EMB torque distribution control method based on road adhesion coefficient estimation algorithm

Abstract

The invention particularly relates to an EMB torque distribution control method based on a road adhesion coefficient estimation algorithm, and relates to the technical field of vehicle braking control, and the method comprises the following steps: estimating a rolling time domain adhesion coefficient of multi-source data fusion; the invention discloses braking force optimal distribution based on yawing moment balance. According to the multi-source data fusion rolling time domain attachment coefficient estimation method, historical data of a camera, an IMU, a GNSS and a wheel speed sensor are utilized to construct a nonlinear adaptive observer, and the limitation of traditional single sensor or current time data estimation is broken through; on one hand, a camera realizes pavement type identification through UNet semantic segmentation and a ShuffleNet network, limits an adhesion coefficient range in advance, and reduces an initial estimation error; and on the other hand, the rolling time domain observer is combined with the vehicle dynamics model and historical data, exponential stable convergence can be achieved as long as the tire enters a nonlinear region at the historical moment, and the method has high robustness for the uncertainty of the model.

Description

Technical Field

[0001] The present invention relates to the technical field of vehicle braking control, and in particular to an EMB torque distribution control method based on a road adhesion coefficient estimation algorithm. Background Art

[0002] As an innovative braking technology, EMB (Electro-Mechanical Braking System) is becoming an essential component of modern vehicles. Combining the advantages of electric braking with mechanical control, the EMB system offers faster response, greater braking precision, and a smaller footprint, making it an ideal alternative to traditional hydraulic braking systems, improving vehicle safety and handling. However, existing EMB technology fails to adequately account for variations in road adhesion and the differences in road adhesion between wheels, significantly limiting its braking capabilities.

[0003] Commonly used EMB systems often default to high-adhesion surfaces when distributing braking torque, making it highly susceptible to wheel lock and drifting when driving on low-adhesion surfaces. Some research has considered incorporating road adhesion coefficient estimation based on vehicle / tire models to improve EMB's adaptability to different road surfaces. However, existing methods often rely solely on current sensor data to estimate the road adhesion coefficient. Due to model uncertainty and insufficient measurement records, the estimated results are prone to low accuracy and slow convergence, resulting in poor performance in practical EMB applications due to road adhesion coefficient mismatch. Other research has considered using camera information to estimate road conditions, but simply classifying the road surface cannot accurately determine the road adhesion coefficient. For example, on slippery roads, the road adhesion coefficient can range from 0.3 to 0.9. Therefore, methods based on vehicle / tire models are still needed to further accurately estimate the road adhesion coefficient to improve EMB performance.

[0004] Therefore, a new EMB torque distribution control method based on the road adhesion coefficient estimation algorithm is needed. By utilizing the current and past information of the camera, IMU (Inertial measurement unit), and GNSS (Global Navigation Satellite System), combined with the vehicle dynamics model and the road classification of the camera, a constrained rolling time-domain nonlinear adaptive observer is designed to reduce the problems of slow estimation speed and low accuracy caused by model uncertainty and measurement excitation. This method is then applied to the torque distribution of the EMB system, thereby improving the optimal braking capability of the EMB system. Summary of the Invention

[0005] The purpose of the present invention is to solve the above problems and to propose an EMB torque distribution control method based on a road adhesion coefficient estimation algorithm.

[0006] In order to achieve the above object, the present invention adopts the following technical solutions:

[0007] An EMB torque distribution control method based on a road adhesion coefficient estimation algorithm includes:

[0008] Rolling time domain adhesion coefficient estimation using multi-source data fusion: A nonlinear adaptive observer is constructed using historical data from cameras, IMUs, GNSS, and wheel speed sensors to output the estimated real-time road adhesion coefficient for each wheel and determine the optimal slip ratio for each wheel.

[0009] Optimal braking force distribution based on yaw moment balance: The driver's operation is analyzed into required braking torque and yaw moment; braking force is distributed according to the road adhesion coefficient and optimal slip rate, combined with the driver's needs and ABS activation status, and then the braking torque of each wheel is dynamically compensated to finally output the braking torque.

[0010] Preferably, the rolling time domain adhesion coefficient estimation based on multi-source data fusion specifically includes road adhesion estimation and observer design and performance analysis.

[0011] Preferably, the road adhesion estimation specifically includes:

[0012] When the vehicle is driving normally, the camera classifies the road conditions in the form of images;

[0013] The semantic segmentation network UNet is used to extract the drivable area of ​​the road surface. The amount of information contained in the road surface image blocks and the area utilization rate of the drivable area are comprehensively considered, and an optimization algorithm is designed to divide the road surface image blocks into appropriate areas.

[0014] The lightweight convolutional neural network ShuffleNet is used to extract road image features and obtain the road type recognition results based on the image; after obtaining the road classification information, the road adhesion coefficient range is defined as [ .

[0015] Preferably, the observer design and performance analysis specifically include:

[0016] Based on the road adhesion range, combined with the vehicle dynamics model and historical sensor data, a nonlinear adaptive observer of the road adhesion coefficient based on the rolling time domain is designed;

[0017] Assume that the current moment The wheel speed is , the wheel end braking torque is , vehicle speed , the road adhesion coefficient range is [ ;past , ,…, The wheel speed at this moment is , ,…, , accordingly, the wheel end drive braking torque is , ,…, , the vehicle speed information is , ,…, ,Design an adaptive observer of road adhesion coefficient based on historical and current information;

[0018]

[0019] in,

[0020]

[0021]

[0022] , , , and is a positive constant, ; … The current moment , and the past ,… time estimated value of; ,… For the past The wheel slip rate at a certain moment; Estimate the road adhesion coefficient for the current moment.

[0023] Preferably, the braking force optimization distribution based on yaw moment balance specifically includes EMB torque distribution and EMB system braking force distribution.

[0024] Preferably, the EMB torque distribution specifically includes:

[0025] Based on the total required braking torque and yaw moment, combined with the road adhesion coefficient constraints of each wheel, the braking torque of each wheel is distributed at the cost of minimizing the road adhesion coefficient of all wheels. The specific optimization problem is as follows:

[0026]

[0027]

[0028] Subject to

[0029] If the ABS activation for each wheel

[0030] Where:

[0031] Indicates the braking torque of each wheel;

[0032] Refers to the vertical load of each wheel;

[0033] Indicates the road adhesion coefficient of each wheel;

[0034] r represents the wheel radius;

[0035] It represents half the distance between the left and right wheels of the front and rear axles;

[0036] is the required yaw moment;

[0037] Indicates that when one or more wheels ABS is activated, the corresponding The braking torque calculated by ABS is dominant and is not used as an optimization variable in the optimization calculation.

[0038] Preferably, the EMB system braking force distribution specifically includes:

[0039] Based on the transmission chain dynamics model, the load torque observer is used to online identify the transmission efficiency attenuation coefficient and nonlinear friction characteristics, and the inertia hysteresis effect of the motor and ball screw is eliminated through the inertia compensation algorithm.

[0040] The system uses a feedforward and feedback composite control architecture. The feedforward link dynamically corrects the target torque based on the road adhesion coefficient and wheel speed fluctuations, while the feedback link estimates the disturbance torque in real time through a sliding mode observer and injects compensation current.

[0041] At the same time, an adaptive weight factor is introduced to integrate the wheel cylinder pressure sensor and motor encoder data to iteratively optimize the compensation amount.

[0042] Preferably, the inertia hysteresis effect of the motor and the ball screw is eliminated by using an inertia compensation algorithm, specifically including:

[0043] In the EMB system transmission chain, the motor rotor, roller screw, and brake caliper components have rotational inertia. , when the control torque When the system changes, the angular acceleration due to inertia , causing the actual torque output to lag behind the control signal;

[0044] According to the rotation law formula: ;

[0045] in is the friction resistance torque;

[0046] moment of inertia The determination process includes:

[0047] Get the mass of the motor rotor ,radius ,length Substitute this into the moment of inertia formula: ;

[0048] Get the rotational definition of inertia ;

[0049] Perform emergency braking on the vehicle at preset time intervals, obtain the temperature change of the motor within the preset time period, and extract the maximum temperature value and the minimum temperature value;

[0050] Calculate the difference between the maximum temperature value and the minimum temperature value to obtain the temperature rise value;

[0051] After obtaining the time points corresponding to the maximum and minimum temperature values, extract the duration between the maximum and minimum temperature values, and divide the temperature rise value by the duration between the maximum and minimum temperature values ​​to obtain the unit temperature rise value;

[0052] A unit temperature rise threshold is preset, and the unit temperature rise value is divided by the unit temperature rise threshold to obtain a unit temperature rise ratio.

[0053] Preferably, the method further includes:

[0054] The surface of the motor rotor is divided into several sub-areas according to a preset size, and the temperature of each sub-area and the center of the motor rotor is obtained after the vehicle performs emergency braking;

[0055] Subtract the corresponding motor rotor center temperature from the temperature of each sub-region of the motor rotor to obtain the surface temperature difference;

[0056] Extract the maximum surface temperature difference value within the sub-region of the motor rotor and record it as the surface maximum temperature difference value; obtain the surface maximum temperature difference values ​​of each sub-region of the electronic rotor in sequence, arrange the surface maximum temperature difference values ​​of each sub-region in descending order according to the value, and extract the two largest surface maximum temperature differences and their corresponding positions; mark the positions of the two largest surface maximum temperature differences as the first positioning point and the second positioning point respectively;

[0057] Mark the rotor center position as the third positioning point, connect the first positioning point, the second positioning point, and the third positioning point with a straight line to obtain a triangular figure, calculate the area of ​​the triangular figure, and record it as the simulated distribution value;

[0058] After normalizing the unit temperature rise ratio and the simulated distribution value, the unit temperature rise ratio and the simulated distribution value are used as the major and minor semi-axis of the ellipse, respectively, to construct an ellipse model, and the area of ​​the ellipse model is calculated and recorded as the added inertia value;

[0059] The inertia matching value is obtained by weighted calculation of the rotational defined inertia and the added inertia value;

[0060] Match the corresponding moment of inertia according to the inertia matching value, and substitute the obtained moment of inertia into the above rotation law formula: That's it.

[0061] In summary, due to the adoption of the above technical solution, the beneficial effects of the present invention are:

[0062] 1. This invention utilizes a rolling time-domain adhesion coefficient estimation method based on multi-source data fusion. This method utilizes historical data from cameras, IMUs, GNSS, and wheel speed sensors to construct a nonlinear adaptive observer, overcoming the limitations of traditional single-sensor or current-time data estimation. The camera uses UNet semantic segmentation and the ShuffleNet network to identify road surface types, pre-define the adhesion coefficient range, and reduce initial estimation errors. Furthermore, the rolling time-domain observer combines the vehicle dynamics model with historical data, achieving exponentially stable convergence only when the tire has entered the nonlinear region at a historical moment, demonstrating strong robustness to model uncertainty.

[0063] 2. This invention utilizes a braking force optimization distribution strategy based on yaw moment balance to analyze driver input into required braking torque and yaw moment. Combining the adhesion constraints of each wheel with the ABS activation state, this method constructs a convex optimization problem with the goal of minimizing the road adhesion coefficient. Dynamic torque distribution is achieved through the OSQP solver, addressing the problem of wheel locking on low-adhesion roads caused by traditional fixed-ratio distribution. Furthermore, the EMB system's braking force distribution utilizes a driveline dynamics model and a load torque observer to online identify friction characteristics. The moment of inertia is dynamically corrected based on the motor rotor's temperature distribution, improving the accuracy of the inertia compensation torque. BRIEF DESCRIPTION OF THE DRAWINGS

[0064] Further details, features and advantages of the present application are disclosed in the following description of exemplary embodiments in conjunction with the accompanying drawings, in which:

[0065] Figure 1 Flowchart of the present invention. DETAILED DESCRIPTION

[0066] Several embodiments of the present application will be described in more detail below with reference to the accompanying drawings so that those skilled in the art can implement the present application. The present application can be embodied in many different forms and for many different purposes and should not be limited to the embodiments described herein. These embodiments are provided to make the present application comprehensive and complete and to fully convey the scope of the present application to those skilled in the art. The embodiments do not limit the present application.

[0067] Unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. It will be further understood that terms such as those defined in commonly used dictionaries should be interpreted as having a meaning consistent with their meaning in the relevant art and / or the context of this specification, and will not be interpreted in an idealized or overly formal sense unless expressly defined as such herein.

[0068] See also Figure 1 As shown, the present invention provides a technical solution:

[0069] An EMB torque distribution control method based on a road adhesion coefficient estimation algorithm includes:

[0070] Rolling time domain adhesion coefficient estimation using multi-source data fusion: A nonlinear adaptive observer is constructed using historical data from cameras, IMUs, GNSS, and wheel speed sensors to output the estimated real-time road adhesion coefficient for each wheel and determine the optimal slip ratio for each wheel.

[0071] Specifically including road adhesion estimation and observer design and performance analysis;

[0072] Road adhesion estimation, including:

[0073] When the vehicle is driving normally, the camera classifies the road conditions in the form of images;

[0074] The semantic segmentation network UNet is used to extract the drivable area of ​​the road surface. The amount of information contained in the road surface image blocks and the area utilization rate of the drivable area are comprehensively considered, and an optimization algorithm is designed to divide the road surface image blocks into appropriate areas.

[0075] The lightweight convolutional neural network ShuffleNet is used to extract road image features and obtain the road type recognition results based on the image; after obtaining the road classification information, the road adhesion coefficient range is defined as [ ;

[0076] Observer design and performance analysis, including:

[0077] Based on the road adhesion range, combined with the vehicle dynamics model and historical sensor data, a nonlinear adaptive observer of the road adhesion coefficient based on the rolling time domain is designed;

[0078] Assume that the current moment The wheel speed is , the wheel end braking torque is , vehicle speed , the road adhesion coefficient range is [ ;past , ,…, The wheel speed at this moment is , ,…, , accordingly, the wheel end drive braking torque is , ,…, , the vehicle speed information is , ,…, ,Design an adaptive observer of road adhesion coefficient based on historical and current information;

[0079]

[0080] in,

[0081]

[0082]

[0083] , , , and is a positive constant, ; … The current moment , and the past ,… time estimated value of; ,… For the past The wheel slip rate at a given moment. Estimate the road adhesion coefficient for the current moment;

[0084] It is worth noting that in this observer, for each moment, as long as the past At each moment, if the tire enters the nonlinear region (i.e., it does not need to stay in the nonlinear region all the time), the observer can converge exponentially and stably, thus ensuring that the estimation result is robust to model uncertainty.

[0085] Furthermore, if the tire operates continuously in the nonlinear region, the observer's convergence rate is proportional to the time domain length of the collected data, demonstrating that the designed observer can accelerate the convergence of the estimation. Furthermore, since the camera is used to constrain the initial value of the road adhesion estimate and its subsequent estimation range, the estimation results can also be accelerated to a certain extent. Finally, because the observer can be applied to each wheel, the road adhesion coefficient of each wheel at every moment can be obtained.

[0086] Optimal braking force distribution based on yaw moment balance: Driver input (such as brake pedal force and steering input) is analyzed into required braking torque and yaw moment. Braking force is distributed based on the road adhesion coefficient and optimal slip ratio, combined with driver demand and ABS activation. The braking torque of each wheel is then dynamically compensated to achieve the final braking torque output.

[0087] Specifically including EMB torque distribution and EMB system braking force distribution;

[0088] EMB torque distribution specifically includes:

[0089] Based on the total required braking torque and yaw moment, combined with the road adhesion coefficient constraints of each wheel, the braking torque of each wheel is distributed at the cost of minimizing the road adhesion coefficient of all wheels. The specific optimization problem is as follows:

[0090]

[0091]

[0092] Subject to

[0093] If the ABS activation for each wheel

[0094] Where:

[0095] Indicates the braking torque of each wheel;

[0096] Refers to the vertical load of each wheel;

[0097] Indicates the road adhesion coefficient of each wheel;

[0098] r represents the wheel radius;

[0099] It represents half the distance between the left and right wheels of the front and rear axles;

[0100] is the required yaw moment;

[0101] Indicates that when one or more wheels ABS is activated, the corresponding The braking torque calculated by ABS is dominant and is not used as an optimization variable in the optimization calculation.

[0102] The above cost function and constraints constitute the optimization problem of braking torque distribution. Since this optimization problem is a convex optimization problem, the braking torque of each wheel is solved through OSQP.

[0103] The following further explains the constraints on wheels with ABS activation. Whether the ABS system is activated must be considered when optimizing braking torque distribution. If the wheel-end ABS is activated on one or more wheels, the braking torque of that wheel is controlled by the ABS on that wheel and does not participate in the EMB system's optimal braking torque distribution.

[0104] For example, if the ABS of the first wheel is activated, the braking torque of the first wheel is equal to the braking torque provided by the ABS of the first wheel, and the optimal distribution of the braking torque of the EMB system can only be distributed to the remaining three wheels.

[0105] The EMB system braking force distribution specifically includes:

[0106] Based on the transmission chain dynamics model, the load torque observer is used to online identify the transmission efficiency attenuation coefficient and nonlinear friction characteristics, and the inertia hysteresis effect of the motor and ball screw is eliminated through the inertia compensation algorithm.

[0107] The system uses a feedforward and feedback composite control architecture. The feedforward link dynamically corrects the target torque based on the road adhesion coefficient and wheel speed fluctuations, while the feedback link estimates the disturbance torque in real time through a sliding mode observer and injects compensation current.

[0108] At the same time, an adaptive weight factor is introduced to integrate the wheel cylinder pressure sensor and motor encoder data to iteratively optimize the compensation amount;

[0109] The inertia hysteresis effect of the motor and ball screw is eliminated through the inertia compensation algorithm, including:

[0110] In the EMB system transmission chain, the motor rotor, roller screw, and brake caliper components have rotational inertia. (unit: ), when the control torque When the system changes, the angular acceleration due to inertia , causing the actual torque output to lag behind the control signal;

[0111] According to the rotation law formula: ;

[0112] in for frictional resistance torque;

[0113] moment of inertia The determination process includes:

[0114] obtaining the mass of the motor rotor , radius , length Descendants into the formula of moment of inertia: ;

[0115] get the moment of inertia ;

[0116] Emergency braking of the vehicle at a predetermined time interval, obtain the motor temperature change within a predetermined time, and extract the maximum temperature and the minimum temperature from it; wherein the minimum temperature is a determined value, and the maximum temperature is the highest temperature obtained by multiple experiments;

[0117] The maximum temperature and the minimum temperature are calculated by difference, and the temperature rise value is obtained;

[0118] After obtaining the time points corresponding to the maximum temperature and the minimum temperature, the time length between the maximum temperature and the minimum temperature is extracted, the temperature rise value is divided by the time length between the maximum temperature and the minimum temperature, and the unit temperature rise value is obtained;

[0119] The preset unit temperature rise threshold value is divided by the unit temperature rise value, and the unit temperature rise ratio is obtained;

[0120] Divide the surface of the motor rotor into several sub-regions with a predetermined size, obtain the temperature of each sub-region of the motor rotor and the center after the vehicle is braked in emergency;

[0121] Subtract the corresponding motor rotor center temperature from the temperature of each sub-region of the motor rotor to obtain the surface temperature difference value;

[0122] Extract the maximum surface temperature difference value in the sub-region of the motor rotor, denoted as the surface maximum temperature difference value; sequentially obtain the surface maximum temperature difference value of each sub-region of the electronic rotor, arrange the surface maximum temperature difference value of each sub-region in descending order according to the numerical value, and extract the maximum two surface maximum temperature difference values and their corresponding positions; The positions of the maximum two surface maximum temperature difference values are marked as the first positioning point and the second positioning point respectively;

[0123] Mark the rotor center position as the third positioning point, connect the first positioning point, the second positioning point and the third positioning point with a straight line to obtain a triangular figure, calculate the area of the triangular figure, denoted as the fitting distribution value;

[0124] After normalizing the unit temperature rise ratio and the pseudo-distribution value, the unit temperature rise ratio and the pseudo-distribution value are taken as the long semi-axis and the short semi-axis of an ellipse respectively, an ellipse model is constructed, the area of the ellipse model is calculated, and the area is recorded as an added inertia value;

[0125] After weighted calculation of the rotation definition inertia and the added inertia value, an inertia matching value is obtained;

[0126] A weight factor of the rotation definition inertia and the added inertia value is preset, the rotation definition inertia and the added inertia value are multiplied by the corresponding weight factors respectively, and then the inertia matching value is obtained by summation;

[0127] According to the inertia matching value, a corresponding rotational inertia is matched, and the obtained rotational inertia is substituted into the above rotation law formula: That is;

[0128] Rotational inertia: the value range of a plurality of preset threshold values is preset, and each threshold value range corresponds to a rotational inertia. The inertia matching value is matched with the value range of the plurality of threshold values to obtain the rotational inertia corresponding to the inertia matching value.

[0129] The surface of the motor rotor is divided into sub-regions, and the rotational inertia is corrected in combination with the temperature distribution characteristics:

[0130] On the one hand, the limitation of traditional average temperature calculation is broken through, the spatial non-uniformity of rotor thermal deformation is captured through sub-region temperature difference analysis, and the influence of temperature gradient on inertia distribution is quantified by using triangular pseudo-distribution value;

[0131] On the other hand, the temperature change rate is dynamically reflected by combining the unit temperature rise ratio, the thermal physical characteristics and the geometric characteristics are coupled through the ellipse model, so that the added inertia value can accurately represent the inertia fluctuation under thermal coupling, and finally the dynamic calibration of the rotational inertia is realized through weighted calculation and threshold matching, which not only improves the adaptability of the inertia compensation algorithm to the temperature field change, but also ensures the real-time calculation efficiency, thereby enhancing the torque response accuracy and stability of the EMB system under complex braking conditions.

[0132] The above formulas are obtained by collecting a large amount of data for software simulation and selecting a formula close to the true value. The influence weight factor and specific coefficient value in the formula are set by the person skilled in the art according to the actual situation, and can be adjusted and modified later.

[0133] The foregoing description of the embodiments enables one of ordinary skill in the art to make or use the application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein can be applied to other embodiments without departing from the spirit or scope of the application. Thus, the present application is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. An EMB torque distribution control method based on a road adhesion coefficient estimation algorithm, characterized in that: include: Rolling time domain adhesion coefficient estimation using multi-source data fusion: A nonlinear adaptive observer is constructed using historical data from cameras, IMUs, GNSS, and wheel speed sensors to output the estimated real-time road adhesion coefficient for each wheel and determine the optimal slip ratio for each wheel. Optimal braking force distribution based on yaw moment balance: The driver's operation is analyzed into required braking torque and yaw moment; According to the road adhesion coefficient and optimal slip rate, combined with the driver's needs and ABS activation, the braking force is distributed, and then the braking torque of each wheel is dynamically compensated to finally output the braking torque.

2. The EMB torque distribution control method based on the road adhesion coefficient estimation algorithm according to claim 1 is characterized in that: The rolling time domain adhesion coefficient estimation based on multi-source data fusion specifically includes road adhesion estimation and observer design and performance analysis.

3. The EMB torque distribution control method based on the road adhesion coefficient estimation algorithm according to claim 2, characterized in that: Road adhesion estimation, including: When the vehicle is driving normally, the camera classifies the road conditions in the form of images; The semantic segmentation network UNet is used to extract the drivable area of ​​the road surface. The amount of information contained in the road surface image blocks and the area utilization rate of the drivable area are comprehensively considered, and an optimization algorithm is designed to divide the road surface image blocks into appropriate areas. The lightweight convolutional neural network ShuffleNet is used to extract road image features and obtain the road type recognition results based on the image; after obtaining the road classification information, the road adhesion coefficient range is defined as [ .

4. The EMB torque distribution control method based on the road adhesion coefficient estimation algorithm according to claim 3 is characterized in that: Observer design and performance analysis, including: Based on the road adhesion range, combined with the vehicle dynamics model and historical sensor data, a nonlinear adaptive observer of the road adhesion coefficient based on the rolling time domain is designed; Assume that the current moment The wheel speed is , the wheel end braking torque is , vehicle speed , the road adhesion coefficient range is [ ;past , ,…, The wheel speed at this moment is , ,…, , accordingly, the wheel end drive braking torque is , ,…, , the vehicle speed information is , ,…, ,Design an adaptive observer of road adhesion coefficient based on historical and current information; ； in, ； ； , , , and is a positive constant, ; … The current moment , and the past ,… time estimated value of; ,… For the past The wheel slip rate at a certain moment; Estimate the road adhesion coefficient for the current moment.

5. The EMB torque distribution control method based on the road adhesion coefficient estimation algorithm according to claim 1, characterized in that: Optimal braking force distribution based on yaw moment balance, specifically including EMB torque distribution and EMB system braking force distribution.

6. The EMB torque distribution control method based on the road adhesion coefficient estimation algorithm according to claim 5, characterized in that: EMB torque distribution specifically includes: Based on the total required braking torque and yaw moment, combined with the road adhesion coefficient constraints of each wheel, the braking torque of each wheel is distributed at the cost of minimizing the road adhesion coefficient of all wheels. The specific optimization problem is as follows: ； ； Subjectto ; If the ABS activation for each wheel Where: Indicates the braking torque of each wheel; Refers to the vertical load of each wheel; Indicates the road adhesion coefficient of each wheel; r represents the wheel radius; It represents half the distance between the left and right wheels of the front and rear axles; is the required yaw moment; Indicates that when one or more wheels ABS is activated, the corresponding The braking torque calculated by ABS is dominant and is not used as an optimization variable in the optimization calculation.

7. The EMB torque distribution control method based on the road adhesion coefficient estimation algorithm according to claim 6, characterized in that: The EMB system braking force distribution specifically includes: Based on the transmission chain dynamics model, the load torque observer is used to online identify the transmission efficiency attenuation coefficient and nonlinear friction characteristics, and the inertia hysteresis effect of the motor and ball screw is eliminated through the inertia compensation algorithm. The system uses a feedforward and feedback composite control architecture. The feedforward link dynamically corrects the target torque based on the road adhesion coefficient and wheel speed fluctuations, while the feedback link estimates the disturbance torque in real time through a sliding mode observer and injects compensation current. At the same time, an adaptive weight factor is introduced to integrate the wheel cylinder pressure sensor and motor encoder data to iteratively optimize the compensation amount.

8. The EMB torque distribution control method based on the road adhesion coefficient estimation algorithm according to claim 7 is characterized in that: The inertia hysteresis effect of the motor and ball screw is eliminated through the inertia compensation algorithm, including: In the EMB system transmission chain, the motor rotor, roller screw, and brake caliper components have rotational inertia. , when the control torque When the system changes, the angular acceleration due to inertia , causing the actual torque output to lag behind the control signal; According to the rotation law formula: ; in is the friction resistance torque; moment of inertia The determination process includes: Get the mass of the motor rotor ,radius ,length Substitute this into the moment of inertia formula: ; Get the rotational definition of inertia ; Perform emergency braking on the vehicle at preset time intervals, obtain the temperature change of the motor within the preset time period, and extract the maximum temperature value and the minimum temperature value; Calculate the difference between the maximum temperature value and the minimum temperature value to obtain the temperature rise value; After obtaining the time points corresponding to the maximum and minimum temperature values, extract the duration between the maximum and minimum temperature values, and divide the temperature rise value by the duration between the maximum and minimum temperature values ​​to obtain the unit temperature rise value; A unit temperature rise threshold is preset, and the unit temperature rise value is divided by the unit temperature rise threshold to obtain a unit temperature rise ratio.

9. The EMB torque distribution control method based on the road adhesion coefficient estimation algorithm according to claim 8, characterized in that: Also includes: The surface of the motor rotor is divided into several sub-areas according to a preset size, and the temperature of each sub-area and the center of the motor rotor is obtained after the vehicle performs emergency braking; Subtract the corresponding motor rotor center temperature from the temperature of each sub-region of the motor rotor to obtain the surface temperature difference; Extract the maximum surface temperature difference value within the sub-region of the motor rotor and record it as the surface maximum temperature difference value; obtain the surface maximum temperature difference values ​​of each sub-region of the electronic rotor in sequence, arrange the surface maximum temperature difference values ​​of each sub-region in descending order according to the value, and extract the two largest surface maximum temperature differences and their corresponding positions; Mark the locations of the two largest surface maximum temperature differences as the first positioning point and the second positioning point respectively; Mark the rotor center position as the third positioning point, connect the first positioning point, the second positioning point, and the third positioning point with a straight line to obtain a triangular figure, calculate the area of ​​the triangular figure, and record it as the simulated distribution value; After normalizing the unit temperature rise ratio and the simulated distribution value, the unit temperature rise ratio and the simulated distribution value are used as the major and minor semi-axis of the ellipse, respectively, to construct an ellipse model, and the area of ​​the ellipse model is calculated and recorded as the added inertia value; The inertia matching value is obtained by weighted calculation of the rotational defined inertia and the added inertia value; Match the corresponding moment of inertia according to the inertia matching value, and substitute the obtained moment of inertia into the above rotation law formula: That's it.

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