Self-learning control method and device for improving EMB execution precision

Abstract

The invention discloses a self-learning control method and device for improving EMB execution precision, and relates to the technical field of automobile control, and the method comprises the following steps: judging whether the current state of a target vehicle meets a self-learning control condition or not; when the self-learning control condition is met, table look-up is carried out according to an EMB system prefabricated estimation model based on a target vehicle EMB motor rotation angle signal, and the prefabricated estimated clamping force of the calipers is obtained; the actual execution clamping force of the calipers is obtained through calculation; and correcting the EMB prefabricated estimation model based on the calipers prefabricated estimation clamping force and the calipers actual execution clamping force. According to the method, the vehicle condition and the road condition are combined and analyzed, control correction is carried out based on the caliper prefabricated estimated clamping force and the caliper actual execution clamping force, the EMB execution precision is improved, and the actual demand is met.

Description

Technical Field

[0001] The present invention relates to the field of automobile control technology, and in particular to a self-learning control method and device for improving EMB execution accuracy. Background Art

[0002] In the control of the electronic mechanical brake system (EMB), in order to ensure that the braking deceleration executed by the EMB can meet the driver's braking intention, it is necessary to evaluate the EMB execution result and control the EMB actuator as a feedback signal of the EMB control. However, it is difficult to measure the actual braking force of the EMB in the use of the whole vehicle. In order to calibrate the results of the EMB caliper execution, there are the following ways to estimate the actual braking force of the EMB: 1. Estimating the braking force through the stroke of the angle sensor and the caliper rigidity model, 2. Estimating the braking force through the motor power characteristics; 3. Estimating the braking force of the caliper by connecting a force sensor in series in the caliper force transmission system. However, the traditional calculation method easily leads to insufficient accuracy in the estimation of the clamping force in the EMB system.

[0003] Therefore, in order to meet practical needs, a self-learning control technology is provided to improve the execution accuracy of EMB. Summary of the invention

[0004] In view of the defects existing in the prior art, the purpose of the present invention is to provide a self-learning control method and device for improving the execution accuracy of EMB, by combining and analyzing the vehicle conditions and road conditions, and performing control correction based on the pre-estimated clamping force of the caliper and the actual clamping force executed by the caliper, so as to improve the execution accuracy of EMB and meet actual needs.

[0005] In order to achieve the above purpose, the technical solution adopted by the present invention is:

[0006] In a first aspect, the present application provides a self-learning control method for improving the execution accuracy of EMB, the method comprising the following steps:

[0007] Monitoring the target vehicle to determine whether the current state of the target vehicle satisfies the self-learning control condition;

[0008] When the self-learning control condition is met, based on the EMB motor angle signal of the target vehicle, a table lookup is performed according to the EMB system prefabricated estimation model to obtain the prefabricated estimated clamping force of the caliper;

[0009] Based on the vehicle mass, wheel angular acceleration, wheel rolling radius, vehicle longitudinal deceleration, motor driving torque, front EMB caliper effective braking radius, front EMB caliper friction coefficient and braking force distribution coefficient of the target vehicle, the actual clamping force of the caliper is calculated;

[0010] The EMB pre-estimation model is modified based on the pre-estimated clamping force of the caliper and the actual implemented clamping force of the caliper.

[0011] On the basis of the above technical solution, the monitoring of the target vehicle and determining whether the current state of the target vehicle meets the self-learning control condition include the following steps:

[0012] Identify the real-time vehicle speed, ABS working state, ESC working state, TCS working state, EBD working state, real-time slip rate, road surface inclination angle and real-time braking intensity of the target vehicle, and when it is determined that all of them meet the corresponding set threshold ranges, determine that the current state of the target vehicle meets the self-learning control condition;

[0013] Otherwise, it is determined that the current state of the target vehicle does not satisfy the self-learning control condition.

[0014] On the basis of the above technical solution, the method further comprises the following steps:

[0015] The road inclination angle is calculated based on the vehicle mass, wheel angular acceleration, wheel rolling radius and vehicle longitudinal deceleration of the target vehicle.

[0016] On the basis of the above technical solution, the monitoring of the target vehicle and determining whether the current state of the target vehicle meets the self-learning control condition include the following steps:

[0017] When the real-time speed of the target vehicle is greater than 20 km / h, the ABS working state, the ESC working state, the TCS working state and the EBD working state are all not triggered, the real-time slip rate is not greater than 1%, the road slope is less than 1%, and the real-time braking intensity is between 0.2g and 0.5g, it is determined that the current state of the target vehicle meets the self-learning control conditions.

[0018] On the basis of the above technical solution, the EMB clamping force correction based on the pre-estimated clamping force of the caliper and the actual clamping force of the caliper includes the following steps:

[0019] When the actual clamping force of the caliper is greater than the pre-estimated clamping force of the caliper, and the ratio of the difference between the two and the pre-estimated clamping force of the caliper is greater than a preset ratio threshold, it is determined that there is a clamping force failure;

[0020] When the actual clamping force of the caliper is greater than the pre-estimated clamping force of the caliper, and the ratio of the difference between the two and the pre-estimated clamping force of the caliper is not greater than a preset ratio threshold, an EMB clamping force correction is performed based on the average value of the sum of the actual clamping force of the caliper and the pre-estimated clamping force of the caliper.

[0021] Based on the above technical solution, the preset ratio threshold is 20%.

[0022] On the basis of the above technical solution, the pre-estimated clamping force of the caliper is obtained by looking up the table according to the EMB motor angle signal and the pre-estimated model.

[0023] Based on the above technical solution, the calculation formula of the clamping force actually performed by the caliper is:

[0024] Fc'=(Fu*Rw+Fwhq)*β / (4*μ*Ra)=(2*m*ω'*Rw-LgtA+Fwhq)*Rw*β / (4*μ*Ra); where,

[0025] Rw is the wheel rolling radius, Ra is the effective braking radius of the front EMB caliper, μ is the friction coefficient of the front EMB caliper, β is the braking force distribution coefficient, Fwhq is the motor driving torque,

[0026] m is the vehicle mass, ω' is the wheel angular acceleration, θ is the road slope, Fu is the ground adhesion, and LgtA is the measurement data of the vehicle longitudinal deceleration sensor.

[0027] On the basis of the above technical solution, the calculation formula of the road surface inclination is:

[0028] m*g*sinθ+m*ω'*Rw=LgtA; where,

[0029] m is the vehicle mass, ω' is the wheel angular acceleration, θ is the road slope, Fu is the ground adhesion, LgtA is the measurement data of the vehicle longitudinal deceleration sensor, g is the gravity acceleration, and Rw is the wheel rolling radius.

[0030] In a second aspect, the present application further provides a measuring device for a self-learning control method for improving the execution accuracy of EMB, the measuring device comprising:

[0031] A monitoring and prediction module is used to monitor the target vehicle and determine whether the current state of the target vehicle meets the self-learning control conditions;

[0032] A clamping force estimation module, which is used to obtain a prefabricated estimated clamping force of the caliper by looking up a table according to a prefabricated estimation model of the EMB system based on the EMB motor rotation angle signal of the target vehicle when the self-learning control condition is met;

[0033] A clamping force acquisition module, which is used to calculate the actual clamping force of the caliper based on the vehicle mass, wheel angular acceleration, wheel rolling radius, vehicle longitudinal deceleration, motor driving torque, front EMB caliper effective braking radius, front EMB caliper friction coefficient and braking force distribution coefficient of the target vehicle;

[0034] The clamping force correction module is used to correct the EMB pre-estimation model based on the pre-estimated clamping force of the caliper and the actual clamping force of the caliper.

[0035] On the basis of the above technical solution, the monitoring and prediction module is also used to identify the real-time vehicle speed, ABS working status, ESC working status, TCS working status, EBD working status, real-time slip rate, road inclination and real-time braking intensity of the target vehicle, and when it is judged that all meet the corresponding set threshold range, it is determined that the current state of the target vehicle meets the self-learning control conditions; otherwise, it is determined that the current state of the target vehicle does not meet the self-learning control conditions.

[0036] On the basis of the above technical solution, the monitoring and prediction module is also used to calculate the road inclination angle based on the vehicle mass, wheel angular acceleration, wheel rolling radius and vehicle longitudinal deceleration of the target vehicle.

[0037] Based on the above technical solution, the monitoring and prediction module is also used to determine that the current state of the target vehicle meets the self-learning control conditions when the real-time vehicle speed of the target vehicle is greater than 20 km / h, the ABS working state, ESC working state, TCS working state and EBD working state are all not triggered, the real-time slip rate is not greater than 1%, the road slope is less than 1%, and the real-time braking intensity is between 0.2g and 0.5g.

[0038] On the basis of the above technical solution, the clamping force correction module is further used to determine a clamping force failure when the clamping force actually executed by the caliper is greater than the pre-estimated clamping force of the caliper, and the ratio of the difference between the two and the pre-estimated clamping force of the caliper is greater than a preset ratio threshold;

[0039] The clamping force correction module is also used to perform EMB clamping force correction based on the average value of the sum of the actual clamping force of the caliper and the pre-estimated clamping force of the caliper when the actual clamping force of the caliper is greater than the pre-estimated clamping force of the caliper, and the ratio of the difference between the two and the pre-estimated clamping force of the caliper is not greater than a preset ratio threshold.

[0040] Based on the above technical solution, the preset ratio threshold is 20%.

[0041] On the basis of the above technical solution, the pre-estimated clamping force of the caliper is obtained by looking up the table according to the EMB motor angle signal and the pre-estimated model.

[0042] Based on the above technical solution, the calculation formula of the clamping force actually performed by the caliper is:

[0043] Fc'=(Fu*Rw+Fwhq)*β / (4*μ*Ra)=(2*m*ω'*Rw-LgtA+Fwhq)*Rw*β / (4*μ*Ra); where,

[0044] Rw is the wheel rolling radius, Ra is the effective braking radius of the front EMB caliper, μ is the friction coefficient of the front EMB caliper, β is the braking force distribution coefficient, Fwhq is the motor driving torque,

[0045] m is the vehicle mass, ω' is the wheel angular acceleration, θ is the road slope, Fu is the ground adhesion, and LgtA is the measurement data of the vehicle longitudinal deceleration sensor.

[0046] On the basis of the above technical solution, the calculation formula of the road surface inclination is:

[0047] m*g*sinθ+m*ω'*Rw=LgtA; where,

[0048] m is the vehicle mass, ω' is the wheel angular acceleration, θ is the road slope, Fu is the ground adhesion, LgtA is the measurement data of the vehicle longitudinal deceleration sensor, g is the gravity acceleration, and Rw is the wheel rolling radius.

[0049] Compared with the prior art, the advantages of the present invention are:

[0050] The present invention combines and analyzes vehicle conditions and road conditions, performs control correction based on the pre-estimated clamping force of the caliper and the clamping force actually executed by the caliper, thereby improving the EMB execution accuracy and meeting actual needs. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0052] Figure 1 A flowchart of the steps of a self-learning control method for improving EMB execution accuracy according to an embodiment of the present invention;

[0053] Figure 2 It is a principle flow chart of a self-learning control method for improving EMB execution accuracy according to an embodiment of the present invention;

[0054] Figure 3 It is a structural block diagram of a self-learning control device for improving EMB execution accuracy according to an embodiment of the present invention. DETAILED DESCRIPTION

[0055] In order to make the purpose, technical solution and advantages of the embodiments of the present application clearer, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of this application.

[0056] The embodiments of the present invention are further described in detail below with reference to the accompanying drawings.

[0057] The embodiments of the present application provide a self-learning control method and device for improving the execution accuracy of EMB. By combining and analyzing the vehicle conditions and road conditions, control correction is performed based on the pre-estimated clamping force of the caliper and the actual clamping force executed by the caliper, thereby improving the execution accuracy of EMB and meeting actual needs.

[0058] In order to achieve the above technical effects, the overall idea of ​​this application is as follows:

[0059] A self-learning control method for improving EMB execution accuracy, the self-learning control method for improving EMB execution accuracy comprising:

[0060] S1. Monitor the target vehicle and determine whether the current state of the target vehicle meets the self-learning control conditions;

[0061] S2. When the self-learning control conditions are met, based on the EMB motor angle signal of the target vehicle, a table lookup is performed according to the EMB system prefabricated estimation model to obtain the prefabricated estimated clamping force of the caliper;

[0062] S3, based on the vehicle mass, wheel angular acceleration, wheel rolling radius, vehicle longitudinal deceleration, motor driving torque, front EMB caliper effective braking radius, front EMB caliper friction coefficient and braking force distribution coefficient of the target vehicle, calculate and obtain the actual clamping force of the caliper;

[0063] S4. Based on the pre-estimated clamping force of the caliper and the actual clamping force of the caliper, the EMB pre-estimation model is modified.

[0064] The embodiments of the present application are further described in detail below in conjunction with the accompanying drawings.

[0065] First, see Figures 1-2 As shown, the embodiment of the present application provides a self-learning control method for improving the execution accuracy of EMB, and the self-learning control method includes the following steps:

[0066] S1. Monitor the target vehicle and determine whether the current state of the target vehicle meets the self-learning control conditions;

[0067] S2. When the self-learning control conditions are met, based on the EMB motor angle signal of the target vehicle, a table lookup is performed according to the EMB system prefabricated estimation model to obtain the prefabricated estimated clamping force of the caliper;

[0068] S3, based on the vehicle mass, wheel angular acceleration, wheel rolling radius, vehicle longitudinal deceleration, motor driving torque, front EMB caliper effective braking radius, front EMB caliper friction coefficient and braking force distribution coefficient of the target vehicle, calculate and obtain the actual clamping force of the caliper;

[0069] S4. Based on the pre-estimated clamping force of the caliper and the actual clamping force of the caliper, the EMB pre-estimation model is modified.

[0070] In the embodiment of the present application, a combined analysis of vehicle conditions and road conditions is performed, and control correction is performed based on the pre-estimated clamping force of the caliper and the actual clamping force executed by the caliper, so as to improve the EMB execution accuracy and meet actual needs.

[0071] It should be noted that, taking the motor angle sensor estimation model as an example, when the driver steps on the brake pedal, the pedal travel sensor detects the brake pedal travel and transmits the travel signal to the EMB controller. The driver intention interpretation module in the EMB controller converts the travel signal into the target clamping force F r .

[0072] After receiving the target clamping force, the EMB motor drive module controls the motor drive caliper to start clamping. At the same time, the motor angle sensor measures the angle signal of the gear in the motor. The EMB motor rotation angle signal sensor clamping force estimation model estimates the executed clamping force Fc according to the measured rotation angle signal and feeds the estimated value back to the motor controller as a feedback signal to form a closed-loop control.

[0073] Assume that the caliper piston stroke is X,

[0074]

[0075] Among them, i1 is the transmission ratio of the motor gear system, i2 is the angular coefficient formed by the EMB caliper screw, and j1 is the EMB caliper stroke efficiency.

[0076] Fc=k*X; where k is the system stiffness of the caliper,

[0077] Thus, the function of clamping force and rotation angle is established And enter the functional relationship into the EMB estimation model.

[0078] The stroke efficiency j1 of the EMB caliper is caused by the gaps between the motor gear system and transmission components such as the screw and the plastic deformation of the components. In the clamping force estimation model, j1 is determined by a limited number of calibration samples during the development process. In actual use, there are differences in component gaps and rigidity between different products. These differences will cause the actual stroke efficiency j1' of the vehicle EMB caliper to differ from the stroke efficiency j1 in the vehicle EMB system estimation model.

[0079] The EMB caliper system rigidity k is determined by the compressibility of the caliper friction pad, the rigidity of the caliper body, and the free clearance between the friction pad and the brake disc. In the clamping force estimation model, the k value is determined by a limited number of calibration samples during the development process. In actual use, different products have different friction pad compressibility, caliper rigidity, and free clearance between the friction pad and the brake disc. These differences will cause the actual rigidity k' of the EMB caliper of the whole vehicle to be different from the caliper system rigidity k prefabricated in the vehicle estimation model.

[0080] The actual clamping force of the vehicle

[0081] The correspondence between the actual clamping force and the rotation angle

[0082] Due to the difference in travel efficiency and rigidity, the clamping force corresponding function preset in the EMB system and There are differences.

[0083] Thus, in actual control, the actual clamping force performed by the vehicle will be different from the target clamping force.

[0084] Further, the monitoring of the target vehicle and determining whether the current state of the target vehicle satisfies the self-learning control condition comprises the following steps:

[0085] identifying the real-time vehicle speed, ABS (Anti-lock Braking System) working state, ESC (Electronic Stability Control) working state, TCS (Traction Control System) working state, EBD (Electronic Brake force Distribution) working state, real-time slip rate, road surface inclination angle, and real-time braking intensity of the target vehicle, and determining that the current state of the target vehicle meets the self-learning control condition when all of them meet the corresponding set threshold ranges;

[0086] Otherwise, it is determined that the current state of the target vehicle does not satisfy the self-learning control condition.

[0087] Furthermore, the method further comprises the following steps:

[0088] The road inclination angle is calculated based on the vehicle mass, wheel angular acceleration, wheel rolling radius and vehicle longitudinal deceleration of the target vehicle.

[0089] On the basis of the above technical solution, the monitoring of the target vehicle and determining whether the current state of the target vehicle meets the self-learning control condition include the following steps:

[0090] When the real-time speed of the target vehicle is greater than 20 km / h, the ABS working state, the ESC working state, the TCS working state and the EBD working state are all not triggered, the real-time slip rate is not greater than 1%, the road slope is less than 1%, and the real-time braking intensity is between 0.2g and 0.5g, it is determined that the current state of the target vehicle meets the self-learning control conditions.

[0091] It should be noted that, when step S1 in the technical solution of the embodiment of the present application is specifically performed, the situation is as follows:

[0092] The EMB self-learning function is triggered when all of the following conditions are met:

[0093] Trigger condition 1: To ensure sufficient sampling data, the vehicle speed is greater than 20 km / h, which can be directly obtained through the vehicle speed signal.

[0094] Trigger condition 2: In order to reduce the influence of brake force regulation on clamping force estimation, the ABS, ESC, TCS, and EBD functions need to be in an untriggered state, which can be directly obtained through the status bits of ABS, ESC, TCS, and EBD.

[0095] Trigger condition 3: The wheel slip rate is ≤ 1%.

[0096] Trigger condition 4: To reduce the effect of road slope on the estimation accuracy of vehicle deceleration, the road slope tanθ of the vehicle is less than 1%, where θ is the road inclination angle.

[0097] The technical solution of the embodiment of the present application estimates the inclination angle of the road on which the vehicle is traveling through the dynamic formula of the vehicle, thereby evaluating whether the road conditions can trigger the clamping force self-learning:

[0098] m*g*sinθ+m*ω'*Rw=LgtA; where,

[0099] m: vehicle mass; ω': tire angular deceleration; Rw: tire rolling radius; LgtA: vehicle longitudinal deceleration sensor measurement data.

[0100] Trigger condition 5: Braking intensity is between 0.2g and 0.5g.

[0101] When all the trigger conditions 1 to 5 of the EMB clamping force self-learning are met, the EMB enters the self-learning state.

[0102] Further, the EMB clamping force correction based on the pre-estimated clamping force of the caliper and the actual clamping force of the caliper includes the following steps:

[0103] When the actual clamping force of the caliper is greater than the pre-estimated clamping force of the caliper, and the ratio of the difference between the two and the pre-estimated clamping force of the caliper is greater than a preset ratio threshold, it is determined that there is a clamping force failure;

[0104] When the actual clamping force of the caliper is greater than the pre-estimated clamping force of the caliper, and the ratio of the difference between the two and the pre-estimated clamping force of the caliper is not greater than a preset ratio threshold, an EMB clamping force correction is performed based on the average value of the sum of the actual clamping force of the caliper and the pre-estimated clamping force of the caliper.

[0105] Furthermore, the preset ratio threshold is 20%.

[0106] Furthermore, the pre-estimated clamping force of the caliper is obtained by looking up the table based on the EMB motor angle signal and the pre-estimated model:

[0107]

[0108] Furthermore, the clamping force calculation formula actually implemented by the caliper is:

[0109] The calculation formula of the actual clamping force performed by the caliper is:

[0110] Fu*Rw=2*Fc'*μ*2*Ra / β-Fwhq;

[0111] Fc'=(Fu*Rw+Fwhq)*β / (4*μ*Ra)=(2*m*ω'*Rw-LgtA+Fwhq)*Rw*β / (4*μ*Ra); where,

[0112] Rw is the wheel rolling radius, Ra is the effective braking radius of the front EMB caliper, μ is the friction coefficient of the front EMB caliper, β is the braking force distribution coefficient, Fwhq is the motor driving torque,

[0113] m is the vehicle mass, ω' is the wheel angular acceleration, θ is the road slope, Fu is the ground adhesion, and LgtA is the measurement data of the vehicle longitudinal deceleration sensor.

[0114] Based on the above technical solution, the calculation formula of the road surface inclination is:

[0115] m*g*sinθ+m*ω'*Rw=LgtA; where,

[0116] m is the vehicle mass, ω' is the wheel angular acceleration, θ is the road slope, Fu is the ground adhesion, LgtA is the measurement data of the vehicle longitudinal deceleration sensor, g is the gravity acceleration, and Rw is the wheel rolling radius.

[0117] Regarding the technical solution of the embodiment of the present application, the previous EMB caliper is taken as an example:

[0118] The calculation function of the caliper pre-estimated clamping force in the EMB clamping force estimation system is recorded as:

[0119] During the braking process of the vehicle in straight-line driving, the motion state of the vehicle conforms to the following calculation formula:

[0120] Fu+m*g*sin(θ)=m*ω'*Rw;

[0121] Fu=2*m*ω'*Ra-LgtA; where,

[0122] m: vehicle mass; ω': wheel angular acceleration; θ: road slope; Fu: ground adhesion;

[0123] LgtA: Vehicle longitudinal deceleration sensor measurement data.

[0124] According to the formula Fu*Rw=2*Fc'*μ*2*Ra / β-Fwhq

[0125] Fc'=(Fu*Rw+Fwhq)*β / (4*μ*Ra)=(2*m*

[0126] ω'*Rw-LgtA+Fwhq)*Rw*β / (4*μ*Ra); where,

[0127] Rw: wheel rolling radius;

[0128] Ra: effective braking radius of the front EMB caliper;

[0129] μ: Friction coefficient of front EMB caliper;

[0130] β: braking force distribution coefficient;

[0131] Fwhq: Motor driving torque.

[0132] The actual clamping force performed by the caliper can thus be calculated from the angular acceleration of the wheel, the LgtA signal.

[0133] Establish the relationship between Fc' and the motor angle signal,

[0134] when When the self-learning is terminated, the clamping force fault signal is fed back.

[0135] when When the clamping force estimated by the steering angle signal and the clamping force estimated by the vehicle body deceleration are averaged and given to the EMB estimation model: This enables the EMB clamping force estimation model to be corrected.

[0136] Second, see Figure 3 As shown, the embodiment of the present application provides a measuring device for a self-learning control method for improving the execution accuracy of EMB, and the measuring device includes:

[0137] A monitoring and prediction module is used to monitor the target vehicle and determine whether the current state of the target vehicle meets the self-learning control conditions;

[0138] A clamping force estimation module, which is used to obtain a prefabricated estimated clamping force of the caliper by looking up a table according to a prefabricated estimation model of the EMB system based on the EMB motor rotation angle signal of the target vehicle when the self-learning control condition is met;

[0139] A clamping force acquisition module, which is used to calculate the actual clamping force of the caliper based on the vehicle mass, wheel angular acceleration, wheel rolling radius, vehicle longitudinal deceleration, motor driving torque, front EMB caliper effective braking radius, front EMB caliper friction coefficient and braking force distribution coefficient of the target vehicle;

[0140] The clamping force correction module is used to correct the EMB pre-estimation model based on the pre-estimated clamping force of the caliper and the actual clamping force of the caliper.

[0141] In the embodiment of the present application, a combined analysis of vehicle conditions and road conditions is performed, and control correction is performed based on the pre-estimated clamping force of the caliper and the actual clamping force executed by the caliper, so as to improve the EMB execution accuracy and meet actual needs.

[0142] Furthermore, the monitoring and prediction module is also used to identify the real-time vehicle speed, ABS working status, ESC working status, TCS working status, EBD working status, real-time slip rate, road inclination and real-time braking intensity of the target vehicle, and when it is determined that all meet the corresponding set threshold ranges, it is determined that the current state of the target vehicle meets the self-learning control conditions; otherwise, it is determined that the current state of the target vehicle does not meet the self-learning control conditions.

[0143] Furthermore, the monitoring and prediction module is also used to calculate the road surface inclination angle based on the vehicle mass, wheel angular acceleration, wheel rolling radius and vehicle longitudinal deceleration of the target vehicle.

[0144] Furthermore, the monitoring and prediction module is also used to determine that the current state of the target vehicle meets the self-learning control conditions when the real-time vehicle speed of the target vehicle is greater than 20 km / h, the ABS working state, the ESC working state, the TCS working state and the EBD working state are all not triggered, the real-time slip rate is not greater than 1%, the road slope is less than 1%, and the real-time braking intensity is between 0.2g and 0.5g.

[0145] Furthermore, the clamping force correction module is also used to determine that there is a clamping force failure when the clamping force actually executed by the caliper is greater than the pre-estimated clamping force of the caliper, and the ratio of the difference between the two and the pre-estimated clamping force of the caliper is greater than a preset ratio threshold;

[0146] The clamping force correction module is also used to perform EMB clamping force correction based on the average value of the sum of the actual clamping force of the caliper and the pre-estimated clamping force of the caliper when the actual clamping force of the caliper is greater than the pre-estimated clamping force of the caliper, and the ratio of the difference between the two and the pre-estimated clamping force of the caliper is not greater than a preset ratio threshold.

[0147] Furthermore, the preset ratio threshold is 20%.

[0148] Furthermore, the pre-estimated clamping force of the caliper is obtained by looking up a table based on the EMB motor angle signal and a pre-estimated model.

[0149] Furthermore, the calculation formula of the clamping force actually performed by the caliper is:

[0150] Fu*Rw=2*Fc'*μ*2*Ra / β-Fwhq;

[0151] Fc'=(Fu*Rw+Fwhq)*β / (4*μ*Ra)=(2*m*ω'*Rw-LgtA+Fwhq)*Rw*β / (4*μ*Ra); where,

[0152] Rw is the wheel rolling radius, Ra is the effective braking radius of the front EMB caliper, μ is the friction coefficient of the front EMB caliper, β is the braking force distribution coefficient, Fwhq is the motor driving torque,

[0153] m is the vehicle mass, ω' is the wheel angular acceleration, θ is the road slope, Fu is the ground adhesion, and LgtA is the measurement data of the vehicle longitudinal deceleration sensor.

[0154] Furthermore, the calculation formula of the road surface inclination is:

[0155] m*g*sinθ+m*ω'*Rw=LgtA; where,

[0156] m is the vehicle mass, ω' is the wheel angular acceleration, θ is the road slope, Fu is the ground adhesion, LgtA is the measurement data of the vehicle longitudinal deceleration sensor, g is the gravity acceleration, and Rw is the wheel rolling radius.

[0157] It should be noted that the self-learning control device for improving the execution accuracy of EMB mentioned in the second aspect is similar to the technical principles of the self-learning control method for improving the execution accuracy of EMB mentioned in the first aspect in terms of technical issues, technical means and technical effects, and will not be elaborated here.

[0158] In the description of the present application, it should be noted that the terms "upper", "lower", etc. indicate orientations or positional relationships based on the orientations or positional relationships shown in the accompanying drawings, and are only for the convenience of describing the present application and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as a limitation on the present application. Unless otherwise clearly specified and limited, the terms "installed", "connected", and "connected" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be an indirect connection through an intermediate medium, or it can be a connection between the two elements. For those of ordinary skill in the art, the specific meanings of the above terms in the present application can be understood according to the specific circumstances.

[0159] It should be noted that, in this application, relational terms such as "first" and "second" are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "comprise a ..." do not exclude the presence of other identical elements in the process, method, article or device including the elements.

[0160] The above description is only a specific implementation of the present application, so that those skilled in the art can understand or implement the present application. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application will not be limited to the embodiments shown herein, but will conform to the widest range consistent with the principles and novel features applied for herein.

Claims

1. A self-learning control method to improve the execution accuracy of EMB, It is characterized in that The method comprises the following steps: Monitoring the target vehicle to determine whether the current state of the target vehicle satisfies the self-learning control condition; When the self-learning control condition is met, based on the EMB motor angle signal of the target vehicle, a table lookup is performed according to the EMB system prefabricated estimation model to obtain the prefabricated estimated clamping force of the caliper; Based on the vehicle mass, wheel angular acceleration, wheel rolling radius, vehicle longitudinal deceleration, motor driving torque, front EMB caliper effective braking radius, front EMB caliper friction coefficient and braking force distribution coefficient of the target vehicle, the actual clamping force of the caliper is calculated; The EMB pre-estimation model is modified based on the pre-estimated clamping force of the caliper and the actual implemented clamping force of the caliper.

2. The self-learning control method for improving EMB execution accuracy as claimed in claim 1, It is characterized in that The monitoring of the target vehicle and determining whether the current state of the target vehicle satisfies the self-learning control condition comprises the following steps: Identify the real-time vehicle speed, ABS working state, ESC working state, TCS working state, EBD working state, real-time slip rate, road surface inclination angle and real-time braking intensity of the target vehicle, and when it is determined that all of them meet the corresponding set threshold ranges, determine that the current state of the target vehicle meets the self-learning control condition; Otherwise, it is determined that the current state of the target vehicle does not satisfy the self-learning control condition.

3. The self-learning control method for improving the execution accuracy of EMB as claimed in claim 2, It is characterized in that The method further comprises the following steps: The road inclination angle is calculated based on the vehicle mass, wheel angular acceleration, wheel rolling radius and vehicle longitudinal deceleration of the target vehicle.

4. The self-learning control method for improving EMB execution accuracy as claimed in claim 1, It is characterized in that The monitoring of the target vehicle and determining whether the current state of the target vehicle satisfies the self-learning control condition comprises the following steps: When the real-time speed of the target vehicle is greater than 20 km / h, the ABS working state, the ESC working state, the TCS working state and the EBD working state are all not triggered, the real-time slip rate is not greater than 1%, the road slope is less than 1%, and the real-time braking intensity is between 0.2g and 0.5g, it is determined that the current state of the target vehicle meets the self-learning control conditions.

5. The self-learning control method for improving EMB execution accuracy as claimed in claim 1, It is characterized in that The EMB clamping force correction based on the pre-estimated clamping force of the caliper and the actual clamping force of the caliper includes the following steps: When the actual clamping force of the caliper is greater than the pre-estimated clamping force of the caliper, and the ratio of the difference between the two and the pre-estimated clamping force of the caliper is greater than a preset ratio threshold, it is determined that there is a clamping force failure; When the actual clamping force of the caliper is greater than the pre-estimated clamping force of the caliper, and the ratio of the difference between the two and the pre-estimated clamping force of the caliper is not greater than a preset ratio threshold, an EMB clamping force correction is performed based on the average value of the sum of the actual clamping force of the caliper and the pre-estimated clamping force of the caliper.

6. The self-learning control method for improving EMB execution accuracy as claimed in claim 1, Features: The preset ratio threshold is 20%.

7. The self-learning control method for improving EMB execution accuracy as claimed in claim 1, Features: The pre-estimated clamping force of the caliper is obtained by looking up a table based on the EMB motor angle signal and a pre-estimated model.

8. The self-learning control method for improving EMB execution accuracy as claimed in claim 1, It is characterized in that The calculation formula of the clamping force actually performed by the caliper is: Fc'=(Fu*Rw+Fwhq)*β / (4*μ*Ra)=(2*m*ω'*Rw-LgtA+Fwhq)*Rw*β / (4*μ*Ra); where, Rw is the wheel rolling radius, Ra is the effective braking radius of the front EMB caliper, μ is the friction coefficient of the front EMB caliper, β is the braking force distribution coefficient, Fwhq is the motor driving torque, m is the vehicle mass, ω' is the wheel angular acceleration, θ is the road slope, Fu is the ground adhesion, and LgtA is the measurement data of the vehicle longitudinal deceleration sensor.

9. The self-learning control method for improving EMB execution accuracy as claimed in claim 1, It is characterized in that The calculation formula of the road surface inclination angle is: m*g*sinθ+m*ω'*Rw=LgtA; where, m is the vehicle mass, ω' is the wheel angular acceleration, θ is the road slope, Fu is the ground adhesion, LgtA is the measurement data of the vehicle longitudinal deceleration sensor, g is the gravity acceleration, and Rw is the wheel rolling radius.

10. A self-learning control device for improving the execution accuracy of EMB, It is characterized in that The device comprises: A monitoring and prediction module is used to monitor the target vehicle and determine whether the current state of the target vehicle meets the self-learning control conditions; A clamping force estimation module, which is used to obtain a prefabricated estimated clamping force of the caliper by looking up a table according to a prefabricated estimation model of the EMB system based on the EMB motor rotation angle signal of the target vehicle when the self-learning control condition is met; A clamping force acquisition module, which is used to calculate the actual clamping force of the caliper based on the vehicle mass, wheel angular acceleration, wheel rolling radius, vehicle longitudinal deceleration, motor driving torque, front EMB caliper effective braking radius, front EMB caliper friction coefficient and braking force distribution coefficient of the target vehicle; The clamping force correction module is used to correct the EMB pre-estimation model based on the pre-estimated clamping force of the caliper and the actual clamping force of the caliper.

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