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Trends in Electrical Engineering Review Article

Advancements in Multiple-Input DC–DC Converters for Hybrid Renewable Energy Systems: Topologies, Control Strategies, and Applications

  1. Narendra Zinjad Department of Electrical Engineering, Bharati Vidyapeeth Deemed to be University, Pune
  2. Deepak Bankar Department of Electrical Engineering, Bharati Vidyapeeth Deemed to be University, Pune
  3. Digvijay Shelke Department of Mechanical Engineering, Rajgad Dnyanpeeth's Shree Chhatrapati Shivajiraje College of Engineering (RD’s SCSCOE), Dhangwadi, Pune

Abstract

Multiple-input DC to DC converters (MICs) are essential components in hybrid energy systems, enabling efficient management of diverse energy inputs from renewable sources such as solar photovoltaic (PV) panels and wind turbines. These converters facilitate the seamless integration of variable power outputs, addressing the intermittent nature of renewable energy through advanced power electronics. This paper provides a comprehensive review of the topologies, control strategies, and applications of MICs within renewable energy systems, emphasizing their role in enhancing system stability and energy yield. Key converter types, including buck, boost, buck–boost, Cuk, Single-Ended Primary-Inductor Converter (SEPIC), flyback, forward, and dual active bridge (DAB), are discussed, alongside recent advancements in efficiency, power density, and sophisticated control techniques. Control methods such as Proportional-Integral-Derivative (PID), fuzzy logic, and Model Predictive Control (MPC) are highlighted for their critical roles in optimizing converter performance under dynamic operating conditions. The paper also examines MIC applications in solar–wind hybrid systems, battery management for energy storage, and electric vehicles (EVs), where they support grid independence and sustainable mobility. Future research directions emphasize improving conversion efficiency, reducing manufacturing and operational costs, and integrating intelligent control algorithms—such as artificial intelligence (AI) and machine learning (ML)—to enhance system reliability and adaptability in the face of evolving energy demands.

Keywords

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