Integrated control method for a distributed electromechanical brake system

Abstract

The application relates to the technical field of vehicle brake control, and discloses an integrated control method of a distributed electronic mechanical brake system, wherein the method comprises the following steps: collecting vehicle operation information and estimating vehicle state variables; generating a control target according to a driver braking intention and the vehicle state variables; performing risk assessment on a vehicle operation condition and self-adaptively adjusting control parameters; solving optimal braking forces of four wheels based on model predictive control rolling optimization; and converting the optimal braking forces into control instructions and delivering the control instructions to each wheel end actuator to perform closed-loop control. The application solves the technical problem of instruction conflicts caused by independent logics of anti-lock control and vehicle body stability control in a traditional scheme by uniformly solving four-wheel braking force distribution, anti-lock control and vehicle body stability control in the same rolling optimization framework based on model predictive control, and the technical effects of coordinating the priorities of multiple control targets and making braking force distribution simultaneously consider braking efficiency and driving stability under complex conditions are achieved.

Description

Technical Field

[0001] This invention relates to the field of vehicle braking control technology, and more specifically to an integrated control method for a distributed electromechanical braking system. Background Technology

[0002] In the field of vehicle braking control, distributed electromechanical braking systems equip each wheel with an independent actuator, allowing for independent adjustment of braking force and providing high dynamic response capabilities. Under complex conditions such as low-traction roads, separated-traction roads, and cornering braking, vehicles simultaneously face the risks of wheel lock-up and yaw instability, placing high demands on the coordination capabilities of the braking control system.

[0003] In the existing technology, anti-lock braking system (ABS) and vehicle stability control are usually implemented using independent control logic and independent control units, and the two are coordinated through preset priority rules or arbitration logic.

[0004] However, the above-mentioned independent control scheme has the following drawbacks: when anti-lock braking and vehicle stability control are triggered at the same time, control command conflicts are likely to occur between the two independent logics, making it difficult to simultaneously take into account braking performance and driving stability within the same control cycle. As a result, the independent wheel braking advantage of the distributed electromechanical braking system cannot be fully utilized, and the overall vehicle braking performance is limited under complex operating conditions. Summary of the Invention

[0005] This invention provides an integrated control method for a distributed electromechanical braking system, solving the technical problem in related technologies that braking systems cannot simultaneously achieve deceleration tracking, yaw stability control, and tire anti-lock braking.

[0006] This invention discloses an integrated control method for a distributed electromechanical braking system, comprising steps A to E. Step A collects vehicle operating status information, driver braking intention information, and actuator feedback information. Based on the collected information, vehicle state variables are estimated, including longitudinal velocity, sideslip angle, yaw rate, four-wheel tire slip ratio, and road surface adhesion coefficient. Step B generates control objectives based on the driver braking intention information and vehicle state variables. These control objectives include the target deceleration of the entire vehicle, total braking force demand, and yaw stability control objective. Step C performs a risk assessment of the vehicle operating conditions based on the vehicle state variables. Based on the risk assessment results, the weights of the control objectives and the boundary conditions of the constraints in model predictive control are adaptively adjusted online. Step D, based on rolling optimization of model predictive control, simultaneously incorporates deceleration tracking, yaw stability, sideslip angle suppression, and slip ratio control objectives into a unified cost function to solve for the optimal braking force for all four wheels. Step E converts the optimal braking force for all four wheels into control variables for each actuator and issues them for execution. Based on the execution feedback, the vehicle state information is updated, and the process returns to step A to form a closed-loop control.

[0007] Further, in step A, the longitudinal velocity is obtained through Kalman filtering, and the input of the Kalman filter is the wheel speeds and longitudinal accelerations of the four wheels; the slip ratio of the four tires is calculated based on the wheel speeds of each wheel and the longitudinal velocity, and the slip ratio of each wheel is equal to the difference between the longitudinal velocity and the wheel speed multiplied by the effective rolling radius of the wheel, divided by the longitudinal velocity; the road adhesion coefficient is estimated online based on the ratio of the tire longitudinal force to the vertical load.

[0008] Further, in step B, the target deceleration of the vehicle is obtained based on the brake pedal travel mapping; the total braking force requirement is equal to the product of the vehicle mass and the target deceleration of the vehicle; the yaw stability control target includes the target yaw rate and the target sideslip angle, the target yaw rate is calculated based on the front wheel steering angle, longitudinal speed and vehicle wheelbase according to the steady-state steering response relationship, and the target yaw rate is equal to the product of the longitudinal speed and the front wheel steering angle divided by the sum of the wheelbase and the understeer gradient coefficient multiplied by the square of the longitudinal speed.

[0009] Furthermore, in step B, an upper limit constraint is applied to the target yaw rate, the upper limit value being equal to the product of the road adhesion coefficient and the gravitational acceleration divided by the longitudinal velocity; when the absolute value of the calculated target yaw rate exceeds the upper limit value, the target yaw rate is truncated to the upper limit value.

[0010] Further, in step C, the risk assessment includes tire slip rate risk assessment, yaw stability risk assessment, and road surface adhesion status assessment; the tire slip rate risk assessment compares the slip rate of each wheel with a preset slip rate threshold, and when the slip rate of a certain wheel exceeds the slip rate threshold, it is determined that the wheel has a risk of locking up; the yaw stability risk assessment calculates the yaw rate deviation and the sideslip angle deviation, and when the deviation exceeds the corresponding threshold, it is determined that the vehicle has a yaw instability trend; the road surface adhesion status assessment determines the low adhesion road surface condition based on the road surface adhesion coefficient, and determines the separated road surface condition based on the difference in the adhesion coefficients of the left and right wheels.

[0011] Further, in step C, the control target weights are continuously scheduled based on the risk level, and each control target weight is a continuous function of the corresponding risk index; the slip ratio control weight is equal to the minimum weight plus the difference between the maximum weight and the minimum weight multiplied by the output value of the Sigmoid function, and the input of the Sigmoid function is the difference between the wheel slip ratio and the slip ratio threshold divided by the scale parameter; the remaining control target weights take the corresponding risk index deviation as input, and after being mapped by the Sigmoid function, they continuously transition between their respective preset minimum and maximum values.

[0012] Further, in step D, a coupled dynamic model of the vehicle's longitudinal, lateral, and yaw rates is established as a prediction model. The state variables of the prediction model include longitudinal velocity, sideslip angle, yaw rate, and four-wheel tire slip ratio, while the control variables are four-wheel braking forces. The continuous-time dynamic equation is discretized to obtain the vehicle state prediction sequence in the prediction time domain. The unified cost function includes a deceleration tracking term, a yaw rate tracking term, a sideslip angle suppression term, a slip ratio control term for each wheel, and a braking force change smoothness term. Each term is normalized and scaled to a dimensionless unified range before being substituted into the cost function.

[0013] Further, in step D, the constraints include: wheel-end braking force constraints, where the braking force of each wheel takes a value between zero and the dynamically adjusted upper limit of braking force; braking force change rate constraints, where the absolute value of the braking force change in adjacent time steps does not exceed the maximum braking force change in a single step; tire adhesion constraints, where the sum of the squares of the longitudinal force and the lateral force of each wheel does not exceed the square of the product of the road adhesion coefficient and the vertical load of that wheel; slip ratio safety constraints, where the slip ratio of each wheel does not exceed the dynamically tightened slip ratio safety upper limit; and vehicle stability boundary constraints, where the absolute value of the yaw rate does not exceed the yaw rate stability boundary value and the absolute value of the sideslip angle does not exceed the sideslip angle stability boundary value.

[0014] Furthermore, in step E, the deviation between the actual braking force and the target braking force is compensated based on the actuator feedback information. The actual braking force fed back by each actuator is collected, the deviation between the actual braking force and the target braking force is calculated, and the deviation is added to the optimization solution of the next control cycle as a correction term.

[0015] This invention discloses an integrated control system for a distributed electromechanical braking system, used to execute the aforementioned integrated control method, comprising: a state acquisition and estimation module for acquiring vehicle operating state information, driver braking intention information, and actuator feedback information, and estimating vehicle state variables based on the acquired information; a control target generation module for generating the vehicle target deceleration, total braking force demand, and yaw stability control targets based on the driver braking intention information and vehicle state variables; a risk assessment and parameter adjustment module for performing risk assessment on the vehicle operating conditions based on the vehicle state variables, and adaptively adjusting the control target weights and constraint boundary conditions in model predictive control online based on the risk assessment results; a rolling optimization solution module for solving the optimal braking force for all four wheels based on rolling optimization of model predictive control, simultaneously incorporating deceleration tracking, yaw stability, sideslip angle suppression, and slip ratio control targets into a unified cost function; and a command conversion and closed-loop execution module for converting the optimal braking force for all four wheels into control variables for each actuator and issuing them for execution, updating vehicle state information based on execution feedback, and triggering the state acquisition and estimation module to enter the next control cycle.

[0016] This invention utilizes model predictive control to uniformly solve four-wheel braking force distribution, anti-lock braking system (ABS), and vehicle stability control within the same rolling optimization framework. It solves the technical problem of command conflicts that arise when ABS and vehicle stability control use independent logic in traditional solutions. This achieves the technical effect of coordinating the priorities of multiple control objectives under complex operating conditions, ensuring that braking force distribution simultaneously considers braking efficiency and driving stability. By continuously scheduling the weights and constraint boundaries of control objectives based on risk assessment results, it avoids step fluctuations in braking force caused by control mode switching, enabling smooth transitions between different control objectives. Furthermore, deviation compensation based on actuator feedback allows the controller to correct execution deviations, improving the tracking accuracy of closed-loop control. Attached Figure Description

[0017] Figure 1 This is a diagram illustrating the overall structure of the distributed EMB system integration control method according to an embodiment of the present invention. Figure 2 This is a flowchart of the MPC integrated control system according to an embodiment of the present invention; Figure 3 This is a flowchart of the MPC anti-lock control under the lock-up risk condition in this embodiment of the invention; Figure 4 This is a flowchart of the MPC vehicle stability control under yaw instability conditions in an embodiment of the present invention. Detailed Implementation

[0018] Example 1 In a distributed electromechanical braking (EMB) system, each of the four wheels is equipped with an independent EMB actuator, and each actuator can independently adjust the wheel-end braking force. Under complex conditions such as low-traction surfaces, separated-traction surfaces, and emergency braking accompanied by steering, traditional solutions employ independent logic and independent control units for anti-lock braking system (ABS) and electronic stability program (ESP), resulting in insufficient coupling between them. When multiple risks occur simultaneously, conflicts easily arise between the independent control logics, making it difficult to fully utilize the high dynamic advantages of independent wheel braking in EMB. This embodiment provides an integrated control method for a distributed EMB system, based on model predictive control (MPC), to uniformly achieve vehicle braking force distribution, anti-lock braking control, and electronic stability program control during the same rolling optimization process.

[0019] In this embodiment, the vehicle is equipped with wheel speed sensors, an inertial measurement unit, a steering angle sensor, a brake pedal sensor, and four wheel-end EMB actuators. Each sensor and actuator is connected to the central brake controller via an onboard communication bus. The central brake controller has real-time computing capabilities and is used to perform MPC rolling optimization solutions.

[0020] A distributed EMB system integration control method according to an embodiment of the present invention includes the following steps: Step 1: Collect vehicle operation information and estimate vehicle state variables. The system collects vehicle operating status information, driver braking intention information, and EMB actuator feedback information. Vehicle operating status information includes four-wheel wheel speeds, longitudinal acceleration, lateral acceleration, yaw rate, and front wheel steering angle. Driver braking intention information includes brake pedal travel and pedal force. EMB actuator feedback information includes the actual braking force currently output by each actuator.

[0021] Based on the information collected above, vehicle state variables are estimated. Vehicle state variables include the vehicle's longitudinal velocity. Side slip angle yaw rate Four-wheel tire slip ratio ( (Corresponding to the front left, front right, rear left, and rear right wheels respectively) Road surface adhesion coefficient And the boundary state of vehicle stability.

[0022] Longitudinal velocity The velocity is obtained through Kalman filtering. The input to the Kalman filter is the wheel speed and longitudinal acceleration of the four wheels, and the output is the estimated longitudinal velocity. Side slip angle. Estimation is based on lateral acceleration, yaw rate, and longitudinal velocity. Four-wheel tire slip ratio. Based on the wheel speed With longitudinal velocity The calculation yields the result, and its expression is:

[0023] in, The effective rolling radius of the wheel, For the first The wheel speed of each wheel For the longitudinal speed of the vehicle, For the first Tire slip ratio of each wheel. Road surface adhesion coefficient. Online estimation is performed based on the ratio of tire longitudinal force to vertical load. The vehicle stability boundary state is determined based on a joint criterion of yaw rate and sideslip angle.

[0024] Furthermore, in the above slip ratio formula, the molecule With denominator Both are units of velocity; dividing them by the units of velocity will result in... It is a dimensionless quantity and can be directly used in the weighting operation in the subsequent cost function.

[0025] Step 2, Generate control target Based on the driver's brake pedal travel and force, and combined with vehicle state variables, a control target is generated. The control target includes the overall vehicle deceleration. Total braking force demand And the target of yaw stability control.

[0026] Target deceleration of the whole vehicle Obtained based on brake pedal travel mapping. Total braking force requirement. Based on the target deceleration of the whole vehicle and the vehicle mass The calculation yields the result, and its expression is:

[0027] in, For vehicle quality, To reduce the overall vehicle speed.

[0028] Yaw stabilization control targets include the target's yaw rate. and target sideslip angle Target yaw rate Based on front wheel steering angle Longitudinal velocity and vehicle wheelbase It is calculated based on the steady-state steering response relationship, and its expression is:

[0029] in, For insufficient steering gradient coefficient, It is the sum of the front and rear wheelbases. This refers to the front wheel steering angle. Vehicle longitudinal velocity. Target sideslip angle. Take the zero value under normal braking conditions.

[0030] In this embodiment of the application, to prevent the target yaw rate from exceeding the road surface adhesion limit, the following measures are taken: Apply an upper limit constraint. The upper limit value is based on the road surface adhesion coefficient. and gravitational acceleration It is confirmed that its expression is:

[0031] in, The road surface adhesion coefficient, It is the acceleration due to gravity. This represents the vehicle's longitudinal velocity. When the calculated target yaw rate exceeds the upper limit, it is truncated to the upper limit to match the control target with road conditions.

[0032] Furthermore, in the above upper limit constraint, the molecule The dimension of is acceleration, and the denominator is 0. The dimension of is velocity, and the dimension obtained by dividing the two is angular velocity. ),and The dimensions are consistent, and the dimensions on both sides are matched, allowing for direct comparison and calculation.

[0033] Step 3: Conduct a risk assessment of the vehicle's operating conditions and adaptively adjust the control parameters. Based on vehicle state parameters, a risk assessment is conducted on the vehicle's operating conditions. This risk assessment includes tire slip rate risk assessment, yaw stability risk assessment, and road surface adhesion status assessment.

[0034] Tire slip rate risk assessment: Assess the slip rate of each wheel. With preset slip rate threshold Comparison. When At that time, it was determined that the first There is a risk of wheel lock-up.

[0035] Yaw stability risk assessment: Calculation of yaw rate deviation and sideslip angle deviation .when Exceeding the yaw deviation threshold or When the sideslip angle deviation threshold is exceeded, the vehicle is considered to have a tendency to yaw.

[0036] Road surface adhesion condition assessment: based on the estimated road surface adhesion coefficient Determine the road surface type. When When the adhesion coefficient is below the low adhesion threshold, the vehicle is considered to be in a low adhesion road condition. When the difference in adhesion coefficient between the left and right wheels exceeds the separation road threshold, the vehicle is considered to be in a separation road condition.

[0037] Based on the risk assessment results, the control objective weights, constraint boundaries, and control modes in MPC are adaptively adjusted online. The adjustment rules are as follows:

[0038] When a wheel is detected to be at risk of locking up, the weight of the slip ratio control objective in the cost function is increased, the slip ratio constraint boundary of that wheel is tightened, and the upper limit of the braking force for that wheel is reduced. At the same time, the braking force distribution of the remaining wheels is coordinated so that they can bear more of the available braking force, in order to maintain the overall braking performance of the vehicle while suppressing lock-up.

[0039] When a tendency for vehicle yaw instability is detected, the weight of the yaw rate tracking target and the weight of the sideslip angle suppression target are increased, and an additional yaw moment control target is introduced as needed. By adjusting the difference in braking force between the left and right wheels, an additional yaw moment that is beneficial to vehicle stability is generated.

[0040] When the vehicle is identified as being in a low-adhesion road surface condition, the weight of the vehicle target deceleration tracking is reduced, the weight of the tire slip ratio control is increased, and the tire adhesion constraint and slip ratio constraint boundary are tightened so that the control target prioritizes meeting the requirements of tire adhesion utilization and driving stability.

[0041] When the vehicle is identified as being on a separated road surface, the difference in braking force distribution between the left and right wheels is adjusted to generate additional yaw moment, suppressing vehicle deviation and maintaining driving stability.

[0042] When both yaw instability and wheel lock-up risk are detected simultaneously, the weights of the yaw control target and the slip ratio control target are increased concurrently, while the slip ratio constraint and vehicle stability boundary constraint are tightened at the same time, coordinating anti-lock braking control and vehicle stability control within a unified optimization framework.

[0043] In this embodiment, the control target weights are continuously scheduled based on the risk level, rather than switching abruptly. Specifically, each control target weight is a continuous function of the risk index, smoothly transitioning with changes in risk level. The weights are controlled using a slip ratio. For example, its following the first The relationship between the changes in the risk level of individual wheel slip ratio is expressed as follows:

[0044] in, This represents the minimum value of the slip ratio control weight. The maximum value of the slip ratio control weight. The slip ratio threshold, To adjust the scale parameter of the transition interval width, For the first Tire slip ratio of each wheel The Sigmoid function has the following expression: in, For the input variables of the Sigmoid function, This represents an exponential function.

[0045] Furthermore, in the above weighting formula, , and All are dimensionless slip ratios. Given a dimensionless input, the output range after mapping with the Sigmoid function is: ,and and The dimensions are consistent, and the resulting weights exist The values ​​are continuously taken within the interval. The weights of other control objectives are processed in the same way, using the corresponding risk indicator deviation as input, and after mapping by the Sigmoid function, they continuously transition between their respective preset minimum and maximum values. This method avoids abrupt fluctuations in braking force caused by sudden changes in control mode, enabling smooth transitions between different control objectives.

[0046] Step 4: Solve for the optimal braking force of the four wheels based on MPC rolling optimization. Step 4 may specifically include: Step 401: Establish a vehicle dynamic response model in the prediction time domain. Based on the current vehicle state variables, a coupled dynamic model of the vehicle's longitudinal, lateral, and yaw states is established as a prediction model for MPC. The state variables of the coupled dynamic model of the vehicle's longitudinal, lateral, and yaw states are: The controlled variable is the braking force of the four wheels. ,in This indicates transpose.

[0047] The longitudinal dynamic equation is: in, For the first The longitudinal force of each wheel For vehicle quality, For wheel index and These correspond to the front left, front right, rear left, and rear right wheels, respectively. The yaw rate is angular velocity. For the longitudinal speed of the vehicle, It is the sideslip angle.

[0048] The transverse dynamic equation is: in, For the first Lateral force on each wheel.

[0049] The equation for yaw dynamics is: in, Let be the moment of inertia of the vehicle about its vertical axis. For the first The yaw moment generated by each wheel relative to the vehicle's center of gravity is calculated by combining the longitudinal and lateral forces of each wheel with their lever arms relative to the center of gravity.

[0050] Discretize the above continuous-time dynamic equations to obtain the prediction time domain. Vehicle state prediction sequence within a step.

[0051] Step 402, establish a unified cost function Based on the control objective and the adjusted weights, a unified cost function is established. The cost function includes the following terms:

[0052] in, To predict the time step index in the time domain, To predict the length of the time domain, To control the length of the time domain, For the first Predicted longitudinal deceleration of the step To reduce the speed of the entire vehicle, For the first Predicted yaw rate of the step For the target yaw rate, For the first Predicted sideslip angle of the step For the first Step 1 Predicted slip ratio of each wheel, For the first Target slip ratio for each wheel For the first The change in braking force of each wheel in adjacent time steps For wheel index and , For target deceleration tracking weights, For yaw rate tracking weights, As the weight for side slip angle suppression, For the first Individual wheel slip ratio control weights The weights are assigned to the smoothness of braking force changes. Each weight value is determined online based on the risk assessment results from step 3.

[0053] Furthermore, the cost function The dimensions of the terms are inconsistent: deceleration tracking term The dimension is the square of acceleration, and the yaw rate tracking term is... The dimension is the square of the angular velocity, and the slip ratio control term is... and sideslip angle suppression term The square of the dimensionless quantity, the term of change in braking force The dimension of the force is the square of the force. To eliminate the influence of dimensional differences on the weighted summation operation, before substituting the cost function, the force is... , , , and Each physical quantity is scaled to a dimensionless uniform range by mean normalization based on the range before being weighted and summed.

[0054] Step 403, Set constraints The constraints include the following: Wheel-end braking force constraints: ,in For the first The upper limit of braking force for each wheel is dynamically adjusted based on the risk assessment results in step 3.

[0055] Braking force change rate constraint: ,in This is the maximum allowable change in braking force per step, and this constraint reflects the response capability of the EMB actuator.

[0056] Tire adhesion constraints: ,in For the first The road surface adhesion coefficient corresponding to each wheel For the first Vertical load on each wheel For the first The longitudinal force of each wheel For the first Lateral force on each wheel.

[0057] Slip ratio safety constraints: ,in For the first The upper limit of slip ratio for each wheel is dynamically tightened based on the risk assessment results in step 3.

[0058] Vehicle stability boundary constraints: , ,in This represents the boundary value for yaw rate stability. This represents the boundary value for sideslip angle stability.

[0059] It should be noted that, and These are the yaw rate stability boundary values ​​and the sideslip angle stability boundary values, which are updated online based on the overall vehicle stability boundary conditions. Take the maximum yaw rate corresponding to the road surface adhesion limit, i.e. ; The steady-state sideslip limit is determined based on the vehicle's lateral stiffness and load distribution, and is updated in each control cycle based on the currently estimated vehicle state variables.

[0060] Step 404, Rolling optimization solution Within each control cycle, based on the current vehicle state variables, the vehicle's longitudinal, lateral, and yaw coupled dynamics model, the cost function, and constraints, the optimal braking force control sequence in the prediction time domain is solved. Only the optimal control quantity corresponding to the current moment is output. As the optimal wheel-end braking force for four wheels, among which Indicates transpose. , , , The optimal braking forces are for the front left, front right, rear left, and rear right wheels, respectively.

[0061] It should be noted that this optimization problem is a constrained quadratic programming problem, which is solved once in each control cycle. Since only the control quantity at the current moment is output and the solution is recalculated based on the updated state at the next moment, the rolling optimization characteristic of MPC enables the central brake controller to continuously correct prediction deviations and adapt to dynamically changing operating conditions.

[0062] Step 5: Convert control commands and execute closed-loop control. Optimal wheel-end braking force of four wheels , , , The mapping relationship is used to convert the variables into control variables for each EMB actuator, and then the variables are sent to each wheel-end EMB actuator to perform braking.

[0063] It should be noted that the control variables of the EMB actuator include motor current, motor torque, and displacement or braking clamping force of the reduction mechanism. The mapping relationship is obtained in advance based on the electromechanical characteristics of the EMB actuator, converting the target braking force into a physical quantity that the actuator can directly execute.

[0064] In this embodiment, compensation is provided for the deviation between the actual braking force and the target braking force based on actuator feedback information. Specifically, the actual braking force fed back by each EMB actuator is collected, the deviation between the actual braking force and the target braking force is calculated, and this deviation is added to the optimization solution of the next control cycle as a correction term. This compensation enables the MPC to consider the impact of the execution deviation in the optimization solution of the next control cycle, thereby improving the tracking accuracy of the actual braking force to the target value.

[0065] After completing the braking execution of the current control cycle, the vehicle status information is updated based on the execution feedback, and the process returns to step 1 to enter the next control cycle, thus forming a closed-loop control.

[0066] Technical effects of this embodiment This implementation method uses MPC to solve for four-wheel braking force distribution, anti-lock braking control, and vehicle stability control within the same rolling optimization framework, thus avoiding command conflicts that occur when ABS and ESP use independent control logic in traditional solutions. Because MPC considers multiple objectives simultaneously in each control cycle, such as deceleration tracking, yaw stability, sideslip angle suppression, and slip ratio control, and continuously schedules weights and constraint boundaries based on risk assessment results, the central brake controller can adaptively coordinate the priorities of various control objectives under complex conditions such as low-adhesion surfaces, separated surfaces, cornering braking, and emergency braking, ensuring that braking force distribution balances braking efficiency and driving stability. Since the control objective weights are continuously scheduled based on the Sigmoid function rather than step switching, the transitions between control modes are smooth, avoiding step fluctuations in braking force. Furthermore, deviation compensation based on actuator feedback enables the central brake controller to correct execution deviations, improving the tracking accuracy of closed-loop control.

[0067] The embodiments of the present invention have been described above. However, the embodiments are not limited to the specific implementation methods described above. The specific implementation methods described above are merely illustrative and not restrictive. Those skilled in the art can make more equivalent embodiments under the guidance of the present embodiments, and all of them are within the protection scope of the present embodiments.

Claims

1. An integrated control method for a distributed electromechanical braking system, characterized in that, It includes at least steps A to E, wherein step A includes: collecting vehicle operating status information, driver braking intention information and actuator feedback information, and estimating vehicle state quantities based on the collected information, wherein the vehicle state quantities include longitudinal speed, sideslip angle, yaw rate, four-wheel tire slip ratio and road surface adhesion coefficient. Step B includes: generating a control target based on the driver's braking intention information and the vehicle state quantity, wherein the control target includes the vehicle target deceleration, total braking force requirement and yaw stability control target; The step C includes: performing a risk assessment of the vehicle operating conditions based on the vehicle state variables, and adaptively adjusting the control target weights and constraint boundary conditions in the model predictive control online according to the risk assessment results; The step D mentioned therein includes: rolling optimization based on model predictive control, which simultaneously incorporates deceleration tracking, yaw stability, sideslip angle suppression and slip ratio control objectives into a unified cost function to solve for the optimal braking force of the four wheels; The step E includes: converting the optimal braking force of the four wheels into control variables for each actuator and issuing them for execution; updating the vehicle status information based on the execution feedback and returning to step A to form closed-loop control.

2. The integrated control method for a distributed electromechanical braking system according to claim 1, characterized in that, In step A, the longitudinal velocity is obtained through Kalman filtering, and the input of the Kalman filter is the wheel speed and longitudinal acceleration of the four wheels; the slip ratio of the four wheels is calculated based on the wheel speed of each wheel and the longitudinal velocity, and the slip ratio of each wheel is equal to the difference between the longitudinal velocity and the wheel speed multiplied by the effective rolling radius of the wheel, divided by the longitudinal velocity; the road adhesion coefficient is estimated online based on the ratio of the tire longitudinal force to the vertical load.

3. The integrated control method for a distributed electromechanical braking system according to claim 1, characterized in that, In step B, the target deceleration of the vehicle is obtained based on the brake pedal travel mapping; the total braking force requirement is equal to the product of the vehicle mass and the target deceleration of the vehicle; the yaw stability control target includes the target yaw rate and the target sideslip angle. The target yaw rate is calculated based on the front wheel steering angle, longitudinal speed, and vehicle wheelbase according to the steady-state steering response relationship. The target yaw rate is equal to the product of the longitudinal speed and the front wheel steering angle divided by the sum of the wheelbase and the understeer gradient coefficient multiplied by the square of the longitudinal speed.

4. The integrated control method for a distributed electromechanical braking system according to claim 3, characterized in that, In step B, an upper limit constraint is applied to the target yaw rate. The upper limit value is equal to the product of the road adhesion coefficient and the gravitational acceleration divided by the longitudinal velocity. When the absolute value of the calculated target yaw rate exceeds the upper limit value, the target yaw rate is truncated to the upper limit value.

5. The integrated control method for a distributed electromechanical braking system according to claim 1, characterized in that, In step C, the risk assessment includes tire slip rate risk assessment, yaw stability risk assessment, and road surface adhesion status assessment. The tire slip rate risk assessment compares the slip rate of each wheel with a preset slip rate threshold. When the slip rate of a wheel exceeds the slip rate threshold, it is determined that the wheel has a risk of locking up. The yaw stability risk assessment calculates the yaw rate deviation and the sideslip angle deviation. When the deviation exceeds the corresponding threshold, it is determined that the vehicle has a yaw instability trend. The road surface adhesion status assessment determines low-adhesion road surface conditions based on the road surface adhesion coefficient and determines separated road surface conditions based on the difference in the adhesion coefficients of the left and right wheels.

6. The integrated control method for a distributed electromechanical braking system according to claim 5, characterized in that, In step C, the control target weights are continuously scheduled based on the risk level, and each control target weight is a continuous function of the corresponding risk index; the slip ratio control weight is equal to the minimum weight plus the difference between the maximum weight and the minimum weight multiplied by the output value of the Sigmoid function, and the input of the Sigmoid function is the difference between the wheel slip ratio and the slip ratio threshold divided by the scale parameter; the remaining control target weights take the corresponding risk index deviation as input, and after being mapped by the Sigmoid function, they continuously transition between their respective preset minimum and maximum values.

7. The integrated control method for a distributed electromechanical braking system according to claim 1, characterized in that, In step D, a coupled dynamic model of the vehicle's longitudinal, lateral, and yaw rates is established as a prediction model. The state variables of the prediction model include longitudinal velocity, sideslip angle, yaw rate, and four-wheel tire slip ratio, while the control variables are the four-wheel braking forces. The continuous-time dynamic equations are discretized to obtain the vehicle state prediction sequence in the prediction time domain. The unified cost function includes a deceleration tracking term, a yaw rate tracking term, a sideslip angle suppression term, a slip ratio control term for each wheel, and a braking force change smoothness term. Each term is normalized and scaled to a dimensionless unified range before being substituted into the cost function.

8. The integrated control method for a distributed electromechanical braking system according to claim 7, characterized in that, In step D, the constraints include: wheel-end braking force constraints, where the braking force of each wheel takes a value between zero and the dynamically adjusted upper limit of braking force; braking force change rate constraints, where the absolute value of the change in braking force in adjacent time steps does not exceed the maximum change in braking force in a single step; tire adhesion constraints, where the sum of the squares of the longitudinal force and the lateral force of each wheel does not exceed the square of the product of the road adhesion coefficient and the vertical load of that wheel; slip ratio safety constraints, where the slip ratio of each wheel does not exceed the dynamically tightened upper limit of slip ratio safety; and vehicle stability boundary constraints, where the absolute value of the yaw rate does not exceed the yaw rate stability boundary value and the absolute value of the sideslip angle does not exceed the sideslip angle stability boundary value.

9. The integrated control method for a distributed electromechanical braking system according to claim 1, characterized in that, In step E, the deviation between the actual braking force and the target braking force is compensated based on the actuator feedback information. The actual braking force fed back by each actuator is collected, the deviation between the actual braking force and the target braking force is calculated, and the deviation is added to the optimization solution of the next control cycle as a correction term.

10. An integrated control system for a distributed electromechanical braking system, used to execute the integrated control method for a distributed electromechanical braking system according to any one of claims 1 to 9, characterized in that, include: The status acquisition and estimation module is used to acquire vehicle operating status information, driver braking intention information and actuator feedback information, and estimate vehicle status quantities based on the acquired information. The control target generation module is used to generate the vehicle target deceleration, total braking force requirement and yaw stability control target based on the driver's braking intention information and the vehicle state quantity. The risk assessment and parameter adjustment module is used to assess the risk of vehicle operating conditions based on the vehicle state variables, and to adaptively adjust the control target weights and constraint boundary conditions in model predictive control online according to the risk assessment results. The rolling optimization solution module is used for rolling optimization based on model predictive control. It incorporates deceleration tracking, yaw stability, sideslip angle suppression and slip ratio control objectives into a unified cost function to solve for the optimal braking force of the four wheels. The instruction conversion and closed-loop execution module is used to convert the optimal braking force of the four wheels into control variables of each actuator and issue them for execution. Based on the execution feedback, it updates the vehicle status information and triggers the status acquisition and estimation module to enter the next control cycle.

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