Article(id=1157001842593980848, tenantId=1146029695717560320, journalId=1146120084050784272, issueId=1157001741804855503, articleNumber=null, orderNo=null, doi=10.19562/j.chinasae.qcgc.2024.09.014, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1716998400000, receivedDateStr=2024-05-30, revisedDate=1722268800000, revisedDateStr=2024-07-30, acceptedDate=null, acceptedDateStr=null, onlineDate=1753780335329, onlineDateStr=2025-07-29, pubDate=1727193600000, pubDateStr=2024-09-25, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1753780335329, onlineIssueDateStr=2025-07-29, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1753780335329, creator=13701087609, updateTime=1753780335329, updator=13701087609, issue=Issue{id=1157001741804855503, tenantId=1146029695717560320, journalId=1146120084050784272, year='2024', volume='46', issue='9', pageStart='1537', pageEnd='1722', issueExtLink='null', onlineDate='null', pubDate='null', beforeIssueId=null, nextIssueId=null, price=null, status=0, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1753780311297, creator=13701087609, updateTime=1756792455058, updator=13701087609, preIssue=null, nextIssue=null, ext={EN=IssueExt(id=1169635588480184833, tenantId=1146029695717560320, journalId=1146120084050784272, issueId=1157001741804855503, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1169635588480184834, tenantId=1146029695717560320, journalId=1146120084050784272, issueId=1157001741804855503, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null}, startPage=1668, endPage=1677, ext={EN=ArticleExt(id=1157001844422697402, articleId=1157001842593980848, tenantId=1146029695717560320, journalId=1146120084050784272, language=EN, title=Control Strategy for Dual-Clutch Transmission Gear Shifting Process in Pure Electric Vehicles Based on LADRC, columnId=null, journalTitle=Automotive Engineering, columnName=null, runingTitle=null, highlight=null, articleAbstract=

For the uncertainty of the shift control model and parameters and the existing unknown disturbances of the two-speed dual clutch transmission (2DCT) of pure electric vehicles, a linear active disturbance rejection controller (LADRC) method is proposed. Firstly, the dynamic model of the 2DCT shifting process is established, and the shifting process is analyzed. Then, considering the uncertainty and unknown disturbance of the control model, the LADRC controller is applied to the shifting process to track the desired rotational speed, and the Extended State Observer (ESO) is used to estimate the disturbance in real time and compensate for it. Finally, it is compared with the PID control. Simulation and experimental results show that the controller has smaller tracking error and strong robustness, which ensures good gear shifting quality.

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针对纯电动汽车两挡双离合自动变速器(two-speed dual-clutch transmission, 2DCT)换挡控制模型及参数的不确定性和存在的未知干扰,提出了一种线性自抗扰(linear active disturbance rejection controller, LADRC)换挡控制策略。首先建立2DCT换挡过程动力学模型,并分析了换挡过程;然后考虑控制模型的不确定性及未知干扰,将LADRC控制器应用到换挡过程对期望角速度进行跟踪,并采用扩张状态观测器(extended state observer, ESO)对扰动进行实时估计,并加以补偿,最后与PID控制进行对比。仿真和实验结果表明,该控制器跟踪误差更小,鲁棒性强,能保证良好的换挡品质。

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陈勇,教授,博士,E-mail:
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参数 单位 数值 参数 单位 数值
b m N·m·s·rad-1 0.002 μ d 0.025
I m kg·m2 0.042 R m 0.15
b L N·m·s·rad-1 0.002 m kg 1 635
I L kg·m2 0.022 f 0.015
b H N·m·s·rad-1 0.002 C D 2.19
I H kg·m2 0.025 i 0 3.91
b w N·m·s·rad-1 0.005 i 1 3
I w kg·m2 0.291 i 2 1.19
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仿真参数

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参数 单位 数值 参数 单位 数值
b m N·m·s·rad-1 0.002 μ d 0.025
I m kg·m2 0.042 R m 0.15
b L N·m·s·rad-1 0.002 m kg 1 635
I L kg·m2 0.022 f 0.015
b H N·m·s·rad-1 0.002 C D 2.19
I H kg·m2 0.025 i 0 3.91
b w N·m·s·rad-1 0.005 i 1 3
I w kg·m2 0.291 i 2 1.19
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LADRC PID
ω 0 15 000 K P 138
b 0 0.042 K i 0.37
k p 3 000 K d 100
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控制器参数

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LADRC PID
ω 0 15 000 K P 138
b 0 0.042 K i 0.37
k p 3 000 K d 100
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项目 LADRC PID
冲击度/(m·s-3 7.56 7.71
滑摩功/kJ 11.48 11.72
抗干扰能力
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控制效果对比

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项目 LADRC PID
冲击度/(m·s-3 7.56 7.71
滑摩功/kJ 11.48 11.72
抗干扰能力
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基于LADRC的纯电动汽车双离合变速器换挡控制
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贺伯林 1 , 陈勇 2 , 代青林 1
汽车工程 | 2024,46(9): 1668-1677
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汽车工程 | 2024, 46(9): 1668-1677
基于LADRC的纯电动汽车双离合变速器换挡控制
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贺伯林1, 陈勇2 , 代青林1
作者信息
  • 1. 河北工业大学,天津市新能源汽车动力传动与安全技术重点实验室,天津 300130
  • 2. 广西大学机械工程学院,南宁 530004

通讯作者:

陈勇,教授,博士,E-mail:
Control Strategy for Dual-Clutch Transmission Gear Shifting Process in Pure Electric Vehicles Based on LADRC
Bolin He1, Yong Chen2 , Qinglin Dai1
Affiliations
  • 1. Hebei University of Technology,Tianjin Key Laboratory of Power Transmission and Safety Technology for New Energy Vehicles,Tianjin 300130
  • 2. School of Mechanical Engineering,Guangxi University,Nanning 530004
出版时间: 2024-09-25 doi: 10.19562/j.chinasae.qcgc.2024.09.014
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针对纯电动汽车两挡双离合自动变速器(two-speed dual-clutch transmission, 2DCT)换挡控制模型及参数的不确定性和存在的未知干扰,提出了一种线性自抗扰(linear active disturbance rejection controller, LADRC)换挡控制策略。首先建立2DCT换挡过程动力学模型,并分析了换挡过程;然后考虑控制模型的不确定性及未知干扰,将LADRC控制器应用到换挡过程对期望角速度进行跟踪,并采用扩张状态观测器(extended state observer, ESO)对扰动进行实时估计,并加以补偿,最后与PID控制进行对比。仿真和实验结果表明,该控制器跟踪误差更小,鲁棒性强,能保证良好的换挡品质。

纯电动汽车  /  双离合变速器  /  换挡控制  /  自抗扰控制  /  扩张状态观测器

For the uncertainty of the shift control model and parameters and the existing unknown disturbances of the two-speed dual clutch transmission (2DCT) of pure electric vehicles, a linear active disturbance rejection controller (LADRC) method is proposed. Firstly, the dynamic model of the 2DCT shifting process is established, and the shifting process is analyzed. Then, considering the uncertainty and unknown disturbance of the control model, the LADRC controller is applied to the shifting process to track the desired rotational speed, and the Extended State Observer (ESO) is used to estimate the disturbance in real time and compensate for it. Finally, it is compared with the PID control. Simulation and experimental results show that the controller has smaller tracking error and strong robustness, which ensures good gear shifting quality.

pure electric vehicles  /  dual-clutch transmissions  /  shifting control  /  active disturbance rejection controller  /  extended state observer
贺伯林, 陈勇, 代青林. 基于LADRC的纯电动汽车双离合变速器换挡控制. 汽车工程, 2024 , 46 (9) : 1668 -1677 . DOI: 10.19562/j.chinasae.qcgc.2024.09.014
Bolin He, Yong Chen, Qinglin Dai. Control Strategy for Dual-Clutch Transmission Gear Shifting Process in Pure Electric Vehicles Based on LADRC[J]. Automotive Engineering, 2024 , 46 (9) : 1668 -1677 . DOI: 10.19562/j.chinasae.qcgc.2024.09.014
面对日益严峻的能源短缺及环境污染问题,发展新能源汽车成为一项重要战略措施1-3,纯电动汽车以其节能、零污染等特点,成为新能源汽车主要发展方向之一4。纯电动车大多采用电机直驱构型并配备单级减速器5,虽然多挡变速器可以有效提高车辆动力性及高速续航能力,但鉴于制造和控制难度大等问题并未得到广泛应用6-7。目前市面上搭载变速器的纯电动乘用车型有两款,分别为保时捷Taycan和奥迪e-tron GT,由于两款均为高端车型,成本并不过多考虑,故均采用双电机构型搭配2AT(两挡自动变速器)来实现无动力中断换挡8。本研究致力于推广自动变速器在纯电动车领域的发展,须综合考虑成本及换挡品质,既要结构简单又能实现动力换挡,故干式双离合自动变速器以其结构简单、传动效率高及换挡无动力中断等优点成为最优选择9。在换挡时间满足要求的情况下,冲击度和滑摩功是双离合变速器换挡品质的两大评价指标,前者反映换挡舒适性,后者则直接影响离合器使用寿命10-11。优良的换挡过程控制策略能直接提升换挡品质,具有重要研究意义。
近年来国内外学者对纯电车两挡变速器进行了深入探索和研究。Walker等12-13提出并验证了两挡变速器对纯电动车经济性的提高。Gao等14-15提出了一种纯电动车用两挡自动变速器(I-AMT)并设计了换挡控制策略,仿真及实验验证了变速器对纯电动车性能的提升。双离合变速器及其控制方面,Choi等16-17设计了分离式纯电动车双离合变速器实验台架,并提出了基于最优控制的换挡策略,但其实验条件过于理想,很多数据在实车中无法获得。Fu等18为实现双离合变速器在转矩阶段的精确控制,提出了闭环自适应控制方法,通过连续自调节控制参数,保证良好的换挡品质。Cheng等19分析了双离合变速器换挡的4种类型,设计了通用控制流程,将换挡过程分为3个阶段,有效地简化了控制算法和参数标定。Li等20考虑摩擦片磨损量、机油和摩擦片温度影响离合器的动摩擦因数,进而影响换挡品质,提出的自适应模型控制不仅在线估计离合器动摩擦因数,且保证良好的换挡品质。Hu等21为提高DCT换挡品质,将外部干扰和未建模部分当作干扰输入,并在惯性阶段采用反步控制技术进行换挡控制,仿真结果表明该控制器跟踪误差较小,且有较强的鲁棒性。Lu等22设计了数据预测驱动控制器,对参考轨迹进行跟踪,为验证控制器的有效性,在不同工况下进行仿真,结果表明实时环境下能有效保证控制性能,对DCT换挡品质有所提高。赵治国等23考虑离合器传递转矩的实时有效估计对离合器转矩控制有较大的影响,采用高阶滑模观测器对输出轴的角加速度进行估计,并设计未知输入观测器估计离合器传递转矩,仿真结果表明该观测的有效性。Bera等24提出了一种基于人工神经网络的发动机控制算法,实现双离合变速器在升挡惯性阶段主从盘转速迅速平稳同步。Hong等25针对搭载干式双速DCT的电动汽车,提出了一种基于离合器传递转矩控制和牵引电机转矩控制的换挡控制算法,换挡品质得到提升。Golkani等26对双离合变速器换挡过程中两个离合器的工作进行优化,提出了以滑摩功和换挡冲击度最小的最优换挡控制方法。Liu等27对双离合变速器换挡过程中转矩阶段和惯性阶段分别提出了相应的控制策略,设计发动机转矩节流阀控制器和离合器转矩控制器,实验和仿真结果表明该控制策略的有效性。上述控制方案的研究大多都是基于对燃油车搭载双离合变速器换挡控制研究,由于纯电动汽车变速器所需挡位、相邻挡位之间传动比比值更大,在惯性阶段须调整的转速差更大,因此具有一定的参考价值,但并不完全适用于纯电动汽车。
本文首先建立2DCT换挡过程动力学模型,并以升挡为例详细分析换挡过程需求,然后考虑到控制对象模型参数和模型的误差以及存在未知干扰,设计了LADRC控制器,采用ESO观测器对干扰进行估计和补偿,完成换挡动作。最后通过MATLAB/Simulink仿真及台架实验验证控制器的有效性和鲁棒性。
以团队自主研发的两挡干式双离合自动变速器为对象,其结构及动力传动系统构型如图1图2所示。传动系统由离合器、输入轴、挡位齿轮、中间轴、主减速齿轮及差速器等零件组成。2DCT通过离合器摩擦片接合与分离实现挡位变换。离合器的接合与分离通过变速器换挡控制单元(transmission control unit, TCU)控制液压系统比例电磁阀实现。以升挡为例对2DCT换挡过程进行研究,面向控制建立2DCT传动系统动力学模型,如图3所示。为便于分析及控制,建模过程中做出以下假设:
• 齿轮之间啮合无间隙;
• 齿轮与齿轮轴为刚性连接;
• 忽略轴的振动;
• 忽略温度对传动系统的影响;
• 忽略中间轴的等效转动惯量和等效旋转阻尼因数;
• 差速器、半轴及车轮为刚性连接。
驱动电机为永磁同步电机,额定功率为80 kW,最大转速为12 000 r/min,最大输出转矩为230 N·m。根据电机参数及特性,将输出转矩转换为与转速和踏板开度相关函数,模型如图4所示,输出转矩函数为
T m = f ( α , n m )
式中:T m为电机输出转矩;α为踏板开度;n m为电机转速。
假设电机输出轴与离合器主动轴为刚性连接,动力平衡方程为
I m ω ˙ m = T m - b m ω m - T L - T H
式中:I m为电机轴等效转动惯量;ω m为电机旋转角速度;b m为电机轴旋转阻尼因数;T LT H分别为离合器L和离合器H摩擦转矩。
电机输出转矩通过离合器传递至变速器,离合器的工作状态为分离、滑摩及接合,模型如式(3)所示。
T c = 0 ,                                      T s = 0       分离 2 3 s g n ( Δ ω m c ) μ d R F n ,   ω m ω c      T m ,                                   T s > T m      接合
其中
s g n x = 1 ,        x > 0 0 ,        x = 0 - 1 ,     x < 0
式中:T c为离合器传递转矩;T s为静态摩擦转矩;Δω mc为离合器主动盘与从动盘角速度差;μ d为离合器动态摩擦因数;R为离合器有效半径;F n为离合器所受的力。
2DCT换挡过程中包括两个状态,分别为转矩阶段与惯性阶段,转矩阶段模型为
I L ω ˙ L = T L - b L ω L - T 1 I H ω ˙ H = T H - b H ω H - T 2 T 0 = i 0 i 1 T 1 + i 2 T 2
式中:I LI H分别为变速器输入轴L和H转动惯量;ω Lω H分别为变速器输入轴L和H的旋转角速度;b Lb H分别为变速器输入轴L和H的旋转阻尼因数;T 1T 2分别为变速器输入轴L和H的负载转矩;i 1为1挡传动比;i 2为2挡传动比;i 0为主传动比;T 0为半轴输入转矩。惯性阶段模型为
I H ω ˙ H = T H - b H ω H - T 2 T 0 = i 0 i 2 T 2
驱动轴将变速器输出转矩传递至车轮,模型如下所示:
I w ' ω ˙ w = T 0 - b w ' ω w - F t r
I w ' = I 0 + I w + I f b w ' = b 0 + b w + b f
式中: I w '为驱动轴等效转动惯量;ω w为驱动轴旋转角速度; b w '为驱动轴的旋转阻尼因数;F t为车轮的驱动力;r为车轮半径;I 0为差速器转动惯量;I w为半轴转动惯量;I f为车轮转动惯量;b 0为差速器旋转阻尼因数;b w为半轴旋转阻尼因数;b f为车轮旋转阻尼因数。
模型由滚动阻力、空气阻力及坡度阻力和加速阻力构成:
F t = G f c o s   β + C D A 21.15 v 2 + G s i n   β + δ m d v d t
式中:G为汽车重力;f为轮胎的滚动阻力系数;C D为空气阻力系数;A为迎风面积;v为车速;β为道路坡度;δ为汽车旋转质量换算系数;m为汽车质量。
离合器摩擦片的分离与接合通过液压系统比例压力电磁阀实现。当主油压为35 bar时,比例电磁阀静态特性如图5所示。液压执行机构油压与比例压力电磁阀电流关系为
p ˙ c v = p c v τ c v + k c v τ c v i
式中:p cv为执行机构油压;τ cv为时间系数;k cv为压力增益系数;i为比例电磁阀电流。
2DCT有两个挡位,通过控制离合器的接合与分离实现挡位的转换,无同步器,以升挡为例,换挡过程转矩和角速度变化如图6所示。根据双离合器特性,换挡过程主要分为准备阶段、转矩阶段及惯性阶段。
降挡过程与升挡过程类似,只是转矩相和惯性相先后顺序不同,本文以升挡为例介绍换挡流程及相关需求。
准备阶段(t 0t 1):当TCU检测到升挡信号,离合器L油压迅速降低到滑摩值,解除锁死状态;离合器H快速充油,消除空行程,达到临界接触点。此时,离合器L传递所有转矩,离合器H不传递转矩。
转矩阶段(t 1t 2):消除空行程后,离合器L压力继续降低,同时离合器H压力升高,当离合器L压力为0时,离合器H压力升高至2挡预设值,电机转矩也随之由1挡初始值升高至2挡预设值,该阶段结束。该阶段离合器L和离合器H都传递转矩,完成电机转矩传递路线转换。
惯性阶段(t 2t 3):此时由于离合器H主、从动盘存在转速差,离合器H处于滑摩状态,需要通过降低电机转矩来实现降速,使主、从动盘转速差减小。当离合器H主、从动盘转速差小于设定阈值,离合器H油压快速增加达到锁定状态,换挡完成。
转矩阶段须通过控制离合器L、H的分离接合实现变速器动力路线切换,该阶段采用开环控制。离合器L分离过快或H接合过慢可能导致转矩中断;离合器L分离过慢可能导致动力循环现象。为避免上述情况,离合器L主动盘转速须等于或稍大于从动盘转速。根据式(2)式(5)得离合器L、H转矩及电机转矩的关系为
i 1 I L ' + i 2 I m T H = i 1 I L ' T m - i 1 I L ' + I m T L -
           i 1 I L ' b m - I m b L ' ω L + T 12 I m
其中
I L ' = I L + ( i 1 / i 2 ) 2 I H b L ' = b L + ( i 1 / i 2 ) 2 b H T 12 = i 1 T L + i 2 T H
为实现精确控制,通常根据动力学模型设计反馈控制器,模型的精确性是控制效果的关键,但在实际应用中,精确的数学模型很难得到,通常还伴随未知干扰。自抗扰控制能够观测补偿模型误差及外部扰动28-29,适用于干扰因素众多且无法准确测量的工业场景,线性自抗扰控制(LADRC)则更加简洁、直接,具有更强鲁棒性。将电机和离合器未建模部分、模型参数误差及未知干扰设计为不确定项dt),由式(2)可得:
I m ω ˙ m = T m - b m ω m - T L - T H + d ( t )
根据式(13)设计LADRC控制器及2阶线性扩张状态观测器(LESO),其结构如图7所示。记 y = x 1 = ω m x 2 = f ˙ 0 ( ω ^ m , T L , T H , t ) h ( t ) = f ¨ 0 b 0 = 1 / I m u = T m,则有:
x ˙ 1 = x 2 + b 0 u x ˙ 2 = h ( t ) y = x 1
f 0 = d ( t ) - T L - T H - b m ω ^ m I m
根据式(14),线性状态观测器为
z ˙ = A z + B u + L ( x 1 - z 1 ) y ^ = z 1
式中: A = 0 1 0 0 B = b 0 0 L = l 1 l 2zx的观测值; L 为观测器反馈增益矩阵。
式(16)展开为
z 1 = f ^ 0 ( z 1 , T L , T H , t ) + b 0 T m + l 1 ( ω m - z 1 ) f ^ ˙ 0 ( z 1 , T L , T H , t ) = l 2 ( ω m - z 1 )
增益l 1=2ω 0l 2=ω 0 2通过特征多项式选取。
λ ( s ) = s 2 + l 1 s + l 2 = ( s + ω 0 ) 2
式中ω 0是LESO带宽。
LADRC的核心在于对扰动进行实时估计,并加以消除,LESO可以实时估计外部扰动和内部扰动,记 b 0 T m = u 0 - f ^ 0,从而式(13)变为
ω ˙ m = b 0 T m + f 0 = u 0 - f ^ 0 + f 0 u 0
由于LESO能实时估计扰动,并对扰动进行补偿,由式(18)式(19)可得控制量为
u = u 0 - z 2 b 0
控制量由两部分组成,u 0/b 0为反馈控制分量,-z 2/b 0是扰动补偿分量。控制规律为
u 0 = k p ( ω d - ω m )
式中:k p为控制器的比例增益;ω dω m期望轨迹。
基于MATLAB/Simulink平台搭建控制器及仿真模型。以加速踏板开度为30%进行升挡仿真,在相同条件下将LADRC控制器与PID控制器进行对比,验证LADRC的跟踪性能及抗干扰能力。模型参数如表1所示。其中LADRC控制器参数及PID控制参数如表2所示。
为验证LADRC控制器的鲁棒性,假设存在随机干扰,如图8所示。
首先验证LADRC的跟踪性能,在模型和参数不变的情况下,采用不同控制器对需求角速度进行跟踪,后加入随机干扰验证LESO观测器对干扰的补偿效果,仿真结果如图9图10所示。
图9可知,无干扰时LADRC控制与PID控制都能跟踪期望角速度,但LADRC控制器跟踪效果更好,最大跟踪误差为0.684 rad/s且响应更快,PID控制器最大跟踪误差为0.747 rad/s,且整个过程中LADRC误差一直小于PID。
图10可知,虽然存在干扰时两者依旧能够完成角速度跟踪,但根据图10(b)可以看出,LADRC控制器波动更小,控制曲线更光滑,对执行器负荷更小,证明通过LESO观测器能对干扰实时估计并补偿,LADRC具有更强的抗干扰能力。
图11图12分别为干扰情况下LADRC控制器换挡过程中电机与离合器的角速度和转矩变化。由图可知,变速器在第19 s进入转矩阶段,19.31 s转矩阶段结束进入惯性阶段,19.60 s惯性阶段结束,换挡完成。整个换挡过程用时0.6 s。图13图14分别为换挡冲击度和滑摩功,最大冲击度为7.56 m/s3,滑摩功为11.48 kJ。冲击度与滑摩功计算公式分别如式(22)式(23)所示。
J = d 2 v d t 2 = 1 δ m r · i 0 ( i 1 d T L + i 2 d T H ) d t
W = T L ω L - ω m + T H ω H - ω m d t
表3可知,LADRC控制器的冲击度、滑摩功及抗干扰性均优于PID控制器,能够提高换挡品质,且更适用于存在复杂干扰的实车场景。
为验证换挡控制策略性能,搭建了两挡双离合变速器动力换挡实验台。实验台架由电源、驱动电机、2DCT、车轮、惯性轮、转矩转速传感器、磁粉制动器、TCU、MCU及上位机等组成。实验台架及原理示意图如图15图16所示。
在本文换挡实验中,磁粉制动器的励磁电流为0.4 A,提供的负载转矩为75.59 N·m,TCU采用的是华海科技的快速原型控制器RapidECU-U3H,数据的采集和标定是通过meca软件来实现的,采样频率为10 ms,换挡过程中液压阀体中电磁阀电流和油路压力如图17图18所示。
图17可知,TCU接收到换挡信号时,1挡电磁阀电流从1 500 mA迅速降低到620 mA;2挡电磁阀电流由0 mA迅速增加到580 mA,完成换挡准备。在50.52 s进入转矩阶段,50.81 s结束,此阶段1挡电磁阀电流先降低到410 mA,然后迅速降为0 mA;2挡电磁阀电流增加到813 mA。惯性阶段结束时间为51.14 s,2挡电磁阀电流在此阶段保持稳定,当惯性阶段结束,电流迅速增加到1 500 mA锁定。由图18可知,油路压力变化与电磁阀电流变化一致。
图19图20分别为电机、离合器的转速和转矩变化。在转矩阶段,电机转速高于离合器L转速,原因可能是电机输出转矩大于离合器传递转矩。在惯性调速阶段末尾,由于惯性力矩,电机转速会低于离合器H转速。由于1挡传动比大于2挡传动比,为保持车辆的加速度变化不大,在转矩阶段,须增加电机转矩。在惯性阶段,通过降低电机转矩来实现降低电机转速,使离合器H主、从动盘转速快速同步。在换挡完成后电机转矩恢复到驾驶员需求转矩。在换挡过程中电机与离合器转速没有突变,实现平稳换挡。
图21为换挡冲击度,最大换挡冲击度8.67 m/s3,符合国家推荐标准。其中在转矩开始阶段和惯性阶段结束产生冲击度最大,可能是传递转矩发生了突变。从图22可知,换挡产生的滑摩功为2 646.76 J。
针对纯电动双离合变速器换挡过程,首先建立了关键传动部件及换挡动力学模型,然后基于MATLAB/Simulink平台搭建相应仿真模型,并对换挡过程及相关需求进行了详细分析。
设计了换挡过程控制策略。在转矩阶段,提出了两离合器协调切换的开环控制方法。在惯性阶段对换挡控制模型及参数的不确定性和存在的未知干扰,采用了LESO观测器对模型的未知扰动进行实时估计及对其补偿,并设计了相应的LADRC控制器。
仿真和实验结果表明,存在外部干扰的情况下,LADRC控制器具有良好的鲁棒性,且能对期望转速跟踪效果良好。整个换挡过程快速、平稳,具有良好的换挡品质。
  • 宁波市2025重大科技专项项目(2019B10111)
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2024年第46卷第9期
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doi: 10.19562/j.chinasae.qcgc.2024.09.014
  • 接收时间:2024-05-30
  • 首发时间:2025-07-29
  • 出版时间:2024-09-25
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  • 收稿日期:2024-05-30
  • 修回日期:2024-07-30
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宁波市2025重大科技专项项目(2019B10111)
作者信息
    1. 河北工业大学,天津市新能源汽车动力传动与安全技术重点实验室,天津 300130
    2. 广西大学机械工程学院,南宁 530004

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陈勇,教授,博士,E-mail:
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2种不同金属材料的力学参数

Family
属数
Number of
genus
种数
Number of
species
占总种数比例
Percentage of
total species (%)

Genus
种数
Number of
species
占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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