Aging in lithium-ion batteries significantly impacts thermal model parameters, altering heat generation and dissipation characteristics. The evolution of thermal properties stems primarily from structural and chemical degradation mechanisms, particularly solid electrolyte interphase (SEI) growth and electrode cracking. These changes necessitate adjustments in thermal conductivity and heat capacity values within degradation-aware modeling frameworks to maintain predictive accuracy over a battery's lifespan.
SEI growth on anode surfaces is a dominant aging mechanism affecting thermal parameters. The SEI layer, primarily composed of lithium salts and organic compounds, exhibits lower thermal conductivity (0.1-0.3 W/m·K) compared to graphite anodes (approximately 150 W/m·K in-plane). As SEI thickness increases from initial values of 10-50 nm to several hundred nanometers after aging, the effective through-plane thermal conductivity of the anode decreases. Empirical studies show a 15-25% reduction in overall cell thermal conductivity after 500 cycles under typical operating conditions. The SEI's thermal properties also influence heat capacity, as its specific heat capacity (1.2-1.8 J/g·K) differs from electrode materials. Composite models must account for the increasing volume fraction of SEI material in aged cells.
Electrode cracking introduces additional complexity to thermal parameter evolution. Mechanical degradation in both anode and cathode materials creates voids and discontinuities that reduce effective thermal conductivity. Silicon-containing anodes demonstrate particularly pronounced effects, where volume expansion-induced cracking can decrease thermal conductivity by 30-40% after extensive cycling. Cathode materials like NMC experience particle cracking that increases interfacial thermal resistance between active material and conductive additives. The resulting air gaps (thermal conductivity ~0.026 W/m·K) create localized hotspots that alter heat transfer pathways. Experimental measurements reveal anisotropic conductivity changes, with through-plane conductivity typically degrading faster than in-plane values due to the layered cell structure.
The combined effects of SEI growth and electrode cracking modify several key parameters in thermal models. Effective thermal conductivity (k_eff) demonstrates a nonlinear relationship with cycle count, often following a form:
k_eff = k_initial - a·N^b
where N is cycle number, and a, b are material-dependent coefficients ranging from 0.001-0.005 and 0.5-0.8 respectively for commercial cells. Heat capacity (C_p) changes less dramatically but shows measurable increases of 3-8% over cell lifetime due to the accumulation of lower-density degradation products. These parameter shifts impact thermal model outputs, with aged cells typically exhibiting 10-15°C higher temperature rises under identical operating conditions compared to fresh cells.
Degradation-aware thermal modeling frameworks incorporate these aging effects through several approaches. Multi-scale models couple electrochemical degradation models with thermal submodels, updating material properties based on local degradation states. Some implementations use look-up tables of measured thermal parameters versus state-of-health (SOH), while others employ physics-based corrections to account for porosity changes and interfacial resistances. A common framework separates the thermal model into three interacting domains: bulk material properties, interfacial resistances, and degradation layer properties. Each domain receives updates from the aging model at defined intervals.
Empirical correlations between measurable aging indicators and thermal parameters enable practical implementation in battery management systems. Resistance increase, a commonly tracked aging metric, shows reasonable correlation (R²=0.75-0.90) with thermal conductivity reduction across multiple cell chemistries. Capacity fade exhibits weaker correlation (R²=0.50-0.70) with heat capacity changes. Some advanced systems employ differential thermal analysis during operation to estimate current thermal parameters by analyzing temperature response times under known load conditions.
The impact of aging on thermal model parameters creates cascading effects in battery performance and safety predictions. Reduced thermal conductivity leads to higher temperature gradients within cells, accelerating local degradation rates in a positive feedback loop. Modified heat capacity values alter the predicted temperature rise rate during fast charging or high-power discharges. These changes necessitate adaptive thermal management strategies, where cooling system operation parameters evolve with cell aging to maintain optimal temperature ranges.
Experimental characterization of aged cells reveals several consistent trends in thermal parameter evolution. Accelerated aging tests at elevated temperatures show faster degradation of thermal conductivity compared to room temperature cycling, with differences attributed to more severe SEI growth mechanisms. Pressure effects significantly influence aged thermal properties, with constrained cells (typical in battery packs) showing 20-30% less conductivity degradation than unconstrained cells due to mitigated electrode delamination.
Several challenges remain in fully capturing aging-thermal property relationships. The heterogeneous nature of degradation makes bulk property measurements insufficient for localized hot spot prediction. Advanced techniques like lock-in thermography provide spatial resolution of thermal conductivity changes but are difficult to implement in operational systems. Machine learning approaches show promise in correlating operational data patterns with thermal parameter changes, though they require extensive training datasets from aged cells.
Practical implementation of degradation-aware thermal models requires balancing accuracy with computational complexity. Reduced-order models that apply uniform degradation factors across cell components can capture 80-90% of aging effects while maintaining real-time capability. More comprehensive models that track local degradation states provide better hot spot prediction but require detailed material history and increased processing power.
The development of standardized testing protocols for aged thermal properties would significantly advance modeling capabilities. Current measurements show considerable variation depending on sample preparation methods and measurement techniques. Consensus on representative aging conditions and measurement procedures would improve the reliability of empirical correlations used in degradation-aware models.
Future improvements in degradation-aware thermal modeling will likely focus on three areas: better understanding of nanoscale interfacial thermal transport in degraded materials, improved in-situ measurement techniques for thermal parameters, and tighter integration between electrochemical-thermal-mechanical aging models. These advancements will enable more accurate prediction of temperature distributions throughout a battery's lifespan, supporting the development of aging-adaptive thermal management systems that optimize both performance and longevity.
The relationship between aging mechanisms and thermal parameters forms a critical link in predicting battery behavior over time. By incorporating these evolving properties into thermal models, engineers can design more robust battery systems that account for performance changes throughout operational life. Continued research in this area will yield improved modeling frameworks that balance physical accuracy with practical implementability across diverse battery applications.