Enhancing state of charge and state of energy estimation in Lithium-ion batteries based on a TimesNet model with Gaussian data augmentation and error correction
Accurately estimating the state of charge (SOC) and state of energy (SOE) of lithium-ion batteries is crucial for their safe and stable operation. This study proposes a hybrid deep learning model based on Gaussian data augmentation (GDA), the TimesNet model, error correction (EC), and an improved Ba...
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| Veröffentlicht in: | Applied energy Jg. 359; S. 122669 |
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| Format: | Journal Article |
| Sprache: | Englisch |
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Elsevier Ltd
01.04.2024
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| ISSN: | 0306-2619, 1872-9118 |
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| Abstract | Accurately estimating the state of charge (SOC) and state of energy (SOE) of lithium-ion batteries is crucial for their safe and stable operation. This study proposes a hybrid deep learning model based on Gaussian data augmentation (GDA), the TimesNet model, error correction (EC), and an improved Bayesian algorithm called Sequential Model-based Algorithm Configuration (SMAC) for SOC and SOE estimation in lithium-ion batteries. Firstly, we compared the performance of the TimesNet model with other benchmark models. Then, GDA data with different signal-to-noise ratios were used for testing, and the model's performance was improved using GDA data with appropriate signal-to-noise ratios. Finally, an error correction method was employed to further enhance the estimation accuracy. During the experiment, SMAC was used to optimize its hyperparameters. In NN and UDDS drive cycles at temperatures of 0 °C, 10 °C, and 25 °C, the highest RMSE values for SOC and SOE estimation of the proposed model were 0.105%, 0.098%, 0.227%, and 0.213%, respectively. Experimental results demonstrate that the TimesNet model achieves good prediction performance for SOC and SOE estimation. GDA and EC effectively enhance the accuracy of the model.
•Using visual networks to analyze the operational data of lithium batteries.•Gaussian data augmentation is applied to optimize the raw data.•Error correction is used to improve the estimation accuracy of SOC and SOE. |
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| AbstractList | Accurately estimating the state of charge (SOC) and state of energy (SOE) of lithium-ion batteries is crucial for their safe and stable operation. This study proposes a hybrid deep learning model based on Gaussian data augmentation (GDA), the TimesNet model, error correction (EC), and an improved Bayesian algorithm called Sequential Model-based Algorithm Configuration (SMAC) for SOC and SOE estimation in lithium-ion batteries. Firstly, we compared the performance of the TimesNet model with other benchmark models. Then, GDA data with different signal-to-noise ratios were used for testing, and the model's performance was improved using GDA data with appropriate signal-to-noise ratios. Finally, an error correction method was employed to further enhance the estimation accuracy. During the experiment, SMAC was used to optimize its hyperparameters. In NN and UDDS drive cycles at temperatures of 0 °C, 10 °C, and 25 °C, the highest RMSE values for SOC and SOE estimation of the proposed model were 0.105%, 0.098%, 0.227%, and 0.213%, respectively. Experimental results demonstrate that the TimesNet model achieves good prediction performance for SOC and SOE estimation. GDA and EC effectively enhance the accuracy of the model.
•Using visual networks to analyze the operational data of lithium batteries.•Gaussian data augmentation is applied to optimize the raw data.•Error correction is used to improve the estimation accuracy of SOC and SOE. |
| ArticleNumber | 122669 |
| Author | Nazir, Muhammad Shahzad Peng, Tian Zhang, Yue Li, Zhengbo Zhang, Zhao Zhang, Chu |
| Author_xml | – sequence: 1 givenname: Chu surname: Zhang fullname: Zhang, Chu organization: Faculty of Automation, Huaiyin Institute of Technology, Huaian 223003, China – sequence: 2 givenname: Yue surname: Zhang fullname: Zhang, Yue organization: Faculty of Automation, Huaiyin Institute of Technology, Huaian 223003, China – sequence: 3 givenname: Zhengbo surname: Li fullname: Li, Zhengbo organization: Faculty of Automation, Huaiyin Institute of Technology, Huaian 223003, China – sequence: 4 givenname: Zhao surname: Zhang fullname: Zhang, Zhao organization: Faculty of Automation, Huaiyin Institute of Technology, Huaian 223003, China – sequence: 5 givenname: Muhammad Shahzad surname: Nazir fullname: Nazir, Muhammad Shahzad organization: Faculty of Automation, Huaiyin Institute of Technology, Huaian 223003, China – sequence: 6 givenname: Tian surname: Peng fullname: Peng, Tian email: husthydropt@126.com organization: Faculty of Automation, Huaiyin Institute of Technology, Huaian 223003, China |
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| Title | Enhancing state of charge and state of energy estimation in Lithium-ion batteries based on a TimesNet model with Gaussian data augmentation and error correction |
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