Thermal behavior monitoring is a critical aspect of assessing the State of Health (SOH) in battery systems. As batteries age, their thermal characteristics change due to degradation mechanisms such as lithium plating, solid electrolyte interphase (SEI) growth, and electrode cracking. These processes increase internal resistance, reduce capacity, and alter heat generation patterns. By analyzing temperature rise, heat dissipation, and thermal gradients, it is possible to derive accurate SOH estimates, enabling predictive maintenance and prolonging battery lifespan.

Temperature rise during charge and discharge cycles is a direct indicator of internal resistance. As a battery degrades, its internal resistance increases, leading to higher Joule heating under the same current load. For example, a lithium-ion battery in an electric vehicle (EV) may exhibit a 10-15% increase in temperature rise over 500 cycles due to cumulative SEI layer growth. Fast-charging exacerbates this effect, as high currents generate more heat, accelerating degradation. Monitoring temperature rise during fast-charging sessions can reveal early signs of capacity fade, allowing for timely intervention.

Heat dissipation patterns also provide insights into SOH. A healthy battery exhibits uniform heat distribution across its surface, while localized hot spots indicate uneven aging or internal defects. In aerospace applications, where batteries operate under extreme conditions, thermal gradients exceeding 5°C between cells can signal impending failure. Passive and active thermal management systems must account for these gradients to prevent thermal runaway. By tracking heat dissipation rates over time, engineers can identify cells with abnormal behavior and replace them before catastrophic failure occurs.

Thermal gradients within a battery pack are influenced by aging mechanisms. For instance, lithium plating during low-temperature charging creates localized resistance variations, leading to uneven heating. In grid-scale energy storage systems, prolonged cycling causes electrode material degradation, which alters thermal conductivity and increases gradient formation. Studies have shown that a gradient of more than 3°C/cm in a large-format cell correlates with a 20% capacity loss, making it a reliable SOH metric.

Infrared thermography is a non-invasive method for monitoring thermal behavior. It captures surface temperature distributions with high spatial resolution, revealing hot spots and gradients without physical contact. In EV battery packs, infrared cameras can detect malfunctioning cells during operation, enabling real-time diagnostics. For example, a study on fast-charging cycles demonstrated that infrared imaging identified cells with early-stage lithium plating, which exhibited 2-3°C higher surface temperatures than healthy cells.

Embedded temperature sensors provide continuous monitoring but face limitations in spatial resolution. Thermocouples and negative temperature coefficient (NTC) sensors are commonly used in battery management systems (BMS) to track core and surface temperatures. However, they only measure discrete points, potentially missing localized anomalies. Data fusion techniques combine sensor readings with infrared thermography or electrochemical models to improve accuracy. For instance, integrating NTC data with impedance spectroscopy measurements can distinguish between reversible heating and irreversible degradation effects.

Data fusion enhances SOH estimation by combining multiple thermal and electrical metrics. Machine learning algorithms process inputs from temperature sensors, voltage profiles, and current loads to predict aging trends. In aerospace battery systems, where reliability is paramount, fused data sets improve SOH predictions by 10-15% compared to single-metric approaches. For example, a neural network trained on thermal and impedance data from satellite batteries achieved 95% accuracy in predicting remaining useful life.

High-demand applications like fast-charging EVs and aerospace systems benefit significantly from thermal behavior monitoring. Fast-charging induces rapid temperature spikes, which accelerate degradation if not managed properly. By correlating temperature rise with cycle count, BMS algorithms can adjust charging rates dynamically to minimize stress. Aerospace batteries, subjected to wide temperature ranges, rely on thermal monitoring to prevent performance drops in critical missions. For instance, NASA’s battery health management systems use real-time thermal data to schedule replacements before capacity falls below operational thresholds.

In conclusion, thermal behavior monitoring is a powerful tool for SOH estimation. Temperature rise, heat dissipation, and thermal gradients reflect underlying aging mechanisms, providing actionable insights for maintenance and optimization. Infrared thermography, embedded sensors, and data fusion techniques enhance monitoring accuracy, particularly in high-stress applications. As battery technology advances, integrating thermal metrics into BMS frameworks will be essential for ensuring safety, longevity, and performance across industries.