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汽车安全与节能学报 ›› 2012, Vol. 3 ›› Issue (2): 165-172.DOI: 10.3969/j.issn.1674-8484.2012.02.010

• 汽车节能与环保 • 上一篇    下一篇

具有实时运算潜力的并联混合动力汽车模型预测控制

曾祥瑞, 黄开胜*, 孟凡博   

  1. 清华大学 汽车安全与节能国家重点实验室,北京 100084
  • 收稿日期:2012-04-26 出版日期:2012-06-18 发布日期:2012-07-04
  • 通讯作者: 黄开胜,副研究员。E-mail :huangks@tsinghua.edu.cn
  • 作者简介:曾祥瑞(1987—),男(汉),湖北,硕士研究生。E-mail :cxr05@mails.tsinghua.edu.cn
  • 基金资助:

    汽车安全与节能国家重点实验室开放基金( KF11081)

Model predictive control for parallel hybrid electric vehicles with potential real-time capability

ZENG Xiangrui, HUANG Kaisheng*, MENG Fanbo   

  1. State Key Laboratory of Automotive Safety and Energy, Tsinghua University, Beijing 100084, China
  • Received:2012-04-26 Online:2012-06-18 Published:2012-07-04

摘要: 混合动力车辆(HEV)的控制策略对提高燃油经济性和排放性能十分重要。该文基于简化过的
一阶柴油发动机动态模型,提出了一种并联混合动力汽车模型预测控制(MPC)转矩分配策略。该策略
提出了基于驾驶员加速踏板位置的需求转矩预测方法,在考虑用电机补偿发动机转矩响应延迟的情况
下,设计了优化问题的约束条件以及目标函数,把非线性的混合动力汽车转矩分配问题转化成为线性
规划问题并进行求解。该模型预测控制转矩分配算法在计算机仿真中的运算时间小于150 ms,具有
实时运算的潜力。仿真结果表明:与一套基于规则的控制策略相比,该模型预测控制策略能将混合动
力车辆的燃油经济性提高2.5 %。

关键词: 混合动力汽车, 模型预测控制, 转矩分配策略, 燃油经济性

Abstract: The control strategy of the hybrid electric vehicle (HEV) plays an important role in the fuel economy
and emission performance. This paper presents a model predictive control (MPC) torque-split strategy for
parallel hybrid electric vehicle powertrains based on a simplified first-order diesel engine transient model. A
torque request prediction method based on the driver’s accelerator pedal is presented for the MPC strategy.
With consideration of the motor’s compensation to the engine’s dynamic torque response delay, the optimization
constraints and objectives were designed with the non-linear HEV torque-split problem simplified to a linear
programming problem. In the simulation the MPC torque-split strategy, which is potentially real-time-capable,
operates in less than 150 ms. Simulation results show that the MPC strategy can improve the fuel economy by
2.5% compared with a rule-based strategy.

Key words: hybrid electric vehicles (HEV), model predictive control, torque-split strategy, fuel economy

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