摘要
基于随机模型预测控制基本原理,研究了四驱混合动力汽车的能量优化管理。采用马尔可夫模型预测加速度变化过程,通过计算得到混合动力汽车未来需求转矩。在保证电池荷电状态平衡的前提下,以燃油经济性最优为目标,建立混合动力汽车能量管理优化模型。针对建立的非线性优化模型,采用动态规划算法进行有限时域内的滚动求解。将提出的控制策略在d SPACE中进行软件在环仿真试验。研究结果表明,随机模型预测控制策略可以实现四驱混合动力汽车基本的能量管理,可在保证各动力部件良好工作状况的前提下,提升燃油经济性。与基于恒值模型的预测控制策略相比,随机模型预测控制策略下的平均燃油经济性提升了8.30%,优化结果接近有先验知识的预测控制策略。
The energy management optimization of 4 WD HEV were studied based on the basic principlesof SMPC. A Markov model was built to describe the changing processes of the acceleration,so as to predictrequired torques. The optimization problem was established to minimize fuel consumption while maintainingthe balance of battery state of charge（SOC）. This nonlinear optimization problem with finite time horizon wassolved with DP algorithm. The proposed control strategy was validated with a software-in-the-loop experimentusing d SPACE. The results show that the SMPC may realize the basic energy management of the 4 WD HEVand the fuel economy is improved while all power components are working well. Average fuel economy ofSMPC is improved by 8.30%comparing with the frozen-time MPC（FTMPC） approach,and is close to theresults of the prescient MPC（PMPC）approach.
作者
钱立军
荆红娟
邱利宏
QIAN Lijun;JING Hongjuan;QIU Lihong(Department of Automotive and Traffic Engineering, Hefei University of Technology, Hefei, 230009;International Center for Automotive Research, Clemson University, Greenville, 29607)
出处
《中国机械工程》
EI
CAS
CSCD
北大核心
2018年第11期1342-1348,共7页
China Mechanical Engineering
基金
国家科技支撑计划资助项目(2013BAG08B01
2015BAG17B04)
关键词
四驱混合动力汽车
能量管理
随机模型预测控制
马尔可夫模型
动态规划
four-wheel-drive （4WD） hybrid electric vehicle （HEV）
energy management
stochasticmodel predictive control （SMPC）
Markov model
dynamic programming （DP）
