﻿ 基于制动边界与意图识别的再生制动策略<sup>*</sup>
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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

Regenerative brake strategy based on braking boundary and intention recognition
WU Zhixin1,2, SHI Jinpeng1, LI Yalun1, YANG Haisheng1, MA Shaodong1
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
Received: 2016-08-08; Accepted: 2016-12-09; Published online: 2016-12-20 15:27
Foundation item: National Key Technology Research and Development Program of China (2015BAG01B00)
Corresponding author. WU Z X, E-mail: wuzhixin@catarc.ac.cn
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).
Key words: electric vehicle     regenerative brake     control strategy     fuzzy recognition     driving range

1 边界最大化再生扭矩计算 1.1 汽车制动动力学曲线

1.1.1 Ⅰ曲线

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1.1.2 ECE法规曲线

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1.1.3 f线组

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1.2 基于4线交点理论的再生制动力分配策略

1.2.1 制动力分配点计算

 图 1 前后轮制动力分配 Fig. 1 Front and rear brake force distribution

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1.2.2 边界最大化再生制动力分配策略

1) 紧急制动：若FfJfFfC，则Fmot_max=0。此时的制动强度超过了f线组的范围，认为是紧急制动情况，并且前轮出现抱死趋势，ABS系统工作。再生制动力必须为0, 以防止影响ABS的正常工作，保证制动安全。

2) 重制动：若FfBFfJf < FfC，则Fmot_max=FfJf-FfJf_v。此时的制动力分配须在Jf点之左，以保证前轮的滑移率。认为机械制动的前后轮制动力在Ⅰ线上分配。那么此时理论的再生制动力在以Jfv为起点、Jf为终点的线段上。

3) 中制动：若FfAFfJECE < FfB，则Fmot_max=FfJECE-FfJECE_v。此时的制动力分配须在JECE点之左，以保证法规要求。同样认为机械制动的前后轮制动力在Ⅰ曲线上分配。那么此时理论的再生制动力在以JECEv为起点、JECE为终点的线段上。

4) 轻制动：若0 < Gz < FfA，则Fmot_max=Gz。此时的制动力分配远离f线组以及ECE法规曲线，故可将制动力全部分配在前轮，并由再生制动力提供。

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 参数 数值 整车整备质量Mc/kg 1 700 整车满载质量Mf/kg 2 075 轴距L/mm 2 640 中心到前轴距离La/mm 1 006 风阻系数Cd 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im 8.867 传动系统总效率ηt 0.9 电池容量C/(kW·h) 20 电池标称电压Un/V 347.5 最大允许充电电流Imax/A 65

 图 2 不同制动强度下的理论最大再生扭矩 Fig. 2 Theoretical maximum regenerative torque under different brake strength
2 制动意图识别与电池充电保护 2.1 基于模糊控制的制动意图识别

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

 图 3 制动踏板深度和输出修正系数隶属函数 Fig. 3 Membership functions of brake pedal depth and output correction factor

Abrk为模糊输入制动踏板深度，Aspd为模糊输入车速，ASOC为模糊输入SOC，Acor为模糊输出修正系数。则Acor的求解式为

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 图 4 模糊制动意图识别结果 Fig. 4 Recognition results of fuzzy braking intention

2.2 基于电机效率曲线的电池充电保护

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 图 5 电机效率曲线 Fig. 5 Motor efficiency curves
3 再生制动最大化控制策略

 图 6 再生制动最大化控制策略 Fig. 6 Control strategy of maximum regenerative brake

 图 7 再生制动最大化控制策略流程图 Fig. 7 Flowchart of control strategy of maximum regenerative brake

4 MATLAB & Cruise联合策略仿真 4.1 联合仿真模型

 图 8 MATLAB & Cruise联合仿真模型 Fig. 8 Model of MATLAB & Cruise united simulation
4.2 仿真结果分析

 图 9 2种工况循环下的车速曲线 Fig. 9 Speed curves under two driving cycles

 图 10 2种工况循环下的再生电流曲线 Fig. 10 Regenerative current curves under two driving cycles

 N·m 车速/(km·h-1) 制动踏板深度/% 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

 图 11 2种工况循环下的能量回收效果 Fig. 11 Effect of energy recovery under two driving cycles

5 结论

1) 本文控制策略通过制动踏板深度、SOC和车速制动意图识别；采用Ⅰ曲线、ECE法规曲线、f线组和等强度制动线交点计算边界最大化再生扭矩；借助基于电机效率曲线的电池充电保护模型进行充电电流限制，得到了优化的再生制动扭矩以驱动电机。

2) NEDC循环工况，本文控制策略相比于车速-踏板查表策略回收制动能量增加23.7%；续航里程相比车速-踏板查表策略增加5.6%，相比于无回收策略增加7.8%。

3) FTP75循环工况，本文控制策略相比于车速-踏板查表策略回收制动能量增加13.0%；续航里程相比车速-踏板查表策略增加11.4%，相比于无回收策略增加27.3%。

4) 通过模型搭建实现控制策略，并对整车联合仿真环境进行二次开发，以某电动汽车整车参数进行仿真，验证了策略的可行性与效能。

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#### 文章信息

WU Zhixin, SHI Jinpeng, LI Yalun, YANG Haisheng, MA Shaodong

Regenerative brake strategy based on braking boundary and intention recognition

Journal of Beijing University of Aeronautics and Astronsutics, 2017, 43(8): 1531-1540
http://dx.doi.org/10.13700/j.bh.1001-5965.2016.0645