A Model-Free Detection Method for Internal Short Circuits in Single Lithium-ion Cells Using Pseudo Open-Circuit Voltage Difference

TL;DR

This paper introduces a simple, model-free online method for detecting internal short circuits in lithium-ion batteries by analyzing voltage differences, achieving perfect detection accuracy with minimal computational resources.

Contribution

The proposed approach is a lightweight, model-free technique that accurately detects internal short circuits using only terminal voltage and current data, without complex observers.

Findings

Achieved 100% detection success rate on tested scenarios.

Requires minimal computational and memory resources.

Validated on multiple real and false fault cases.

Abstract

This letter proposes a lightweight, model-free online diagnostic framework for detecting internal short circuits (ISC) in single lithium-ion cells under dynamic operating conditions. The core of the method lies in computing the first-order difference of pseudo open-circuit voltage ($\boldsymbol{\mathrm{OCV}_{\text{pseudo}}}$) to extract high-frequency deviations caused by ISC events from low-frequency polarization variations. The method relies solely on terminal voltage, current measurements, and an offline $R_0$--SOC look-up table, thereby eliminating the need for electrochemical or equivalent-circuit observers. Validated on ten real and one false fault scenarios, the proposed approach achieves a 100\% detection success rate with no missed or false alarms. In addition, the proposed method exhibits extremely low computational and memory requirements, making it highly suitable for real-time deployment in battery management systems (BMS).