个人信息Personal Information
教授
博士生导师
硕士生导师
性别:男
毕业院校:天津大学
学位:硕士
所在单位:机械工程学院
学科:车辆工程. 电机与电器
办公地点:综合2号实验楼417B
联系方式:dlzyf@dlut.edu.cn
电子邮箱:dlzyf@dlut.edu.cn
A Mixed Logical Dynamical-Model Predictive Control (MLD-MPC) Energy Management Control Strategy for Plug-in Hybrid Electric Vehicles (PHEVs)
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论文类型:期刊论文
发表时间:2017-01-01
发表刊物:ENERGIES
收录刊物:SCIE
卷号:10
期号:1
ISSN号:1996-1073
关键字:driving intention; mixed logic dynamical model; mixed integer linear programming (MILP); model predictive control (MPC); nonlinear auto-regressive (NAR) neural network
摘要:Plug-in hybrid electric vehicles (PHEVs) can be considered as a hybrid system (HS) which includes the continuous state variable, discrete event, and operation constraint. Thus, a model predictive control (MPC) strategy for PHEVs based on the mixed logical dynamical (MLD) model and short-term vehicle speed prediction is proposed in this paper. Firstly, the mathematical model of the controlled PHEV is set-up to evaluate the energy consumption using the linearized models of core power components. Then, based on the recognition of driving intention and the past vehicle speed data, a nonlinear auto-regressive (NAR) neural network structure is designed to predict the vehicle speed for known driving profiles of city buses and the predicted vehicle speed is used to calculate the total required torque. Next, a MLD model is established with appropriate constraints for six possible driving modes. By solving the objective function with the Mixed Integer Linear Programming (MILP) algorithm, the optimal motor torque and the corresponding driving mode sequence within the speed prediction horizon can be obtained. Finally, the proposed energy control strategy shows substantial improvement in fuel economy in the simulation results.