On-board torque management 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,Japan2.College of Mechanical and Electronic Engineering,Dalian Minzu University,Dalian 116600,Liaoning,China
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.
机标关键词：predictionbenchmarkmanagementtorqueenginewithapproachassignment
论文发表日期：2022-01-01
在线出版日期：2025-08-15（本平台首次上网日期，不代表文献的发表时间）
页数：12( 173-184 )
英文信息