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Identification and Quantification of Decomposition Mechanisms in Lithium-Ion Batteries; Input to Heat Flow Simulation for Modeling Thermal Runaway

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A Combined Data-Driven and Model-Based Algorithm for Accurate Battery Thermal Runaway Warning.

Qingyang Chen¹, Yinghui He¹, Nengjie Fang²

¹College of Information Science and Electronic Engineering, Zhejiang University, Hangzhou 310027, China.

Sensors (Basel, Switzerland)

|August 10, 2024

Summary

This summary is machine-generated.

This study introduces a novel algorithm for early detection of battery thermal runaway (TR). Combining data-driven and model-based methods, it provides advanced warnings and reduces false alarms in energy storage systems.

Keywords:

Bernardi equation K-Means data-driven model-driven thermal runaway warning

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Area of Science:

Battery safety engineering
Energy storage systems
Anomaly detection algorithms

Background:

Rising demand for large-scale energy storage necessitates improved battery safety.
Existing thermal runaway (TR) warning algorithms have limitations: model-driven methods are complex and lack versatility, while data-driven methods incur high training costs and false alarm rates.

Purpose of the Study:

To develop a hybrid algorithm for accurate and early detection of battery thermal runaway.
To overcome the limitations of existing model-driven and data-driven TR warning approaches.

Main Methods:

A combined data-driven (K-Means algorithm for outlier detection) and model-based (Bernardi equation for temperature evaluation) approach was developed.
Outputs from both modules were weighted and combined for comprehensive battery abnormality assessment.

Main Results:

The proposed hybrid algorithm achieved a 25-minute advance warning for thermal runaway events.
Significantly reduced probability of false alarms compared to existing methods.

Conclusions:

The hybrid algorithm effectively integrates the strengths of model-driven and data-driven techniques for enhanced battery safety.
This approach offers a promising solution for reliable early warning of thermal runaway in large-scale energy storage systems.