PNNL is working with national laboratories and academia to provide electric vehicle manufacturers with batteries that are more reliable, high-performing, safe, and less expensive.

A multi-institution research team led by PNNL is addressing curb usage management challenges in large urban areas by developing a city-scale dynamic curb use simulation tool and an open-source curb management platform. 

PNNL partners with agencies and industry to identify and engage historically disadvantaged populations in regulatory decision-making, environmental assessment, and impact estimation of the consequences of complex polices and projects.

PNNL administers two research buoys for the U.S. Department of Energy that allows collection of wind meteorological and oceanographic data off the nation's coasts.

PNNL designs, delivers, and manages training programs that enable partners worldwide to understand their individual or organizational roles and responsibilities, fulfill a job function, or strengthen a particular skill set.

Physics-informed machine learning (PIML) is a modeling approach that harnesses the power of machine learning and big data to improve the understanding of coupled, dynamic systems.

PNNL is working on behalf of the U.S. Department of Energy to create a prototype system that enables homes to help provide services to the power grid while delivering economic benefits to residents.

The Salish Sea Model (SSM) is a predictive coastal ocean model for estuarine research, restoration planning, water-quality management, and climate change response assessment.

PNNL develops training, exercises, and assessments to prepare and equip border security officers to detect, identify, and interdict the illicit movements of materials, commodities, and components associated with WMD.