A vehicle yaw stability control method, system, and storage medium

Abstract

The application provides a vehicle yaw stability control method, system and storage medium, comprising: taking the yaw measurement data of the vehicle as input, generating a yaw stability control instruction through a preset model-free adaptive control model; driving the Onebox braking system to execute the yaw stability control instruction, obtaining the actual execution duration of the Onebox braking system executing the yaw stability control instruction and the energy recovery data generated; determining the theoretical execution duration according to the energy recovery data; and adjusting the yaw stability control strategy of the Onebox braking system based on the actual execution duration and the theoretical execution duration. The model-free adaptive control model is used to adjust the yaw stability of the vehicle, which is less affected by the external environment, can adapt to various complex working conditions and actively correct errors, so that the vehicle can maintain the yaw stability. The mechanical energy generated during the execution of the yaw stability control instruction by the Onebox braking system is converted into electrical energy and stored, so as to increase the cruising range of the new energy vehicle.

Description

Technical Field

[0001] This invention relates to the field of vehicle auxiliary control, and in particular to a method, system and storage medium for controlling vehicle yaw stability. Background Technology

[0002] Traffic accidents involving vehicles traveling at high speeds or on uneven roads, often resulting in injury or death, are easily caused by loss of controllability. When a vehicle skids or fishtails at high speeds, the lateral force provided by the tires is limited, making it impossible to fully guarantee vehicle stability, and the driver finds it difficult to avoid an accident by correcting the steering. Furthermore, during cornering, the vehicle directly generates yaw rate; when the speed exceeds a critical value, the yaw rate gain increases dramatically, also easily leading to dangerous accidents such as skidding or fishtailing.

[0003] Related technologies use fuzzy control to control the yaw rate and yaw moment of a vehicle to prevent dangerous events such as skidding or fishtailing. However, fuzzy control methods rely on a large amount of historical data, but the scenarios in which a vehicle yaws occurs are uncertain, and historical data is difficult to cover comprehensively, thus having certain limitations and not being fully applicable to yaw control under various complex operating conditions. Summary of the Invention

[0004] In view of this, the purpose of the present invention is to provide a method, system and storage medium for controlling vehicle yaw stability, which solves the problem that the prior art is difficult to adapt to various complex working conditions for vehicle yaw control.

[0005] In a first aspect, this application provides a method for controlling vehicle yaw stability, comprising the following steps:

[0006] Using the vehicle's yaw measurement data as input, yaw stability control commands are generated through a preset modelless adaptive control model;

[0007] The Onebox braking system is driven to execute the yaw stability control command, and the actual execution time of the Onebox braking system executing the yaw stability control command and the generated energy recovery data are obtained.

[0008] The theoretical execution time is determined based on the energy recovery data.

[0009] The yaw stability control strategy of the Onebox braking system is adjusted based on the actual execution time and the theoretical execution time.

[0010] In one embodiment, determining the theoretical execution time based on the energy recovery data specifically includes:

[0011] The braking distance of the vehicle during the execution of the yaw stability control command by the Onebox braking system is obtained; the initial speed of the vehicle at the start of the execution of the yaw stability control command and the final speed of the vehicle at the end of the execution of the yaw stability control command are obtained; and the mass information of the vehicle is obtained.

[0012] The theoretical execution time is generated based on the energy recovery data, braking distance, initial vehicle speed, final vehicle speed, and mass information using a preset energy recovery model.

[0013] In one embodiment, the energy recovery model is specifically as follows:

[0014]

[0015] Wherein, E is the energy recovery data, m is the vehicle's mass information, V is the termination speed, V0 is the initial speed, t is the theoretical execution time, and S is the braking distance.

[0016] In one embodiment, the Onebox braking system executes the yaw stability control command, specifically including:

[0017] The power unit of the Onebox braking system performs differential braking and drives the energy recovery unit of the Onebox braking system to convert the mechanical energy generated during braking of the power mechanism into electrical energy, and stores the electrical energy in the energy storage unit of the Onebox braking system.

[0018] In one embodiment, before generating yaw stability control commands using a preset model-free adaptive control model with the vehicle's yaw measurement data as input, the method further includes:

[0019] Based on the vehicle's driving posture data, yaw judgment data is generated through a preset fuzzy prediction model;

[0020] Calculate the yaw deviation value between the yaw measurement data and the yaw judgment data;

[0021] When the yaw deviation value exceeds the preset yaw deviation threshold range, the yaw measurement data is input into the model-free adaptive control model.

[0022] In one embodiment, using vehicle yaw measurement data as input, yaw stability control commands are generated through a preset model-free adaptive control model, specifically including:

[0023] The yaw measurement data is used as input, and the yaw differential data is output through a preset vehicle system dynamics model.

[0024] A Hessian matrix controller is constructed based on the yaw measurement data and the yaw differential data;

[0025] Based on the Hessian matrix controller, yaw stability control commands are generated using the expected yaw data.

[0026] In one embodiment, before using the yaw measurement data as input and outputting the yaw differential data through a preset vehicle system dynamics model, the method further includes:

[0027] Based on a preset two-degree-of-freedom vehicle model, a dynamic model of the vehicle system is constructed according to the yaw measurement data; wherein, the yaw measurement data includes the torque of the four wheels of the vehicle, the angular velocity of the four wheels, the lateral velocity, and the yaw angular velocity.

[0028] In one embodiment, the step of generating yaw stability control commands based on the Hessian matrix controller and the expected yaw data specifically includes:

[0029] Obtain the estimated value of the Hessian matrix controller at the t-th time node;

[0030] Based on the estimated value at the t-th time node and the expected yaw data at the (t+1)-th time node, calculate the input value of the Hessian matrix controller at the t-th time node;

[0031] Based on the input value at the t-th time node and the estimated value at the t-th time node, the yaw prediction data for the (t+1)-th time node is generated through the vehicle system dynamics model.

[0032] Based on the expected yaw data at the (t+1)th time node and the predicted yaw data at the (t+1)th time node, a yaw stability control command is generated.

[0033] Secondly, this application provides a vehicle yaw stability control system, comprising: a processor and a memory; wherein the memory stores a computer program, the computer program being loaded by the processor and executed as described in any one of the first aspects of the vehicle yaw stability control method.

[0034] Thirdly, this application provides a computer-readable storage medium storing instructions for loading by a processor and executing the vehicle yaw stability control method as described in any one of the first aspects.

[0035] In this embodiment, a model-free adaptive control model is used to adjust the vehicle's yaw stability. This model is less affected by external environmental factors, can actively correct errors under various complex operating conditions, and ensures the vehicle maintains yaw stability, effectively preventing dangerous accidents such as skidding or fishtailing. Furthermore, the mechanical energy generated by the Onebox braking system during the execution of yaw stability control commands is converted into electrical energy and stored to increase the driving range of the new energy vehicle. The energy recovered by the Onebox braking system allows for the determination of its energy recovery efficiency, facilitating timely adjustments to the yaw stability control strategy and improving energy recovery efficiency. Attached Figure Description

[0036] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of the present invention and should not be regarded as a limitation on the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0037] Figure 1 This is a flowchart illustrating the method for controlling vehicle yaw stability in the embodiments of this application.

[0038] Figure 2 This is a schematic diagram of the vehicle yaw stability control device in the embodiments of this application. Detailed Implementation

[0039] The specific embodiments of the present invention will now be described in detail with reference to the accompanying drawings. Obviously, the described embodiments are merely some, not all, of the embodiments of the present invention. Based on the description of the present invention, all other embodiments obtained by those skilled in the art without inventive effort are within the scope of protection of the present invention.

[0040] In the description of this invention, unless otherwise explicitly specified and limited, the terms "set," "install," "connect," etc., should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium. Those skilled in the art can understand the specific meaning of the above terms according to the specific circumstances.

[0041] The terms “upper,” “lower,” “left,” “right,” “front,” “back,” “top,” “bottom,” “inner,” and “outer,” etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings, or the orientation or positional relationship commonly used when the product of the invention is in use. They are only for the convenience of description and simplification, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on the present invention.

[0042] The terms “first,” “second,” “third,” etc., are used merely to distinguish elements with similar attributes, not to indicate or imply relative importance or a specific order.

[0043] The terms “include,” “comprising,” or any other variation thereof are intended to cover non-exclusive inclusion, which includes not only the elements listed but also other elements not expressly listed.

[0044] With the significant increase in car ownership in my country in recent years, the number of deaths caused by traffic accidents has also been rising year by year. As the most typical type of traffic accident, those caused by loss of control and stability while driving at high speeds or on uneven roads usually result in injuries or fatalities.

[0045] When a vehicle experiences skidding or fishtailing at high speeds, the lateral force provided by the tires has reached its limit, and therefore can no longer provide any lateral force to maintain vehicle stability. At this point, any steering correction by the driver will be unable to prevent an accident. Furthermore, when driving on uneven roads, gaps will form between the tires and the chassis due to the unevenness, affecting handling stability and consequently ride comfort. Simultaneously, during cornering, the vehicle directly generates yaw rate. When the vehicle speed exceeds a critical value, the yaw rate gain will increase dramatically, leading to dangerous situations such as skidding or fishtailing.

[0046] These dangerous and unstable operating conditions can seriously affect the vehicle's driving stability and threaten the personal safety of the occupants. Therefore, it is essential to implement yaw stability control during vehicle cornering.

[0047] The related technology discloses a vehicle yaw stability control method based on saturation adaptation. The control method includes the steps of establishing a two-degree-of-freedom vehicle yaw dynamics model when the vehicle is turning, designing a saturation-adaptive vehicle yaw stability controller based on the two-degree-of-freedom vehicle yaw dynamics model, and adjusting the vehicle's direct yaw moment through the saturation-adaptive vehicle yaw stability controller so that the vehicle's yaw angular velocity tracks its reference steady-state value.

[0048] Related technologies also disclose a vehicle yaw stability control method and a vehicle yaw stability control device. A first reference yaw rate is set by measuring the vehicle's yaw rate, and a yaw rate difference is set. When the value of the yaw rate difference exceeds a limit defined by a yaw rate difference threshold, a stabilization braking intervention is triggered. Information about the road shape ahead of the vehicle is obtained. Once the stabilization braking intervention is triggered, the reliability of the driver's steering input is evaluated. If the driver's steering input is considered unreliable, a replacement reference yaw rate is set based on the obtained road shape, and a replacement yaw rate difference is set, thereby performing stabilization braking intervention based on the replacement yaw rate difference.

[0049] The two technical methods mentioned above rely on fuzzy control methods. However, fuzzy control methods rely on a large amount of historical data. But the scenarios in which a vehicle yaws occurs are uncertain, and historical data is difficult to cover comprehensively, which has certain limitations and cannot be fully applied to vehicle yaw control under various complex working conditions.

[0050] like Figure 1 As shown, this embodiment provides a method for controlling vehicle yaw stability, including the following steps:

[0051] Step S100: Using the vehicle's yaw measurement data as input, generate yaw stability control commands through a preset modelless adaptive control model;

[0052] Step S200: Drive the Onebox braking system to execute the yaw stability control command, and obtain the actual execution time of the Onebox braking system executing the yaw stability control command and the generated energy recovery data;

[0053] Step S300: Determine the theoretical execution time based on the energy recovery data;

[0054] Step S400: Calculate the energy recovery efficiency of the Onebox braking system based on the actual execution time and the theoretical execution time.

[0055] In step S100, the model-free adaptive control (MFAC) model is a control system that relies solely on the system's input and output data. It adjusts and compensates the controller's output data in real time based on the system's input and output data from the previous moment. It can achieve a good control system without establishing a system model. It has advantages such as high robustness, reliability, adaptability, real-time performance, and scalability. At the same time, it does not depend on the system's mathematical model, so it can adapt to situations where the system model is difficult to establish or where there are uncertainties. It can be applied to various control systems and has closed-loop system stability analysis and criteria to ensure the stability of the control system.

[0056] Specifically, using vehicle yaw measurement data as input, yaw stability control commands are generated through a preset model-free adaptive control model, including:

[0057] Step S101: Using the yaw measurement data as input, output the yaw differential data through a preset vehicle system dynamics model;

[0058] Step S102: Construct a Hessian matrix controller based on the yaw measurement data and the yaw differential data;

[0059] Step S103: Based on the Hessen matrix controller, generate yaw stability control commands through the yaw expectation data.

[0060] Before using the yaw measurement data as input and outputting the yaw differential data through a preset vehicle system dynamics model, the method further includes step S104: constructing the vehicle system dynamics model based on the preset two-degree-of-freedom vehicle model and the yaw measurement data; wherein, the yaw measurement data includes the torque of the four wheels of the vehicle, the angular velocity of the four wheels, the lateral velocity, and the yaw angular velocity.

[0061] In steps S101 and S104, a vehicle dynamics model, a vehicle lateral and longitudinal dynamics model, a vehicle steering dynamics model, and a vehicle steering dynamics model need to be established first. Then, based on these models, a vehicle system dynamics model is integrated using a two-degree-of-freedom vehicle model, as detailed below:

[0062] (1) Vehicle dynamics model:

[0063]

[0064] Where J is the moment of inertia of each wheel, f is the coefficient of viscous friction of the road surface, and T(.) is the driving torque of each wheel, including T fl T fr T lr T rr , For each wheel angular velocity, including ω fl ω fr ω rl ω rr .

[0065] (2) Vehicle lateral and longitudinal dynamics model:

[0066]

[0067]

[0068] (3) Vehicle yaw dynamics model:

[0069]

[0070] Where m is the total vehicle mass, v x v is the longitudinal speed of the vehicle. p F represents the vehicle's lateral speed. x(f,l) F x(f,r) F x(r,l) F x(r,r) F represents the longitudinal force on the four wheels in the vehicle coordinate system. p(f,l) F p(f,r) F p(r,l) F p(r,r) Let ω be the lateral force on the four wheels in the vehicle coordinate system. r Let δ be the yaw rate of the vehicle. f Let be the front wheel steering angle, I be the vehicle's moment of inertia about the z-axis, a be the distance between the front axle and the center of mass, b be the distance between the rear axle and the center of mass, and c be half the wheel track.

[0071] (4) Vehicle steering dynamics model:

[0072]

[0073] Among them, J s Let F be the moment of inertia of the steering system, fs be the coefficient of friction of the steering system, and F be the moment of inertia of the steering system. l(f,l) F l(f,r) Let d be the longitudinal force of the two front wheels in the tire coordinate system. w T is the contact width between the tire and the ground. s Let the steering wheel torque be... M w For the yaw moment of the four wheels, This is the conversion factor.

[0074] By integrating the above four dynamic vehicle models using a two-degree-of-freedom vehicle model, a vehicle system dynamic model can be generated:

[0075]

[0076] in, For state variables, v = [M fl M fr M lr M rr ω fl ω fr ω lr ω rr ω r ] T , f(x), B(x), and C(x) are all nonlinear state functions;

[0077] Among them, v xv is the lateral speed of the vehicle. y Let δ be the longitudinal velocity of the vehicle. f For the front wheel steering angle, For δ f The differential component, M fl M fr M lr M rr ω represents the torque of the four wheels of the vehicle. fl ω fr ω lr ω rr Let ω be the angular velocity of the four wheels of the vehicle. r This refers to the yaw rate; M respectively fl M fr M lr M rr ω fl ω fr ω lr ω rr ω r The corresponding differential components.

[0078] In step S102, a Hessian matrix controller is constructed based on the yaw measurement data and the yaw differential data. First, a hierarchical data model based on the Hessian matrix controller is constructed:

[0079]

[0080] Among them, Ω c (t) represents the Hessian matrix controller; using u = (M fl M fr M lr M rr ) T , q=(ω fl ω fr ω lr ω rr ω r ) T and If these are taken as the input and output quantities in a hierarchical data model, respectively, then:

[0081] Δq(t+1)=q(t+1)-q(t)=[Δω fl (t+1), Δω fr (t+1), Δω rl (t+1), Δω rr (t+1), Δω r (t+1)] T ;

[0082] Δu(t)=u(t)-u(t-1)=[ΔM fl (t), ΔM fr (t), ΔM lr (t), ΔM rr (t), ΔM w (t)] T ;

[0083]

[0084]

[0085] Where Δq(t+1) represents the output change between two adjacent times t and t+1, and Δu(t) represents the input change between two adjacent times t-1 and t, where t, t+1, and t-1 all represent discrete-time parameters, indicating a specific moment. Δω fl (t+1), Δω fr (t+1), Δω rl (t+1), Δω rr (t+1), Δω r (t+1) ω at time t and time t+1 respectively fl ω fr ω lr ω rr ω r , The difference. ΔM fl (t), ΔM fr (t), ΔM rl (t), ΔM rr (t), ΔM w (t), M at time t-1 and time t, respectively fl M fr M lr M rr M w , The difference, where M w Based on M fl M fr M lr M rr generate, based on generate.

[0086] Preferably, before inputting the yaw measurement data into the model-free adaptive control model, the data needs to be optimized to remove invalid data. For example, for ordinary passenger cars, the limit angular velocity range is 20–40° / s. The limit angular velocity of sports cars or high-performance vehicles may be higher, reaching 60° / s or even higher. When yaw measurement data contains data that significantly exceeds the limit angular velocity, that data is removed, and yaw stability control commands are no longer generated based on this data.

[0087] In step S103, based on the Hessian matrix controller, a yaw stability control command is generated using the expected yaw data, specifically including the following steps:

[0088] Step S1031: Obtain the estimated value of the Hessian matrix controller at the t-th time node;

[0089] Step S1032: Calculate the input value of the Hessian matrix controller at the t-th time node based on the estimated value at the t-th time node and the expected yaw data at the (t+1)-th time node;

[0090] Step S1033: Based on the input value at the t-th time node and the estimated value at the t-th time node, generate the yaw prediction data for the (t+1)-th time node through the vehicle system dynamics model;

[0091] Step S1034: Generate a yaw stability control command based on the expected yaw data at the (t+1)th time node and the predicted yaw data at the (t+1)th time node.

[0092] In step S1031, by formula

[0093]

[0094] The estimated value at time point t can be obtained.

[0095] Where Δq(t)=q(t)-q(t-1); Δu(t-1)=u(t-1)-u(t-2); η is the step size factor, which takes values ​​from 0 to 2; μ is the weight factor, which is greater than 0.

[0096] In step S1032, by formula

[0097]

[0098] The input value u(t) at the t-th time node can be obtained.

[0099] Where λ is the penalty factor, which is greater than 0; ρ is the step size factor, which ranges from 0 to 1; q* (t+1) represents the expected yaw rate at time point (t+1), which can be obtained through the PID fuzzy prediction model; q(t) represents the output value at time point t.

[0100] In step S1033, based on the input value at the t-th time node and the estimated value at the t-th time node, the yaw prediction data q(t+1) at the (t+1)-th time node is generated through the vehicle system dynamics model, that is:

[0101]

[0102] In step S1034, a yaw stability control command is generated based on the expected yaw data at the (t+1)th time node and the predicted yaw data at the (t+1)th time node. The predicted yaw data q(t+1) and the expected yaw data q at the (t+1)th time node are then compared. * Compare the two data points (t+1) based on the expected yaw rate q. * The comparison between (t+1) and the yaw prediction data q(t+1) generates anti-yaw data, and yaw stability control commands can be generated based on the anti-yaw data.

[0103] For example, in a dual-wheel drive electric vehicle, each front wheel has its own independent electric drive system, which can control the yaw moment of both front wheels. When the vehicle needs to turn left, the left front wheel is accelerated while the right front wheel is decelerated. At this time, an anti-yaw moment to the left is generated based on the yaw stability control command, causing the vehicle to turn left, thereby achieving yaw stability in the steering condition.

[0104] Before generating yaw stability control commands using a preset model-free adaptive control model with the vehicle's yaw measurement data as input, the following steps are also included:

[0105] Step S501: Based on the vehicle's driving posture data, generate yaw judgment data through a preset fuzzy prediction model;

[0106] Step S502: Calculate the yaw deviation value between the yaw measurement data and the yaw judgment data;

[0107] Step S503: When the yaw deviation value exceeds the preset yaw deviation threshold range, the yaw measurement data is input into the model-free adaptive control model.

[0108] In step S501, the fuzzy prediction model can be a neural network model or a PID model. The driving posture data and yaw judgment data both include accelerator pedal position, brake pedal position, vehicle speed, longitudinal acceleration, steering wheel angle, and yaw rate.

[0109] Specifically, a training set can be constructed based on the historical driving posture data of multiple vehicles, and then a fuzzy prediction model can be trained based on the training set. For example, two-thirds of the historical driving posture data in the training set can be used as the experimental set, and the other one-third can be used as the test set. First, a preliminary fuzzy prediction model can be obtained by training the historical driving posture data in the experimental set, and then the preliminary fuzzy prediction model can be debugged by using the historical driving posture data in the test set, and finally the fuzzy prediction model can be obtained.

[0110] In steps S502 and S503, under non-ideal conditions, there will definitely be a yaw deviation between the yaw measurement data and the yaw judgment data. The yaw deviation is usually within a range; within the range, it is defined as no deviation, and outside the range, it is defined as having a deviation. For example, on a straight road, the yaw deviation of the yaw angular velocity is 0 to 0.5 r / s, while on a curve, the yaw deviation of the yaw angular velocity is 15 to 20 r / s. For example, according to the two-degree-of-freedom model of a vehicle, when the vehicle rotates 30° at a speed of 30km / h, the yaw rate w is approximately 80° / s. The yaw moment M = moment of inertia Iz × w, and the moment of inertia is approximately 1500kg / m2. Therefore, the yaw moment is approximately 12000Nm. Assuming that the distance between each wheel and the vehicle's center of gravity is the same, the yaw moment is evenly distributed, so each wheel is 3000Nm. The threshold is 3000Nm. If the yaw moment measured in the yaw measurement data exceeds the yaw moment judged in the yaw judgment data by 5%, it is defined as having a deviation.

[0111] Specifically, the yaw measurement data can be labeled as "no deviation" and the yaw measurement data can be labeled as "deviation exists". When the yaw measurement data carries the "deviation exists" label, the yaw deviation value between the yaw measurement data and the yaw judgment data is calculated and controlled by a model-free adaptive control model; when the yaw measurement data carries the "no deviation" label, control by a model-free adaptive control model is not required.

[0112] In step S200, the Onebox (Onebox Line Control System) braking system is an intelligent braking system that can manage the pressure of the vehicle's braking system through a controller to ensure sufficient stopping power under any circumstances, thereby protecting vehicle occupants and cargo.

[0113] The Onebox braking system offers advantages in both structure and performance. Structurally, its integrated design merges these components into a single system, simplifying the structure and reducing the number of parts, making it lighter and easier to arrange and assemble within the vehicle. In terms of performance, the Onebox braking system boasts faster response times and more precise braking force control. Its response time of less than 150 milliseconds is significantly faster than traditional braking systems, making it more suitable for scenarios with higher braking performance requirements, such as autonomous driving.

[0114] The Onebox system employs advanced sensors and intelligent control algorithms to perceive vehicle status and driver behavior in real time, adjusting braking force and wheel cylinder pressure as needed to improve driving safety. Preferably, the torque of the four wheels in the yaw measurement data of this embodiment can be obtained through the Onebox braking system.

[0115] Furthermore, the Onebox braking system offers advantages in energy consumption. Traditional hydraulic vacuum-assisted braking systems require a vacuum pump to generate vacuum to provide auxiliary braking force, consuming a certain amount of energy. The One-Box brake-by-wire system eliminates the need for a vacuum pump, thus reducing energy consumption. The One-Box system also features energy recovery capabilities, converting and storing the energy released during braking to increase the driving range of new energy vehicles.

[0116] The execution of the yaw stability control command by the Onebox braking system specifically includes step S201: driving the power unit of the Onebox braking system to perform differential braking, and driving the energy recovery unit of the Onebox braking system to convert the mechanical energy generated when the power mechanism brakes into electrical energy, and storing the electrical energy in the energy storage unit of the Onebox braking system.

[0117] The Onebox brake-by-wire system uses brakes, brake pads, and other components to generate anti-yaw moments on each of the four wheels. The mechanical energy generated in this process is converted into electrical energy and stored, thus achieving energy recovery. The energy recovery unit of the Onebox brake-by-wire system typically includes an energy recovery device, usually a power inverter or frequency converter. When the vehicle brakes, the braking system generates mechanical energy, which is then converted into electrical energy by the energy recovery device. The energy storage unit is typically a battery pack, which stores the electrical energy.

[0118] This embodiment utilizes the Onebox brake-by-wire system to recover the kinetic energy generated during braking and convert it into electrical energy, thereby reducing energy waste and environmental pollution. Furthermore, storing the recovered energy in the battery can increase the driving range and endurance of the electric vehicle.

[0119] In step S300, the theoretical execution time is determined based on the energy recovery data, specifically including:

[0120] Step S301: Obtain the braking distance of the vehicle during the execution of the yaw stability control command by the Onebox braking system; obtain the initial vehicle speed at the start of the execution of the yaw stability control command and the final vehicle speed at the end of the execution of the yaw stability control command; obtain the vehicle's mass information;

[0121] Step S302: Based on the energy recovery data, the braking distance, the initial vehicle speed, the final vehicle speed, and the mass information, the theoretical execution time is generated using a preset energy recovery model.

[0122] In step S301, the braking distance refers to the distance the vehicle moves forward from the moment the Onebox braking system begins executing the yaw stability control command to the moment the yaw stability control command ends. Specifically, the start time for the Onebox braking system to execute the yaw stability control command is the moment when the vehicle's ECU (Electronic Control Unit) sends the control command to the Onebox braking system, and the end time is the moment when the Onebox braking system completes the yaw stability control command and sends a completion signal to the ECU. The distance the vehicle moves between the start and end times is the total distance traveled.

[0123] In step S302, the energy recovery model is specifically as follows:

[0124]

[0125] Wherein, E is the energy recovery data, m is the vehicle's mass information, V is the termination speed, V0 is the initial speed, t is the theoretical execution time, and S is the braking distance.

[0126] In step S400, the Onebox brake-by-wire system is also equipped with a feedback control unit. The feedback control unit is used to monitor and control the energy recovery process. It can adjust the operating parameters of the energy recovery device according to the vehicle's braking situation and energy demand to achieve maximum energy recovery efficiency.

[0127] The energy recovery efficiency of the Onebox braking system can be calculated based on the actual and theoretical execution times. The yaw stability control strategy of the Onebox braking system can then be adjusted according to the energy recovery efficiency. For example, if the theoretical execution time is 4 seconds, but the actual execution time is 6 seconds, the actual execution time is much longer than the theoretical execution time, indicating significant energy waste during recovery and low energy recovery efficiency. In this case, the yaw stability control strategy of the Onebox braking system needs to be adjusted to improve the energy recovery efficiency, bringing it closer to 100%.

[0128] The yaw stability control strategies include: reducing the energy recovery ratio of the front axle and distributing the reduced front axle energy recovery ratio to the rear axle; or reducing the energy recovery ratio of the rear axle and distributing the reduced front axle energy recovery ratio to the front axle, etc. For example, when a vehicle exhibits oversteer, it indicates insufficient lateral traction of the rear wheels. By reducing the energy recovery ratio of the rear axle, the reduced rear axle energy recovery ratio can be transferred to the front axle, allowing the front axle, which has higher energy recovery efficiency, to recover more energy. In other words, by increasing the lateral support force of the rear axle, the vehicle can better track its trajectory.

[0129] In summary, firstly, this embodiment adjusts the vehicle's yaw stability using a model-free adaptive control model, which is less affected by the external environment and can actively correct errors under various complex operating conditions, ensuring the vehicle maintains yaw stability and effectively preventing dangerous accidents such as skidding or fishtailing. Secondly, the mechanical energy generated by the Onebox braking system during the execution of yaw stability control commands is converted into electrical energy and stored to increase the driving range of new energy vehicles. Thirdly, the energy recovered by the Onebox braking system allows for the determination of its energy recovery efficiency, facilitating timely adjustments to the Onebox braking system's yaw stability control strategy and improving energy recovery efficiency.

[0130] like Figure 2 As shown, this embodiment also provides a vehicle yaw stability control device, including:

[0131] The yaw stability control command generation module M10 is used to generate yaw stability control commands by taking the vehicle's yaw measurement data as input and using a preset modelless adaptive control model.

[0132] The yaw stability control command execution module M20 is used to drive the Onebox braking system to execute the yaw stability control command, and to obtain the actual execution time of the Onebox braking system executing the yaw stability control command and the energy recovery data generated.

[0133] The theoretical execution time generation module M30 is used to determine the theoretical execution time based on the energy recovery data;

[0134] The yaw stability control strategy adjustment module M40 is used to adjust the yaw stability control strategy of the Onebox braking system based on the actual execution time and the theoretical execution time.

[0135] This application also provides a vehicle yaw stability control system, comprising: a processor and a memory; wherein the memory stores a computer program, the computer program being loaded by the processor and executed as described above for controlling vehicle yaw stability.

[0136] This application also provides a computer-readable storage medium storing instructions for loading and executing by a processor the vehicle yaw stability control method described above.

[0137] The embodiments of the mobile terminal and computer-readable storage medium provided in this application include all the technical features of the embodiments of the above control method. The extended and explanatory content of the specification is basically the same as that of the embodiments of the above method, and will not be repeated here.

[0138] This application also provides a computer program product, which includes computer program code. When the computer program code is run on a computer, it causes the computer to perform the methods described in the various possible implementations above.

[0139] This application also provides a chip, including a memory and a processor. The memory is used to store a computer program, and the processor is used to call and run the computer program from the memory, so that a device with the chip installed performs the methods described in the various possible implementations above.

[0140] The sequence numbers of the embodiments in this application are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.

[0141] In this application, the same or similar terms, concepts, technical solutions and / or application scenario descriptions are generally described in detail only when they appear for the first time. When they appear again, they are generally not repeated for the sake of brevity. When understanding the technical solutions and other contents of this application, the same or similar terms, concepts, technical solutions and / or application scenario descriptions that are not described in detail later can be referred to their previous relevant detailed descriptions.

[0142] In this application, the descriptions of the various embodiments have different focuses. For parts that are not described in detail or recorded in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0143] The technical features of the present application can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of the present application.

[0144] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in the above-mentioned storage medium and includes several instructions to cause a terminal device to execute the methods of each embodiment of this application. The above are only preferred embodiments of this application and do not limit the patent scope of this application. Any equivalent structural or procedural transformations made based on the content of this application's specification and drawings, or direct or indirect applications in other related technical fields, are similarly included within the patent protection scope of this application.

[0145] It should be noted that the various embodiments in this specification are described in a progressive manner, with each embodiment focusing on the differences from other embodiments. The same or similar parts between the various embodiments can be referred to each other.

[0146] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the appended claims.

Claims

1. A method for controlling vehicle yaw stability, characterized in that, Includes the following steps: Using the vehicle's yaw measurement data as input, yaw stability control commands are generated through a preset modelless adaptive control model; The Onebox braking system is driven to execute the yaw stability control command, and the actual execution time of the Onebox braking system executing the yaw stability control command and the generated energy recovery data are obtained. The theoretical execution time is determined based on the energy recovery data. The energy recovery efficiency of the Onebox braking system is calculated based on the actual execution time and the theoretical execution time, and the yaw stability control strategy of the Onebox braking system is adjusted according to the energy recovery efficiency.

2. The method for controlling vehicle yaw stability according to claim 1, characterized in that, The step of determining the theoretical execution time based on the energy recovery data specifically includes: The braking distance of the vehicle during the execution of the yaw stability control command by the Onebox braking system is obtained; the initial speed of the vehicle at the start of the execution of the yaw stability control command and the final speed of the vehicle at the end of the execution of the yaw stability control command are obtained; and the mass information of the vehicle is obtained. The theoretical execution time is generated based on the energy recovery data, braking distance, initial vehicle speed, final vehicle speed, and mass information using a preset energy recovery model.

3. The method for controlling vehicle yaw stability according to claim 2, characterized in that, The energy recovery model is specifically as follows: Among them, the For energy recovery data, the For vehicle quality information, the To stop the vehicle speed, the For the initial vehicle speed, the The theoretical execution time is... This is the braking distance.

4. The method for controlling vehicle yaw stability according to claim 1, characterized in that, The Onebox braking system executes the yaw stability control command, specifically including: The power unit of the Onebox braking system is driven to perform differential braking, and the energy recovery unit of the Onebox braking system is driven to convert the mechanical energy generated by the power unit during braking into electrical energy, and the electrical energy is stored in the energy storage unit of the Onebox braking system.

5. The method for controlling vehicle yaw stability according to claim 1, characterized in that, Before generating yaw stability control commands using a preset model-free adaptive control model with vehicle yaw measurement data as input, the process also includes: Based on the vehicle's driving posture data, yaw judgment data is generated through a preset fuzzy prediction model; Calculate the yaw deviation value between the yaw measurement data and the yaw judgment data; When the yaw deviation value exceeds the preset yaw deviation threshold range, the yaw measurement data is input into the model-free adaptive control model.

6. The method for controlling vehicle yaw stability according to claim 1, characterized in that, Using vehicle yaw measurement data as input, yaw stability control commands are generated through a preset model-free adaptive control model, specifically including: The yaw measurement data is used as input, and the yaw differential data is output through a preset vehicle system dynamics model. A Hessian matrix controller is constructed based on the yaw measurement data and the yaw differential data; Based on the Hessian matrix controller, yaw stability control commands are generated using the expected yaw data.

7. The method for controlling vehicle yaw stability according to claim 6, characterized in that, Before using the yaw measurement data as input and outputting the yaw differential data through a preset vehicle system dynamics model, the process also includes: Based on a preset two-degree-of-freedom vehicle model, a dynamic model of the vehicle system is constructed according to the yaw measurement data; wherein, the yaw measurement data includes the torque of the four wheels of the vehicle, the angular velocity of the four wheels, the lateral velocity, and the yaw angular velocity.

8. The method for controlling vehicle yaw stability according to claim 6, characterized in that, The process of generating yaw stability control commands based on the Hessian matrix controller and the expected yaw data specifically includes: Obtain the estimated value of the Hessian matrix controller at the t-th time node; Based on the estimated value at the t-th time node and the expected yaw data at the (t+1)-th time node, calculate the input value of the Hessian matrix controller at the t-th time node; Based on the input value at the t-th time node and the estimated value at the t-th time node, the yaw prediction data for the (t+1)-th time node is generated through the vehicle system dynamics model. Based on the expected yaw data at the (t+1)th time node and the predicted yaw data at the (t+1)th time node, a yaw stability control command is generated.

9. A control system for vehicle yaw stability, comprising: A processor and a memory; wherein the memory stores a computer program for being loaded by the processor and executed as described in any one of claims 1-8 to control vehicle yaw stability.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores instructions for loading by a processor and executing the vehicle yaw stability control method as described in any one of claims 1-8.

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