QUALISYS: Unified Framework to Quantify Lithium-Ion Battery Aging Across Cell, Module, and System Scales

Invention Reference Number

202505993

Unified Framework to Quantify Lithium-Ion Battery Aging Across Cell, Module, and System Scales

Battery energy storage systems (BESS) are supporting critical services for anchoring grid reliability – dynamic frequency services, peak shifting and arbitrage, relieving congestion, and supplying grid-forming inertia and black start - making modern grids more resilient. However, predicting how these batteries age under real-world conditions remains a challenge. This technology introduces a data-driven modeling framework that improves the prediction of battery health and degradation across scales—from individual cells to full energy storage systems. By combining simulated and real-world data, the approach enhances forecasting accuracy, extends battery life, and supports more efficient maintenance strategies for grid operators and energy storage developers.

Description

This invention provides a unified, machine learning–based framework to quantify lithium-ion battery aging at the cell, module, and system levels. Traditional simulation and laboratory models often fail to represent real-world degradation, while large-scale testing is costly and time intensive. The framework bridges this gap by integrating simulated datasets with limited field data to capture realistic patterns of battery wear and performance decline.

Through advanced computational modeling, the system learns from both idealized and empirical data to provide more reliable projections of long-term battery behavior under variable operating conditions. This approach supports the optimization of maintenance schedules, performance forecasting, and lifecycle cost reduction for energy storage assets. The model is adaptable to different battery chemistries and architectures, making it broadly applicable across grid-scale and commercial storage systems.