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| On-board torquemanagement approach to the E-COSM benchmark problem with a prediction-based engine assignment |
| BoZhang1,JiangyanZhang2,FuguoXu1 |
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| (1 Department of Engineering and Applied Sciences, Faculty of Science and Technology, Sophia University, Tokyo 102-8554, Japan;2 College of Mechanical and Electronic Engineering, Dalian Minzu University, Dalian 116600, Liaoning, China) |
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| 摘要: |
| This paper proposes an energy management strategy for the benchmark problem of E-COSM 2021 to improve the energy
efficiency of hybrid electric vehicles (HEVs) on a road with a slope. We assume that HEVs are in a connected environment
with real-time vehicle-to-everything information, including geographic information, vehicle-to-infrastructure information
and vehicle-to-vehicle information. The benchmark problem to be solved is based on HEV powertrain control using traffic
information to achieve fuel economy improvements while satisfying the constraints of driving safety and travel time. The
proposed strategy includes multiple rules and model predictive control (MPC). The rules of this strategy are designed based on
external environment information to maintain safe driving and to determine the driving mode. To improve fuel economy, the
optimal energy management strategy is primarily considered, and to perform real-time energy management via RHC-based
optimization in a connected environment with safety constraints, a key issue is to predict the dynamics of the preceding vehicle
during the targeted horizon. Therefore, this paper presents a real-time model-based optimization strategy with learning-based
prediction of the vehicle’s future speed. To validate the proposed optimization strategy, a powertrain control simulation
platform in a traffic-in-the-loop environment is constructed, and case study results performed on the constructed platform are
reported and discussed. |
| 关键词: Hybrid powertrain control · Connected and automated vehicles · Vehicle-to-everything · Benchmark problem |
| DOI：https://doi.org/10.1007/s11768-022-00089-9 |
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| 基金项目:This work was partially supported by the National Natural Science Foundation of China (No. 61973053). The authors would like to thank the Toyota Motor Corporation for the technical support on this research work. |
|
| On-board torquemanagement approach to the E-COSM benchmark problem with a prediction-based engine assignment |
| Bo Zhang1,Jiangyan Zhang2,Fuguo Xu1 |
| (1 Department of Engineering and Applied Sciences, Faculty of Science and Technology, Sophia University, Tokyo 102-8554, Japan;2 College of Mechanical and Electronic Engineering, Dalian Minzu University, Dalian 116600, Liaoning, China) |
| Abstract: |
| This paper proposes an energy management strategy for the benchmark problem of E-COSM 2021 to improve the energy
efficiency of hybrid electric vehicles (HEVs) on a road with a slope. We assume that HEVs are in a connected environment
with real-time vehicle-to-everything information, including geographic information, vehicle-to-infrastructure information
and vehicle-to-vehicle information. The benchmark problem to be solved is based on HEV powertrain control using traffic
information to achieve fuel economy improvements while satisfying the constraints of driving safety and travel time. The
proposed strategy includes multiple rules and model predictive control (MPC). The rules of this strategy are designed based on
external environment information to maintain safe driving and to determine the driving mode. To improve fuel economy, the
optimal energy management strategy is primarily considered, and to perform real-time energy management via RHC-based
optimization in a connected environment with safety constraints, a key issue is to predict the dynamics of the preceding vehicle
during the targeted horizon. Therefore, this paper presents a real-time model-based optimization strategy with learning-based
prediction of the vehicle’s future speed. To validate the proposed optimization strategy, a powertrain control simulation
platform in a traffic-in-the-loop environment is constructed, and case study results performed on the constructed platform are
reported and discussed. |
| Key words: Hybrid powertrain control · Connected and automated vehicles · Vehicle-to-everything · Benchmark problem |
