Co-Optimization of Boost Converter Reliability and Volumetric Power Density Using Genetic Algorithm

In power electronic applications, reliability and power density are a few of the many important performance metrics that require continual improvement in order to meet the demand of today’s complex electrical systems. However, due to the complexity of the synergy between various components, it is challenging to visualize and evaluate the effects of choosing one component over another and what certain design parameters impose on the overall reliability and lifetime of the system. Furthermore, many areas of electronics have realized remarkable innovation in the integration of new materials of passive and active components; wide-bandgap semiconductor devices and new magnetic materials allow higher operating temperature, blocking voltage, and switching frequency; all of which enable much more compact power converter designs. However, uncertainty remains in the overall electronics reliability in different design variations. Hence, in order to better understand the relationship between reliability and power density in a power electronic system, this paper utilizes a genetic algorithm (GA) to provide pareto optimal solution sets in a multi-variate trade space that relates the Mean Time Between Failures (MTBF) and volumetric power density for the design of a 5 kW synchronous boost converter. Different designs of the synchronous boost converter based on the variation of the electrical parameters and material types for the passive (input and output capacitors, the boost inductor, and the heatsink) and active components (switches) have been studied. A few candidate designs have been evaluated and verified through hardware experiments.