With growing demand for energy storage in electric vehicles and renewable energy systems, the safety and performance of lithium-ion batteries have become critical. Thermal runaway—a dangerous exothermic reaction caused by mechanical, electrical, or thermal abuse—poses significant safety risks. While most thermal runaway models focus on new batteries, this study emphasizes how battery aging affects thermal behavior and safety.
An electrochemical thermal coupling model incorporating aging effects was developed to predict thermal behavior and runaway conditions in cylindrical lithium-ion batteries. Validated with experimental data from fresh NMC and aged NCA batteries, the model identified key factors like internal resistance (10-40 mΩ), capacity (1-5 Ah), and current rate (1C-8C). Using logistic regression, the study achieved 95% prediction accuracy for thermal runaway thresholds.
1. Uncontrolled Heating and Safety Hazards
Lithium-ion batteries, essential for EVs and renewable energy storage, are prone to thermal runaway—a failure mode that causes uncontrolled temperature rise, SEI decomposition, and electrolyte combustion. Traditional research often overlooks battery aging, which significantly alters thermal safety boundaries. This study reveals how aging impacts thermal runaway, offering new insights into battery safety.
2. Battery Life and Thermal Safety Mechanisms
Cyclic Aging: Frequent charging and discharging accelerate electrode degradation and SEI film thickening, increasing internal resistance.
Idle Aging: Even during storage, ambient temperature and state of charge (SOC) reduce battery health, decreasing thermal stability.
The Role of Internal Resistance: Increased internal resistance exacerbates heat generation, while capacity decay reduces energy storage. This interaction complicates predicting thermal safety in aged batteries.
3. Data-Driven Battery Monitoring
Simplified Modeling: Using a single-particle model, the study simulated electrode reactions and heat accumulation. Cylindrical batteries’ core areas showed 10-15% higher temperatures than surfaces due to poor heat dissipation.
Full Factorial Design: 64 simulations revealed:
- Current Rate (C-rate): Doubling the charging current triples the heating rate. At 8C, temperatures can exceed 90°C.
- Internal Resistance (R): Every 10mΩ increase raises the temperature rise by 15-20%. Aged batteries (40mΩ) show 25°C higher temperatures than new ones at 3C.
- Capacity (Q): Low-capacity batteries heat slower, but high-capacity aged batteries generate more heat due to internal resistance.
4. Thermal Runaway Warning Signs
- Low SOC Anomaly: Temperatures exceeding 35°C at 10% SOC increase thermal runaway risk by 40%.
- Temperature Rise Rate: dT/dt > 0.2°C/s (especially at 50% SOC) indicates accelerated internal reactions.
- Temperature Acceleration: d²T/dt² > 0.1°C/s² signals unavoidable thermal runaway, requiring active cooling.
Machine Learning Prediction: Logistic regression identified an internal resistance threshold of 35mΩ under 3C charging, increasing thermal runaway probability from 5% to 65%. The model achieves 95% accuracy, enabling 40-minute advance warnings for BMS interventions.
5. The Aging Paradox and Thermal Management
Aged batteries may exhibit higher thermal runaway temperatures due to gas release reducing reactivity. However, increased internal resistance causes heating at lower SOC, heightening real-world risks. Solutions include:
- Multi-scale modeling integrating SEI growth and thermal distribution.
- Dynamic charging strategies for high-resistance batteries.
- Temperature-adaptive electrolytes to suppress exothermic reactions.
6. Conclusion
Battery aging profoundly impacts thermal safety, intertwining performance and risk. This study deciphers the relationship between aging and thermal runaway, highlighting the need for predictive monitoring and management. With machine learning and real-time data, batteries can have “personalized health profiles,” turning aging from a safety blind spot into a manageable factor.
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