Data-driven thermal fault detection of lithium-ion batteries
The electric vehicle (EV) industry is beginning to prosper at an unprecedented rate due to laws against the production of internal combustion engine (ICE) vehicles. The laws were implemented as the supply of petrol is running out and due to global warming concerns. Most if not all electric vehicles...
Saved in:
Main Author: | |
---|---|
Other Authors: | |
Format: | Final Year Project |
Language: | English |
Published: |
Nanyang Technological University
2022
|
Subjects: | |
Online Access: | https://hdl.handle.net/10356/157662 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Institution: | Nanyang Technological University |
Language: | English |
id |
sg-ntu-dr.10356-157662 |
---|---|
record_format |
dspace |
spelling |
sg-ntu-dr.10356-1576622023-07-07T18:58:59Z Data-driven thermal fault detection of lithium-ion batteries Yee, Eugene Jun Jian Xu Yan School of Electrical and Electronic Engineering xuyan@ntu.edu.sg Engineering::Electrical and electronic engineering The electric vehicle (EV) industry is beginning to prosper at an unprecedented rate due to laws against the production of internal combustion engine (ICE) vehicles. The laws were implemented as the supply of petrol is running out and due to global warming concerns. Most if not all electric vehicles rely on lithium-ion batteries (LIBs) and rapid technological advancement of LIBs in their power density and energy poses reliability and safety concerns. One of the critical hazards is thermal fault as it can be potentially catastrophic and may cause fire accidents or even explosions. This report illustrates the algorithm and technique used to implement an anomaly detection for thermal fault. Cell 1 parameters are modelled to simulate thermal fault and the data is used to train a machine learning (ML) model. The model is then used to predict for cell 2 to detect thermal fault. The algorithms were programmed in Python and the LIBs used during experiments were 18650 cells. Bachelor of Engineering (Electrical and Electronic Engineering) 2022-05-21T13:41:17Z 2022-05-21T13:41:17Z 2022 Final Year Project (FYP) Yee, E. J. J. (2022). Data-driven thermal fault detection of lithium-ion batteries. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/157662 https://hdl.handle.net/10356/157662 en application/pdf Nanyang Technological University |
institution |
Nanyang Technological University |
building |
NTU Library |
continent |
Asia |
country |
Singapore Singapore |
content_provider |
NTU Library |
collection |
DR-NTU |
language |
English |
topic |
Engineering::Electrical and electronic engineering |
spellingShingle |
Engineering::Electrical and electronic engineering Yee, Eugene Jun Jian Data-driven thermal fault detection of lithium-ion batteries |
description |
The electric vehicle (EV) industry is beginning to prosper at an unprecedented rate due to laws against the production of internal combustion engine (ICE) vehicles. The laws were implemented as the supply of petrol is running out and due to global warming concerns. Most if not all electric vehicles rely on lithium-ion batteries (LIBs) and rapid technological advancement of LIBs in their power density and energy poses reliability and safety concerns. One of the critical hazards is thermal fault as it can be potentially catastrophic and may cause fire accidents or even explosions.
This report illustrates the algorithm and technique used to implement an anomaly detection for thermal fault. Cell 1 parameters are modelled to simulate thermal fault and the data is used to train a machine learning (ML) model. The model is then used to predict for cell 2 to detect thermal fault. The algorithms were programmed in Python and the LIBs used during experiments were 18650 cells. |
author2 |
Xu Yan |
author_facet |
Xu Yan Yee, Eugene Jun Jian |
format |
Final Year Project |
author |
Yee, Eugene Jun Jian |
author_sort |
Yee, Eugene Jun Jian |
title |
Data-driven thermal fault detection of lithium-ion batteries |
title_short |
Data-driven thermal fault detection of lithium-ion batteries |
title_full |
Data-driven thermal fault detection of lithium-ion batteries |
title_fullStr |
Data-driven thermal fault detection of lithium-ion batteries |
title_full_unstemmed |
Data-driven thermal fault detection of lithium-ion batteries |
title_sort |
data-driven thermal fault detection of lithium-ion batteries |
publisher |
Nanyang Technological University |
publishDate |
2022 |
url |
https://hdl.handle.net/10356/157662 |
_version_ |
1772826394219249664 |