Hybrid Estimation Method for the State of Charge of Lithium Batteries Using a Temporal Convolutional Network and XGBoost
Lithium batteries have recently attracted significant attention as highly promising energy storage devices within the secondary battery industry. However, it is important to note that they may pose safety risks, including the potential for explosions during use. Therefore, achieving stable and safe...
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2023-11-01
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| Series: | Batteries |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2313-0105/9/11/544 |
| _version_ | 1827640570409910272 |
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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 have recently attracted significant attention as highly promising energy storage devices within the secondary battery industry. However, it is important to note that they may pose safety risks, including the potential for explosions during use. Therefore, achieving stable and safe utilization of these batteries necessitates accurate state-of-charge (SOC) estimation. In this study, we propose a hybrid model combining temporal convolutional network (TCN) and eXtreme gradient boosting (XGBoost) to investigate the nonlinear and evolving characteristics of batteries. The primary goal is to enhance SOC estimation performance by leveraging TCN’s long-effective memory capabilities and XGBoost’s robust generalization abilities. We conducted experiments using datasets from NASA, Oxford, and a vehicle simulator to validate the model’s performance. Additionally, we compared the performance of our model with that of a multilayer neural network, long short-term memory, gated recurrent unit, XGBoost, and TCN. The experimental results confirm that our proposed TCN–XGBoost hybrid model outperforms the other models in SOC estimation across all datasets. |
| first_indexed | 2024-03-09T17:01:31Z |
| format | Article |
| id | doaj.art-539e9c01c4d4401493a9b6de1e77826d |
| institution | Directory Open Access Journal |
| issn | 2313-0105 |
| language | English |
| last_indexed | 2024-03-09T17:01:31Z |
| publishDate | 2023-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Batteries |
| spelling | doaj.art-539e9c01c4d4401493a9b6de1e77826d2023-11-24T14:29:09ZengMDPI AGBatteries2313-01052023-11-0191154410.3390/batteries9110544Hybrid Estimation Method for the State of Charge of Lithium Batteries Using a Temporal Convolutional Network and XGBoostJong-Hyun Lee0In-Soo Lee1School of Electronic and Electrical Engineering, Kyungpook National University, Daegu 41566, Republic of KoreaSchool of Electronic and Electrical Engineering, Kyungpook National University, Daegu 41566, Republic of KoreaLithium batteries have recently attracted significant attention as highly promising energy storage devices within the secondary battery industry. However, it is important to note that they may pose safety risks, including the potential for explosions during use. Therefore, achieving stable and safe utilization of these batteries necessitates accurate state-of-charge (SOC) estimation. In this study, we propose a hybrid model combining temporal convolutional network (TCN) and eXtreme gradient boosting (XGBoost) to investigate the nonlinear and evolving characteristics of batteries. The primary goal is to enhance SOC estimation performance by leveraging TCN’s long-effective memory capabilities and XGBoost’s robust generalization abilities. We conducted experiments using datasets from NASA, Oxford, and a vehicle simulator to validate the model’s performance. Additionally, we compared the performance of our model with that of a multilayer neural network, long short-term memory, gated recurrent unit, XGBoost, and TCN. The experimental results confirm that our proposed TCN–XGBoost hybrid model outperforms the other models in SOC estimation across all datasets.https://www.mdpi.com/2313-0105/9/11/544lithium batterySOCestimationTCNXGBoosthybrid model |
| spellingShingle | Jong-Hyun Lee In-Soo Lee Hybrid Estimation Method for the State of Charge of Lithium Batteries Using a Temporal Convolutional Network and XGBoost Batteries lithium battery SOC estimation TCN XGBoost hybrid model |
| title | Hybrid Estimation Method for the State of Charge of Lithium Batteries Using a Temporal Convolutional Network and XGBoost |
| title_full | Hybrid Estimation Method for the State of Charge of Lithium Batteries Using a Temporal Convolutional Network and XGBoost |
| title_fullStr | Hybrid Estimation Method for the State of Charge of Lithium Batteries Using a Temporal Convolutional Network and XGBoost |
| title_full_unstemmed | Hybrid Estimation Method for the State of Charge of Lithium Batteries Using a Temporal Convolutional Network and XGBoost |
| title_short | Hybrid Estimation Method for the State of Charge of Lithium Batteries Using a Temporal Convolutional Network and XGBoost |
| title_sort | hybrid estimation method for the state of charge of lithium batteries using a temporal convolutional network and xgboost |
| topic | lithium battery SOC estimation TCN XGBoost hybrid model |
| url | https://www.mdpi.com/2313-0105/9/11/544 |
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