Aging data collected during formation and lifecycle testing plays a critical role in determining the viability of batteries for second-life applications. As batteries degrade in their primary use, such as in electric vehicles (EVs), they often retain significant capacity—typically between 70% and 80% of their original capacity—making them suitable for less demanding applications like grid storage or backup power systems. The process of evaluating, repurposing, and deploying these batteries relies on robust predictive modeling, standardized grading criteria, and specialized equipment for health assessment.

Formation testing occurs during battery manufacturing and establishes baseline performance metrics, including capacity, impedance, and thermal behavior. Lifecycle testing, conducted over extended periods, tracks degradation patterns under various operational conditions. By analyzing this data, engineers can predict remaining useful life (RUL) and identify failure mechanisms such as lithium plating, solid-electrolyte interphase (SEI) layer growth, or cathode material degradation. Predictive models leverage this data to estimate how a battery will perform in a second-life scenario, where operational demands differ from initial use.

One common approach to predictive modeling involves machine learning algorithms trained on historical aging data. These models correlate degradation trends with factors like charge-discharge cycles, depth of discharge (DOD), temperature exposure, and charge rates. For example, a battery that experienced frequent fast charging in an EV may exhibit accelerated capacity fade, influencing its suitability for grid storage where slower, more controlled cycling is typical. Empirical models based on electrochemical impedance spectroscopy (EIS) data can also isolate degradation mechanisms, helping to classify batteries by their dominant failure modes.

Grading criteria for second-life batteries typically include capacity retention, internal resistance, self-discharge rate, and cycle stability. A widely used framework categorizes batteries into tiers:
- Tier 1: Above 80% capacity, low resistance, suitable for high-performance applications.
- Tier 2: 70-80% capacity, moderate resistance, ideal for stationary storage.
- Tier 3: Below 70% capacity, high resistance, relegated to low-demand uses or recycling.

These tiers guide repurposing decisions. For instance, Tier 1 batteries might be redeployed in commercial energy storage systems, while Tier 2 batteries could serve residential solar storage. Tier 3 batteries may still find use in less critical applications like off-grid lighting before final recycling.

Equipment used for health assessment includes cyclers, impedance analyzers, and thermal imaging tools. Cyclers perform capacity verification by subjecting batteries to standardized charge-discharge protocols. Impedance analyzers measure internal resistance and detect inhomogeneities in cell aging. Thermal imaging identifies hotspots that indicate localized degradation or potential safety risks. Advanced techniques like X-ray diffraction (XRD) and scanning electron microscopy (SEM) provide insights into material-level changes, though these are more common in R&D settings than in large-scale repurposing facilities.

Real-world examples demonstrate the practical application of these methods. In one grid storage project, retired EV batteries with 75-82% residual capacity were integrated into a 2 MWh system after passing rigorous health checks. The batteries underwent formation-style testing to confirm stability under grid conditions, followed by continuous monitoring to track performance degradation. Another example involves an EV battery repurposing initiative where second-life packs were used for peak shaving in industrial facilities. Predictive models based on historical cycling data ensured that only batteries with uniform aging across modules were selected, minimizing the risk of premature failure.

Challenges remain in standardizing second-life battery assessment. Variability in aging patterns due to differing usage histories complicates grading, and economic viability depends on balancing repurposing costs against projected lifespan. However, as predictive models improve and testing equipment becomes more automated, the second-life battery market is poised to grow, contributing to sustainability by extending battery utility and reducing waste.

The integration of aging data into second-life applications underscores the importance of comprehensive lifecycle management. From predictive analytics to graded redeployment, the process ensures that batteries deliver value beyond their initial use while supporting broader energy storage and circular economy goals. As the industry matures, advancements in testing protocols and modeling accuracy will further enhance the reliability and scalability of second-life battery solutions.