| Two-stage on-board optimization of merging velocity planning with energy management for HEVs |
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| 作者姓名： | Bo ZHANG Wenjing CAO Tielong SHEN |
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| 作者单位： | Department of Engineering and Applied Sciences, Faculty of Science and Technology, Sophia University, Tokyo, Japan |
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| 基金项目： | This work was supported in part by the Foundation of Japan MEXT Scientific Research B (KAKENHI) (No. 17H03284), and in part by Toyota Motor Corporation, Japan. |
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| 摘 要： | This paper proposes a two-stage hierarchy control system with model predictive control (MPC) for connected parallel HEVs with available traffic information. In the first stage, a coordination of on-ramp merging problem using MPC is presented to optimize the merging point and trajectory for cooperative merging. After formulating the merging problem into a nonlinear optimization problem, a continuous/GMRES method is used to generate the real-time vehicle acceleration for two considered HEVs running on main road and merging road, respectively. The real-time acceleration action is used to calculate the torque demand for the dynamic system of the second stage. In the second stage, an energy management strategy (EMS) for powertrain control that optimizes the torque-split and gear ratio simultaneously is composed to improve fuel efficiency. The formulated nonlinear optimization problem is solved by sequential quadratic programming (SQP) method under the same receding horizon. The simulation results demonstrate that the vehicles can merge cooperatively and smoothly with a reasonable torque distribution and gear shift schedule.
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| 关 键 词： | Merging model predictive control (MPC) powertrain control parallel hybrid electric vehicles (HEVs) |
Two-stage on-board optimization of merging velocity planning with energy management for HEVs |
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| Bo ZHANG,Wenjing CAO,Tielong SHEN.Two-stage on-board optimization of merging velocity planning with energy management for HEVs[J].Journal of Control Theory and Applications,2019,17(4):335-345. |
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| Authors: | Bo ZHANG Wenjing CAO and Tielong SHEN |
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| Affiliation: | Department of Engineering and Applied Sciences, Faculty of Science and Technology, Sophia University, Tokyo, Japan,Department of Engineering and Applied Sciences, Faculty of Science and Technology, Sophia University, Tokyo, 102 8554, Japan and Department of Engineering and Applied Sciences, Faculty of Science and Technology, Sophia University, Tokyo, 102 8554, Japan |
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| Abstract: | This paper proposes a two-stage hierarchy control system with model predictive control (MPC) for connected parallel HEVs with available traffic information. In the first stage, a coordination of on-ramp merging problem using MPC is presented to optimize the merging point and trajectory for cooperative merging. After formulating the merging problem into a nonlinear optimization problem, a continuous/GMRES method is used to generate the real-time vehicle acceleration for two considered HEVs running on main road and merging road, respectively. The real-time acceleration action is used to calculate the torque demand for the dynamic system of the second stage. In the second stage, an energy management strategy (EMS) for powertrain control that optimizes the torque-split and gear ratio simultaneously is composed to improve fuel efficiency. The formulated nonlinear optimization problem is solved by sequential quadratic programming (SQP) method under the same receding horizon. The simulation results demonstrate that the vehicles can merge cooperatively and smoothly with a reasonable torque distribution and gear shift schedule. |
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| Keywords: | Merging model predictive control (MPC) powertrain control parallel hybrid electric vehicles (HEVs) |
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