Neural Network-Based Li-Ion Battery Aging Model at Accelerated C-Rate
Lithium-ion (Li-ion) batteries are widely used in electric vehicles (EVs) because of their high energy density, low self-discharge, and superior performance. Despite this, Li-ion batteries’ performance and reliability become critical as they lose their capacity with increasing charge and discharging...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-01-01
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| Series: | Batteries |
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| Online Access: | https://www.mdpi.com/2313-0105/9/2/93 |
| _version_ | 1797622351811051520 |
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| author | Md Azizul Hoque Mohd Khair Hassan Abdulrahman Hajjo Mohammad Osman Tokhi |
| author_facet | Md Azizul Hoque Mohd Khair Hassan Abdulrahman Hajjo Mohammad Osman Tokhi |
| author_sort | Md Azizul Hoque |
| collection | DOAJ |
| description | Lithium-ion (Li-ion) batteries are widely used in electric vehicles (EVs) because of their high energy density, low self-discharge, and superior performance. Despite this, Li-ion batteries’ performance and reliability become critical as they lose their capacity with increasing charge and discharging cycles. Moreover, Li-ion batteries are subject to aging in EVs due to load variations in discharge. Monitoring the battery cycle life at various discharge rates would enable the battery management system (BMS) to implement control parameters to resolve the aging issue. In this paper, a battery lifetime degradation model is proposed at an accelerated current rate (C-rate). Furthermore, an ideal lifetime discharge rate within the standard C-rate and beyond the C-rate is proposed. The consequence of discharging at an accelerated C-rate on the cycle life of the batteries is thoroughly investigated. Moreover, the battery degradation model is investigated with a deep learning algorithm-based feed-forward neural network (FNN), and a recurrent neural network (RNN) with long short-term memory (LSTM) layer. A comparative assessment of performance of the developed models is carried out and it is shown that the LSTM-RNN battery aging model has superior performance at accelerated C-rate compared to the traditional FNN network. |
| first_indexed | 2024-03-11T09:09:03Z |
| format | Article |
| id | doaj.art-69576daf432d460da3657e73ab1c50a2 |
| institution | Directory Open Access Journal |
| issn | 2313-0105 |
| language | English |
| last_indexed | 2024-03-11T09:09:03Z |
| publishDate | 2023-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Batteries |
| spelling | doaj.art-69576daf432d460da3657e73ab1c50a22023-11-16T19:07:28ZengMDPI AGBatteries2313-01052023-01-01929310.3390/batteries9020093Neural Network-Based Li-Ion Battery Aging Model at Accelerated C-RateMd Azizul Hoque0Mohd Khair Hassan1Abdulrahman Hajjo2Mohammad Osman Tokhi3Department of Electrical and Electronic Engineering, Universiti Putra Malaysia (UPM), Serdang 43400, Selangor, MalaysiaDepartment of Electrical and Electronic Engineering, Universiti Putra Malaysia (UPM), Serdang 43400, Selangor, MalaysiaDepartment of Electrical and Electronic Engineering, Universiti Putra Malaysia (UPM), Serdang 43400, Selangor, MalaysiaSchool of Engineering, London South Bank University, 103 Borough Road, London SE1 0AA, UKLithium-ion (Li-ion) batteries are widely used in electric vehicles (EVs) because of their high energy density, low self-discharge, and superior performance. Despite this, Li-ion batteries’ performance and reliability become critical as they lose their capacity with increasing charge and discharging cycles. Moreover, Li-ion batteries are subject to aging in EVs due to load variations in discharge. Monitoring the battery cycle life at various discharge rates would enable the battery management system (BMS) to implement control parameters to resolve the aging issue. In this paper, a battery lifetime degradation model is proposed at an accelerated current rate (C-rate). Furthermore, an ideal lifetime discharge rate within the standard C-rate and beyond the C-rate is proposed. The consequence of discharging at an accelerated C-rate on the cycle life of the batteries is thoroughly investigated. Moreover, the battery degradation model is investigated with a deep learning algorithm-based feed-forward neural network (FNN), and a recurrent neural network (RNN) with long short-term memory (LSTM) layer. A comparative assessment of performance of the developed models is carried out and it is shown that the LSTM-RNN battery aging model has superior performance at accelerated C-rate compared to the traditional FNN network.https://www.mdpi.com/2313-0105/9/2/93aginglithium-ioncurrent-ratebattery management systemartificial neural networkrecurrent neural network |
| spellingShingle | Md Azizul Hoque Mohd Khair Hassan Abdulrahman Hajjo Mohammad Osman Tokhi Neural Network-Based Li-Ion Battery Aging Model at Accelerated C-Rate Batteries aging lithium-ion current-rate battery management system artificial neural network recurrent neural network |
| title | Neural Network-Based Li-Ion Battery Aging Model at Accelerated C-Rate |
| title_full | Neural Network-Based Li-Ion Battery Aging Model at Accelerated C-Rate |
| title_fullStr | Neural Network-Based Li-Ion Battery Aging Model at Accelerated C-Rate |
| title_full_unstemmed | Neural Network-Based Li-Ion Battery Aging Model at Accelerated C-Rate |
| title_short | Neural Network-Based Li-Ion Battery Aging Model at Accelerated C-Rate |
| title_sort | neural network based li ion battery aging model at accelerated c rate |
| topic | aging lithium-ion current-rate battery management system artificial neural network recurrent neural network |
| url | https://www.mdpi.com/2313-0105/9/2/93 |
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