基于制动边界与意图识别的再生制动策略

吴志新; 石金蓬; 李亚伦; 杨海圣; 马少东

doi:10.13700/j.bh.1001-5965.2016.0645

基于制动边界与意图识别的再生制动策略

doi: 10.13700/j.bh.1001-5965.2016.0645

吴志新^{1, 2, ,},
石金蓬¹,
李亚伦¹,
杨海圣¹,
马少东¹

1.
北京航空航天大学交通科学与工程学院, 北京 100083
2.
中国汽车技术研究中心, 天津 224100

基金项目:

国家科技支撑计划 2015BAG01B00

详细信息

作者简介:
吴志新男, 博士生导师, 教授级高级工程师。主要研究方向:电动汽车设计

石金蓬男，硕士研究生。主要研究方向：电动汽车整车控制系统

通讯作者:
吴志新, E-mail: wuzhixin@catarc.ac.cn

中图分类号: U462.3
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- 被引次数: 0
出版历程
- 收稿日期: 2016-08-08
- 录用日期: 2016-12-09
- 网络出版日期: 2017-08-20

Regenerative brake strategy based on braking boundary and intention recognition

1.
School of Transportation Science and Engineering, Beijing University of Aeronautics and Astronautics, Beijing 100083, China
2.
China Automotive Technology & Research Center, Tianjin 224100, China

Funds:

National Key Technology Research and Development Program of China 2015BAG01B00

More Information

Corresponding author: WU Z X, E-mail: wuzhixin@catarc.ac.cn

摘要

摘要:
有效的再生制动策略能够增加电动汽车回收能量，提高其续航能力。通过汽车制动动力学以及相关法规的分析，提出了基于边界最大化的再生制动力分配策略，建立了基于制动踏板深度、车速、SOC的模糊制动意图识别模型，识别驾驶员制动意图；建立了基于电机效率曲线的电池充电保护模型，限制电池充电电流。通过对Cruise仿真平台的二次开发，研究本文提出的再生制动策略对于电动汽车续驶里程的影响。在新欧洲行驶循环（NEDC）条件下，该策略能够增加电动汽车续驶里程7.8%；在美国联邦环保局测试工况（FTP75）条件下，该策略能够增加电动汽车续驶里程27.3%。
- 电动汽车 /
- 再生制动 /
- 控制策略 /
- 模糊识别 /
- 续驶里程
Abstract:
Effective strategy of regenerative brake can increase the recycling energy and improve the driving range of electric vehicles. A recycling brake force distribution strategy on the basis of maximum boundary was proposed from the analysis of the vehicle brake dynamics and related regulations. The models of fuzzy braking intention identification based on brake pedal depth, vehicle speed and SOC wereestablished to identify the driver braking intentions; the models of battery charging protection based on the motor efficiency map were established to limit the battery charging current. The influence of the regenerative brake strategy ondriving range was researched by means of the second development of Cruise simulation platform. The driving range of electric vehicles rises by 7.8% in accordance with the regenerative brake strategy for the new European driving cycle (NEDC). The driving range of electric vehicles rises by 27.3% in accordance with the regenerative brake strategy for the EPA Federal Test Procedure (FTP75).
- electric vehicle /
- regenerative brake /
- control strategy /
- fuzzy recognition /
- driving range

HTML全文

图 1 前后轮制动力分配

Figure 1. Front and rear brake force distribution

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图 2 不同制动强度下的理论最大再生扭矩

Figure 2. Theoretical maximum regenerative torque under different brake strength

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图 3 制动踏板深度和输出修正系数隶属函数

Figure 3. Membership functions of brake pedal depth and output correction factor

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图 4 模糊制动意图识别结果

Figure 4. Recognition results of fuzzy braking intention

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图 5 电机效率曲线

Figure 5. Motor efficiency curves

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图 6 再生制动最大化控制策略

Figure 6. Control strategy of maximum regenerative brake

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图 7 再生制动最大化控制策略流程图

Figure 7. Flowchart of control strategy of maximum regenerative brake

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图 8 MATLAB & Cruise联合仿真模型

Figure 8. Model of MATLAB & Cruise united simulation

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图 9 2种工况循环下的车速曲线

Figure 9. Speed curves under two driving cycles

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图 10 2种工况循环下的再生电流曲线

Figure 10. Regenerative current curves under two driving cycles

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图 11 2种工况循环下的能量回收效果

Figure 11. Effect of energy recovery under two driving cycles

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表 1 某纯电动汽车整车参数

Table 1. Parameters of an electric vehicle

参数	数值
整车整备质量M_c/kg	1 700
整车满载质量M_f/kg	2 075
轴距L/mm	2 640
中心到前轴距离L_a/mm	1 006
风阻系数C_d	0.393
迎风面积A/m²	2.46
车轮滚动半径R/m	0.314
机械传动比i_m	8.867
传动系统总效率η_t	0.9
电池容量C/(kW·h)	20
电池标称电压U_n/V	347.5
最大允许充电电流I_max/A	65

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表 2 车速、SOC模糊子集阈值

Table 2. Threshold values for speed and SOC fuzzy subset

车速阈值	SOC阈值
[0, 40)	[0, 0.3)
[15, 90)	[0.2, 0.9)
[75, 100]	[0.7, 1]

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表 3 车速-踏板查表策略

Table 3. TLU strategy of speed-pedal

N·m
车速/ (km·h^-1)	制动踏板深度/%
车速/ (km·h^-1)	0	10	15	20	25	30	40	50	60	80	100
0	0	0	0	0	0	0	0	0	0	0	0
15	0	5	5	5	6	7	7	9	10	10	10
20	0	6	6	6	7	9	9	10	12	12	13
25	0	8	8	8	9	10	10	10	14	15	15
30	0	8	9	9	11	12	14	15	16	18	20
35	0	8	9	10	11	13	15	18	20	23	25
40	0	9	10	11	13	16	19	22	25	29	32
50	0	10	12	14	17	20	23	26	29	32	36
60	0	10	14	17	20	23	27	30	34	37	40
70	0	12	16	19	22	25	29	33	37	41	45
90	0	15	18	21	25	29	32	36	40	46	50
120	0	20	23	26	29	33	37	41	45	50	55
150	0	25	28	31	34	38	42	46	50	55	60

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