Hybrid accelerated testing methods that combine calendar aging at high states of charge (SOC) with partial cycling are critical for evaluating battery degradation under realistic usage conditions. These tests provide insights into how batteries degrade not only from active use but also from periods of inactivity, which is particularly relevant for applications like electric vehicles and grid storage where batteries may experience intermittent usage patterns.
Calendar aging refers to the degradation that occurs when a battery is stored under specific conditions, such as elevated temperature or high SOC, without undergoing charge-discharge cycles. High SOC storage accelerates chemical side reactions, including electrolyte decomposition and solid-electrolyte interphase (SEI) growth, leading to capacity fade and increased impedance. Partial cycling, on the other hand, involves repeated charge and discharge within a limited SOC window, simulating real-world usage where full cycles are rare. Combining these two stressors in an interleaved manner allows researchers to capture synergistic effects that may not be apparent when studying them separately.
The National Renewable Energy Laboratory (NRL) has developed modeling frameworks within its Battery Lifetime Toolbox to analyze such hybrid degradation mechanisms. These models incorporate empirical data from accelerated tests to predict long-term performance under varied operational profiles. A key aspect of NREL’s approach is the separation of calendar aging and cycling-induced degradation, followed by their reintegration to assess cumulative effects. The toolbox uses semi-empirical equations to describe capacity loss and resistance increase, with parameters calibrated using experimental data.
For calendar aging, the model typically follows an Arrhenius-type relationship where degradation rates depend on temperature and SOC. The general form of the equation may include terms for time dependence, often represented by a power law or exponential function. Cycling degradation is modeled based on factors such as depth of discharge (DOD), C-rate, and cycle count. When interleaving calendar and cycling stresses, the model accounts for their combined impact by considering whether degradation mechanisms are additive, multiplicative, or interactive.
One challenge in hybrid testing is determining the appropriate weighting between calendar and cycling contributions. NREL’s framework addresses this by using accelerated test data to fit degradation coefficients for each stressor. For example, a test might involve storing cells at 80% SOC and 45°C for a set duration, followed by a series of 10% DOD cycles, repeating this sequence to simulate real-world intermittency. The resulting data helps refine the model’s ability to predict performance under arbitrary usage patterns.
These models are instrumental in defining battery warranty terms. Manufacturers use them to estimate end-of-life criteria, such as 80% remaining capacity or a doubling of resistance, under expected customer usage scenarios. By simulating different combinations of storage and cycling conditions, they can set warranty periods that balance risk and competitiveness. For instance, if a model predicts that a battery will retain 80% capacity after 10 years under moderate cycling and average storage conditions, the warranty may be set at 8 years to account for variability in real-world usage.
The Battery Lifetime Toolbox also supports sensitivity analyses to identify which stressors have the greatest impact on longevity. This informs design improvements, such as optimizing electrode materials or electrolyte formulations to mitigate dominant degradation pathways. Additionally, the models help evaluate the trade-offs between different usage patterns, such as the impact of frequent shallow cycling versus occasional deep discharges.
In summary, hybrid accelerated testing interleaving calendar aging with partial cycling provides a more comprehensive understanding of battery degradation than traditional single-stress approaches. NREL’s modeling frameworks translate experimental data into predictive tools that guide warranty definitions and product development. By capturing the interplay between storage and cycling effects, these methods enable more accurate lifetime estimations, ultimately supporting the deployment of reliable and durable energy storage systems.
The continued refinement of these models will be essential as battery technologies evolve. Emerging chemistries, such as silicon-anode or solid-state batteries, may exhibit different degradation behaviors under hybrid stresses, necessitating updates to existing frameworks. Furthermore, the growing diversity of applications—from electric vehicles to stationary storage—demands tailored testing protocols that reflect specific usage profiles. Advances in machine learning and data-driven modeling could further enhance the precision of lifetime predictions, enabling even more robust warranty strategies and performance guarantees.
Ultimately, the integration of accelerated testing with sophisticated modeling is a cornerstone of battery reliability engineering. It bridges the gap between laboratory experiments and real-world performance, ensuring that batteries meet consumer expectations while supporting the transition to sustainable energy systems.