Distributed driving vehicle control method and system based on multi-strategy optimization algorithm

Abstract

The invention provides a distributed driving vehicle control method and system based on a multi-strategy optimization algorithm, and the method comprises the steps: building a seven-degree-of-freedom nonlinear vehicle model and a linear two-degree-of-freedom dynamic model based on the Newton second law; based on a seven-degree-of-freedom nonlinear vehicle model, estimating the side slip angle of the vehicle in real time through a weighted adaptive fusion strategy by adopting an observation method of fusing a fuzzy sliding-mode observer and extended Kalman filtering; based on the estimated value of the vehicle side slip angle, the actual yaw velocity, the expected side slip angle and the expected yaw velocity calculated by the linear two-degree-of-freedom dynamical model, establishing a sliding mode controller, and calculating the yaw moment of the vehicle; and a multi-objective optimization function and constraint conditions are constructed, the yaw moment of the vehicle is distributed through a whale optimization algorithm fusing multiple strategies, an optimal moment distribution strategy is obtained, and distributed independent electric drive control over the wheels is completed.

Description

Technical Field

[0001] This invention belongs to the field of distributed drive control technology, specifically relating to a distributed drive vehicle control method and system based on a multi-strategy optimization algorithm. Background Technology

[0002] With the rapid development of electric vehicles and intelligent driving technologies, distributed drive technology, as a novel drive method, has gradually attracted widespread attention. Distributed drive systems, by independently arranging hub motors on each wheel, give vehicles a high degree of flexibility and controllability. Compared to traditional centralized drive systems, distributed drive systems not only achieve independent control of each wheel but also optimize power distribution, improving vehicle handling, stability, and efficiency, thus providing crucial technical support for the future development of intelligent and autonomous driving technologies. The most significant feature of distributed drive systems is their ability to precisely adjust vehicle power through individual control of each wheel. Compared to traditional drive systems, distributed drive systems can precisely distribute torque to each wheel through intelligent control algorithms, thereby maintaining higher stability and handling performance under various complex driving conditions.

[0003] However, despite the numerous advantages of distributed drive systems, existing research and applications still face a series of challenges. First, the dynamic modeling methods proposed in existing research still have significant shortcomings in practical applications under complex road conditions and high-speed driving. Accurate estimation of vehicle state parameters is also a challenge for distributed drive control. Many important state parameters of a vehicle during operation cannot be directly measured by sensors and require advanced state estimation techniques. While current observer designs can estimate some states, the accuracy of existing methods is still insufficient to meet the requirements of high-performance control systems under conditions of large sideslip angles, severe road conditions, and high lateral acceleration. Furthermore, while traditional stability control methods can ensure vehicle stability to a certain extent, existing control strategies often cannot respond quickly and accurately adjust yaw moment in complex dynamic environments, especially under conditions such as sharp turns and high-speed lane changes. Traditional torque distribution methods often rely on empirical models or fixed rules, lacking precise consideration of real-time vehicle conditions and dynamics. How to achieve intelligent torque distribution under multi-objective constraints, combining dynamic characteristics and driving needs, remains a pressing technical challenge.

[0004] Therefore, research on distributed drive control has significant practical implications and promising application prospects. Summary of the Invention

[0005] To address the problems existing in current technologies, this invention provides a distributed drive vehicle control method and system based on a multi-strategy optimization algorithm. By precisely controlling the torque of each wheel, distributed drive can significantly improve vehicle handling and stability, and also provide higher safety and response speed in complex road conditions. With the widespread application of distributed drive technology, intelligent vehicles are expected to develop towards greater efficiency, safety, and environmental friendliness, thereby promoting the construction of green transportation and smart cities.

[0006] To achieve the above objectives, the present invention provides the following solution:

[0007] A distributed drive vehicle control method based on a multi-strategy optimization algorithm includes:

[0008] Based on Newton's second law, a seven-degree-of-freedom nonlinear vehicle model and a linear two-degree-of-freedom dynamics model are established respectively.

[0009] Based on the seven-degree-of-freedom nonlinear vehicle model, an observation method that integrates a fuzzy sliding mode observer and an extended Kalman filter is adopted. Through a weighted adaptive fusion strategy, the vehicle's centroid sideslip angle is estimated in real time.

[0010] Based on the estimated value of the vehicle's center of gravity sideslip angle, the actual yaw rate, the desired center of gravity sideslip angle, and the desired yaw rate calculated by the linear two-degree-of-freedom dynamic model, a sliding mode controller is established to calculate the vehicle's yaw moment.

[0011] A multi-objective optimization function and constraints are constructed, and the vehicle yaw moment is allocated by a whale optimization algorithm that integrates multiple strategies to obtain the optimal moment allocation strategy, thereby completing the distributed independent electric drive control of the wheels.

[0012] Preferably, in the seven-degree-of-freedom nonlinear vehicle model, the seven degrees of freedom include the vehicle's longitudinal motion, lateral motion, yaw motion, and rotational motion of the four wheels; the method for constructing the seven-degree-of-freedom nonlinear vehicle model includes:

[0013] Based on the vehicle's mass, longitudinal velocity, lateral velocity, yaw rate, front wheel angle, longitudinal acceleration, longitudinal and lateral forces of the wheels in the vehicle coordinate system, longitudinal resultant force, air density, frontal area, and air resistance coefficient, the vehicle's longitudinal motion equation is constructed.

[0014] Based on the vehicle mass, longitudinal velocity, lateral velocity, lateral acceleration, lateral force, front wheel rotation angle, and longitudinal and lateral forces of the wheels in the vehicle coordinate system, the vehicle lateral motion equations are constructed.

[0015] Based on the vehicle's yaw moment, vehicle's yaw moment of inertia, vehicle's yaw angular velocity, the distance from the vehicle's center of mass to the front and rear axles, the vehicle's front wheel rotation angle, the longitudinal and lateral forces of the wheels in the vehicle's coordinate system, and the wheelbase between the two wheels on the front axle and the two wheels on the rear axle, the vehicle's yaw motion equation is constructed.

[0016] Based on the wheel's moment of inertia, the driving / braking torque of each wheel hub motor, the rolling resistance coefficient between the wheel and the road surface, the tire's vertical load, and the wheel's effective rolling radius, the rotational motion equation of the vehicle wheel is constructed.

[0017] Based on the vehicle's longitudinal motion equation, lateral motion equation, yaw motion equation, and wheel rotation equation, the seven-degree-of-freedom nonlinear vehicle model is obtained.

[0018] Preferably, the linear two-degree-of-freedom dynamic model includes equations for the vehicle's lateral motion and yaw motion.

[0019] The preferred expression for the observation method that combines the fuzzy sliding mode observer and the extended Kalman filter is as follows:

[0020] ,

[0021] in, This represents the estimated value of the fuzzy sliding mode observer. This represents the estimated value of the extended Kalman filter, and the weighting factor. Used for smooth switching between two estimation methods and weighted averaging.

[0022] Preferably, methods for constructing a multi-strategy whale optimization algorithm include:

[0023] Initialize the parameters of the whale optimization algorithm, generate the initial population using Logistic chaotic mapping, calculate the fitness of each individual whale, select the individual with the best fitness as the leader, and iterate the population.

[0024] During population iteration, when the generated random number is greater than a preset threshold, the position of the individual whale is updated through a double helix mechanism; when the random number is less than a preset threshold and the absolute value of the exploration-development balance coefficient A is greater than or equal to a preset parameter threshold, the position of the individual whale is updated through a search mechanism; when the random number is less than a preset threshold and the absolute value of the exploration-development balance coefficient A is less than a preset parameter threshold, the position of the individual whale is updated through an encirclement mechanism.

[0025] For the population after updating the individual whale positions, Gaussian mutation is used to compare the fitness values ​​of all whale individuals and update the global optimum. The iteration terminates when the termination condition is met.

[0026] Preferably, the multi-objective optimization function is constructed by integrating the objective function characterizing the efficiency optimization of the drive system, the objective function characterizing the driving safety of the vehicle, and the objective function based on the torque change rate using weighting coefficients.

[0027] The present invention also provides a distributed drive vehicle control system based on a multi-strategy optimization algorithm for implementing the method, comprising:

[0028] The model building module is used to build a seven-degree-of-freedom nonlinear vehicle model and a linear two-degree-of-freedom dynamics model based on Newton's second law.

[0029] The estimation module is used to estimate the vehicle's centroid sideslip angle in real time based on the seven-degree-of-freedom nonlinear vehicle model by using an observation method that combines a fuzzy sliding mode observer and an extended Kalman filter, and by employing a weighted adaptive fusion strategy.

[0030] The control module is used to establish a sliding mode controller and calculate the vehicle yaw moment based on the estimated value of the vehicle's center of gravity sideslip angle, the actual yaw rate, the desired center of gravity sideslip angle, and the desired yaw rate calculated by the linear two-degree-of-freedom dynamic model.

[0031] The optimization module is used to construct multi-objective optimization functions and constraints. It uses a multi-strategy whale optimization algorithm to distribute the vehicle's yaw moment, obtain the optimal moment distribution strategy, and complete the distributed independent electric drive control of the wheels.

[0032] Preferably, the model building module includes:

[0033] The longitudinal motion equation construction unit is used to construct the vehicle's longitudinal motion equation based on the vehicle's mass, longitudinal velocity, lateral velocity, yaw rate, front wheel angle, longitudinal acceleration, longitudinal and lateral forces of the wheels in the vehicle coordinate system, longitudinal resultant force, air density, frontal area, and air drag coefficient.

[0034] The lateral motion equation construction unit is used to construct the vehicle's lateral motion equations based on the vehicle's mass, longitudinal velocity, lateral velocity, lateral acceleration, lateral force, front wheel steering angle, and longitudinal and lateral forces of the wheels in the vehicle coordinate system.

[0035] The yaw motion equation construction unit is used to construct the vehicle yaw motion equation based on the vehicle yaw moment, vehicle yaw moment of inertia, vehicle yaw angular velocity, distance from the vehicle's center of mass to the front and rear axles, vehicle front wheel rotation angle, longitudinal and lateral forces of the wheels in the vehicle coordinate system, wheel track between the two front axles and the two rear axles.

[0036] The rotational motion equation construction unit is used to construct the rotational motion equation of the vehicle wheel based on the wheel's moment of inertia, the driving / braking torque of each wheel hub motor, the rolling resistance coefficient between the wheel and the road surface, the tire's vertical load, and the wheel's effective rolling radius.

[0037] The seven-degree-of-freedom model building unit is used to obtain the seven-degree-of-freedom nonlinear vehicle model based on the vehicle's longitudinal motion equation, the vehicle's lateral motion equation, the vehicle's yaw motion equation, and the vehicle's wheel rotational motion equation.

[0038] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0039] Improving the accuracy and robustness of state estimation: A weighted adaptive fusion strategy of fuzzy sliding mode observer and extended Kalman filter is adopted, combined with the refined representation of a seven-degree-of-freedom nonlinear vehicle model, which makes up for the adaptability defects of single observation methods under complex working conditions, effectively reduces the estimation bias of the centroid sideslip angle, and provides a reliable data foundation for the accurate execution of subsequent control strategies.

[0040] Enhance vehicle handling stability and responsiveness: By constructing a sliding mode controller that fuses the dual errors of center of gravity sideslip angle and yaw rate, and combining it with a design for online parameter adjustment using fuzzy logic, the system can quickly respond to changes in vehicle attitude, effectively suppress attitude deviation during driving, and improve the vehicle's handling stability in dynamic driving scenarios.

[0041] Achieving multi-objective collaborative optimization of torque distribution: Constructing a multi-objective optimization function that integrates drive system efficiency, driving safety, and torque change smoothness, combining dynamic weight coefficients adapted to different operating conditions, and then using a whale optimization algorithm that integrates multiple strategies for torque distribution. Compared with traditional fixed rules or single-objective distribution methods, it can balance drive efficiency and driving smoothness while ensuring safety.

[0042] Improve the adaptability and search efficiency of the optimization algorithm: The improved whale optimization algorithm initializes the population through Logistic chaotic mapping, updates the position through double helix mechanism and optimizes the solution space through Gaussian mutation, taking into account both global exploration and local development capabilities. It can effectively avoid getting trapped in local optima, improve the search efficiency of torque distribution strategy and ensure the real-time performance of control.

[0043] With a wide range of applicable scenarios, relying on precise dynamic modeling and adaptive control logic, there is no need to adjust parameters separately for a single working condition. It can be adapted to various driving scenarios such as straight driving, steering, and complex road conditions, providing a logically complete and highly practical technical approach for high-performance control of distributed drive vehicles. Attached Figure Description

[0044] To more clearly illustrate the technical solution of the present invention, the drawings used in the embodiments are briefly introduced below. Obviously, the 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.

[0045] Figure 1 This is a schematic diagram illustrating the relationship between input and output variables in the Magic Formula Tire Model of this invention.

[0046] Figure 2 The following are the identification result curves for embodiments of the present invention: (a) is the identification result curve for pure tire sliding longitudinal force; (b) is the identification result curve for pure tire side slip lateral force.

[0047] Figure 3 This is a schematic diagram of a seven-degree-of-freedom nonlinear vehicle model according to an embodiment of the present invention;

[0048] Figure 4 This is a schematic diagram of the wheel dynamics model according to an embodiment of the present invention;

[0049] Figure 5 This is a schematic diagram of a linear two-degree-of-freedom dynamic model according to an embodiment of the present invention;

[0050] Figure 6 This is a diagram of the whale optimization algorithm architecture according to an embodiment of the present invention;

[0051] Figure 7 This is a flowchart of a distributed drive vehicle control method based on a multi-strategy optimization algorithm according to an embodiment of the present invention. Detailed Implementation

[0052] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0053] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0054] Example 1

[0055] like Figure 7 As shown, a distributed drive vehicle control method based on a multi-strategy optimization algorithm includes:

[0056] S1: Based on Newton's second law, establish a seven-degree-of-freedom nonlinear vehicle model and a linear two-degree-of-freedom dynamics model respectively.

[0057] To accurately describe the dynamic characteristics of vehicle tires, researchers have proposed various tire models. Currently, common tire models include the Dugoff model and the Magic Formula tire model. However, the Magic Formula tire model is the most widely used. Based on theoretical analysis and experimental research, this model uses a unified mathematical expression to describe the tire's lateral force, longitudinal force, and self-aligning torque through fitting experimental data, comprehensively reflecting the tire's dynamic behavior. This model has the advantages of fewer parameters, easy numerical determination, and clear parameter meanings, enabling it to fit ordinary tires with high accuracy and high reliability. The Magic Formula comprehensively describes the combined effects of longitudinal force, lateral force, rollover moment, rolling resistance torque, and self-aligning torque during tire operation, while also revealing the relationship between factors such as slip ratio, sideslip angle, camber angle, and wheel vertical load. Figure 1 As shown. Therefore, this invention will use the Magic Formula tire model to calculate the various forces acting on the tires during vehicle operation.

[0058] The magic formula for the longitudinal force of the tire model under pure longitudinal slip conditions is:

[0059] , (1)

[0060] In the formula, For pure longitudinal sliding force of the tire, , These represent the front left, front right, rear left, and rear right, respectively. This represents the longitudinal slip ratio of the wheel; This refers to the vertical load on the tire; ; , , and The relevant parameters are calculated using the following formula:

[0061] (2)

[0062] In the formula, The second-order fitting parameters for the tire model (see details) ).

[0063] The magic formula for the tire model under pure lateral slip conditions is:

[0064] (3)

[0065] In the formula, The lateral force is the force exerted by the tire during pure side deviation. The wheel slip angle; ; , , and The relevant parameters are calculated using the following formula:

[0066] (4)

[0067] In the formula, The second-order fitting parameters for the tire model (see details) ).

[0068] Table 1 Fitting parameters of the magic tire model for longitudinal and lateral forces

[0069]

[0070] This invention performs vehicle dynamics modeling in CarSim software, and performs parameter fitting of the Magic Formula tire model on a 215 / 55 R17 tire in the software. The tire pure sliding longitudinal force identification result curve is shown below. As shown in a), the curve of the tire pure side-deviation lateral force identification result is as follows: As shown in b), it can be seen from the two figures that the longitudinal force and lateral force of the tire fit very well.

[0071] Considering the coupled constraints of longitudinal and lateral forces on the tire under extreme operating conditions—that is, the actual tire exhibits both sliding and rolling phenomena during operation—the magic tire model needs to be modified. The results are as follows.

[0072] (5)

[0073] (6)

[0074] In the formula,

[0075] (7)

[0076] (8)

[0077] In the formula, This is the road surface adhesion coefficient.

[0078] A further implementation method involves a seven-degree-of-freedom nonlinear vehicle model where the seven degrees of freedom include the vehicle's longitudinal motion, lateral motion, yaw motion, and rotational motion of the four wheels. Therefore, the coupling effects of the vehicle's roll and pitch motions are not considered during modeling, and a model is established as follows: Figure 3 The seven-degree-of-freedom nonlinear vehicle model shown is illustrated.

[0079] Methods for constructing a seven-degree-of-freedom nonlinear vehicle model include:

[0080] Based on the vehicle's mass, longitudinal velocity, lateral velocity, yaw rate, front wheel steering angle, longitudinal acceleration, longitudinal and lateral forces of the wheels in the vehicle coordinate system, resultant longitudinal force, air density, frontal area, and drag coefficient, the vehicle's longitudinal motion equations are constructed; the specific equations are as follows:

[0081] Longitudinal motion (x-axis direction):

[0082] (9)

[0083] In the formula, Indicates the overall vehicle weight; and These represent the vehicle's longitudinal speed and lateral speed, respectively. Indicates the vehicle's yaw rate; The steering angle of the vehicle's front wheels; For longitudinal acceleration, For the longitudinal resultant force of the vehicle; and These represent the distances from the vehicle's center of gravity to its front and rear axles, respectively. air density; The vehicle's frontal area; This refers to the air drag coefficient; and These represent the longitudinal force and lateral force of the wheel in the vehicle coordinate system, respectively. The longitudinal force and lateral force of the wheel are calculated using the magic formula tire model (1) and (3), where the vertical load of each wheel is... slip ratio and sideslip angle The calculation methods are shown in formulas (10), (11) and (12).

[0084] (10)

[0085] (11)

[0086] (12)

[0087] In the formula, This refers to the vehicle's lateral acceleration. The height of the vehicle's center of gravity; and These represent the track width between the two front wheels and the track width between the two rear wheels of the vehicle, respectively. The effective rolling radius of the wheel; The rotational speed of the four wheels; This is the acceleration due to gravity.

[0088] Based on the vehicle's mass, longitudinal velocity, lateral velocity, lateral acceleration, net lateral force, front wheel steering angle, and the longitudinal and lateral forces of the wheels in the vehicle coordinate system, the vehicle's lateral motion equations are constructed; the specific equations are as follows:

[0089] Lateral motion (y-axis direction).

[0090] (13)

[0091] In the formula, The resultant force is the lateral force on the vehicle.

[0092] Based on the vehicle's yaw moment, yaw moment of inertia, yaw angular velocity, distance from the vehicle's center of mass to the front and rear axles, front wheel rotation angle, longitudinal and lateral forces on the wheels in the vehicle coordinate system, and wheelbase between the two front and rear axles, the vehicle's yaw motion equations are constructed; the specific equations are as follows:

[0093] Lateral motion,

[0094] (14)

[0095] In the formula, This refers to the yaw moment of the vehicle. Let yaw moment of inertia be the moment of inertia of the vehicle.

[0096] Transform the vehicle body coordinate system values ​​into the inertial coordinate system values:

[0097] (15)

[0098] X and Y represent the vehicle's longitudinal and lateral positions in the inertial coordinate system, respectively.

[0099] Based on the wheel's moment of inertia, the drive / braking torque of each wheel's hub motor, the rolling resistance coefficient between the wheel and the road surface, the tire's vertical load, and the wheel's effective rolling radius, the rotational motion equations of the vehicle's wheels are constructed. Specifically, each wheel of a distributed drive electric vehicle is controlled by a corresponding in-wheel motor. To accurately describe the wheel's rotational motion, a system is established as follows: Figure 4 The wheel dynamics model shown is shown.

[0100] Considering the rolling resistance of the wheels on the road surface, the rotation of the four wheels,

[0101] (16)

[0102] In the formula, The moment of inertia of the wheel; This refers to the drive / braking torque of the hub motors for each wheel; This represents the rolling resistance coefficient between the wheel and the road surface. .

[0103] A seven-degree-of-freedom nonlinear vehicle model is obtained based on the vehicle's longitudinal motion equation, lateral motion equation, yaw motion equation, and wheel rotational motion equation.

[0104] A further implementation method is that the linear two-degree-of-freedom dynamic model includes equations for the vehicle's lateral motion and yaw motion.

[0105] In vehicle dynamics control analysis, the two-degree-of-freedom (DOF) model is widely used. The two-DOF model only considers the lateral and yaw motions of the vehicle, reflecting the most important dynamic characteristics in vehicle handling stability. The two-DOF model significantly simplifies the entire vehicle system, retaining only the lateral and yaw motions that directly affect lateral stability; therefore, the following assumptions are necessary:

[0106] (1) Ignore vertical motion and suspension system characteristics, and only consider the vehicle's motion in the field of motion. Planar motion;

[0107] (2) Ignoring the drive system and vehicle driving resistance, assume the longitudinal speed is constant;

[0108] (3) Assume that the left and right tires have the same motion state and mechanical characteristics, and do not consider the load transfer to the left and right;

[0109] (4) Without considering the steering system, assume that the steering angle is directly input from the first two wheels.

[0110] Based on the above assumptions, establish 8 8. Nonlinear two-degree-of-freedom model of distributed electric drive vehicles, such as Figure 5 As shown. First, the equations for the lateral motion of the two-degree-of-freedom model are listed below:

[0111] (17)

[0112] The equations of freedom for the lateral yaw motion are:

[0113] (18)

[0114] The tire slip angles of each wheel are:

[0115] (19)

[0116] The steering angles of the first two wheels can be derived from geometric relationships. and The expression is:

[0117] (20)

[0118] S2: Based on a seven-DOF nonlinear vehicle model, an observation method fusing fuzzy sliding mode observers and extended Kalman filters is adopted. A weighted adaptive fusion strategy is used to estimate the vehicle's center of gravity sideslip angle in real time. Specifically, the extended Kalman filter is used to linearize the seven-DOF nonlinear vehicle model to obtain accurate estimates. These estimates are then combined with the estimation results obtained by fuzzy logic through real-time adjustment of the sliding mode observer parameters to obtain a state fusion strategy, achieving more accurate and robust state estimation.

[0119] A further implementation method is described by the following expression for the observation method that integrates the fuzzy sliding mode observer and the extended Kalman filter:

[0120] , (twenty one)

[0121] in, This represents the estimated value of the fuzzy sliding mode observer. This represents the estimated value of the extended Kalman filter, and the weighting factor. Used for smooth switching and weighted averaging between two estimation methods. Weights The adjustment is made dynamically based on the size of the sideslip angle; it increases at larger sideslip angles. .

[0122] S3: Based on the estimated value of the vehicle's center of gravity sideslip angle, the actual yaw rate, the desired center of gravity sideslip angle, and the desired yaw rate calculated by the linear two-degree-of-freedom dynamic model, a sliding mode controller is established to calculate the vehicle's yaw moment.

[0123] Specifically, the desired sideslip angle and desired yaw rate of the vehicle are calculated using a linear two-degree-of-freedom dynamic model, with the desired sideslip angle set to 0 to ensure vehicle stability. The actual sideslip angle is estimated using the observation method obtained in the previous step (S2) based on a seven-degree-of-freedom nonlinear vehicle model by fusing a fuzzy sliding mode observer and an extended Kalman filter. The actual yaw rate is directly measured by the onboard IMU inertial measurement unit.

[0124] Two types of tracking errors are defined: centroid sideslip angle tracking error. and yaw rate tracking error .

[0125] To simultaneously ensure both error convergence speed and steady-state control accuracy, an integral term is introduced to suppress steady-state error, and the sliding surface is designed as follows:

[0126] (twenty two)

[0127] in, , For positive sliding surface parameters, fuzzy logic is used to adjust them online to adapt to different driving conditions: for example, during dynamic conditions such as emergency steering and high-speed lane changes, the parameters are increased. To improve error convergence speed and quickly suppress vehicle attitude deviation; to reduce [errors] during steady-state conditions such as straight-line driving and smooth steering. Increase This enhances the steady-state error suppression capability of the integral term and avoids control jitter.

[0128] in This represents the tracking error of the centroid sideslip angle. , Represents the integral variable. Represents the current moment.

[0129] S4: Construct a multi-objective optimization function and constraints, and use a multi-strategy whale optimization algorithm to distribute the vehicle yaw moment, obtain the optimal moment distribution strategy, and complete the distributed independent electric drive control of the wheels.

[0130] like Figure 6 As shown, a further implementation method for constructing a multi-strategy whale optimization algorithm includes:

[0131] Initialize the whale optimization algorithm parameters, define the whale population size, maximum number of iterations, and convergence constant. Use a Logistic chaotic mapping to generate the initial population. Calculate the fitness of each individual whale, and select the individual with the best fitness as the leader for population iteration. Specifically, the initial population generation using a Logistic chaotic mapping is implemented as follows:

[0132] Let the search space dimension be . , No. dimensional boundary is Generate a uniformly distributed sequence using a chaotic iterative formula: ,

[0133] in is the chaos coefficient, with a value range of [3.57, 4]; Let the initial values ​​be random values ​​in the interval (0,1). Map these values ​​to the solution space as follows: Finally, the initial population position is obtained. .

[0134] In the main loop phase, a random number p is generated, and the convergence factor a is updated. a controls the intensity of encirclement and exploration behavior; its decrease with iteration indicates a gradual shift from global exploration to local exploitation. Parameters A and C are updated. A determines the whale's movement pattern, and C provides random perturbations. Specifically, parameter A is the exploration-exploitation balance coefficient, primarily used to regulate the individual whale's movement step size and direction, achieving a dynamic balance between the algorithm's global exploration and local exploitation capabilities. Its calculation formula is A = 2a・r1 - a (r1 is a uniformly distributed random number in the interval [0,1], and a is the convergence constant). When |A| ≥ 1, a larger movement step size is used, and the whale moves away from the current optimal individual to perform global exploration, traversing unknown regions of the search space to avoid getting trapped in local optima. When |A| < 1, a smaller movement step size is used, and the whale moves closer to the current optimal individual to perform local exploitation, finely searching near the optimal solution to improve the accuracy of the torque allocation scheme. Every whale in the population is traversed. During population iteration, when the generated random number p is greater than a preset threshold, the whale's position is updated using a double helix mechanism; when the random number is less than the preset threshold and the absolute value of parameter A is greater than or equal to the preset parameter threshold, a search mechanism is used to update the whale's position; when the random number is less than the preset threshold and the absolute value of parameter A is less than the preset parameter threshold, an encirclement mechanism is used to update the whale's position. Specifically:

[0135] If P > 0.5, then the individual position is updated using the double helix mechanism.

[0136] If P < 0.5 and |A| ≥ 1, then update the individual position using a search mechanism.

[0137] If P < 0.5 and |A| < 1, then update the individual position using the enclosing mechanism.

[0138] The position of an individual whale is updated using a hyperbolic function composite spiral path, where the basic update formula is: ,in Let l be a distance vector, and l be a uniformly distributed random number in the interval [-1, 1]. The double helix function is also relevant. Defined as: , where b is the helical shape parameter, k is the double helical coupling parameter in the interval [0,1], and cosh(·) and sinh(·) are the hyperbolic cosine function and hyperbolic sine function, respectively.

[0139] For the updated whale population positions, Gaussian mutation is applied. The fitness values ​​of all whales are compared, and the global optimum is updated. The iteration terminates when the termination condition is met. Specifically, an adaptive Gaussian perturbation is applied to elite individuals during the iteration process: the mutation operation is defined as follows: ,in This represents a Gaussian-distributed random vector. Coefficient of variation. Adaptive decay with iteration, Where t is the current iteration number, The maximum number of iterations, , , which are the preset upper and lower bounds of the coefficient of variation, respectively.

[0140] A further implementation involves using weighting coefficients to integrate the objective function characterizing the drive system efficiency optimization, the objective function characterizing vehicle driving safety, and the objective function based on the torque change rate to construct a multi-objective optimization function.

[0141] The multi-objective optimization function is set as follows:

[0142] Design an objective function to characterize the efficiency optimization of the driving system.

[0143] When the motor's input power is minimized, the corresponding system drive efficiency will be significantly improved. Therefore, minimizing the vehicle's overall input power is used as the objective function to characterize energy efficiency.

[0144] (twenty three)

[0145] in It can be obtained from the motor efficiency function.

[0146] Design an objective function to characterize vehicle driving safety:

[0147] The output reserve of longitudinal force of a tire can be represented by the tire load factor:

[0148] (twenty four)

[0149] in: F represents the road surface adhesion coefficient, F represents the longitudinal force of each tire, and F represents the vertical force of each tire. The longitudinal force can be obtained from the tire torque.

[0150] (25)

[0151] Where T is the torque of each tire, and R is the rolling radius. Based on this, an objective function characterizing driving safety is determined. as follows:

[0152] (26)

[0153] Design an objective function based on the rate of torque change.

[0154] The magnitude of torque change is measured by calculating the ratio of the difference between the current torque and the distributed motor torque to the current torque.

[0155] (27)

[0156] In the formula, T represents the torque of each wheel at the current moment. This is a very small factor, and to avoid the denominator being 0 during the calculation, it is ignored in the subsequent calculation and derivation.

[0157] By integrating the three objective functions using weighting coefficients, the final multi-objective optimization allocation objective function for vehicle torque can be obtained:

[0158] (28)

[0159] In the formula, , , These correspond to the weight coefficients of the three vehicle torque distribution objective functions mentioned above, and satisfy the following conditions:

[0160] =1.

[0161] The constraints are set as follows:

[0162] (1) Equality constraints

[0163] Overall vehicle drive force constraints:

[0164] (29)

[0165] Overall vehicle yaw moment constraint:

[0166] (30)

[0167] (2) Inequality constraints

[0168] Under the condition of satisfying the above equality constraints, it is also necessary to consider that the motor torque is limited by the maximum power of the motor, and the ground force on the tire is constrained by the road adhesion conditions. The following inequality constraints are established: (31)

[0169] Torque allocation is performed using a multi-strategy fusion whale optimization algorithm to obtain the optimal torque allocation coefficient.

[0170] The torque distribution coefficients of each drive motor are used as optimization variables to construct a vector:

[0171] (32)

[0172] in This represents the torque distribution coefficient of the i motors, satisfying the following constraints: (33)

[0173] Define the mapping relationship between the individual whale position vector and the torque distribution coefficient:

[0174] (34)

[0175] in The original positional components are transformed into an assignment function through normalization:

[0176] (35)

[0177] Construct a fitness evaluation function based on a weighted objective function:

[0178] (36)

[0179] in: For a weighted multi-objective function, To constrain the penalty for violations, its full definition is:

[0180] (37)

[0181] in, Penalty for the upper limit of torque of a single motor. Penalty for the lower limit of single motor torque. Penalty for exceeding total power limits. The penalty is designed to ensure its feasibility.

[0182] Example 2

[0183] This invention also provides a distributed drive vehicle control system based on a multi-strategy optimization algorithm, and a method for implementing this system, comprising:

[0184] The model building module is used to build a seven-degree-of-freedom nonlinear vehicle model and a linear two-degree-of-freedom dynamics model based on Newton's second law.

[0185] The estimation module is used to estimate the vehicle's centroid sideslip angle in real time based on a seven-degree-of-freedom nonlinear vehicle model by employing an observation method that fuses a fuzzy sliding mode observer and an extended Kalman filter, and through a weighted adaptive fusion strategy.

[0186] The control module is used to establish a sliding mode controller and calculate the vehicle yaw moment based on the estimated value of the vehicle's center of gravity sideslip angle, the actual yaw rate, the desired center of gravity sideslip angle, and the desired yaw rate calculated by the linear two-degree-of-freedom dynamic model.

[0187] The optimization module is used to construct multi-objective optimization functions and constraints. It uses a multi-strategy whale optimization algorithm to distribute the vehicle's yaw moment, obtain the optimal moment distribution strategy, and complete the distributed independent electric drive control of the wheels.

[0188] A further implementation method is that the model building module includes:

[0189] The longitudinal motion equation construction unit is used to construct the vehicle's longitudinal motion equation based on the vehicle's mass, longitudinal velocity, lateral velocity, yaw rate, front wheel angle, longitudinal acceleration, longitudinal and lateral forces of the wheels in the vehicle coordinate system, longitudinal resultant force, air density, frontal area, and air drag coefficient.

[0190] The lateral motion equation construction unit is used to construct the vehicle's lateral motion equations based on the vehicle's mass, longitudinal velocity, lateral velocity, lateral acceleration, lateral force, front wheel steering angle, and longitudinal and lateral forces of the wheels in the vehicle coordinate system.

[0191] The yaw motion equation construction unit is used to construct the vehicle yaw motion equation based on the vehicle yaw moment, vehicle yaw moment of inertia, vehicle yaw angular velocity, distance from the vehicle's center of mass to the front and rear axles, vehicle front wheel rotation angle, longitudinal and lateral forces of the wheels in the vehicle coordinate system, wheel track between the two front axles and the two rear axles.

[0192] The rotational motion equation construction unit is used to construct the rotational motion equation of the vehicle wheel based on the wheel's moment of inertia, the driving / braking torque of each wheel hub motor, the rolling resistance coefficient between the wheel and the road surface, the tire's vertical load, and the wheel's effective rolling radius.

[0193] The seven-degree-of-freedom model building unit is used to obtain a seven-degree-of-freedom nonlinear vehicle model based on the vehicle's longitudinal motion equation, vehicle's lateral motion equation, vehicle's yaw motion equation, and the vehicle's wheel rotational motion equation.

[0194] The embodiments described above are merely preferred embodiments of the present invention and are not intended to limit the scope of the present invention. Various modifications and improvements made to the technical solutions of the present invention by those skilled in the art without departing from the spirit of the present invention should fall within the protection scope defined by the claims of the present invention.

Claims

1. A distributed drive vehicle control method based on a multi-strategy optimization algorithm, characterized in that, include: Based on Newton's second law, a seven-degree-of-freedom nonlinear vehicle model and a linear two-degree-of-freedom dynamics model were established respectively. Based on the seven-degree-of-freedom nonlinear vehicle model, an observation method that integrates a fuzzy sliding mode observer and an extended Kalman filter is adopted. Through a weighted adaptive fusion strategy, the vehicle's centroid sideslip angle is estimated in real time. Based on the estimated value of the vehicle's center of gravity sideslip angle, the actual yaw rate, the desired center of gravity sideslip angle, and the desired yaw rate calculated by the linear two-degree-of-freedom dynamic model, a sliding mode controller is established to calculate the vehicle's yaw moment. A multi-objective optimization function and constraints are constructed, and the vehicle yaw moment is allocated by a whale optimization algorithm that integrates multiple strategies to obtain the optimal moment allocation strategy, thereby completing the distributed independent electric drive control of the wheels.

2. The method according to claim 1, characterized in that, In the seven-degree-of-freedom nonlinear vehicle model, the seven degrees of freedom include the vehicle's longitudinal motion, lateral motion, yaw motion, and rotational motion of the four wheels. The method for constructing the seven-degree-of-freedom nonlinear vehicle model includes: Based on the vehicle's mass, longitudinal velocity, lateral velocity, yaw rate, front wheel angle, longitudinal acceleration, longitudinal and lateral forces of the wheels in the vehicle coordinate system, longitudinal resultant force, air density, frontal area, and air resistance coefficient, the vehicle's longitudinal motion equation is constructed. Based on the vehicle mass, longitudinal velocity, lateral velocity, lateral acceleration, lateral force, front wheel rotation angle, and longitudinal and lateral forces of the wheels in the vehicle coordinate system, the vehicle lateral motion equations are constructed. Based on the vehicle's yaw moment, vehicle's yaw moment of inertia, vehicle's yaw angular velocity, the distance from the vehicle's center of mass to the front and rear axles, the vehicle's front wheel rotation angle, the longitudinal and lateral forces of the wheels in the vehicle's coordinate system, and the wheelbase between the two wheels on the front axle and the two wheels on the rear axle, the vehicle's yaw motion equation is constructed. Based on the wheel's moment of inertia, the driving / braking torque of each wheel hub motor, the rolling resistance coefficient between the wheel and the road surface, the tire's vertical load, and the wheel's effective rolling radius, the rotational motion equation of the vehicle wheel is constructed. Based on the vehicle's longitudinal motion equation, lateral motion equation, yaw motion equation, and wheel rotation equation, the seven-degree-of-freedom nonlinear vehicle model is obtained.

3. The method according to claim 1, characterized in that, The linear two-degree-of-freedom dynamic model includes equations for the vehicle's lateral motion and yaw motion.

4. The method according to claim 1, characterized in that, The expression for the observation method that combines the fuzzy sliding mode observer and the extended Kalman filter is as follows: ， in, This represents the estimated value of the fuzzy sliding mode observer. This represents the estimated value of the extended Kalman filter, and the weighting factor. Used for smooth switching between two estimation methods and weighted averaging.

5. The method according to claim 1, characterized in that, Methods for constructing whale optimization algorithms that integrate multiple strategies include: Initialize the parameters of the whale optimization algorithm, generate the initial population using Logistic chaotic mapping, calculate the fitness of each individual whale, select the individual with the best fitness as the leader, and iterate the population. During population iteration, when the generated random number is greater than a preset threshold, the position of the individual whale is updated through a double helix mechanism; when the random number is less than a preset threshold and the absolute value of the exploration-development balance coefficient A is greater than or equal to a preset parameter threshold, the position of the individual whale is updated through a search mechanism; when the random number is less than a preset threshold and the absolute value of the exploration-development balance coefficient A is less than a preset parameter threshold, the position of the individual whale is updated through an encirclement mechanism. For the population after updating the individual whale positions, Gaussian mutation is used to compare the fitness values ​​of all whale individuals and update the global optimum. The iteration terminates when the termination condition is met.

6. The method according to claim 1, characterized in that, The multi-objective optimization function is constructed by integrating the objective functions representing drive system efficiency optimization, vehicle driving safety, and torque change rate based on weighting coefficients.

7. A distributed drive vehicle control system based on a multi-strategy optimization algorithm, used to implement the method described in any one of claims 1-6, characterized in that, include: The model building module is used to build a seven-degree-of-freedom nonlinear vehicle model and a linear two-degree-of-freedom dynamics model based on Newton's second law. The estimation module is used to estimate the vehicle's centroid sideslip angle in real time based on the seven-degree-of-freedom nonlinear vehicle model by using an observation method that combines a fuzzy sliding mode observer and an extended Kalman filter, and by employing a weighted adaptive fusion strategy. The control module is used to establish a sliding mode controller and calculate the vehicle yaw moment based on the estimated value of the vehicle's center of gravity sideslip angle, the actual yaw rate, the desired center of gravity sideslip angle, and the desired yaw rate calculated by the linear two-degree-of-freedom dynamic model. The optimization module is used to construct multi-objective optimization functions and constraints. It uses a multi-strategy whale optimization algorithm to distribute the vehicle's yaw moment, obtain the optimal moment distribution strategy, and complete the distributed independent electric drive control of the wheels.

8. The system according to claim 7, characterized in that, The model building module includes: The longitudinal motion equation construction unit is used to construct the vehicle's longitudinal motion equation based on the vehicle's mass, longitudinal velocity, lateral velocity, yaw rate, front wheel angle, longitudinal acceleration, longitudinal and lateral forces of the wheels in the vehicle coordinate system, longitudinal resultant force, air density, frontal area, and air drag coefficient. The lateral motion equation construction unit is used to construct the vehicle's lateral motion equations based on the vehicle's mass, longitudinal velocity, lateral velocity, lateral acceleration, lateral force, front wheel steering angle, and longitudinal and lateral forces of the wheels in the vehicle coordinate system. The yaw motion equation construction unit is used to construct the vehicle yaw motion equation based on the vehicle yaw moment, vehicle yaw moment of inertia, vehicle yaw angular velocity, distance from the vehicle's center of mass to the front and rear axles, vehicle front wheel rotation angle, longitudinal and lateral forces of the wheels in the vehicle coordinate system, wheel track between the two front axles and the two rear axles. The rotational motion equation construction unit is used to construct the rotational motion equation of the vehicle wheel based on the wheel's moment of inertia, the driving / braking torque of each wheel hub motor, the rolling resistance coefficient between the wheel and the road surface, the tire's vertical load, and the wheel's effective rolling radius. The seven-degree-of-freedom model building unit is used to obtain the seven-degree-of-freedom nonlinear vehicle model based on the vehicle's longitudinal motion equation, the vehicle's lateral motion equation, the vehicle's yaw motion equation, and the vehicle's wheel rotational motion equation.

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