Aging models for second-life batteries present unique challenges compared to first-life applications due to the nonlinear degradation patterns that emerge after repurposing. Batteries retired from electric vehicles (EVs) and redeployed in grid storage or other stationary applications have already undergone partial degradation, but their remaining useful life depends on complex factors such as prior usage history, chemistry, and operational conditions in their new role. Accurate residual lifetime prediction is critical for economic viability and safety, requiring tailored modeling approaches that differ from those used in first-life scenarios.

First-life battery aging models typically assume linear or predictable degradation rates based on standardized cycling and calendar aging tests. However, second-life batteries exhibit nonlinear behavior due to heterogeneous wear mechanisms accumulated during their initial service. For example, an EV battery may have experienced uneven capacity fade across its cells due to localized stress, temperature variations, or charge-discharge patterns. When repurposed, these batteries often operate under lower charge-discharge rates but face longer durations at high states of charge, accelerating different degradation pathways such as solid electrolyte interface (SEI) growth or lithium plating.

Residual lifetime prediction for second-life batteries relies on advanced modeling techniques that incorporate historical data and real-time monitoring. Empirical models based on capacity fade and impedance rise are common but must be adjusted for second-life conditions. Machine learning approaches, such as Gaussian process regression or neural networks, have shown promise in capturing nonlinear degradation by training on datasets from retired EV batteries. These models integrate variables like cycle count, depth of discharge, temperature, and resting periods to forecast remaining capacity with higher accuracy. Physics-based models, such as pseudo-two-dimensional (P2D) electrochemical frameworks, are also adapted to second-life scenarios by incorporating initial state parameters inferred from diagnostic tests.

Economic viability assessments for second-life batteries depend heavily on the accuracy of aging models. The total cost of ownership must account for reduced performance, increased maintenance, and potential refurbishment costs. Levelized cost of storage (LCOS) calculations for second-life systems often show competitiveness with first-life batteries when the repurposing costs are low and the residual lifetime is sufficiently long. However, uncertainties in degradation rates can lead to underestimation of replacement intervals, undermining financial projections. Sensitivity analyses are essential to evaluate how variations in aging rates impact payback periods and internal rates of return.

Safety considerations are heightened in second-life applications due to the cumulative wear from prior use. Aging models must identify risks such as thermal runaway triggered by latent defects or accelerated degradation in repurposed cells. Non-destructive testing methods, including electrochemical impedance spectroscopy (EIS) and incremental capacity analysis (ICA), are employed to detect anomalies like lithium plating or electrode cracking. Safety protocols for second-life batteries often exceed those for first-life systems, requiring additional redundancy in battery management systems (BMS) and stricter thermal monitoring.

Regulatory implications for second-life batteries are still evolving, as existing standards were primarily designed for first-life applications. Certification processes must address the lack of uniformity in aging across repurposed batteries, necessitating case-by-case evaluations. Policies in some regions mandate rigorous testing and documentation of prior usage before redeployment, while others lack clear guidelines, creating market barriers. Harmonizing aging model requirements with safety and performance standards is critical to scaling second-life battery adoption.

Key differences between first-life and second-life aging models include the initial conditions and stress factors. First-life models start with pristine cells and predictable degradation trajectories, whereas second-life models must account for unknown histories and compounded wear mechanisms. Additionally, second-life applications often prioritize energy throughput over power performance, shifting the focus of aging analysis to calendar aging rather than cycle aging. This requires recalibration of acceleration factors used in aging tests.

In summary, aging models for second-life batteries must address nonlinear degradation through advanced data-driven and physics-based approaches. Accurate residual lifetime prediction enhances economic viability, while robust safety protocols mitigate risks from prior usage. Regulatory frameworks need updating to accommodate the unique challenges of repurposed batteries, ensuring reliable and safe integration into energy storage systems. The development of standardized aging assessment methodologies will be pivotal in unlocking the full potential of second-life battery markets.