Battery aging models are critical for predicting performance degradation over time, especially in extreme temperature environments. Cryogenic conditions, such as those faced by electric vehicle batteries in polar regions, and high-temperature environments, like desert energy storage systems, introduce unique degradation mechanisms that challenge conventional aging models. Understanding these mechanisms and adapting models accordingly is essential for accurate lifetime predictions and system design.

At cryogenic temperatures, the primary aging mechanisms revolve around electrolyte behavior and charge transfer limitations. Most lithium-ion batteries use liquid electrolytes that become viscous or freeze at low temperatures, severely reducing ionic conductivity. This leads to increased internal resistance, lithium plating, and capacity loss. Lithium plating occurs when lithium ions cannot intercalate into the anode quickly enough, depositing as metallic lithium instead. This not only reduces capacity but also increases the risk of dendrite formation and internal short circuits. Additionally, repeated cycling at low temperatures causes mechanical stress in electrode materials due to uneven expansion and contraction, accelerating particle cracking and solid-electrolyte interphase (SEI) layer growth.

Aging models for cryogenic conditions must account for these factors by modifying traditional Arrhenius-based approaches. The Arrhenius equation, which describes temperature-dependent reaction rates, often underestimates aging at very low temperatures because it does not fully capture phase changes in the electrolyte or the nonlinear behavior of charge transfer resistance. Instead, models must incorporate electrolyte freezing points and ionic conductivity thresholds. For example, some studies introduce a viscosity-dependent term to adjust the reaction kinetics when temperatures fall below a critical threshold. Another adaptation involves coupling mechanical stress models with electrochemical degradation to simulate particle fracture and SEI growth under repeated cold cycling.

In contrast, high-temperature environments, such as those found in desert energy storage applications, introduce different aging mechanisms. Elevated temperatures accelerate chemical reactions, including electrolyte decomposition, SEI layer growth, and transition metal dissolution from cathodes. Electrolyte decomposition produces gaseous byproducts, increasing internal pressure and potentially leading to cell swelling. SEI growth consumes active lithium, reducing capacity, while transition metal dissolution degrades cathode structure and increases impedance. Corrosion of current collectors and other metallic components also becomes significant at high temperatures, particularly in humid desert conditions where moisture may be present.

Models for high-temperature aging must integrate thermal degradation pathways that are often negligible at moderate temperatures. For instance, the Eyring equation, which extends the Arrhenius concept to include entropy effects, is useful for capturing reaction rate changes under extreme heat. Additionally, models must account for the interaction between temperature and state of charge (SOC). High SOC levels exacerbate degradation at elevated temperatures due to increased electrode potentials, leading to faster electrolyte oxidation and cathode instability. Some advanced models incorporate a coupled thermal-electrochemical framework to simulate localized hot spots and their impact on aging gradients within the cell.

A critical challenge in modeling extreme-temperature aging is the lack of long-term empirical data. Most battery testing occurs under controlled laboratory conditions, which may not fully replicate real-world extremes. To address this, researchers use accelerated aging tests with temperature extremes beyond normal operating ranges. However, these tests must be carefully designed to avoid introducing unrealistic failure modes. For example, excessively rapid temperature cycling can cause mechanical failures that would not occur under natural conditions.

Another consideration is the interaction between temperature and other stress factors, such as high charge/discharge rates or mechanical vibrations. In electric vehicles operating in cold climates, high-power demands during acceleration can exacerbate lithium plating, while in desert storage systems, daily temperature fluctuations induce thermal cycling stresses. Aging models must therefore be multidimensional, integrating thermal, electrochemical, and mechanical effects. Some recent approaches use machine learning to identify patterns in degradation data from extreme conditions, improving prediction accuracy where traditional physics-based models fall short.

Material selection also plays a key role in extreme-temperature battery performance. For cryogenic applications, electrolytes with low freezing points, such as those incorporating sulfolane or ionic liquids, can mitigate viscosity issues. Anode materials less prone to lithium plating, like hard carbon or lithium titanate, are also beneficial. In high-temperature environments, thermally stable cathodes, such as lithium iron phosphate (LFP), and corrosion-resistant current collectors, like aluminum with protective coatings, improve longevity. Aging models tailored to these materials require specific parameter adjustments to reflect their unique degradation pathways.

Finally, operational strategies can influence aging in extreme temperatures. Preheating systems for cryogenic environments reduce the risk of lithium plating during cold starts, while active cooling in desert storage systems minimizes thermal degradation. Aging models used for system design must incorporate these strategies to evaluate their effectiveness. For example, a model might simulate the trade-offs between preheating energy consumption and cycle life extension in an EV battery.

In summary, aging models for batteries in extreme temperatures must move beyond conventional approaches to address unique degradation mechanisms. Cryogenic conditions demand adjustments for electrolyte freezing and lithium plating, while high-temperature environments require enhanced treatment of thermal decomposition and corrosion. Integrating multidimensional stress factors, material-specific parameters, and operational strategies into these models is essential for accurate predictions. As battery applications expand into harsher climates, continued refinement of these models will be crucial for ensuring reliability and longevity.