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...

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Main Author: Yee, Eugene Jun Jian
Other Authors: Xu Yan
Format: Final Year Project
Language:English
Published: Nanyang Technological University 2022
Subjects:
Online Access:https://hdl.handle.net/10356/157662
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Institution: Nanyang Technological University
Language: English
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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
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