Adaptive Equivalent Consumption Minimization Strategy for Hybrid Heavy-Duty Truck Based on Driving Condition Recognition and Parameter Optimization
The accurate determination and dynamic adjustment of key control parameters are challenges for equivalent consumption minimization strategy (ECMS) to be implemented in real-time control of hybrid electric vehicles. An adaptive real-time ECMS is proposed for hybrid heavy-duty truck in this paper. Thr...
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MDPI AG
2020-10-01
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| Series: | Energies |
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| Online Access: | https://www.mdpi.com/1996-1073/13/20/5407 |
| _version_ | 1827704139391434752 |
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| author | Pei Zhang Xianpan Wu Changqing Du Hongming Xu Huawu Wang |
| author_facet | Pei Zhang Xianpan Wu Changqing Du Hongming Xu Huawu Wang |
| author_sort | Pei Zhang |
| collection | DOAJ |
| description | The accurate determination and dynamic adjustment of key control parameters are challenges for equivalent consumption minimization strategy (ECMS) to be implemented in real-time control of hybrid electric vehicles. An adaptive real-time ECMS is proposed for hybrid heavy-duty truck in this paper. Three efforts have been made in this study. First, six kinds of typical driving cycle for hybrid heavy-duty truck are obtained by hierarchical clustering algorithm, and a driving condition recognition (DCR) algorithm based on a neural network is put forward. Second, particle swarm optimization (PSO) is applied to optimize three key parameters of ECMS under a specified driving cycle, including equivalent factor, scale factor of penalty function, and vehicle speed threshold for engine start-up. Finally, combining all the above two efforts, a novel adaptive ECMS based on DCR and key parameter optimization of ECMS by PSO is presented and validated through numerical simulation. The simulation results manifest that proposed adaptive ECMS can further improve the fuel economy of a hybrid heavy-duty truck while keeping the battery charge-sustainability, compared with ECMS and PSO-ECMS under a composite driving cycle. |
| first_indexed | 2024-03-10T15:34:33Z |
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| id | doaj.art-734de503d47c4c4a9a812287dce556f7 |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-03-10T15:34:33Z |
| publishDate | 2020-10-01 |
| publisher | MDPI AG |
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| series | Energies |
| spelling | doaj.art-734de503d47c4c4a9a812287dce556f72023-11-20T17:21:23ZengMDPI AGEnergies1996-10732020-10-011320540710.3390/en13205407Adaptive Equivalent Consumption Minimization Strategy for Hybrid Heavy-Duty Truck Based on Driving Condition Recognition and Parameter OptimizationPei Zhang0Xianpan Wu1Changqing Du2Hongming Xu3Huawu Wang4Hubei Key Laboratory of Advanced Technology for Automotive Components, Wuhan University of Technology, Wuhan 430070, ChinaHubei Key Laboratory of Advanced Technology for Automotive Components, Wuhan University of Technology, Wuhan 430070, ChinaHubei Key Laboratory of Advanced Technology for Automotive Components, Wuhan University of Technology, Wuhan 430070, ChinaDepartment of Mechanical Engineering, University of Birmingham, Birmingham B15 2TT, UKDongfeng Commercial Vehicle Technical Center of DFCV, Wuhan 430056, ChinaThe accurate determination and dynamic adjustment of key control parameters are challenges for equivalent consumption minimization strategy (ECMS) to be implemented in real-time control of hybrid electric vehicles. An adaptive real-time ECMS is proposed for hybrid heavy-duty truck in this paper. Three efforts have been made in this study. First, six kinds of typical driving cycle for hybrid heavy-duty truck are obtained by hierarchical clustering algorithm, and a driving condition recognition (DCR) algorithm based on a neural network is put forward. Second, particle swarm optimization (PSO) is applied to optimize three key parameters of ECMS under a specified driving cycle, including equivalent factor, scale factor of penalty function, and vehicle speed threshold for engine start-up. Finally, combining all the above two efforts, a novel adaptive ECMS based on DCR and key parameter optimization of ECMS by PSO is presented and validated through numerical simulation. The simulation results manifest that proposed adaptive ECMS can further improve the fuel economy of a hybrid heavy-duty truck while keeping the battery charge-sustainability, compared with ECMS and PSO-ECMS under a composite driving cycle.https://www.mdpi.com/1996-1073/13/20/5407hybrid heavy-duty vehicleparticle swarm optimizationequivalent consumption minimum strategydriving condition recognition |
| spellingShingle | Pei Zhang Xianpan Wu Changqing Du Hongming Xu Huawu Wang Adaptive Equivalent Consumption Minimization Strategy for Hybrid Heavy-Duty Truck Based on Driving Condition Recognition and Parameter Optimization Energies hybrid heavy-duty vehicle particle swarm optimization equivalent consumption minimum strategy driving condition recognition |
| title | Adaptive Equivalent Consumption Minimization Strategy for Hybrid Heavy-Duty Truck Based on Driving Condition Recognition and Parameter Optimization |
| title_full | Adaptive Equivalent Consumption Minimization Strategy for Hybrid Heavy-Duty Truck Based on Driving Condition Recognition and Parameter Optimization |
| title_fullStr | Adaptive Equivalent Consumption Minimization Strategy for Hybrid Heavy-Duty Truck Based on Driving Condition Recognition and Parameter Optimization |
| title_full_unstemmed | Adaptive Equivalent Consumption Minimization Strategy for Hybrid Heavy-Duty Truck Based on Driving Condition Recognition and Parameter Optimization |
| title_short | Adaptive Equivalent Consumption Minimization Strategy for Hybrid Heavy-Duty Truck Based on Driving Condition Recognition and Parameter Optimization |
| title_sort | adaptive equivalent consumption minimization strategy for hybrid heavy duty truck based on driving condition recognition and parameter optimization |
| topic | hybrid heavy-duty vehicle particle swarm optimization equivalent consumption minimum strategy driving condition recognition |
| url | https://www.mdpi.com/1996-1073/13/20/5407 |
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