EPCL Funding Opportunity - Energy Power Control Learning Research Grants
Summary
The NSF Directorate for Engineering announced the Energy, Power, Control, and Learning (EPCL) funding opportunity supporting fundamental research in engineered systems, AI, optimization, and electric power systems. The program advances capabilities in power grid, transportation, manufacturing, healthcare, and other critical infrastructure. Full proposals are accepted on a rolling basis through Program Director PD 26-7607.
What changed
The NSF announced a new funding opportunity for the Energy, Power, Control, and Learning program, supporting fundamental research in systems and control, machine learning, optimization, networked multi-agent systems, and electric power systems. Research areas include AI-assisted tools, battery management, power electronics, hybrid vehicles, and power grid challenges from renewable energy integration.
For academic researchers and institutions in engineering, this announcement opens access to NSF grant funding for work spanning energy, transportation, robotics, and biomedical systems. Researchers should prepare proposals aligned with EPCL priorities and submit via Research.gov or Grants.gov using the applicable proposal preparation guidelines.
Archived snapshot
Apr 16, 2026GovPing captured this document from the original source. If the source has since changed or been removed, this is the text as it existed at that time.
Synopsis
The Energy, Power, Control, and Learning (EPCL) program invests in fundamental research to advance the capabilities, performance, security and resilience of engineered systems. These advances can benefit the U.S. power grid, transportation, manufacturing, healthcare and other critical infrastructure systems that enable economic growth and prosperity.
EPCL supports research on systems and control, learning, optimization, and networked multi-agent systems. The program addresses a wide variety of systems and decision-making issues; examples include higher-level decision making, dynamic resource allocation, risk management in the presence of uncertainty, sub-system failures, and game theory for system control and learning, as well as stochastic and hybrid systems. EPCL research may also involve advances in artificial intelligence (AI); examples include novel machine learning algorithms, new AI-assisted tools, adaptive programming, and brain-like networked architectures for real-time learning.
The program encourages collaboration among different fields to advance knowledge that will lead to new methods and technologies. While projects focus on fundamental advances in knowledge, they should ideally provide a clear vision of how research can influence real-world applications. These may include energy, transportation, robotics, biomedical devices and systems, or other uses.
EPCL is committed to supporting advances in the theory and technology of electric power systems. Such research can address issues related to generation, transmission, storage, inverter-based energy sources; power electronics and drives; battery management systems; energy harvesting; hybrid and electric vehicles; and the interplay of power system operation with regulatory and economic structures and consumer behavior. The program also supports research that addresses emerging challenges stemming from societal trends in energy production and consumption, such as changes in energy sources for the power grid or growth in data centers.
Partnerships: To speed discovery and innovation, NSF partners with federal agencies, industry, international groups, and others. Current opportunities are at NSF ENG Partnerships.
This program advances NSF’s mission as given in the NSF organic statute (42 U.S.C. 1861, et seq.).
Program contacts
| Name | |
|---|---|
| EPCL Program Team | eccs-epcl@nsf.gov |
Awards made through this program
Browse projects funded by this program
Map of recent awards made through this program
Organization(s)
- Directorate for Engineering (ENG)
- Division of Electrical, Communications and Cyber Systems (ENG/ECCS)
Upcoming due dates
Full proposal accepted anytime
Program guidelines
Apply to PD 26-7607 as follows:
Full proposals submitted via Research.gov: NSF Proposal & Award Policies & Procedures Guide proposal preparation guidelines apply.
Full proposals submitted via Grants.gov: NSF Grants.gov Application Guide guidelines apply. See Grants.gov Proposal Processing in Research.gov for more information.
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Published:
April 16, 2026
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