DC-DC converters are crucial in the architecture of contemporary power systems, facilitating the integration of renewable energy sources such as wind turbines, battery storage, and fuel cells into mainstream power grids. The relentless drive toward higher power densities and efficiencies defines modern converter technology. However, as these systems become more prevalent, the importance of reliability and robust lifecycle management has come to the forefront. Recent research has increasingly focused on enhancing the control mechanisms and lifecycle management of these converters. Traditional model-based approaches, while robust, often fail to account fully for the complexities and uncertainties inherent in DC-DC converter applications. Conversely, emerging data-driven techniques are showing promise, potentially overturning long-standing modeling and control paradigms.
This Research Topic aims to spotlight and further the development of advanced control techniques and life cycle management for DC-DC converters. By integrating cutting-edge control strategies and comprehensive lifecycle management approaches, the goal is to ensure that these crucial components not only meet immediate operational demands but also maintain high reliability and efficiency throughout their operational life. The emphasis is on unveiling novel research that can offer significant advancements in the design, modulation, diagnosis, and management practices, thereby setting new standards for DC-DC converter technology.
The topic encompasses a broad spectrum of themes related to the enhancement of DC-DC converters, specifically in terms of lifecycle and control innovations. We welcome contributions that address, but are not limited to, the following aspects:
• Comprehensive life cycle management strategies for DC-DC converters
• Advanced modeling techniques that enhance the understanding and performance of power electronics converters
• Optimization of converter designs with an emphasis on lifecycle durability and efficiency
• Innovative control and modulation techniques tailored for new and existing systems
• Implementation of real-time monitoring, fault diagnosis, and predictive maintenance within lifecycle frameworks
• Strategies for fault-tolerant control and fault ride-through capabilities
• Utilization of digital twins in the lifecycle management of converters
• Application of artificial intelligence in modeling, design, control, and maintenance processes
By exploring these components, this Research Topic aims to foster a transformative impact on the DC-DC converter field, enhancing their performance across all phases of their lifecycle and ensuring these systems are equipped to meet the evolving demands of modern power distribution networks.
Keywords:
DC-DC Converter, Modeling, Co-design optimization, Advanced control and modulation, Condition Monitoring, Fault detection and diagnosis, Artificial Intelligence, Life Cycle Management
Important Note:
All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.
DC-DC converters are crucial in the architecture of contemporary power systems, facilitating the integration of renewable energy sources such as wind turbines, battery storage, and fuel cells into mainstream power grids. The relentless drive toward higher power densities and efficiencies defines modern converter technology. However, as these systems become more prevalent, the importance of reliability and robust lifecycle management has come to the forefront. Recent research has increasingly focused on enhancing the control mechanisms and lifecycle management of these converters. Traditional model-based approaches, while robust, often fail to account fully for the complexities and uncertainties inherent in DC-DC converter applications. Conversely, emerging data-driven techniques are showing promise, potentially overturning long-standing modeling and control paradigms.
This Research Topic aims to spotlight and further the development of advanced control techniques and life cycle management for DC-DC converters. By integrating cutting-edge control strategies and comprehensive lifecycle management approaches, the goal is to ensure that these crucial components not only meet immediate operational demands but also maintain high reliability and efficiency throughout their operational life. The emphasis is on unveiling novel research that can offer significant advancements in the design, modulation, diagnosis, and management practices, thereby setting new standards for DC-DC converter technology.
The topic encompasses a broad spectrum of themes related to the enhancement of DC-DC converters, specifically in terms of lifecycle and control innovations. We welcome contributions that address, but are not limited to, the following aspects:
• Comprehensive life cycle management strategies for DC-DC converters
• Advanced modeling techniques that enhance the understanding and performance of power electronics converters
• Optimization of converter designs with an emphasis on lifecycle durability and efficiency
• Innovative control and modulation techniques tailored for new and existing systems
• Implementation of real-time monitoring, fault diagnosis, and predictive maintenance within lifecycle frameworks
• Strategies for fault-tolerant control and fault ride-through capabilities
• Utilization of digital twins in the lifecycle management of converters
• Application of artificial intelligence in modeling, design, control, and maintenance processes
By exploring these components, this Research Topic aims to foster a transformative impact on the DC-DC converter field, enhancing their performance across all phases of their lifecycle and ensuring these systems are equipped to meet the evolving demands of modern power distribution networks.
Keywords:
DC-DC Converter, Modeling, Co-design optimization, Advanced control and modulation, Condition Monitoring, Fault detection and diagnosis, Artificial Intelligence, Life Cycle Management
Important Note:
All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.