CN106121847B - 内燃发动机平均值模型的辨识方法 - Google Patents

内燃发动机平均值模型的辨识方法 Download PDF

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CN106121847B
CN106121847B CN201610291438.9A CN201610291438A CN106121847B CN 106121847 B CN106121847 B CN 106121847B CN 201610291438 A CN201610291438 A CN 201610291438A CN 106121847 B CN106121847 B CN 106121847B
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D.帕赫纳
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Abstract

本发明涉及内燃发动机平均值模型的辨识方法，更具体地一种用于辨识具有一组方程和限定模型有效性的适当约束的平均值模型的系统或方法。可利用一个模型来设计用于发动机系统的算法，采集检测的数据，基于模型和数据而优化控制参数，并且提供发动机系统的控制。可对这些过程进行重新迭代从而更新发动机系统的控制。

Description

内燃发动机平均值模型的辨识方法
背景技术
本公开是关于发动机的建模、分析和控制。
发明内容
本公开揭示了一种利用一组方程和限定模型有效性的适当约束来辨识平均值模型的系统或方法。可利用模型来设计用于发动机系统的算法、采集被检测的数据、基于该模型和数据而优化控制参数、以及提供发动机系统的控制。可对这些过程重新迭代以更新发动机系统的控制。
附图说明
图1是涡轮增压柴油发动机结构的图示；
图2是显示更多信息的涡轮增压柴油发动机结构的图示；
图3是总体系统布置的图示；
图4是算法流程图的图示；
图5是具有两个状态变量及一个线性主动约束、一个自由变量的情况的图示。
具体实施方式
在本文中所描述和/或图示的一个实施例中，本发明的系统和方法可包括一个或多个处理器、计算机、控制器、用户界面、无线和/或有线连接等。
本说明书可提供一个或多个说明性的和具体的实例或者本发明系统和方法的实施方案。可存在许多其它实例或者本发明系统和方法的实施方案。
可注意到平均值模型。内燃发动机的平均值模型可描述流动经过发动机的物质的化学和热力学过程。特别地对于发动机的实时最优控制，尤其是排放控制，这种模型是有用的。该模型可预测控制参数的变化的效果，包括效果随时间的演化。该模型可用于优化控制参数从而实现在稳态中和在瞬态中的最优性。为了这种目的，可设想出一种快速可靠的模型辨识方法。本文中所描述的这些数值方法可被开发用于改进在这方面的辨识方法。
平均值模型与曲轴转角分辨模型的不同之处在于发动机质量流量是在独立于曲轴转角的情况下而实现。当代表在一个固定工作点的未受控制的发动机时，特别是在恒定的速度和负荷并且所有外部条件不变的情况下，该平均值模型可利用等效的常数值来近似质量流量、压力等。相反，曲轴转角分辨模型也涉及到进气阀和排气阀的开启和闭合以及经过进气口和排气口的非平稳质量流量。在一个固定的工作点，曲轴转角分辨模型可预测周期性的流量、压力等。因此，平均值模型可足以对控制参数进行优化，这些控制参数未必需要在一次曲轴旋转中进行调节相反被缓慢地适应使得它们的值在一次旋转中没有显著变化。平均值模型的有效性可始终限于特定的工作状态。作为一个例子，如果经过模型元件的质量流量的方向发生变化，那么这种平均值模型未必代表发动机物理特征。许多这种约束可表示为关于模型内部状态的简单不等式，例如在一个点处的压力必须高于（低于）在其它点处的压力。
该算法的一个目的是执行关于稳态数据的模型辨识使得该模型产生与测量值相似的输出值，与此同时模型信号是在其有效性的范围内。这避免了用无意义模型拟合数据的常见问题。在该方法中，模型有效性可相当于满足用于模型内部状态“x”的线性不等式约束“Ax<=b”。这种状态可被称为是可行的。
图4中示出了算法流程图。该流程图可使用逻辑矩阵L1和L2，这些逻辑矩阵可以被表示为具有与问题所具有的“x”约束同样多的行和与数据中的单独工作点同样多的列的逻辑矩阵。
值特征可包括：（1）逻辑矩阵L1中的一个真值可表示在该工作点中的相应约束的活动；和（2）在逻辑矩阵L2中的一个真值可表明可以在下一次迭代中对该约束活动进行转换。
逻辑矩阵L1和L2可用于以高效率的方式组织计算。辨识过程包括：（1）搜索给定参数的可行向量状态的内部优化；（2）基于来自在所有稳态数据点的内部优化的信息而更新模型参数的外部优化。该过程开始于仅一次求解内部优化从而获得初始可行点。然后，可通过迭代地执行相当于有效集（active set）方法的过程而求解外部优化。仅对需要改变约束活动的工作点进行更新。
可在流程图中示出本发明方法的优点。尽管可在模型参数的优化期间限制模型状态，但优化法分别仅优化在各工作点处的参数（在外部）或者仅优化固定参数的模型状态（在内部）。因此，在任意点未必解决了高维优化问题。然而，低维优化会需要进行许多次求解，而且线性约束的次数会较高。由于这个原因，可高效率地处理这种情况的二次规划方法可以用于执行外部优化。
图4中的图示的算法可求解与非线性辨识问题相关的成本函数的二次近似。当基于来自前一次迭代的结果对近似进行更新时，可迭代地重复该算法。更新可代表未必在图示中可见的一个附加的迭代循环。在这种应用中，最初的方框“开始于初始模型参数”可变为“开始于来自前一次迭代的参数”。
图4的流程图10可在符号11处的开始于初始模型参数而开始。在符号12，下一步骤可以是对用于实际上确定对状态的主动约束的所有稳态工作点的可行状态模型向量“x”进行优化，即，确定逻辑矩阵L1。符号12的步骤可解决内部优化问题。
在符号13，可将逻辑矩阵L2全部初始化为“真”。在符号14，可用L2列中的任何真元素在各工作点进行迭代。就一个工作点而言，在符号15，可利用方程（16）将L1列中的有效集转换成对参数的相应约束。
在符号16，仅利用方程（15）将在工作点处的对成本函数的贡献表示为参数的函数。然后，在符号17，可对与涉及到实际上对参数的所有约束的方程（10）的R(k)的和有关的模型参数进行优化。主动约束可定义L2元素。符号17的步骤可解决外部优化问题。
在符号18，可询问任何L2元素是否为真的问题。如果回答为“否”，那么可认为建立了一个解，并且过程可在符号19终止。如果回答为“是”，那么在符号20可将与L2矩阵中的真值相对应的L1元素求逆（invert）。然后，作为迭代循环，可重复符号14至18的操作。
可存在当近似是基于来自前一次迭代的结果时可重复的循环的另一次迭代。这种过程被称作顺序优化。对迭代循环的更新可开始于符号11，这可以被重新表述为“开始于来自前一次迭代的参数”，如本文中所述。
图1是涡轮增压柴油发动机结构25的图示。该平均值模型可由类似于是发动机结构的近似的图1的结构而图形地表示。就各种发动机架构而言，该结构可略有不同，并且辨识算法将需要涵盖实际上所有的结构。该图示可将主要热力学过程组合成近似实际过程的理想化部件。例如，冷却器、中间冷却器26或其它冷却器可以是理想化部件，其中只在流动的气体与它们的环境（冷却液）之间发生热传递。相反，受控制的阀27、34或多个阀可使绝热压力变化模型化（即，在没有热的增加或损失的情况下发生），该绝热压力变化可以通过改变阀门开度而加以控制。压缩机28和涡轮29可以是由共同的旋转轴31而联接的两个部件。压缩机28可将进气加压。涡轮29通常可具有通过使一部分的排气旁通经过排气门27到达点36而加入到来自涡轮29的排气中以便从系统中排出、或者控制长宽比（aspect ratio）（即，具有涡轮叶片位置控制的可变几何涡轮增压器）而控制涡轮功率的参数。来自压缩机28的增压空气可经过冷却器26和混合点32到达内燃发动机33。混合点32也可经由阀34和冷却器35而接收再循环的发动机排气（EGR）。来自发动机33的排气可流动经过分流器30，该分流器30将排气划分至涡轮29和阀34，但未必以50/50的方式进行划分。
在图1的图示中所揭示的本发明系统或方法中的部件可在各种构造中重新布置或连接。可将各部件添加到该系统或方法中或者从其中除去。根据图1的图示所揭示部件的构造是（除了其它情况）本发明系统或方法的一个例子或者说明性实例。
图2是显示更多信息的涡轮增压柴油发动机结构25的图示。可用模型代表发动机结构25及其部件。来自周围环境41的空气可以是压缩机28的入流42。可检测环境41的环境压力、环境温度。涡轮29可经由连接件或轴31而驱动压缩机28，这些部件一起可以构成涡轮增压器43。来自压缩机28的出流44可到达冷却器或增压空气冷却器26。来自冷却器26的输出45可到达混合点或阀32。输出45可与来自EGR冷却器35的一些输出46混合。可利用来自发动机控制模块48的信号来控制输出45与输出46的混合的量。来自混合点32的输出47可以是发动机33的入流。
发动机控制模块（ECM）48可包含模型和来自传感器49的输入（如新鲜空气流量、压缩机28后的温度、中间冷却器26后的温度、进气歧管中的温度和发动机33的进气歧管中的压力等），并且通过对输入进行处理而确定通向发动机33的最优值和控制信号（例如，发动机转速、喷射量、喷射的开始、轨压等）。来自发动机33的出流54可到达分流器30，该分流器30将出流54划分成出流55和出流56。出流55可到达EGR阀，该EGR阀控制经过EGR冷却器35的排气的出流50和流到混合阀32的输出46。出流56可以是涡轮29的入流，该涡轮29经由连接件31使压缩机28旋转。来自涡轮29的出流57可到达周围环境41。阀或排气门27可在连接件36处将入流56连接到出流57从而允许部分的入流56绕过涡轮29。可利用阀27和叶片位置输入58来控制涡轮29和压缩机28的转速。可由发动机控制模块48提供对阀27的输入和叶片位置输入58的控制。也可由发动机控制模块48提供对混合阀32的输入和EGR位置输入59的控制。
在图2的图示中所揭示的本发明系统或方法中的部件可在各种构造中重新布置或连接。可将这些部件添加到本发明系统或方法中或者从其中除去。根据图2的图示所揭示部件的构造是（除了其它情况）本发明系统或方法的一个例子或说明性实例。
图3是系统布置的总体图示。方程组61可构成算法62。可由算法62得出模型63。出于将经优化的控制信号65提供给发动机系统66的目的，发动机控制模块可包括模型。发动机系统可将来自传感器和其它来源的信息和数据67提供给发动机控制模块64，以便进一步优化控制信号65。
实际上所有模型部件的方程可以是基于第一原理并且包含从数据中所确定的某些未知的参数值。该模型表示可类似于由用理想化物理部件（如理想的电感、电容、电阻等）所制成的电路所代表的实际电气装置。
可注意到一种消除方法。模型辨识问题可以是如下面所陈述。可通过操纵模型参数而使由模型所预测的值与在实际发动机上所测量的值之间的差值的范数最小化。在本文中，可仅考虑稳态辨识。这意味着误差最小化可仅涉及在任何外源信号的最后一次改变之后已被设定为稳态的在发动机中所测量的数据。具体地，当不考虑对曲轴转角的依赖性时，可用平均值模型中的恒定的控制变量、压力、温度、浓度和速度来描述发动机稳态。
可通过求解一个微分方程组而计算模型的预测，该微分方程组可通过将实际上所有理想化部件的所有代数和微分方程加以组合而获得，并且涉及到模型结构。这种模型的微分方程通常可由如下的一阶非线性微分方程和非线性输出函数（非线性状态空间模型）的系统所代表。
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（1）
这里，
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是内部状态变量的向量；u是外生变量的向量；
Figure 690450DEST_PATH_IMAGE003
是模型输出的向量，θ是模型参数的向量。当考虑稳态时，该模型可用以下的非线性方程组进行描述。
Figure 898709DEST_PATH_IMAGE004
（2）
辨识问题可表示为以下的非线性最小二乘法优化问题，考虑到一组所观测到的输入值和输出值
Figure 86982DEST_PATH_IMAGE005
：
Figure 784811DEST_PATH_IMAGE006
（3）
该优化可由以下的等式约束所限制。
Figure 697184DEST_PATH_IMAGE007
（4）
用于使（3）最小化的方法似乎可求解关于
Figure 692822DEST_PATH_IMAGE008
的等式约束（4）并且将解
Figure 237067DEST_PATH_IMAGE009
替代到（3）。这可将等式约束优化转变成普通的无约束优化。这样，可将
Figure 604332DEST_PATH_IMAGE010
变量消除并且问题现在是维度优化问题。
通过求解该代数方程或者通过施加恒定的输入
Figure 234028DEST_PATH_IMAGE011
达充分长的时间而从初始状态
Figure 837617DEST_PATH_IMAGE012
求解微分方程，可获得非线性方程（4）的一个解。该方法可更好地用于如下稳定系统。
Figure 174051DEST_PATH_IMAGE013
（5）
尽管
Figure 977797DEST_PATH_IMAGE010
消元似乎可很好地解决问题，但在该方法中可存在一些可能的问题。
可将这些问题列出为如在下面的P1、P2、P3和P4。
（P1）可将非线性方程（4）的一个解与预测误差最小化（3）完全地分离。因此，即使在发动机中测量了许多信号，例如，将会测量所有的状态变量
Figure 94789DEST_PATH_IMAGE014
，但这未必有助于非线性方程求解器从多个解中辨识正确的稳态。可存在代数方程（4）的多个解。就基于微分方程解（5）的一个方法而言，当初始状态为固定时，有多个解的问题未必存在。然而，当模型在辨识的过程中变得不稳定时，该方法会失效。
（P2）当初始参数值尽可能地接近最优值并且初始最小化目标函数值接近最优值时，非线性辨识可工作良好。当观看图1并考虑反馈结构时，会发生以下情况：不准确的初始参数估值将驱使模型状态非常远离最优值以及初始目标函数值将较大。这会是真的，特别是如果初始参数将模型推进到稳定性边界。当足够地接近该边界时，初始目标函数值会是任意地大。这意味着非线性优化必须执行许多次迭代从而进行优化。这将不仅花费时间，而且可使算法发散更有可能发生。
（P3）对于任何
Figure 432229DEST_PATH_IMAGE014
值，模型结构未必普遍地有效。当观看作为一个例子的图1时，可看到在混合点32将压缩的新鲜空气与再循环的排气混合。只要压缩空气和再循环流两者具有由箭头所表示的方向，这会是真的。当初始参数值不限定压缩空气和再循环流两者中的任一个正确流动方向时，在该点实际上将未必发生混合。然后，具有代表进气中的质量和能量总和的这个混合点的模型结构将未必代表将会在具有相同参数的实际发动机上发生的物理现实。该模型的行为然后可不同于假设遵守物理学定律的预计的行为。
（P4）同样地，对于任何
Figure 387284DEST_PATH_IMAGE014
值模型件未必是普遍地有效。在某些和一般所满足的条件下，各部件模型通常是有效的。作为一个例子，如果压缩机上的压力比小于1或者如果涡轮增压器转速为负值，那么简化的压缩机流量方程经常会不代表物理现实。
换句话说，利用平均值模型对发动机进行建模可未必仅采用方程，而是可以采用确保模型有效性的约束。这种模型的辨识（参数优化）可采用实际上在任何时候表示这种约束的算法，即，不用在容许范围外的值来评价模型方程。例如，在对其参数进行优化的整个过程中，该算法将未必试图用低于1的压力比来评价压缩机模型。
可注意一种新的公式。求解部分的所表示难点的努力可以是基于使模型结构更加通用且更加复杂从而在由
Figure 597817DEST_PATH_IMAGE014
所限定的模型信号值的更大范围中合理地表现。然而，本发明方法相反地是基于明确地约束模型信号，从而不允许存在将会使部件模型或者模型结构无效的信号值。这意味着可将模型辨识问题表示为如下的不等式约束优化。
Figure 389056DEST_PATH_IMAGE015
（6）
这里，可假设约束
Figure 779454DEST_PATH_IMAGE016
被定义用于保持模型的有效性并且目标函数
Figure 339749DEST_PATH_IMAGE017
惩罚模型预测误差。代替明确地考虑非线性等式约束（4），可提出将非线性等式约束（4）转变成惩罚状态导数的范数的软约束。然后，可将目标函数
Figure 986762DEST_PATH_IMAGE017
表示为两个目标的凸组合：
Figure 254844DEST_PATH_IMAGE018
（7）
目标函数J假设形式可以是测量数据与模型预测之间的差值的第二平方范数：
Figure 885152DEST_PATH_IMAGE020
（7a）
这里，
Figure 113002DEST_PATH_IMAGE021
是模型状态变量的序列，
Figure 447030DEST_PATH_IMAGE022
是模型输入的序列，
Figure 947282DEST_PATH_IMAGE023
是测量值的序列，
Figure 554849DEST_PATH_IMAGE024
是模型参数的向量。方程（7a）可被称为最小二乘的方法。
在这个目标函数中，状态变量需是如果被代入模型微分方程（1）则它们的导数为零。这是为何加入该等式约束或者至少对时间导数的范数的惩罚，即，需将最小二乘法表示为方程（7b）：
Figure 89736DEST_PATH_IMAGE026
（7b）
参数λ应对给目标赋予相对权重的优化过程加以控制。可假设，当把输入信号
Figure 78552DEST_PATH_IMAGE027
施加给该模型时，该最优模型参数可以始终限定正确的可行方程（7）模型信号。不等式约束
Figure 377684DEST_PATH_IMAGE028
可意味着仅在辨识过程中有效，但对于成功确定的模型却未必如此。
本发明的公式可解决如下的消除方法的问题。
（P1）（4）的解未必与（3）分离，因为可同时地对这两个目标进行优化。具体地，当在测量值中存在关于状态变量
Figure 415041DEST_PATH_IMAGE010
的一些信息时，优化可具有更好的机会发现物理上有意义的解。
（P2）目标函数值不应分割，因为在此方法中状态变量
Figure 850439DEST_PATH_IMAGE010
未必是由微分方程（1）所驱动。它们是直接地由优化算法所控制。P3和P4两种可被不等式
Figure 197107DEST_PATH_IMAGE016
所涵盖。
可注意到一种优化技术。该新公式的一个明显问题是优化变量的数量会是巨大的。在一个典型的发动机辨识实例中，可测量500至1,000个稳态工作点。考虑到5至10个状态变量
Figure 677810DEST_PATH_IMAGE010
，优化状态的数量可以是2,500至10,000。与这些数量（通常小于50）相比，模型参数θ的数量似乎是可忽略的。因此，未必容易利用新公式的潜在优势，并且可使用采用该模型结构的特殊辨识算法。本发明的新优化方法可利用序列二次规划技术来求解这个大维度优化问题。该技术可以是基于利用二次正定函数来近似目标函数和利用下面的线性约束来近似不等式约束。
Figure 79710DEST_PATH_IMAGE029
（8）
可提出利用结构（6）。目标函数可以是可加的，并且在特定工作点处为有效的状态变量的值可始终只存在于一个被加数。因此，有利的是以如下方式来分配优化。
Figure 323610DEST_PATH_IMAGE030
（9）
在方程（9）中，关于在特定工作点的状态变量
Figure 388649DEST_PATH_IMAGE031
的优化可分别地进行。然后，外部最小化可使关于θ的总体值最小化。只有内部优化可以给出用θ的值参数化的最优值，这才可以进行。换句话说，内部优化需要提供以下的θ的函数作为它们的结果，如下式中。
Figure 600056DEST_PATH_IMAGE032
（10）
然后，外部优化可使关于θ的和
Figure 228484DEST_PATH_IMAGE033
最小化。就具有线性约束的二次目标（8）而言，函数
Figure 343201DEST_PATH_IMAGE034
可以是具有取决于内部优化问题中的一组主动约束的参数的二次函数。假定已利用二次规划法算法解决了内部迭代问题并且在约束优化中某些不等式约束是弱主动的；这意味着各自的拉格朗日乘子将是如下的非负值。
Figure 766092DEST_PATH_IMAGE035
（11）
可定义如下地将
Figure 464796DEST_PATH_IMAGE036
用作伪逆矩阵
Figure 772280DEST_PATH_IMAGE037
并且将矩阵
Figure 30918DEST_PATH_IMAGE038
用作
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零空间矩阵的有效集中的降维坐标
Figure 623759DEST_PATH_IMAGE039
。
Figure 859569DEST_PATH_IMAGE040
（12）
通过要求相对于约化坐标的导数必须为零，可确定最优约化坐标。可以发现它们是参数的仿射函数：
Figure 745616DEST_PATH_IMAGE041
（13）
相对于
Figure 87474DEST_PATH_IMAGE042
，如果说对状态变量的某些线性约束是主动的，那么实际上将状态的向量约束到与约束区邻接的某些线性子空间。在方程（12）和（13）中，将在该子空间中的坐标表示为
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。图5是在两个状态变量和一个主动线性约束的情况下的图示51，这可给一个留有一个自由变量
Figure 82160DEST_PATH_IMAGE042
。该自由变量可限定状态向量52在“主动”线上的位置。
当代入（13）至（12）然后代入（8）时，
Figure 822714DEST_PATH_IMAGE043
可以是模型参数的二次函数。因此，内部优化可提供所需的信息。然而，内部优化也必须提供对θ的约束，这保证该有效集未必在内部问题中发生变化。可以如下方式得出这些约束。Karush-Kuhn-Tucker条件可表明，在优化中目标函数的梯度必须是如下的主动约束的梯度的某个线性组合。
Figure 652917DEST_PATH_IMAGE044
（14）
连同（11），可表示为下式。
Figure 111711DEST_PATH_IMAGE045
（15）
这意味着弱主动约束
Figure 673011DEST_PATH_IMAGE046
的约束最优
Figure 720602DEST_PATH_IMAGE008
和拉格朗日乘子是模型参数θ的仿射函数。因此，用于将这组弱主动约束保持在内部优化问题的参数的必需条件可以是如下。
Figure DEST_PATH_IMAGE047
（16）
这可以是关于模型参数的一组线性不等式。可由以下的非主动不等式
Figure 217311DEST_PATH_IMAGE048
形成矩阵
Figure 350352DEST_PATH_IMAGE049
。
解决问题（9）的优化结构可描述如下。用“O”表示的步骤可从属于外部优化，而用“I”表示的步骤可从属于内部优化。
O1.外部优化可设定模型参数θ的初始估值。
O2.可顺序地执行内部优化：
I1.二次规划法（QP1）可用于优化
Figure 216808DEST_PATH_IMAGE008
以便发现约束优化和一组严格主动约束，即，具有正的拉格朗日乘子的不等式。
I2.代入方程（13）至（12）然后至（8）可用于评价此有效集中的
Figure 118905DEST_PATH_IMAGE050
。
I3.方程（16）可用于定义对参数的约束，这些参数确保在具有优化的θ之后以前的严格主动约束可以是至少弱主动的。
O3.外部优化可对
Figure 928728DEST_PATH_IMAGE050
（将它们求和）和参数的约束（将所有参数求和）进行合计。
O4.外部优化可利用二次规划法（QP2）来发现参数的约束最优值。可将它们解释为最优值，这些最优值将未必改变在任何内部优化中的一组主动约束。
O5.外部优化可在QP2处对一组主动约束进行检查。如果该组主动约束是空的，那么参数可以是最优的。如果存在主动约束，那么这可表示需对稳态数据点进行转换（从非主动变化到主动，反之亦然）的“x”的约束的活动。这意味着可在一次内部优化中改变主动约束。该内部优化可用参数的新值重新计算。其它内部优化未必需要进行重新计算，因为它们的
Figure 283485DEST_PATH_IMAGE050
仍然是有效的。然后算法可从O4前进。
可注意到一个结论。可将基于稳态数据的平均值模型参数的辨识表示为一个优化问题。已证明该优化问题可以是非常高的维度或者需将处于稳态的状态变量的值消除。该消除可导致状态值不再处于控制下并且可以不被约束从而保持模型有效性和与测量值的稳态一致性。因此，技术人员在此可提出不消除状态变量。相反可提出解决高维约束优化问题的一个方法，如本文中所示。优化结构可利用该问题结构。如果目标函数是二次的并且不等式约束是线性的，这可以很好地进行。尽管目标函数（6）未必是二次的，但仍然可以采用该算法，如果例如相继地利用二次函数局部地近似目标函数。可将这看作是序列二次规划技术。如何利用该算法的另一个方法可以是基于模型非线性的分段线性近似。
本发明的辨识技术对于由辨识和数值优化的非专业人员所使用的商业模型和控制设计工具而言是非常重要的。由于许多原因，优化的可靠性和稳健性对于客户的接收度和满意度而言是关键因素。
总的来说，用于发动机系统的建模机构可包括连接到发动机系统的发动机控制模块（ECM）、和代表发动机系统的模型。发动机系统可包括发动机、连接到ECM的发动机的致动器和传感器、具有连接到发动机的输入的输出的涡轮增压器、和具有连接到发动机的输出的输入的后处理装置。代表发动机系统的模型可位于ECM中。代表发动机系统的模型可以是使用方程和约束的平均值模型。这些约束可确保防止用被代表的发动机系统容许的范围之外的值对模型的方程中的模型有效性进行评价。
代表处于稳态的发动机的模型可包括由分别代表内部状态变量、外生变量、模型输出和模型参数的项
Figure 953632DEST_PATH_IMAGE002
、u、
Figure 21820DEST_PATH_IMAGE003
和θ所组成的一个或多个方程。所述一个或多个方程可包括：
Figure 735698DEST_PATH_IMAGE004
。
模型的辨识可表示为用于所观测到的输入和输出值
Figure 594064DEST_PATH_IMAGE005
的最小二乘法优化
Figure 363175DEST_PATH_IMAGE006
。
最小二乘法优化可由
Figure 974285DEST_PATH_IMAGE007
所约束
其中,
Figure 875376DEST_PATH_IMAGE052
表示f是用于k的序列。
ECM可接收来自发动机系统的传感器的数据，这些数据是基于模型而进行处理。ECM可将基于模型而优化的信号发送给发动机系统的致动器。ECM可提供发动机系统的实时控制。
用于与发动机系统的平均值模型的非线性辨识有关的成本函数的近似方法可包括：选择发动机系统的初始模型参数、和对用于稳态工作点的状态模型向量进行优化，其确定对状态的主动约束，亦即确定元素的第一逻辑矩阵。该第一逻辑矩阵可具有与状态模型向量约束的数量同样多的行及与数据中的单独工作点同样多的列。
所述方法还可包括将元素的第二逻辑矩阵实际上全部初始化真。该第二逻辑矩阵可具有与状态模型向量约束的数量同样多的行及与数据中的单独工作点同样多的列。
所述方法还可包括用在第二逻辑矩阵的一列中的真元素对稳态工作点进行迭代。就稳态工作点而言，可将在第一逻辑矩阵中的一列中的有效集转换成对参数的相应的约束。
所述方法还可包括仅将对在稳态工作点中的成本函数的贡献表示为参数的函数、和关于对参数的约束的和的优化模型参数。主动约束可定义第二逻辑矩阵的元素。
如果第二逻辑矩阵的任何元素为“假”，那么可发现一个解。
如果第二逻辑矩阵的任何元素为“真”，那么可将与第二逻辑矩阵中的真值相对应的第一逻辑矩阵的元素求逆。所述方法还可包括重复前述方法的一个或多个部分。
涡轮增压发动机模型系统可包括：发动机系统的发动机的发动机模型；具有连接到发动机模型的入流口的输出的混合点模型；具有连接到发动机模型的出流口的涡轮入流口、连接到混合点模型的输入的压缩机出流口并且具有涡轮出流口和压缩机入流口的涡轮增压器模型；及包含多个模型（包括发动机模型、混合点模型和涡轮增压器模型）且连接到发动机的处理器。
这些模型可以是采用方程和约束的平均值模型。这些约束可确保可以防止用在容许范围外的值对方程中的模型有效性进行评价。处理器可接收来自发动机系统的传感器的数据，并且根据模型及计算的方程和约束将经优化的信号提供给发动机系统的致动器。
所述多个模型还可包括：具有连接到发动机模型的出流口的输入的排气再循环阀模型；及具有连接到排气再循环阀模型的输出的输入和连接到混合点模型的第二输入的输出的排气再循环冷却器模型。
所述多个模型还可包括具有连接到出流压缩机的输入、并且具有连接到混合点模型的输入的输出的增压空气冷却器模型。
所述多个模型还可包括分流器模型，该分流器模型具有连接到发动机模型的出流口的输入、连接到涡轮增压器模型的涡轮入流口的第一输出、和连接到排气再循环阀模型的入流口的第二输出。
于2010年5月18日提交的名称为“Distributed Model Identification（分布式模型辨识）”的美国专利申请第12/782,004号以参考的方式并入本文中。
在本说明书中，部分的主题可具有假设的或预言的性质，尽管以另一种方式或时态所陈述。
尽管已通过至少一个说明性实例描述了本发明的系统和/或方法，但在阅读本说明书时许多变型和修改对于本领域技术人员将变得清楚。因此，意图是基于相关技术尽可能宽泛地解释所附权利要求，从而包括所有的这种变型和修改。

Claims (13)

1.一种用于发动机系统的建模机构，包括：
连接到发动机系统的发动机控制模块；和
代表所述发动机系统的平均值模型；并且其中，
所述发动机系统包括：
发动机；
连接到所述发动机控制模块的所述发动机的致动器和传感器；
具有输出的涡轮增压器，所述输出连接到所述发动机的输入；和
具有输入的后处理装置，所述输入连接到所述发动机的输出；
代表所述发动机系统的所述平均值模型位于所述发动机控制模块中；
所述平均值模型使用方程和约束，所述约束给所述方程提供用被代表的发动机系统容许的范围之内的值；
所述平均值模型包括限定目标函数
Figure DEST_PATH_IMAGE002
的以下方程：
Figure DEST_PATH_IMAGE004
其中，
Figure DEST_PATH_IMAGE006
、
Figure DEST_PATH_IMAGE008
、
Figure DEST_PATH_IMAGE010
和θ分别代表内部状态变量、模型输入的序列、测量输出的序列和模型参数，并且g和f是代表所述平均值模型的一阶非线性微分方程；并且
所述发动机控制模块构造成使用包括序列二次规划技术的优化过程来求解所述目标函数，并且其中，λ控制所述优化过程。
2.如权利要求1所述的机构，其中，
所述发动机控制模块接收基于所述模型进行处理的来自所述发动机系统的所述传感器的数据；并且
所述发动机控制模块将基于所述模型而优化的信号发送给所述发动机系统的所述致动器。
3.如权利要求2所述的机构，其中，所述发动机控制模块提供所述发动机系统的实时控制。
4.如权利要求1所述的机构，还包括：
具有连接到代表所述发动机系统的所述平均值模型的入流口的输出的混合点模型；
具有连接到代表所述发动机系统的所述平均值模型的出流口的涡轮入流口、连接到所述混合点模型的输入的压缩机出流口并且具有涡轮出流口和压缩机入流口的涡轮增压器模型；和
所述发动机控制模块包括多个模型，所述多个模型包括代表所述发动机系统的所述平均值模型、所述混合点模型和所述涡轮增压器模型，并且其中，
所述平均值模型是利用方程和约束的平均值模型，所述约束给所述方程提供用被代表的发动机系统容许的范围之内的值；并且
所述发动机控制模块接收来自所述发动机系统的传感器的数据，并且根据所述模型及计算的方程和约束将优化的信号提供给所述发动机系统的致动器。
5.如权利要求4所述的机构，其中，所述多个模型还包括：
具有连接到所述发动机模型的出流口的输入的排气再循环阀模型；和
具有连接到所述排气再循环阀模型的输出的输入和连接到所述混合点模型的第二输入的输出的排气再循环冷却器模型。
6.如权利要求5所述的机构，其中，所述多个模型还包括具有连接到所述压缩机出流口的输入和连接到所述混合点模型的输入的输出的增压空气冷却器模型。
7.如权利要求6所述的机构，其中，所述多个模型还包括分流器模型，所述分流器模型具有连接到所述发动机模型的所述出流口的输入、连接到所述涡轮增压器模型的所述涡轮入流口的第一输出和连接到所述排气再循环阀模型的入流口的第二输出。
8.一种在如权利要求1所述的用于发动机系统的建模机构中执行优化过程的方法，由所述发动机控制模块执行，所述方法包括：
选择用于所述发动机系统的初始模型参数；和
对稳态工作点的状态模型向量进行优化，其确定对状态的主动约束，亦即确定元素的第一逻辑矩阵；并且
其中第一逻辑矩阵具有与状态模型向量约束的数量同样多的行及与数据中的单独工作点的数量同样多的列。
9.如权利要求8所述的方法，还包括：
将元素的第二逻辑矩阵实际上全部初始化为真；并且
其中所述第二逻辑矩阵具有与状态模型向量约束的数量同样多的行及与所述数据中的单独工作点的数量同样多的列。
10.如权利要求9所述的方法，还包括：
用所述第二逻辑矩阵的一列中的真元素对稳态工作点进行迭代；并且
其中就稳态工作点而言，将所述第一逻辑矩阵中的一列中的有效集转换成对参数的相应约束。
11.如权利要求9所述的方法，还包括：
将对在所述稳态工作点处的成本函数的贡献表示为仅为参数的函数；和
对关于参数的约束的和的模型参数进行优化；并且
其中主动约束定义所述第二逻辑矩阵的元素。
12.如权利要求11所述的方法，其中，如果所述第二逻辑矩阵的任何元素为假，则发现解。
13.如权利要求11所述的方法，其中，
如果所述第二逻辑矩阵的任何元素为真，则将与所述第二逻辑矩阵中的真值相对应的所述第一逻辑矩阵的元素求逆；并且
还包括重复如权利要求8-12中任一项所述的方法。
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