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PVDeg: Enhancing Usability and AI-Driven Multi-Mechanism Degradation Modeling

Daxini, Rajiv [National Laboratory of the Rockies, Golden, CO (United States)] (ORCID:0000000319939408)·Ford, Tobin [National Laboratory of the Rockies, Golden, CO (United States)] (ORCID:0009000074285625)·Ovaitt, Silvana [National Laboratory of the Rockies, Golden, CO (United States)] (ORCID:000000030180728X)·Kempe, Michael [National Laboratory of the Rockies, Golden, CO (United States)] (ORCID:0000000333120482)·Jordan, Dirk [National Laboratory of the Rockies, Golden, CO (United States)] (ORCID:0000000221837489)·Li, Baojie [Lawrence Berkeley National Laboratory]·Jain, Anubhav [Lawrence Berkeley National Laboratory]·Springer, Martin [National Laboratory of the Rockies, Golden, CO (United States)] (ORCID:000000016803108X)
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Abstract

PVDeg version 0.7.0, released in December 2025, introduced major enhancements to improve usability and performance. This update reorganized tutorials and tool notebooks to create a more intuitive experience, enabling users to easily follow and adapt workflows for their specific analyses. In addition to structural improvements, both the notebooks and core logic underwent significant optimization for efficiency, robustness, and style. These refinements were supported by new testing frameworks built on nbval and pytest, adherence to PEP8 standards, and extensive code refactoring, which collectively simplify onboarding for new developers. Looking ahead, version 0.8.0 will deliver advanced AI-driven capabilities. The primary focus is to further develop and automate the degradation workflow, designed to analyze PV module degradation across diverse locations and system configurations. By integrating large language models (LLMs) to scan literature and compile a comprehensive database of materials and degradation rates, this feature will enable modeling of multiple materials and mechanisms within a single, streamlined workflow. Users will be able to evaluate degradation impacts on different system architectures under varying environmental conditions, facilitating informed decisions on bill-of-materials optimization for specific deployment scenarios. These advancements position PVDeg as a powerful, user-friendly tool for accelerating PV reliability research and system design.

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14 SOLAR ENERGY42 ENGINEERING97 MATHEMATICS AND COMPUTINGdegradationopen-sourcephotovoltaicPVDegpython

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Daxini, Rajiv [National Laboratory of the Rockies, Golden, CO (United States)] (ORCID:0000000319939408), Ford, Tobin [National Laboratory of the Rockies, Golden, CO (United States)] (ORCID:0009000074285625), Ovaitt, Silvana [National Laboratory of the Rockies, Golden, CO (United States)] (ORCID:000000030180728X), Kempe, Michael [National Laboratory of the Rockies, Golden, CO (United States)] (ORCID:0000000333120482), Jordan, Dirk [National Laboratory of the Rockies, Golden, CO (United States)] (ORCID:0000000221837489), Li, Baojie [Lawrence Berkeley National Laboratory], Jain, Anubhav [Lawrence Berkeley National Laboratory], Springer, Martin [National Laboratory of the Rockies, Golden, CO (United States)] (ORCID:000000016803108X). 2026-07-09. PVDeg: Enhancing Usability and AI-Driven Multi-Mechanism Degradation Modeling. https://doi.org/10.66816/po6364222

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