CN117601872A - Rapid recognition method for flat pavement during running of electric automobile - Google Patents

Abstract

The invention provides a method for quickly identifying a flat road surface when an electric automobile runs, which CAN quickly and accurately identify the flat road surface by only utilizing three signals, namely a driving motor output torque, a brake master cylinder pressure and a wheel speed of a non-driving wheel, which CAN be acquired by a vehicle chassis CAN network. The method is insensitive to an estimated initial value in road gradient and whole vehicle mass estimation based on a double unscented Kalman filtering algorithm, so that two parameters can be estimated more reliably. By utilizing the estimation results of the two parameters, the invention can identify the flat road surface in real time according to the characteristic difference of the vehicle running on the flat road surface or the ramp road surface, thereby providing necessary priori knowledge for the development of the automobile dynamics control system such as braking force distribution and the like.

Description

Rapid recognition method for flat pavement during running of electric automobile

Technical Field

The invention belongs to the technical field of electric automobile driving braking force distribution control, and particularly relates to a flat pavement rapid identification method during electric automobile driving.

Background

When an electric automobile runs on a flat road surface and a ramp road surface, the load distribution of front and rear axles of the electric automobile has obvious difference, and if the electric automobile is not treated differently for roads with different gradients during braking of the electric automobile, the control efficiency of a braking force distribution system is necessarily affected, so that the safety and the economy of the electric automobile during running of the electric automobile are affected. To ensure the braking effect of the electric vehicle, it is critical to perform rapid and accurate flat road surface recognition to solve the problem. Some recent prior art approaches mostly adopt a way to measure the vehicle body posture by using sensors such as an on-board GNSS positioning module, an accelerometer, a gyroscope, etc. for road gradient estimation, or calculate the whole vehicle mass and the road gradient based on a kalman filtering algorithm, for example, chinese patent applications CN202211448013.6, CN202111417841.9, CN202111526176.7, CN201810548335.5, etc. However, the problems of inaccurate whole vehicle quality and road gradient estimation or incapability of meeting the requirements of algorithm instantaneity in the prior art still exist, so that the method for judging whether the current running road of the automobile is flat or not by only using the gradient estimation value with larger error is unreliable. In addition, various sensors used in the prior art often need additional mounting, which increases the cost of the device. Therefore, how to provide a flat pavement identification method with higher accuracy, strong real-time performance and low cost on the premise of unknown quality of the whole vehicle is a technical problem which needs to be solved in the field.

Disclosure of Invention

In view of the above, the invention provides a method for rapidly identifying a flat road surface when an electric automobile runs, which specifically comprises the following steps:

step one, building a vehicle longitudinal dynamics model aiming at an electric vehicle, and collecting output torque T of a driving motor in vehicle operation under a preset working condition _m Wheel speed omega of left and right non-driving wheel and brake master cylinder pressure p _rl And omega _rr An operating parameter;

secondly, taking a longitudinal vehicle speed u, a whole vehicle mass m and a road gradient i as state vectors, and simultaneously taking the longitudinal vehicle speed u as an observation vector to respectively establish a state equation and an observation equation of the system; based on the established vehicle longitudinal dynamics model and the acquired operation parameters, executing an unscented Kalman filtering algorithm to obtain road gradient estimated values at all moments

Step three, longitudinal vehicle speed u and whole vehicle mass m are used as state vectors, and longitudinal vehicle speedu is used as an observation vector at the same time, and a state equation and an observation equation of the system are respectively established; based on the established longitudinal dynamics model of the vehicle, the acquired operating parameters and the gradient estimation in step twoExecuting unscented Kalman filtering algorithm to obtain the whole vehicle quality estimation value +.>And calculating the change rate k of the whole vehicle quality estimation result along with time _m ；

Step four, the results of the step two and the step three are respectively matched with a set road gradient estimation threshold value i ₀ And a threshold k of the rate of change of the estimated value of the mass of the whole vehicle ₀ And comparing to identify whether the road on which the vehicle is currently located is a flat road surface.

Further, the vehicle longitudinal dynamics model in the first step is specifically based on the following steps: total transmission ratio i of drive train _g The total mechanical efficiency eta of the transmission system, the rolling radius r of the wheels and the hydraulic braking force proportionality coefficient k _p Coefficient of air resistance C _D Building vehicle parameters including a windward area A, a tire rolling resistance coefficient f and a rotating mass conversion coefficient delta; the reference longitudinal vehicle speed u is calculated by using the collected wheel speeds of the left non-driving wheel and the right non-driving wheel:

the working conditions of the operation parameter collection and test are selected to be carried out under the non-rain and snow weather conditions that the atmospheric temperature is 5-32 ℃, the atmospheric pressure is 91-104 kPa, the relative humidity is less than 95 percent, and the average wind speed at the position which is higher than 0.7m of the road surface is less than 3 m/s; the test vehicle starts from a static state on a straight road, and the road surface is hard, smooth and clean and ensures that the adhesion coefficient meets the requirement.

Further, in the second step, the state vector is specifically defined as x ₁ (t)＝[u(t),m(t),i(t)] ^T The observation vector is z ₁ (t) =u (t), where t is a continuous time variable; the established state equation takes the following form:

in the method, in the process of the invention,

the established observation equation specifically takes the following form:

wherein q ₁ (k-1) is the process noise of the system at time k-1; r is (r) ₁ (k) The observation noise of the system at the moment k; Δt is the calculated step length, ρ is the air density, g is the gravitational acceleration;

the unscented Kalman filtering algorithm is executed to obtain the road gradient estimated values at all timesThe specific flow of (a) comprises:

I. calculating x of each sigma point _1,1 、χ _1,i+1 、χ _1,i+4 And corresponding weight W _1,1 、W _1,i+1 、W _1,i+4 ：

Wherein, κ is any constant;an ith column representing a matrix square root;

II, calculating predicted values of the state vector and the corresponding covariance matrix:

wherein Q is ₁ A process noise covariance matrix;

thirdly, calculating predicted values of the observation vector and the corresponding covariance matrix:

wherein R is ₁ To observe the noise covariance matrix

IV, calculating Kalman gain:

calculating a state vector estimated value:

calculating covariance matrix:

further, in the third step, the state vector is specifically defined as x ₂ (t)＝[u(t),m(t)] ^T The observation vector is z ₂ (t) =u (t), where t is a continuous time variable; the established state equation takes the following form:

in the method, in the process of the invention,

the established observation equation specifically takes the following form:

wherein q ₂ (k-1) is the process noise of the system at time k-1; r is (r) ₂ (k) The observation noise of the system at the moment k; Δt is the calculated step length, ρ is the air density, g is the gravitational acceleration;

execution of trace-freeKalman filtering algorithm obtains whole vehicle quality estimated values at all momentsThe specific flow of (a) comprises:

I. calculating x of each sigma point _2,1 、χ _2,i+1 、χ _2,i+3 And corresponding weight W _2,1 、W _2,i+1 、W _2,i+3 ：

Wherein, κ is any constant;an ith column representing a matrix square root;

II, calculating predicted values of the state vector and the corresponding covariance matrix:

wherein Q is ₂ A process noise covariance matrix;

thirdly, calculating predicted values of the observation vector and the corresponding covariance matrix:

wherein R is ₂ To observe the noise covariance matrix

IV, calculating Kalman gain:

calculating a state vector estimated value:

calculating covariance matrix:

further, the change rate k of the whole vehicle quality estimation result along with time _m Specifically based on the following formula:

further, the specific process of identifying whether the road is a flat road surface in the fourth step includes:

(1) Setting a threshold i for road gradient estimation ₀ Threshold k of time change rate of whole vehicle quality estimated value ₀ ；

(2) The road gradient estimate is acquired once every certain time τ (τ=nΔt)And the whole vehicle quality estimation value->Rate of change over time k _m And sequentially compared with corresponding thresholds;

(3) If it meetsExecuting step (4), otherwise returning to executing step (2) and outputting a signal of 0;

(4) If satisfy k _m <k ₀ And (3) judging the current road to be a flat road surface and outputting a signal '1', otherwise, returning to the execution step (2) and outputting a signal '0'.

According to the method for quickly identifying the flat road surface during running of the electric automobile, which is provided by the invention, the flat road surface CAN be quickly and accurately identified by only utilizing three types of signals, namely the output torque of the driving motor, the pressure of the brake master cylinder and the wheel speed of the non-driving wheel, which CAN be acquired by the CAN network of the chassis of the automobile, and compared with the prior art, the method for quickly identifying the flat road surface is free from dependence on sensors such as a GNSS positioning module, an accelerometer and a gyroscope, and the cost is obviously reduced. The method is insensitive to an estimated initial value in road gradient and whole vehicle mass estimation based on a double unscented Kalman filtering algorithm, so that two parameters can be estimated more reliably. By utilizing the estimation results of the two parameters, the invention can identify the flat road surface in real time according to the characteristic difference of the vehicle running on the flat road surface or the ramp road surface, thereby providing necessary priori knowledge for the development of the automobile dynamics control system such as braking force distribution and the like.

Drawings

FIG. 1 is a graph of output torque of a drive motor and a reference longitudinal vehicle speed signal obtained in an example based on the present invention;

FIG. 2 is an estimation of road grade and vehicle mass in an example based on the present invention;

FIG. 3 is a graph showing real-time identification of actual road grade and flat road surface in an example according to the present invention;

fig. 4 is an algorithm flow of the present invention.

Detailed Description

The following description of the embodiments of the present invention will be made apparent and fully in view of the accompanying drawings, in which some, but not all embodiments of the invention are shown. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.

The method for quickly identifying the flat pavement when the electric automobile runs, as shown in fig. 4, specifically comprises the following steps:

step one, building a vehicle longitudinal dynamics model aiming at an electric vehicle, and collecting output torque T of a driving motor in vehicle operation under a preset working condition _m Wheel speed omega of left and right non-driving wheel and brake master cylinder pressure p _rl And omega _rr An operating parameter;

secondly, taking a longitudinal vehicle speed u, a whole vehicle mass m and a road gradient i as state vectors, and simultaneously taking the longitudinal vehicle speed u as an observation vector to respectively establish a state equation and an observation equation of the system; based on the established vehicle longitudinal dynamics model and the acquired operation parameters, executing an unscented Kalman filtering algorithm to obtain road gradient estimated values at all moments

Step three, longitudinal vehicle speed u and whole vehicle mass m are used as state vectors, and longitudinal vehicleThe speed u is simultaneously used as an observation vector, and a state equation and an observation equation of the system are respectively established; based on the established longitudinal dynamics model of the vehicle, the acquired operating parameters and the gradient estimation in step twoExecuting unscented Kalman filtering algorithm to obtain the whole vehicle quality estimation value +.>And calculating the change rate k of the whole vehicle quality estimation result along with time _m ；

Step four, the results of the step two and the step three are respectively matched with a set road gradient estimation threshold value i ₀ And a threshold k of the rate of change of the estimated value of the mass of the whole vehicle ₀ And comparing to identify whether the road on which the vehicle is currently located is a flat road surface.

In a preferred embodiment of the invention, the vehicle longitudinal dynamics model described in step one is based in particular on the method comprising: total transmission ratio i of drive train _g The total mechanical efficiency eta of the transmission system, the rolling radius r of the wheels and the hydraulic braking force proportionality coefficient k _p Coefficient of air resistance C _D Building vehicle parameters including a windward area A, a tire rolling resistance coefficient f and a rotating mass conversion coefficient delta; the reference longitudinal vehicle speed u is calculated by using the collected wheel speeds of the left non-driving wheel and the right non-driving wheel:

the working conditions of the operation parameter collection and test are selected to be carried out under the non-rain and snow weather conditions that the atmospheric temperature is 5-32 ℃, the atmospheric pressure is 91-104 kPa, the relative humidity is less than 95 percent, and the average wind speed at the position which is higher than 0.7m of the road surface is less than 3 m/s; the test vehicle starts from a static state on a straight road, and the road surface is hard, smooth and clean and ensures that the adhesion coefficient meets the requirement.

In the second step, the state vector is specifically defined as x ₁ (t)＝[u(t),m(t),i(t)] ^T Direction of observationThe amount is z ₁ (t) =u (t), where t is a continuous time variable; the established state equation takes the following form:

in the method, in the process of the invention,

the established observation equation specifically takes the following form:

wherein q ₁ (k-1) is the process noise of the system at time k-1; r is (r) ₁ (k) The observation noise of the system at the moment k; Δt is the calculated step length, ρ is the air density, g is the gravitational acceleration;

the unscented Kalman filtering algorithm is executed to obtain the road gradient estimated values at all timesThe specific flow of (a) comprises:

I. calculating x of each sigma point _1,1 、χ _1,i+1 、χ _1,i+4 And corresponding weight W _1,1 、W _1,i+1 、W _1,i+4 ：

Wherein, κ is an arbitrary constant, and n+κ=3 is generally taken;an ith column representing a matrix square root;

II, calculating predicted values of the state vector and the corresponding covariance matrix:

wherein Q is ₁ A process noise covariance matrix;

thirdly, calculating predicted values of the observation vector and the corresponding covariance matrix:

wherein R is ₁ To observe the noise covariance matrix

IV, calculating Kalman gain:

calculating a state vector estimated value:

calculating covariance matrix:

in the third step, the state vector is specifically defined as x ₂ (t)＝[u(t),m(t)] ^T The observation vector is z ₂ (t) =u (t), where t is a continuous time variable; the established state equation takes the following form:

in the method, in the process of the invention,

the established observation equation specifically takes the following form:

wherein q ₂ (k-1) is the process noise of the system at time k-1; r is (r) ₂ (k) The observation noise of the system at the moment k; Δt is the calculated step length, ρ is the air density, g is the gravitational acceleration;

the unscented Kalman filtering algorithm is executed to obtain the whole vehicle quality estimated value at each momentThe specific flow of (a) comprises:

I. calculating x of each sigma point _2,1 、χ _2,i+1 、χ _2,i+3 And corresponding weight W _2,1 、W _2,i+1 、W _2,i+3 ：

Wherein, κ is any constant;an ith column representing a matrix square root;

II, calculating predicted values of the state vector and the corresponding covariance matrix:

wherein Q is ₂ A process noise covariance matrix;

thirdly, calculating predicted values of the observation vector and the corresponding covariance matrix:

wherein R is ₂ To observe the noise covariance matrix

IV, calculating Kalman gain:

calculating a state vector estimated value:

calculating covariance matrix:

rate of change k of vehicle mass estimation result over time _m Specifically based on the following formula:

the specific process of identifying whether the road is a flat road surface in the fourth step comprises the following steps:

(1) Setting a threshold i for road gradient estimation ₀ Threshold k of time change rate of whole vehicle quality estimated value ₀ ；

(2) The road gradient estimate is acquired once every certain time τ (τ=nΔt)And the whole vehicle quality estimation value->Rate of change over time k _m And sequentially compared with corresponding thresholds;

(3) If it meetsExecuting step (4), otherwise returning to executing step (2) and outputting a signal of 0;

(4) If satisfy k _m <k ₀ And (3) judging the current road to be a flat road surface and outputting a signal '1', otherwise, returning to the execution step (2) and outputting a signal '0'.

Fig. 1-3 show specific examples of implementing the invention based on straight-line driving experiments of half-load (main driving position and right rear driving position loading, and the whole vehicle mass is 1732 kg) of a brand of front-drive pure electric vehicle. The experimental road consisted of a flat road section (average gradient of 1.22%) and an uphill road section (average gradient of 7.99%). Fig. 1 shows the output torque of the drive motor and a reference longitudinal vehicle speed signal. Fig. 2 shows the estimation results of the road gradient and the vehicle mass, wherein the broken line represents the true value and the solid line represents the estimated value. Fig. 3 shows the actual road gradient and the recognition result of the flat road surface, which proves that the invention can rapidly and reliably recognize the flat road surface and the ramp road surface.

It should be understood that, the sequence number of each step in the embodiment of the present invention does not mean that the execution sequence of each process should be determined by the function and the internal logic of each process, and should not limit the implementation process of the embodiment of the present invention.

Although embodiments of the present invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made therein without departing from the principles and spirit of the invention, the scope of which is defined in the appended claims and their equivalents.

Claims (6)

1. A method for quickly identifying a flat road surface when an electric automobile runs is characterized by comprising the following steps of: the method specifically comprises the following steps:

step one, building a vehicle longitudinal dynamics model aiming at an electric vehicle, and collecting output torque T of a driving motor in vehicle operation under a preset working condition _m Wheel speed omega of left and right non-driving wheel and brake master cylinder pressure p _rl And omega _rr An operating parameter;

secondly, taking a longitudinal vehicle speed u, a whole vehicle mass m and a road gradient i as state vectors, and simultaneously taking the longitudinal vehicle speed u as an observation vector to respectively establish a state equation and an observation equation of the system; based on the established vehicle longitudinal dynamics model and the acquired operation parameters, executing an unscented Kalman filtering algorithm to obtain road gradient estimated values at all moments

Step three, taking the longitudinal vehicle speed u and the whole vehicle mass m as state vectors, and simultaneously taking the longitudinal vehicle speed u as an observation vector, and respectively establishing a state equation and an observation equation of the system; based on the established longitudinal dynamics model of the vehicle, the acquired operating parameters and the gradient estimation in step twoThe unscented Kalman filtering algorithm is executed to obtain the whole vehicle quality estimated value at each momentAnd calculating the change rate k of the whole vehicle quality estimation result along with time _m ；

Step four, the results of the step two and the step three are respectively matched with a set road gradient estimation threshold value i ₀ And a threshold k of the rate of change of the estimated value of the mass of the whole vehicle ₀ And comparing to identify whether the road on which the vehicle is currently located is a flat road surface.

2. The method of claim 1, wherein: the longitudinal dynamics model of the vehicle in the first step is specifically based on the following steps: total transmission ratio i of drive train _g The total mechanical efficiency eta of the transmission system, the rolling radius r of the wheels and the hydraulic braking force proportionality coefficient k _p Coefficient of air resistance C _D Building vehicle parameters including a windward area A, a tire rolling resistance coefficient f and a rotating mass conversion coefficient delta; the reference longitudinal vehicle speed u is calculated by using the collected wheel speeds of the left non-driving wheel and the right non-driving wheel:

the working conditions of the operation parameter collection and test are selected to be carried out under the non-rain and snow weather conditions that the atmospheric temperature is 5-32 ℃, the atmospheric pressure is 91-104 kPa, the relative humidity is less than 95 percent, and the average wind speed at the position which is higher than 0.7m of the road surface is less than 3 m/s; the test vehicle starts from a static state on a straight road, and the road surface is hard, smooth and clean and ensures that the adhesion coefficient meets the requirement.

3. The method of claim 2, wherein: in the second step, the state vector is specifically defined as x ₁ (t)＝[u(t),m(t),i(t)] ^T The observation vector is z ₁ (t) =u (t), where t is a continuous time variable; the established state equation takes the following form:

in the method, in the process of the invention,

the established observation equation specifically takes the following form:

wherein q ₁ (k-1) is the process noise of the system at time k-1; r is (r) ₁ (k) The observation noise of the system at the moment k; Δt is the calculated step length, ρ is the air density, g is the gravitational acceleration;

the unscented Kalman filtering algorithm is executed to obtain the road gradient estimated values at all timesThe specific flow of (a) comprises:

I. calculating x of each sigma point _1,1 、χ _1,i+1 、χ _1,i+4 And corresponding weight W _1,1 、W _1,i+1 、W _1,i+4 ：

Wherein, κ is any constant;an ith column representing a matrix square root;

II, calculating predicted values of the state vector and the corresponding covariance matrix:

wherein Q is ₁ A process noise covariance matrix;

thirdly, calculating predicted values of the observation vector and the corresponding covariance matrix:

wherein R is ₁ To observe the noise covariance matrix

IV, calculating Kalman gain:

calculating a state vector estimated value:

calculating covariance matrix:

4. a method as claimed in claim 3, wherein: in the third step, the state vector is specifically defined as x ₂ (t)＝[u(t),m(t)] ^T The observation vector is z ₂ (t) =u (t), where t is a continuous time variable; the established state equation takes the following form:

in the method, in the process of the invention,

the established observation equation specifically takes the following form:

wherein q ₂ (k-1) is the system at the time of k-1Process noise; r is (r) ₂ (k) The observation noise of the system at the moment k; Δt is the calculated step length, ρ is the air density, g is the gravitational acceleration;

the unscented Kalman filtering algorithm is executed to obtain the whole vehicle quality estimated value at each momentThe specific flow of (a) comprises:

I. calculating x of each sigma point _2,1 、χ _2,i+1 、χ _2,i+3 And corresponding weight W _2,1 、W _2,i+1 、W _2,i+3 ：

Wherein, κ is any constant;an ith column representing a matrix square root;

II, calculating predicted values of the state vector and the corresponding covariance matrix:

wherein Q is ₂ A process noise covariance matrix;

thirdly, calculating predicted values of the observation vector and the corresponding covariance matrix:

wherein R is ₂ To observe the noise covariance matrix

IV, calculating Kalman gain:

calculating a state vector estimated value:

calculating covariance matrix:

5. the method of claim 4, wherein: rate of change k of vehicle mass estimation result over time _m Specifically based on the following formula:

6. the method of claim 5, wherein: the specific process of identifying whether the road is a flat road surface in the fourth step comprises the following steps:

(1) Setting a threshold i for road gradient estimation ₀ Threshold k of time change rate of whole vehicle quality estimated value ₀ ；

(2) The road gradient estimate is acquired once every certain time τ (τ=nΔt)And the whole vehicle quality estimation value->Rate of change over time k _m And sequentially compared with corresponding thresholds;

(3) If it meetsExecuting step (4), otherwise returning to executing step (2) and outputting a signal of 0;

(4) If satisfy k _m <k ₀ And (3) judging the current road to be a flat road surface and outputting a signal '1', otherwise, returning to the execution step (2) and outputting a signal '0'.