ZHAO Xuan, MA Jian, WANG Gui-ping. Composite braking control strategy of pure electric bus based on brake driving intention recognition[J]. Journal of Traffic and Transportation Engineering, 2014, 14(4): 64-75.
Citation: ZHAO Xuan, MA Jian, WANG Gui-ping. Composite braking control strategy of pure electric bus based on brake driving intention recognition[J]. Journal of Traffic and Transportation Engineering, 2014, 14(4): 64-75.

Composite braking control strategy of pure electric bus based on brake driving intention recognition

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  • Author Bio:

    ZHAO Xuan(1983-), male, engineer, PhD, +86-29-82334462, zhaoxuan@chd.edu.cn

  • Received Date: 2014-05-11
  • Publish Date: 2014-08-25
  • To research braking force distribution ratio of composite braking system for pure electric bus, a composite braking control strategy based on brake driving intention recognition was presented.A double-layer brake driving intention recognition model based on hidden Markov theory was set up and identified by using road experiment data.Based on recognized driving intention and vehicle speed, the distribution ratios of braking forces for front and rear wheels, ECE regulation, motor characteristics, slip ratios, battery characteristics, super capacitor characteristics and transmission system characteristics were taken as constraint conditions, the braking force distribution strategy of composite braking system was proposed, and the control strategy of composite braking system was simulated by Simulink software under 9 operating conditions.Simulation result shows that friction braking system and motor regenerative braking system can work coordinately and steadily under various operating conditions when the braking control strategy is applied, and braking energy can be recovered as much as possible under the premise of ensuring braking safety.Energy recovery efficiency is highest under slight brake when vehicle speed was low, and the efficiency can reach to 43.84%.Energy recovery efficiency is lowest under emergency brake when vehicle speed is high, and the efficiency is only 0.89%.

     

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  • [1]
    CAO Bing-gang, ZHANG Chuan-wei, BAI Zhi-feng, et al. Technology progress and trends of electric vehicles[J]. Journal of Xi'an Jiaotong University, 2004, 38(1): 1-5. (in Chinese). https://www.cnki.com.cn/Article/CJFDTOTAL-XAJT200401001.htm
    [2]
    CHAN C C, WONG Y S. The state of the art of electric vehicles technology[C]//IEEE. The 4th International Power Electronics and Motion Control Conference. Xi'an: Xi'an Jiaotong University Press, 2004: 46-57.
    [3]
    SCHMIDT M, ISERMANN R, LENZEN B, et al. Potential of regenerative braking using an integrated starter alternator[J]. SAE Paper, 2000-01-1020.
    [4]
    PENG Dong. Study on combined control of regenerative braking and anti-lock braking system for hybrid electric vehicle[D]. Shanghai: Shanghai Jiaotong University, 2007. (in Chinese).
    [5]
    ZHANG J Z, CHEN X, ZHANG P J. Integrated control of braking energy regeneration and pneumatic anti-lock braking[J]. Journal of Automobile Engineering, 2010, 224(5): 587-610. doi: 10.1243/09544070JAUTO1307
    [6]
    ZHANG Chang-li, ZHANG Ya-jun, YAN Mao-de, et al. Fuzzy control modeling and simulation of regenerative braking system for pure electric vehicle with dual-source energy storage system[J]. Journal of System Simulation, 2011, 23(2): 233-238. (in Chinese). https://www.cnki.com.cn/Article/CJFDTOTAL-XTFZ201102004.htm
    [7]
    WALKER A M, LAMPERTH M U, WILKINS S. On friction braking demand with regenerative braking[J]. SAE Paper, 2002-01-2581.
    [8]
    ZHOU Lei, LUO Yu-gong, LI Ke-qiang, et al. Braking control of electric vehicles while coordinating regenerative and antilock brakes[J]. Journal of Tsinghua University: Science and Technology, 2009, 49(5): 728-732. (in Chinese). doi: 10.3321/j.issn:1000-0054.2009.05.027
    [9]
    KO J, KIM J, LEE G, et al. Development of a co-operative control algorithm during regenerative braking for a fuel cell electric vehicle[C]//IEEE. The 7th IEEE Vehicle Power and Propulsion Conference. Chicago: IEEE, 2011: 7-13.
    [10]
    ZHANG Yuan-cai, YU Zhuo-ping, XU Le, et al. A study on the strategy of braking force distribution for the hybrid braking system in electric vehicles based on braking intention[J]. Automotive Engineering, 2009, 31(3): 244-249. (in Chinese). doi: 10.3321/j.issn:1000-680X.2009.03.011
    [11]
    MA Qi-zhen. Study on regenerative brake control algorithm based on braking intention identification[D]. Changchun: Jilin University, 2013. (in Chinese).
    [12]
    SUN Lu, YU Ye, GU Wen-jun. Car ownership prediction method based on principal component analysis and hiddenMarkov model[J]. Journal of Traffic and Transportation Engineering, 2013, 13(2): 92-98. (in Chinese). http://transport.chd.edu.cn/article/id/201302014
    [13]
    CAO Yuan, MA Lian-chuan, LI Wang. Monitoring method of safety computer condition for railway signal system[J]. Journal of Traffic and Transportation Engineering, 2013, 13(3): 107-112. (in Chinese). http://transport.chd.edu.cn/article/id/201303015
    [14]
    YANG Qi, YANG Yun-feng, FENG Zhong-xiang, et al. Prediction method for passenger volume of city public transit based on grey theory and Markov model[J]. China Journal of Highway and Transport, 2013, 26(6): 169-175. (in Chinese). doi: 10.3969/j.issn.1001-7372.2013.06.023
    [15]
    ZHANG Liang-li. Research on motorist's intention recognition for traffic safety precaution[D]. Wuhan: Wuhan University of Technology, 2011. (in Chinese).
    [16]
    WANG Zhen. Real time gathering event detection based on layered hidden Markov model[D]. Guilin: Guilin University of Electronic Technology, 2010. (in Chinese).
    [17]
    SATHYANARAYANA A, BOYRAZ P, HANSEN J H L. Driver behavior analysis and route recognition by hidden Markov models[C]//IEEE. 2008 IEEE International Conference on Vehicular Electronics and Safety. Columbus: IEEE, 2008: 276-281.
    [18]
    ZONG Chang-fu, WANG Chang, YANG De-jun, et al. Driving intention identification and maneuvering behavior prediction of drivers on cornering[C]//IEEE. 2009 IEEE International Conference on Mechatronics and Automation. Changchun: IEEE, 2009: 4055-4060.
    [19]
    33(8): 701-706. ZONG Chang-fu, WANG Chang, HE Lei, et al. Driving intention recognition based on double-layer HMM[J]. Automotive Engineering, 2011, 33(8): 701-706. (in Chinese). https://www.cnki.com.cn/Article/CJFDTOTAL-QCGC201108013.htm
    [20]
    YIN An-dong, ZHAO Han, ZHANG Bing-li. Study on regenerative braking and control strategy for electric vehicles[J]. Journal of Hefei University of Technology: Natural Science Edition, 2008, 31(11): 1760-1763, 1777. (in Chinese). https://www.cnki.com.cn/Article/CJFDTOTAL-HEFE200811009.htm
    [21]
    ZHOU Yu-cai, CHEN Shi-an, WANG Jun-cheng. Two-acceleration-error-input proportional-integral-derivative control for vehicle active suspension[J]. Journal of Traffic and Transportation Engineering: English Edition, 2014, 1(3): 228-234.
    [22]
    ZHAO Xuan. Study on control strategy for powertrain of pure electric bus[D]. Xi'an: Chang'an University, 2012. (in Chinese).
    [23]
    YAO Zuo, WEI Heng, PERUGU H, et al. Sensitivity analysis of project level MOVES running emission rates for light and heavy duty vehicles[J]. Journal of Traffic and Transportation Engineering: English Edition, 2014, 1(2): 81-96.

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