In-situ steering hierarchical cooperative control system and method for distributed drive electric vehicle

Abstract

This invention discloses a distributed drive electric vehicle in-situ steering hierarchical cooperative control system and method, belonging to the field of electric vehicle control technology. It includes: real-time acquisition of vehicle state signals, and obtaining the slip ratio and road adhesion coefficient required for in-situ steering control through dynamic calculations; determining the nominal yaw rate and desired wheel speed based on the driver's operating intention, slip ratio, and road adhesion coefficient; obtaining the final drive torque based on the error between the nominal yaw rate and the actual yaw rate, and the error between the desired wheel speed and the actual wheel speed; real-time monitoring of the vehicle state, and when an unstable state is detected, resetting the target nominal yaw rate and final drive torque to zero, and outputting a braking pressure command; converting the final drive torque and braking pressure command into actual execution actions, forming a state feedback closed loop, thereby completing vehicle motion control and achieving precise, stable, and safe control of the in-situ steering process.

Description

Technical Field

[0001] This invention relates to the field of electric vehicle control technology, and in particular to a distributed drive electric vehicle in-situ steering hierarchical cooperative control system and method. Background Technology

[0002] In the field of electric vehicle technology, distributed drive technology provides vehicles with excellent maneuverability and control flexibility by independently controlling the wheel torque through four wheel-side motors. In-situ steering allows vehicles to rotate around their own center in confined spaces, effectively solving the problem of vehicle turning in crowded parking lots and narrow alleys. However, existing technologies still have the following problems: the tire force state during in-situ steering is complex, involving nonlinear stages such as elastic deformation and sideslip, making traditional differential steering control strategies difficult to apply directly; the coupling relationship between yaw rate and wheel speed is complex, and controlling either variable alone can easily lead to steering center deviation, affecting control accuracy; changes in road surface adhesion coefficient and unknown disturbances significantly affect system stability, lacking effective robust control methods; current research is mostly at the simulation stage, lacking complete control schemes verified in real vehicles. Summary of the Invention

[0003] To address the problems of existing technologies, this invention provides a distributed drive electric vehicle in-situ steering hierarchical cooperative control system and method.

[0004] On the one hand, a hierarchical cooperative control method for in-situ steering of a distributed drive electric vehicle is provided, the method comprising:

[0005] S1: Real-time acquisition of vehicle status signals, and obtains the slip ratio and road adhesion coefficient required for in-situ steering control through dynamic calculations;

[0006] S2: Determine the nominal yaw rate and the desired wheel speed based on the driver's operating intention, slip ratio and road adhesion coefficient;

[0007] S3: Based on the error between the nominal yaw rate and the actual yaw rate, and the error between the expected wheel speed and the actual wheel speed, the final driving torque is obtained, thereby achieving a dynamic balance between the yaw rate and the wheel speed during the steering process.

[0008] S4: Monitor vehicle status in real time. When it is determined that the vehicle has entered an unstable state, reset the nominal yaw rate and final drive torque of the target to zero and output a braking pressure command.

[0009] S5: Transforms the final drive torque and braking pressure commands into actual actions and forms a state feedback closed loop to complete vehicle motion control.

[0010] Furthermore, S1 specifically includes:

[0011] S11: Collect the rotational speed, longitudinal acceleration, lateral acceleration and yaw rate signals of each wheel. Calculate the angular acceleration based on the rotational speed signal using the differential method. At the same time, use the vehicle dynamics equations, combined with the longitudinal and lateral accelerations, to calculate the vertical load on each wheel.

[0012] S12: Based on angular acceleration, driving torque signal and braking pressure signal, the longitudinal force of each wheel is estimated using the single wheel dynamics equation. Combining the vertical load and the longitudinal force of the wheel, the road adhesion coefficient of each wheel is calculated according to the adhesion circle theory. At the same time, based on the speed signal and the motion state of the vehicle's center of gravity, the slip ratio of each wheel is calculated.

[0013] S13: Summarize the road surface adhesion coefficient and slip ratio, verify them through the set threshold, and remove outliers or perform filtering.

[0014] Furthermore, S2 specifically includes:

[0015] S21: Read the driver's accelerator pedal opening signal and determine the nominal yaw rate through the preset dead zone and adjustable slope curve;

[0016] S22: The nominal yaw rate and road surface adhesion coefficient are used as important inputs for fuzzy logic control. Reasoning is performed according to preset fuzzy rules, and the desired wheel speed is output through membership function and fuzzy reasoning mechanism.

[0017] Furthermore, the fuzzy rules follow the following core principles: under the same road surface adhesion coefficient, the higher the nominal yaw rate, the higher the expected wheel speed; under the same nominal yaw rate, the lower the road surface adhesion coefficient, the lower the expected wheel speed.

[0018] Furthermore, S3 specifically includes:

[0019] S31: Based on the error between the nominal yaw rate and the actual yaw rate, the total disturbance is estimated through a first-order linear extended state observer, and the first driving torque is output.

[0020] S32: Based on the error between the desired wheel speed and the actual wheel speed, an improved single-neuron adaptive PID algorithm is used to dynamically adjust the control parameters and output the second driving torque;

[0021] S33: Based on the error between the current nominal yaw rate and the actual yaw rate, and the error between the expected wheel speed and the actual wheel speed, a weighting coefficient is dynamically allocated to weightedly fuse the first driving torque and the second driving torque to obtain the final driving torque.

[0022] Furthermore, S4 specifically includes: real-time monitoring of the slip ratio of each wheel, the longitudinal speed, the lateral speed and the yaw rate tracking error of the vehicle; when the monitored values ​​meet the preset instability criteria, it is determined that the vehicle has entered an unstable state, and the nominal yaw rate and the final driving torque of the target are immediately cleared to zero, triggering the safety braking logic and outputting the braking pressure command.

[0023] Furthermore, S5 specifically includes:

[0024] S51: Based on the final drive torque and braking pressure command, and according to the steering direction requirements of each wheel, the final drive torque is distributed to the four wheel-side motors, with the left wheel and the right wheel outputting torque in opposite directions, enabling the vehicle to rotate around its own center.

[0025] S52: When the braking command is triggered, the corresponding braking force is applied to each wheel according to the received braking pressure command. The vehicle is brought to a smooth stop in conjunction with the final drive torque withdrawal strategy. Under normal steering conditions, the synergistic effect of the final drive torque and the braking force causes the vehicle to move in accordance with the desired yaw rate and wheel speed.

[0026] S53: The actual yaw rate and the rotational speed of each wheel are fed back in real time to form a closed-loop adjustment mechanism, ensuring that the control strategy can continuously adapt to the dynamic changes of the vehicle and achieve precise and stable control of the in-situ steering process.

[0027] On the other hand, a distributed drive electric vehicle in-situ steering hierarchical cooperative control system is provided to implement the aforementioned distributed drive electric vehicle in-situ steering hierarchical cooperative control method, the system comprising:

[0028] The observation layer is used to collect vehicle status signals in real time and obtain the slip ratio and road adhesion coefficient required for in-situ steering control through dynamic calculations.

[0029] The decision layer is used to determine the nominal yaw rate and the desired wheel speed based on the driver's operating intention, slip ratio and road adhesion coefficient;

[0030] The upper control layer is used to obtain the final drive torque based on the error between the nominal yaw rate and the actual yaw rate, and the error between the desired wheel speed and the actual wheel speed, so as to achieve dynamic balance between yaw rate and wheel speed during steering; it is also used to monitor the vehicle status in real time, and when it is determined that the vehicle has entered an unstable state, it will clear the target's nominal yaw rate and final drive torque to zero and output a braking pressure command.

[0031] The execution layer is used to translate the final drive torque and braking pressure commands into actual actions and form a state feedback closed loop to complete vehicle motion control.

[0032] Furthermore, the observation layer includes:

[0033] Wheel speed sensors are used to collect the rotational speed signals of each wheel;

[0034] An inertial measurement unit is used to collect longitudinal acceleration, lateral acceleration, and yaw rate signals of each wheel.

[0035] The first calculation module is used to calculate angular acceleration based on the rotational speed signal using the differential method, and at the same time, it uses the vehicle dynamics equations to calculate the vertical load of each wheel by combining longitudinal and lateral acceleration.

[0036] The second calculation module is used to estimate the longitudinal force of each wheel based on angular acceleration, driving torque signal and braking pressure signal using the single wheel dynamics equation, and calculate the road adhesion coefficient of each wheel based on the adhesion circle theory by combining vertical load and longitudinal force of the wheel; at the same time, it calculates the slip ratio of each wheel based on speed signal and vehicle center of gravity motion state.

[0037] The summary module is used to summarize the road surface adhesion coefficient and slip ratio, verify them by setting thresholds, and remove outliers or perform filtering.

[0038] The decision-making body includes:

[0039] The accelerator pedal opening acquisition module is used to read the driver's accelerator pedal opening signal and determine the nominal yaw rate through a preset dead zone and an adjustable slope curve.

[0040] The fuzzy logic controller uses the nominal yaw rate and road adhesion coefficient as important inputs for fuzzy logic control. It performs inference based on preset fuzzy rules and outputs the desired wheel speed through membership functions and fuzzy inference mechanisms.

[0041] Furthermore, the upper control layer includes:

[0042] The yaw rate active disturbance rejection controller is used to estimate the total disturbance based on the error between the nominal yaw rate and the actual yaw rate, and output the first driving torque through a first-order linear extended state observer.

[0043] An adaptive wheel speed controller is used to dynamically adjust control parameters and output a second driving torque based on the error between the desired wheel speed and the actual wheel speed, using an improved single-neuron adaptive PID algorithm.

[0044] The collaborative control module is used to dynamically allocate weighting coefficients based on the error between the current nominal yaw rate and the actual yaw rate and the error between the expected wheel speed and the actual wheel speed, and to weight and fuse the first driving torque and the second driving torque to obtain the final driving torque.

[0045] The collaborative control module is also used to monitor the slip ratio of each wheel, the longitudinal speed, the lateral speed and the yaw rate tracking error in real time. When the monitored values ​​meet the preset instability criteria, it determines that an instability state has been entered and immediately clears the nominal yaw rate and the final driving torque of the target to zero, triggers the safety braking logic and outputs the braking pressure command.

[0046] The execution layer includes:

[0047] The motor controller is used to distribute the final drive torque to the four wheel-side motors according to the final drive torque and braking pressure command and the steering direction requirements of each wheel. The left wheel outputs torque in opposite directions to the right wheel, so that the vehicle can rotate around its own center.

[0048] The integrated braking control system is used to apply corresponding braking force to each wheel according to the received braking pressure command when the braking command is triggered. It works in conjunction with the final drive torque withdrawal strategy to achieve a smooth stop of the vehicle. Under normal steering conditions, the synergistic effect of the final drive torque and braking force causes the vehicle to move in accordance with the desired yaw rate and wheel speed.

[0049] The feedback module is used to provide real-time feedback of the actual yaw rate and the rotational speed of each wheel, forming a closed-loop adjustment mechanism to ensure that the control strategy can continuously adapt to the dynamic changes of the vehicle and achieve precise and stable control of the in-situ steering process.

[0050] The beneficial effects of the technical solution provided by the embodiments of the present invention are as follows: The present invention provides basic data for decision-making by estimating the road surface adhesion coefficient and wheel slip ratio in real time. Then, it determines the nominal yaw rate based on the driver's throttle operation and obtains the desired wheel speed through fuzzy logic in combination with the road surface adhesion coefficient. Subsequently, it adopts yaw rate active disturbance rejection control and adaptive wheel speed control to work together, output the final driving torque through dynamic weight allocation, and at the same time monitor the vehicle status in real time. When an instability risk is detected, it triggers safety braking. Through this method, the precise, stable and safe control of the in-situ steering process is achieved. Attached Figure Description

[0051] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0052] Figure 1 This is a schematic diagram of the in-situ steering power principle provided by the present invention;

[0053] Figure 2This is a comparison curve of the yaw rate tracking effect under a vehicle cooperative control strategy provided by the present invention;

[0054] Figure 3 This is a comparison curve of the vehicle yaw angle tracking control effect provided by the present invention;

[0055] Figure 4 This is a curve illustrating the wheel speed control effect of a vehicle provided by the present invention;

[0056] Figure 5 This is a wheel-end torque distribution curve diagram of a vehicle provided by the present invention. Detailed Implementation

[0057] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0058] To make the objectives, technical solutions, and advantages of the present invention clearer, the embodiments of the present invention will be described in further detail below with reference to the accompanying drawings.

[0059] Example 1

[0060] First, it should be noted that in-situ steering is a function of distributed drive electric vehicles that uses the opposing driving torques from the left and right wheels to allow the vehicle to rotate around its own center of mass. For example... Figure 1 As shown, the distance 'a' from the center of gravity to the front axle, the distance 'b' from the center of gravity to the rear axle, and the track width 'L' are all marked. The longitudinal force 'F' exerted by the ground on the tire is also marked at each wheel. x With lateral force F y , where F xfr F xfl F xrr F xrl F represents the longitudinal force on the right front, left front, right rear, and left rear wheels, respectively. yfr F yfl F yrr F yrl These represent the lateral forces on the right front, left front, right rear, and left rear wheels, respectively.

[0061] based on Figure 1 The force and geometric relationships shown can be used to establish the equations of the vehicle's yaw motion about its center of mass:

[0062] (1)

[0063] (2)

[0064] In equation (1), M is the yaw moment generated by the difference in longitudinal forces on the left and right sides, and B is the wheelbase. In equation (2), The yaw acceleration is... Let yaw moment of inertia be the moment of inertia of the vehicle.

[0065] In the initial stage, the wheel is only subjected to longitudinal force F. x As the driving torque increases, the vehicle begins to experience yaw acceleration, but the tires have not yet deformed significantly. As the driving torque increases, the tires enter the elastic deformation stage, and the lateral force F... y Gradually build up, longitudinal force F x The descent is due to the constraint of the attached ellipse, resulting in a steering resistance torque M. r =(F yfl +F yfr )a+(F yrl +F yrr As b increases, the vehicle's rotational tendency is further enhanced. When the lateral force F... y When the ground adhesion limit is reached, the tire enters the sideslip phase, the sideslip angle reaches its maximum, and the wheel's longitudinal velocity V x With lateral velocity V y Under the combined effect, the tangential motion continues, and the yaw rate ω continuously increases. Ultimately, when the steering resistance torque M... r When the yaw moment M is balanced, the system enters the steady-state steering phase, where the yaw rate ω and the longitudinal force F of the tires are constant. x With lateral force F y All remained stable, and the vehicle achieved smooth and continuous rotation.

[0066] in addition, Figure 1 α fl ɑ fr ɑ rl ɑ rr These represent the tire slip angles of the left front wheel, right front wheel, left rear wheel, and right rear wheel, respectively. fl v fr v rl v rr These represent the linear velocities of the centers of the left front wheel, right front wheel, left rear wheel, and right rear wheel, respectively.

[0067] Based on the aforementioned in-situ steering mechanism, it is known that the tire force is always constrained by the attachment ellipse during this process, and there is a strong coupling relationship between the yaw rate ω and the wheel speed, resulting in the overall system exhibiting critical stability characteristics. Traditional control methods relying on precise dynamic models are insufficient to effectively address disturbances caused by tire nonlinearity and road surface changes. Therefore, this invention designs a distributed drive electric vehicle in-situ steering hierarchical cooperative control method to achieve coordinated adjustment of yaw rate and wheel speed, ensuring the stability and accuracy of in-situ steering without relying on precise models.

[0068] A hierarchical cooperative control method for in-situ steering of a distributed drive electric vehicle includes the following steps:

[0069] Step 1: Real-time acquisition of vehicle status signals, and obtaining the slip ratio and road adhesion coefficient required for in-situ steering control through dynamic calculations. This includes the following steps:

[0070] Step 1: First, receive the rotational speed signals of each wheel collected by the wheel speed sensors. and the longitudinal acceleration output by the inertial measurement unit lateral acceleration With yaw rate The signal, based on wheel speed signals, calculates the angular acceleration of each wheel using a differential method. :

[0071] (3)

[0072] In equation (3), k is the current calculation cycle, and n is the number of cycle intervals. This represents the duration of a single calculation cycle.

[0073] Simultaneously, by utilizing vehicle dynamics equations and combining longitudinal acceleration... With lateral acceleration Calculate the vertical load F of each wheel. zij :

[0074] (4)

[0075] (5)

[0076] (6)

[0077] (7)

[0078] In equations (4), (5), (6), and (7), F zfl F zfr、 F zrl、 F zrr These represent the vertical loads on the left front, right front, left rear, and right rear wheels, respectively; m = 2633 kg is the total vehicle mass; m s =2000kg is the spring load mass; h g =0.74m is the height of the center of gravity; a=1.517 m, b=1.293m, track width B=2.81 m, wheelbase L=2.81m; g=9.8m / s²; k f k r These are the lateral load transfer coefficients for the front and rear axles, respectively, determined by testing.

[0079] Step 2: Based on the angular acceleration of the wheel The driving torque signal and braking pressure signal are used to estimate the longitudinal force F of each wheel using the single-wheel dynamics equation. xij :

[0080] (8)

[0081] In equation (8), For the moment of inertia of the wheel, For rolling resistance, The equivalent rolling resistance coefficient is given by r = 0.338m, where r is the tire rolling radius. This represents the driving torque of each wheel. This indicates the braking torque of each wheel.

[0082] Combined with vertical load F zij With longitudinal force F xij The real-time road adhesion coefficient of each wheel was calculated based on the adhesion circle theory. :

[0083] (9)

[0084] In equation (9), F yij The lateral force of each wheel is estimated using the yaw dynamics equation.

[0085] Meanwhile, based on wheel speed signals Calculate the slip ratio of each wheel based on the motion state of the vehicle's center of gravity. :

[0086] (10)

[0087] In equation (10), The longitudinal velocity component at the wheel center is calculated from vehicle kinematics.

[0088] Step 3: Calculate the road adhesion coefficient for each wheel. With slip ratio The data is aggregated, validated using set thresholds, and outliers are removed or filtered to ensure accuracy and reliability.

[0089] Step Two: Based on the driver's operating intention, slip ratio, and road adhesion coefficient, determine the nominal yaw rate and the desired wheel speed. This includes the following steps:

[0090] Step 1: Read the driver's accelerator pedal opening signal, and determine the nominal yaw rate through the preset dead zone and adjustable slope curve. :

[0091] (11)

[0092] In Equation (11), represents the driver's throttle pedal opening signal, and the dead zone threshold is taken as 5%, and the adjustable slope coefficient k slope is taken as 0.35, which is used to adjust the response sensitivity.

[0093] Step 2: Input the nominal yaw rate (range 0 - 20° / s) and the road surface adhesion coefficient (range 0 - 1) into the fuzzy logic controller, and output the desired wheel speed (range 0 - 4m / s). The fuzzy rules follow the following core principles: Under the condition of the same road surface adhesion coefficient, the higher the nominal yaw rate, the higher the desired wheel speed; under the condition of the same nominal yaw rate, the lower the road surface adhesion coefficient, the lower the desired wheel speed. Specifically:

[0094] (1) If is "slow" and is "low", then is "medium";

[0095] (2) If is "fast" and is "low", then is "large";

[0096] (3) If is "fast" and is "high", then is "medium".

[0097] Step 3: According to the errors between the nominal yaw rate and the actual yaw rate, and between the desired wheel speed and the actual wheel speed, obtain the final driving torque to achieve the dynamic balance of the yaw rate and the wheel speed during the steering process. Specifically, it includes the following steps:

[0098] Step 1: Receive the nominal yaw rate and the desired wheel speed as the tracking control target.

[0099] Step 2: Based on the error between the nominal yaw rate and the actual yaw rate , the yaw rate active disturbance rejection controller estimates the total system disturbance through an extended state observer, and designs a first-order linear extended state observer:

[0100] (12)

[0101] In Equation (12), z1 is the yaw rate The estimated value, z2 is the total disturbance estimate, u=M is ​​the control input, y= For system output, l1=100, l2=200 are the observer gain. Let yaw moment of inertia be the moment of inertia of the vehicle.

[0102] The control law is designed as follows:

[0103] (13)

[0104] In equation (13), K p =50 is the proportional control gain. The yaw moment M is converted into the first driving torque, which is then output as the first driving torque. :

[0105] (14)

[0106] In equation (14), the positive and negative signs correspond to the left and right wheels respectively, and r represents the tire rolling radius.

[0107] Adaptive wheel speed controller based on desired wheel speed Compared with actual wheel speed The error e(t) = - An improved single-neuron adaptive PID algorithm is adopted to dynamically adjust the control parameters and output the second driving torque. :

[0108] (15)

[0109] In equation (15), the proportionality coefficient K p Integral coefficient K i Differential coefficient K d The weights are adjusted in real time using a single neuron weight update rule, with the weight adjustment amount Δ. p Δ i Δ d The learning rate η is dynamically determined by the error and its rate of change, and the integral term, and a multi-segment linear dynamic learning rate η is introduced. p (t), η i (t), η d (t) performs adaptive adjustment.

[0110] Step 3: Control the error e based on the current yaw rate. ω = - Wheel speed control error e v = - Dynamically allocate weight coefficients wω With w v :

[0111] (16)

[0112] (17)

[0113] In equation (16), ε is a very small positive number to prevent the denominator from being zero.

[0114] The first driving torque T ω With the second driving torque T v Weighted fusion yields the final driving torque. :

[0115] (18)

[0116] Step 4: Real-time monitoring of wheel slip ratio Vehicle longitudinal speed V x Lateral velocity V y and yaw rate tracking error. The instability criterion is:

[0117] (19)

[0118] In Equation (19), the instability criterion is: (1) Calculate the slip ratio of the wheel with the largest slip ratio among the four wheels, compare its slip ratio with the average slip ratio of the other three wheels, and if the difference exceeds 0.3, it indicates that the longitudinal adhesion between a certain wheel and the road surface has decreased significantly, and the vehicle stability has been compromised.

[0119] (2) Calculate the wheel with the smallest slip ratio among the four wheels, and compare the average slip ratio of the other three wheels with the minimum slip ratio. If the difference exceeds 0.3, it indicates that the slip ratio of a certain wheel is abnormally low. This may be due to brake drag, excessive mechanical resistance, or insufficient drive, which may cause the wheel speed to be too low and easily cause the vehicle to deflect.

[0120] (3) Monitor the lateral and longitudinal velocities at the vehicle's center of gravity in real time. If the absolute value of the lateral velocity exceeds 0.5 meters per second, or the absolute value of the longitudinal velocity exceeds 0.1 meters per second, it indicates that the vehicle is no longer rotating around its own center, but has undergone lateral slip or forward / backward displacement, that is, the steering center has shifted significantly.

[0121] (4) If the absolute difference between the actual yaw rate and the target nominal yaw rate is always greater than 3 degrees per second within any consecutive 1-second time period, it indicates that the control system cannot effectively track the expected rotation speed, and the system may be subjected to excessive disturbance or has lost control capability.

[0122] When any of the conditions is met, the system is determined to be in an unstable state, and the target's nominal yaw rate and driving torque are immediately reset to zero. And output braking pressure command P b =3MPa.

[0123] Step 4: Transform the final drive torque and braking pressure commands into actual actions and form a state feedback closed loop to complete vehicle motion control. This includes the following steps:

[0124] Step 1: Receive the final drive torque T output. d,final In response to braking pressure commands, the motor controller distributes the final drive torque to the four wheel-side motors according to the steering direction requirements of each wheel. The left wheel outputs torque in the opposite direction to the right wheel, enabling the vehicle to rotate around its own center.

[0125] Step 2: When the system triggers a braking command, the integrated braking control system applies corresponding braking force to each wheel based on the received braking pressure command, coordinating with the drive torque disengagement strategy to achieve a smooth stop for the vehicle. Under normal steering conditions, the combined effect of drive torque and braking force causes the vehicle to move according to the desired yaw rate and wheel speed.

[0126] Step 3: Combine the actual yaw rate ω with the actual wheel speed v of each wheel. wat Real-time feedback forms a closed-loop adjustment mechanism, ensuring that the control strategy can continuously adapt to changes in vehicle dynamics, achieving precise and stable control of the in-situ steering process. The final test results are as follows: Figures 2-5 As shown.

[0127] Figure 2 The paper demonstrates the rapid tracking of the actual yaw rate to the nominal yaw rate, with small steady-state error and low overshoot. Figure 3 The data shows that the left and right wheels rotate at similar speeds but in opposite directions, verifying the vehicle's motion characteristics of rotating around its own center. Figure 4 The paper presents the symmetrical distribution of driving torque on the left and right sides and the timely intervention of braking pressure in case of instability; Figure 5 The results show that the vehicle's center of gravity basically rotates in place, while the estimated slip ratio and adhesion coefficient remain stable in the tire nonlinear region, which demonstrates the effectiveness and robustness of the proposed hierarchical cooperative control method.

[0128] Example 2

[0129] A distributed drive electric vehicle in-situ steering hierarchical cooperative control system, used in the distributed drive electric vehicle in-situ steering hierarchical cooperative control method in Embodiment 1, the system comprising:

[0130] The observation layer is used to collect vehicle status signals in real time and obtain the slip ratio and road adhesion coefficient required for in-situ steering control through dynamic calculations.

[0131] The decision layer is used to determine the nominal yaw rate and the desired wheel speed based on the driver's operating intention, slip ratio and road adhesion coefficient;

[0132] The upper control layer is used to obtain the final drive torque based on the error between the nominal yaw rate and the actual yaw rate, and the error between the desired wheel speed and the actual wheel speed, so as to achieve dynamic balance between yaw rate and wheel speed during steering; it is also used to monitor the vehicle status in real time, and when it is determined that the vehicle has entered an unstable state, it will clear the target's nominal yaw rate and final drive torque to zero and output a braking pressure command.

[0133] The execution layer is used to translate the final drive torque and braking pressure commands into actual actions and form a state feedback closed loop to complete vehicle motion control.

[0134] The observation layer includes:

[0135] Wheel speed sensors are used to collect the rotational speed signals of each wheel;

[0136] An inertial measurement unit is used to collect longitudinal acceleration, lateral acceleration, and yaw rate signals of each wheel.

[0137] The first calculation module is used to calculate angular acceleration based on the rotational speed signal using the differential method, and at the same time, it uses the vehicle dynamics equations to calculate the vertical load of each wheel by combining longitudinal and lateral acceleration.

[0138] The second calculation module is used to estimate the longitudinal force of each wheel based on angular acceleration, driving torque signal and braking pressure signal using the single wheel dynamics equation, and calculate the road adhesion coefficient of each wheel based on the adhesion circle theory by combining vertical load and longitudinal force of the wheel; at the same time, it calculates the slip ratio of each wheel based on speed signal and vehicle center of gravity motion state.

[0139] The summary module is used to summarize the road surface adhesion coefficient and slip ratio, verify them by setting thresholds, and remove outliers or perform filtering.

[0140] The decision-making body includes:

[0141] The accelerator pedal opening acquisition module is used to read the driver's accelerator pedal opening signal and determine the nominal yaw rate through a preset dead zone and an adjustable slope curve.

[0142] The fuzzy logic controller uses the nominal yaw rate and road adhesion coefficient as important inputs for fuzzy logic control. It performs inference based on preset fuzzy rules and outputs the desired wheel speed through membership functions and fuzzy inference mechanisms.

[0143] The upper control layer includes:

[0144] The yaw rate active disturbance rejection controller is used to estimate the total disturbance based on the error between the nominal yaw rate and the actual yaw rate, and output the first driving torque through a first-order linear extended state observer.

[0145] An adaptive wheel speed controller is used to dynamically adjust control parameters and output a second driving torque based on the error between the desired wheel speed and the actual wheel speed, using an improved single-neuron adaptive PID algorithm.

[0146] The collaborative control module is used to dynamically allocate weighting coefficients based on the error between the current nominal yaw rate and the actual yaw rate, and the error between the expected wheel speed and the actual wheel speed, to weight and fuse the first driving torque and the second driving torque to obtain the final driving torque. The collaborative control module is also used to monitor the slip ratio of each wheel, the longitudinal speed, the lateral speed and the yaw rate tracking error in real time. When the monitored values ​​meet the preset instability criteria, it is determined that an instability state has been entered, and the nominal yaw rate and the final driving torque of the target are immediately cleared to zero, triggering the safety braking logic and outputting the braking pressure command.

[0147] The execution layer includes:

[0148] The motor controller is used to distribute the final drive torque to the four wheel-side motors according to the final drive torque and braking pressure command and the steering direction requirements of each wheel. The left wheel outputs torque in opposite directions to the right wheel, so that the vehicle can rotate around its own center.

[0149] The integrated braking control system is used to apply corresponding braking force to each wheel according to the received braking pressure command when the braking command is triggered. It works in conjunction with the final drive torque withdrawal strategy to achieve a smooth stop of the vehicle. Under normal steering conditions, the synergistic effect of the final drive torque and braking force causes the vehicle to move in accordance with the desired yaw rate and wheel speed.

[0150] The feedback module is used to provide real-time feedback of the actual yaw rate and the rotational speed of each wheel, forming a closed-loop adjustment mechanism to ensure that the control strategy can continuously adapt to the dynamic changes of the vehicle and achieve precise and stable control of the in-situ steering process.

[0151] The sequence numbers of the above embodiments of the present invention are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.

[0152] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A hierarchical cooperative control method for in-situ steering of a distributed drive electric vehicle, characterized in that, The method includes: S1: Real-time acquisition of vehicle status signals, and obtains the slip ratio and road adhesion coefficient required for in-situ steering control through dynamic calculations; S2: Determine the nominal yaw rate and the desired wheel speed based on the driver's operating intention, slip ratio and road adhesion coefficient; S3: Based on the error between the nominal yaw rate and the actual yaw rate, and the error between the expected wheel speed and the actual wheel speed, the final driving torque is obtained, thereby achieving a dynamic balance between the yaw rate and the wheel speed during the steering process. S4: Monitor vehicle status in real time. When it is determined that the vehicle has entered an unstable state, reset the nominal yaw rate and final drive torque of the target to zero and output a braking pressure command. S5: Transforms the final drive torque and braking pressure commands into actual actions and forms a state feedback closed loop to complete vehicle motion control.

2. The distributed drive electric vehicle in-situ steering hierarchical cooperative control method according to claim 1, characterized in that, S1 specifically includes: S11: Collect the rotational speed, longitudinal acceleration, lateral acceleration and yaw rate signals of each wheel. Calculate the angular acceleration based on the rotational speed signal using the differential method. At the same time, use the vehicle dynamics equations, combined with the longitudinal and lateral accelerations, to calculate the vertical load on each wheel. S12: Based on angular acceleration, driving torque signal and braking pressure signal, the longitudinal force of each wheel is estimated using the single wheel dynamics equation. Combining the vertical load and the longitudinal force of the wheel, the road adhesion coefficient of each wheel is calculated according to the adhesion circle theory. At the same time, based on the speed signal and the motion state of the vehicle's center of gravity, the slip ratio of each wheel is calculated. S13: Summarize the road surface adhesion coefficient and slip ratio, verify them through the set threshold, and remove outliers or perform filtering.

3. The distributed drive electric vehicle in-situ steering hierarchical cooperative control method according to claim 2, characterized in that, S2 specifically includes: S21: Read the driver's accelerator pedal opening signal and determine the nominal yaw rate through the preset dead zone and adjustable slope curve; S22: The nominal yaw rate and road surface adhesion coefficient are used as important inputs for fuzzy logic control. Reasoning is performed according to preset fuzzy rules, and the desired wheel speed is output through membership function and fuzzy reasoning mechanism.

4. The distributed drive electric vehicle in-situ steering hierarchical cooperative control method according to claim 3, characterized in that, The fuzzy rules follow these core principles: Under the same road surface adhesion coefficient, the higher the nominal yaw rate, the higher the expected wheel speed; under the same nominal yaw rate, the lower the road surface adhesion coefficient, the lower the expected wheel speed.

5. The distributed drive electric vehicle in-situ steering hierarchical cooperative control method according to claim 3, characterized in that, S3 specifically includes: S31: Based on the error between the nominal yaw rate and the actual yaw rate, the total disturbance is estimated through a first-order linear extended state observer, and the first driving torque is output. S32: Based on the error between the desired wheel speed and the actual wheel speed, an improved single-neuron adaptive PID algorithm is used to dynamically adjust the control parameters and output the second driving torque; S33: Based on the error between the current nominal yaw rate and the actual yaw rate, and the error between the expected wheel speed and the actual wheel speed, a weighting coefficient is dynamically allocated to weightedly fuse the first driving torque and the second driving torque to obtain the final driving torque.

6. The distributed drive electric vehicle in-situ steering hierarchical cooperative control method according to claim 5, characterized in that, The S4 specifically includes: real-time monitoring of the slip ratio of each wheel, the longitudinal speed, the lateral speed and the yaw rate tracking error of the vehicle; when the monitored values ​​meet the preset instability criteria, it is determined that the vehicle has entered an unstable state, and the nominal yaw rate and the final drive torque of the target are immediately cleared to zero, triggering the safety braking logic and outputting the braking pressure command.

7. The distributed drive electric vehicle in-situ steering hierarchical cooperative control method according to claim 6, characterized in that, S5 specifically includes: S51: Based on the final drive torque and braking pressure command, and according to the steering direction requirements of each wheel, the final drive torque is distributed to the four wheel-side motors, with the left wheel and the right wheel outputting torque in opposite directions, enabling the vehicle to rotate around its own center. S52: When the braking command is triggered, the corresponding braking force is applied to each wheel according to the received braking pressure command. The vehicle is brought to a smooth stop in conjunction with the final drive torque withdrawal strategy. Under normal steering conditions, the synergistic effect of the final drive torque and the braking force causes the vehicle to move in accordance with the desired yaw rate and wheel speed. S53: The actual yaw rate and the rotational speed of each wheel are fed back in real time to form a closed-loop adjustment mechanism, ensuring that the control strategy can continuously adapt to the dynamic changes of the vehicle and achieve precise and stable control of the in-situ steering process.

8. A distributed drive electric vehicle in-situ steering hierarchical cooperative control system, characterized in that, The system is used to implement the distributed drive electric vehicle in-situ steering hierarchical cooperative control method according to any one of claims 1 to 7, the system comprising: The observation layer is used to collect vehicle status signals in real time and obtain the slip ratio and road adhesion coefficient required for in-situ steering control through dynamic calculations. The decision layer is used to determine the nominal yaw rate and the desired wheel speed based on the driver's operating intention, slip ratio and road adhesion coefficient; The upper control layer is used to obtain the final drive torque based on the error between the nominal yaw rate and the actual yaw rate, and the error between the desired wheel speed and the actual wheel speed, so as to achieve dynamic balance between yaw rate and wheel speed during steering; it is also used to monitor the vehicle status in real time, and when it is determined that the vehicle has entered an unstable state, it will clear the target's nominal yaw rate and final drive torque to zero and output a braking pressure command. The execution layer is used to translate the final drive torque and braking pressure commands into actual actions and form a state feedback closed loop to complete vehicle motion control.

9. The distributed drive electric vehicle in-situ steering hierarchical cooperative control system according to claim 8, characterized in that, The observation layer includes: Wheel speed sensors are used to collect the rotational speed signals of each wheel; An inertial measurement unit is used to collect longitudinal acceleration, lateral acceleration, and yaw rate signals of each wheel. The first calculation module is used to calculate angular acceleration based on the rotational speed signal using the differential method, and at the same time, it uses the vehicle dynamics equations to calculate the vertical load of each wheel by combining longitudinal and lateral acceleration. The second calculation module is used to estimate the longitudinal force of each wheel based on angular acceleration, driving torque signal and braking pressure signal using the single wheel dynamics equation, and calculate the road adhesion coefficient of each wheel based on the adhesion circle theory by combining vertical load and longitudinal force of the wheel; at the same time, it calculates the slip ratio of each wheel based on speed signal and vehicle center of gravity motion state. The summary module is used to summarize the road surface adhesion coefficient and slip ratio, verify them by setting thresholds, and remove outliers or perform filtering. The decision-making body includes: The accelerator pedal opening acquisition module is used to read the driver's accelerator pedal opening signal and determine the nominal yaw rate through a preset dead zone and an adjustable slope curve. The fuzzy logic controller uses the nominal yaw rate and road adhesion coefficient as important inputs for fuzzy logic control. It performs inference based on preset fuzzy rules and outputs the desired wheel speed through membership functions and fuzzy inference mechanisms.

10. The distributed drive electric vehicle in-situ steering hierarchical cooperative control system according to claim 9, characterized in that, The upper control layer includes: The yaw rate active disturbance rejection controller is used to estimate the total disturbance based on the error between the nominal yaw rate and the actual yaw rate, and output the first driving torque through a first-order linear extended state observer. An adaptive wheel speed controller is used to dynamically adjust control parameters and output a second driving torque based on the error between the desired wheel speed and the actual wheel speed, using an improved single-neuron adaptive PID algorithm. The collaborative control module is used to dynamically allocate weighting coefficients based on the error between the current nominal yaw rate and the actual yaw rate and the error between the expected wheel speed and the actual wheel speed, and to weight and fuse the first driving torque and the second driving torque to obtain the final driving torque. The collaborative control module is also used to monitor the slip ratio of each wheel, the longitudinal speed, the lateral speed and the yaw rate tracking error in real time. When the monitored value meets the preset instability criterion, it is determined that the vehicle has entered an unstable state, and the nominal yaw rate and the final driving torque of the target are immediately cleared to zero, triggering the safety braking logic and outputting the braking pressure command. The execution layer includes: The motor controller is used to distribute the final drive torque to the four wheel-side motors according to the final drive torque and braking pressure command and the steering direction requirements of each wheel. The left wheel outputs torque in opposite directions to the right wheel, so that the vehicle can rotate around its own center. The integrated braking control system is used to apply corresponding braking force to each wheel according to the received braking pressure command when the braking command is triggered. It works in conjunction with the final drive torque withdrawal strategy to achieve a smooth stop of the vehicle. Under normal steering conditions, the synergistic effect of the final drive torque and braking force causes the vehicle to move in accordance with the desired yaw rate and wheel speed. The feedback module is used to provide real-time feedback of the actual yaw rate and the rotational speed of each wheel, forming a closed-loop adjustment mechanism to ensure that the control strategy can continuously adapt to the dynamic changes of the vehicle and achieve precise and stable control of the in-situ steering process.

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