Braking strategy and simulation of electric vehicle based on traffic information

doi:10.3969/j.issn.1674-8484.2021.01.003

Abstract

Abstract:

A braking coordination strategy of electric vehicles (EV) was proposed based on traffic information to improve the economy of coasting recovery and the safety and comfort of pedal braking. After analyzed slide braking economy, the coasting intensity was determined by the traffic condition and the vehicle state information. A vehicle safety distance model and a collision warning strategy were established based on the traffic information and the forward vehicle information with the warning information being used to coordinate the coasting braking and pedal braking strength. The strategies were verified by simulation. The results show that the comprehensive energy consumption is reduced by 1.1% in good traffic conditions; the braking fatigue of drivers is alleviated in congestion conditions by using the coasting strategy based on traffic information; warning and coordination strategies avoid frequent warning and reduce the probability of triggering emergency braking. Therefore, using traffic information can assist driver to brake more reasonably.

Key words: electric vehicles (EV), braking coordination, traffic information, coast braking, safety distance model

CLC Number:

U461.8

DONG Zhenpeng, ZU Bingfeng, ZHOU Jianwei, XU Jiachen. Braking strategy and simulation of electric vehicle based on traffic information[J]. Journal of Automotive Safety and Energy, 2021, 12(1): 35-42.

Figures/Tables 12

References 12

[1]	王科, 李霖, 韩维建. 智能汽车关键技术与设计方法[M]. 北京: 机械工业出版社, 2019: 15-28.
	WANG Ke, LI Lin, HAN Weijian. Key Technology and Design Method of Intelligent Vehicle [M]. Beijing: Mechanical Industry Press, 2019: 15-28. (in Chinese)
[2]	GAO Yimin, CHEN Liping, Ehsani M. Investigation of the effectiveness of regenerative braking for EV and HEV[C]// Future Transp Tech Conf & Expo, Costa Mesa CA USA: Elec and Hyb Elec Vehi, Hyb Ele Contr Syst, 1999:3184-3190.
[3]	姬芬竹, 杜发荣, 朱文博. 基于制动意图识别的电动汽车能量经济性[J]. 北京航空航天大学学报, 2016,42(1):21-27.
	JI Fenzhu, DU Farong, ZHU Wenbo. Electric vehicle energy economy based on braking intention identification[J]. J Beijing Univ of Aero Astro, 2016,42(1):21-27. (in Chinese)
[4]	林程, 刘夏红, 董爱道. 基于加速踏板行程的再生制动控制策略研究[J]. 汽车工程, 2015,37(7):802-806.
	LIN Cheng, LIU Xiahong, DONG Aidao. A research on regenerative braking control strategy based on acceleration pedal travel[J]. Automotive Engineering, 2015,37(7):802-806. (in Chinese)
[5]	胡蕾蕾. 电动汽车主动安全避撞控制系统研究[D]. 长春: 吉林大学, 2014.
	HU Leilei. On the active safety collision avoidance control system for electric vehicle[D]. Changchun: Jinlin University, 2014. (in Chinese)
[6]	Nakaoka M, Raksincharoensak P, Nagai M. Study on forward collision system to driver characteristics and road environment[C]// IEEE Int’l Conf Control, Automation and Syst, Seoul, Oct 14-17, 2008: 2890-2895.
[7]	胡远志, 吕章洁, 刘西. 基于PreScan的AEB系统纵向避撞算法及仿真验证[J]. 汽车安全与节能学报, 2017,8(2):136-142.
	HU Yuanzhi, LÜ Zhangjie, LIU Xi. Algorithm and simulation verification of longitudinal collision avoidance for autonomous emergency break (AEB) system based on PreScan[J]. J Automotive Safety Energy, 2017,8(2):136-142. (in Chinese)
[8]	Kyongsu Y. Estimation of tire-road friction using observer based identifiers[J]. Vehi Syst Dyna, 1999,31(4):233-261.
[9]	何仁, 冯海鹏. 自动紧急制动(AEB)技术的研究与进展[J]. 汽车安全与节能学报, 2019,10(1):1-15.
	HE Ren, FENG Haipeng. Research and development of autonomous emergency brake (AEB) technology[J]. J Automotive Safety Energy, 2019,10(1):1-15. (in Chinese)
[10]	尹小庆, 汪浩, 莫宇迪, 等. 考虑路面附着因数的车辆向前碰撞预警时间的优化算法[J]. 汽车安全与节能学报, 2019,10(2):178-183.
	YIN Xiaoqing, WANG Hao, MO Yudi, et al. Optimized algorithm for vehicle forward collision pre-warning time considering road adhesion coefficient[J]. J Automotive Safety Energy, 2019,10(2):178-183. (in Chinese)
[11]	吴浩然. 基于驾驶意图与动态环境的车辆纵向碰撞预警研究[D]. 武汉: 武汉理工大学, 2013.
	WU Haoran. Forward Collision Warning algorithm based on driving intention and driving environment[D]. Wuhan: Wuhan University of Technology, 2013. (in Chinese)
[12]	Moon S, Yi K. Human driving databased design of a vehicle adaptive cruise control algorithm[J]. Vehi Syst Dyna, 2008,46(8):661-690.

β	滑行制动强度等级
β	市区交通	市郊交通	高速交通
≤0	3	1	1
0~0.1	4	2	2
>0.1	4	4	3

β	滑行制动强度等级
β	市区交通	市郊交通	高速交通
≤0	3	1	1
0~0.1	4	2	2
>0.1	4	4	3

路面种类		μ	a / (m·s^-2)
干燥路面	水泥路面	0.60~0.75	5.58~7.35
	沥青路面	0.55~0.70	5.39~6.86
	积雪路面	0.20~0.35	1.96~3.43
潮湿路面	水泥路面	0.45~0.65	4.41~6.37
潮湿路面	沥青路面	0.40~0.65	3.92~6.37

路面种类		μ	a / (m·s^-2)
干燥路面	水泥路面	0.60~0.75	5.58~7.35
	沥青路面	0.55~0.70	5.39~6.86
	积雪路面	0.20~0.35	1.96~3.43
潮湿路面	水泥路面	0.45~0.65	4.41~6.37
潮湿路面	沥青路面	0.40~0.65	3.92~6.37

ε	CW	动作
>1	无
0.5~1	I	预先填充制动系统,产生碰撞警报。
0~0.5	II	碰撞警报增强。
≤0	III	触发紧急避撞系统,本车完全停车后方可退出。