A Multi-Model Probability Based Two-Layer Fusion Modeling Approach of Supercapacitor for Electric Vehicles
The improvement of the supercapacitor model redundancy is a significant method to guarantee the reliability of the power system in electric vehicle application. In order to enhance the accuracy of the supercapacitor model, eight conventional supercapacitor models were selected for parameter identifi...
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| Format: | Article |
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MDPI AG
2021-07-01
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| Series: | Energies |
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| Online Access: | https://www.mdpi.com/1996-1073/14/15/4644 |
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| author | Bo Huang Yuting Ma Chun Wang Yongzhi Chen Quanqing Yu |
| author_facet | Bo Huang Yuting Ma Chun Wang Yongzhi Chen Quanqing Yu |
| author_sort | Bo Huang |
| collection | DOAJ |
| description | The improvement of the supercapacitor model redundancy is a significant method to guarantee the reliability of the power system in electric vehicle application. In order to enhance the accuracy of the supercapacitor model, eight conventional supercapacitor models were selected for parameter identification by genetic algorithm, and the model accuracies based on standard diving cycle are further discussed. Then, three fusion modeling approaches including Bayesian fusion, residual normalization fusion, and state of charge (SOC) fragment fusion are presented and compared. In order to further improve the accuracy of these models, a two-layer fusion model based on SOC fragments is proposed in this paper. Compared with other fusion models, the root mean square error (RMSE), maximum error, and mean error of the two-layer fusion model can be reduced by at least 23.04%, 8.70%, and 30.13%, respectively. Moreover, the two-layer fusion model is further verified at 10, 25, and 40 °C, and the RMSE can be correspondingly reduced by 60.41%, 47.26%, 23.04%. The results indicate that the two-layer fusion model proposed in this paper achieves better robustness and accuracy. |
| first_indexed | 2024-03-10T09:15:38Z |
| format | Article |
| id | doaj.art-206b7432f6d34b93ab40774ea1b97419 |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-03-10T09:15:38Z |
| publishDate | 2021-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-206b7432f6d34b93ab40774ea1b974192023-11-22T05:35:57ZengMDPI AGEnergies1996-10732021-07-011415464410.3390/en14154644A Multi-Model Probability Based Two-Layer Fusion Modeling Approach of Supercapacitor for Electric VehiclesBo Huang0Yuting Ma1Chun Wang2Yongzhi Chen3Quanqing Yu4School of Mechanical Engineering, Sichuan University of Science and Engineering, Zigong 643000, ChinaArtificial Intelligence Key Laboratory of Sichuan Province, Sichuan University of Science and Engineering, Zigong 643000, ChinaSchool of Mechanical Engineering, Sichuan University of Science and Engineering, Zigong 643000, ChinaSchool of Mechanical Engineering, Sichuan University of Science and Engineering, Zigong 643000, ChinaSchool of Automotive Engineering, Harbin Institute of Technology, Weihai 264209, ChinaThe improvement of the supercapacitor model redundancy is a significant method to guarantee the reliability of the power system in electric vehicle application. In order to enhance the accuracy of the supercapacitor model, eight conventional supercapacitor models were selected for parameter identification by genetic algorithm, and the model accuracies based on standard diving cycle are further discussed. Then, three fusion modeling approaches including Bayesian fusion, residual normalization fusion, and state of charge (SOC) fragment fusion are presented and compared. In order to further improve the accuracy of these models, a two-layer fusion model based on SOC fragments is proposed in this paper. Compared with other fusion models, the root mean square error (RMSE), maximum error, and mean error of the two-layer fusion model can be reduced by at least 23.04%, 8.70%, and 30.13%, respectively. Moreover, the two-layer fusion model is further verified at 10, 25, and 40 °C, and the RMSE can be correspondingly reduced by 60.41%, 47.26%, 23.04%. The results indicate that the two-layer fusion model proposed in this paper achieves better robustness and accuracy.https://www.mdpi.com/1996-1073/14/15/4644supercapacitorparameter identificationgenetic algorithmfusion model |
| spellingShingle | Bo Huang Yuting Ma Chun Wang Yongzhi Chen Quanqing Yu A Multi-Model Probability Based Two-Layer Fusion Modeling Approach of Supercapacitor for Electric Vehicles Energies supercapacitor parameter identification genetic algorithm fusion model |
| title | A Multi-Model Probability Based Two-Layer Fusion Modeling Approach of Supercapacitor for Electric Vehicles |
| title_full | A Multi-Model Probability Based Two-Layer Fusion Modeling Approach of Supercapacitor for Electric Vehicles |
| title_fullStr | A Multi-Model Probability Based Two-Layer Fusion Modeling Approach of Supercapacitor for Electric Vehicles |
| title_full_unstemmed | A Multi-Model Probability Based Two-Layer Fusion Modeling Approach of Supercapacitor for Electric Vehicles |
| title_short | A Multi-Model Probability Based Two-Layer Fusion Modeling Approach of Supercapacitor for Electric Vehicles |
| title_sort | multi model probability based two layer fusion modeling approach of supercapacitor for electric vehicles |
| topic | supercapacitor parameter identification genetic algorithm fusion model |
| url | https://www.mdpi.com/1996-1073/14/15/4644 |
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