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| Real-time energy-efficient anticipative driving control of connected and automated hybrid electric vehicles |
| ShiyingDong1,2,HongChen2,3,LuluGuo3,QifangLiu2,BingzhaoGao4 |
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| (1 State Key Laboratory of Automotive Simulation and Control, Jilin University, Changchun 130025, Jilin, China
2 Department of Control Science and Engineering, Jilin University, Changchun 130025, Jilin, China;3 Department of Control Science and Engineering, Tongji University, Shanghai 200092, China;4 Clean Energy Automotive Engineering Center, Tongji University, Shanghai 200092, China) |
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| 摘要: |
| In this paper, we propose a real-time energy-efficient anticipative driving control strategy for connected and automated
hybrid electric vehicles (HEVs). Considering the inherent complexities brought about by the velocity profile optimization
and energy management control, a hierarchical control architecture in the model predictive control (MPC) framework is
developed for real-time implementation. In the higher level controller, a novel velocity optimization problem is proposed
to realize safe and energy-efficient anticipative driving. The real-time control actions are derived through a computationally
efficient algorithm. In the lower level controller, an explicit solution of the optimal torque split ratio and gear shift schedule
is introduced for following the optimal velocity profile obtained from the higher level controller. The comparative simulation
results demonstrate that the proposed strategy can achieve approximately 13% fuel consumption saving compared with a
benchmark strategy. |
| 关键词: Connected and automated vehicle · Hybrid electric vehicle · Anticipative driving · Hierarchical control architecture · Real-time solution |
| DOI：https://doi.org/10.1007/s11768-022-00092-0 |
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| 基金项目:This work was supported by in part by the China Automobile Industry Innovation and Development Joint Fund (No. U1864206), in part by the National Nature Science Foundation of China (No. 62003244), in part by the Jilin Provincial Science and Technology Department (No. 20200301011RQ) and in part by the Jilin Provincial Science Foundation of China (No. 20200201062JC). |
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| Real-time energy-efficient anticipative driving control of connected and automated hybrid electric vehicles |
| Shiying Dong1,2,Hong Chen2,3,Lulu Guo3,Qifang Liu2,Bingzhao Gao4 |
| (1 State Key Laboratory of Automotive Simulation and Control, Jilin University, Changchun 130025, Jilin, China
2 Department of Control Science and Engineering, Jilin University, Changchun 130025, Jilin, China;3 Department of Control Science and Engineering, Tongji University, Shanghai 200092, China;4 Clean Energy Automotive Engineering Center, Tongji University, Shanghai 200092, China) |
| Abstract: |
| In this paper, we propose a real-time energy-efficient anticipative driving control strategy for connected and automated
hybrid electric vehicles (HEVs). Considering the inherent complexities brought about by the velocity profile optimization
and energy management control, a hierarchical control architecture in the model predictive control (MPC) framework is
developed for real-time implementation. In the higher level controller, a novel velocity optimization problem is proposed
to realize safe and energy-efficient anticipative driving. The real-time control actions are derived through a computationally
efficient algorithm. In the lower level controller, an explicit solution of the optimal torque split ratio and gear shift schedule
is introduced for following the optimal velocity profile obtained from the higher level controller. The comparative simulation
results demonstrate that the proposed strategy can achieve approximately 13% fuel consumption saving compared with a
benchmark strategy. |
| Key words: Connected and automated vehicle · Hybrid electric vehicle · Anticipative driving · Hierarchical control architecture · Real-time solution |
