Design of Early Internal Short-Circuit Fault Diagnosis and Safety Early Warning System for Lithium-ion Battery Energy Storage Systems

Abstract

Lithium-ion batteries are widely adopted in electric vehicles, energy storage systems, and portable electronic devices, leading to
increasing concerns about battery safety. Internal short-circuit faults, a common safety hazard in lithium-ion batteries, can potentially cause
thermal runaway, fires, or even explosions. To address this issue, this study proposes a machine learning-based lithium-ion battery internal
short-circuit fault diagnosis and safety alert system. This system continuously monitors key battery parameters such as voltage, current, temperature, and internal resistance, and integrates machine learning algorithms, including Support Vector Machine (SVM), to form a multi-parameter fusion fault diagnosis model capable of effectively identifying abnormal battery states.