• CN:11-2187/TH
  • ISSN:0577-6686

机械工程学报 ›› 2019, Vol. 55 ›› Issue (10): 86-108.doi: 10.3901/JME.2019.10.086

• 运载工程 • 上一篇    下一篇

混合动力汽车模型预测能量管理研究现状与展望

张风奇1, 胡晓松2, 许康辉1, 唐小林2, 崔亚辉1   

  1. 1. 西安理工大学机械与精密仪器工程学院 西安 710048;
    2. 重庆大学机械传动国家重点实验室 重庆 400044
  • 收稿日期:2018-11-04 修回日期:2019-03-24 出版日期:2019-05-20 发布日期:2019-05-20
  • 通讯作者: 胡晓松(通信作者),男,1983年出生,博士,教授。主要研究方向为电动汽车电池管理技术、机电复合动力传动系统优化与控制。E-mail:xiaosonghu@ieee.org
  • 作者简介:张风奇,男,1987年出生,博士,讲师。主要研究方向为混合动力汽车能量管理及优化控制。E-mail:zfqdy@126.com;许康辉,男,1993年出生,硕士研究生。主要研究方向为混合动力系统建模及优化控制。E-mail:xukanghui@stu.xaut.edu.cn;唐小林,男,1984年出生,博士,副教授。主要研究方向为混合动力系统建模与控制。E-mail:tangxl0923@cqu.edu.cn;崔亚辉,男,1963年出生,博士,教授,博士研究生导师。主要研究方向为混合动力车辆传动技术与控制。E-mail:cyhxut@xaut.edu.cn
  • 基金资助:
    国家自然科学基金(51875054、51705044)和陕西省教育厅专项科研计划(18JK0578)资助项目

Current Status and Prospects for Model Predictive Energy Management in Hybrid Electric Vehicles

ZHANG Fengqi1, HU Xiaosong2, XU Kanghui1, TANG Xiaolin2, CUI Yahui1   

  1. 1. School of Mechanical and Precision Instrument Engineering, Xi'an University of Technology, Xi'an 710048;
    2. State Key Lab of Mechanical Transmission, Chongqing University, Chongqing 400044
  • Received:2018-11-04 Revised:2019-03-24 Online:2019-05-20 Published:2019-05-20

摘要: 能量管理策略是混合动力汽车的核心技术,其直接决定了整车燃油经济性、动力性及驾驶性。然而,实际工况的不确定性和扰动性极大地增加了能量管理算法的设计难度。为此,开发高效、适应性强的能量管理算法至关重要。模型预测能量管理由于具有滚动优化、反馈校正等优点,可减少未来工况扰动对优化性能的影响,提升工况适应性和整车经济性。重点阐述基于模型预测控制的混合动力汽车能量管理策略的发展状况,并对其基本原理、优势、适用范围进行了综合分析。通过对比分析总结不同控制方法的优缺点,并运用具体算例阐释模型预测能量管理策略的特点。最后从不同角度对预测能量管理的发展方向进行了展望,为先进混合动力汽车能量管理控制器的研发提供一些参考。

关键词: 车联网, 工况预测, 混合动力汽车, 模型预测控制, 能量管理策略

Abstract: Energy management strategies are a core technology in hybrid electric vehicles and plug-in hybrid electric vehicles (HEVs/PHEVs), which directly determines fuel economy, power performance, and drivability. However, the uncertainty, and perturbation of realistic driving conditions greatly increase the difficulty of devising an effective energy management algorithm. It is therefore critical to develop efficient, adaptive, and resilient energy management algorithms. The model predictive energy management can reduce the impact of future operating disturbances on the optimization performance, improving the adaptability of driving conditions and vehicle economy due to the advantages of optimization over receding horizon and feedback compensation. It systematically surveys the state of the art in energy management strategies based on model predictive control (MPC). Their principles, advantages, and applicability are comprehensively analyzed. The advantages and disadvantages of different control methods are compared and summarized. A case study is conducted to illustrate features of MPC-based energy management for HEVs. Finally, future research trends are presented from different perspectives, in order to shed some light on the development of advanced supervisory energy management controllers in HEVs/PHEVs.

Key words: driving cycle prediction, energy management strategies, hybrid electric vehicles, internet of vehicles, model predictive control

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