AI

New Modeling Method and Design Optimization for a Soft-Switched DC-DC Converter

Abstract

High performance cloud computing enables many key future technologies such as artificial intelligence (AI), self-driving vehicle, big data analysis, and internet of things (IoT), using clustered CPU and GPU servers in the datacenter. To improve the power efficiency and the infrastructure flexibility, the computing industry is adopting 54VDC to power the servers in the open compute racks. In this paper, a new modeling technique for a soft-switched DC-DC converter is presented and can be used to guide optimal design in different applications, for example, 54V to point-of-load (PoL) for the new open compute rack. To improve the model accuracy and reduce the complexity, this paper proposes a reduced order linear differential equation (LDE) based modeling technique to discover 1) the tank resonance involving the output inductor; 2) output current ripple and its impact on power efficiency; 3) the proper on-time control for soft switching; 4) unique bleeding mode under the heavy load; 5) output power capability of the converter; 6) the inherent output droop of the converter for phase current sharing and 7) component tolerance analysis and impact on the performance of the converter. With the power loss estimation, design guideline is provided for a reference design and design improvement based on this new modeling technique. Using the proposed method, great accuracy can be expected in the efficiency estimation. Simulation and experimental results are provided to verify the modeling technique in a 54V-1.2V 25A DC-DC converter prototype.