Stochastic power loss analysis of differential power processing

Ping Wang, Robert Pilawa-Podgurski, Philip Krein, Minjie Chen

Research output: Contribution to journalArticlepeer-review

Abstract

This article presents stochastic power loss analysis for differential power processing (DPP). A stochastic model is developed to analyze power loss scaling in a DPP system based on probability distributions of loads or sources. Scaling factors are introduced to describe how losses change with DPP system size and load or source power variance. Expected power losses of representative DPP topologies are analyzed and compared to losses of a conventional dc-dc converter with the same total switch die area and magnetic volume. The results quantify performance trends of DPP architectures. Models and scaling factors are verified with SPICE simulations and experimental results. The analytical framework, scaling factors, and quantitative models provide useful guidelines for designing large-scale DPP systems. This article is accompanied by a video file demonstrating the modeling procedures and the experimental setup.

Original languageEnglish (US)
Pages (from-to)81-99
Number of pages19
JournalIEEE Transactions on Power Electronics
Volume37
Issue number1
DOIs
StatePublished - Jan 2022
Externally publishedYes

Keywords

  • Analytical models
  • DC-DC power converters
  • Differential power processing (DPP)
  • Integrated circuit modeling
  • Load modeling
  • Probability distribution
  • Stochastic processes
  • Topology
  • battery management systems
  • data center power management
  • dc-dc converters
  • photovoltaic systems
  • series modules
  • stochastic models
  • differential power processing (DPP)
  • Battery management systems

ASJC Scopus subject areas

  • Electrical and Electronic Engineering

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