An improved dung beetle optimizer- hybrid kernel least square support vector regression algorithm for state of health estimation of lithium-ion batteries based on variational model decomposition
Accurate prediction of the state of health (SOH) of lithium-ion batteries is important for real-time monitoring and safety control of lithium-ion batteries. In this paper, a hybrid kernel least square support vector regression (HKLSSVR) prediction model based on variational modal decomposition (VMD)...
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| Vydané v: | Energy (Oxford) Ročník 306; s. 132464 |
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| Médium: | Journal Article |
| Jazyk: | English |
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Elsevier Ltd
15.10.2024
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| ISSN: | 0360-5442 |
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| Abstract | Accurate prediction of the state of health (SOH) of lithium-ion batteries is important for real-time monitoring and safety control of lithium-ion batteries. In this paper, a hybrid kernel least square support vector regression (HKLSSVR) prediction model based on variational modal decomposition (VMD) and improved dung beetle optimization (IDBO) is proposed. First, the original data is decomposed using VMD to reduce the non-smoothness of the data and to reduce the impact of non-smoothness on the prediction performance. The prediction is then carried out using the IDBO-HKLSSVR model, where the parameters in the prediction model are optimized using the IDBO optimization algorithm. Finally, all prediction components are superimposed to obtain the final results. The experimental results show that the coefficients of determination of the SOH of the six batteries predicted by the model are above 0.98388, which are higher than those of the other algorithms, confirming the high accuracy of the model in predicting the SOH of lithium-ion batteries. Meanwhile, compared with the existing prediction methods, the VMD-IDBO-HKLSSVR model proposed in this paper can predict the SOH of lithium-ion batteries more accurately.
•The algorithm was validated using both NASA and CALCE datasets.•Improving the dung beetle optimization algorithm using three strategies.•The superiority, robustness and stability of the algorithm are verified. |
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| AbstractList | Accurate prediction of the state of health (SOH) of lithium-ion batteries is important for real-time monitoring and safety control of lithium-ion batteries. In this paper, a hybrid kernel least square support vector regression (HKLSSVR) prediction model based on variational modal decomposition (VMD) and improved dung beetle optimization (IDBO) is proposed. First, the original data is decomposed using VMD to reduce the non-smoothness of the data and to reduce the impact of non-smoothness on the prediction performance. The prediction is then carried out using the IDBO-HKLSSVR model, where the parameters in the prediction model are optimized using the IDBO optimization algorithm. Finally, all prediction components are superimposed to obtain the final results. The experimental results show that the coefficients of determination of the SOH of the six batteries predicted by the model are above 0.98388, which are higher than those of the other algorithms, confirming the high accuracy of the model in predicting the SOH of lithium-ion batteries. Meanwhile, compared with the existing prediction methods, the VMD-IDBO-HKLSSVR model proposed in this paper can predict the SOH of lithium-ion batteries more accurately. Accurate prediction of the state of health (SOH) of lithium-ion batteries is important for real-time monitoring and safety control of lithium-ion batteries. In this paper, a hybrid kernel least square support vector regression (HKLSSVR) prediction model based on variational modal decomposition (VMD) and improved dung beetle optimization (IDBO) is proposed. First, the original data is decomposed using VMD to reduce the non-smoothness of the data and to reduce the impact of non-smoothness on the prediction performance. The prediction is then carried out using the IDBO-HKLSSVR model, where the parameters in the prediction model are optimized using the IDBO optimization algorithm. Finally, all prediction components are superimposed to obtain the final results. The experimental results show that the coefficients of determination of the SOH of the six batteries predicted by the model are above 0.98388, which are higher than those of the other algorithms, confirming the high accuracy of the model in predicting the SOH of lithium-ion batteries. Meanwhile, compared with the existing prediction methods, the VMD-IDBO-HKLSSVR model proposed in this paper can predict the SOH of lithium-ion batteries more accurately. •The algorithm was validated using both NASA and CALCE datasets.•Improving the dung beetle optimization algorithm using three strategies.•The superiority, robustness and stability of the algorithm are verified. |
| ArticleNumber | 132464 |
| Author | Wang, Shunli Zhu, Tao Fan, Yongcun Huang, Qi Fernandez, Carlos Hai, Nan |
| Author_xml | – sequence: 1 givenname: Tao surname: Zhu fullname: Zhu, Tao organization: School of Information Engineering, Southwest University of Science and Technology, Mianyang, 621010, China – sequence: 2 givenname: Shunli orcidid: 0000-0003-0485-8082 surname: Wang fullname: Wang, Shunli email: wangshunli1985@qq.com organization: School of Information Engineering, Southwest University of Science and Technology, Mianyang, 621010, China – sequence: 3 givenname: Yongcun surname: Fan fullname: Fan, Yongcun organization: School of Information Engineering, Southwest University of Science and Technology, Mianyang, 621010, China – sequence: 4 givenname: Nan surname: Hai fullname: Hai, Nan organization: School of Information Engineering, Southwest University of Science and Technology, Mianyang, 621010, China – sequence: 5 givenname: Qi orcidid: 0000-0002-8637-0269 surname: Huang fullname: Huang, Qi organization: School of Information Engineering, Southwest University of Science and Technology, Mianyang, 621010, China – sequence: 6 givenname: Carlos orcidid: 0000-0001-6588-9590 surname: Fernandez fullname: Fernandez, Carlos organization: School of Pharmacy and Life Sciences, Robert Gordon University, Aberdeen, AB10-7GJ, UK |
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| Keywords | Lithium-ion battery Improved dung beetle optimizer State of health Hybrid kernel least square support vector regression |
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| Title | An improved dung beetle optimizer- hybrid kernel least square support vector regression algorithm for state of health estimation of lithium-ion batteries based on variational model decomposition |
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