具体实施方式

以下将参考附图详细说明本公开的各种示例性实施例、特征和方面。附图中相同的附图标记表示功能相同或相似的元件。尽管在附图中示出了实施例的各种方面，但是除非特别指出，不必按比例绘制附图。

另外，为了更好的说明本发明，在下文的具体实施例中给出了众多的具体细节。本领域技术人员应当理解，没有某些具体细节，本发明同样可以实施。在一些实例中，对于本领域技术人员熟知的手段未作详细描述，以便于凸显本发明的主旨。

如图1所示，本发明一实施例提出一种车辆俯仰角估计方法，本实施例方法包括步骤S10～S40：

步骤S10、获取车辆当前车速，并根据所述当前车速获得第一纵向加速度；

具体而言，步骤S10中对所述当前车速的纵向分量进行求导获得第一纵向加速度，车辆当前车速可以通过CAN总线采集获取。

步骤S20、获取当前坡度角、车辆加速度传感器当前检测得到的第二纵向加速度；

具体而言，当前坡度角可以通过GPS导航信息，从地图上获取，地图数据包括各道路的坡度角信息，因此根据车辆位置信息可以获得对应车辆所在道路的坡度角。当然，也可以利用其它估计坡度角的方法进行实时估计，本实施例中不对坡度角的获取方式进行具体限定，其均在本发明的保护范围之内。

一般而言，车辆均配置有车辆加速度传感器(例如ESP中的惯性传感器)用于检测车辆纵向加速度，本实施例中将车辆加速度传感器所检测得到的纵向加速度定义为第二纵向加速度。

步骤S30、根据所述第一纵向加速度、当前坡度角、第二纵向加速度获得量测俯仰角；

具体而言，车辆俯仰角变化主要由于前后轴载荷变化，导致悬架变形变化，从而引起车辆前倾或者翘起。因此，可以通过估计前后轴荷，并且结合悬架特性估计车辆俯仰角。由于轮胎的垂向刚度明显高于悬架垂向刚度，假设行驶时车辆簧下质量的姿态不变，车辆俯仰一维动力学方程如式(1)所示。

其中，θ为俯仰角，为俯仰角加速度，F_x为纵向力，h为质心高度，m为簧载质量，M为车辆总质量，I′_y为等效转动惯量，M_y为俯仰阻力矩。

I′_y＝I_y+mh² (2)

其中，I_y为车辆绕y轴转动惯量，可以通过提前测量得到。

F_x＝m·a_x (3)

其中，a_x为车辆纵向加速度，可以通过纵向车速求导得到。

其中，l₁为前轴与质心在X方向上的距离，l₂为后轴与质心在X方向上的距离，K₁、K₂分别为前轴和后轴悬挂所用弹簧刚度，R₁、R₂分别为前后减震器特性。

一般而言，车辆设计状态下重心高度，质心X轴向位置，绕Y轴转动惯量均可以通过KC台架试验获取。通过采用半载时车辆的相关参数，结合悬架特性，车辆纵向加速度，可以估算车辆俯仰角。但车辆实际载荷经常变换，重心位置也相应变化，公式(1)具有一定的不确定性，因此本实施例中通过纵向传感器测量的纵向加速度与车辆实际纵向加速度的偏移来估计俯仰角，来提高判断精度。

车辆ESP中安装的惯性传感器X方向与车辆X轴重合，车辆俯仰角的变化会引起第一纵向加速度a_x与第二纵向加速度a_xs有偏差，a_x与a_xs的关系为：

其中，l_c为转弯时传感器与旋转中心横向偏移量，为横摆角加速度，r为横摆角速度，V_y为Y向速度，g为重力加速度，α为坡度角。忽略横摆运动影响，则有：

a_xs＝a_xcosθ+g sin(α+θ) (6)

通过公式(6)，根据所述第一纵向加速度、当前坡度角、第二纵向加速度可以获得量测俯仰角。

步骤S40、将所述量测俯仰角作为观测变量，进行卡尔曼滤波获得车辆的俯仰角估计值。

基于以上内容可知，本实施例方法在进行车辆俯仰角估计时，充分综合考虑了由当前车速求导获得的第一纵向加速度、当前道路的坡度角以及车辆加速度传感器当前检测得到的第二纵向加速度，根据所述第一纵向加速度、当前坡度角、第二纵向加速度获得量测俯仰角，并将所述量测俯仰角作为观测变量，进行卡尔曼滤波获得车辆的俯仰角估计值。从而在不增加车辆硬件成本情况下，解决目前根据现有其他车辆传感器信号进行车辆俯仰角估计所存在的估计精度不高的问题。

在一较佳实施例中，所述根据所述第一纵向加速度、当前坡度角、第二纵向加速度获得量测俯仰角，具体如下公式所示：

其中，θ_s为量测俯仰角，a_xs为第二纵向加速度，a_x为第一纵向加速度，α为坡度角，g为重力加速度。

具体而言，基于上述公式(6)，考虑到大多工况车辆俯仰角低于3°，cosθ≈1，因而通过传感器观测的量测俯仰角如公式(7)所示。

在一较佳实施例中，采用卡尔曼滤波进行车辆俯仰角估计，基于公式(1)进行系统方程建模，令系统状态向量为观测变量y即为俯仰角θ_s，则系统状态空间方程为：

其中，C＝[1 0]。

在矩阵A中，

将公式(8)离散化，得到：

其中，T为步长，即为俯仰角估计的当前循环与上一循环的时间间隔，其为预先设定的参数值。

基于以上考虑车辆模型的卡尔曼滤波模型，设当前时刻k时刻，k时刻观测变量为y(k)，则所述步骤S40具体包括：

步骤S401、进行一步状态预测获得k时刻状态预测量其中，为k-1时刻系统状态量，u(k-1)为k-1时刻u的值；

步骤S402、获取k-1时刻的过程噪声Q(k-1)，并根据所述过程噪声Q(k-1)进行一步预测方差获得k时刻方差预测量P(k,k-1)，其中，P(k,k-1)＝GP(k-1)G′+Q(k-1)，P(k-1)为k-1时刻方差；

步骤S403、获取k时刻的观测噪声R(k)，根据k时刻的状态预测量方差预测量P(k,k-1)、观测变量y(k)以及观测噪声R(k)估计k时刻系统状态量其中，

K(k)为滤波增益矩阵，K(k)＝P(k,k-1)C/[C·P(k,k-1)·C′+R(k)]；

步骤S404、根据k时刻的方差预测量P(k,k-1)进行方差更新获得k时刻方差P(k)，并进行保存，P(k)＝[I-K(k)·C]·P(k,k-1)，I为单位矩阵；其中，k时刻方差P(k)用于后续k+1时刻俯仰角估计；

步骤S405、根据所述k时刻的系统状态量获得k时刻车辆的俯仰角估计值。

具体而言，步骤S405中，根据观测矩阵y＝CX和k时刻的系统状态量即获得k时刻车辆的俯仰角估计值。

具体而言，相对于现有的俯仰角的卡尔曼滤波模型而言，本实施例中卡尔曼滤波模型考虑了俯仰角变化的动力学因素，对常规的卡尔曼滤波模型进行了改进，将传感器测量数据估计的量测俯仰角与一维动力学模型估计俯仰角结合起来，使得卡尔曼滤波中的方差参数随工况变化，从而进一步提高了俯仰角估计值的精度。

在一较佳实施例中，所述获取k时刻的观测噪声R(k)，包括：

根据k时刻的第一纵向加速度以及第一纵向加速度与观测噪声的对应关系确定k时刻的观测噪声R(k)；

或者，根据k时刻的第二纵向加速度以及第二纵向加速度与观测噪声的对应关系确定k时刻的观测噪声R(k)。

具体而言，车辆的纵向加速度与观测噪声存在一定对应关系，该对应关系大致可以描述为：在直线运动时，车辆纵向加速度变化不大时，给测量噪声方差较小的值；在车辆侧向加速度较大，纵向加速度变化剧烈时，给测量噪声方差较大的值。

需说明的是，对应关系预先根据实车测量数据进行标定，不同车型的对应关系有所不同。由于本实施例中综合考虑了第一纵向加速度和第二纵向加速度，因此，在具体应用中，可以单独考虑第一纵向加速度与观测噪声的对应关系或者第二纵向加速度与观测噪声的对应关系来确定k时刻的观测噪声R(k)，也可以综合考虑第一纵向加速度、第二纵向加速度与观测噪声的对应关系，具体考虑的因素进行实车测量数据标定获得相应的对应关系，以用于观测噪声R(k)的获取。

如图2所示，本发明实施例提出一种车辆俯仰角估计系统，包括：

第一信号获取单元1，用于获取车辆当前车速，并根据所述当前车速获得第一纵向加速度；

第二信号获取单元2，用于获取当前坡度角、车辆加速度传感器当前检测得到的第二纵向加速度；

观测变量获取单元3，根据所述第一纵向加速度、当前坡度角、第二纵向加速度获得量测俯仰角；以及

卡尔曼滤波单元4，用于将所述量测俯仰角作为观测变量，进行卡尔曼滤波获得车辆的俯仰角估计值。

在一较佳实施例中，所述观测变量获取单元3具体用于根据公式获得量测俯仰角，

其中，θ_s为量测俯仰角，a_xs为第二纵向加速度，a_x为第一纵向加速度，α为坡度角，g为重力加速度。

在一较佳实施例中，所述卡尔曼滤波单元4具体包括：

一步状态预测单元41，用于进行一步状态预测获得k时刻状态预测量其中，k时刻表示当前时刻，为k-1时刻系统状态量，u(k-1)为k-1时刻u的值，F_x为车辆纵向力，h为车辆质心高度，m为车辆簧载质量，M为车辆总质量，I′_y为车辆等效转动惯量，M_y为车辆俯仰阻力矩，T为步长，即为程序当前循环与上一循环的时间间隔；

一步方差预测单元42，用于获取k-1时刻的过程噪声Q(k-1)，并根据所述过程噪声Q(k-1)进行一步预测方差获得k时刻方差预测量P(k,k-1)，其中，P(k,k-1)＝GP(k-1)G′+Q(k-1)，P(k-1)为k-1时刻方差；

系统状态量估计单元43，用于获取k时刻的观测噪声R(k)，根据k时刻的状态预测量方差预测量P(k,k-1)、观测变量y(k)以及观测噪声R(k)估计k时刻系统状态量其中，C＝[1 0]，K(k)＝P(k,k-1)C/[C·P(k,k-1)·C′+R(k)]；

方差估计单元44，用于根据k时刻的方差预测量P(k,k-1)进行方差更新获得k时刻方差P(k)，P(k)＝[I-K(k)·C]·P(k,k-1)，I为单位矩阵；以及

俯仰角估计单元45，用于根据所述k时刻的系统状态量和方差P(k)获得k时刻车辆的俯仰角估计值。

在一较佳实施例中，所述系统状态量估计单元43具体还用于：

根据k时刻的第一纵向加速度以及第一纵向加速度与观测噪声的对应关系确定k时刻的观测噪声R(k)；

或者，根据k时刻的第二纵向加速度以及第二纵向加速度与观测噪声的对应关系确定k时刻的观测噪声R(k)。

或者，根据k时刻的第一纵向加速度、第二纵向加速度以及第一纵向加速度、第二纵向加速度与观测噪声的对应关系确定k时刻的观测噪声R(k)。

以上所描述的系统实施例仅仅是示意性的，其中所述作为分离部件说明的单元可以是或者也可以不是物理上分开的，作为单元显示的部件可以是或者也可以不是物理单元，即可以位于一个地方，或者也可以分布到多个网络单元上。可以根据实际的需要选择其中的部分或者全部模块来实现本实施例方案的目的。

需说明的是，上述实施例所述系统与上述实施例所述方法对应，因此，上述实施例所述系统未详述部分可以参阅上述实施例所述方法的内容得到，此处不再赘述。

并且，上述实施例所述车辆俯仰角估计系统如果以软件功能单元的形式实现并作为独立的产品销售或使用时，可以存储在一个计算机可读取存储介质中。

本发明另一实施例还提出一种计算机设备，包括：根据上述实施例所述的车辆俯仰角估计系统；或者，存储器和处理器，所述存储器中存储有计算机可读指令，所述计算机可读指令被所述处理器执行时，使得所述处理器执行根据上述实施例所述车辆俯仰角估计方法的步骤。

当然，所述计算机设备还可以具有有线或无线网络接口、键盘以及输入输出接口等部件，以便进行输入输出，该计算机设备还可以包括其他用于实现设备功能的部件，在此不做赘述。

示例性的，所述计算机程序可以被分割成一个或多个单元，所述一个或者多个单元被存储在所述存储器中，并由所述处理器执行，以完成本发明。所述一个或多个单元可以是能够完成特定功能的一系列计算机程序指令段，该指令段用于描述所述计算机程序在所述计算机设备中的执行过程。

所述处理器可以是中央处理单元(Central Processing Unit，CPU)，还可以是其他通用处理器、数字信号处理器(Digital Signal Processor，DSP)、专用集成电路(Application Specific Integrated Circuit，ASIC)、现成可编程门阵列(Field-Programmable Gate Array，FPGA)或者其他可编程逻辑器件、分立门或者晶体管逻辑器件、分立硬件组件等。通用处理器可以是微处理器或者该处理器也可以是任何常规的处理器等，所述处理器是所述计算机设备的控制中心，利用各种接口和线路连接整个所述计算机设备的各个部分。

所述存储器可用于存储所述计算机程序和/或单元，所述处理器通过运行或执行存储在所述存储器内的计算机程序和/或单元，以及调用存储在存储器内的数据，实现所述计算机设备的各种功能。此外，存储器可以包括高速随机存取存储器，还可以包括非易失性存储器，例如硬盘、内存、插接式硬盘，智能存储卡(Smart Media Card,SMC)，安全数字(Secure Digital,SD)卡，闪存卡(Flash Card)、至少一个磁盘存储器件、闪存器件、或其他易失性固态存储器件。

本发明另一实施例还提出一种计算机可读存储介质，其上存储有计算机程序，所述计算机程序被处理器执行时实现上述实施例所述车辆俯仰角估计方法的步骤。

具体而言，所述计算机可读存储介质可以包括：能够携带所述计算机程序代码的任何实体或装置、记录介质、U盘、移动硬盘、磁碟、光盘、计算机存储器、只读存储器(ROM，Read-Only Memory)、随机存取存储器(RAM，Random Access Memory)、电载波信号、电信信号以及软件分发介质等。

以上已经描述了本发明的各实施例，上述说明是示例性的，并非穷尽性的，并且也不限于所披露的各实施例。在不偏离所说明的各实施例的范围和精神的情况下，对于本技术领域的普通技术人员来说许多修改和变更都是显而易见的。本文中所用术语的选择，旨在最好地解释各实施例的原理、实际应用或对市场中的技术改进，或者使本技术领域的其它普通技术人员能理解本文披露的各实施例。

Mode of execution

Various exemplary embodiments, features, and aspects of the present disclosure will be described in detail below with reference to the accompanying drawings. Like reference numerals in the drawings denote identical or similar elements. While various aspects of the embodiments are shown in the drawings, the drawings are not necessarily drawn to scale unless specifically noted.

In addition, in order to better illustrate the present invention, numerous specific details are set forth in the detailed description that follows. It should be understood by those skilled in the art that the present invention may be practiced without some specific details. In some instances, well-known means to those skilled in the art are not described in detail in order to highlight the subject matter of the present invention.

As shown 1, an embodiment of the present invention provides a method for estimating a pitch angle of a vehicle, which comprises the following steps S10 - S40:

Step S10: obtaining a current vehicle speed of the vehicle and obtaining first longitudinal acceleration according to the current vehicle speed.

, In step S10, a longitudinal component of the current vehicle speed is obtained first longitudinal acceleration, and the current vehicle speed of the vehicle can be acquired through CAN bus acquisition.

In step S20, the current gradient angle is acquired, and the vehicle acceleration sensor detects second longitudinal acceleration currently detected.

, The current gradient angle may be acquired from the map by GPS navigation information, the map data including the gradient angle information of each road, and thus the gradient angle of the road where the corresponding vehicle is located may be obtained according to the vehicle position information. Of course, other methods for estimating the gradient angle may be used for real-time estimation, and the method of acquiring the gradient angle in the present embodiment is not specifically limited, and is all within the protection scope of the present invention.

In general, the vehicle is equipped with a vehicle acceleration sensor (e.g. an inertial sensor in ESP) for detecting vehicle longitudinal acceleration, and the longitudinal acceleration detected by the vehicle acceleration sensor in the present embodiment is defined as second longitudinal acceleration.

Step S30. Based on the first longitudinal acceleration, the current gradient angle, second longitudinal acceleration, the measurement pitch angle is obtained.

, The vehicle pitch angle variation mainly causes the suspension deformation to be changed due to the change in the load of the front and rear shafts, thereby causing the vehicle to lean forward or tilt. , The vehicle pitch can be estimated by estimating the front-rear axle load and combining the suspension characteristics. Because the vertical rigidity of the tire is obviously higher than the vertical rigidity of the suspension frame, the attitude of the lower mass of the vehicle spring during driving is assumed to be constant, and the vehicle pitching one-dimensional kinetic equation is as shown in Equation (1).

Here, θ is a pitch angle. -pitch angular F acceleration_x The longitudinal force, h is the mass center height, m is the sprung mass, M is the total mass of vehicle, I '. _y M For equivalent rotational inertia_y A pitch drag torque.

I '_y ＝ I_y + Mmh² (2)

In-flight I_y The rotational inertia of the vehicle around y axis can be obtained by measuring in advance.

F_x ＝ m·a_x (3)

In-flight a_x For vehicle longitudinal acceleration, it is possible to derive by longitudinal vehicle speed.

In-flight l₁ Distance between front axle and centroid in X direction, l₂ Distance of rear axle and centroid in X direction, K₁ , K₂ Spring rigidity and R for suspension of front axle and rear axle respectively₁ , R₂ Shock and back damper characteristics, respectively.

, The center of gravity height and the center of mass X in the vehicle design state can be acquired through Y bench tests around KC-axis rotational inertia. The vehicle pitch angle can be estimated by combining the suspension characteristics, the vehicle longitudinal acceleration, and the vehicle longitudinal acceleration when the vehicle is half-loaded. However, since the actual load of the vehicle is frequently changed, the position of the center of gravity also changes correspondingly, the formula (1) has certain uncertainty, and therefore, the pitching angle is estimated by the longitudinal acceleration measured by the longitudinal sensor and the deviation of the actual longitudinal acceleration of the vehicle.

The inertial sensor ESP installed in the vehicle X coincides with the vehicle X axis, and the variation in the pitch angle of the vehicle causes first longitudinal acceleration a. _x Longitudinal acceleration second with a_xs Deviation, a_x AND a_xs The relationship is as follows.

In-flight l_c The sensor is transversely offset from the center of rotation during cornering. For yaw angular acceleration, r is the yaw rate, V. _y To Y speed, g is gravitational acceleration, α is the slope angle. If the influence of the transverse pendulum motion is ignored, there are:

a_xs ＝ a_x Cos θ θ θ θ + g sin (α + θ) (6)

From Equation (6), according to the first longitudinal acceleration, the current slope angle, second longitudinal acceleration, a measured pitch angle can be obtained.

Step S40: A pitch angle estimated value of the vehicle is obtained by performing Kalman filtering on the measured pitch angle as an observation variable.

Based on first longitudinal acceleration, current gradient angle, and longitudinal acceleration, and second longitudinal acceleration obtained from the current vehicle speed, the measured pitch angle is obtained as an observation variable, and the pitch angle estimation value of the vehicle is obtained by Kalman filtering first 2nd. , Under the condition that the hardware cost of the vehicle is not increased, the problem of low estimation accuracy existing in the vehicle pitch angle estimation according to the existing other vehicle sensor signals is solved.

In a preferred embodiment, the measured pitch angle is obtained from the first longitudinal acceleration, the current slope angle, second longitudinal acceleration, as shown in the following formula.

In which θ is_s Measuring pitch angle, a_xs Longitudinal acceleration of second a_x For first longitudinal acceleration, α is the slope angle, g is gravitational acceleration.

, Based on Equation (6), the pitch angle of the most operating vehicle is considered to be below 3°, cos θ ≈ 1, and thus the pitching angle observed by the sensor is as shown in Equation (7).

In a preferred embodiment, vehicle pitch angle estimation is carried out by Kalman filtering, system equation modeling is performed based on formula (1), and system state vectors are made. The observation variable y is a pitch angle θ. _s The system state space equation is: the system state space equation.

Wherein. C 1 0

In matrix A.

The formula (8) is discretized and obtained.

Wherein. T Is a step size, that is, the time interval between the current cycle estimated for the pitch angle and the last cycle, which is a preset parameter value.

Based on the Kalman filtering model considering the vehicle model above, the current time k time is set, k time observation variable is y (k), and the step S40 specifically includes the following steps.

Step S401, performing one-step state prediction to obtain k-time-time state prediction amount Wherein. For k - a system state quantity, u (k-1) is the value of k -timing u.

Step S402: The process noise Q (k-1) at k - is acquired, and a Q-time variance prediction k (1, k-P) is obtained according to the process noise k (k-1), where P (k, k-1) ＝ GP (k-1) G ''' + Q P (k k 1-1) is k -timing variance.

Step S403. Observation noise k (R) at k time is acquired, and state prediction according to k time is obtained. The variance prediction P (k, k-1), the observed variable y (k) and, and the observed noise R (k) estimate k time system state quantities. Wherein.

K (k) Is a filter gain matrix, K (k) ＝ P (k, k-1) C / [C··P (k, k-1) ·C C C C + R (k)].

Step S404: The variance update at k times P (k, k-1) performs variance update to obtain k time variance P (k) and holds, P (k) ＝ [I-K(k) ·C] ·P (k, k-1), I. Wherein, k time variance P (k) is used for follow-up k + 1 timing depression angle estimation.

Step S405. System state quantities according to said k times A pitch angle estimate of the vehicle k is obtained.

, In step S405, system state quantities at times of CX and k are determined according to observation matrix y In other words, k times the pitch angle estimated value of the vehicle is obtained.

In particular, with respect to a Kalman filter model of the existing pitch angle, the Kalman filtering model in this embodiment takes into account the dynamics factors of the pitch angle variation, combines the measured pitch angle of the sensor measurement data estimation with the one-dimensional kinetic model estimation pitch angle, and further improves the accuracy of the pitch angle estimation value.

In a preferred embodiment, the acquisition k instant observation noise R (k) comprises:

At k times, the observed noise first (and 1st) at k is determined by R longitudinal acceleration k longitudinal acceleration versus observed noise correlation.

Alternatively, the observed noise k (2nd) at and second time is determined based on the corresponding relationship k longitudinal acceleration R of k longitudinal acceleration and observed noise.

, The longitudinal acceleration of the vehicle and the observed noise have a certain relationship, and the corresponding relationship may be generally described as: when the longitudinal acceleration of the vehicle is not large in the linear motion, a smaller value is given to the measured noise variance. When the vehicle lateral acceleration is large and the longitudinal acceleration changes violently, a larger value is given to the measured noise variance.

It needs to be noted that the correspondence relationship is preliminarily calibrated according to the real vehicle measurement data, and the corresponding relation of different vehicle types is different. Since first longitudinal acceleration and second longitudinal acceleration are taken into account in the present embodiment, the observation noise first (2nd) at k time may be determined individually in consideration R longitudinal acceleration and the corresponding relationship of k longitudinal acceleration and observed noise, and first longitudinal acceleration may be comprehensively considered. The second Longitudinal acceleration and observation noise's corresponding relation, the factor that specifically considered carries out real vehicle measurement data calibration and obtains corresponding relation to be used for observation noise R (k)'s acquisition.

As shown 2, an embodiment of the present invention provides a vehicle pitch angle estimation system.

The first Signal acquisition unit 1 is used for acquiring the current vehicle speed of the vehicle and obtaining first longitudinal acceleration according to the current vehicle speed.

The second Signal acquisition unit 2 is used for obtaining current slope angle, and the current detected second longitudinal acceleration of vehicle acceleration sensor.

The observation variable acquisition unit 3 obtains a measurement pitch angle according to the first longitudinal acceleration, the current slope angle, second longitudinal acceleration. and

The Kalman filtering unit 4 is configured to obtain a pitch angle estimated value of the vehicle by performing Kalman filtering on the measured pitch angle as an observation variable.

In a preferred embodiment, the observation variable acquisition unit 3 is specifically configured to obtain a measurement pitch angle according to a formula.

In which θ is_s Measuring pitch angle, a_xs Longitudinal acceleration of second a_x For first longitudinal acceleration, α is the slope angle, g is gravitational acceleration.

In a preferred embodiment, the Kalman filtering unit 4 specifically comprises:

One-step state prediction unit 41 for performing one-step state prediction to obtain k-time-time state prediction amount Here, k time represents the current time. At k - 1, the system state quantity, u (k-1), is the value of k - type time u, F. _x For the vehicle longitudinal force, h is the vehicle mass center height, m is vehicle sprung mass, M is vehicle total mass, I '_y Equivalent rotational inertia of vehicle M_y For the pitch drag torque of the vehicle, T is the step size, that is, the time interval between the current cycle of the program and the previous cycle.

One-step variance prediction unit 42 is used to acquire the process noise Q (k-1) at the time of k - 1 and obtain Q-time variance prediction k (1, k-P) at the time of one-step prediction on the process noise k (k-1), where P (1 k, k k k 1-1) G P '1' ' + Q (＝ GP-k) is k .

A system state amount estimation unit 43 acquires observation noise k (R) at k time, and estimates the state prediction according to k times. The variance is pre-measured P (k, k-1). The observation variable y (k) and observes the noise R (k) estimates k the system state quantity at the moment of time. Wherein. C 1 0 [K], k (＝ P) 1 (k, k-1) C / [C··P (k, k-k) ·C C C C + R ()].

The variance estimation unit 44 estimates k (P, k-k) variance updates at 1 times to obtain k time variance P (k), P (k) ＝ [I-K(k) ·C] ·P (k, k-1), I. and

Pitch angle estimation unit 45 for estimating system state quantities based on said k times And Variance P (k) At k times the pitch angle estimate of the vehicle is obtained.

In a preferred embodiment, the system state quantity estimation unit 43 is specifically also used for:

At k times, the observed noise first (and 1st) at k is determined by R longitudinal acceleration k longitudinal acceleration versus observed noise correlation.

Alternatively, the observed noise k (2nd) at and second time is determined based on the corresponding relationship k longitudinal acceleration R of k longitudinal acceleration and observed noise.

Alternatively, k longitudinal acceleration according to first times. The second Longitudinal acceleration and first longitudinal acceleration, second longitudinal acceleration and observed noise correlation determine k-time observation noise R (k).

The system embodiments described above are illustrative only, wherein the elements illustrated as separate components may or may not physically separate, may or may not be physical units, i.e. may be located in one place, or may also be distributed over multiple network elements. Part or all of the modules can be selected according to actual needs to achieve the object of the embodiment of the present embodiment.

It should be noted that the system according to the above-mentioned embodiment corresponds to the method described in the above-mentioned embodiment, and therefore, the system not described in detail in the above-mentioned embodiments can be obtained by reference to the contents of the method described above.

Also, the vehicle pitch angle estimation system described in the above embodiment may be stored in a computer-readable storage medium if implemented in the form of a software functional unit and sold or used as a separate product.

Another embodiment of the present invention further provides a computer device, comprising: the vehicle pitch angle estimation system according to the above embodiment. Alternatively, the memory and the processor, the memory stores therein computer readable instructions which, when executed by the processor, cause the processor to perform the steps of the vehicle pitch angle estimating method according to the embodiments described above.

Of course, the computer device may also have a wired or wireless network interface. The keyboard and is input to an output interface or the like so as to perform an input/output, and the computer device may further include other components for implementing the functions of the device, which will not be described in detail herein.

, The computer program may be divided into one or more units that are stored in the memory and executed by the processor to complete the present invention. The one or more units may be a series of computer program instruction segments capable of performing a particular function for describing an execution process of the computer program in the computer device.

The processor may be a central processing unit (Central Processing Unit, CPU), other general purpose processors, digital signal processors (Digital Signal Processor, DSP), specific integrated circuits (Application Specific Integrated Circuit, ASIC), existing programmable gate arrays (Field-Programmable Gate Array, FPGA), or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components, and the like. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like, the processor being a control center of the computer device, connecting the entire computer device with various interfaces and lines.

The memory may be used to store the computer program and/or unit that, by running or executing a computer program and/or a unit stored in the memory, and invokes data stored in a memory to implement various functions of the computer device. In addition, the memory may include a high speed random access memory, and may also include non-volatile memory such as hard disk, memory, plug-in hard disk, smart memory card (Smart Media Card, SMC), secure digital (Secure Digital, SD) card, flash memory card (Flash Card), at least one disk storage device, flash memory device, or other volatile solid state storage device.

Another embodiment of the present invention further provides a computer-readable storage medium having stored thereon a computer program which, when executed by a processor, implements the steps of the method for estimating the pitch angle of the vehicle according to the embodiment described above.

, The computer readable storage medium may include any entity or device, recording medium, U disk, mobile hard disk, magnetic disk, optical disk, computer memory, read only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), a carrier signal, a telecommunication signal and software distribution medium, and the like that can carry the computer program code.

While various embodiments of the present invention have been described above, the foregoing description is exemplary and is not intended to be exhaustive or limited to the disclosed embodiments. Many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the described embodiments. The terminology used herein is for the purpose of best explaining the principles of the embodiments, the actual application, or technical improvements in the market, or to enable others of ordinary skill in the art to understand the embodiments disclosed herein.