Assessment of DC-DC Converter Selection Metrics

Abstract

The exponential growth of Internet of Things (IoT) devices, powered by diverse energy sources, poses significant challenges in power electronics. Despite advances in DC-DC converter topologies, a gap remains in the literature regarding standardized performance metrics for selecting suitable converters, making the selection process complex. This study critically assesses metrics from seminal works of the 1960s to contemporary state-of-the-art, proposing a systematic approach to converter assessment. Two major categories of metrics are identified: averaging metrics and waveform-preserving metrics. Averaging metrics, grounded in Wolaver's foundational work, are effective for high-level comparisons among many converter options, establishing a performance baseline. The study introduces an average modeling tool to reveal core converter characteristics for objective comparison. Waveform-preserving metrics, on the other hand, provide detailed performance insights and are suitable for a narrower set of converter options. The study further categorizes these metrics to assess converter switches and reactive components. A new RMS metric is proposed, refining the existing processed power metric for better accuracy. By integrating both averaging and waveform-preserving metrics at relevant design stages, this study offers a systematic framework for converter assessment. This approach bridges the gap between high-level comparison and detailed performance evaluation, facilitating informed decision-making in converter selection.