SOC and SOH Prediction of Lithium‐Ion Batteries Based on LSTM–AUKF Joint Algorithm
ABSTRACT Lithium batteries are increasingly favored for energy storage due to their high energy density, long cycle life, and robust charge and discharge rates. However, safety concerns necessitate the implementation of a battery management system (BMS) to monitor battery status, maintain energy bal...
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| Format: | Article |
| Language: | English |
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Wiley
2025-01-01
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| Series: | Energy Science & Engineering |
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| Online Access: | https://doi.org/10.1002/ese3.1992 |
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| author | Yancheng Song Jiaqi Lu Huai Zhang Guangjun Liu |
| author_facet | Yancheng Song Jiaqi Lu Huai Zhang Guangjun Liu |
| author_sort | Yancheng Song |
| collection | DOAJ |
| description | ABSTRACT Lithium batteries are increasingly favored for energy storage due to their high energy density, long cycle life, and robust charge and discharge rates. However, safety concerns necessitate the implementation of a battery management system (BMS) to monitor battery status, maintain energy balance, and provide failure warnings to ensure safe operation. This paper proposes an efficient BMS for high‐voltage, high‐current lithium battery energy storage. The approach leverages a multihead‐attention‐enhanced long short‐term memory (LSTM) neural network combined with an adaptive unscented Kalman filter to accurately calculate the battery's state of charge (SOC) and state of health (SOH). To improve accuracy, various factors such as temperature and internal resistance were considered. The algorithm was validated through hardware and simulation experiments, with experimental data compared to estimation results to demonstrate its precision. The findings show strong convergence and tracking capabilities, with SOC estimation presenting a maximum error of 1.5% and SOH estimation a maximum error of under 0.4%. We expect that this approach will allow for a more refined evaluation of SOC and SOH in lithium‐ion batteries, potentially improving Li‐ion battery system management. |
| format | Article |
| id | doaj-art-6452e9ebedb94b30898446af84a47e45 |
| institution | Kabale University |
| issn | 2050-0505 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Energy Science & Engineering |
| spelling | doaj-art-6452e9ebedb94b30898446af84a47e452025-01-21T11:38:24ZengWileyEnergy Science & Engineering2050-05052025-01-0113124025410.1002/ese3.1992SOC and SOH Prediction of Lithium‐Ion Batteries Based on LSTM–AUKF Joint AlgorithmYancheng Song0Jiaqi Lu1Huai Zhang2Guangjun Liu3School of Mechanical Engineering Tongji University Shanghai ChinaSchool of Mechanical Engineering Tongji University Shanghai ChinaWuhu ChuRui Intelligent Technology Co., Ltd. Wuhu ChinaSchool of Mechanical Engineering Tongji University Shanghai ChinaABSTRACT Lithium batteries are increasingly favored for energy storage due to their high energy density, long cycle life, and robust charge and discharge rates. However, safety concerns necessitate the implementation of a battery management system (BMS) to monitor battery status, maintain energy balance, and provide failure warnings to ensure safe operation. This paper proposes an efficient BMS for high‐voltage, high‐current lithium battery energy storage. The approach leverages a multihead‐attention‐enhanced long short‐term memory (LSTM) neural network combined with an adaptive unscented Kalman filter to accurately calculate the battery's state of charge (SOC) and state of health (SOH). To improve accuracy, various factors such as temperature and internal resistance were considered. The algorithm was validated through hardware and simulation experiments, with experimental data compared to estimation results to demonstrate its precision. The findings show strong convergence and tracking capabilities, with SOC estimation presenting a maximum error of 1.5% and SOH estimation a maximum error of under 0.4%. We expect that this approach will allow for a more refined evaluation of SOC and SOH in lithium‐ion batteries, potentially improving Li‐ion battery system management.https://doi.org/10.1002/ese3.1992AUKFLSTMnumber‐model fusion methodSOC and SOH prediction |
| spellingShingle | Yancheng Song Jiaqi Lu Huai Zhang Guangjun Liu SOC and SOH Prediction of Lithium‐Ion Batteries Based on LSTM–AUKF Joint Algorithm Energy Science & Engineering AUKF LSTM number‐model fusion method SOC and SOH prediction |
| title | SOC and SOH Prediction of Lithium‐Ion Batteries Based on LSTM–AUKF Joint Algorithm |
| title_full | SOC and SOH Prediction of Lithium‐Ion Batteries Based on LSTM–AUKF Joint Algorithm |
| title_fullStr | SOC and SOH Prediction of Lithium‐Ion Batteries Based on LSTM–AUKF Joint Algorithm |
| title_full_unstemmed | SOC and SOH Prediction of Lithium‐Ion Batteries Based on LSTM–AUKF Joint Algorithm |
| title_short | SOC and SOH Prediction of Lithium‐Ion Batteries Based on LSTM–AUKF Joint Algorithm |
| title_sort | soc and soh prediction of lithium ion batteries based on lstm aukf joint algorithm |
| topic | AUKF LSTM number‐model fusion method SOC and SOH prediction |
| url | https://doi.org/10.1002/ese3.1992 |
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