Energy management strategy for methanol hybrid commercial vehicles based on improved dung beetle algorithm optimization.
In order to solve the problem of poor adaptability and robustness of the rule-based energy management strategy (EMS) in hybrid commercial vehicles, leading to suboptimal vehicle economy, this paper proposes an improved dung beetle algorithm (DBO) optimized multi-fuzzy control EMS. First, the rule-ba...
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| Main Authors: | , , , , |
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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.0313303 |
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| author | Zhihao Li Ping Xiao Jiabao Pan Wenjun Pei Aoning Lv |
| author_facet | Zhihao Li Ping Xiao Jiabao Pan Wenjun Pei Aoning Lv |
| author_sort | Zhihao Li |
| collection | DOAJ |
| description | In order to solve the problem of poor adaptability and robustness of the rule-based energy management strategy (EMS) in hybrid commercial vehicles, leading to suboptimal vehicle economy, this paper proposes an improved dung beetle algorithm (DBO) optimized multi-fuzzy control EMS. First, the rule-based EMS is established by dividing the efficient working areas of the methanol engine and power battery. The Tent chaotic mapping is then used to integrate strategies of cosine, Lévy flight, and Cauchy Gaussian mutation, improving the DBO. This integration compensates for the traditional dung beetle algorithm's tendency to fall into local optima and enhances its global search capability. Subsequently, fuzzy controllers for the driving charging mode and hybrid driving mode are designed under this rule-based EMS. Finally, the improved DBO is used to obtain the optimal control of the fuzzy controller by taking the fuel consumption of the whole vehicle and the fluctuation change of the battery state of charge (SOC) as the optimization objectives. Compared to traditional rule-based energy management strategies, the optimized fuzzy control using the enhanced DBO continuously adjusts the torque distribution between the engine and motor based on the vehicle's real-time state, resulting in a 9.07% reduction in fuel consumption and a 3.43% decrease in battery SOC fluctuations. |
| format | Article |
| id | doaj-art-7a29193476ef4193aed8d6f20b8527a1 |
| institution | Kabale University |
| issn | 1932-6203 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj-art-7a29193476ef4193aed8d6f20b8527a12025-01-08T05:31:59ZengPublic Library of Science (PLoS)PLoS ONE1932-62032025-01-01201e031330310.1371/journal.pone.0313303Energy management strategy for methanol hybrid commercial vehicles based on improved dung beetle algorithm optimization.Zhihao LiPing XiaoJiabao PanWenjun PeiAoning LvIn order to solve the problem of poor adaptability and robustness of the rule-based energy management strategy (EMS) in hybrid commercial vehicles, leading to suboptimal vehicle economy, this paper proposes an improved dung beetle algorithm (DBO) optimized multi-fuzzy control EMS. First, the rule-based EMS is established by dividing the efficient working areas of the methanol engine and power battery. The Tent chaotic mapping is then used to integrate strategies of cosine, Lévy flight, and Cauchy Gaussian mutation, improving the DBO. This integration compensates for the traditional dung beetle algorithm's tendency to fall into local optima and enhances its global search capability. Subsequently, fuzzy controllers for the driving charging mode and hybrid driving mode are designed under this rule-based EMS. Finally, the improved DBO is used to obtain the optimal control of the fuzzy controller by taking the fuel consumption of the whole vehicle and the fluctuation change of the battery state of charge (SOC) as the optimization objectives. Compared to traditional rule-based energy management strategies, the optimized fuzzy control using the enhanced DBO continuously adjusts the torque distribution between the engine and motor based on the vehicle's real-time state, resulting in a 9.07% reduction in fuel consumption and a 3.43% decrease in battery SOC fluctuations.https://doi.org/10.1371/journal.pone.0313303 |
| spellingShingle | Zhihao Li Ping Xiao Jiabao Pan Wenjun Pei Aoning Lv Energy management strategy for methanol hybrid commercial vehicles based on improved dung beetle algorithm optimization. PLoS ONE |
| title | Energy management strategy for methanol hybrid commercial vehicles based on improved dung beetle algorithm optimization. |
| title_full | Energy management strategy for methanol hybrid commercial vehicles based on improved dung beetle algorithm optimization. |
| title_fullStr | Energy management strategy for methanol hybrid commercial vehicles based on improved dung beetle algorithm optimization. |
| title_full_unstemmed | Energy management strategy for methanol hybrid commercial vehicles based on improved dung beetle algorithm optimization. |
| title_short | Energy management strategy for methanol hybrid commercial vehicles based on improved dung beetle algorithm optimization. |
| title_sort | energy management strategy for methanol hybrid commercial vehicles based on improved dung beetle algorithm optimization |
| url | https://doi.org/10.1371/journal.pone.0313303 |
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