Electromechanical braking device based on optimal slip rate recognition and anti-lock control method

Abstract

The invention provides an electronic mechanical braking device based on optimal slip rate recognition and further provides an anti-lock control method adopting the electronic mechanical braking device. The ECU recognizes the braking intention of a driver according to the input of the electronic brake pedal, obtains the expected braking strength, calculates the required conventional braking torque, and drives the EMB to apply the corresponding braking torque; collecting a vehicle information signal, and calculating the optimal slip rate of the current road surface; the ABS controller calculates the ABS braking torque by adopting a fuzzy PID control method based on the difference between the current wheel slip rate and the optimal slip rate of the road surface; and the ECU judges the size of the conventional braking torque and the size of the ABS braking torque, and the slip rate of the vehicle is controlled to be close to the optimal slip rate. According to the method, the road surface optimal slip rate can be estimated in real time when the vehicle is braked and is used for ABS control, so that the vehicle can utilize the road surface adhesive force to the greatest extent when the vehicle is subjected to ABS braking on different road surfaces.

Description

Technical Field

[0001] The present invention relates to the technical field of wire-controlled braking, and in particular to an electronic mechanical braking device and an anti-lock braking control method based on optimal slip rate identification. Background Art

[0002] The braking system is the most important part of automobile safety. Traditional cars use a hydraulic braking system based on vacuum assistance, which makes it difficult to accurately control the braking force. Entering the new energy era, the demand for intelligence and electrification continues to increase, and the wire control brake system (brake bywire) is widely used in new energy vehicles. The wire control brake system is divided into wire control hydraulic brake (Electro-Hydraulic Brake, EHB) and wire control mechanical brake. Compared with EHB, EMB completely abandons hydraulic components, has a more flexible layout, and more precise control of braking force. Traditional vehicles use a logical threshold value method based on wheel angular acceleration and slip rate or their combination for ABS control. This method requires setting threshold values ​​according to different road surfaces and different models, and the braking force fluctuates greatly when it is applied, and it cannot be guaranteed to be controlled at the optimal slip rate. The wheel braking force of vehicles using EMB is independently controllable, and there is no need to set up a separate ABS control device. The ABS function can be effectively integrated, and a control method different from that of traditional vehicles is used to maximize the use of ground adhesion. Summary of the invention

[0003] The technical problem to be solved by the present invention is to provide an anti-lock control method of an electronic mechanical brake device based on optimal road slip rate identification.

[0004] In order to solve the problem described in the background technology, the present invention provides an electronic mechanical braking device based on optimal slip ratio recognition, comprising an electronic control unit ECU, an electronic pedal module, an ABS controller, an EMB control system, and a road surface recognition module, wherein the EMB control system comprises an EMB controller and an EMB actuator; the vehicle electronic control unit ECU recognizes the driver's braking intention according to the input of the electronic pedal module, obtains the expected braking intensity and calculates the required conventional braking torque, drives the EMB control system to apply the corresponding braking torque, collects the vehicle speed, wheel speed, wheel load and EMB braking torque signal, and calculates the current slip ratio and the utilized adhesion coefficient of the wheel; the road surface recognition module calculates the optimal slip ratio of the current road surface according to the slip ratio and the utilized adhesion coefficient; the ABS controller calculates the ABS braking torque by a fuzzy PID control method based on the difference between the current wheel slip ratio and the optimal slip ratio of the road surface; the electronic control unit ECU determines the size of the conventional braking torque and the ABS braking torque, enters the ABS control mode when the conventional braking torque is greater than the ABS braking torque, takes the ABS braking torque as the EMB input, adjusts the braking torque applied by the EMB control system, and controls the vehicle's slip ratio to be near the optimal slip ratio.

[0005] The present invention also provides an electronic mechanical anti-lock braking control method based on optimal slip ratio identification, comprising the above-mentioned electronic mechanical braking device, specifically comprising the following steps:

[0006] Step S1: The vehicle electronic control unit ECU identifies the driver's braking intensity a according to the electronic brake pedal module d The intention of the brake is determined and the required normal braking torque T0 is calculated;

[0007] Step S2: the EMB control system receives the braking force command from the ECU, controls the drive motor according to the built-in clamping force controller, and causes the EMB actuator in the EMB control system to apply a braking torque Tb;

[0008] Step S3: collecting vehicle speed, wheel speed, wheel load and EMB braking torque signals through the CAN network;

[0009] Step S4: Based on the collected wheel speed signal v w With the vehicle speed signal v c Calculate the current wheel slip rate s, calculate the wheel acceleration dw / dt based on the collected wheel speed signal, and calculate the wheel acceleration based on the wheel acceleration, the braking torque Tb of the EMB and the wheel load F z Calculate the adhesion coefficient

[0010] Step S5: Input the current slip rate and the utilized adhesion coefficient into the road surface recognition module, and the road surface recognition module calculates the optimal slip rate s under the current road surface through the Burckhardt tire model. op t;

[0011] Step S6: The ABS controller uses the deviation between the slip ratio and the optimal slip ratio and its change rate as input, and uses fuzzy control to adjust the PID control parameters to calculate the ABS braking torque T abs ;

[0012] Step S7: Determine the normal braking torque T0 and the ABS braking torque T abs size, take the smaller value as the EMB controller input, when T0>T abs Enter ABS control mode.

[0013] In the above-mentioned electronic mechanical anti-lock braking control method based on optimal slip ratio identification, the conventional braking torque calculation formula described in step S1 is:

[0014] T0=a d *F z *R

[0015] where a d is the expected braking deceleration, F z is the vertical load on the wheel, and R is the rolling radius of the wheel.

[0016] In the above-mentioned electronic mechanical anti-lock braking control method based on optimal slip ratio identification, the slip ratio calculation formula described in step S4 is:

[0017] s=(v c -v w ) / v c ,

[0018] The current road adhesion coefficient The calculation formula is as follows:

[0019]

[0020] Where J is the wheel moment of inertia and dw / dt is the wheel angular acceleration.

[0021] In the above-mentioned electronic mechanical anti-lock braking control method based on optimal slip ratio identification, the Burckhardt tire model described in step S5 is expressed as:

[0022]

[0023] Among them, c1, c2, c3 are parameters of different road surfaces.

[0024] In the above-mentioned electronic mechanical anti-lock braking control method based on optimal slip ratio identification, the road surface identification module in step S5 calculates the adhesion coefficients under 6 standard road surfaces according to the slip ratio s using the Burckhardt tire model formula. The similarity between the current road surface and the i-th road surface is calculated by the Lagrange interpolation basis function The optimal slip ratio s for the six road surfaces opti After multiplying them respectively and summing them, the optimal slip rate of the current road surface is estimated, where the similarity expression is:

[0025]

[0026] The expression for calculating the optimal slip ratio is:

[0027]

[0028] In the above-mentioned electronic mechanical anti-lock braking control method based on optimal slip ratio identification, the fuzzy controller in step S6 is based on the optimal slip ratio s opt The adjustment parameter ΔK is calculated based on the deviation e from the current slip ratio s and the rate of change of the deviation ec p , ΔK i , ΔK d , first respectively with their weight coefficient ω p ,ω i ,ω d Multiply it by the initial value K of the control parameter of the PID controller p0 , K i0 , K d0 Add them respectively to form a new control parameter K p , K i , K d The required braking torque T in ABS mode is obtained through PID calculation abs .

[0029] In the above-mentioned electronic mechanical braking anti-lock control method based on optimal slip ratio identification, the fuzzy domain of the deviation e is [-0.8, 0.2], the fuzzy domain of the deviation change rate ec is [-30, 30], and ΔK is set p , ΔK i , ΔK d The fuzzy domain is [-6,6]; the slip rate deviation e, slip rate deviation change rate ec, fuzzy control output parameter ΔK p , ΔK i , ΔK dEach of them is divided into the following seven fuzzy subsets: {NB, NM, NS, ZO, PS, PM, PB}, where NB means negative large, NM means negative medium, NS means negative small, ZO means zero, PS means positive small, PM means positive medium, and PB means positive large;

[0030] For the slip rate deviation e, Gaussian functions with different shapes are selected as the membership functions in the domain distribution interval, and the distribution interval is NB(-0.8, 0.1), NM(-0.55, 0.1), NS(-0.3, 0.1), ZO(-0.05, 0.05), PS(0.03, 0.07), PM(0.03, 0.14), PB(0.03, 0.2); for the slip rate deviation change rate ec, trigonometric functions are selected as the membership functions in different distribution intervals in the domain, and the distribution interval is NB[-40, -30, -20], NM[-30, -20, -10], NS[-20, -10, 0], ZO[-10, 0, 10], PS[0, 10, 20], PM[10, 20, 30], PB[20, 30, 40];

[0031] For ΔK p , ΔK i , ΔK d The domain distribution intervals of the membership function are all trigonometric functions, and the distribution intervals are NB[-8,-6,-4], NM[-6,-4,-2], NS[-4,-2,0], ZO[-2,0,2], PS[0,2,4], PM[2,4,6], PB[4,6,8], ΔK p , ΔK i , ΔK d Each has independent fuzzy control rules;

[0032] For ΔK p When the absolute value of the slip ratio deviation e is large, a larger value should be taken to improve the response speed; when the absolute value of e is medium, a medium value should be taken to control the overshoot of the control system and ensure a certain response speed; when the absolute value of e is small, a larger value should be taken to reduce the net difference;

[0033] For ΔK i When the absolute value of the slip ratio deviation e is large, a smaller value should be taken to reduce the integral saturation; when the absolute value of e is medium, a medium value should be taken to control the overshoot of the control system and ensure a certain response speed; when the absolute value of e is small, a larger value should be taken to reduce the net difference;

[0034] For ΔK d When the absolute value of the slip ratio deviation e is large, a larger value should be taken to reduce overshoot; when the absolute value of e is medium, a medium value should be taken; when the absolute value of e is small, a smaller value should be taken to reduce the adjustment time; the centroid method is used to clarify the reasoning results, and the calculation formula is:

[0035]

[0036] In the formula, u represents the output of the fuzzy controller, y i is the center of the fuzzy set corresponding to the i-th rule, A(y i ) is the area under the membership function corresponding to the i-th rule;

[0037] The initial value of the fuzzy PID controller is K p0 , K i0 , K d0 , the output value of the fuzzy rule ΔK p , ΔK i , ΔK d First, their weight coefficients ω p ,ω i ,ω d Multiply them, and then add the initial values ​​of the control parameters to get the proportional, integral, and differential control parameters, namely:

[0038] K p =K p0 +ω p *ΔK p ;

[0039] K i =K i0 +ω i *ΔK i ;

[0040] K p =K d0 +ω d *ΔK d ;

[0041] The expression of the braking torque T_abs of the ABS braking mode is:

[0042] T abs =K p e+K i ∫edt+K d ·ec.

[0043] The remarkable effects of the present invention are:

[0044] (1) Real-time identification of the type of road surface on which the wheel is currently riding and calculation of its optimal slip ratio for ABS control. This allows the vehicle to maintain the optimal slip ratio when performing ABS braking on different road surfaces, making the most of road adhesion and improving ABS braking experience.

[0045] (2) The fuzzy control method is used to realize the self-tuning of PID control parameters, which improves the problem of unstable control effect when switching road surfaces under fixed parameter control. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] Figure 1 A system block diagram of an electronic mechanical braking device based on optimal slip ratio identification according to the present invention;

[0047] Figure 2 is a flow chart of an anti-lock control method of an electronic mechanical brake device based on optimal slip ratio calculation according to the present invention;

[0048] Figure 3 It is the relationship between slip rate and road adhesion coefficient of different types of road surfaces in the Burckhardt tire model;

[0049] Figure 4 This is the ABS control block diagram of the fuzzy PID of the present invention;

[0050] Figure 5 It is the membership function graph of slip rate deviation e;

[0051] Figure 6 It is the membership function graph of slip ratio deviation change rate ec;

[0052] Figure 7 It is the membership function graph of control parameters ΔKp, ΔKi, ΔKd;

[0053] Figure 8 It is the fuzzy rule table of ΔKp, ΔKi, and ΔKd. DETAILED DESCRIPTION

[0054] In order to further illustrate the present invention, specific embodiments are described below in conjunction with the accompanying drawings. However, it should be understood that these descriptions are only for further illustrating the features and advantages of the present invention, rather than limiting the claims of the present invention.

[0055] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly connected by those skilled in the art to which the present invention belongs. The terms used in the specification of the present invention herein are for describing specific embodiments and are not intended to limit the present invention.

[0056] refer to Figure 1 and Figure 2The present invention provides an electronic mechanical braking device based on optimal slip ratio recognition, comprising an electronic control unit ECU, an electronic pedal module, an ABS controller, an EMB control system, and a road surface recognition module, wherein the EMB control system comprises an EMB controller and an EMB actuator; the vehicle electronic control unit ECU recognizes the driver's braking intention according to the input of the electronic pedal module, obtains the expected braking intensity and calculates the required conventional braking torque, drives the EMB control system to apply the corresponding braking torque, collects the vehicle speed, wheel speed, wheel load and EMB braking torque signal, and calculates the current slip ratio and the utilized adhesion coefficient of the wheel; the road surface recognition module calculates the optimal slip ratio of the current road surface according to the slip ratio and the utilized adhesion coefficient; the ABS controller calculates the ABS braking torque by a fuzzy PID control method based on the difference between the current wheel slip ratio and the optimal slip ratio of the road surface; the electronic control unit ECU determines the size of the conventional braking torque and the ABS braking torque, enters the ABS control mode when the conventional braking torque is greater than the ABS braking torque, takes the ABS braking torque as the EMB input, adjusts the braking torque applied by the EMB control system, and controls the vehicle's slip ratio to be near the optimal slip ratio.

[0057] An electronic mechanical anti-lock braking control method based on optimal slip ratio identification specifically comprises the following steps:

[0058] Step S1: The electronic brake pedal module identifies the driver's braking intensity a according to the distance, speed and force of the driver's pedal pressing. d The electronic control unit ECU determines the braking intensity a d Calculate the required normal braking torque T0;

[0059] Step S2: The EMB control system receives the braking force command from the ECU, and its built-in clamping force controller controls the EMB actuator, which includes a drive motor, a deceleration torque increasing mechanism, and a motion conversion mechanism. The torque of the drive motor is increased by the deceleration torque increasing mechanism, and the rotational motion is converted into the clamping force required for braking by the motion conversion mechanism to apply a braking torque Tb to the wheel;

[0060] Step S3: The vehicle collects wheel speed signals, vehicle speed signals, and EMB brake torque signals through a CAN network, wherein the wheel speed signals can be obtained by wheel speed sensors on the wheels, the vehicle speed signals can be obtained by a vehicle inertial navigation system, and the braking torque signals can be obtained by an EMB control system;

[0061] Step S4: Based on the wheel speed signal v w With the vehicle speed signal v cCalculate the current wheel slip rate s, calculate the wheel deceleration dw / dt based on the wheel speed signal, and calculate the current road adhesion coefficient based on the wheel deceleration, the EMB braking torque and the wheel load

[0062] The slip ratio is calculated as follows:

[0063] s=(v c -v w ) / v c (1)

[0064] The conventional braking torque T0 calculation formula is as follows:

[0065] T0=a d *F z *R (2)

[0066] Among them, F z is the vertical load of the wheel, and R is the rolling radius of the wheel.

[0067] The calculation formula of the current road adhesion coefficient is as follows:

[0068]

[0069] Where J is the moment of inertia of the tire

[0070] Step S5: Input the current slip rate and the current road adhesion coefficient into the road recognition module, and the road recognition module calculates the optimal slip rate s under the current road surface through the Burckhardt tire model. opt ;

[0071] Step S6: The ABS controller takes the deviation between the slip rate and the optimal slip rate and the rate of change thereof as input, and the braking torque of the ABS braking mode is calculated and determined by a fuzzy PID controller, wherein the fuzzy PID controller includes a fuzzy controller and a PID controller, and the fuzzy controller calculates the adjustment parameter ΔK according to the deviation e between the optimal slip rate s_opt and the current slip rate s and the rate of change ec of the deviation. p , ΔK i , ΔK d , first respectively with its weight coefficient ω p ,ω i ,ω d Multiply it by the initial value K of the control parameter of the PID controller p0 , K i0 , K d0 Add them respectively to form a new control parameter K p , K i , Kd The required braking torque T in ABS mode is obtained by PID calculation of the slip ratio deviation e. abs . ;

[0072] Step S7: Determine the normal braking torque T0 and the ABS braking torque T abs size, take the smaller value as the EMB controller input, when T0>T abs Enter ABS control mode;

[0073] The Burckhardt tire model is expressed as: The c1, c2, and c3 parameters corresponding to different road types are different. For details, please refer to the following table:

[0074] Road surface type c1 c2 c3 s_opt Dry Asphalt 1.2801 23.99 0.52 0.17 Dry cement 1.1973 25.168 2.3 0.16 Wet asphalt 0.857 33.822 0.347 0.13 cobblestone 0.4004 3.708 0.1204 0.14 Snow 0.1946 94.129 0.0646 0.06 ice 0.05 306.39 0.001 0.03

[0075] The road surface recognition module calculates the adhesion coefficients of 6 standard road surfaces using the Burckhardt tire model formula according to the current slip rate s. The similarity between the current road surface and the i-th road surface is calculated by the Lagrange interpolation basis function Then compare the optimal slip rate s of the six road surfaces opti By multiplying them separately and taking the sum, the optimal slip rate of the current road surface can be estimated.

[0076] The similarity and the optimal slip ratio estimate s opt The calculation formula is as follows

[0077]

[0078] After the ABS braking mode is triggered, the deviation e between the estimated value of the optimal slip rate of the road surface and the current slip rate value and the deviation change rate ec are input into the fuzzy controller. The fuzzy domain of e is [-0.8, 0.2], and the fuzzy domain of ec is [-30, 30]. Set ΔK p , ΔK i , ΔK d The fuzzy domain of is [-6,6].

[0079] The slip rate deviation e, slip rate deviation change rate ec, fuzzy control output parameter ΔK p , ΔK i , ΔK d Each of them is divided into the following seven fuzzy subsets: {NB, NM, NS, ZO, PS, PM, PB}, where NB represents large negative, NM represents medium negative, NS represents small negative, ZO represents zero, PS represents small positive, PM represents medium positive, and PB represents large positive.

[0080] For the slip rate deviation e, Gaussian functions with different shapes are selected as membership functions in the domain distribution interval, and the distribution interval is NB (-0.8, 0.1), NM (-0.55, 0.1), NS (-0.3, 0.1), ZO (-0.05, 0.05), PS (0.03, 0.07), PM (0.03, 0.14), PB (0.03, 0.2).

[0081] For the slip rate deviation change rate ec, trigonometric functions are selected as membership functions in different distribution intervals on the domain, and the distribution intervals are NB[-40,-30,-20], NM[-30,-20,-10], NS[-20,-10,0], ZO[-10,0,10], PS[0,10,20], PM[10,20,30], and PB[20,30,40].

[0082] For ΔK p , ΔK i , ΔK d The domain distribution interval of the membership function is trigonometric function, and the distribution interval is NB[-8,-6,-4], NM[-6,-4,-2], NS[-4,-2,0], ZO[-2,0,2], PS[0,2,4], PM[2,4,6], and PB[4,6,8].

[0083] ΔK p , ΔK i , ΔK d Each has an independent fuzzy control rule.

[0084] For ΔK p When the absolute value of the slip ratio deviation e is large, a larger value should be taken to improve the response speed; when the absolute value of e is medium, a medium value should be taken to control the overshoot of the control system and ensure a certain response speed; when the absolute value of e is small, a larger value should be taken to reduce the net difference;

[0085] For ΔK i When the absolute value of the slip ratio deviation e is large, a smaller value should be taken to reduce the integral saturation; when the absolute value of e is medium, a medium value should be taken to control the overshoot of the control system and ensure a certain response speed; when the absolute value of e is small, a larger value should be taken to reduce the net difference;

[0086] For ΔK d , when the absolute value of the slip ratio deviation e is large, a larger value should be taken to reduce overshoot; when the absolute value of e is medium, a medium value should be taken; when the absolute value of e is small, a smaller value should be taken to reduce the adjustment time;

[0087] The centroid method is used to clarify the reasoning results. The calculation formula is:

[0088]

[0089] In the formula, u represents the output of the fuzzy controller, y i is the center of the fuzzy set corresponding to the i-th rule, A(y i ) is the area under the membership function corresponding to the i-th rule.

[0090] The initial value of the fuzzy PID controller is K p0 , K i0 , K d0 , the output value of the fuzzy rule ΔK p , ΔK i , ΔK d First, their weight coefficients ω p ,ω i ,ω d Multiply them, and then add the initial values ​​of the control parameters to get the proportional, integral, and differential control parameters, namely:

[0091] K p =K p0 +ω p *ΔK p ;

[0092] K i =K i0 +ω i *ΔK i ;

[0093] K p =K d0 +ω d *ΔK d ;

[0094] The expression of the braking torque T_abs of the ABS braking mode is:

[0095] T abs =K p e+K i ∫edt+K d ·ec.

[0096] The above is a detailed introduction to an electronic mechanical braking device and an anti-lock control method based on optimal slip rate identification provided by an embodiment of the present invention. Specific examples are used herein to illustrate the principle and implementation mode of the present invention. The description of the above embodiments is only used to help understand the technical solutions disclosed by the present invention. At the same time, for those skilled in the art, according to the ideas of the present invention, there will be changes in the specific implementation modes and application scopes. In summary, the content of this specification should not be understood as a limitation of the present invention.

Claims

1. An electromechanical brake device based on optimal slip ratio identification, characterized in that: It includes an electronic control unit ECU, an electronic pedal module, an ABS controller, an EMB control system, and a road surface recognition module, wherein the EMB control system includes an EMB controller and an EMB actuator; the vehicle electronic control unit ECU recognizes the driver's braking intention according to the input of the electronic pedal module, obtains the expected braking intensity and calculates the required conventional braking torque, drives the EMB control system to apply the corresponding braking torque, collects the vehicle speed, wheel speed, wheel load and EMB braking torque signal, and calculates the current slip rate and utilization adhesion coefficient of the wheel; the road surface recognition module calculates the optimal slip rate of the current road surface according to the slip rate and the utilization adhesion coefficient; the ABS controller calculates the ABS braking torque by a fuzzy PID control method based on the difference between the current wheel slip rate and the optimal slip rate of the road surface; the electronic control unit ECU determines the size of the conventional braking torque and the ABS braking torque, and enters the ABS control mode when the conventional braking torque is greater than the ABS braking torque, takes the ABS braking torque as the EMB input, and adjusts the braking torque applied by the EMB control system.

2. An electronic mechanical anti-lock braking control method based on optimal slip ratio identification, characterized in that: The electronic mechanical brake device according to claim 1 specifically comprises the following steps: Step S1: The vehicle electronic control unit ECU identifies the driver's braking intensity a according to the electronic brake pedal module d The intention of the brake is determined and the required normal braking torque T0 is calculated; Step S2: the EMB control system receives the braking force command from the ECU, controls the drive motor according to the built-in clamping force controller, and causes the EMB actuator in the EMB control system to apply a braking torque Tb; Step S3: collecting vehicle speed, wheel speed, wheel load and EMB braking torque signals through the CAN network; Step S4: Based on the collected wheel speed signal v w With the vehicle speed signal v c Calculate the current wheel slip rate s, calculate the wheel acceleration dw / dt based on the collected wheel speed signal, and calculate the wheel acceleration based on the wheel acceleration, the braking torque Tb of the EMB and the wheel load F z Calculate the adhesion coefficient Step S5: Input the current slip rate and the utilized adhesion coefficient into the road surface recognition module, and the road surface recognition module calculates the optimal slip rate s under the current road surface through the Burckhardt tire model. op t; Step S6: The ABS controller uses the deviation between the slip ratio and the optimal slip ratio and its change rate as input, and uses fuzzy control to adjust the PID control parameters to calculate the ABS braking torque T abs ; Step S7: Determine the normal braking torque T0 and the ABS braking torque T abs size, take the smaller value as the EMB controller input, when T0>T abs Enter ABS control mode.

3. The electronic mechanical anti-lock braking control method based on optimal slip ratio identification according to claim 2, characterized in that: The conventional braking torque calculation formula described in step S1 is: T0=a d *F z *R where a d is the expected braking deceleration, F z is the vertical load on the wheel, and R is the rolling radius of the wheel.

4. The electronic mechanical anti-lock braking control method based on optimal slip ratio identification according to claim 2, characterized in that: The slip rate calculation formula described in step S4 is: s=(v c -v w ) / v c , The current road adhesion coefficient The calculation formula is as follows: Where J is the wheel moment of inertia and dw / dt is the wheel angular acceleration.

5. The electronic mechanical anti-lock braking control method based on optimal slip ratio identification according to claim 2, characterized in that: The Burckhardt tire model described in step S5 is expressed as: Among them, c1, c2, c3 are parameters of different road surfaces.

6. The electronic mechanical anti-lock braking control method based on optimal slip ratio identification according to claim 2, characterized in that: The road surface recognition module in step S5 calculates the adhesion coefficients of the six standard road surfaces using the Burckhardt tire model formula according to the slip rate s. The similarity between the current road surface and the i-th road surface is calculated by the Lagrange interpolation basis function The optimal slip ratio s for the six road surfaces opti After multiplying them respectively and summing them, the optimal slip rate of the current road surface is estimated, where the similarity expression is: The expression for calculating the optimal slip ratio is:

7. The electronic mechanical anti-lock braking control method based on optimal slip ratio identification according to claim 2, characterized in that: The fuzzy controller in step S6 is based on the optimal slip ratio s opt The adjustment parameter ΔK is calculated based on the deviation e from the current slip ratio s and the rate of change of the deviation ec p , ΔK i , ΔK d , first respectively with its weight coefficient ω p ,ω i ,ω d Multiply it by the initial value K of the control parameter of the PID controller p0 , K i0 , K d0 Add them respectively to form a new control parameter K p , K i , K d The required braking torque T in ABS mode is obtained through PID calculation abs .

8. The electronic mechanical anti-lock braking control method based on optimal slip ratio identification according to claim 7, characterized in that: The fuzzy domain of the deviation e is [-0.8, 0.2], the fuzzy domain of the deviation change rate ec is [-30, 30], and ΔK is set p , ΔK i , ΔK d The fuzzy domain is [-6,6]; the slip rate deviation e, slip rate deviation change rate ec, fuzzy control output parameter ΔK p , ΔK i , ΔK d Each of them is divided into the following seven fuzzy subsets: {NB, NM, NS, ZO, PS, PM, PB}, where NB means negative large, NM means negative medium, NS means negative small, ZO means zero, PS means positive small, PM means positive medium, and PB means positive large; For the slip rate deviation e, Gaussian functions with different shapes are selected as the membership functions in the domain distribution interval, and the distribution interval is NB(-0.8, 0.1), NM(-0.55, 0.1), NS(-0.3, 0.1), ZO(-0.05, 0.05), PS(0.03, 0.07), PM(0.03, 0.14), PB(0.03, 0.2); for the slip rate deviation change rate ec, trigonometric functions are selected as the membership functions in different distribution intervals in the domain, and the distribution interval is NB[-40, -30, -20], NM[-30, -20, -10], NS[-20, -10, 0], ZO[-10, 0, 10], PS[0, 10, 20], PM[10, 20, 30], PB[20, 30, 40]; For ΔK p , ΔK i , ΔK d The domain distribution intervals of the membership function are all trigonometric functions, and the distribution intervals are NB[-8,-6,-4], NM[-6,-4,-2], NS[-4,-2,0], ZO[-2,0,2], PS[0,2,4], PM[2,4,6], PB[4,6,8], ΔK p , ΔK i , ΔK d Each has independent fuzzy control rules; For ΔK p When the absolute value of the slip ratio deviation e is large, a larger value should be taken to increase the response speed; when the absolute value of e is medium, a medium value should be taken to control the overshoot of the control system and ensure a certain response speed; when the absolute value of e is small, a larger value should be taken to reduce the net difference; For ΔK i When the absolute value of the slip ratio deviation e is large, a smaller value should be taken to reduce the integral saturation; when the absolute value of e is medium, a medium value should be taken to control the overshoot of the control system and ensure a certain response speed; when the absolute value of e is small, a larger value should be taken to reduce the net difference; For ΔK d When the absolute value of the slip ratio deviation e is large, a larger value should be taken to reduce overshoot; when the absolute value of e is medium, a medium value should be taken; when the absolute value of e is small, a smaller value should be taken to reduce the adjustment time; the centroid method is used to clarify the reasoning results, and the calculation formula is: In the formula, u represents the output of the fuzzy controller, y i is the center of the fuzzy set corresponding to the i-th rule, A(y i ) is the area under the membership function corresponding to the i-th rule; The initial value of the fuzzy PID controller is K p0 , K i0 , K d0 , the output value of the fuzzy rule ΔK p , ΔK i , ΔK d First, their weight coefficients ω p ,ω i ,ω d Multiply them, and then add the initial values ​​of the control parameters to get the proportional, integral, and differential control parameters, namely: K p =K p0 +ω p *ΔK p ; K i =K i0 +ω i *ΔK i ; K p =K d0 +ω d *ΔK d ; The expression of the braking torque T_abs of the ABS braking mode is: T abs =K p ·e+K i ∫edt+K d ·ec。

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