Advanced Simulations Offer New Insights into Grid-Scale Battery Aging

A new study by researchers at the U.S. Department of Energy’s Oak Ridge National Laboratory (ORNL) highlights how the way grid-scale battery energy storage systems (BESS) are operated can significantly influence their aging, performance, and long-term economics. The findings, presented at the 2026 IEEE Electrical Energy Storage Applications and Technologies (EESAT) Conference, demonstrate how advanced simulations can help optimize battery system design before deployment.

According to ORNL researchers, the team developed a physics-based, high-performance computing (HPC) modeling framework capable of simulating battery degradation from individual cells to complete battery packs. The framework can analyze more than 10,000 lithium-ion cells simultaneously, enabling engineers to evaluate how different operating strategies affect battery health over hundreds of charge-discharge cycles in a fraction of the time required for conventional testing.

The simulations compared two common grid applications—frequency regulation and energy arbitrage—and found that each creates distinct degradation pathways within lithium-ion batteries. While frequency regulation involves frequent, shallow charge-discharge cycles, energy arbitrage requires deeper cycling, resulting in faster battery wear. The researchers also observed that combining multiple grid services could help balance immediate revenue opportunities with long-term battery lifetime and replacement costs.

The study further revealed that battery aging is influenced not only by operating conditions but also by electrical architecture. Low-voltage battery systems exhibited greater variation in cell degradation than high-voltage configurations, underscoring the importance of system design in improving performance consistency across large-scale storage installations.

According to Oak Ridge National Laboratory, the modeling framework could significantly reduce the need for extensive full-system testing while accelerating the development of more durable and cost-effective BESS deployments. The research team plans to expand the framework to evaluate additional battery chemistries, operating temperatures, and real-world use cases, including battery systems supporting AI data centers, paving the way for smarter and longer-lasting energy storage solutions.