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Predictive Modeling and Fault Detection of Thermal Runaway in Lithium-Ion Batteries

  • Unique Paper ID: 171314
  • Volume: 11
  • Issue: 7
  • PageNo: 3889-3892
  • Abstract:
  • Lithium-ion batteries (LIBs) are critical for modern energy applications, such as electric vehicles (EVs) and renewable energy systems. However, their vulnerability to thermal runaway (TR)—a self-sustaining thermal failure—poses significant safety challenges. This research introduces a hybrid predictive framework combining physics-based thermal modeling and machine learning (ML) techniques. The Bernardi equation simulated heat dynamics, while Random Forest and XGBoost classified multi-sensor data to detect TR risks. The XGBoost model achieved 95.1% accuracy with a time-to-fault prediction error of ±5 seconds. Multi-sensor fusion of temperature, voltage, and state of charge (SOC) data enhanced detection accuracy by 10%. These findings underscore the potential of integrating predictive models into battery management systems (BMS) to improve LIB safety and reliability.
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Cite This Article

  • ISSN: 2349-6002
  • Volume: 11
  • Issue: 7
  • PageNo: 3889-3892

Predictive Modeling and Fault Detection of Thermal Runaway in Lithium-Ion Batteries

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Impact Factor
8.01 (Year 2024)
UGC Approved
Journal no 47859

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