Data analytics for energy storage safety

The lithium-ion battery has become the common type of rechargeable battery in consumer electronics. They are a well-known type of battery due to being one of the best energy-to-weight ratios, high open-circuit voltage, low self-discharge rate, no memory effect and a slow loss of charge when not i...

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Main Author: Tiah, Jye Chen
Other Authors: Hung Dinh Nguyen
Format: Final Year Project
Language:English
Published: Nanyang Technological University 2022
Subjects:
Online Access:https://hdl.handle.net/10356/157524
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Institution: Nanyang Technological University
Language: English
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spelling sg-ntu-dr.10356-1575242023-07-07T19:16:54Z Data analytics for energy storage safety Tiah, Jye Chen Hung Dinh Nguyen School of Electrical and Electronic Engineering hunghtd@ntu.edu.sg Engineering::Computer science and engineering::Software::Software engineering Engineering::Electrical and electronic engineering::Power electronics The lithium-ion battery has become the common type of rechargeable battery in consumer electronics. They are a well-known type of battery due to being one of the best energy-to-weight ratios, high open-circuit voltage, low self-discharge rate, no memory effect and a slow loss of charge when not in use [1]. Due to the high complexity in calculation cost of the lithium-ion battery being an electrochemical device, it creates a unique pattern caused by internal and external effects. Hence, this article proposes a method for accurately forecasting the remaining useful life (RUL) of lithium-ion batteries. This will prevent problems caused by continuous usage of the battery after reaching its life threshold. This paper presents the use of 2 algorithms to predict the RUL of the battery. The first proposed algorithm is using a Deep Neural Network (DNN) approach to train and predict the State of Health (SoH) of the battery. Next, the second proposed algorithm will be using a Long Short Term Memory type network (LSTM), to estimate the RUL of the battery. The proposed approach is implemented in a case study with a battery dataset obtained from the NASAAmes Prognostic Center of Excellence (PCoE) database. The results revealed that the performance accuracy is better with the second algorithms working together with the first algorithms. In addition, the result was capable of reaching the battery end of life cycle earlier than the actual battery. Bachelor of Engineering (Electrical and Electronic Engineering) 2022-05-19T06:22:37Z 2022-05-19T06:22:37Z 2022 Final Year Project (FYP) Tiah, J. C. (2022). Data analytics for energy storage safety. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/157524 https://hdl.handle.net/10356/157524 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::Computer science and engineering::Software::Software engineering
Engineering::Electrical and electronic engineering::Power electronics
spellingShingle Engineering::Computer science and engineering::Software::Software engineering
Engineering::Electrical and electronic engineering::Power electronics
Tiah, Jye Chen
Data analytics for energy storage safety
description The lithium-ion battery has become the common type of rechargeable battery in consumer electronics. They are a well-known type of battery due to being one of the best energy-to-weight ratios, high open-circuit voltage, low self-discharge rate, no memory effect and a slow loss of charge when not in use [1]. Due to the high complexity in calculation cost of the lithium-ion battery being an electrochemical device, it creates a unique pattern caused by internal and external effects. Hence, this article proposes a method for accurately forecasting the remaining useful life (RUL) of lithium-ion batteries. This will prevent problems caused by continuous usage of the battery after reaching its life threshold. This paper presents the use of 2 algorithms to predict the RUL of the battery. The first proposed algorithm is using a Deep Neural Network (DNN) approach to train and predict the State of Health (SoH) of the battery. Next, the second proposed algorithm will be using a Long Short Term Memory type network (LSTM), to estimate the RUL of the battery. The proposed approach is implemented in a case study with a battery dataset obtained from the NASAAmes Prognostic Center of Excellence (PCoE) database. The results revealed that the performance accuracy is better with the second algorithms working together with the first algorithms. In addition, the result was capable of reaching the battery end of life cycle earlier than the actual battery.
author2 Hung Dinh Nguyen
author_facet Hung Dinh Nguyen
Tiah, Jye Chen
format Final Year Project
author Tiah, Jye Chen
author_sort Tiah, Jye Chen
title Data analytics for energy storage safety
title_short Data analytics for energy storage safety
title_full Data analytics for energy storage safety
title_fullStr Data analytics for energy storage safety
title_full_unstemmed Data analytics for energy storage safety
title_sort data analytics for energy storage safety
publisher Nanyang Technological University
publishDate 2022
url https://hdl.handle.net/10356/157524
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