Estimation of Online State of Charge and State of Health Based on Neural Network Model Banks Using Lithium Batteries
Lithium batteries are secondary batteries used as power sources in various applications, such as electric vehicles, portable devices, and energy storage devices. However, because explosions frequently occur during their operation, improving battery safety by developing battery management systems wit...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-07-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/22/15/5536 |
| _version_ | 1827618343851393024 |
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| author | Jong-Hyun Lee In-Soo Lee |
| author_facet | Jong-Hyun Lee In-Soo Lee |
| author_sort | Jong-Hyun Lee |
| collection | DOAJ |
| description | Lithium batteries are secondary batteries used as power sources in various applications, such as electric vehicles, portable devices, and energy storage devices. However, because explosions frequently occur during their operation, improving battery safety by developing battery management systems with excellent reliability and efficiency has become a recent research focus. The performance of the battery management system varies depending on the estimated accuracy of the state of charge (SOC) and state of health (SOH). Therefore, we propose a SOH and SOC estimation method for lithium–ion batteries in this study. The proposed method includes four neural network models—one is used to estimate the SOH, and the other three are configured as normal, caution, and fault neural network model banks for estimating the SOC. The experimental results demonstrate that the proposed method using the long short-term memory model outperforms its counterparts. |
| first_indexed | 2024-03-09T10:05:17Z |
| format | Article |
| id | doaj.art-32eeae59e8c244aaadf04312f9544142 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-09T10:05:17Z |
| publishDate | 2022-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-32eeae59e8c244aaadf04312f95441422023-12-01T23:09:15ZengMDPI AGSensors1424-82202022-07-012215553610.3390/s22155536Estimation of Online State of Charge and State of Health Based on Neural Network Model Banks Using Lithium BatteriesJong-Hyun Lee0In-Soo Lee1School of Electronic and Electrical Engineering, Kyungpook National University, Daegu 41566, KoreaSchool of Electronic and Electrical Engineering, Kyungpook National University, Daegu 41566, KoreaLithium batteries are secondary batteries used as power sources in various applications, such as electric vehicles, portable devices, and energy storage devices. However, because explosions frequently occur during their operation, improving battery safety by developing battery management systems with excellent reliability and efficiency has become a recent research focus. The performance of the battery management system varies depending on the estimated accuracy of the state of charge (SOC) and state of health (SOH). Therefore, we propose a SOH and SOC estimation method for lithium–ion batteries in this study. The proposed method includes four neural network models—one is used to estimate the SOH, and the other three are configured as normal, caution, and fault neural network model banks for estimating the SOC. The experimental results demonstrate that the proposed method using the long short-term memory model outperforms its counterparts.https://www.mdpi.com/1424-8220/22/15/5536lithium batteriesstate of chargestate of healthmultilayer neural networkslong short-term memoryestimation |
| spellingShingle | Jong-Hyun Lee In-Soo Lee Estimation of Online State of Charge and State of Health Based on Neural Network Model Banks Using Lithium Batteries Sensors lithium batteries state of charge state of health multilayer neural networks long short-term memory estimation |
| title | Estimation of Online State of Charge and State of Health Based on Neural Network Model Banks Using Lithium Batteries |
| title_full | Estimation of Online State of Charge and State of Health Based on Neural Network Model Banks Using Lithium Batteries |
| title_fullStr | Estimation of Online State of Charge and State of Health Based on Neural Network Model Banks Using Lithium Batteries |
| title_full_unstemmed | Estimation of Online State of Charge and State of Health Based on Neural Network Model Banks Using Lithium Batteries |
| title_short | Estimation of Online State of Charge and State of Health Based on Neural Network Model Banks Using Lithium Batteries |
| title_sort | estimation of online state of charge and state of health based on neural network model banks using lithium batteries |
| topic | lithium batteries state of charge state of health multilayer neural networks long short-term memory estimation |
| url | https://www.mdpi.com/1424-8220/22/15/5536 |
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