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Updated: Dec 29, 2025

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

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

Published on: March 7, 2022

Internal short circuit detection in Li-ion batteries using supervised machine learning.

Arunava Naha¹, Ashish Khandelwal¹, Samarth Agarwal²

¹Mobile Battery Research Lab, Samsung R&D Institute India - Bangalore (SRIB), #2870, Phoenix Building, Bagmane Constellation Business Park, Outer ring road, Doddanekundi circle, Marathahalli Post, Bangalore, 560037, India.

Scientific Reports

|January 30, 2020

Summary

This summary is machine-generated.

This study introduces a machine learning method for real-time detection of internal short circuits in lithium-ion (Li-ion) batteries, enhancing safety in electronic devices. The advanced algorithm achieves over 97% accuracy, ensuring reliable battery fault detection during normal operation.

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Published on: March 31, 2023

Area of Science:

Materials Science
Electrical Engineering
Computer Science

Background:

Lithium-ion (Li-ion) batteries are ubiquitous in modern electronics, but safety concerns, particularly internal short circuits, are paramount.
Internal short circuits are a primary cause of Li-ion battery failures and associated safety incidents.
Current methods for detecting these faults are often insufficient for real-time monitoring during device usage.

Purpose of the Study:

To develop and validate a novel, real-time method for detecting internal short circuits in Li-ion batteries.
To leverage advanced machine learning techniques for accurate and reliable fault identification.
To ensure the proposed method integrates seamlessly with device operation without user interference.

Main Methods:

An equivalent electric circuit model was used to identify and extract physics-based features indicative of short circuits from charge-discharge cycles.
A training dataset was generated, including scenarios with and without external short-circuit resistance.
Internal shorts were simulated via mechanical abuse, and a testing dataset was created from pre- and post-abuse charge-discharge data.
A random forest classifier was trained using the extracted features for fault detection.

Main Results:

The developed machine learning algorithm demonstrated high accuracy in detecting internal short circuits.
Fault detection accuracy on the testing dataset exceeded 97%.
The method effectively identified faults induced by mechanical abuse, simulating real-world failure scenarios.

Conclusions:

The proposed machine learning approach provides an effective solution for real-time online detection of internal short circuits in Li-ion batteries.
The algorithm's high accuracy and non-intrusive nature make it suitable for implementation in various electronic devices.
This advancement contributes significantly to improving the safety and reliability of Li-ion battery-powered systems.