CN114765438A - 线性时变模型预测转矩控制 - Google Patents

线性时变模型预测转矩控制 Download PDF

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CN114765438A
CN114765438A CN202111534642.6A CN202111534642A CN114765438A CN 114765438 A CN114765438 A CN 114765438A CN 202111534642 A CN202111534642 A CN 202111534642A CN 114765438 A CN114765438 A CN 114765438A
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torque
current
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Y-Y·王
L·郝
S·戈帕拉克里希南
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GM Global Technology Operations LLC
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    • B60L50/50—Electric propulsion with power supplied within the vehicle using propulsion power supplied by batteries or fuel cells
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    • B60L15/20—Methods, circuits, or devices for controlling the traction-motor speed of electrically-propelled vehicles for control of the vehicle or its driving motor to achieve a desired performance, e.g. speed, torque, programmed variation of speed
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    • H02P27/00—Arrangements or methods for the control of AC motors characterised by the kind of supply voltage
    • H02P27/04—Arrangements or methods for the control of AC motors characterised by the kind of supply voltage using variable-frequency supply voltage, e.g. inverter or converter supply voltage
    • H02P27/06—Arrangements or methods for the control of AC motors characterised by the kind of supply voltage using variable-frequency supply voltage, e.g. inverter or converter supply voltage using dc to ac converters or inverters
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    • B60L2210/00—Converter types
    • B60L2210/40—DC to AC converters
    • B60L2210/42—Voltage source inverters
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    • B60L2240/00—Control parameters of input or output; Target parameters
    • B60L2240/40—Drive Train control parameters
    • B60L2240/42—Drive Train control parameters related to electric machines
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    • B60L2240/00—Control parameters of input or output; Target parameters
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    • B60L2240/00—Control parameters of input or output; Target parameters
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    • B—PERFORMING OPERATIONS; TRANSPORTING
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    • H02P2207/01—Asynchronous machines

Abstract

一种用于机动车中的电机的转矩控制系统包括电源逆变器和模型预测控制模块，电源逆变器向电机传递电流以调节电机的转矩，模型预测控制模块向电源逆变器发送三相电压以控制电源逆变器的运行。

Description

线性时变模型预测转矩控制
技术领域
本公开涉及电机。更具体地，本公开涉及用于机动车的电机的转矩控制。
背景技术
许多机动车使用电机。特别是，混合动力车辆除了使用内燃机外，还使用一种或多种电机，且机动车使用一种或多种电机作为主要动力装置。
这些电机的运行需要控制电机的转矩输出。目前，电机的转矩控制基于开环系统。此类系统需要对查找表进行大量的校准以实现机动车的稳健性能。
因此，虽然当前的电机转矩控制系统实现了其预期目的，但需要一种新的和改进的系统来调节电机的转矩输出。
发明内容
根据若干方面，一种用于机动车中的电机的转矩控制的系统，包括电源逆变器和模型预测控制(MPC)模块，电源逆变器向电机传递电流以调节电机的转矩，MPC模块向电源逆变器发送三相电压以控制电源逆变器的运行。
在本公开的另一方面中，MPC模块基于在每个采样时间电机的线性时变模型。
在本公开的另一个方面中，MPC模块基于MPC控制范围内的线性参数变化模型。
在本公开的另一个方面中，MPC模块利用来自卡尔曼滤波器中的磁通反馈。
在本公开的另一个方面中，MPC模块利用电流反馈。
在本公开的另一个方面中，该系统进一步包括卡尔曼滤波器，用于估计电流以消除在选定电机运行条件下电流测量带来的噪声。
在本公开的另一个方面中，该系统进一步包括测量电机转矩的转矩传感器。
在本公开的另一个方面中，该系统进一步包括卡尔曼滤波器，该卡尔曼滤波器根据电机的电压和电机的测量转矩来估计用于MPC模块的磁通或电流。
在本公开的另一个方面中，该MPC模块基于双环控制结构。
在本公开的另一个方面中，该双环控制结构包括内环，内环控制到电机的电流。
在本公开的另一个方面中，该双环控制结构包括外环，外环基于来自电机的转矩反馈。
根据若干方面，一种用于机动车中的电机的转矩控制的系统，包括电源逆变器和模型预测控制(MPC)模块，电源逆变器向电机传递电流以调节电机的转矩，MPC模块向电源逆变器发送三相电压以控制电源逆变器的运行。MPC模块基于线性时变模型或线性参数变化模型。
在本公开的另一方面中，MPC模块利用来自卡尔曼滤波器中的磁通反馈。
在本公开的另一方面中，MPC模块利用电流反馈。该系统进一步包括卡尔曼滤波器，用于估计电流以消除在选定电机运行条件下电流测量带来的噪声。
在本公开的另一方面中，该系统进一步包括测量电机转矩的转矩传感器。
在本公开的另一个方面中，该系统进一步包括卡尔曼滤波器，该卡尔曼滤波器根据电机的电压和电机的测量转矩来估计用于MPC模块的磁通或电流。
在本公开的另一个方面中，该MPC模块基于双环控制结构。
在本公开的另一个方面中，该双环控制结构包括内环，内环控制到电机的电流。
在本公开的另一个方面中，该双环控制结构包括外环，外环基于来自电机的转矩反馈。
根据若干方面，一种用于机动车中的电机的转矩控制的系统，包括电源逆变器、电池组和模型预测控制(MPC)模块。电源逆变器向电机传递三相交流电流以调节电机的转矩，电池组向电源逆变器施加直流电压，MPC模块向电源逆变器发送三相电压以控制电源逆变器的运行。MPC模块基于线性时变模型或线性参数变化模型。
进一步适用性领域将从此文提供的描述中变得显而易见。应理解的是，描述和具体实例仅仅是示例性的，并不旨在限制本公开的范围。
附图说明
此文描述的附图仅仅用于示例性目的，并不意在以任何方式限制本公开的范围。
图1A是一种根据示例性实施例的用于电机的转矩控制的系统的示意图；
图1B是图1A所示系统的子组件的示意图；
图1C是在图1A所示系统中使用的卡尔曼滤波器的示意图；
图2是另一种根据示例性实施例的用于电机的转矩控制的系统的示意图；
图3是再一种根据示例性实施例的用于电机的转矩控制的系统的示意图；以及
图4是又一种根据示例性实施例的用于电机的转矩控制的系统的示意图。
具体实施方式
下述描述本质上仅是示例性的，并不旨在限制本公开、其应用或用途。
参阅图1A、1B和1C，示出了一种用于控制交流电机24的转矩(Tq)的系统。该系统10包括向电源逆变器16提供电压14的电池组12。电源逆变器继而向电机24提供三相电流20、21和22。
该系统10进一步包括模型预测控制(MPC)模块36，用于调节从电源逆变器16向电机24(例如三相交流电机)传递的电流。在一些设置中，MPC模块36是线性时变(LTV)的，即电机系统模型参数是随时间变化的。在其他设置中，系统模型参数是线性参数变化(LPV)的，即一些系统参数缓慢变化。电机24是永磁(PM)电机，也称为永磁异步电机(PMSM)。
作为参考，本公开使用了下列术语：r(k)：采样时间k处的控制参考；Tq_ref：电机转矩参考或指令，r(k)＝Tq_ref；va、vb和vc：向电机24施加的三相电压，用于转矩控制；ia、ib、ic：电机24的三相电流；θ，ω：电机转子位置和电机转速；电机转速计算为ωe，电气的电机转速，和ωm，机械的电机转速或物理转速，它们的关系式为ωe＝p/2ωm，其中p为电机磁极数；Tq：电机转矩；id，iq：转子旋转参考系中的电机电流(这两股电流在磁场中垂直)；Vd，Vq：转子旋转参考系中的电机电压(它们在磁场中垂直，又被称为直流电压和正交电压)；相位转换(θ)将旋转参考系电流或电压转换为三相电流或电压，反之亦然；λd，λq，：旋转参考系中的电机磁通；
Figure BDA0003412700830000031
增加斜度，表示估计的电机磁通。
MPC36根据传感器26和测量三相电流的传感器估计的指令电机转矩(或参考转矩)Tq_ref、电机速度和电机磁通反馈来生成预期的电机控制电压Vd和Vq。通过相位转换(θ)32，最佳控制电压Vd和Vq被转换为所需的三相电压va、vb和vc，施加给电机24。根据所需的三相电压va、vb和vc，空间矢量调制或PWM34应用于变换器控制开关16以产生所需的三相电流20、21和22，并施加给电机24，电机24产生所需转矩Tq。如此，该系统10通过磁通反馈来利用电机转矩的模型预测控制。
MPC36利用优化算法获得所需电压Vd和Vq来控制所需转矩Tq。控制目标是在具有N个样本数量的预测时间范围内最小化成本函数(1)。
Figure BDA0003412700830000032
其中vi＝[vd(i)vq(i)] (1)
在该成本函数中，i表示第i个采样步骤处的离散采样时间，Tq(i)是采样时间i处的转矩，r(i)是采样时间i处的转矩控制参考，k是当前采样时间，vref(i)是Vd和Vq的参考指令，设置为0，Δvi是Vd和Vq的变化率，Wy、Wu和WΔu是可调加权函数，||*||表示向量范数。
权值调整通过速率限制来平衡快速转矩跟踪响应、控制输入能量和控制输入的积极性。该算法求解使成本函数(1)最小化的最佳控制Vd和Vq以实现所需的转矩跟踪控制。该优化控制器的求解取决于以电机磁通状态空间方程表征的电机动态响应：
Figure BDA0003412700830000033
Figure BDA0003412700830000034
Figure BDA0003412700830000041
该优化还受转矩、最大逆变器电压和电流以及温度的约束：
Figure BDA0003412700830000042
在方程式(2)中，rs是定子线圈绕组，Ld和Lq是电机感应值，是id和iq的非线性函数。电机动态方程式是线性时变的(ωe(t)、Lq(id(t)、iq(t))、Ld(id(t)、iq(t)))，其中λm：机械连杆磁通，被视为常量；Vmax：最大逆变器电压极限；Imax：最大逆变器电流极限；Tq，min(ωm)：最小转矩极限，作为电机速度的函数；以及Tq，max(ωm)：最大转矩极限，作为电机速度的函数(当电机速度超出某个极限时，弱磁通范围内存在典型的转矩极限，电机转矩随速度函数而减小)；以及Tm，min＜Tm＜Tm，max，电机温度以及最小/最大极限。
由于计算和优化控制是在线性系统的离散采样时间执行的，电机微分方程在预测时间窗口的每个采样时间内被线性化和离散化，以定义MPC36的线性时变模型预控制(LTV/MPC)。每个采样时间I处的线性化电机离散状态空间方程为：
Figure BDA0003412700830000043
Figure BDA0003412700830000044
Figure BDA0003412700830000045
所得到的结果MPC26是磁通估量的状态反馈
Figure BDA0003412700830000046
由于磁通不可测量，故采用卡尔曼滤波器30根据电压指令Vd和Vq以及经过相位转变(θ)的测量电流id和iq来估计用于反馈控制的电机磁通，如图1C所示的详细图。
现转到图2，示出了用于控制交流电机24的转矩(Tq)的可选系统100。在此处，MPC36采用直流反馈102。由于电流id和iq是从三相电流ia、ib和ic转变而来的，故它们可直接测量。选项包括使用不带卡尔曼滤波器的直流反馈或使用带卡尔曼滤波器的直流反馈来估计电流，以消除电流测量带来的噪声。电机状态空间方程式由电流id和iq以及方程组表示为：
Figure BDA0003412700830000047
Figure BDA0003412700830000051
其中，电流观测器的输出方程为y＝[iq id]′。
现转到图3，示出了另一种用于控制交流电机24的转矩(Tq)的系统200。该系统利用转矩传感器或虚拟转矩传感器202进行直接转矩反馈控制。因此，消除了所有三相电流测量。系统200采用的算法类似于上述描述的系统10或100采用的算法。然而，卡尔曼滤波器却是不同的。当采用直接转矩传感器反馈时，卡尔曼滤波器根据指令Vd和Vq和测量的电机转矩而不是测量电流id和iq来估计MPC反馈的磁通或电流。
现转到图4，示出了又一种用于控制交流电机24的转矩(Tq)的系统300。该系统300包括两环：控制电流id和iq的内环和提供转矩反馈控制的外环。深度学习神经网络(NNT)302根据所需的电机转矩(Tq)、电机速度(rpm)、电池电压(Vdc)和电机温度(Temp)作为NNT302的输入，产生用于电机24所需的电流指令id *和iq *。然后，内环MPC30控制电流id和iq以跟踪电流指令id *和iq *。
MPC36最小化成本函数：
Figure BDA0003412700830000052
vk＝[vd(k)vq(k)]，Ik＝[id(k)iq(k)]，r(k)＝[id*(k) iq*(k)]
其中，k表示k处的采样时间。这种优化是为了找到最佳控制Vd和Vq，以便id和iq可以根据上述描述的电机动态方程(4)跟踪指令id *和iq *。外环转矩控制是通过比较实际的转矩测量和转矩指令而实现的。转矩跟踪误差通过PID控制器318调制，PID控制器318修改指令id *和iq *以更好地跟踪转矩，其中α＜1 316和(1-α)314分配PID调制以修改单独的指令id *和iq *。
本公开的电机转矩控制系统提供了若干优点。这些优点包括减少校准次数以及消除电机运行时采用的一个或多个传感器的潜在可能。
本公开的说明本质上仅仅是示例性的，不脱离本公开要旨的变更旨在落入本公开的范围内。这些变更不应被视为脱离本公开的精神和范围。

Claims (10)

1.一种用于机动车中电机的转矩控制的系统，所述系统包括：
电源逆变器，向所述电机传递电流以调节所述电机的转矩；以及
模型预测控制(MPC)模块，向所述电源逆变器发送三相电压以控制所述电源逆变器的运行。
2.根据权利要求1所述的系统，其中，所述MPC模块基于在每个采样时间所述电机的线性时变模型。
3.根据权利要求1所述的系统，其中，所述MPC模块基于MPC控制范围内的线性参数变化模型。
4.根据权利要求1所述的系统，其中，所述MPC模块利用来自卡尔曼滤波器的估计磁通反馈。
5.根据权利要求1所述的系统，其中，所述MPC模块利用电流反馈。
6.根据权利要求5所述的系统，进一步包括卡尔曼滤波器，用于估计电流以消除在选定电机运行条件下电流测量带来的噪声。
7.根据权利要求1所述的系统，进一步包括测量所述电机的转矩的转矩传感器。
8.根据权利要求7所述的系统，进一步包括卡尔曼滤波器，其根据所述电机的电压和所述电机的测量转矩来估计用于所述MPC模块的磁通或电流。
9.根据权利要求1所述的系统，其中，所述MPC模块基于双环控制结构。
10.根据权利要求9所述的系统，其中，所述双环控制结构包括内环，所述内环控制到所述电机的电流。
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