Energy storage is becoming critical to grid resilience and electricity affordability because battery systems can help balance supply and demand and stabilize power.
Long battery lifetimes are key to unlocking those benefits. But that requires understanding how project design and operation affect stress on large-scale battery systems over hundreds of cycles—before spending the time and money to deploy them.
Researchers at the Department of Energy's Oak Ridge National Laboratory are tackling this problem by coupling high-performance computing (HPC) with a physics-based modeling framework to rapidly evaluate how different operating strategies affect battery aging over time. The result is a powerful computational tool for designing megawatt-hour battery storage systems so they will last longer and reduce costs.
A full battery pack—what most people think of as a battery—is made up of modules filled with many battery cells. The ORNL modeling framework starts with cell-level aging simulation, then scales up to module- and pack-level performance. This multiscale approach enables optimization of electrical architecture, control strategies and operating schedules.
HPC speeds insight into battery wear
