelligence (AI), big data analysis, machine learning, data-driven, automation control and other system design, modeling, control and management methods, new and efficient solutions of renewable energy systems can be expected. The Special Issue of “Intelligent Renewable Energy System: A Focus on Hydrogen Fuel Cells and Battery Storage with AI” aims to cover recent advances and future perspectives related to fuel cells and battery storage with system design, modeling, control strategy, energy management, AI methods, and applications on mobility and stationary scenarios. The Special Issue welcomes outstanding research papers, as well as review articles, devoted to innovative suggestions for AI-based or advanced modeling and control technologies in the field of renewable energy, especially for the hydrogen fuel cell and battery storage.

The main topics of this Special Issue include but are not limited to:

• Hydrogen fuel cell and electrolyzer design, modeling, and control;
• Fuel cell vehicle powertrains;
• Advances in battery storage technologies;
• Battery management system (BMS);
• Energy management system (EMS);
• Electric vehicle control;
• Fuel cell combined heat and power (CHP);
• Thermal management of renewable energy systems;
• Advanced Grid-to-Vehicle and Vehicle-to-Grid (V2G and G2V) systems;
• Charging/discharging infrastructure;
• DC/DC converters;
• Data-driven control of renewable energy system;
• Development of artificial intelligence in the new energy field;
• Real-time simulation and analysis tools for hydrogen fuel cells;
• Battery thermal system optimization;
• Electrification of remoted areas;
• Deep learning and machine learning for renewable energy system;
• Data-driven-based models and physics-based models;
• Hydrogen storage and renewable hydrogen production.

Prof. Dr. Hongwen He
Prof. Dr. Liangfei Xu
Prof. Dr. Ya-Xiong Wang
Guest Editors

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