Approximate Global Energy Management Based on Macro–Micro Mixed Traffic Model for Plug-in Hybrid Electric Vehicles
A multi-scale physical process management system is presented in this paper, taking the plug-in hybrid electric vehicle system as the physical interface connecting the macro traffic system to the micro energy conversion process, with the ultimate goal of global energy management in the full temporal...
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MDPI AG
2023-10-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/13/20/11196 |
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| author | Zhisheng He Haiyong Peng Yanfei Gao Jun Yang Shenxue Hao Guangde Han Jian Wang |
| author_facet | Zhisheng He Haiyong Peng Yanfei Gao Jun Yang Shenxue Hao Guangde Han Jian Wang |
| author_sort | Zhisheng He |
| collection | DOAJ |
| description | A multi-scale physical process management system is presented in this paper, taking the plug-in hybrid electric vehicle system as the physical interface connecting the macro traffic system to the micro energy conversion process, with the ultimate goal of global energy management in the full temporal–spatial domain for autonomous plug-in hybrid electric vehicles. This novel method adopts a macro traffic flow model at a large time scale, in which only the initial conditions and the traffic information of key road sections are required, and a car following model at the micro scale. Furthermore, local replanning of energy management is carried out by adjusting the power threshold and the efficiency weight through the type of reinforcement learning that is closest to human learning, once a short term speed disturbance is induced by unknown disturbances in the macro traffic flow. Due to the nonlinear relationship between speed fluctuation and power fluctuation, it is necessary to map the vehicle speed and acceleration characteristics to the power characteristics, instead of directly utilizing the traffic model characterized by the speed and acceleration characteristics. The results show that novel multi-scale physical management can achieve a smaller deviation from the global optimal solution and enhanced robustness of global energy management. Additionally, close coupling between the dynamic characteristics of vehicle components and speed fluctuation ensures correct tracking of the optimized target value. |
| first_indexed | 2024-03-10T21:28:36Z |
| format | Article |
| id | doaj.art-a7205c0d81d04decbb80165239e41335 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-10T21:28:36Z |
| publishDate | 2023-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-a7205c0d81d04decbb80165239e413352023-11-19T15:29:52ZengMDPI AGApplied Sciences2076-34172023-10-0113201119610.3390/app132011196Approximate Global Energy Management Based on Macro–Micro Mixed Traffic Model for Plug-in Hybrid Electric VehiclesZhisheng He0Haiyong Peng1Yanfei Gao2Jun Yang3Shenxue Hao4Guangde Han5Jian Wang6Zhejiang Hexia Technology Co., Ltd., Shanghai 201615, ChinaSchool of Mechanical and Automotive Engineering, Shanghai University of Engineering Science, Shanghai 201620, ChinaKey Laboratory of Transportation Industry for Transport Vehicle Detection, Diagnosis and Maintenance Technology, School of Automotive Engineering, Shandong Jiaotong University, Jinan 250357, ChinaKey Laboratory of Transportation Industry for Transport Vehicle Detection, Diagnosis and Maintenance Technology, School of Automotive Engineering, Shandong Jiaotong University, Jinan 250357, ChinaSchool of Transportation and Logistics Engineering, Shandong Jiaotong University, Jinan 250357, ChinaKey Laboratory of Transportation Industry for Transport Vehicle Detection, Diagnosis and Maintenance Technology, School of Automotive Engineering, Shandong Jiaotong University, Jinan 250357, ChinaKey Laboratory of Transportation Industry for Transport Vehicle Detection, Diagnosis and Maintenance Technology, School of Automotive Engineering, Shandong Jiaotong University, Jinan 250357, ChinaA multi-scale physical process management system is presented in this paper, taking the plug-in hybrid electric vehicle system as the physical interface connecting the macro traffic system to the micro energy conversion process, with the ultimate goal of global energy management in the full temporal–spatial domain for autonomous plug-in hybrid electric vehicles. This novel method adopts a macro traffic flow model at a large time scale, in which only the initial conditions and the traffic information of key road sections are required, and a car following model at the micro scale. Furthermore, local replanning of energy management is carried out by adjusting the power threshold and the efficiency weight through the type of reinforcement learning that is closest to human learning, once a short term speed disturbance is induced by unknown disturbances in the macro traffic flow. Due to the nonlinear relationship between speed fluctuation and power fluctuation, it is necessary to map the vehicle speed and acceleration characteristics to the power characteristics, instead of directly utilizing the traffic model characterized by the speed and acceleration characteristics. The results show that novel multi-scale physical management can achieve a smaller deviation from the global optimal solution and enhanced robustness of global energy management. Additionally, close coupling between the dynamic characteristics of vehicle components and speed fluctuation ensures correct tracking of the optimized target value.https://www.mdpi.com/2076-3417/13/20/11196macro–micro mixed traffic modelreinforcement learninglocal replanningenergy managementplug-in hybrid vehiclescyber hierarchy and interactional network |
| spellingShingle | Zhisheng He Haiyong Peng Yanfei Gao Jun Yang Shenxue Hao Guangde Han Jian Wang Approximate Global Energy Management Based on Macro–Micro Mixed Traffic Model for Plug-in Hybrid Electric Vehicles Applied Sciences macro–micro mixed traffic model reinforcement learning local replanning energy management plug-in hybrid vehicles cyber hierarchy and interactional network |
| title | Approximate Global Energy Management Based on Macro–Micro Mixed Traffic Model for Plug-in Hybrid Electric Vehicles |
| title_full | Approximate Global Energy Management Based on Macro–Micro Mixed Traffic Model for Plug-in Hybrid Electric Vehicles |
| title_fullStr | Approximate Global Energy Management Based on Macro–Micro Mixed Traffic Model for Plug-in Hybrid Electric Vehicles |
| title_full_unstemmed | Approximate Global Energy Management Based on Macro–Micro Mixed Traffic Model for Plug-in Hybrid Electric Vehicles |
| title_short | Approximate Global Energy Management Based on Macro–Micro Mixed Traffic Model for Plug-in Hybrid Electric Vehicles |
| title_sort | approximate global energy management based on macro micro mixed traffic model for plug in hybrid electric vehicles |
| topic | macro–micro mixed traffic model reinforcement learning local replanning energy management plug-in hybrid vehicles cyber hierarchy and interactional network |
| url | https://www.mdpi.com/2076-3417/13/20/11196 |
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