Battery (electricity)

Quantum mechanics

Random forest

Linguistics

Voltage

Engineering

Philosophy

Short circuit

Fault detection and isolation

Actuator

Classifier (UML)

Power (physics)

Set (abstract data type)

Computer science

Internal resistance

Physics

Pattern recognition (psychology)

Feature (linguistics)

Artificial intelligence

Programming language

Electrical engineering

Scientific Reports

With the proliferation of Li-ion batteries in smart phones, safety is the main concern and an on-line detection of battery faults is much wanting. Internal short circuit is a very critical issue that is often ascribed to be a cause of many accidents involving Li-ion batteries. A novel method that can detect the Internal short circuit in real time based on an advanced machine leaning approach, is proposed. Based on an equivalent electric circuit model, a set of features encompassing the physics of Li-ion cell with short circuit fault are identified and extracted from each charge-discharge cycle. The training feature set is generated with and without an external short-circuit resistance across the battery terminals. To emulate a real user scenario, internal short is induced by mechanical abuse. The testing feature set is generated from the battery charge-discharge data before and after the abuse. A random forest classifier is trained with the training feature set. The fault detection accuracy for the testing dataset is found to be more than 97%. The proposed algorithm does not interfere with the normal usage of the device, and the trained model can be implemented in any device for online fault detection.

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