Deep transfer-learning based lithium-ion battery fault diagnosis

Fault detection in lithium-ion batteries (LiB) is paramount to ensuring the long life and proper functioning of the batteries. To that end, this thesis proposes a combined fault diagnosis framework that leverages voltage charging curves and voltage charging curve fault residuals to accurately detect multiple faults within a LIB during partial and full charging regimes. This framework removes the need for parameter tuning and is also adaptable to varying battery chemistries and performs well with a small amount of available data. The framework leverages voltage residuals generated via a randomly initialized or pre-trained LSTM (Long Short Term Memory) model. Experimental results show its ability to accurately detect the different types of faults utilizing full voltage charging curve residuals with an accuracy of 95%. The framework can also detect faults utilizing partial voltage charging curve residuals & a pre-trained LSTM model with an accuracy of 94%.

Name thesis:degree_name

Master of Applied Science (MASc)

Discipline thesis:degree_discipline

Automotive Engineering

Grantor

University of Ontario Institute of Technology

Year dc:date.issued

2022

Author dc:creator

• Nwauche, Chukwuemeka Nelson

Advisor dc:contributor.advisor

• Lin, Xianke

• Lithium-ion batteries
• Transfer learning
• Convolutional neural networks
• Electric vehicles
• Battery fault diagnosis

Language dc:language.iso

en

Handle dc:identifier.uri

https://hdl.handle.net/10155/1517

OAI identifier oai:identifier

oai:ontariotechu.scholaris.ca:10155/1517