A Mixed Logical Dynamical-Model Predictive Control (MLD-MPC) Energy Management Control Strategy for Plug-in Hybrid Electric Vehicles (PHEVs)

被引：38

作者：

Lian, Jing ^{[1
]}

Liu, Shuang ^{[1
]}

Li, Linhui ^{[1
]}

Liu, Xuanzuo ^{[1
]}

Zhou, Yafu ^{[1
]}

Yang, Fan ^{[1
]}

Yuan, Lushan ^{[1
]}

机构：

[1] Dalian Univ Technol, Sch Automot Engn, Fac Vehicle Engn & Mech, State Key Lab Struct Anal Ind Equipment, Dalian 116024, Peoples R China

来源：

ENERGIES | 2017年 / 10卷 / 01期

基金：

中国国家自然科学基金;

关键词：

driving intention; mixed logic dynamical model; mixed integer linear programming (MILP); model predictive control (MPC); nonlinear auto-regressive (NAR) neural network; NEURAL-NETWORK;

D O I：

10.3390/en10010074

中图分类号：

TE [石油、天然气工业]; TK [能源与动力工程];

学科分类号：

0807 ; 0820 ;

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.

引用

页数：18

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