A road surface feature recognition method based on electromechanical brake vehicle

Abstract

The application discloses a road surface feature recognition method based on an electromechanical brake vehicle, and comprises the following steps: determining whether each wheel clamping force sensor is normally working; if the sensor is normally working, directly obtaining the brake clamping force as the final brake clamping force; if the sensor is not working, obtaining the first brake clamping force through a motor model, obtaining the second brake clamping force through a brake friction plate model, and obtaining the final brake clamping force through a fuzzy logic weight algorithm; determining whether the wheel has a risk of being locked; if not, calculating the current ground longitudinal force of the wheel according to a wheel dynamics model; if yes, triggering a longitudinal force locking logic, and obtaining the current locking ground longitudinal force as the current ground longitudinal force of the wheel; combining the vertical force to calculate the current utilization adhesion coefficient of each wheel; and judging the road surface type where the vehicle is located based on the utilization adhesion coefficient of each wheel and by using a fuzzy rule. The application solves the problem of low recognition precision.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of automotive electromechanical braking, and particularly relates to a road surface feature recognition method based on an electromechanical brake vehicle. BACKGROUND

[0002] Vehicle driving safety is closely related to road surface features, and accurate identification of road surface types, such as uniform road surfaces, split road surfaces, and joint road surfaces, is a core prerequisite for vehicle stability control and brake system optimization. Traditional road surface recognition methods rely on additional sensors such as vehicle-mounted cameras and laser radars, which have high costs, are greatly affected by environmental light, and have a lagging response.

[0003] An electromechanical brake (EMB) as a new type of braking technology can directly obtain key parameters such as clamping force and motor torque, providing a new data source for road surface feature recognition.

[0004] However, existing EMB-based recognition methods rely on sensors for clamping force measurement, and lack reliable redundancy estimation schemes when the sensors fail, resulting in low recognition accuracy when the schemes are applied.

[0005] Therefore, there is an urgent need for a road surface feature recognition method based on an electromechanical brake vehicle to solve the problem of low recognition accuracy caused by the traditional EMB-based road surface feature recognition method, which only relies on sensor measurement of clamping force. SUMMARY

[0006] The present application provides a road surface feature recognition method based on an electromechanical brake vehicle to solve the problem of low recognition accuracy caused by the traditional EMB-based road surface feature recognition method, which only relies on sensor measurement of clamping force.

[0007] To achieve the above-mentioned purpose, the present application adopts the following technical solutions:

[0008] Determine whether the clamping force sensor of the electromechanical brake of each wheel is working normally, if the clamping force sensor is working normally, directly obtain the brake clamping force as the final brake clamping force, if the clamping force sensor fails, obtain the first brake clamping force through the motor model, obtain the second brake clamping force through the brake friction plate model, and fuse the final brake clamping force through the fuzzy logic weight algorithm;

[0009] After obtaining the final brake clamping force, determine whether the wheel has a risk of locking, if not, calculate the current ground longitudinal force of the wheel according to the dynamics model of the wheel, if so, trigger the longitudinal force locking logic, and obtain the current locking ground longitudinal force, and take the obtained locking ground longitudinal force as the current ground longitudinal force of the wheel;

[0010] According to the ground longitudinal force of each wheel and the final brake clamping force, the current utilization adhesion coefficient of each wheel is calculated;

[0011] Based on the utilization adhesion coefficient of each wheel, the road surface type where the vehicle is located is determined by using fuzzy rules.

[0012] To optimize the above technical solutions, the specific measures taken also include:

[0013] Further, the first brake clamping force is obtained through the motor model, including the following steps:

[0014] The motor model based on the torque of the permanent magnet synchronous motor is constructed, and the first brake clamping force is calculated :

[0015] ;

[0016] In the formula, is the q-axis current of the permanent magnet synchronous motor, is the number of pole pairs, is the magnetic flux of the permanent magnet, is the wheel rotational inertia, is the damping coefficient, is the mechanical angular velocity, is the angular acceleration, is the transmission ratio of the speed reduction mechanism, is the lead of the screw rod.

[0017] Further, the second brake clamping force is obtained through the brake friction plate model, including the following steps:

[0018] The brake friction plate model based on the deformation of the friction plate is constructed, the total angular displacement of the motor during braking is decomposed to obtain the angular displacement eliminating the mechanical clearance and the angular displacement of the elastic deformation of the friction plate , and the second brake clamping force generated by the deformation of the friction plate is calculated :

[0019] ;

[0020] In the formula, , , and are nonlinear stiffness coefficients.

[0021] Further, the final brake clamping force is obtained by fusing through the fuzzy logic weight algorithm, including the following steps:

[0022] The final brake clamping force is obtained by fusing through the fuzzy logic weight algorithm wherein, is a weight factor, is taken from [0, 1].

[0023] Further, the determination of the weight factor comprises the following steps:

[0024] The preset maximum clamping force is , the clamping force residual is defined as , the maximum residual limit is ;

[0025] The preset fixed period is defined, and the three membership functions of the last period clamping force , and , and respectively represent the low, medium and high three degrees of the last period clamping force ; the three membership functions of the clamping force residual , and , and respectively represent the small, medium and large three degrees of the clamping force residual ; the three membership functions of the weight factor , and , , and respectively represent the displacement dominant, balanced, torque dominant three degrees of the weight factor ;

[0026] The fuzzy rule base of the weight factor is established with rules, then for the th rule, the activation strength is calculated:

[0027] ;

[0028] wherein, represents taking the smaller value on both sides of the symbol as the operation result, represents the membership function of the last period clamping force in the th rule, the value range of and , represents the​​​​​ the clamping force residual of the rule,

[0029] aggregate the output fuzzy sets and defuzzify using the centroid method, and the weight factor is determined by dividing the universe of discourse [0, 1] into 100 discrete points and calculating the weighted sum, according to which the weight factor is determined:

[0030]

[0031] wherein denotes the "fuzzy or" of the results of the rules, denotes the output of the membership function corresponding to the weight factor determined according to the i-th rule, and wherein ; wherein is the i-th discrete point.

[0032] Further, the determination of whether the wheel is at risk of locking includes the following steps:

[0033] an index of locking tendency is introduced to evaluate the change tendency of the wheel angular deceleration; when , it is determined that the wheel is at risk of locking, otherwise, it is not at risk of locking, wherein is the change rate of the wheel angular deceleration.

[0034] Further, when it is determined that the wheel is not at risk of locking, the ground longitudinal force of the wheel is calculated according to the wheel dynamics model, including the following steps:

[0035] According to the wheel dynamics model, the relationship between the ground longitudinal force and the final brake clamping force is:

[0036]

[0037] wherein is the friction factor of the brake disc friction surface, is the effective braking radius of the brake disc, is the wheel moment of inertia, is the angular deceleration of the wheel, ​​​​​​​​​​​​The longitudinal force exerted on the wheel by the ground. Let be the rolling radius of the wheel.

[0038] Furthermore, if there is a risk of seizure, the longitudinal force locking logic is triggered, and the current locked ground longitudinal force is obtained, including the following steps:

[0039] When a risk of wheel lockup is detected, the longitudinal force locking logic is triggered, and the initial wheel speed at the current moment is recorded. And continuously monitor wheel speed When the wheel speed decreases to a preset threshold wheel speed At this point, it is considered that the wheel has entered the deep slip phase, at which time the longitudinal force on the ground is... As it approaches the road surface adhesion limit, the final brake clamping force is then determined. Calculate the longitudinal force locking the ground. for:

[0040] ;

[0041] In the formula, The wheel speed is The angular deceleration of the wheel at that time, The coefficient of friction of the brake disc friction surface. The effective braking radius of the brake disc. For the moment of inertia of the wheel, Let be the rolling radius of the wheel.

[0042] Furthermore, the step of calculating the current coefficient of adhesion for each wheel based on the longitudinal ground force and the final brake clamping force includes the following steps:

[0043] Based on the linear two-degree-of-freedom model of the vehicle, the height of the center of mass is set to... The wheelbase between the front and rear axles is set to , In the formula, This is the distance from the vehicle's center of gravity to the front axle. This is the distance from the vehicle's center of gravity to the rear axle.

[0044] Based on the longitudinal force on the ground of each wheel That is, the longitudinal force on the left front of the ground. Longitudinal force on the right front ground Longitudinal force on the left rear ground and right rear longitudinal force And the final brake clamping force of each wheel. That is, the clamping force of the left front final brake. Right front final brake clamping force Left rear final brake clamping force and right rear final brake clamping force , respectively, to obtain the left front utilization adhesion coefficient , the right front utilization adhesion coefficient , the left rear utilization adhesion coefficient and the right rear utilization adhesion coefficient :

[0045] ;

[0046] wherein, is the friction factor of the friction surface of the brake disc, is the mass of the vehicle, a is the deceleration of the vehicle, and g is the acceleration of gravity, is the effective braking radius of the brake disc, is the rolling radius of the wheel, is the moment of inertia of the wheel, is the angular deceleration of the left front wheel, is the angular deceleration of the right front wheel, is the angular deceleration of the left rear wheel, is the angular deceleration of the right rear wheel.

[0047] Further, the road surface type in which the vehicle is located is determined based on the utilization adhesion coefficients of the wheels, and the road surface state is provided to the vehicle stability control system by using fuzzy rules, including the following steps: according to the left front utilization adhesion coefficient , the right front utilization adhesion coefficient , the left rear utilization adhesion coefficient and the right rear utilization adhesion coefficient , three core features are calculated:

[0048] ;

[0049] ;

[0050] ;

[0051] wherein, is the overall average of the utilization adhesion coefficients of the four wheels, is the difference value of the utilization adhesion coefficients of the left and right wheels, is the difference value of the utilization adhesion coefficients of the front and rear wheels;

[0052] Then, the , and are normalized, and the corresponding , and are obtained; the fuzzy set of the difference variable is {small ( ), medium ( ), big ( )} and determine the membership functions of the three degrees of difference variable , and and use the difference variable membership function; make the fuzzy set of mean variable {low ( ), medium ( ), high ( )} and determine the membership functions of the three degrees of mean variable ; make the fuzzy set of road feature {uniform low ( ), uniform medium ( ), uniform high ( ), opposite ( ), butt joint ( ), mixed ( )} and determine the membership functions of the six degrees of road feature , and correspondingly, the elements of the fuzzy set of road feature are 1, 2, 3, 4, 5, 6 respectively;

[0053] According to the road adhesion coefficient characteristics of each wheel, make fuzzy rules; use fuzzy inference mechanism to infer according to the fuzzified input and fuzzy rules, and for each fuzzy rule, use product method to calculate the activation strength corresponding to the fuzzy rule:

[0054] ;

[0055] In the formula, and are the membership functions of , and corresponding to the fuzzy rule respectively, the value range of and , the value range of and , the value range of and ;

[0056] According to the activation strength , aggregate the output fuzzy set, divide the road feature into 100 discrete points in the range of [1, 7] and calculate the weighted sum; get the According to the down rounding method, and output the final result, according to the final result corresponding to the integer corresponding to the element in the fuzzy set of the road surface feature Confirm the current road surface condition:

[0057] ; ;

[0058] In the formula, The membership function of the output road surface feature corresponding to the first rule The value range of , , , , , And ; The discrete point of the first road surface feature .

[0059] The beneficial effects of the present application are:

[0060] The present application adopts a "direct measurement and double model redundant estimation" double mode to obtain the brake disc clamping force. When the sensor fails, it can be estimated by combining the motor torque model, the friction plate deformation model and the fuzzy logic weight algorithm, avoiding the recognition interruption caused by the dependence of a single sensor, and ensuring the continuity of the core data.

[0061] The present application introduces a locking logic of the skid tendency index and the longitudinal force, and when the wheel appears the risk of skid, the longitudinal force value approaching the limit of road adhesion can be locked, so that the problem of sudden drop in calculation accuracy of the ground longitudinal force in the wheel skid state and large deviation in estimation using the adhesion coefficient can be solved.

[0062] The present application does not need to additionally carry the sensors such as vehicle-mounted cameras and laser radars, and directly relies on the clamping force, motor torque, wheel speed and other parameters that can be obtained by the electronic mechanical brake to realize recognition, which not only reduces the hardware cost investment, but also avoids the problem that the external sensors are affected by environmental factors such as light and weather.

[0063] The present application realizes multi-dimensional data processing through fuzzy control logic, from clamping force fusion, utilization of adhesion coefficient calculation, to road type judgment based on "overall mean value, left-right difference value and front-rear difference value", all through efficient algorithm reasoning and de-fuzzification operation to output results, which can quickly and accurately identify uniform road surface, split road surface, butt joint road surface and mixed road surface, and provide timely and reliable road surface state basis for vehicle stability control. BRIEF DESCRIPTION OF DRAWINGS

[0064] Figure 1A flowchart of a road surface feature recognition method based on an electromechanical brake vehicle according to the present application. DETAILED DESCRIPTION

[0065] The present application will now be further described in greater detail in connection with the accompanying drawings.

[0066] As shown in the accompanying drawings, Figure 1 a road surface feature recognition method based on an electromechanical brake vehicle according to an embodiment of the present application comprises the following steps:

[0067] determining whether the clamping force sensor of the electromechanical brake of each wheel is working normally, if the clamping force sensor is working normally, directly obtaining the brake clamping force as the final brake clamping force, if the clamping force sensor fails, obtaining the first brake clamping force through a motor model, obtaining the second brake clamping force through a brake friction plate model, and obtaining the final brake clamping force through a fuzzy logic weight algorithm;

[0068] after obtaining the final brake clamping force, determining whether the wheel has the risk of locking, if not, calculating the current ground longitudinal force of the wheel according to the dynamics model of the wheel, if yes, triggering the longitudinal force locking logic and obtaining the current locking ground longitudinal force, taking the obtained locking ground longitudinal force as the current ground longitudinal force of the wheel;

[0069] calculating the current utilization adhesion coefficient of each wheel according to the ground longitudinal force of each wheel and the final brake clamping force;

[0070] judging the road type where the vehicle is located based on the utilization adhesion coefficient of each wheel using fuzzy rules.

[0071] The motor model for obtaining the first brake clamping force comprises the following steps:

[0072] a motor model based on the torque of a permanent magnet synchronous motor (PMSM) is constructed, and the equation of voltage and torque is:

[0073] ;

[0074] wherein, is the torque, , is the d-q axis current of the permanent magnet synchronous motor, which is obtained by collecting the current characteristics of the motor, , is the d-q axis inductance of the permanent magnet synchronous motor, is the number of pole pairs, is the magnetic flux of the permanent magnet;

[0075] for a surface-mounted permanent magnet synchronous motor, let then:

[0076] ;

[0077] Neglecting the Coulomb friction, static friction torque of the motor, the electromagnetic torque equation of the permanent magnet synchronous motor is:

[0078] ;

[0079] In the formula, is the wheel rotational inertia, is the damping coefficient, is the load torque of the motor rotor, that is, the ball screw resistance, is the mechanical angular velocity, is the angular acceleration, which is the corresponding angular velocity and angular acceleration ;

[0080] The ball screw cooperates with the speed reduction mechanism to convert the motor torque into the axial clamping force, so the first brake clamping force is derived from the motor dynamics equation The calculation equation is:

[0081] ;

[0082] In the formula, is the speed reduction mechanism transmission ratio, is the lead screw lead;

[0083] Then, the first brake clamping force is obtained by arranging The calculation equation is:

[0084] .

[0085] Wherein, the above brake friction plate model obtains the second brake clamping force, including the following steps:

[0086] The brake friction plate model is constructed based on the deformation of the friction plate. Considering that the electromechanical brake includes two stages of brake gap elimination and clamping force following when applying the brake force, the total angular displacement of the motor during braking is first decomposed:

[0087] ;

[0088] In the formula, is the total angular displacement of the motor during braking, is the angular displacement of eliminating the mechanical gap, is the angular displacement of the elastic deformation of the friction plate;

[0089] The second brake clamping force generated by the deformation of the friction plate is The nonlinear relationship satisfied is:

[0090] ;

[0091] In the formula, , , and are nonlinear stiffness coefficients, which are obtained by fitting bench test.

[0092] The final brake clamping force obtained by fusing through the fuzzy logic weight algorithm includes the following steps:

[0093] The final brake clamping force obtained by fusing through the fuzzy logic weight algorithm is:

[0094] ;

[0095] In the formula, is a weight factor, takes [0, 1], when the weight factor , completely trust , when , completely trust .

[0096] In the formula, the determination of the weight factor includes the following steps:

[0097] In a low load scenario, the electromagnetic torque estimation is preferred, mainly based on the fact that the mechanical structure has small internal friction in a low load scenario, and the transmission is relatively smooth; in a high load scenario, the displacement estimation is preferred, at this time the internal pressure of the ball screw is large, and the deformation can well reflect the value of the clamping force, in different situations, need to be dynamically adjusted, here a fuzzy control logic is used to adaptively estimate the weight factor ;

[0098] The preset maximum clamping force is , generally, the of a passenger car is about 30kN, define the clamping force residual , the maximum value of the residual is limited to ;

[0099] A preset fixed period is defined, and the membership function of the clamping force of the last period is:

[0100] ;

[0101] ;

[0102] ;

[0103] wherein, respectively represent low, medium, high three degrees of the clamping force of the last cycle, and 'exp()' is an exponential function, respectively correspond to low, medium, high three clamping force of the last cycle, and the proportional coefficient of the membership function can be preset according to experience;

[0104] The membership function of the clamping force residual error is defined as:

[0105] ;

[0106] ;

[0107] ;

[0108] wherein, respectively represent small, medium, large three degrees of the clamping force residual error , and 'exp()' is an exponential function, respectively correspond to small, medium, large three clamping force residual errors , and the proportional coefficient of the membership function can be preset according to experience;

[0109] The membership function of the weight factor is defined as:

[0110] ;

[0111] ;

[0112] ;

[0113] wherein, respectively represent displacement dominant, balanced, torque dominant three degrees of the weight factor , and 'exp()' is an exponential function, respectively correspond to displacement dominant, balanced, torque dominant three weight factors , and the proportional coefficient of the membership function can be preset according to experience;

[0114] The fuzzy rule base of the weight factor is established, and based on the physical characteristics that "the greater the clamping force, the more reliable the displacement estimation", the rules are designed as follows:

[0115] R1: when the clamping force of the last cycle is low, and the clamping force residual error e is small, the weight factor tends to be balanced;

[0116] R2: When the clamping force level of the previous cycle is low, and the clamping force residual e level is medium, the weight factor tends to be balanced; tends to be torque dominant;

[0117] R3: When the clamping force level of the previous cycle is low, and the clamping force residual e level is large, the weight factor tends to be torque dominant; tends to be torque dominant;

[0118] R4: When the clamping force level of the previous cycle is medium, and the clamping force residual e level is small, the weight factor tends to be balanced; tends to be balanced;

[0119] R5: When the clamping force level of the previous cycle is medium, and the clamping force residual e level is medium, the weight factor tends to be balanced; tends to be balanced;

[0120] R6: When the clamping force level of the previous cycle is medium, and the clamping force residual e level is large, the weight factor tends to be torque dominant; tends to be torque dominant;

[0121] R7: When the clamping force level of the previous cycle is high, and the clamping force residual e level is small, the weight factor tends to be displacement dominant; tends to be displacement dominant;

[0122] R8: When the clamping force level of the previous cycle is high, and the clamping force residual e level is medium, the weight factor tends to be displacement dominant; tends to be displacement dominant;

[0123] R9: When the clamping force level of the previous cycle is high, and the clamping force residual e level is large, the weight factor tends to be displacement dominant; tends to be displacement dominant;

[0124] Next, the activation strength of the rule is calculated, and for the i-th rule, the activation strength is calculated as follows:

[0125] ;

[0126] wherein, represents taking the smaller value of the two sides as the operation result, represents the membership function of the clamping force of the previous cycle in the i-th rule, ​​​The value range of , The membership function of the clamping force residual error of the rule in the th The value range of ;

[0127] The aggregated output fuzzy set:

[0128] ;

[0129] Wherein, The "fuzzy or" of the results of the 9 rules is taken, which is commonly implemented by taking the maximum value (Max) in engineering, The membership function output corresponding to the weight factor determined according to the rule in the th The value range of ;

[0130] Finally, the defuzzification is performed using the barycenter method, the weight factor is divided into 100 discrete points on the domain [0,1], and the weighted sum is calculated:

[0131] ;

[0132] Wherein, is the th ;

[0133] According to the calculation result, the weight factor is determined.

[0134] Wherein, the above-mentioned determination of whether the wheel has the risk of locking includes the following steps:

[0135] A locking trend index is introduced to evaluate the change trend of the wheel angular deceleration; when , it is determined that the wheel has the risk of locking, otherwise, it has no risk of locking, wherein, is the wheel angular deceleration change rate.

[0136] Wherein, the above-mentioned if there is no risk of locking, the ground longitudinal force of the wheel is calculated according to the wheel dynamics model, including the following steps:

[0137] The brake torque acting on the wheel is calculated according to the final brake clamping force :

[0138] ;

[0139] wherein, is the friction coefficient of the brake disc friction surface, is the effective braking radius of the brake disc;

[0140] According to the longitudinal dynamics equation, the brake torque on the wheel is obtained :

[0141] ;

[0142] wherein, is the moment of inertia of the wheel, is the angular deceleration of the wheel, which is written as a positive value according to the wheel deceleration, and the angular deceleration of the wheel can be obtained by differentiating the wheel speed, and the wheel speed is measured by the vehicle-mounted speed sensor, is the longitudinal force of the ground on the wheel, is the rolling radius of the wheel;

[0143] The relationship between the longitudinal force of the ground and the final brake clamping force is:

[0144] .

[0145] wherein, if there is a risk of locking, the longitudinal force locking logic is triggered, and the current locking ground longitudinal force is obtained, including the following steps:

[0146] When the wheel is locked, it cannot be simply considered that the brake clamping force and the wheel longitudinal force satisfy the above relationship, because the final brake clamping force of the electronic mechanical brake and the angular deceleration of the wheel can be quickly obtained through the vehicle communication bus, so corresponding processing needs to be made before the wheel is locked to ensure that a more reasonable wheel longitudinal force value can be calculated.

[0147] When the risk of locking is detected, the longitudinal force locking logic is triggered, the initial wheel speed at the current time is recorded , and the wheel speed is continuously monitored , when the wheel speed decreases to a preset threshold wheel speed , , it is considered that the wheel enters the deep slip stage, at this time the ground longitudinal force tends to the limit of road adhesion, then the locking ground longitudinal force is calculated according to the final brake clamping force :

[0148] ;

[0149] In the formula, is the wheel speed the angular deceleration of the wheel, the friction factor of the friction surface of the brake disc, the effective braking radius of the brake disc, the moment of inertia of the wheel, the rolling radius of the wheel.

[0150] wherein the vertical force resulting from the combined braking deceleration is further used to calculate the current utilization adhesion coefficient of each wheel, including the following steps:

[0151] The wheel utilization adhesion coefficient calculation formula is:

[0152] ;

[0153] wherein, is the vertical force;

[0154] According to the linear two-degree-of-freedom model of the vehicle, the height of the center of mass is set as , and the front and rear axle track is set as , wherein, is the distance from the center of mass of the vehicle to the front axle, is the distance from the center of mass of the vehicle to the rear axle;

[0155] During braking, due to the effect of inertial force, the front axle load of the vehicle increases, and the rear axle load decreases, so the vertical load of the front axle and the vertical load of the rear axle are respectively:

[0156] ;

[0157] ;

[0158] wherein, is the mass of the vehicle, a is the vehicle braking deceleration, and g is the acceleration of gravity;

[0159] According to the ground longitudinal force of each wheel , i.e. the left front ground longitudinal force , the right front ground longitudinal force , the left rear ground longitudinal force , and the right rear ground longitudinal force , wherein, , , , are the marks of the left front wheel, the right front wheel, the left rear wheel, and the right rear wheel, respectively, and the left front utilization adhesion coefficient , the right front utilization adhesion coefficient , the left rear utilization adhesion coefficient , and the right rear utilization adhesion coefficient is:

[0160]

[0161] When the final brake clamp force can be read directly by the clamp force sensor, and the longitudinal force locking logic is not triggered, then according to the ground longitudinal force and the final brake clamp force of each wheel , i.e. the left front final brake clamp force , the right front final brake clamp force , the left rear final brake clamp force , the right rear final brake clamp force , the final expression of each wheel using the adhesion coefficient is obtained as:

[0162]

[0163] When the clamp force sensor fails, and the longitudinal force locking logic is not triggered, then the final expression of each wheel using the adhesion coefficient is obtained as:

[0164]

[0165] In the formula, is the weight factor of the left front wheel, is the weight factor of the right front wheel, is the weight factor of the left rear wheel, is the weight factor of the right rear wheel, is the first brake clamp force of the left front wheel, is the first brake clamp force of the right front wheel, is the first brake clamp force of the left rear wheel, is the first brake clamp force of the right rear wheel, is the second brake clamp force of the left front wheel, is the second brake clamp force of the right front wheel, is the second brake clamp force of the left rear wheel, is the second brake clamp force of the right rear wheel.

[0166] When the longitudinal force locking logic is triggered, then the angular deceleration of the left front wheel , the angular deceleration of the right front wheel , the angular deceleration of the left rear wheel , the angular deceleration of the right rear wheel in the final expression of each wheel using the adhesion coefficient is replaced by the angular deceleration when the left front wheel speed is a threshold value , the angular deceleration when the right front wheel speed is a threshold value , the angular deceleration when the left rear wheel speed is a threshold value , the angular deceleration when the right rear wheel speed is a threshold value​​​ , the left rear wheel speed is a threshold value , the angular deceleration , the right rear wheel speed is a threshold value , the angular deceleration .

[0167] Wherein, the above-mentioned based on each wheel using the adhesion coefficient, using fuzzy rules to determine the road surface type where the vehicle is, providing the road surface state basis for the vehicle stability control system, including the following steps:

[0168] limiting the left front using adhesion coefficient , the right front using adhesion coefficient , the left rear using adhesion coefficient , the right rear using adhesion coefficient The value is in the interval [0.1, 0.8], when the calculated wheel using adhesion coefficient is less than 0.1, it is calculated as 0.1, when the calculated wheel using adhesion coefficient is greater than 0.8, it is calculated as 0.8;

[0169] According to the four wheel using adhesion coefficient, three core features are calculated:

[0170] ;

[0171] ;

[0172] ;

[0173] Wherein, is the overall mean of the four wheel using adhesion coefficient, is the difference value of the left and right wheel using adhesion coefficient, is the difference value of the front and rear wheel using adhesion coefficient;

[0174] Then normalize , and :

[0175] ;

[0176] ;

[0177] ;

[0178] The fuzzy set of difference variable is {small ( ), medium ( ), large ( )} and the membership function of difference variable is:

[0179] ;

[0180] ;

[0181] ;

[0182] wherein, , both use difference variable membership function, i.e. replacing the above with membership function of , replacing the above with membership function of ;

[0183] form fuzzy set of mean variable {low ( ), medium ( ), high ( )} and determine its membership function as:

[0184] ;

[0185] ;

[0186] ;

[0187] form fuzzy set of road surface feature {uniform low ( ), uniform medium ( ), uniform high ( ), split ( ), butt joint ( ), mixed ( )} and determine its membership function as:

[0188] ;

[0189] ;

[0190] ;

[0191] ;

[0192] ;

[0193] ;

[0194] ​According to the road adhesion coefficient characteristics of each wheel, the fuzzy rules are made as follows:

[0195] R1: if is , is and is , then is uniform low;

[0196] R2: if is , is and is , then is uniform medium;

[0197] R3: if is , is and is , then is uniform high;

[0198] R4: if is , is and is , then is opposite open;

[0199] R5: if is , is and is , then is opposite open;

[0200] R6: if is , is and is , then is opposite joint;

[0201] R7: if is , is and is , then is opposite joint;

[0202] R8: if is 、 is and is then is mixed;

[0203] R9: if is , is and is then is mixed;

[0204] Using the fuzzy inference mechanism, inference is made according to the fuzzified input and fuzzy rules, and for each fuzzy rule, the product method is used to calculate the activation strength of the corresponding road surface feature:

[0205] ;

[0206] wherein are the membership functions of the , , , corresponding to the , , , , , , , ,

[0207] , , ,

[0208] , , , , , , , , , , ;

[0209] The fuzzy representation of the road surface adhesion coefficient feature obtained by fuzzy inference is converted into a specific numerical value, and the discrete barycentric method is used to defuzzify, dividing the road surface feature into 100 discrete points in the range of [1, 7] and calculating the weighted sum:

[0210] ;

[0211] In the formula, is the discrete point of the first road feature ;

[0212] The obtained is rounded down, and if more than 6, it is calculated as 6, and if less than 1, it is calculated as 1, and the final result is output;

[0213] The elements in the fuzzy set of the road feature {Uniform low ( ), uniform medium ( ), uniform high ( ), split ( ), butt joint ( ), mixed ( )} are respectively corresponding to integers 1, 2, 3, 4, 5, 6, and according to the integer corresponding to the final result, the corresponding element is corresponded to confirm the current road condition.

[0214] The application is based on the characteristics of EMB itself, without additional sensors, and can still maintain a high-precision road feature recognition method under the risk of sensor failure and wheel lock, to meet the real-time and reliability requirements of vehicle dynamic control.

[0215] The above is only the preferred embodiment of the present application, and the protection scope of the present application is not limited to the above-mentioned embodiments, and any technical solution belonging to the idea of the present application belongs to the protection scope of the present application. It should be noted that for ordinary technical personnel in the technical field, it can be understood that the embodiments can be changed, modified, replaced, polished and modified without departing from the principles and spirits of the present application, and should be regarded as the protection scope of the present application, the scope of the present application is defined by the appended claims and their equivalents.

Claims

1. A road feature recognition method for an electromechanical brake-based vehicle, characterized by, The method comprises the following steps: determining whether the clamping force sensor of the electromechanical brake of each wheel is working normally, if the clamping force sensor is working normally, directly obtaining the brake clamping force as the final brake clamping force, if the clamping force sensor is not working, obtaining the first brake clamping force through the motor model, obtaining the second brake clamping force through the brake friction plate model, and fusing the final brake clamping force through the fuzzy logic weight algorithm; after obtaining the final brake clamping force, determining whether the wheel has the risk of locking, if not, calculating the current ground longitudinal force of the wheel according to the dynamics model of the wheel, if yes, triggering the longitudinal force locking logic and obtaining the current locking ground longitudinal force, taking the obtained locking ground longitudinal force as the current ground longitudinal force of the wheel; calculating the current utilization adhesion coefficient of each wheel according to the ground longitudinal force of each wheel and the final brake clamping force; judging the road type where the vehicle is located based on the utilization adhesion coefficient of each wheel and using fuzzy rules; the first brake clamping force is obtained through the motor model, comprising the following steps: A motor model based on the torque of the permanent magnet synchronous motor is constructed, and the first brake clamping force is calculated : ； wherein, is the q-axis current of the permanent magnet synchronous motor, is the number of pole pairs, is the flux of the permanent magnet, is the wheel moment of inertia, is the damping coefficient, is the mechanical angular velocity, is the angular acceleration, is the transmission ratio of the deceleration mechanism, is the lead of the screw. the second brake clamping force is obtained through the brake friction plate model, comprising the following steps: A brake friction plate model based on friction plate deformation is constructed, and the total angular displacement of the motor during braking is decomposed to obtain the angular displacement for eliminating mechanical clearance and the angular displacement for elastic deformation of the friction plate , and the second brake clamping force generated by the deformation of the friction plate is calculated : ； wherein , , and are non-linear stiffness coefficients.

2. The road feature recognition method for an electromechanical brake-based vehicle according to claim 1, characterized by, the final brake clamping force is fused through the fuzzy logic weight algorithm, comprising the following steps: The final brake clamping force is obtained by fusing through a fuzzy logic weight algorithm To: ; in which, is a weight factor, take [0, 1].

3. The road feature recognition method for an electromechanical brake vehicle according to claim 2, characterized by, The weight factor determination, comprising the steps of: The preset maximum clamping force is , the clamping force residual error is defined as , and the residual error maximum limit is ; Pre-set fixed period, define the last period clamping force Three membership functions of the last period clamping force , and , and represent low, medium and high three degrees of the last period clamping force respectively; define three membership functions of the clamping force residual , , and , and represent small, medium and large three degrees of the clamping force residual respectively; define three membership functions of the weight factor , , and , , and represent displacement dominant, balanced, torque dominant three degrees of the weight factor respectively; Established Weighting factors of the rules The fuzzy rule base, then for the first The rules are used to calculate the activation strength. : ； In the formula, represents taking the smaller value of the two sides of the symbol as the operation result, represents the membership function of the clamping force of the last period in the rule of item , , the value range of and , represents the membership function of the clamping force residual error in the rule of item , , the value range of and ; The aggregated output fuzzy set is de-fuzzified using the centroid method, and a weight factor is determined based on the result of the calculation : ； ； In the formula, represents the result of "fuzzy or" of the rules, represents the weight factor determined according to the rule of the first corresponding to the membership function output, the value range of is and ; in the formula, is the first discrete point.​​ 4. The road feature recognition method for an electromechanical brake-based vehicle according to Claim 1, characterized by, the risk of locking of the wheel is determined, comprising the following steps: An index of tendency to lock is introduced for evaluating the tendency of the wheel angular deceleration rate to change; when then the wheel is judged to be at risk of locking, otherwise, no risk of locking, wherein, is the wheel angular deceleration rate change rate.

5. The road feature recognition method for an electromechanical brake-based vehicle according to Claim 1, characterized by, if not, the ground longitudinal force of the wheel is calculated according to the dynamics model of the wheel, comprising the following steps: According to the dynamic model of the wheel, the longitudinal force of the ground is obtained The relationship between the final brake clamp force is: ； wherein is the friction factor of the brake disc friction surface, is the effective brake radius of the brake disc, is the moment of inertia of the wheel, is the angular deceleration of the wheel, is the longitudinal force of the ground on the wheel, is the rolling radius of the wheel.

6. The road feature recognition method for an electromechanical brake-based vehicle according to Claim 1, characterized by, if yes, the longitudinal force locking logic is triggered, and the current locking ground longitudinal force is obtained, comprising the following steps: When detecting the risk of wheel lock, trigger the longitudinal force locking logic, record the initial wheel speed at the current time , and continuously monitor the wheel speed When the wheel speed decreases to the preset threshold wheel speed , it is considered that the wheel enters the deep slip stage, at which time the ground longitudinal force tends to the road surface adhesion limit, and the locking ground longitudinal force is calculated according to the final brake clamping force : ； wherein is the wheel speed, is the angular deceleration of the wheel, is the friction factor of the brake disc friction surface, is the effective brake radius of the brake disc, is the moment of inertia of the wheel, is the rolling radius of the wheel.

7. The road feature recognition method for an electromechanical brake-based vehicle according to Claim 1, characterized by, the current utilization adhesion coefficient of each wheel is calculated according to the ground longitudinal force of each wheel and the final brake clamping force, comprising the following steps: According to a linear two-degree-of-freedom model of the vehicle, the height of the center of mass is set as , the distance between the front and rear axles is set as , , where is the distance from the center of mass of the vehicle to the front axle, is the distance from the center of mass of the vehicle to the rear axle; longitudinal ground force of each wheel i.e. left front longitudinal ground force , right front longitudinal ground force , left rear longitudinal ground force and right rear longitudinal ground force , and final brake clamp force of each wheel i.e. left front final brake clamp force , right front final brake clamp force , left rear final brake clamp force and right rear final brake clamp force , left front utilization coefficient of adhesion , right front utilization coefficient of adhesion , left rear utilization coefficient of adhesion and right rear utilization coefficient of adhesion are obtained, respectively: ； wherein is the friction factor of the friction surface of the brake disc, is the mass of the vehicle, a is the deceleration of the vehicle, and g is the acceleration of gravity, is the effective brake radius of the brake disc, is the rolling radius of the wheel, is the moment of inertia of the wheel, is the angular deceleration of the front left wheel, is the angular deceleration of the front right wheel, is the angular deceleration of the rear left wheel, is the angular deceleration of the rear right wheel.

8. The road feature recognition method for an electromechanical brake-based vehicle according to claim 7, characterized by, The method is based on the utilization of the adhesion coefficients of each wheel, and the road surface type is judged by fuzzy rules, so as to provide the road surface state basis for the vehicle stability control system, and includes the following steps: calculating three core features according to the left front adhesion coefficient , the right front adhesion coefficient , the left rear adhesion coefficient and the right rear adhesion coefficient . ； ； ； In the formula, is the overall average of the utilization of the adhesion coefficient for the four wheels, is the difference in the utilization of the adhesion coefficient for the left and right wheels, is the difference in the utilization of the adhesion coefficient for the front and rear wheels; Then normalize , and , correspondingly obtain , and ; make the fuzzy set of difference variable {small ( ), medium ( ), large ( )} and determine the membership functions of difference variable in three degrees, and and use the membership functions of difference variable ; make the fuzzy set of mean variable {low ( ), medium ( ), high ( )} and determine the membership functions of mean variable in three degrees; make the fuzzy set of road surface feature {uniform low ( ), uniform medium ( ), uniform high ( ), opposite opening ( ), butt joint ( ), mixed ( )} and determine the membership functions of road surface feature in six degrees, and the elements in the fuzzy set of road surface feature correspond to integers 1, 2, 3, 4, 5 and 6 respectively. Based on the road adhesion coefficient characteristics of each wheel, formulate The first fuzzy rule is determined; using a fuzzy inference mechanism, inference is performed based on the fuzzified input and fuzzy rules, and for each fuzzy rule, a product method is used to calculate the first fuzzy rule. The activation intensity corresponding to each fuzzy rule: ； In the formula, and are the membership functions of the first fuzzy rules corresponding to , and respectively, the value range of and , the value range of and , the value range of and ; According to the activation intensity Aggregate the output fuzzy set, and divide the road surface feature into 100 discrete points in the range of [1, 7], and calculate the weighted sum; the result is rounded down, and the final result is output, and the final result is corresponded to the integer corresponding to the element in the fuzzy set of the road surface feature to confirm the current road surface condition: ； ； In the formula, is the th road surface feature corresponding to the output of the th rule, , , , , and ; is the th discrete point of the th road surface feature.

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