Predictive Controller for Power Supply Prognostication

Award Information
Agency:
Department of Defense
Amount:
$749,898.00
Program:
SBIR
Contract:
FA8650-07-C-2726
Solitcitation Year:
2006
Solicitation Number:
2006.1
Branch:
Air Force
Award Year:
2007
Phase:
Phase II
Agency Tracking Number:
F061-178-2944
Solicitation Topic Code:
AF06-178
Small Business Information
SATCON APPLIED TECHNOLOGY, INC.
27 Drydock Avenue, Boston, MA, 02210
Hubzone Owned:
N
Woman Owned:
N
Socially and Economically Disadvantaged:
N
Duns:
187659425
Principal Investigator
 Leo Casey, ScD
 Vice President & Chief Te
 (617) 897-2435
 leo.casey@satcon.com
Business Contact
 William O'Donnell
Title: President
Phone: (617) 897-2408
Email: bill.odonnell@satcon.com
Research Institution
N/A
Abstract
Building on the feasibility determination made in Phase 1 SatCon will demonstrate an application of prognostic control techniques for switching power converters utilizing emerging Silicon Carbide (SiC) power devices, while using predictive modeling. Silicon Carbide is an emerging technology, that promises dramatic enhancements in size, weight, and reliability of power converters, and therefore significant work is required to accurately model the aging and wear out mechanisms. The significant benefits of SiC technology, and the increased confidence level in critical survivability of the converter, for manned and unmanned airborne applications, motivates this work. It is expected that the prognostic control techniques demonstrated here can accelerate adoption and application of this and other new semiconductor technologies, by addressing concerns with the new devices and unknown failure mechanisms. The methodology developed uses predictive modeling and simple robust sensing to avoid excessive sensing requirements in the prognostics of complex converters. This approach requires detailed knowledge of the aging mechanisms, the physical driving forces behind these mechanisms and the observable precursors of failure. The precise history of the system, combined with measurement and modeling, can then predict that the system will not fail, within a given time frame, with a high degree of confidence.

* information listed above is at the time of submission.

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