The health of a battery is a critical parameter in determining its remaining useful life and performance. Among various state-of-health (SOH) estimation methods, thermal behavior analysis has emerged as a powerful tool due to its non-invasive nature and strong correlation with degradation mechanisms. Heat generation in batteries is primarily governed by Joule heating and entropic effects, both of which evolve as the battery ages, making thermal signatures a reliable indicator of SOH.

Batteries generate heat during operation through irreversible and reversible processes. Joule heating, an irreversible process, results from internal resistance to ion and electron flow. As a battery degrades, increased electrode polarization, electrolyte decomposition, and solid-electrolyte interphase (SEI) growth raise internal resistance, amplifying Joule heating. Entropic heat, a reversible contribution, stems from thermodynamic entropy changes during electrochemical reactions. Aging alters the entropy coefficient due to material phase transformations and loss of active material, modifying the entropic heat profile. These shifts in thermal behavior provide measurable signals for SOH estimation.

Infrared thermography is a widely used technique for capturing surface temperature distributions. High-resolution IR cameras detect spatial temperature variations during charge-discharge cycles, revealing localized hotspots indicative of uneven aging or internal defects. Studies show that aged cells exhibit higher surface temperature gradients due to increased inhomogeneity in current distribution. By tracking temperature rise rates and peak differentials over cycles, degradation trends can be quantified. However, IR thermography is limited to surface measurements and requires controlled environmental conditions to avoid noise from external heat sources.

Distributed temperature sensor networks address the limitations of IR thermography by embedding sensors at critical locations within battery modules. Fiber-optic sensors, thermocouples, and negative temperature coefficient (NTC) thermistors provide real-time internal temperature data. Multi-point measurements enable detection of thermal anomalies caused by localized degradation, such as lithium plating or electrode delamination. Sensor fusion algorithms integrate data from multiple points to construct a three-dimensional thermal map, improving the resolution of SOH estimation. Research indicates that distributed sensing can detect early-stage degradation with higher sensitivity than single-point measurements.

Thermal impedance modeling is another key approach for linking thermal behavior to SOH. The thermal impedance network, analogous to an electrical circuit, represents heat transfer paths within a battery. Key components include conduction resistance through layers, interfacial resistance between materials, and convection resistance at cooling surfaces. Aging increases thermal impedance due to gas generation in cells, contact resistance growth from electrode swelling, and dry-out of electrolytes. Electrochemical-thermal models simulate heat generation and dissipation dynamics, enabling SOH prediction by comparing simulated and measured temperature profiles. Parameter identification techniques adjust model coefficients to match observed thermal responses, with deviations indicating degradation levels.

Aging distinctly alters heat dissipation patterns in batteries. Fresh cells exhibit uniform temperature distributions and predictable cooling rates under load. As cells degrade, several changes occur. First, increased internal resistance causes higher heat generation for the same current, leading to faster temperature rises. Second, loss of thermal contact between layers due to electrode swelling reduces effective thermal conductivity, slowing heat dissipation. Third, non-uniform aging creates localized high-resistance zones that generate disproportionate heat, visible as asymmetric temperature profiles. These patterns serve as fingerprints for specific degradation modes, such as lithium plating or active material loss.

Combining thermal data with electrical measurements enhances SOH prediction accuracy. Electrical signals like voltage hysteresis, capacity fade, and internal resistance provide complementary information to thermal data. Data fusion techniques, including weighted averaging, Kalman filtering, and machine learning models, integrate multiple inputs for robust estimation. For example, a temperature rise rate combined with charge voltage curvature improves detection of SEI growth. Similarly, differential thermal analysis during discharge can isolate anode degradation when paired with differential voltage analysis. Research demonstrates that hybrid methods reduce SOH estimation errors by 30-50% compared to single-mode approaches.

Practical implementation requires consideration of operating conditions. Ambient temperature, cooling system performance, and load profiles influence thermal behavior and must be accounted for in SOH algorithms. Baseline thermal signatures should be established under controlled conditions before field deployment. Adaptive algorithms that continuously update reference parameters accommodate environmental variations and aging-dependent changes in thermal response.

Challenges remain in standardizing thermal-based SOH prediction methods. Variability in cell designs, materials, and cooling systems necessitates customized models for different battery types. Sensor placement optimization is critical for capturing representative thermal data without interfering with battery operation. Long-term validation under real-world conditions is essential to verify prediction accuracy across diverse usage scenarios.

Despite these challenges, thermal behavior analysis offers significant advantages for SOH prediction. It provides real-time monitoring capability without requiring full discharge cycles, enabling continuous health assessment during normal operation. The non-invasive nature makes it suitable for deployed systems where intrusive testing is impractical. As battery systems grow in complexity and scale, integrating thermal monitoring with battery management systems will become increasingly important for ensuring safety, reliability, and optimal performance throughout the battery lifecycle.

Future advancements may include higher-resolution thermal imaging, advanced materials with embedded thermal sensors, and improved multi-physics models that couple thermal, electrical, and mechanical aging effects. Standardized thermal fingerprint databases could enable faster deployment across different battery chemistries and form factors. The integration of thermal SOH indicators with cloud-based analytics platforms may facilitate fleet-wide health monitoring and predictive maintenance for large-scale energy storage systems.