CN103264669B - Heavy vehicle weight real-time identification method based on CAN information and function principle - Google Patents

Abstract

The invention discloses a real-time identification method for heavy-duty vehicle quality based on CAN information and functional principles. The method obtains wheel speed, engine speed, actual engine torque percentage, gear position, accelerator pedal opening and closing speed through the vehicle CAN bus when the vehicle is running. Acceleration, clutch pedal signal and brake pedal signal; calculate the engine torque, acceleration and rotating mass conversion coefficient; establish the vehicle longitudinal dynamics equation based on the functional principle, on the basis of the dynamics equation, drive force and air resistance The work done by the equivalent external force generated is the input of the system, and the work done by the equivalent acceleration generated by the sum of rolling resistance, gradient resistance and acceleration resistance is regarded as an observable data vector, and the forgetting factor is selected according to the characteristics of the vehicle mass. Recursive least squares method for real-time estimation of heavy vehicle mass.

Description

A real-time identification method of heavy vehicle quality based on CAN information and functional principles

technical field

The invention relates to a method for real-time identification of the quality of a heavy vehicle, in particular to a method for real-time identification of the quality of a heavy vehicle based on CAN information and functional principles.

Background technique

For the dynamic measurement method of heavy-duty vehicle quality, the most common method is the dynamic truck scale, but the weighing platform cannot be installed on the vehicle body, and it is not convenient to obtain the actual vehicle mass online in real time.

Parameter estimation is an indirect measure to obtain the mass of a heavy vehicle. The required parameter information is measured by the sensors installed on the heavy-duty vehicle, and some algorithm is used to obtain the estimation result of the vehicle quality. This method can obtain the result of quality estimation online in real time, and the accuracy of the estimation result can be improved by improving the algorithm.

The vehicle mass directly affects the relationship between the vehicle's longitudinal force and its longitudinal acceleration. Starting from this simple fact, many researchers have proposed methods for estimating the vehicle's mass. One of the methods is a real-time updated recursive least squares estimator, which uses longitudinal forces, accelerations, and road gradients based on GPS measurements to determine vehicle mass and air resistance, but this method uses GPS measurements directly Calculation, denoising ability is not strong. In another method, the multi-rate forgetting factor is used to adapt to the time-varying nature of air resistance and road gradient, and the recursive least squares method is used to estimate the vehicle mass and road gradient. However, this method needs to use the engine characteristics to calculate the driving torque, which is relatively complex complex. In another method, Bachmann-Landau big-O symbols and singular perturbation theory are used to eliminate the influence of air resistance, slope resistance and rolling resistance in the longitudinal dynamics formula, but the convergence and accuracy of the quality estimation results depend on the Continuous incentives for vehicles. Another method uses the adaptive Kalman filter to estimate the vehicle mass, but the information required by this method comes from the standard sensors installed on the test vehicle, and the statistical characteristics of the noise need to be determined in advance, so the calculation is complicated.

Contents of the invention

The purpose of the present invention is to provide a high-accuracy heavy-duty vehicle quality real-time identification method based on CAN information and functional principles, so as to improve the precision of quality estimation results.

The present invention comprises the following steps:

Step 1: Obtain relevant parameters through CAN bus measurement, and input relevant parameters and vehicle inherent parameters into the calculation system to calculate the required parameters of the model;

Step 2: Establish a vehicle longitudinal dynamics model based on functional principles;

Step 3: Establish a recursive least squares quality identification model with a forgetting factor based on the vehicle longitudinal dynamics model based on the functional principle;

Step 4: Formulate quality identification conditions based on vehicle information;

Step 5: Input the parameters required by the model and the quality identification conditions into the RLS quality identification system to estimate the mass of the heavy-duty vehicle.

The concrete steps of described step one are:

(1) Obtain the wheel speed v, engine speed n, engine actual torque percentage T _% , gear position, accelerator pedal opening, clutch pedal signal and brake pedal signal through the vehicle CAN bus when the vehicle is running;

(2) Calculate the engine torque T _tq and acceleration and rotating mass conversion factor δ;

The engine torque _T in the model parameters can be calculated by engine speed n, engine actual torque percentage T _% and engine maximum torque T _max information;

The acceleration in the model parameters It can be obtained from the differential of the wheel speed v;

The rotating mass conversion factor δ in the model parameters can be calculated as follows:

$\mathrm{<span\; class="notranslate">\; \&delta;</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; +</span>\frac{\mathrm{<span\; class="notranslate">\; 1</span>}}{\mathrm{<span\; class="notranslate">\; m</span>}}\frac{\mathrm{<span\; class="notranslate">\; \&Sigma;</span>}{\mathrm{<span\; class="notranslate">\; I</span>}}_{\mathrm{<span\; class="notranslate">\; w</span>}}}{{\mathrm{<span\; class="notranslate">\; r</span>}}^{\mathrm{<span\; class="notranslate">\; 2</span>}}}<span\; class="notranslate">\; +</span>\frac{\mathrm{<span\; class="notranslate">\; 1</span>}}{\mathrm{<span\; class="notranslate">\; m</span>}}\frac{{\mathrm{<span\; class="notranslate">\; I</span>}}_{\mathrm{<span\; class="notranslate">\; f</span>}}{\mathrm{<span\; class="notranslate">\; i</span>}}_{\mathrm{<span\; class="notranslate">\; g</span>}}^{\mathrm{<span\; class="notranslate">\; 2</span>}}{\mathrm{<span\; class="notranslate">\; i</span>}}_{\mathrm{<span\; class="notranslate">\; 0</span>}}^{\mathrm{<span\; class="notranslate">\; 2</span>}}{\mathrm{<span\; class="notranslate">\; \&eta;</span>}}_{\mathrm{<span\; class="notranslate">\; T</span>}}}{{\mathrm{<span\; class="notranslate">\; r</span>}}^{\mathrm{<span\; class="notranslate">\; 2</span>}}}$

(3) Record the inherent parameters of the vehicle, such as the transmission ratio i _g of the final drive, the transmission ratio i ₀ of the final drive, the mechanical efficiency η _T of the drive train, the wheel radius r, the air resistance coefficient C _D , the windward area A, and the rolling resistance coefficient f. Moment of inertia I _w of the wheel, moment of inertia I _f of the flywheel and acceleration of gravity g.

(4) Input the above-mentioned parameters into the calculation system to calculate the required parameters of the model.

The concrete steps of described step 2 are:

(1) Establish vehicle longitudinal dynamics equation based on force method;

The vehicle longitudinal dynamics equation includes the gear ratio i _g of the transmission, the gear ratio i ₀ of the final drive, the mechanical efficiency η _T of the drive train, the air resistance coefficient C _D , the windward area A, the rolling resistance coefficient f, the wheel's Intrinsic parameters of moment of inertia I _w , flywheel moment of inertia _If and wheel radius r.

The vehicle longitudinal dynamics equation includes engine torque T _tq , acceleration and the calculation parameters of the rotating mass conversion factor δ.

(2) On the basis of the dynamic equation described in step (1), multiply both ends of the model by the vehicle speed v, and then integrate the time t at the same time to obtain the vehicle longitudinal dynamics model based on the functional principle.

The concrete method of described step 3 is:

On the basis of the longitudinal dynamic equation based on the functional principle described in step two, the work done by the equivalent external force generated by the driving force F _t and air resistance F _w is the system input, the work done by the equivalent acceleration generated by the sum of rolling resistance F _f , gradient resistance F _i and acceleration resistance F _j Considering it as an observable data vector, the heavy vehicle mass m is estimated in real time by using the recursive least squares method with forgetting factor.

In the heavy vehicle mass identification method, the vehicle mass m is a system parameter to be identified.

In the heavy vehicle mass identification method, aiming at improving the identification accuracy, the forgetting factor μ is selected according to the characteristics of the vehicle mass.

The detailed identification conditions in the step 4 are:

(1) The identification method is in the starting stage, that is, the first 200m after starting, the driving speed v is greater than 2m/s, the acceleration Execute when it is greater than -1.2m/s ² , the opening of the accelerator pedal is greater than 20%, neither the clutch nor the brake pedal is depressed, and it is not at the moment of shifting gears.

(2) The identification method is greater than or equal to 200m after the start of the halfway driving stage, the driving speed v is greater than 5m/s, the acceleration Between -0.1m/s ² and 0.1m/s ² , the opening of the accelerator pedal is greater than 20%, neither the clutch nor the brake pedal is depressed, the engine output torque T _tq is large enough, the gear is in the 3rd to 9th gear and no Execute when the absolute value of gear shifting and engine speed change rate is less than 100.

The beneficial effect of the present invention is: compared with the traditional quality identification method, the identification method only needs the information of the CAN bus of the vehicle or the sensor with lower cost to complete the identification process, and the identification method is easy to apply in real time and online, The recognition accuracy is high. The vehicle quality identification method of the invention is applicable to various roads and has strong applicability.

Description of drawings

Figure 1 is a schematic diagram of the force when the vehicle accelerates on a ramp.

Fig. 2 is a flow chart of quality identification in the present invention.

Detailed ways

Refer to Figure 1. The present invention comprises the following steps:

Step 1: Obtain relevant parameters through CAN bus measurement, and input relevant parameters and vehicle inherent parameters into the calculation system to calculate the required parameters of the model;

Step 2: Establish a vehicle longitudinal dynamics model based on functional principles;

Step 3: Establish a recursive least squares quality identification model with a forgetting factor based on the vehicle longitudinal dynamics model based on the functional principle;

Step 4: Formulate quality identification conditions based on vehicle information;

Step 5: Input the parameters required by the model and the quality identification conditions into the RLS quality identification system to estimate the mass of the heavy-duty vehicle.

The concrete steps of described step one are:

(1) Obtain the wheel speed v, engine speed n, engine actual torque percentage T _% , gear position, accelerator pedal opening, clutch pedal signal and brake pedal signal through the vehicle CAN bus when the vehicle is running;

(2) Calculate the engine torque T _tq and acceleration and rotating mass conversion factor δ;

The engine torque _T in the model parameters can be calculated by engine speed n, engine actual torque percentage T _% and engine maximum torque T _max information;

The acceleration in the model parameters It can be obtained from the differential of the wheel speed v;

The rotating mass conversion factor δ in the model parameters can be calculated as follows:

$\mathrm{<span\; class="notranslate">\; \&delta;</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; +</span>\frac{\mathrm{<span\; class="notranslate">\; 1</span>}}{\mathrm{<span\; class="notranslate">\; m</span>}}\frac{\mathrm{<span\; class="notranslate">\; \&Sigma;</span>}{\mathrm{<span\; class="notranslate">\; I</span>}}_{\mathrm{<span\; class="notranslate">\; w</span>}}}{{\mathrm{<span\; class="notranslate">\; r</span>}}^{\mathrm{<span\; class="notranslate">\; 2</span>}}}<span\; class="notranslate">\; +</span>\frac{\mathrm{<span\; class="notranslate">\; 1</span>}}{\mathrm{<span\; class="notranslate">\; m</span>}}\frac{{\mathrm{<span\; class="notranslate">\; I</span>}}_{\mathrm{<span\; class="notranslate">\; f</span>}}{\mathrm{<span\; class="notranslate">\; i</span>}}_{\mathrm{<span\; class="notranslate">\; g</span>}}^{\mathrm{<span\; class="notranslate">\; 2</span>}}{\mathrm{<span\; class="notranslate">\; i</span>}}_{\mathrm{<span\; class="notranslate">\; 0</span>}}^{\mathrm{<span\; class="notranslate">\; 2</span>}}{\mathrm{<span\; class="notranslate">\; \&eta;</span>}}_{\mathrm{<span\; class="notranslate">\; T</span>}}}{{\mathrm{<span\; class="notranslate">\; r</span>}}^{\mathrm{<span\; class="notranslate">\; 2</span>}}}$

(3) Record the inherent parameters of the vehicle, such as the transmission ratio i _g of the final drive, the transmission ratio i ₀ of the final drive, the mechanical efficiency η _T of the drive train, the wheel radius r, the air resistance coefficient C _D , the windward area A, and the rolling resistance coefficient f. Moment of inertia I _w of the wheel, moment of inertia I _f of the flywheel and acceleration of gravity g.

(4) Input the above-mentioned parameters into the calculation system to calculate the required parameters of the model.

The concrete steps of described step 2 are:

(1) Establish vehicle longitudinal dynamics equation based on force method;

The vehicle longitudinal dynamics equation includes the gear ratio i _g of the transmission, the gear ratio i ₀ of the final drive, the mechanical efficiency η _T of the drive train, the air resistance coefficient C _D , the windward area A, the rolling resistance coefficient f, the wheel's Intrinsic parameters of moment of inertia I _w , flywheel moment of inertia _If and wheel radius r.

The vehicle longitudinal dynamics equation includes engine torque T _tq , acceleration and the calculation parameters of the rotating mass conversion factor δ.

(2) On the basis of the dynamic equation described in step (1), multiply both ends of the model by the vehicle speed v, and then integrate the time t at the same time to obtain the vehicle longitudinal dynamics model based on the functional principle.

The concrete method of described step 3 is:

On the basis of the longitudinal dynamic equation based on the functional principle described in step two, the work done by the equivalent external force generated by the driving force F _t and air resistance F _w is the system input, the work done by the equivalent acceleration generated by the sum of rolling resistance F _f , gradient resistance F _i and acceleration resistance F _j Considering it as an observable data vector, the heavy vehicle mass m is estimated in real time by using the recursive least squares method with forgetting factor.

In the heavy vehicle mass identification method, the vehicle mass m is a system parameter to be identified.

In the heavy vehicle mass identification method, aiming at improving the identification accuracy, the forgetting factor μ is selected according to the characteristics of the vehicle mass.

The detailed identification conditions in the step 4 are:

(1) The identification method is in the starting stage, that is, the first 200m after starting, the driving speed v is greater than 2m/s, the acceleration Execute when it is greater than -1.2m/s ² , the opening of the accelerator pedal is greater than 20%, neither the clutch nor the brake pedal is depressed, and it is not at the moment of shifting gears.

(2) The identification method is greater than or equal to 200m after the start of the halfway driving stage, the driving speed v is greater than 5m/s, the acceleration Between -0.1m/s ² and 0.1m/s ² , the opening of the accelerator pedal is greater than 20%, neither the clutch nor the brake pedal is depressed, the engine output torque T _tq is large enough, the gear is in the 3rd to 9th gear and no Execute when the absolute value of gear shifting and engine speed change rate is less than 100.

As shown in Figure 1, when the vehicle is in the normal driving state, the dynamic equation of the vehicle when driving along the slope can be expressed as:

${\mathrm{<span\; class="notranslate">\; f</span>}}_{\mathrm{<span\; class="notranslate">\; t</span>}}<span\; class="notranslate">\; =</span>{\mathrm{<span\; class="notranslate">\; f</span>}}_{\mathrm{<span\; class="notranslate">\; f</span>}}<span\; class="notranslate">\; +</span>{\mathrm{<span\; class="notranslate">\; f</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; +</span>{\mathrm{<span\; class="notranslate">\; f</span>}}_{\mathrm{<span\; class="notranslate">\; w</span>}}<span\; class="notranslate">\; +</span>{\mathrm{<span\; class="notranslate">\; f</span>}}_{\mathrm{<span\; class="notranslate">\; j</span>}}<span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; )</span>$

In the formula, , is the driving force of the car;

F _f =mgf, is the rolling resistance;

F _i =mgi, is the slope resistance;

, is the air resistance;

, is the acceleration resistance.

Therefore, the following transformations can be done:

$\frac{{\mathrm{<span\; class="notranslate">\; T</span>}}_{\mathrm{<span\; class="notranslate">\; q</span>}}{\mathrm{<span\; class="notranslate">\; i</span>}}_{\mathrm{<span\; class="notranslate">\; g</span>}}{\mathrm{<span\; class="notranslate">\; i</span>}}_{\mathrm{<span\; class="notranslate">\; 0</span>}}{\mathrm{<span\; class="notranslate">\; \&eta;</span>}}_{\mathrm{<span\; class="notranslate">\; T</span>}}}{\mathrm{<span\; class="notranslate">\; r</span>}}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; mgf</span>}<span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; mgi</span>}<span\; class="notranslate">\; +</span>\frac{{\mathrm{<span\; class="notranslate">\; C</span>}}_{\mathrm{<span\; class="notranslate">\; D.</span>}}{\mathrm{<span\; class="notranslate">\; Av</span>}}^{\mathrm{<span\; class="notranslate">\; 2</span>}}}{\mathrm{<span\; class="notranslate">\; 21.15</span>}}<span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; \&delta;m</span>}\frac{\mathrm{<span\; class="notranslate">\; dv</span>}}{\mathrm{<span\; class="notranslate">\; dt</span>}}<span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 2</span>}<span\; class="notranslate">\; )</span>$

In the above formula, T _tq is the output torque of the engine, i _g is the transmission ratio of the transmission, i ₀ is the transmission ratio of the main reducer, η _T is the mechanical efficiency of the drive train, r is the radius of the wheel, g is the acceleration of gravity, f is the rolling resistance coefficient, i is the road gradient, C _D is the air resistance coefficient, A is the effective frontal area, v is the vehicle speed (m/s), is the longitudinal acceleration of the vehicle (m/s ² ); δ is the conversion factor of the rotating mass of the vehicle.

According to the method of the functional principle, the two ends of the formula (2) are multiplied by the speed v respectively, and the power expression is obtained:

$\frac{{\mathrm{<span\; class="notranslate">\; T</span>}}_{\mathrm{<span\; class="notranslate">\; q</span>}}{\mathrm{<span\; class="notranslate">\; i</span>}}_{\mathrm{<span\; class="notranslate">\; g</span>}}{\mathrm{<span\; class="notranslate">\; i</span>}}_{\mathrm{<span\; class="notranslate">\; 0</span>}}{\mathrm{<span\; class="notranslate">\; \&eta;</span>}}_{\mathrm{<span\; class="notranslate">\; T</span>}}}{\mathrm{<span\; class="notranslate">\; r</span>}}<span\; class="notranslate">\; \&CenterDot;</span>\mathrm{<span\; class="notranslate">\; v</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; mgf</span>}<span\; class="notranslate">\; \&Center\; Dot;</span>\mathrm{<span\; class="notranslate">\; v</span>}<span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; mgi</span>}<span\; class="notranslate">\; \&CenterDot;</span>\mathrm{<span\; class="notranslate">\; v</span>}<span\; class="notranslate">\; +</span>\frac{{\mathrm{<span\; class="notranslate">\; C</span>}}_{\mathrm{<span\; class="notranslate">\; D.</span>}}\mathrm{<span\; class="notranslate">\; A</span>}}{\mathrm{<span\; class="notranslate">\; 21.15</span>}}<span\; class="notranslate">\; \&CenterDot;</span>{\mathrm{<span\; class="notranslate">\; v</span>}}^{\mathrm{<span\; class="notranslate">\; 3</span>}}<span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; \&delta;m</span>}\frac{\mathrm{<span\; class="notranslate">\; dv</span>}}{\mathrm{<span\; class="notranslate">\; dt</span>}}<span\; class="notranslate">\; \&CenterDot;</span>\mathrm{<span\; class="notranslate">\; v</span>}<span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 3</span>}<span\; class="notranslate">\; )</span>$

Both ends of formula (3) are integrated with respect to time t at the same time, and the energy expression of vehicle longitudinal dynamics is obtained:

$<span\; class="notranslate">\; \&Integral;</span>\frac{{\mathrm{<span\; class="notranslate">\; T</span>}}_{\mathrm{<span\; class="notranslate">\; q</span>}}{\mathrm{<span\; class="notranslate">\; i</span>}}_{\mathrm{<span\; class="notranslate">\; g</span>}}{\mathrm{<span\; class="notranslate">\; i</span>}}_{\mathrm{<span\; class="notranslate">\; 0</span>}}{\mathrm{<span\; class="notranslate">\; \&eta;</span>}}_{\mathrm{<span\; class="notranslate">\; T</span>}}}{\mathrm{<span\; class="notranslate">\; r</span>}}\mathrm{<span\; class="notranslate">\; vdt</span>}<span\; class="notranslate">\; =</span><span\; class="notranslate">\; \&Integral;</span>\mathrm{<span\; class="notranslate">\; wxya</span>}<span\; class="notranslate">\; +</span><span\; class="notranslate">\; \&Integral;</span>\mathrm{<span\; class="notranslate">\; mgivdt</span>}<span\; class="notranslate">\; +</span><span\; class="notranslate">\; \&Integral;</span>\frac{{\mathrm{<span\; class="notranslate">\; C</span>}}_{\mathrm{<span\; class="notranslate">\; D.</span>}}\mathrm{<span\; class="notranslate">\; A</span>}}{\mathrm{<span\; class="notranslate">\; 21.15</span>}}{\mathrm{<span\; class="notranslate">\; v</span>}}^{\mathrm{<span\; class="notranslate">\; 3</span>}}\mathrm{<span\; class="notranslate">\; dt</span>}<span\; class="notranslate">\; +</span><span\; class="notranslate">\; \&Integral;</span>\mathrm{<span\; class="notranslate">\; \&delta;m</span>}\frac{\mathrm{<span\; class="notranslate">\; dv</span>}}{\mathrm{<span\; class="notranslate">\; dt</span>}}\mathrm{<span\; class="notranslate">\; vdt</span>}<span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 4</span>}<span\; class="notranslate">\; )</span>$

combine computable integral terms, ,have to:

$<span\; class="notranslate">\; \&Integral;</span>\frac{{\mathrm{<span\; class="notranslate">\; T</span>}}_{\mathrm{<span\; class="notranslate">\; q</span>}}{\mathrm{<span\; class="notranslate">\; i</span>}}_{\mathrm{<span\; class="notranslate">\; g</span>}}{\mathrm{<span\; class="notranslate">\; i</span>}}_{\mathrm{<span\; class="notranslate">\; 0</span>}}{\mathrm{<span\; class="notranslate">\; \&eta;</span>}}_{\mathrm{<span\; class="notranslate">\; T</span>}}}{\mathrm{<span\; class="notranslate">\; r</span>}}\mathrm{<span\; class="notranslate">\; vdt</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; mgf</span>}<span\; class="notranslate">\; \&Integral;</span>\mathrm{<span\; class="notranslate">\; vdt</span>}<span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; mg</span>}<span\; class="notranslate">\; \&Integral;</span>\mathrm{<span\; class="notranslate">\; ivdt</span>}<span\; class="notranslate">\; +</span>\frac{{\mathrm{<span\; class="notranslate">\; C</span>}}_{\mathrm{<span\; class="notranslate">\; D.</span>}}\mathrm{<span\; class="notranslate">\; A</span>}}{\mathrm{<span\; class="notranslate">\; 21.15</span>}}<span\; class="notranslate">\; \&Integral;</span>{\mathrm{<span\; class="notranslate">\; v</span>}}^{\mathrm{<span\; class="notranslate">\; 3</span>}}\mathrm{<span\; class="notranslate">\; dt</span>}<span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; \&delta;m</span>}<span\; class="notranslate">\; \&Integral;</span>\mathrm{<span\; class="notranslate">\; vdt</span>}<span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 5</span>}<span\; class="notranslate">\; )</span>$

After transforming formula (5), we get

$<span\; class="notranslate">\; \&Integral;</span>\frac{{\mathrm{<span\; class="notranslate">\; T</span>}}_{\mathrm{<span\; class="notranslate">\; q</span>}}{\mathrm{<span\; class="notranslate">\; i</span>}}_{\mathrm{<span\; class="notranslate">\; g</span>}}{\mathrm{<span\; class="notranslate">\; i</span>}}_{\mathrm{<span\; class="notranslate">\; 0</span>}}{\mathrm{<span\; class="notranslate">\; \&eta;</span>}}_{\mathrm{<span\; class="notranslate">\; T</span>}}}{\mathrm{<span\; class="notranslate">\; r</span>}}\mathrm{<span\; class="notranslate">\; vdt</span>}<span\; class="notranslate">\; -</span>\frac{{\mathrm{<span\; class="notranslate">\; C</span>}}_{\mathrm{<span\; class="notranslate">\; D.</span>}}\mathrm{<span\; class="notranslate">\; A</span>}}{\mathrm{<span\; class="notranslate">\; 21.15</span>}}{<span\; class="notranslate">\; \&Integral;</span>\mathrm{<span\; class="notranslate">\; v</span>}}^{\mathrm{<span\; class="notranslate">\; 3</span>}}\mathrm{<span\; class="notranslate">\; dt</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; mgf</span>}<span\; class="notranslate">\; \&Integral;</span>\mathrm{<span\; class="notranslate">\; vdt</span>}<span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; mg</span>}<span\; class="notranslate">\; \&Integral;</span>\mathrm{<span\; class="notranslate">\; ivdt</span>}<span\; class="notranslate">\; +</span><span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; \&delta;m</span>}<span\; class="notranslate">\; \&Integral;</span>\mathrm{<span\; class="notranslate">\; vdv</span>}<span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 6</span>}<span\; class="notranslate">\; )</span>$

After obtaining the vehicle longitudinal dynamic equation based on the functional principle as described above, the present invention will use the recursive least square method with forgetting factor to estimate the quality of the heavy vehicle. The algorithm adopted for estimating the vehicle mass according to the embodiment of the present invention will be introduced below.

First, take the work done by the equivalent external force as follows:

${\stackrel{<span\; class="notranslate">\; \&OverBar;</span>}{\mathrm{<span\; class="notranslate">\; E.</span>}}}_{\mathrm{<span\; class="notranslate">\; tw</span>}}<span\; class="notranslate">\; =</span><span\; class="notranslate">\; \&Integral;</span>\frac{{\mathrm{<span\; class="notranslate">\; T</span>}}_{\mathrm{<span\; class="notranslate">\; q</span>}}{\mathrm{<span\; class="notranslate">\; i</span>}}_{\mathrm{<span\; class="notranslate">\; g</span>}}{\mathrm{<span\; class="notranslate">\; i</span>}}_{\mathrm{<span\; class="notranslate">\; 0</span>}}{\mathrm{<span\; class="notranslate">\; \&eta;</span>}}_{\mathrm{<span\; class="notranslate">\; T</span>}}}{\mathrm{<span\; class="notranslate">\; r</span>}}\mathrm{<span\; class="notranslate">\; vdt</span>}<span\; class="notranslate">\; -</span>\frac{{\mathrm{<span\; class="notranslate">\; 3.6</span>}}^{\mathrm{<span\; class="notranslate">\; 2</span>}}{\mathrm{<span\; class="notranslate">\; C</span>}}_{\mathrm{<span\; class="notranslate">\; D.</span>}}\mathrm{<span\; class="notranslate">\; A</span>}}{\mathrm{<span\; class="notranslate">\; 21.15</span>}}<span\; class="notranslate">\; \&Integral;</span>{\mathrm{<span\; class="notranslate">\; v</span>}}^{\mathrm{<span\; class="notranslate">\; 3</span>}}\mathrm{<span\; class="notranslate">\; dt</span>}$

Take the work done by the equivalent acceleration as follows:

${\stackrel{<span\; class="notranslate">\; \&OverBar;</span>}{\mathrm{<span\; class="notranslate">\; E.</span>}}}_{\mathrm{<span\; class="notranslate">\; a</span>}}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; gf</span>}<span\; class="notranslate">\; \&Integral;</span>\mathrm{<span\; class="notranslate">\; vdt</span>}<span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; g</span>}<span\; class="notranslate">\; \&Integral;</span>\mathrm{<span\; class="notranslate">\; ivdt</span>}<span\; class="notranslate">\; +</span><span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; \&delta;</span>}<span\; class="notranslate">\; \&Integral;</span>\mathrm{<span\; class="notranslate">\; vdv</span>}$

According to the least squares format:

In the formula, is the output of the system;

is an observable data vector;

θ=m is the system parameter to be identified;

ω(t) is random noise with zero mean.

Discretize formula (7), we can get:

As the recursive algorithm grows with time and the amount of data increases, the phenomenon of "data saturation" will occur, so the concept of forgetting factor is introduced. From this, the iterative format of the recursive least squares quality identification method with forgetting factor can be obtained:

$\left\{\begin{array}{c}\stackrel{<span\; class="notranslate">\; ^</span>}{\mathrm{<span\; class="notranslate">\; m</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; k</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; =</span>\stackrel{<span\; class="notranslate">\; ^</span>}{\mathrm{<span\; class="notranslate">\; m</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; k</span>}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; \&gamma;</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; k</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; [</span>{\stackrel{<span\; class="notranslate">\; \&OverBar;</span>}{\mathrm{<span\; class="notranslate">\; E.</span>}}}_{\mathrm{<span\; class="notranslate">\; tw</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; k</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; -</span>{\stackrel{<span\; class="notranslate">\; \&OverBar;</span>}{\mathrm{<span\; class="notranslate">\; E.</span>}}}_{\mathrm{<span\; class="notranslate">\; a</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; k</span>}<span\; class="notranslate">\; )</span>\stackrel{<span\; class="notranslate">\; ^</span>}{\mathrm{<span\; class="notranslate">\; m</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; k</span>}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; ]</span>\\ \mathrm{<span\; class="notranslate">\; \&gamma;</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; k</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; P</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; k</span>}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; )</span>{\stackrel{<span\; class="notranslate">\; \&OverBar;</span>}{\mathrm{<span\; class="notranslate">\; E.</span>}}}_{\mathrm{<span\; class="notranslate">\; a</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; k</span>}<span\; class="notranslate">\; )</span>{<span\; class="notranslate">\; [</span>{\stackrel{<span\; class="notranslate">\; \&OverBar;</span>}{\mathrm{<span\; class="notranslate">\; E.</span>}}}_{\mathrm{<span\; class="notranslate">\; a</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; k</span>}<span\; class="notranslate">\; )</span>\mathrm{<span\; class="notranslate">\; P</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; k</span>}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; )</span>{\stackrel{<span\; class="notranslate">\; \&OverBar;</span>}{\mathrm{<span\; class="notranslate">\; E.</span>}}}_{\mathrm{<span\; class="notranslate">\; a</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; k</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; \&mu;</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; k</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; ]</span>}^{<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 1</span>}}\\ \mathrm{<span\; class="notranslate">\; P</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; k</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; =</span>\frac{\mathrm{<span\; class="notranslate">\; 1</span>}}{\mathrm{<span\; class="notranslate">\; \&mu;</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; k</span>}<span\; class="notranslate">\; )</span>}<span\; class="notranslate">\; [</span>\mathrm{<span\; class="notranslate">\; I</span>}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; \&gamma;</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; k</span>}<span\; class="notranslate">\; )</span>{\stackrel{<span\; class="notranslate">\; \&OverBar;</span>}{\mathrm{<span\; class="notranslate">\; E.</span>}}}_{\mathrm{<span\; class="notranslate">\; a</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; k</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; ]</span>\mathrm{<span\; class="notranslate">\; P</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; k</span>}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; )</span>\end{array}\right.<span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 9</span>}<span\; class="notranslate">\; )</span>$

Among them, μ(k) is the forgetting factor, and the value range is (0,1];

${\stackrel{<span\; class="notranslate">\; \&OverBar;</span>}{\mathrm{<span\; class="notranslate">\; E.</span>}}}_{\mathrm{<span\; class="notranslate">\; tw</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; k</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; =</span>{<span\; class="notranslate">\; \&Integral;</span>}_{\mathrm{<span\; class="notranslate">\; k</span>}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 1</span>}}^{\mathrm{<span\; class="notranslate">\; k</span>}}\frac{{\mathrm{<span\; class="notranslate">\; T</span>}}_{\mathrm{<span\; class="notranslate">\; q</span>}}{\mathrm{<span\; class="notranslate">\; i</span>}}_{\mathrm{<span\; class="notranslate">\; g</span>}}{\mathrm{<span\; class="notranslate">\; i</span>}}_{\mathrm{<span\; class="notranslate">\; 0</span>}}{\mathrm{<span\; class="notranslate">\; \&eta;</span>}}_{\mathrm{<span\; class="notranslate">\; T</span>}}}{\mathrm{<span\; class="notranslate">\; r</span>}}\mathrm{<span\; class="notranslate">\; vdt</span>}<span\; class="notranslate">\; -</span>\frac{{\mathrm{<span\; class="notranslate">\; 3.6</span>}}^{\mathrm{<span\; class="notranslate">\; 2</span>}}{\mathrm{<span\; class="notranslate">\; C</span>}}_{\mathrm{<span\; class="notranslate">\; D.</span>}}\mathrm{<span\; class="notranslate">\; A</span>}}{\mathrm{<span\; class="notranslate">\; 21.15</span>}}{<span\; class="notranslate">\; \&Integral;</span>}_{\mathrm{<span\; class="notranslate">\; k</span>}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 1</span>}}^{\mathrm{<span\; class="notranslate">\; k</span>}}{\mathrm{<span\; class="notranslate">\; v</span>}}^{\mathrm{<span\; class="notranslate">\; 3</span>}}\mathrm{<span\; class="notranslate">\; dt</span>}<span\; class="notranslate">\; ;</span>$

${\stackrel{<span\; class="notranslate">\; \&OverBar;</span>}{\mathrm{<span\; class="notranslate">\; E.</span>}}}_{\mathrm{<span\; class="notranslate">\; a</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; k</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; gf</span>}{<span\; class="notranslate">\; \&Integral;</span>}_{\mathrm{<span\; class="notranslate">\; k</span>}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 1</span>}}^{\mathrm{<span\; class="notranslate">\; k</span>}}\mathrm{<span\; class="notranslate">\; vdt</span>}<span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; g</span>}{<span\; class="notranslate">\; \&Integral;</span>}_{\mathrm{<span\; class="notranslate">\; k</span>}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 1</span>}}^{\mathrm{<span\; class="notranslate">\; k</span>}}\mathrm{<span\; class="notranslate">\; ivdt</span>}<span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; \&delta;</span>}{<span\; class="notranslate">\; \&Integral;</span>}_{\mathrm{<span\; class="notranslate">\; k</span>}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 1</span>}}^{\mathrm{<span\; class="notranslate">\; k</span>}}\mathrm{<span\; class="notranslate">\; vdv</span>}<span\; class="notranslate">\; ;</span>$

Equation (9) is an expression based on the vehicle mass identification algorithm. The initial value of P is 1, and the initial value of m is 0.

In the above-mentioned vehicle quality identification algorithm invented, the least square method with forgetting factor is adopted. In the calculation process of the above-mentioned vehicle quality identification algorithm, in order to obtain the output value accurately, it is necessary to determine the forgetting factor used in the algorithm. The following describes the process of determining the forgetting factor according to the embodiment of the present invention.

Vehicle mass is a vehicle mass is a relatively stable slow variable. At the beginning of the identification, since the initial value may deviate greatly from the real value, a large trust decay is required, that is, a small forgetting factor should be selected, which can reduce the impact of the current large data error on the next moment ; When the identification result converges to the real value, the error between the quality identification result and the real value should be small, so a small trust decay is required, that is, a larger forgetting factor (closer to 1) is selected. Therefore, it is selected according to the following rules:

$\mathrm{<span\; class="notranslate">\; \&mu;</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 0.05</span>}<span\; class="notranslate">\; \&CenterDot;</span>{\mathrm{<span\; class="notranslate">\; 0.98</span>}}^{\mathrm{<span\; class="notranslate">\; t</span>}}<span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 10</span>}<span\; class="notranslate">\; )</span>$

Obviously, the accuracy of identification can be improved by choosing the value of μ reasonably.

Based on the above, the present invention proposes a new quality identification method for heavy vehicles. Compared with the traditional vehicle quality identification method, this identification method only needs the information of vehicle CAN bus or low-cost sensors to complete the identification process, and this identification method is easy to be applied in real time and online.

The vehicle quality identification method of the invention is applicable to various roads and has strong applicability.

Claims (1)

1. A real-time identification method for heavy vehicle quality based on CAN information and functional principles, the method comprising the following steps: Step 1: Obtain relevant parameters through CAN bus measurement, and input relevant parameters and vehicle inherent parameters into the calculation system to calculate the required parameters of the model; Step 2: Establish a vehicle longitudinal dynamics model based on functional principles; Step 3: Establish a recursive least squares quality identification model with a forgetting factor based on the vehicle longitudinal dynamics model based on the functional principle; Step 4: Formulate quality identification conditions based on vehicle information; Step 5: Input the parameters required by the model and the quality identification conditions into the RLS quality identification system to estimate the mass of the heavy-duty vehicle; The concrete steps of described step one are: (1) Obtain the wheel speed v, engine speed n, engine actual torque percentage T _% , gear position, accelerator pedal opening, clutch pedal signal and brake pedal signal through the vehicle CAN bus when the vehicle is running; (2) Obtain engine torque T _tq and acceleration by calculation and rotating mass conversion factor δ; The engine torque T _tq in the model parameters can be calculated by the engine speed n, the actual engine torque percentage T _% and the engine maximum torque T _max information; The acceleration in the model parameters It can be obtained from the differential of the wheel speed v; The rotating mass conversion factor δ in the model parameters can be calculated as follows: (3) Record the inherent parameters of the vehicle, such as the transmission ratio i _g , the transmission ratio i ₀ of the final drive, the mechanical efficiency η _T of the drive train, the wheel radius r, the air resistance coefficient C _D , the windward area A, and the rolling resistance coefficient f. The moment of inertia I _w of the wheel, the moment of inertia I _f of the flywheel and the acceleration of gravity g: (4) Input the above-mentioned parameters into the calculation system to calculate the required parameters of the model: The concrete steps of described step 2 are: (1) Establish vehicle longitudinal dynamics equation based on force method; The vehicle longitudinal dynamics equation includes the gear ratio i _g of the transmission, the gear ratio i ₀ of the final drive, the mechanical efficiency η _T of the drive train, the air resistance coefficient C _D , the windward area A, the rolling resistance coefficient f, the wheel's Intrinsic parameters of moment of inertia I _w , flywheel moment of inertia I _f and wheel radius r: The vehicle longitudinal dynamics equation includes engine torque T _tq , acceleration and the calculation parameters of the rotating mass conversion factor δ: (2) On the basis of the dynamic equation described in step (1), multiply both ends of the model by the vehicle speed v, and then integrate the time t simultaneously to obtain the vehicle longitudinal dynamics model based on the functional principle: The concrete method of described step 3 is: On the basis of the longitudinal dynamic equation based on the functional principle described in step two, the work done by the equivalent external force generated by the driving force F _t and air resistance F _w is the system input, the work done by the equivalent acceleration generated by the sum of rolling resistance F _f , gradient resistance F _i and acceleration resistance F _j As an observable data vector, use the recursive least squares method with forgetting factor to estimate the mass m of heavy vehicles in real time: In the heavy vehicle mass identification method, the vehicle mass m is the system parameter to be identified: In the heavy vehicle mass identification method described above, aiming at improving the accuracy of identification, the forgetting factor μ is selected according to the characteristics of the vehicle mass: The detailed identification conditions in the step 4 are: (1) The identification method is in the starting stage, that is, the first 200m after starting, the driving speed v is greater than 2m/s, the acceleration Execute when it is greater than -1.2m/s ² , the opening of the accelerator pedal is greater than 20%, neither the clutch nor the brake pedal is depressed, and it is not at the time of shifting gears: (2) The identification method is greater than or equal to 200m after being started in the midway driving stage, the driving speed v is greater than 5m/s, and the acceleration Between -0.1m/s ² and 0.1m/s ² , the opening of the accelerator pedal is greater than 20%, neither the clutch nor the brake pedal is depressed, the engine output torque T _tq is large enough, the gear is in the 3rd to 9th gear and no Execute when the absolute value of gear shifting and engine speed change rate is less than 100; The vehicle quality is a relatively stable slow variable; at the beginning of the identification, since the initial value may deviate greatly from the real value, a large trust decay is required, that is, a small forgetting factor should be selected, which can reduce The impact of the current large data error on the next moment; when the identification result converges to the real value, the error between the quality identification result and the real value should not be large, so a small trust decay is required, that is, a larger value is selected. The forgetting factor of is close to 1; so it is selected according to the following rules: μ(t)＝1-0.05· ^0.98t Obviously, the accuracy of identification can be improved by choosing the value of μ reasonably.