A CMOS-Based Energy Harvesting Approach for Laterally-Arrayed Multi-Bandgap Concentrated Photovoltaic Systems
Author(s)
Zhang, Haoquan; Martynov, Konstantin; Li, Duanhui; Perreault, David J.
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This paper presents an energy harvesting approach for a concentrated photovoltaics (CPV) system based on cell-block-level integrated CMOS converters. The CPV system, built upon the Laterally-Arrayed Multi-Bandgap (LAMB) cell structure, is a potentially higher-efficiency and lower-cost alternative to traditional tandem-based systems. The cells within a sub-module block are connected for approximate voltage matching, and a CMOS-based multi-input single-output (MISO) buck converter harvests and combines the energy while performing maximum power point tracking (MPPT) locally. First, a comparison of modeled performances achievable with traditional tandem CPV and LAMB CPV with a MISO converter is presented using day-long outdoor measured solar spectrum. The model predicts on average >19% more energy can be extracted from LAMB modules on a typical day. Then, a prototype miniaturized MISO dc-dc converter operating at 10MHz is developed in a 130nm CMOS process. For 45-160mW power levels, the prototype converter achieves >92% nominal and >95% peak efficiency in a small form factor designed to fit within available space in a LAMB PV cell block. The results demonstrate the potential of the LAMB CPV architecture for enhanced solar energy capture.
Date issued
2019-11Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer ScienceJournal
2019 IEEE Energy Conversion Congress and Exposition
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Citation
Zhang, Haoquan et al. "A CMOS-Based Energy Harvesting Approach for Laterally-Arrayed Multi-Bandgap Concentrated Photovoltaic Systems." 2019 IEEE Energy Conversion Congress and Exposition, September-October 2019, Baltimore, Maryland, Institute of Electrical and Electronics Engineers, November 2019. © 2019 IEEE
Version: Author's final manuscript
ISBN
9781728103952
9781728103969
ISSN
2329-3748