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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| Format: | Final Year Project (FYP) |
| Language: | English |
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Nanyang Technological University
2022
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| Online Access: | https://hdl.handle.net/10356/157524 |
| _version_ | 1826118841244057600 |
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| author | Tiah, Jye Chen |
| author2 | Hung Dinh Nguyen |
| author_facet | Hung Dinh Nguyen Tiah, Jye Chen |
| author_sort | Tiah, Jye Chen |
| collection | NTU |
| 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. |
| first_indexed | 2024-10-01T04:50:07Z |
| format | Final Year Project (FYP) |
| id | ntu-10356/157524 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T04:50:07Z |
| publishDate | 2022 |
| publisher | Nanyang Technological University |
| record_format | dspace |
| spelling | ntu-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 |
| 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 |
| title | 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_short | Data analytics for energy storage safety |
| title_sort | data analytics for energy storage safety |
| topic | Engineering::Computer science and engineering::Software::Software engineering Engineering::Electrical and electronic engineering::Power electronics |
| url | https://hdl.handle.net/10356/157524 |
| work_keys_str_mv | AT tiahjyechen dataanalyticsforenergystoragesafety |