A high-accuracy switching loss model of SiC MOSFETs in a motor drive for electric vehicles
Xiaofeng Ding,
Peng Lu and
Zhenyu Shan
Applied Energy, 2021, vol. 291, issue C, No S0306261921003275
Abstract:
Power loss estimation of power electronic devices is essential to the efficiency optimization of motor drives in electric vehicles. However, most existing power loss models of silicon carbide (SiC) metal-oxidesemiconductor field-effect transistors (MOSFETs) have a low accuracy due to the neglection of parasitic parameters in the motor drive circuitry. In this paper, the voltage and current trajectories of SiC MOSFETs in the switching transition of a motor drive system are analyzed in detail. Based on the analysis, the conduction and switching losses of SiC MOSFETs in the motor drive inverter are modeled. Compared with the traditional power loss models, the proposed analysis model includes parasitic inductances and capacitances in circuitry and the MOSFET, and the reverse recovery loss of the body diode, etc. The experimental results verified that the proposed power loss model has higher accuracy than the conventional model.
Keywords: Electric vehicle traction systems; Losses; Parasitic parameters; SiC MOSFETs (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)
Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0306261921003275
Full text for ScienceDirect subscribers only
Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text
Persistent link: https://EconPapers.repec.org/RePEc:eee:appene:v:291:y:2021:i:c:s0306261921003275
Ordering information: This journal article can be ordered from
http://www.elsevier.com/wps/find/journaldescription.cws_home/405891/bibliographic
http://www.elsevier. ... 405891/bibliographic
DOI: 10.1016/j.apenergy.2021.116827
Access Statistics for this article
Applied Energy is currently edited by J. Yan
More articles in Applied Energy from Elsevier
Bibliographic data for series maintained by Catherine Liu ().