特邀专栏:汽车先进动力系统的设计、优化与控制专栏(上)

速比离散型车辆加速-滑行式节油巡航策略

  • 徐少兵 ,
  • 刘学冬 ,
  • 李升波 ,
  • 杜雪瑾 ,
  • 林庆峰 ,
  • 成波
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  • 1. 清华大学汽车安全与节能国家重点实验室 北京 100084;
    2. 北京航空航天大学交通科学与工程学院 北京 100191
徐少兵,男,1989年出生,博士。主要研究方向为智能车辆控制与决策、最优控制理论及应用。E-mail:xsbing2008@foxmail.com;刘学冬,男,1992年出生,硕士研究生。主要研究方向为车辆经济性驾驶、混合动力车辆的周期性节能控制。E-mail:buaalxd@foxmail.com;成波,男,1962年出生,教授,博士研究生导师。清华大学苏州汽车研究院院长,汽车安全与节能国家重点实验室副主任,兼任北京汽车工程学会学术委员会主任,国家863计划主题专家,中国交通运输科技项目专家,公安部交通管理专家委员会专家等。主要研究方向为智能化汽车,汽车安全技术,驾驶行为建模与仿真,汽车人机工程等。E-mail:chengbo@tsinghua.edu.cn

收稿日期: 2016-11-28

  修回日期: 2017-03-24

  网络出版日期: 2017-07-20

基金资助

国家自然科学基金(51575293,51622504)和十三五国家重点研发计划(2016YFB0100906)资助项目。

Fuel-saving Pulse-and-glide Cruising Strategy for Road Vehicles with Discrete Gear Ratio

  • XU Shaobing ,
  • LIU Xuedong ,
  • LI Shengbo ,
  • DU Xuejin ,
  • LIN Qingfeng ,
  • CHENG Bo
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  • 1. State Key Laboratory of Automotive Safety and Energy, Tsinghua University, Beijing 100084;
    2. School of Transportation Science and Engineering, Beihang University, Beijing 100191

Received date: 2016-11-28

  Revised date: 2017-03-24

  Online published: 2017-07-20

摘要

巡航过程的节油驾驶是汽车经济性驾驶技术的重要组成,其核心问题是节油巡航策略的辨识。为此,定量化地求解了速比离散型变速器车辆的加速-滑行式(Pulse-and-glide,PnG)节油巡航策略。将节油策略的辨识构建为一最优控制问题;由于发动机的油耗呈强非线性,且PnG策略的加速阶段与滑行阶段动力学特性不一致,导致该问题属于非线性切换型最优控制。采用拼接策略将该非连续问题转化为加速和滑行两段光滑子问题的组合,通过伪谱法实现对积分型性能函数和微分型状态空间方程的代数转化,从而将原最优控制问题转化为非线性规划问题,并进而实现发动机输出转矩、变速器档位、模式切换时刻等参数的数值优化。结果表明:对所研究的车辆,平均巡航速度在中速范围内,PnG策略相对匀速巡航具有显著的节油效果,最高节油率接近25%。最后分析PnG策略实现节油的物理机理,并对节油效果进行了实车试验验证。

本文引用格式

徐少兵 , 刘学冬 , 李升波 , 杜雪瑾 , 林庆峰 , 成波 . 速比离散型车辆加速-滑行式节油巡航策略[J]. 机械工程学报, 2017 , 53(14) : 49 -58 . DOI: 10.3901/JME.2017.14.049

Abstract

The fuel-saving cruising technique is the momentous part of economical driving, of which the key issue is how to identify the fuel-saving operating strategies. The pulse-and-glide (PnG) cruising strategy for road vehicles with discrete gear ratio transmission is presented aiming to minimize the fuel consumption in cruising scenarios. The optimization of PnG cruising strategy naturally casts into the optimal control framework. The studied vehicle is featured with strong nonlinear fuel characteristics in engine and different dynamics in the pulse and the glide modes. Those features lead to a non-smooth switching problem, for which the multi-phase knotting technique is employed to cut this problem into two smooth sub-phases, i.e., accelerating and coasting. The pseudo-spectral method is employed to convert the differential type space state equation and integral type performance index. The associated nonlinear programming is finally solved to achieve optimal engine torque, transmission gear ratio, and switching time of pulse and glide. The optimization results show that for the studied vehicle, the PnG strategy can save fuel when cruising at middle speed as compared to the constant speed (CS) operation, and the highest fuel saving rate can reach up to 25%. The mechanism of PnG is discussed, and the fuel saving performance is verified by the real-vehicle tests.

参考文献

[1] BARKENBUS J N. Eco-driving:An overlooked climate change initiative[J]. Energy Policy, 2010, 38(2):762-769.
[2] BARTH M, BORIBOONSOMSIN K. Energy and emissions impacts of a freeway-based dynamic eco-driving system[J]. Transportation Research Part D:Transport and Environment, 2009, 14(6):400-410.
[3] 李升波, 徐少兵, 王文军, 等. 汽车经济性驾驶技术及应用概述[J]. 汽车安全与节能学报, 2014, 5(2):121-131. LI Shengbo, XU Shaobing, WANG Wenjun, et al. Overview of ecological driving technology and application for ground vehicles[J]. Journal of Automotive Safety and Energy, 2014, 5(2):121-131.
[4] LI S, XU S, HUANG X, CHENG B, et al. Eco-departure of connected vehicles with V2X communication at signalized intersections[J]. IEEE Transactions on Vehicular Technology, 2015, 64(12):5439-5449.
[5] XU S, LI S, CHENG B, et al. Instantaneous feedback control for fuel-prioritized vehicle cruising system on highways with varying slope.[J].IEEE Transaction on Intelligent Transportation Systems. 2017, 18(5):1210-1220.
[6] TONG H Y, HUNG W T, CHEUNG C S. On-road motor vehicle emissions and fuel consumption in urban driving conditions[J]. Journal of the Air & Waste Management Association, 2000, 50(4):543-554.
[7] ERICSSON E. Independent driving pattern factors and their influence on fuel-use and exhaust emission factors[J]. Transportation Research Part D:Transport and Environment, 2001, 6(5):325-345.
[8] LI S E, PENG H. Strategies to minimize the fuel consumption of passenger cars during car-following scenarios[J]. Proceedings of the Institution of Mechanical Engineers, Part D:Journal of Automobile Engineering, 2012, 226(3):419-429.
[9] LI S E, PENG H, LI K, et al. Minimum fuel control strategy in automated car-following scenarios[J]. IEEE Trans. Vehicular Technology, 2012, 61(3):998-1007.
[10] LEE J, NELSON D J, LOHSE-BUSCH H. Vehicle inertia impact on fuel consumption of conventional and hybrid electric vehicles using acceleration and coast driving strategy[R]. SAE, 2009-01-1322, 2009.
[11] IOANNOU P A, CHIEN C C. Autonomous intelligent cruise control[J]. IEEE Transactions on Vehicular Techndogy, 1993, 42(4):657-672.
[12] LABINAZ G, BAYOUMI M M, RUDIE K. A survey of modeling and control of hybrid systems[J]. Annual Reviews in Control, 1997, 21:79-92.
[13] SHAKOURI P, ORDYS A, DARNELL P, et al. Fuel efficiency by coasting in the vehicle[J] International Journal of Vehicular Technology, 2013, 2013(1):1-14.
[14] SUZUKI T, MORIMOTO Y, HAMADA H, et al. Fuel cut-off control system in fuel injection internal combustion engine with automatic power transmission:U.S., 4539643[P]. 1985-09-03.
[15] 格里什克维奇. 电子计算机在汽车设计与计算中的应用[M]. 北京:人民交通出版社,1983. GRISHKEVICH. Computer applications in automotive design and calculation[M]. Beijing:China Communications Press, 1983.
[16] ROSS I M, FAHROO F. Pseudospectral knotting methods for solving non-smooth optimal control problems[J]. Journal of Guidance, Control, and Dynamics, 2004, 27(3):397-405.
[17] ELNAGAR G, KAZEMI M A, RAZZAGHI M. The pseudospectral Legendre method for discretizing optimal control problems[J]. IEEE Trans. Automatic Control, 1995, 40(10):1793-1796.
[18] XU S, LI S E, DENG K, et al. A unified pseudospectral computational framework for optimal control of road vehicles[J]. IEEE/ASME Trans. Mechatronics, 2015, 20(4):1499-1510.
[19] GARG D, PATTERSON M, HAGER W W, et al. A unified framework for the numerical solution of optimal control problems using pseudospectral methods[J]. Automatica, 2010, 46(11):1843-1851.
[20] GILL P E, MURRAY W, SAUNDERS M A. SNOPT:An SQP algorithm for large-scale constrained optimization[J]. SIAM Review, 2005, 47(1):99-131.
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