SDLE SunFarm
Global SunFarm Network
Automated Image Processing

About SDLE

Lifetime and Degradation Science

In its 2010 Science For Energy Technology workshop, the U.S. Department of Energy Basic Energy Sciences program identified photovoltaic module lifetime and degradation science as an energy research priority. Researchers at the SDLE Center build on years of solar PV industry experience to address this priority, engaging students and industry partners in dynamic research programs that move beyond basic qualification testing of systems to determine actual degradation mechanisms and rates – the scientific underpinning of reliability and qualification standards.

By facilitating this collaborative, applied scientific exploration, the SDLE Center pushes the boundaries of lifetime and degradation science to enable the design of better, longer-lasting materials and systems, and accelerated more accurate testing protocols.


Crosscutting Applications

Focusing on solar PV buildings envelope and energy efficiency technologies, the lifetime and degradation science research at the SDLE Center has broader applications to all materials. The advanced exposure techniques, rigorous evaluation processes, and quantitative degradation rate modeling performed at the Center connects materials, components, and  systems to address  crosscutting research challenges not only for PV but for all environmentally exposed technologies.


Research Topics

Researchers at the SDLE Center are continually exploring new research areas and applying the data science approach to a wide range of fields. Some research topics include:

  • Lifetime and degradation science of solar photovoltaic (PV) materials, and other environmentally exposed, long lived (>25 years) technologies.
  • Accelerated laboratory and outdoor real-world exposures and evaluations of outdoor exposed technologies such as solar, led lighting and building envelope materials
  • Energy efficiency and virtual energy auditing of buildings, using engineering epidemiology and data analytics
  • Data-mining and statistical- and machine-learning applied to materials
  • Petabyte/Petaflop big data analytics applied to time-series, spectral and image datasets
  • Non-relational data warehousing and analytics environment for complex systems. 

Funding is provided by:

Funded Under TECH 11-060 and TECH 12-004