Real-Time State of Charge Estimation for Each Cell of Lithium Battery Pack Using Neural Networks
With the emergence of problems on environmental pollutions, lithium batteries have attracted considerable attention as an efficient and nature-friendly alternative energy storage device owing to their advantages, such as high power density, low self-discharge rate, and long life cycle. They are wide...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-12-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/10/23/8644 |
| _version_ | 1827700938769432576 |
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| author | JaeHyung Park JongHyun Lee SiJin Kim InSoo Lee |
| author_facet | JaeHyung Park JongHyun Lee SiJin Kim InSoo Lee |
| author_sort | JaeHyung Park |
| collection | DOAJ |
| description | With the emergence of problems on environmental pollutions, lithium batteries have attracted considerable attention as an efficient and nature-friendly alternative energy storage device owing to their advantages, such as high power density, low self-discharge rate, and long life cycle. They are widely used in numerous applications, from everyday items, such as smartphones, wireless vacuum cleaners, and wireless power tools, to transportation means, such as electric vehicles and bicycles. In this paper, the state of charge (SOC) of each cell of the lithium battery pack was estimated in real time using two types of neural networks: Multi-layer Neural Network (MNN) and Long Short-Term Memory (LSTM). To determine the difference in the SOC estimation performance under various conditions, the input values were compared using 2, 6, and 8 input values, and the difference according to the use of temperature variable data was compared, and finally, the MNN and LSTM. The differences were compared. Real-time SOC was estimated using the method with the lowest error rate. |
| first_indexed | 2024-03-10T14:21:54Z |
| format | Article |
| id | doaj.art-ebddd1d5e82e4a50b4c83c4bd81b7e2b |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-10T14:21:54Z |
| publishDate | 2020-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-ebddd1d5e82e4a50b4c83c4bd81b7e2b2023-11-20T23:19:53ZengMDPI AGApplied Sciences2076-34172020-12-011023864410.3390/app10238644Real-Time State of Charge Estimation for Each Cell of Lithium Battery Pack Using Neural NetworksJaeHyung Park0JongHyun Lee1SiJin Kim2InSoo Lee3School of Electronic and Electrical Engineering, Kyungpook National University, Daegu 41566, KoreaSchool of Electronic and Electrical Engineering, Kyungpook National University, Daegu 41566, KoreaSchool of Electronic and Electrical Engineering, Kyungpook National University, Daegu 41566, KoreaSchool of Electronic and Electrical Engineering, Kyungpook National University, Daegu 41566, KoreaWith the emergence of problems on environmental pollutions, lithium batteries have attracted considerable attention as an efficient and nature-friendly alternative energy storage device owing to their advantages, such as high power density, low self-discharge rate, and long life cycle. They are widely used in numerous applications, from everyday items, such as smartphones, wireless vacuum cleaners, and wireless power tools, to transportation means, such as electric vehicles and bicycles. In this paper, the state of charge (SOC) of each cell of the lithium battery pack was estimated in real time using two types of neural networks: Multi-layer Neural Network (MNN) and Long Short-Term Memory (LSTM). To determine the difference in the SOC estimation performance under various conditions, the input values were compared using 2, 6, and 8 input values, and the difference according to the use of temperature variable data was compared, and finally, the MNN and LSTM. The differences were compared. Real-time SOC was estimated using the method with the lowest error rate.https://www.mdpi.com/2076-3417/10/23/8644lithium battery PackState of ChargeMulti-Layer Neural NetworkLong Short-Term Memoryreal-time |
| spellingShingle | JaeHyung Park JongHyun Lee SiJin Kim InSoo Lee Real-Time State of Charge Estimation for Each Cell of Lithium Battery Pack Using Neural Networks Applied Sciences lithium battery Pack State of Charge Multi-Layer Neural Network Long Short-Term Memory real-time |
| title | Real-Time State of Charge Estimation for Each Cell of Lithium Battery Pack Using Neural Networks |
| title_full | Real-Time State of Charge Estimation for Each Cell of Lithium Battery Pack Using Neural Networks |
| title_fullStr | Real-Time State of Charge Estimation for Each Cell of Lithium Battery Pack Using Neural Networks |
| title_full_unstemmed | Real-Time State of Charge Estimation for Each Cell of Lithium Battery Pack Using Neural Networks |
| title_short | Real-Time State of Charge Estimation for Each Cell of Lithium Battery Pack Using Neural Networks |
| title_sort | real time state of charge estimation for each cell of lithium battery pack using neural networks |
| topic | lithium battery Pack State of Charge Multi-Layer Neural Network Long Short-Term Memory real-time |
| url | https://www.mdpi.com/2076-3417/10/23/8644 |
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