An optimized informer model design for electric vehicle SOC prediction.
SOC prediction is of great value to electric vehicle status assessment. Informer model is better than other models in SOC prediction, but there is still a gap in practical application. Therefore, based on the health assessment algorithm, a new optimized Informer model is proposed to predict SOC. Fir...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Public Library of Science (PLoS)
2025-01-01
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| Series: | PLoS ONE |
| Online Access: | https://doi.org/10.1371/journal.pone.0314255 |
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| _version_ | 1850128448006127616 |
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| author | Xin Xie Feng Huang Yefeng Long Youyuan Peng Wenjuan Zhou |
| author_facet | Xin Xie Feng Huang Yefeng Long Youyuan Peng Wenjuan Zhou |
| author_sort | Xin Xie |
| collection | DOAJ |
| description | SOC prediction is of great value to electric vehicle status assessment. Informer model is better than other models in SOC prediction, but there is still a gap in practical application. Therefore, based on the health assessment algorithm, a new optimized Informer model is proposed to predict SOC. Firstly, the health assessment is carried out through the historical running data of the electric vehicle to obtain the health matrix. Then, the health matrix is used to improve Encoder and Decoder modules and improve the prediction accuracy and speed of Informer model. Subsequently, the health matrix is utilized to optimize the prediction logic, reduce the influence of truncation error, and further improve the SOC prediction accuracy. Finally, using the Informer model before and after optimization, SOC prediction is performed using four different datasets. The results indicate that after optimizing the En-De module of Informer, prediction accuracy improved by approximately 15%, with prediction speed increasing by about 100%. Furthermore, optimizing the prediction logic to reduce truncation error further enhanced Informer's prediction accuracy by around 20%. |
| format | Article |
| id | doaj-art-da8f4d29d0a34c53a1836f9692a981c0 |
| institution | OA Journals |
| issn | 1932-6203 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj-art-da8f4d29d0a34c53a1836f9692a981c02025-08-20T02:33:18ZengPublic Library of Science (PLoS)PLoS ONE1932-62032025-01-01203e031425510.1371/journal.pone.0314255An optimized informer model design for electric vehicle SOC prediction.Xin XieFeng HuangYefeng LongYouyuan PengWenjuan ZhouSOC prediction is of great value to electric vehicle status assessment. Informer model is better than other models in SOC prediction, but there is still a gap in practical application. Therefore, based on the health assessment algorithm, a new optimized Informer model is proposed to predict SOC. Firstly, the health assessment is carried out through the historical running data of the electric vehicle to obtain the health matrix. Then, the health matrix is used to improve Encoder and Decoder modules and improve the prediction accuracy and speed of Informer model. Subsequently, the health matrix is utilized to optimize the prediction logic, reduce the influence of truncation error, and further improve the SOC prediction accuracy. Finally, using the Informer model before and after optimization, SOC prediction is performed using four different datasets. The results indicate that after optimizing the En-De module of Informer, prediction accuracy improved by approximately 15%, with prediction speed increasing by about 100%. Furthermore, optimizing the prediction logic to reduce truncation error further enhanced Informer's prediction accuracy by around 20%.https://doi.org/10.1371/journal.pone.0314255 |
| spellingShingle | Xin Xie Feng Huang Yefeng Long Youyuan Peng Wenjuan Zhou An optimized informer model design for electric vehicle SOC prediction. PLoS ONE |
| title | An optimized informer model design for electric vehicle SOC prediction. |
| title_full | An optimized informer model design for electric vehicle SOC prediction. |
| title_fullStr | An optimized informer model design for electric vehicle SOC prediction. |
| title_full_unstemmed | An optimized informer model design for electric vehicle SOC prediction. |
| title_short | An optimized informer model design for electric vehicle SOC prediction. |
| title_sort | optimized informer model design for electric vehicle soc prediction |
| url | https://doi.org/10.1371/journal.pone.0314255 |
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