Journal of Innovative Science

Abstract: This study presents the design and development of an automated test bench for characterizing the efficiency of DC-DC converters under dynamic load conditions, addressing the limitations of conventional static testing methods. The proposed system integrates programmable hardware, real-time data acquisition, and automated control algorithms to simulate realistic load variations and capture high-resolution performance data. A synchronous buck converter was used as the test model, operating over a load range of 10%–100%. Key parameters such as input/output voltage, current, ripple voltage, temperature, efficiency, and transient response were continuously monitored and statistically analyzed. Results revealed that converter efficiency decreased from 96.6% to 93.5% as load increased, primarily due to thermal rise and increased switching losses, with strong negative correlations observed between efficiency and temperature (r = -0.987). Regression analysis confirmed temperature as the dominant factor influencing performance, while cluster analysis classified operational states into high-efficiency (10–50%) and high-stress (75–100%) regimes. The developed test bench demonstrated exceptional accuracy, repeatability, and adaptability, successfully replicating real-world conditions and providing comprehensive insights into converter behavior. Overall, this automated system establishes a robust, scalable, and intelligent framework for DC-DC converter testing facilitating efficient design validation, performance benchmarking, and predictive diagnostics in power electronics research and industry applications.