摘要
针对ISG混合动力汽车的能量管理问题,提出了一种控制车辆驾驶员行为的模型预测控制策略。首先由马尔可夫链预测车辆未来需求功率模型,对求得的需求功率模型结合驾驶员行为进行随机学习控制;运用动态规划算法以燃油消耗最小为目标进行滚动优化;最后在Advisor和Matlab/Simulink平台上搭建仿真模型。仿真结果表明:与逻辑门限控制策略相比,该控制策略能有效地改善ISG混合动力汽车燃油经济性,并具有良好的实时性。
In this paper,a model predictive control strategy is proposed to control the behavior of vehicle drivers based on the energy management problem of ISG hybrid electric vehicle. First predict the future required power vehicle model by the markov chain,to stochastic learn control the required power model with driver behavior,and then use the dynamic programming algorithm to minimum the fuel consumption as the target of rolling optimization. The simulation model is established in Advisor and Matlab/Simulink platform,comparing with logic threshold control strategy,simulation results show that the fuel economy of ISG hybrid electric vehicle is increased by 6.5 % with good real time performance.
作者
徐赛培
宋璐
付主木
宋书中
XU Sai-pei;SONG Lu;FU Zhu-mu;SONG Shu-zhong(School of Information Engineering,Henan Univercity of Science and Technology,Luoyang 471023,China;School of Electronic Engineering,Henan University of Science and Technology,Luoyang 471023,China)
出处
《火力与指挥控制》
CSCD
北大核心
2018年第6期171-174,共4页
Fire Control & Command Control
基金
国家自然科学基金(61473115)
河南省科技创新人才杰出青年计划基金(144100510004)
河南省高校科技创新人才支持计划基金资助项目(13HASTIT038)
关键词
ISG混合动力汽车
模型预测控制
能量管理
马尔可夫链
动态规划
ISG Hybrid Electric Vehicle
model predictive control
energy management
the markovchain
dynamic programming
