阅读说明：本技术 一种坡度估计方法、装置、电子设备以及存储介质 (Gradient estimation method and device, electronic equipment and storage medium ) 是由 李林润 王兴 厉健峰 张建 姜洪伟 刘秋铮 王宇 王御 于 2021-09-10 设计创作，主要内容包括：本发明公开了一种坡度估计方法、装置、电子设备以及存储介质,属于计算机技术领域。该方法根据车辆在当前时刻的行驶加速度,以及车辆在上一时刻的坡度最优估计值确定车辆在当前时刻的坡度预测值；确定车辆在当前时刻的坡度预测值的误差协方差；根据车辆在当前时刻的坡度测量值的误差协方差和所述车辆在当前时刻的坡度预测值的误差协方差,确定当前时刻的卡尔曼增益；根据所述当前时刻的卡尔曼增益、当前时刻的坡度测量值、所述当前时刻的坡度预测值、所述上一时刻的坡度最优估计值、坡度估计阈值、以及上一时刻的坡度最优估计值与当前时刻的坡度预测值之间的差值,确定车辆在当前时刻的坡度最优估计值。为坡度估计提供了一种新思路。(The invention discloses a slope estimation method and device, electronic equipment and a storage medium, and belongs to the technical field of computers. Determining a slope prediction value of the vehicle at the current moment according to the running acceleration of the vehicle at the current moment and the optimal slope estimation value of the vehicle at the previous moment; determining the error covariance of the gradient predicted value of the vehicle at the current moment; determining a Kalman gain at the current moment according to the error covariance of the gradient measured value of the vehicle at the current moment and the error covariance of the gradient predicted value of the vehicle at the current moment; and determining the optimal slope estimation value of the vehicle at the current moment according to the Kalman gain at the current moment, the slope measurement value at the current moment, the slope prediction value at the current moment, the optimal slope estimation value at the previous moment, the slope estimation threshold value and the difference value between the optimal slope estimation value at the previous moment and the slope prediction value at the current moment. Provides a new idea for slope estimation.)

1. A gradient estimation method characterized by comprising:

determining a slope prediction value of the vehicle at the current moment according to the running acceleration of the vehicle at the current moment and the optimal slope estimation value of the vehicle at the previous moment;

determining the error covariance of the slope prediction value of the vehicle at the current moment according to the error covariance of the optimal slope estimation value of the vehicle at the previous moment;

determining a Kalman gain at the current moment according to the error covariance of the gradient measured value of the vehicle at the current moment and the error covariance of the gradient predicted value of the vehicle at the current moment;

and determining the optimal slope estimation value of the vehicle at the current moment according to the Kalman gain at the current moment, the slope measurement value at the current moment, the slope prediction value at the current moment, the optimal slope estimation value at the previous moment, the slope estimation threshold value and the difference value between the optimal slope estimation value at the previous moment and the slope prediction value at the current moment.

2. The method of claim 1, wherein determining the optimal slope estimation value at the current time according to the kalman gain at the current time, the slope measurement value at the current time, the slope prediction value at the current time, the optimal slope estimation value at the previous time, the slope estimation threshold value, and the difference between the optimal slope estimation value at the previous time and the slope prediction value at the current time comprises:

if the absolute value of the difference between the optimal slope estimation value at the previous moment and the predicted slope value at the current moment is greater than the slope estimation threshold, determining the optimal slope estimation value at the current moment according to the slope estimation threshold and the optimal slope estimation value at the previous moment;

and if the absolute value of the difference value between the optimal slope estimation value at the previous moment and the predicted slope value at the current moment is smaller than the slope estimation threshold, determining the optimal slope estimation value at the current moment according to the Kalman gain at the current moment, the measured slope value at the current moment and the predicted slope value at the current moment.

3. The method of claim 2, wherein the grade estimation threshold is determined by:

determining a driving time period required by the vehicle from a preparation uphill state to an initial uphill state;

determining the gradient change rate of the gradient of the vehicle according to the maximum gradient of the road and the running time;

and determining the slope estimation threshold according to the slope change rate, the slope change proportion coefficient and the time interval between two adjacent slope estimations.

4. The method of claim 3, wherein the gradient change proportionality coefficient is determined based on a wheel slip level.

5. The method of claim 4, further comprising:

determining theoretical longitudinal acceleration according to the gradient resistance and the acceleration resistance;

determining an acceleration error according to the theoretical longitudinal acceleration and the actual longitudinal acceleration;

and determining the wheel slip grade according to the acceleration error and the slip judgment threshold value.

6. The method of claim 5, wherein determining a wheel slip level based on the acceleration error and a slip determination threshold comprises:

if the absolute value of the acceleration error is smaller than a first slip judgment threshold value, determining that the wheel slip grade is a first slip grade;

if the absolute value of the acceleration error is greater than a first slip judgment threshold and less than a second slip judgment threshold, or the absolute value of the acceleration error is greater than a third slip judgment threshold, determining that the wheel slip grade is a second slip grade;

if the absolute value of the acceleration error is greater than a second slip judgment threshold and less than a third slip judgment threshold, determining the wheel slip level as a third slip level; wherein the third slip determination threshold is greater than a second slip determination threshold, which is greater than the first slip determination threshold.

7. A gradient estimation device characterized by comprising:

the gradient predicted value determining module is used for determining a gradient predicted value of the vehicle at the current moment according to the running acceleration of the vehicle at the current moment and the optimal gradient estimated value of the vehicle at the previous moment;

the error covariance determination module is used for determining the error covariance of the slope prediction value of the vehicle at the current moment according to the error covariance of the slope optimal estimation value of the vehicle at the previous moment;

the Kalman gain determination module is used for determining the Kalman gain at the current moment according to the error covariance of the slope measurement value of the vehicle at the current moment and the error covariance of the slope prediction value of the vehicle at the current moment;

and the slope estimation value determination module is used for determining the optimal slope estimation value of the vehicle at the current moment according to the Kalman gain at the current moment, the slope measurement value at the current moment, the slope prediction value at the current moment, the optimal slope estimation value at the previous moment, the slope estimation threshold value and the difference value between the optimal slope estimation value at the previous moment and the slope prediction value at the current moment.

8. The apparatus of claim 7, wherein the slope estimate determination module is specifically configured to:

if the absolute value of the difference between the optimal slope estimation value at the previous moment and the predicted slope value at the current moment is greater than the slope estimation threshold, determining the optimal slope estimation value at the current moment according to the slope estimation threshold and the optimal slope estimation value at the previous moment;

and if the absolute value of the difference value between the optimal slope estimation value at the previous moment and the predicted slope value at the current moment is smaller than the slope estimation threshold, determining the optimal slope estimation value at the current moment according to the Kalman gain at the current moment, the measured slope value at the current moment and the predicted slope value at the current moment.

9. An electronic device, comprising:

one or more processors;

a memory for storing one or more programs;

when executed by the one or more processors, cause the one or more processors to implement the grade estimation method of any one of claims 1-6.

10. A computer-readable storage medium, on which a computer program is stored, which program, when being executed by a processor, is adapted to carry out the gradient estimation method according to any one of claims 1-6.

Technical Field

The embodiment of the invention relates to the technical field of computers, in particular to a slope estimation method and device, electronic equipment and a storage medium.

Background

Current road gradient estimation algorithms may be classified into a kinematic model-based method and a dynamic model-based method according to the vehicle model used. The method based on the dynamic model takes the pressure of a brake wheel cylinder and the output torque of a power source as the input of an estimator, and depends on a vehicle model, and each parameter in the vehicle model is greatly influenced by high-frequency noise, the estimated value is unstable, and the influence of operations such as braking, gear shifting and the like is large; the method based on the kinematics model adopts a small number of sensors, and based on signals of a longitudinal acceleration sensor, signals in a vehicle body coordinate system measured by the method can be influenced by the vehicle body pose, and particularly when a vehicle starts at a low speed, accelerates and decelerates, the vehicle body pose is unstable, so that a certain deviation exists between the vehicle body pose and the actual vehicle longitudinal acceleration, and a certain influence is brought to the slope estimation precision.

Disclosure of Invention

The invention provides a slope estimation method, a slope estimation device, electronic equipment and a storage medium, which are used for solving the problem that a slope estimation value is unreliable finally due to serious fluctuation of an acceleration sensor value caused by instability of a vehicle when the vehicle starts at a low speed, accelerates and decelerates.

In a first aspect, an embodiment of the present invention provides a gradient estimation method, including:

determining a slope prediction value of the vehicle at the current moment according to the running acceleration of the vehicle at the current moment and the optimal slope estimation value of the vehicle at the previous moment;

determining the error covariance of the slope prediction value of the vehicle at the current moment according to the error covariance of the optimal slope estimation value of the vehicle at the previous moment;

determining a Kalman gain at the current moment according to the error covariance of the gradient measured value of the vehicle at the current moment and the error covariance of the gradient predicted value of the vehicle at the current moment;

and determining the optimal slope estimation value of the vehicle at the current moment according to the Kalman gain at the current moment, the slope measurement value at the current moment, the slope prediction value at the current moment, the optimal slope estimation value at the previous moment, the slope estimation threshold value and the difference value between the optimal slope estimation value at the previous moment and the slope prediction value at the current moment.

In a second aspect, an embodiment of the present invention further provides a gradient estimation apparatus, including:

the gradient predicted value determining module is used for determining a gradient predicted value of the vehicle at the current moment according to the running acceleration of the vehicle at the current moment and the optimal gradient estimated value of the vehicle at the previous moment;

the error covariance determination module is used for determining the error covariance of the slope prediction value of the vehicle at the current moment according to the error covariance of the slope optimal estimation value of the vehicle at the previous moment;

the Kalman gain determination module is used for determining the Kalman gain at the current moment according to the error covariance of the slope measurement value of the vehicle at the current moment and the error covariance of the slope prediction value of the vehicle at the current moment;

and the slope estimation value determination module is used for determining the optimal slope estimation value of the vehicle at the current moment according to the Kalman gain at the current moment, the slope measurement value at the current moment, the slope prediction value at the current moment, the optimal slope estimation value at the previous moment, the slope estimation threshold value and the difference value between the optimal slope estimation value at the previous moment and the slope prediction value at the current moment.

In a third aspect, an embodiment of the present invention further provides an electronic device, including:

one or more processors;

a memory for storing one or more programs;

when executed by the one or more processors, cause the one or more processors to implement a grade estimation method as provided by any of the embodiments of the invention.

In a fourth aspect, embodiments of the present invention further provide a computer-readable storage medium, on which a computer program is stored, where the computer program, when executed by a processor, implements a gradient estimation method as provided in any of the embodiments of the present invention.

The technical scheme of the embodiment of the invention determines the slope prediction value of the vehicle at the current moment according to the running acceleration of the vehicle at the current moment and the optimal slope estimation value of the vehicle at the previous moment, then determines the error covariance of the slope prediction value of the vehicle at the current moment according to the error covariance of the optimal slope estimation value of the vehicle at the previous moment, then, determining the Kalman gain at the current moment according to the error covariance of the gradient measured value of the vehicle at the current moment and the error covariance of the gradient predicted value of the vehicle at the current moment, and determining the optimal slope estimation value of the vehicle at the current moment according to the Kalman gain at the current moment, the slope measurement value at the current moment, the slope prediction value at the current moment, the optimal slope estimation value at the previous moment, the slope estimation threshold value and the difference value between the optimal slope estimation value at the previous moment and the slope prediction value at the current moment. The technical scheme solves the problem that the gradient estimation value is unreliable due to serious fluctuation of the acceleration sensor value caused by instability of the vehicle when the vehicle starts at a low speed, accelerates and decelerates, improves the robustness of the gradient estimation algorithm under the condition of unstable vehicle body without increasing the complexity of the original gradient estimation algorithm and the requirements of the sensor, and provides a new idea for gradient estimation.

Drawings

FIG. 1 is a flow chart of a method for estimating grade according to an embodiment of the present invention;

fig. 2A is a flowchart of a gradient estimation method according to a second embodiment of the present invention;

FIG. 2B is a schematic diagram of a vehicle traveling uphill according to a second embodiment of the present invention;

fig. 3 is a schematic structural diagram of a gradient estimation apparatus according to a third embodiment of the present invention;

fig. 4 is a schematic structural diagram of an electronic device according to a fourth embodiment of the present invention.

Detailed Description

The present invention will be described in further detail with reference to the accompanying drawings and examples. It is to be understood that the specific embodiments described herein are merely illustrative of the invention and are not limiting of the invention. It should be further noted that, for the convenience of description, only some of the structures related to the present invention are shown in the drawings, not all of the structures.

Before the embodiments of the present invention are introduced, the basic idea of the present invention is explained, and the specific process is as follows:

firstly, a vehicle motion model of a longitudinal acceleration sensor measurement result of a vehicle and a road gradient is constructed based on a running acceleration of the vehicle, a longitudinal acceleration sensor measurement result of the vehicle, the road gradient, a gravitational acceleration and the like, and specifically, the vehicle motion model can be determined by the following formula:

wherein, a_xRepresents the longitudinal acceleration sensor measurements of the vehicle,represents the running acceleration of the vehicle, g represents the gravitational acceleration, and i represents the road gradient.

Then, the vehicle motion model is converted into a linear system state space model, specifically, since the road gradient changes slowly relative to the vehicle dynamic state, the time derivative can be approximated to zero, and a differential equation set can be obtained:

further, discretizing the above formula to obtain a state space equation of the gradient estimation system at the time k, as follows:

wherein the content of the first and second substances,v represents a vehicle speed, i represents a road gradient,a matrix of inputs to the system is represented,represented as an observation matrix, which is,expressed as a state transition matrix, z_kRepresents an observed value of the system, w_kRepresenting the deviation, v, between the system state space equation and the actual measurement process for process noise_kTo measure noise, an error in the accuracy of the sensor is indicated.

And then slope estimation is carried out based on the improved Kalman filtering algorithm, and a state updating link in the Kalman filtering algorithm is mainly improved, and specific explanation is provided in the following embodiments.

Example one

Fig. 1 is a flowchart of a gradient estimation method according to an embodiment of the present invention, where the embodiment is applicable to a situation where a vehicle performs gradient estimation when starting at a low speed, accelerating or decelerating, and the method may be executed by a gradient estimation apparatus, which may be implemented by software and/or hardware, and may be integrated in an electronic device carrying a gradient estimation function, such as a vehicle controller.

As shown in fig. 1, the method may specifically include:

and S110, determining a gradient predicted value of the vehicle at the current moment according to the running acceleration of the vehicle at the current moment and the optimal gradient estimated value of the vehicle at the previous moment.

In this embodiment, the running acceleration of the vehicle at the current time and the optimal estimated value of the gradient of the vehicle at the previous time may be input into the gradient prediction model in combination with the state space equation of the gradient estimation system, so as to obtain the predicted value of the gradient of the vehicle at the current time. For example, the predicted value of the gradient of the vehicle at the present time may be determined by the following formula:

wherein the content of the first and second substances,represents the predicted value of the gradient at the present time,expressed as the best estimate of the slope at the previous time, a is expressed as the state transition matrix, B is expressed as the system input matrix, u_kThe longitudinal acceleration sensor measurement result indicating the current time includes the running acceleration of the vehicle at the current time.

And S120, determining the error covariance of the slope prediction value of the vehicle at the current moment according to the error covariance of the slope optimal estimation value of the vehicle at the previous moment.

In this embodiment, the error covariance of the optimal slope estimation value of the vehicle at the previous time is input into the error determination model, so as to obtain the error covariance of the predicted slope value of the vehicle at the current time. For example, the error covariance of the slope prediction value of the vehicle at the present time may be determined by the following equation:

wherein the content of the first and second substances,error covariance, P, expressed as slope prediction value at current time_k-1The error covariance, denoted as the optimal estimate of the slope at the previous time, and Q, denoted as the process noise covariance, can be obtained by those skilled in the art through comparative experiments depending on the actual situation.

And S130, determining the Kalman gain at the current moment according to the error covariance of the gradient measured value of the vehicle at the current moment and the error covariance of the gradient predicted value of the vehicle at the current moment.

In this embodiment, the error covariance of the slope measurement value of the vehicle at the current time and the error covariance of the slope prediction value of the vehicle at the current time are input to the kalman gain determination model, and the kalman gain at the current time is output. For example, the kalman gain at the current time may be determined by the following equation:

wherein, K_kExpressed as the kalman gain at the current time,and R is a measurement variance obtained by long-term probability statistics of the sensor measurement data.

And S140, determining the optimal slope estimation value of the vehicle at the current moment according to the Kalman gain at the current moment, the slope measurement value at the current moment, the slope prediction value at the current moment, the optimal slope estimation value at the previous moment, the slope estimation threshold value and the difference value between the optimal slope estimation value at the previous moment and the slope prediction value at the current moment.

Optionally, if the absolute value of the difference between the optimal slope estimation value at the previous time and the predicted slope value at the current time is greater than the slope estimation threshold, the optimal slope estimation value at the current time is determined according to the slope estimation threshold and the optimal slope estimation value at the previous time. Specifically, if the optimal slope estimation value at the previous time is smaller than the predicted slope value at the current time, and the absolute value of the difference between the optimal slope estimation value at the previous time and the predicted slope value at the current time is greater than the slope estimation threshold, the optimal slope estimation value at the previous time and the slope estimation threshold are summed, and the summed result is used as the optimal slope estimation value of the vehicle at the current time. And if the optimal slope estimation value at the previous moment is larger than the predicted slope value at the current moment, and the absolute value of the difference between the optimal slope estimation value at the previous moment and the predicted slope value at the current moment is larger than the slope estimation threshold, subtracting the optimal slope estimation value at the previous moment from the slope estimation threshold, and taking the subtraction result as the optimal slope estimation value of the vehicle at the current moment.

Optionally, if the absolute value of the difference between the optimal slope estimation value at the previous time and the predicted slope value at the current time is smaller than the slope estimation threshold, the optimal slope estimation value at the current time is determined according to the kalman gain at the current time, the slope measurement value at the current time, and the predicted slope value at the current time.

For example, the optimal estimated value of the gradient at the present time may be determined by the following formula:

wherein the content of the first and second substances,expressed as the best estimate of the slope at the current time,expressed as the optimum estimate of the slope, Δ i, at the previous moment_maxExpressed as a slope estimation threshold value, is,expressed as the gradient measurement, K, at the current time_kKalman gain, denoted as the current time, and View, denoted as HA test matrix,Expressed as the absolute value of the difference between the optimal slope estimation value at the previous moment and the predicted slope value at the current moment is less than the slope estimation threshold value, z_kExpressed as the predicted value of the grade at the present moment.

After the optimal slope estimation value of the vehicle at the current moment is determined, the error covariance of the optimal slope estimation value at the current moment is determined according to the Kalman gain at the current moment and the error covariance of the predicted slope value at the current moment, so that the error covariance of the optimal slope estimation value at the next moment is determined, namely the error covariance of the optimal slope estimation value is updated. Specifically, it can be determined by the following formula:

wherein, P_kThe covariance of error is expressed as the optimal estimated value of gradient at the current moment, I is an identity matrix, H is an observation matrix, and K_kExpressed as the kalman gain at the current time,and R is a measurement variance obtained by long-term probability statistics of the sensor measurement data.

The technical scheme of the embodiment of the invention determines the slope prediction value of the vehicle at the current moment according to the running acceleration of the vehicle at the current moment and the optimal slope estimation value of the vehicle at the previous moment, then determines the error covariance of the slope prediction value of the vehicle at the current moment according to the error covariance of the optimal slope estimation value of the vehicle at the previous moment, then, determining the Kalman gain at the current moment according to the error covariance of the gradient measured value of the vehicle at the current moment and the error covariance of the gradient predicted value of the vehicle at the current moment, and determining the optimal slope estimation value of the vehicle at the current moment according to the Kalman gain at the current moment, the slope measurement value at the current moment, the slope prediction value at the current moment, the optimal slope estimation value at the previous moment, the slope estimation threshold value and the difference value between the optimal slope estimation value at the previous moment and the slope prediction value at the current moment. The technical scheme solves the problem that the gradient estimation value is unreliable due to serious fluctuation of the acceleration sensor value caused by instability of the vehicle when the vehicle starts at a low speed, accelerates and decelerates, improves the robustness of the gradient estimation algorithm under the condition of unstable vehicle body without increasing the complexity of the original gradient estimation algorithm and the requirements of the sensor, and provides a new idea for gradient estimation.

Example two

Fig. 2A is a flowchart of a gradient estimation method according to a second embodiment of the present invention; fig. 2B is a schematic diagram of a vehicle traveling uphill according to a second embodiment of the present invention. On the basis of the above embodiment, further optimization is carried out, and an alternative implementation scheme is provided.

As shown in fig. 2A, the method may specifically include:

and S210, determining a gradient predicted value of the vehicle at the current moment according to the running acceleration of the vehicle at the current moment and the optimal gradient estimated value of the vehicle at the previous moment.

S220, determining the error covariance of the slope prediction value of the vehicle at the current moment according to the error covariance of the slope optimal estimation value of the vehicle at the previous moment.

And S230, determining the Kalman gain at the current moment according to the error covariance of the gradient measured value of the vehicle at the current moment and the error covariance of the gradient predicted value of the vehicle at the current moment.

S240, determining the optimal slope estimation value of the vehicle at the current moment according to the Kalman gain at the current moment, the slope measurement value at the current moment, the slope prediction value at the current moment, the optimal slope estimation value at the previous moment, the slope estimation threshold value and the difference value between the optimal slope estimation value at the previous moment and the slope prediction value at the current moment.

Alternatively, the gradient estimation threshold is determined by: determining a driving time period required by the vehicle from a preparation uphill state to an initial uphill state; determining the gradient change rate of a slope road where a vehicle is located according to the maximum gradient and the running time of the road; and determining the slope estimation threshold according to the slope change rate, the slope change proportion coefficient and the time interval between two adjacent slope estimations.

The pre-uphill state refers to a state in which the front wheels of the vehicle reach the bottom of a slope and are preparing to go uphill, as shown by the dotted line in fig. 2B; the initial uphill state refers to a state in which the rear wheel of the vehicle is just off the bottom of the slope and the vehicle is completely on the slope of the slope, as shown by the solid line vehicle in fig. 2B.

Specifically, twice the vehicle wheel base is taken as the travel path of the vehicle from the preliminary uphill state to the initial uphill state, as shown in fig. 2B, where L denotes the vehicle wheel base and v denotes the vehicle speed. Further, the travel time period required for the vehicle to go from the preliminary uphill state to the initial uphill state is determined based on the travel path and the vehicle speed, and may be determined by, for example, the following equation:

wherein, t_slopeRepresents a travel time period required for the vehicle to go from the preliminary ascent state to the initial ascent state, L represents a vehicle wheel base, and v represents a vehicle speed.

After the driving time length required by the vehicle from the preparation uphill state to the initial uphill state is determined, the gradient change rate of the slope road where the vehicle is located is determined according to the maximum gradient of the road and the driving time length. For example, the maximum gradient of the road is divided by the travel time, and the division result is shown in fig. 2B as the gradient change rate of the road on which the vehicle is located, θ_maxIndicating the road maximum slope.

After determining the gradient change rate, determining the gradient estimation threshold value according to the gradient change rate, the gradient change proportionality coefficient, and the time interval between two adjacent gradient estimation may be that a result of multiplying the gradient change rate, the gradient change proportionality coefficient, and the time interval between two adjacent gradient estimation is used as the gradient estimation threshold value.

Alternatively, the gradient change proportionality coefficient may be set by a person skilled in the art according to actual conditions. When the vehicle starts at a low speed, accelerates or decelerates, wheel slip occurs, so that the pitching of the vehicle body is unstable, the longitudinal speed of the vehicle estimated based on the wheel speed is unstable, and finally the slope estimation result fluctuates. To further ensure stability and accuracy of the grade estimation, as an alternative to the embodiments of the present invention, the grade change proportionality coefficient is determined according to the wheel slip level, with different grade change proportionality coefficients corresponding to different wheel slip levels.

For example, the wheel slip level may be determined by determining a theoretical longitudinal acceleration based on the grade resistance and the acceleration resistance; determining an acceleration error according to the theoretical longitudinal acceleration and the actual longitudinal acceleration; and determining the wheel slip level according to the acceleration error and the slip judgment threshold. The slip determination threshold value may be set by those skilled in the art according to actual conditions.

The slope resistance refers to a component force of gravity along a slope when a wheel runs on a slope, which is expressed as a resistance to the wheel running, and can be determined by a gravitational acceleration, a rolling resistance coefficient and the weight of the vehicle, and optionally, a result of multiplying the gravitational acceleration, the rolling resistance coefficient and the weight of the vehicle can be used as the slope resistance. This can be determined, for example, by the following equation: f_fMgf, wherein F_fRepresents the gradient resistance, m represents the vehicle weight, g represents the gravitational acceleration, and f represents the rolling resistance coefficient.

The acceleration resistance is an inertia force that keeps a constant motion while the wheels are running, and may be determined by a vehicle running acceleration and a vehicle weight, and optionally, a result of multiplying the vehicle running speed and the vehicle weight may be used as the acceleration resistance. This can be determined, for example, by the following equation:wherein, F_jRepresenting the acceleration resistance, m representing the vehicle weight,indicating the vehicle running acceleration.

The theoretical longitudinal acceleration is an acceleration of the vehicle in the direction of the slope which is theoretically calculated.

The actual longitudinal acceleration refers to the longitudinal acceleration of the vehicle measured by a longitudinal acceleration sensor in the vehicle.

Optionally, the sum of the gradient resistance and the acceleration resistance is subjected to quotient with the vehicle weight, the quotient result is used as a theoretical longitudinal acceleration, then, a difference value between the theoretical longitudinal acceleration and an actual longitudinal acceleration is used as an acceleration error, and further, the wheel slip level is determined according to the acceleration error and the slip judgment threshold.

For example, determining the wheel slip level according to the acceleration error and the slip determination threshold may be determining the wheel slip level as the first slip level if the absolute value of the acceleration error is smaller than the first slip determination threshold; if the absolute value of the acceleration error is greater than the first slip judgment threshold and less than the second slip judgment threshold, or the absolute value of the acceleration error is greater than the third slip judgment threshold, determining the wheel slip grade as the second slip grade; if the absolute value of the acceleration error is greater than the second slip judgment threshold and less than a third slip judgment threshold, determining the wheel slip level as a third slip level; wherein the third slip judgment threshold is greater than the second slip judgment threshold, and the second slip judgment threshold is greater than the first slip judgment threshold. The third slip determination threshold value, the second slip determination threshold value, and the first slip determination threshold value may be set by those skilled in the art according to actual circumstances.

It can be understood that the wheel slip condition can be timely and accurately distinguished by introducing the wheel slip grade, so that the gradient change proportion coefficient can be accurately determined, the accuracy of the gradient estimation threshold value is further improved, and the accuracy of the gradient estimation is further improved.

The technical scheme of the embodiment of the invention determines the slope prediction value of the vehicle at the current moment according to the running acceleration of the vehicle at the current moment and the optimal slope estimation value of the vehicle at the previous moment, then determines the error covariance of the slope prediction value of the vehicle at the current moment according to the error covariance of the optimal slope estimation value of the vehicle at the previous moment, then, determining the Kalman gain at the current moment according to the error covariance of the gradient measured value of the vehicle at the current moment and the error covariance of the gradient predicted value of the vehicle at the current moment, and determining the optimal slope estimation value of the vehicle at the current moment according to the Kalman gain at the current moment, the slope measurement value at the current moment, the slope prediction value at the current moment, the optimal slope estimation value at the previous moment, the slope estimation threshold value and the difference value between the optimal slope estimation value at the previous moment and the slope prediction value at the current moment. The technical scheme solves the problem that the gradient estimation value is unreliable due to serious fluctuation of the acceleration sensor value caused by instability of the vehicle when the vehicle starts at a low speed, accelerates and decelerates, improves the robustness of the gradient estimation algorithm under the condition of unstable vehicle body without increasing the complexity of the original gradient estimation algorithm and the requirements of the sensor, and provides a new idea for gradient estimation.

EXAMPLE III

Fig. 3 is a schematic structural diagram of a gradient estimation apparatus according to a third embodiment of the present invention; the present embodiment may be applied to the case that the vehicle performs the slope estimation when starting at a low speed, accelerating or decelerating, and the device may be implemented by software and/or hardware, and may be integrated in an electronic device carrying the slope estimation function, such as a vehicle controller.

As shown in fig. 3, the apparatus includes a slope prediction value determination module 310, an error covariance determination module 320, a kalman gain determination module 330, and a slope estimate determination module 340, wherein,

the gradient predicted value determining module 310 is used for determining a gradient predicted value of the vehicle at the current moment according to the running acceleration of the vehicle at the current moment and the optimal gradient estimated value of the vehicle at the previous moment;

the error covariance determination module 320 is used for determining the error covariance of the slope prediction value of the vehicle at the current moment according to the error covariance of the slope optimal estimation value of the vehicle at the previous moment;

a kalman gain determining module 330, configured to determine a kalman gain at the current time according to an error covariance of a slope measurement value of the vehicle at the current time and an error covariance of a slope prediction value of the vehicle at the current time;

the slope estimation value determination module 340 is configured to determine an optimal slope estimation value of the vehicle at the current time according to the kalman gain at the current time, the slope measurement value at the current time, the slope prediction value at the current time, the optimal slope estimation value at the previous time, the slope estimation threshold, and a difference between the optimal slope estimation value at the previous time and the slope prediction value at the current time.

The technical scheme of the embodiment of the invention determines the slope prediction value of the vehicle at the current moment according to the running acceleration of the vehicle at the current moment and the optimal slope estimation value of the vehicle at the previous moment, then determines the error covariance of the slope prediction value of the vehicle at the current moment according to the error covariance of the optimal slope estimation value of the vehicle at the previous moment, then, determining the Kalman gain at the current moment according to the error covariance of the gradient measured value of the vehicle at the current moment and the error covariance of the gradient predicted value of the vehicle at the current moment, and determining the optimal slope estimation value of the vehicle at the current moment according to the Kalman gain at the current moment, the slope measurement value at the current moment, the slope prediction value at the current moment, the optimal slope estimation value at the previous moment, the slope estimation threshold value and the difference value between the optimal slope estimation value at the previous moment and the slope prediction value at the current moment. The technical scheme solves the problem that the gradient estimation value is unreliable due to serious fluctuation of the acceleration sensor value caused by instability of the vehicle when the vehicle starts at a low speed, accelerates and decelerates, improves the robustness of the gradient estimation algorithm under the condition of unstable vehicle body without increasing the complexity of the original gradient estimation algorithm and the requirements of the sensor, and provides a new idea for gradient estimation.

Further, the slope estimate determination module 340 is specifically configured to:

if the absolute value of the difference value between the optimal slope estimation value at the previous moment and the predicted slope value at the current moment is greater than the slope estimation threshold, determining the optimal slope estimation value at the current moment according to the slope estimation threshold and the optimal slope estimation value at the previous moment;

and if the absolute value of the difference value between the optimal slope estimation value at the previous moment and the predicted slope value at the current moment is smaller than the slope estimation threshold, determining the optimal slope estimation value at the current moment according to the Kalman gain at the current moment, the measured slope value at the current moment and the predicted slope value at the current moment.

Further, the slope estimation value determination module 340 includes a slope estimation threshold determination unit, which is specifically configured to:

determining a driving time period required by the vehicle from a preparation uphill state to an initial uphill state;

determining the gradient change rate of a slope road where a vehicle is located according to the maximum gradient and the running time of the road;

and determining the slope estimation threshold according to the slope change rate, the slope change proportion coefficient and the time interval between two adjacent slope estimations.

Further, the gradient change proportionality coefficient is determined in accordance with a wheel slip level.

Further, the slope estimation value determination module 340 further includes a theoretical longitudinal acceleration determination unit, an acceleration error determination unit, and a wheel slip level determination unit, wherein,

a theoretical longitudinal acceleration determining unit for determining a theoretical longitudinal acceleration according to the gradient resistance and the acceleration resistance;

the acceleration error determining unit is used for determining an acceleration error according to the theoretical longitudinal acceleration and the actual longitudinal acceleration;

and the wheel slip grade determining unit is used for determining the wheel slip grade according to the acceleration error and the slip judgment threshold value.

Further, the wheel slip level determination unit is specifically configured to:

if the absolute value of the acceleration error is smaller than the first slip judgment threshold, determining that the wheel slip grade is the first slip grade;

if the absolute value of the acceleration error is greater than the first slip judgment threshold and less than the second slip judgment threshold, or the absolute value of the acceleration error is greater than the third slip judgment threshold, determining the wheel slip grade as the second slip grade;

if the absolute value of the acceleration error is greater than the second slip judgment threshold and less than a third slip judgment threshold, determining the wheel slip level as a third slip level; wherein the third slip judgment threshold is greater than the second slip judgment threshold, and the second slip judgment threshold is greater than the first slip judgment threshold.

The slope estimation device can execute the slope estimation method provided by any embodiment of the invention, and has corresponding functional modules and beneficial effects of the execution method.

Example four

Fig. 4 is a schematic structural diagram of an electronic device according to a fourth embodiment of the present invention, and fig. 4 shows a block diagram of an exemplary device suitable for implementing the embodiment of the present invention. The device shown in fig. 4 is only an example and should not bring any limitation to the function and the scope of use of the embodiments of the present invention.

As shown in FIG. 4, electronic device 12 is embodied in the form of a general purpose computing device. The components of electronic device 12 may include, but are not limited to: one or more processors or processing units 16, a system memory 28, and a bus 18 that couples various system components including the system memory 28 and the processing unit 16.

Bus 18 represents one or more of any of several types of bus structures, including a memory bus or memory controller, a peripheral bus, an accelerated graphics port, and a processor or local bus using any of a variety of bus architectures. By way of example, such architectures include, but are not limited to, Industry Standard Architecture (ISA) bus, micro-channel architecture (MAC) bus, enhanced ISA bus, Video Electronics Standards Association (VESA) local bus, and Peripheral Component Interconnect (PCI) bus.

Electronic device 12 typically includes a variety of computer system readable media. Such media may be any available media that is accessible by electronic device 12 and includes both volatile and nonvolatile media, removable and non-removable media.

The system memory 28 may include computer system readable media in the form of volatile memory, such as Random Access Memory (RAM)30 and/or cache memory (cache 32). The electronic device 12 may further include other removable/non-removable, volatile/nonvolatile computer system storage media. By way of example only, storage system 34 may be used to read from and write to non-removable, nonvolatile magnetic media (not shown in FIG. 4, and commonly referred to as a "hard drive"). Although not shown in FIG. 4, a magnetic disk drive for reading from and writing to a removable, nonvolatile magnetic disk (e.g., a "floppy disk") and an optical disk drive for reading from or writing to a removable, nonvolatile optical disk (e.g., a CD-ROM, DVD-ROM, or other optical media) may be provided. In these cases, each drive may be connected to bus 18 by one or more data media interfaces. System memory 28 may include at least one program product having a set (e.g., at least one) of program modules that are configured to carry out the functions of embodiments of the invention.

A program/utility 40 having a set (at least one) of program modules 42 may be stored, for example, in system memory 28, such program modules 42 including, but not limited to, an operating system, one or more application programs, other program modules, and program data, each of which examples or some combination thereof may comprise an implementation of a network environment. Program modules 42 generally carry out the functions and/or methodologies of embodiments described herein.

Electronic device 12 may also communicate with one or more external devices 14 (e.g., keyboard, pointing device, display 24, etc.), with one or more devices that enable a user to interact with electronic device 12, and/or with any devices (e.g., network card, modem, etc.) that enable electronic device 12 to communicate with one or more other computing devices. Such communication may be through an input/output (I/O) interface 22. Also, the electronic device 12 may communicate with one or more networks (e.g., a Local Area Network (LAN), a Wide Area Network (WAN), and/or a public network, such as the Internet) via the network adapter 20. As shown, the network adapter 20 communicates with other modules of the electronic device 12 via the bus 18. It should be understood that although not shown in the figures, other hardware and/or software modules may be used in conjunction with electronic device 12, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems, among others.

The processing unit 16 executes various functional applications and data processing, such as implementing a grade estimation method provided by an embodiment of the present invention, by running a program stored in the system memory 28.

EXAMPLE five

Fifth embodiment of the present invention further provides a computer-readable storage medium, on which a computer program (or referred to as computer-executable instructions) is stored, where the computer program is used, when executed by a processor, to perform a gradient estimation method provided in the fifth embodiment of the present invention, where the method includes:

determining a slope prediction value of the vehicle at the current moment according to the running acceleration of the vehicle at the current moment and the optimal slope estimation value of the vehicle at the previous moment;

determining the error covariance of the slope prediction value of the vehicle at the current moment according to the error covariance of the optimal slope estimation value of the vehicle at the previous moment;

determining a Kalman gain at the current moment according to the error covariance of the gradient measured value of the vehicle at the current moment and the error covariance of the gradient predicted value of the vehicle at the current moment;

and determining the optimal slope estimation value of the vehicle at the current moment according to the Kalman gain at the current moment, the slope measurement value at the current moment, the slope prediction value at the current moment, the optimal slope estimation value at the previous moment, the slope estimation threshold value and the difference value between the optimal slope estimation value at the previous moment and the slope prediction value at the current moment.

Computer storage media for embodiments of the invention may employ any combination of one or more computer-readable media. The computer readable medium may be a computer readable signal medium or a computer readable storage medium. A computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples (a non-exhaustive list) of the computer readable storage medium would include the following: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the context of this document, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device.

A computer readable signal medium may include a propagated data signal with computer readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device.

Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.

Computer program code for carrying out operations for embodiments of the present invention may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, Smalltalk, C + +, and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet service provider).

It is to be noted that the foregoing is only illustrative of the preferred embodiments of the present invention and the technical principles employed. It will be understood by those skilled in the art that the present invention is not limited to the particular embodiments described herein, but is capable of various obvious changes, rearrangements and substitutions as will now become apparent to those skilled in the art without departing from the scope of the invention. Therefore, although the embodiments of the present invention have been described in more detail through the above embodiments, the embodiments of the present invention are not limited to the above embodiments, and many other equivalent embodiments may be included without departing from the spirit of the present invention, and the scope of the present invention is determined by the scope of the appended claims.