CN111580494B - Self-adaptive calibration method and system for control parameters of parallel driving vehicle - Google Patents

Self-adaptive calibration method and system for control parameters of parallel driving vehicle Download PDF

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CN111580494B
CN111580494B CN202010349073.7A CN202010349073A CN111580494B CN 111580494 B CN111580494 B CN 111580494B CN 202010349073 A CN202010349073 A CN 202010349073A CN 111580494 B CN111580494 B CN 111580494B
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CN111580494A (en
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张利
雷鸣
董士琦
王薇
詹建华
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Dongfeng Motor Corp
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    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
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Abstract

The application discloses a method and a system for adaptively calibrating control parameters of a parallel driving vehicle, which relate to the technical field of control parameter calibration, and the method comprises the following steps: the driving simulator sends simulation signal parameters to the parallel driving controller; the parallel driving controller controls the real vehicle to run according to the analog signal parameters, acquires running data and sends the running data to the server; the server constructs a Kalman filtering prediction model according to the vehicle parameters and the driving data, acquires vehicle end quality and gradient value serving as prediction results in real time by taking the driving data as observed quantity, and transmits the prediction results to the parallel driving controller; and the parallel driving controller constructs a calibration equation between the analog signal parameters and the vehicle control parameters, and calibrates the vehicle control parameters in real time according to the driving data, the vehicle end mass and the gradient value. According to the method and the device, the quality of the remote controlled vehicle and the gradient information of the road where the remote controlled vehicle is located are combined, the vehicle control parameters are calibrated in real time, and the remote driver can control the vehicle safely and efficiently.

Description

Self-adaptive calibration method and system for control parameters of parallel driving vehicle
Technical Field
The application relates to the technical field of control parameter calibration, in particular to a method and a system for adaptively calibrating control parameters of a parallel driving vehicle.
Background
The initial idea of parallel driving is formed in the middle of the 90 s of the 20 th century, and in recent years, with the development of technologies such as internet, big data, cloud computing, internet of things, artificial intelligence and the like, the development of parallel driving is also promoted. The vehicles in the parallel driving system can be various types of vehicles, such as manned sightseeing vehicles, cargo-carrying unmanned vending vehicles, engineering vehicles and the like.
In the related art, the relation between the simulation parameters of the parallel cab rack and the actual controlled vehicle control parameters is generally directly determined by using real vehicle tests, and then parallel driving is performed according to the relation. However, the influence of different vehicle masses and running gradients on actual control parameters in the actual running process is not considered.
In the actual driving process, such as an unmanned goods-carrying vending vehicle and the like, the mass of the whole vehicle can be greatly changed along with time, and a driver who remotely controls the vehicle has no direct sense of the change of the mass of the whole vehicle because the driver is in an unreal driving environment, so that steering and unreasonable operation of acceleration and deceleration are easy to occur, and safety accidents or inefficient driving are caused.
In addition, a driver who remotely operates the vehicle is not in a real road environment, cannot acquire road gradient information by virtue of the perception of an environment video, and is easy to operate dangerously, such as excessively rapid acceleration or excessively slow inefficient operation during ascending and descending, particularly descending.
Disclosure of Invention
Aiming at one of the defects in the prior art, the application aims to provide a parallel driving vehicle control parameter self-adaptive calibration method and system to solve the problem that the influence of the whole vehicle mass and the running gradient on vehicle control parameters is not considered.
The application provides a self-adaptive calibration method for control parameters of a parallel driving vehicle in a first aspect, which comprises the following steps:
the driving simulator sends simulation signal parameters to the parallel driving controller;
the parallel driving controller controls the real vehicle to run according to the analog signal parameters, acquires running data and sends the running data to the server through the on-board unit (OBU);
the server constructs a Kalman filtering prediction model according to the vehicle parameters and the running data, acquires vehicle end quality and gradient value serving as prediction results in real time by using the Kalman filtering prediction model and taking the running data as observed quantity, and transmits the prediction results to the parallel driving controller through the OBU;
and the parallel driving controller constructs a calibration equation between the analog signal parameters and the vehicle control parameters, and calibrates the vehicle control parameters in real time according to the driving data, the vehicle end mass and the gradient value.
In some embodiments, further comprising: and when the vehicle control parameter exceeds a preset range, recalibrating the mapping relation between the analog signal parameter and the vehicle control parameter.
In some embodiments, the travel data includes travel speed and acceleration;
the acceleration is obtained through collection or through differential calculation of the running speed.
In some embodiments, further comprising:
and when the real vehicle reaches the stop condition, stopping prediction, and obtaining the vehicle end mass and the gradient value as final values at the previous moment.
In some embodiments, the stop condition comprises at least one of:
the brake pressure of the real vehicle is greater than a brake pressure threshold value;
the gear shifting flag bit of the gearbox of the real vehicle is true in the gear shifting process;
the current vehicle speed of the real vehicle is less than a vehicle speed threshold value;
the engine torque of the real vehicle is less than a torque threshold value;
after filtering, the engine torque change rate of the real vehicle is greater than a change rate threshold;
the current gear of the real vehicle is neutral or reverse gear.
In some embodiments, the obtaining, in real time, the vehicle end mass and the gradient value as the prediction result by using the driving data as the observed quantity specifically includes:
the dynamic equation of the automobile system is taken as a state equation, the acceleration resistance is assumed as process noise,
∑F＝Fair+Fg+Fμ+G
Figure GDA0002987194580000031
wherein, FairAs air resistance, FgFor acceleration resistance, FμFor rolling resistance, G is system noise, F is vehicle running driving force, TwheelIs engine torque, rwheelIs the wheel radius;
based on the form of converting the vehicle longitudinal dynamics model into a discrete state space, selecting a trigonometric function of the driving speed, the reciprocal of the vehicle end mass and the rolling resistance coefficient as a state quantity, the state equation can be described as follows,
Figure GDA0002987194580000032
wherein the content of the first and second substances,
Figure GDA0002987194580000033
is the state quantity estimated value at the time k, mu is the rolling resistance coefficient, beta is the gradient, VkFor speed of travel, m is the end mass, βμIs the inverse tangent value of the rolling resistance coefficient,
Figure GDA0002987194580000034
three state components;
discretizing the state equation to obtain a one-step prediction equation as follows,
Figure GDA0002987194580000035
Figure GDA0002987194580000036
Figure GDA0002987194580000037
Figure GDA0002987194580000041
wherein alpha is1For wind resistance related quantity, ρ is air density, CdAs aerodynamic coefficient, AfIs the frontal area, alpha2G is the acceleration of gravity,
Figure GDA0002987194580000042
which is the differential of the speed of the vehicle,
Figure GDA0002987194580000043
for the predicted value of the state quantity at time k to time k +1, TsCalculating a time step for the EKF;
the observation equation for building the kalman filter prediction model is as follows,
Figure GDA0002987194580000044
wherein Z isk+1Is the observed quantity at time k +1, akIs a measure of acceleration;
predicting value of state quantity at k +1 time by using k time
Figure GDA0002987194580000045
And observed quantity Z at time k +1k+1And estimating to obtain a state quantity estimated value at the moment of k +1, and obtaining real-time vehicle end quality and gradient value.
In some embodiments, before calibrating the vehicle control parameter in real time, the method further includes:
and carrying out normalization processing on the driving speed, the vehicle end mass and the gradient value in the driving data so as to enable the driving speed, the vehicle end mass and the gradient value to be positioned in a [0, 1] interval.
The second aspect of the present application provides a parallel driving vehicle control parameter adaptive calibration system, which includes:
a driving simulator for transmitting analog signal parameters;
the parallel driving controller is used for controlling the real vehicle to run according to the analog signal parameters and acquiring running data;
the server is used for constructing a Kalman filtering prediction model according to the vehicle parameters and the running data, and acquiring vehicle end quality and gradient value serving as prediction results in real time by using the dynamic Kalman filtering prediction model and taking the running data as observed quantity;
and the parallel driving controller is also used for constructing a calibration equation between the analog signal parameters and the vehicle control parameters and calibrating the vehicle control parameters in real time according to the driving data, the vehicle end mass and the gradient value.
In some embodiments, the parallel driving controller is further configured to recalibrate the mapping relationship between the analog signal parameter and the vehicle control parameter when the vehicle control parameter is out of a preset range.
In some embodiments, further comprising:
and the OBU is in communication connection with the server through the 5G customer terminal equipment CPE and is used for sending the running data acquired by the parallel driving controller to the server and sending the prediction result acquired by the server to the parallel driving controller.
The beneficial effect that technical scheme that this application provided brought includes:
according to the parallel driving vehicle control parameter self-adaptive calibration method and system, the parallel driving controller controls the real vehicle to run according to the analog signal parameters and obtains the running data, so that the server can obtain the vehicle end quality and the gradient value in real time according to the whole vehicle parameters and the running data, and the parallel driving controller can carry out real-time calibration, therefore, the vehicle control parameters can be calibrated in real time by combining the quality of the remote controlled vehicle and the gradient information of the road where the remote controlled vehicle is located, and the remote driver can control the vehicle safely and efficiently.
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FIG. 1 is a flow chart of a method for adaptive calibration of control parameters of a parallel-drive vehicle according to an embodiment of the present disclosure;
FIG. 2 is a flowchart of a calculation of the end mass and the grade value provided in the embodiment of the present application;
FIG. 3 is a schematic diagram of a vehicle longitudinal dynamics model provided by an embodiment of the present application;
fig. 4 is a schematic diagram of a parallel driving vehicle control parameter adaptive calibration system according to an embodiment of the present application.
Detailed Description
The present application will be described in further detail with reference to the accompanying drawings and examples.
Referring to fig. 1, an embodiment of the application provides a parallel driving vehicle control parameter adaptive calibration method, which is implemented based on a 5G network. The self-adaptive calibration method comprises the following steps:
s1, the driving simulator sends simulation signal parameters to the parallel driving controller.
The driving simulator is connected with the server through a Universal Serial Bus (USB), and the driving simulator and the server are both located at the driving cabin end of the parallel driving background. The server provides drive for the driving simulator, converts the analog signal of the driving simulator into a network signal and is linked to the 5G private network segment through the Ethernet.
And S2, the parallel driving controller controls the real vehicle to run according to the analog signal parameters, acquires running data and sends the running data to the server through the on-board unit (OBU).
The OBU is located at a vehicle end, is connected with the parallel driving controller through a vehicle CAN bus, and is linked to the 5G private network through 5G customer premise equipment CPE. The OBU receives CAN data sent by the parallel driving controller, analyzes the CAN data to obtain driving data, converts the driving data into network signals, and sends the network signals to the server through the 5 GCPE.
And S3, the server constructs a Kalman filtering prediction model according to the acquired running data, the vehicle longitudinal dynamics model and vehicle parameters such as transmission ratio, tire efficiency and the like, acquires vehicle end quality and gradient value serving as a prediction result in real time by using the dynamic Kalman filtering prediction model and the running data as observed quantity, converts the prediction result into a network signal and transmits the network signal to a 5G network, and transmits the network signal to the parallel driving controller after being received by the OBU.
And after receiving the vehicle end mass and the gradient value acquired by the server, the OBU sends the vehicle end mass and the gradient value to a vehicle CAN bus through a CAN module of the OBU, and forwards the vehicle end mass and the gradient value to the parallel driving controller.
And S4, the parallel driving controller constructs a calibration equation between the analog signal parameters and the vehicle control parameters, and calibrates the vehicle control parameters in real time according to the driving data, the vehicle end quality and the gradient value so as to send the calibrated vehicle control parameters such as steering, accelerator, brake and other control parameters to a vehicle end CAN bus for execution by an actuator.
According to the self-adaptive calibration method for the control parameters of the parallel driving vehicle, the parallel driving controller controls the real vehicle to run according to the analog signal parameters and obtains running data, so that the server can obtain the quality and the gradient value of the vehicle end in real time according to the parameters and the running data of the whole vehicle, and the parallel driving controller can carry out real-time calibration, therefore, the control parameters of the vehicle can be calibrated in real time by combining the quality of the remote controlled vehicle and the gradient information of a road where the remote controlled vehicle is located, and a remote driver can control the vehicle safely and efficiently.
In this embodiment, before calibrating the vehicle control parameter in real time, the method further includes:
and normalizing the running speed, the vehicle end mass and the gradient value in the running data to be positioned in a [0, 1] interval.
For the steering parameter theta of the vehicle, it is compared with the simulated steering parameter of the cockpit frame
Figure GDA0002987194580000076
The mapping relationship of (1) is as follows:
Figure GDA0002987194580000071
wherein k is1，k2，k3B1 is a standard quantity; and m, beta and V are respectively the normalized vehicle end mass, gradient and running speed.
For the vehicle throttle parameter acc, the vehicle throttle parameter acc and the cockpit bench simulation throttle parameter
Figure GDA0002987194580000072
The mapping relationship of (1) is as follows:
Figure GDA0002987194580000073
wherein k is4，k5And b2 is the standard quantity.
For the vehicle brake parameter brk, the simulation brake parameter of the cockpit rack
Figure GDA0002987194580000074
The mapping relationship of (1) is as follows:
Figure GDA0002987194580000075
wherein k is6，k7，k8And b3 is the standard quantity.
The adaptive calibration method of the embodiment further includes: and when the vehicle control parameter exceeds the preset range, recalibrating the mapping relation between the analog signal parameter and the vehicle control parameter. And then, acquiring the vehicle control parameters in real time according to the re-calibrated mapping relation.
In the present embodiment, the travel data includes a travel speed and an acceleration of the vehicle. Alternatively, the acceleration is directly acquired.
In other embodiments, when the vehicle cannot acquire the acceleration signal, the acquired running speed may be subjected to differential calculation to obtain the acceleration of the vehicle.
Preferably, the adaptive calibration method of the present embodiment further includes: when the real vehicle reaches the stop condition, the prediction is stopped, and the vehicle end mass and the gradient value are obtained at the previous moment and are used as final values.
Further, the stop condition includes at least one of:
1. the brake pressure of the real vehicle is larger than the brake pressure threshold value, namely the estimation needs to be stopped when the vehicle is in a braking state.
2. The gear shifting flag bit of the gearbox of the real vehicle is true in the gear shifting process, namely the estimation needs to be stopped when the gearbox is in the gear shifting process.
3. And stopping the estimation when the current vehicle speed of the real vehicle is less than the vehicle speed threshold value.
4. The estimation needs to be stopped when the engine torque of the real vehicle is smaller than the torque threshold value.
5. After filtering, the estimation needs to be stopped when the engine torque change rate of the real vehicle is greater than the change rate threshold.
6. The estimation needs to be stopped when the current gear of the real vehicle is neutral or reverse gear.
In the embodiment, the accuracy of the estimation of the mass and the gradient value can be ensured under the bad vehicle running condition or when the sensor per se has deviation by increasing the stopping condition of the prediction of the mass and the gradient value of the vehicle end.
Referring to fig. 2, the vehicle end mass and gradient values are calculated by using an extended kalman filter EKF, which specifically includes:
1) calculating the state quantity predicted value of the previous moment to the current moment according to the state quantity estimated value of the previous moment,
Figure GDA0002987194580000081
wherein the content of the first and second substances,
Figure GDA0002987194580000082
is the state quantity estimated value at the k moment;
Figure GDA0002987194580000083
the predicted value is from time k to time k + 1.
2) A covariance prediction of the error is calculated,
Figure GDA0002987194580000091
Figure GDA0002987194580000092
Figure GDA0002987194580000093
wherein A iskIs f to xkCalculating the Jacobi moment of the partial derivativesArray, WkIs f to wkThe jacobian matrix of the partial derivatives is solved,
Figure GDA0002987194580000094
is a covariance predictor of the error at time k,
Figure GDA0002987194580000095
covariance prediction, P, for the error at time k +1kCovariance as error at time k, Q process noise variance, wkIs process noise.
3) Computing kalman gain
Figure GDA0002987194580000096
Figure GDA0002987194580000097
Figure GDA0002987194580000098
Wherein K is the Kalman gain,
Figure GDA0002987194580000099
predicted value of observed value at time k, HkIs h to xkJacobian matrix, V, of partial derivativeskIs h to VkCalculating a jacobian matrix of partial derivatives, RkTo observe the variance of the noise.
4) Estimation update
Figure GDA00029871945800000910
Wherein the content of the first and second substances,
Figure GDA00029871945800000911
is an estimate of the state quantity at time k +1,
Figure GDA00029871945800000912
is a predicted value of the observed value at the time k +1, Zk+1Is the observed value at the time k + 1.
5) Updating covariance
Figure GDA00029871945800000913
Wherein, PkIs the covariance of the error at time K.
In this embodiment, in step S3, the obtaining the vehicle end mass and the gradient value as the prediction result in real time by using the driving data as the observed quantity specifically includes:
referring to fig. 3, a vehicle longitudinal dynamics model is established as follows,
∑F＝Fair+Fg+Fμ+G
Figure GDA0002987194580000101
wherein, FairAs air resistance, FgFor acceleration resistance, FμFor rolling resistance, G is system noise, F is vehicle running driving force, TwheelIs engine torque, rwheelIs the wheel radius.
Based on the form of converting the vehicle longitudinal dynamics model into the discrete state space, selecting a trigonometric function of the driving speed, the reciprocal of the vehicle end mass and the rolling resistance coefficient as a state quantity, wherein the state equation can be described as follows:
Figure GDA0002987194580000102
μ＝tanβμ
wherein the content of the first and second substances,
Figure GDA0002987194580000103
is an estimated value of the state quantity at the time k, mu is the rolling resistance coefficient, and beta isGradient, VkIs the speed of travel at time k, m is the end mass, βμIs the inverse tangent value of the rolling resistance coefficient,
Figure GDA0002987194580000104
Figure GDA0002987194580000105
three state components.
In order to realize real-time recursive estimation of the vehicle end mass and the gradient value, the state equation is discretized, namely a one-step prediction equation is as follows:
Figure GDA0002987194580000106
Figure GDA0002987194580000107
Figure GDA0002987194580000108
Figure GDA0002987194580000109
wherein alpha is1For wind resistance related quantity, ρ is air density, CdAs aerodynamic coefficient, AfIs the frontal area, alpha2G is the acceleration of gravity,
Figure GDA00029871945800001010
which is the differential of the speed of the vehicle,
Figure GDA0002987194580000111
for the predicted value of the state quantity at time k to time k +1, TsThe time step is calculated for the EKF.
The observation equation for building the kalman filter prediction model is as follows,
Figure GDA0002987194580000112
wherein Z isk+1Observed quantity (system input value) at the time of k +1, akIs a measure of acceleration.
The process noise variance Q of this embodiment is
Figure GDA0002987194580000113
Wherein σ1，σ2，σ3Respectively three state components
Figure GDA0002987194580000114
Root variance of process noise random quantities.
In this embodiment, the predicted value and the observed quantity of the state quantity at the current time are calculated based on the estimated value of the state quantity at the previous time, and then the estimated value of the state quantity at the current time can be estimated by using the predicted value and the observed quantity of the state field at the current time at the previous time, so that real-time vehicle end quality and gradient value are obtained by recursion.
In the embodiment, the acceleration resistance is assumed as the process noise, the visibility of the state equation can be improved, and the vehicle end mass and the gradient value serving as the prediction result can be obtained only by respectively obtaining two observed quantities of the running speed and the acceleration through the speed sensor and the acceleration sensor, so that the cost of the sensors is reduced, the running speed, the vehicle end mass and the gradient value in the running data are arranged in the interval of [0, 1], the convergence time of the estimation of the mass and the gradient value is shortened, and the vehicle control parameters are effectively calibrated in real time.
Referring to fig. 4, an embodiment of the present application further provides a parallel driving vehicle control parameter adaptive calibration system, which includes a driving simulator, a parallel driving controller, and a server.
The driving simulator is used for sending the analog signal parameters to the server, and the server is used for forwarding the analog signal parameters to the parallel driving controller.
And the parallel driving controller is used for controlling the real vehicle to run according to the analog signal parameters and acquiring running data.
The server is further used for constructing a Kalman filtering prediction model according to the vehicle parameters and the running data, and acquiring vehicle end quality and gradient values serving as prediction results in real time by using the running data as observed quantities through the dynamic Kalman filtering prediction model.
The parallel driving controller is also used for constructing a calibration equation between the analog signal parameters and the vehicle control parameters, and calibrating the vehicle control parameters in real time according to the driving data, the vehicle end mass and the gradient value.
Further, when the vehicle control parameter exceeds a preset range, the parallel driving controller is further configured to recalibrate a mapping relationship between the analog signal parameter and the vehicle control parameter, so that the vehicle control parameter is kept within the preset range. The vehicle control parameters comprise a steering parameter, an accelerator parameter and a brake parameter.
The adaptive calibration system of the embodiment further comprises an On Board Unit (OBU). The OBU is in communication connection with the server through the 5G customer terminal equipment CPE. The OBU is used for sending the running data acquired by the parallel driving controller to the server and sending the prediction result acquired by the server to the parallel driving controller.
The OBU transmits the acquired and analyzed driving data to the 5GCPE through the network control module, and the 5GCPE performs secondary relay transmission to the server at the background.
The self-adaptive calibration system of the embodiment is suitable for the self-adaptive calibration method, and can estimate the quality of the parallel driving vehicle and the information of the road gradient where the parallel driving vehicle is located only by two observed quantities of the driving speed and the acceleration, so as to calibrate the vehicle control parameters in real time. When the vehicle control parameters exceed the preset range, the mapping relation between the analog signal parameters and the vehicle control parameters can be calibrated again, and the problems of low-efficiency vehicle control and potential safety hazards caused by the fact that a driver is in a remote unreal environment in a parallel driving system are effectively solved.
The present application is not limited to the above embodiments, and it will be apparent to those skilled in the art that various modifications and improvements can be made without departing from the principle of the present application, and such modifications and improvements are also considered to be within the scope of the present application.

Claims (9)

1. A self-adaptive calibration method for control parameters of a parallel driving vehicle is characterized by comprising the following steps:
the driving simulator sends simulation signal parameters to the parallel driving controller;
the parallel driving controller controls the real vehicle to run according to the analog signal parameters, acquires running data and sends the running data to the server through the on-board unit (OBU);
the server constructs a Kalman filtering prediction model according to the vehicle parameters and the running data, acquires vehicle end quality and gradient value serving as prediction results in real time by using the Kalman filtering prediction model and taking the running data as observed quantity, and transmits the prediction results to the parallel driving controller through the OBU;
the parallel driving controller constructs a calibration equation between the analog signal parameters and vehicle control parameters, and calibrates the vehicle control parameters in real time according to the driving data, the vehicle end mass and the gradient value;
the method for acquiring the vehicle end quality and the gradient value serving as the prediction result in real time by taking the driving data as the observed quantity specifically comprises the following steps:
the dynamic equation of the automobile system is taken as a state equation, the acceleration resistance is assumed as process noise,
ΣF＝Fair+Fg+Fμ+G
Figure FDA0003005559880000011
wherein, FairAs air resistance, FgFor acceleration resistance, FμIs rolling resistance, G isNoise, F is the driving force for vehicle running, TwheelIs engine torque, rwheelIs the wheel radius;
based on the form of converting the vehicle longitudinal dynamics model into a discrete state space, selecting a trigonometric function of the driving speed, the reciprocal of the vehicle end mass and the rolling resistance coefficient as a state quantity, the state equation can be described as follows,
Figure FDA0003005559880000021
wherein the content of the first and second substances,
Figure FDA0003005559880000022
is the state quantity estimated value at the time k, mu is the rolling resistance coefficient, beta is the gradient, VkFor speed of travel, m is the end mass, βμIs the inverse tangent value of the rolling resistance coefficient,
Figure FDA0003005559880000023
three state components;
discretizing the state equation to obtain a one-step prediction equation as follows,
Figure FDA0003005559880000024
Figure FDA0003005559880000025
Figure FDA0003005559880000026
Figure FDA0003005559880000027
wherein alpha is1For wind resistance related quantity, ρ is air density, CdAs aerodynamic coefficient, AfIs the frontal area, alpha2G is the acceleration of gravity,
Figure FDA0003005559880000028
which is the differential of the speed of the vehicle,
Figure FDA0003005559880000029
for the predicted value of the state quantity at time k to time k +1, TsCalculating a time step for the EKF;
the observation equation for building the kalman filter prediction model is as follows,
Figure FDA00030055598800000210
wherein Z isk+1Is the observed quantity at time k +1, akIs a measure of acceleration;
predicting value of state quantity at k +1 time by using k time
Figure FDA00030055598800000211
And observed quantity Z at time k +1k+1And estimating to obtain a state quantity estimated value at the moment of k +1, and obtaining real-time vehicle end quality and gradient value.
2. The adaptive calibration method for the control parameters of the parallel driving vehicle as claimed in claim 1, characterized by further comprising: and when the vehicle control parameter exceeds a preset range, recalibrating the mapping relation between the analog signal parameter and the vehicle control parameter.
3. The adaptive calibration method for the control parameters of the parallel driving vehicle as claimed in claim 1, characterized in that: the travel data includes a travel speed and an acceleration;
the acceleration is obtained through collection or through differential calculation of the running speed.
4. The adaptive calibration method for the control parameters of the parallel driving vehicle as claimed in claim 1, characterized by further comprising:
and when the real vehicle reaches the stop condition, stopping prediction, and obtaining the vehicle end mass and the gradient value as final values at the previous moment.
5. The parallel-drive vehicle control parameter adaptive calibration method according to claim 4, wherein the stop condition includes at least one of:
the brake pressure of the real vehicle is greater than a brake pressure threshold value;
the gear shifting flag bit of the gearbox of the real vehicle is true in the gear shifting process;
the current vehicle speed of the real vehicle is less than a vehicle speed threshold value;
the engine torque of the real vehicle is less than a torque threshold value;
after filtering, the engine torque change rate of the real vehicle is greater than a change rate threshold;
the current gear of the real vehicle is neutral or reverse gear.
6. The adaptive calibration method for the control parameters of the parallel driving vehicle as claimed in claim 1, wherein before calibrating the control parameters of the vehicle in real time, the method further comprises:
and carrying out normalization processing on the driving speed, the vehicle end mass and the gradient value in the driving data so as to enable the driving speed, the vehicle end mass and the gradient value to be positioned in a [0, 1] interval.
7. A parallel driving vehicle control parameter adaptive calibration system is characterized by comprising:
a driving simulator for transmitting analog signal parameters;
the parallel driving controller is used for controlling the real vehicle to run according to the analog signal parameters and acquiring running data;
the server is used for constructing a Kalman filtering prediction model according to the vehicle parameters and the running data, and acquiring vehicle end quality and gradient value serving as prediction results in real time by using the dynamic Kalman filtering prediction model and taking the running data as observed quantity;
the parallel driving controller is also used for constructing a calibration equation between the analog signal parameters and the vehicle control parameters and calibrating the vehicle control parameters in real time according to the driving data, the vehicle end mass and the gradient value;
the server is also used for taking the automobile system dynamics equation as a state equation, assuming the acceleration resistance as process noise,
∑F＝Fair+Fg+Fμ+G
Figure FDA0003005559880000041
wherein, FairAs air resistance, FgFor acceleration resistance, FμFor rolling resistance, G is system noise, F is vehicle running driving force, TwheelIs engine torque, rwheelIs the wheel radius;
the server is also used for converting the longitudinal dynamic model of the vehicle into a discrete state space form, selecting a trigonometric function of the driving speed, the reciprocal of the vehicle end mass and the rolling resistance coefficient as a state quantity, and the state equation can be described as follows,
Figure FDA0003005559880000042
wherein the content of the first and second substances,
Figure FDA0003005559880000043
is the state quantity estimated value at the time k, mu is the rolling resistance coefficient, beta is the gradient, VkFor speed of travel, m is the end mass, βμIs the inverse tangent value of the rolling resistance coefficient,
Figure FDA0003005559880000044
three state components;
the server is also used for discretizing the state equation to obtain a one-step prediction equation as follows,
Figure FDA0003005559880000045
Figure FDA0003005559880000051
Figure FDA0003005559880000052
Figure FDA0003005559880000053
wherein alpha is1For wind resistance related quantity, ρ is air density, CdAs aerodynamic coefficient, AfIs the frontal area, alpha2G is the acceleration of gravity,
Figure FDA0003005559880000054
which is the differential of the speed of the vehicle,
Figure FDA0003005559880000055
for the predicted value of the state quantity at time k to time k +1, TsCalculating a time step for the EKF;
the server also builds the observation equations for the kalman filter prediction model as follows,
Figure FDA0003005559880000056
wherein Z isk+1Is the observed quantity at time k +1, akTo accelerateA measure of the degree;
predicting value of state quantity at k +1 time by using k time
Figure FDA0003005559880000057
And observed quantity Z at time k +1k+1And estimating to obtain a state quantity estimated value at the moment of k +1, and obtaining real-time vehicle end quality and gradient value.
8. The parallel-drive vehicle control parameter adaptive calibration system of claim 7, wherein:
and when the vehicle control parameter exceeds a preset range, the parallel driving controller is also used for re-calibrating the mapping relation between the analog signal parameter and the vehicle control parameter.
9. The parallel-drive vehicle control parameter adaptive calibration system of claim 8, further comprising:
and the OBU is in communication connection with the server through the 5G customer terminal equipment CPE and is used for sending the running data acquired by the parallel driving controller to the server and sending the prediction result acquired by the server to the parallel driving controller.
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