Electric vehicle braking energy recovery control method integrating fuzzy control and improved firefly algorithm.
Braking energy recovery is crucial for improving the energy efficiency and extending the range of electric vehicles. If a large amount of braking energy is wasted, it will lead to problems such as reduced range and increased battery burden for electric vehicles. Therefore, an electric vehicle brakin...
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| Format: | Article |
| Language: | English |
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Public Library of Science (PLoS)
2025-01-01
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| Series: | PLoS ONE |
| Online Access: | https://doi.org/10.1371/journal.pone.0320537 |
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| author | Jinfeng Xiong Jingbin Song Zhiqiang Zhang |
| author_facet | Jinfeng Xiong Jingbin Song Zhiqiang Zhang |
| author_sort | Jinfeng Xiong |
| collection | DOAJ |
| description | Braking energy recovery is crucial for improving the energy efficiency and extending the range of electric vehicles. If a large amount of braking energy is wasted, it will lead to problems such as reduced range and increased battery burden for electric vehicles. Therefore, an electric vehicle braking energy recovery control model that integrates fuzzy control algorithm with genetic firefly algorithm is proposed. Experimental analysis showed that the decrease in the state of charge of the model was 12.44%, and the braking energy recovery rate reached 52.1% in practical applications. Based on the above data, the proposed method can effectively control the amount of energy recovery. In addition, when the system chip value was 10%, the total amount of recovered energy at the battery end was the highest. Conversely, the total amount of recovered energy at the battery end was relatively small. In summary, the designed electric vehicle braking energy recovery control model can effectively control the amount of braking energy recovery of electric vehicles, ensuring the maximum recovery while also considering the durability and driving stability of the vehicle battery. The method can effectively extend mileage range in the electric vehicle industry, promoting the development and technological innovation of the new energy industry. |
| format | Article |
| id | doaj-art-d8e653fc646545b9bd528d730e39f7e1 |
| institution | DOAJ |
| issn | 1932-6203 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj-art-d8e653fc646545b9bd528d730e39f7e12025-08-20T03:16:46ZengPublic Library of Science (PLoS)PLoS ONE1932-62032025-01-01203e032053710.1371/journal.pone.0320537Electric vehicle braking energy recovery control method integrating fuzzy control and improved firefly algorithm.Jinfeng XiongJingbin SongZhiqiang ZhangBraking energy recovery is crucial for improving the energy efficiency and extending the range of electric vehicles. If a large amount of braking energy is wasted, it will lead to problems such as reduced range and increased battery burden for electric vehicles. Therefore, an electric vehicle braking energy recovery control model that integrates fuzzy control algorithm with genetic firefly algorithm is proposed. Experimental analysis showed that the decrease in the state of charge of the model was 12.44%, and the braking energy recovery rate reached 52.1% in practical applications. Based on the above data, the proposed method can effectively control the amount of energy recovery. In addition, when the system chip value was 10%, the total amount of recovered energy at the battery end was the highest. Conversely, the total amount of recovered energy at the battery end was relatively small. In summary, the designed electric vehicle braking energy recovery control model can effectively control the amount of braking energy recovery of electric vehicles, ensuring the maximum recovery while also considering the durability and driving stability of the vehicle battery. The method can effectively extend mileage range in the electric vehicle industry, promoting the development and technological innovation of the new energy industry.https://doi.org/10.1371/journal.pone.0320537 |
| spellingShingle | Jinfeng Xiong Jingbin Song Zhiqiang Zhang Electric vehicle braking energy recovery control method integrating fuzzy control and improved firefly algorithm. PLoS ONE |
| title | Electric vehicle braking energy recovery control method integrating fuzzy control and improved firefly algorithm. |
| title_full | Electric vehicle braking energy recovery control method integrating fuzzy control and improved firefly algorithm. |
| title_fullStr | Electric vehicle braking energy recovery control method integrating fuzzy control and improved firefly algorithm. |
| title_full_unstemmed | Electric vehicle braking energy recovery control method integrating fuzzy control and improved firefly algorithm. |
| title_short | Electric vehicle braking energy recovery control method integrating fuzzy control and improved firefly algorithm. |
| title_sort | electric vehicle braking energy recovery control method integrating fuzzy control and improved firefly algorithm |
| url | https://doi.org/10.1371/journal.pone.0320537 |
| work_keys_str_mv | AT jinfengxiong electricvehiclebrakingenergyrecoverycontrolmethodintegratingfuzzycontrolandimprovedfireflyalgorithm AT jingbinsong electricvehiclebrakingenergyrecoverycontrolmethodintegratingfuzzycontrolandimprovedfireflyalgorithm AT zhiqiangzhang electricvehiclebrakingenergyrecoverycontrolmethodintegratingfuzzycontrolandimprovedfireflyalgorithm |