Multidirectional balance adjusting method and system for electric vehicle hub

Abstract

The invention provides a multidirectional balance adjustment method and system for an electric vehicle hub, and relates to the technical field of electric vehicle balance testing, and the method comprises the steps: carrying out the dynamic modeling based on component information and three-rail degree of freedom, and determining a balance adjustment model; interacting driving state data of the electric vehicle, wherein the driving state data comprises road excitation, load mass and a driving mode; and the driving state data is transmitted to a balance adjustment model for balance adjustment decision making, a multi-wheel balance adjustment strategy is determined, the multi-wheel balance adjustment strategy is responded to the hub motor, and balance adjustment management is carried out. The technical problems that a traditional balance adjustment method is generally based on a simple control algorithm and cannot effectively cope with complex and changeable road conditions, the vehicle control precision is insufficient, and the driving stability and safety are affected can be solved, the vehicle control precision can be remarkably improved, and the vehicle driving safety is improved. The balance and stability of the vehicle under various complex road conditions are ensured, and the effects of improving the stability of the vehicle and optimizing the driving experience and safety are achieved.

Description

Technical Field

[0001] The present application relates to the technical field of electric vehicle balance testing, and in particular to a multi-directional balance adjustment method and system for an electric vehicle wheel hub. Background Art

[0002] Electric tricycles usually use hub motor drive technology, in which the hub motor and wheel are integrated into one design, which can effectively save space and improve vehicle driving efficiency. In order to ensure the driving stability and safety of the vehicle, electric tricycles need to perform precise balance adjustments, especially under complex and changeable road conditions, such as ramps, slippery roads, bumpy roads, etc.

[0003] Traditional wheel hub motor balancing adjustment methods are usually based on simple control algorithms, such as PID control, fuzzy control, etc. Although these methods can provide basic control functions to a certain extent, due to the simplicity and static characteristics of their algorithms, they cannot fully cope with rapid changes in complex road conditions. They fail to consider the impact of different road conditions, load changes and driving methods on the dynamic characteristics of the vehicle, resulting in insufficient vehicle control accuracy and affecting the vehicle's driving stability and safety. Summary of the invention

[0004] The purpose of this application is to provide a multi-directional balance adjustment method and system for an electric vehicle wheel hub, so as to solve the technical problems that traditional balance adjustment methods are usually based on simple control algorithms, cannot effectively cope with complex and changeable road conditions, have insufficient vehicle control accuracy, and affect driving stability and safety.

[0005] In view of the above problems, the present application provides a multi-directional balance adjustment method and system for an electric vehicle wheel hub.

[0006] In a first aspect, the present application provides a multi-directional balance adjustment method for an electric vehicle wheel hub, which is implemented by a multi-directional balance adjustment system for an electric vehicle wheel hub, including: obtaining component information of the electric vehicle and determining three-track degrees of freedom, wherein the component information at least includes basic information of the electric vehicle's hub motor, suspension and wheels, and the three-track degrees of freedom include the movement degrees of freedom of three tires, and the movement degrees of freedom include the degrees of freedom of rotation, lateral, longitudinal and yaw movement; based on the component information and the three-track degrees of freedom, dynamic modeling is performed to determine a balance adjustment model; interactive driving status data of the electric vehicle, wherein the driving status data includes road excitation, load mass and driving mode, and the driving mode includes straight driving and turning; transmitting the driving status data to the balance adjustment model, making a balance adjustment decision, determining a multi-wheel balance adjustment strategy, responding the multi-wheel balance adjustment strategy to the hub motor, and performing balance adjustment management.

[0007] In a second aspect, the present application also provides a multi-directional balance adjustment system for an electric vehicle wheel hub, which is used to execute a multi-directional balance adjustment method for an electric vehicle wheel hub as described in the first aspect, including: an electric vehicle information acquisition module, used to acquire component information of the electric vehicle and determine three-track degrees of freedom, wherein the component information at least includes basic information of the electric vehicle's hub motor, suspension and wheels, and the three-track degrees of freedom include the movement degrees of freedom of three tires, and the movement degrees of freedom include the degrees of freedom of rotation, lateral, longitudinal and yaw movement; a balance adjustment model determination module, used to perform dynamic modeling and determine a balance adjustment model based on the component information and the three-track degrees of freedom; a driving status data interaction module, used to interact with the driving status data of the electric vehicle, wherein the driving status data includes road excitation, load mass and driving mode, and the driving mode includes straight driving and turning; a balance adjustment management module, used to transmit the driving status data to the balance adjustment model, make a balance adjustment decision, determine a multi-wheel balance adjustment strategy, respond the multi-wheel balance adjustment strategy to the hub motor, and perform balance adjustment management.

[0008] One or more technical solutions provided in this application have at least the following technical effects or advantages: By acquiring the component information of the electric vehicle and determining the three-track degrees of freedom, wherein the component information at least includes the basic information of the wheel hub motor, suspension and wheels of the electric vehicle, and the three-track degrees of freedom include the movement degrees of freedom of three tires, and the movement degrees of freedom include the degrees of freedom of rotation, lateral, longitudinal and yaw movement; then based on the component information and the three-track degrees of freedom, dynamic modeling is performed to determine the balance adjustment model; then the driving state data of the electric vehicle is interacted, wherein the driving state data includes road excitation, load mass and driving mode, and the driving mode includes straight driving and turning; finally, the driving state data is transmitted to the balance adjustment model, a balance adjustment decision is made, a multi-wheel balance adjustment strategy is determined, and the multi-wheel balance adjustment strategy is responded to the wheel hub motor to perform balance adjustment management; that is, through dynamic control, real-time feedback and multi-wheel adjustment strategy, the control accuracy of the vehicle can be significantly improved, ensuring that the vehicle maintains balance and stability under various complex road conditions, so as to achieve the technical effect of improving vehicle stability, optimizing driving experience and safety.

[0009] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented according to the contents of the specification, and in order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are specifically cited below. It should be understood that the content described in this section is not intended to identify the key or important features of the embodiments of the present application, nor is it intended to limit the scope of the present application. Other features of the present application will become easy to understand through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] In order to more clearly illustrate the technical solutions in the present application or the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings in the following description are only exemplary, and for ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying any creative work.

[0011] Figure 1 A schematic flow chart of a multi-directional balancing adjustment method for an electric vehicle wheel hub according to the present application.

[0012] Figure 2 This is a schematic structural diagram of a multi-directional balancing adjustment system for an electric vehicle wheel hub according to the present application.

[0013] Description of reference numerals: An electric vehicle information acquisition module 11 , a balance adjustment model determination module 12 , a driving status data interaction module 13 , and a balance adjustment management module 14 . DETAILED DESCRIPTION

[0014] This application provides a multi-directional balance adjustment method and system for electric vehicle wheel hubs, which solves the technical problems that traditional balance adjustment methods are usually based on simple control algorithms, cannot effectively cope with complex and changeable road conditions, have insufficient vehicle control accuracy, and affect driving stability and safety. Through dynamic control, real-time feedback and multi-wheel adjustment strategies, the vehicle's control accuracy can be significantly improved, ensuring that the vehicle maintains balance and stability under various complex road conditions, achieving the technical effect of improving vehicle stability, optimizing driving experience and safety.

[0015] Below, the technical solutions in the present application will be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all of the embodiments of the present application. It should be understood that the present application is not limited to the example embodiments described herein. Based on the embodiments of the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present application. It should also be noted that, for the convenience of description, only the parts related to the present application are shown in the accompanying drawings, rather than all of them.

[0016] For example, please refer to the attached Figure 1 The present application provides a multi-directional balance adjustment method for an electric vehicle wheel hub, which is applied to a multi-directional balance adjustment system for an electric vehicle wheel hub, and specifically includes the following steps: Step 1: Obtain component information of the electric vehicle and determine the three-track degrees of freedom, wherein the component information at least includes basic information of the electric vehicle's hub motor, suspension and wheels, and the three-track degrees of freedom include the movement degrees of freedom of three tires, and the movement degrees of freedom include the degrees of freedom of rotation, lateral, longitudinal and yaw motion.

[0017] Specifically, first, obtain the component information of the electric vehicle. The component information is the basis for the balance adjustment of the electric vehicle, and at least includes the basic information of the electric vehicle's hub motor, suspension and wheels. Among them, electric tricycles usually use hub motors to drive the wheels. The parameters of the hub motors include power, torque, speed, efficiency, etc. The output power and torque of the motor directly affect the driving force and control response of the wheels; the suspension system is used to isolate the impact between the wheels and the body, and determines the comfort and stability of the vehicle during driving. The design parameters of the suspension include spring stiffness, damping coefficient, suspension type (such as independent suspension, non-independent suspension, etc.), suspension travel, etc.; the parameters of the wheels include the friction coefficient of the tire, the hardness of the tire, the type of tire (such as solid tire, pneumatic tire), tire pressure, etc. The physical properties of the tire directly affect the friction and traction between the vehicle and the ground.

[0018] On the other hand, the three degrees of freedom are determined. The three degrees of freedom refer to the independent degrees of freedom of the vehicle in the three main directions during the movement, including the movement degrees of freedom of the three tires, wherein the movement degrees of freedom include rotational, lateral, longitudinal and yaw movement. The rotational freedom refers to the rotational movement of the wheel around its central axis. In electric vehicles, the hub motor drives the wheel to rotate, generating forward or backward movement of the vehicle. The rotational freedom usually determines the speed, traction and stability of the vehicle. The rotational freedom is the basis for the driving force provided by the electric motor; the lateral freedom refers to the independent movement of the vehicle in the horizontal direction (i.e., left and right direction), which is usually related to the steering system and the lateral direction of the wheel. The lateral degree of freedom determines the steering and lateral stability of the vehicle, especially affecting the vehicle's handling performance when turning, avoiding obstacles and encountering uneven roads; the longitudinal degree of freedom refers to the independent movement of the vehicle in the forward or backward direction, which is usually related to acceleration, braking and the vehicle's dynamic response. The longitudinal degree of freedom determines the vehicle's acceleration performance, braking performance, and the traction and friction between the vehicle and the road; the yaw degree of freedom refers to the vehicle's rotational movement around its vertical axis. This degree of freedom is mainly related to the vehicle's steering response, the lateral force of the wheels and the road conditions. The yaw motion usually occurs when turning and determines the stability of the vehicle, which is particularly important when driving at high speeds or making sharp turns.

[0019] By obtaining the component information of the electric vehicle's hub motor, suspension and wheels, and determining the three degrees of freedom including rotation, lateral, longitudinal and yaw, the necessary dynamic basis can be provided for the electric vehicle's balance control system. This step provides a theoretical basis for subsequent model establishment, control algorithm design and optimization of balance control strategies.

[0020] Step 2: Based on the component information and the three-track degrees of freedom, dynamic modeling is performed to determine a balance adjustment model.

[0021] Furthermore, step 2 of this application also includes: Based on the component information, three-dimensional modeling is performed to determine a three-dimensional simulation model; based on the three-track degrees of freedom, a balance adjustment mechanism is determined, and a simplified adjustment mechanism is determined by simplifying the mechanism elements, wherein the balance adjustment mechanism includes a single-track adjustment mechanism and a coupling adjustment mechanism.

[0022] Specifically, first, use 3D modeling software (such as AutoCAD, SolidWorks, CATIA, etc.) to perform 3D modeling of the electric tricycle. Based on the obtained component information, create geometric models of the various components of the electric vehicle in the 3D modeling software. Each component needs to be modeled in detail, including parameters such as size, shape, and mass. According to the actual design, model the wheels, hub motors, and suspension systems. The rotation axis of the wheels, the connection structure of the suspension, and the geometric relationship need to be reflected in the modeling process to build a 3D simulation model of the electric tricycle, that is, a model that can simulate the real situation in a virtual environment. Through the establishment of 3D modeling and simulation models based on component information, powerful digital support can be provided for the balance adjustment of electric tricycles.

[0023] Next, the balance adjustment mechanism is determined based on the three-track freedom. The balance adjustment mechanism includes a single-track adjustment mechanism and a coupling adjustment mechanism. The single-track adjustment mechanism refers to adjusting a certain independent freedom of the vehicle to solve the problem in a specific motion mode, including rotational freedom adjustment, lateral freedom adjustment, longitudinal freedom adjustment, etc. Rotational freedom adjustment refers to controlling the rotation state of the wheel by adjusting the speed of the wheel hub motor. This adjustment can be used to adjust the power output of the wheel during acceleration or braking to ensure that the vehicle maintains appropriate stability under different working conditions; lateral freedom adjustment refers to controlling the lateral stability of the vehicle through auxiliary adjustment of the steering system or the wheel hub motor. During the turning process, the steering angle of the front wheel is adjusted to keep the vehicle in a stable driving trajectory and avoid skidding. Longitudinal freedom adjustment refers to adjusting the longitudinal stability of the vehicle by controlling the motor power and braking force. For example, during the braking process, the longitudinal stability of the vehicle is ensured by adjusting the braking force of the rear wheel to avoid violent forward rush or sliding.

[0024] Since the movement of the vehicle is coupled with multiple degrees of freedom, the coupling adjustment mechanism needs to consider the interaction between multiple degrees of freedom to achieve precise balance control. For example, the coupling between the yaw degree of freedom and the lateral degree of freedom. When the vehicle turns, in addition to adjusting the steering angle, it is also necessary to consider the rotation of the wheels and the yaw angle of the vehicle to ensure the stability of the vehicle. Among them, the stability of the vehicle not only depends on the adjustment of a single degree of freedom, but also needs to maintain balance through the comprehensive adjustment of multiple degrees of freedom. For example, when the vehicle is subject to external disturbances (such as side winds, vehicle speed changes, etc.), it is necessary to adjust the wheel speed and the lateral angle of the wheel dynamically to offset the impact of external disturbances. The coupling adjustment mechanism can handle more complex dynamic situations, ensuring that the vehicle can remain stable under complex driving conditions, and is suitable for practical application scenarios that require high-precision balance adjustment.

[0025] Then the mechanism elements are simplified. Since the factors involved in the movement of electric tricycles are complex and have strong nonlinear characteristics, overly precise modeling and control will increase the amount of calculation, resulting in a long reaction time of the control system and reducing the control effect of the system. Therefore, in the modeling process, a certain degree of simplification is required to optimize the calculation efficiency of the model and ensure the control performance. For example, the rolling resistance of the tire is ignored, and tires with the same characteristics in all aspects are used. It is assumed that the forces acting on the tires are consistent when subjected to lateral forces; lateral forces such as lateral winds when the vehicle is in motion are ignored to simplify the decision-making mechanism of balance adjustment. Through single-track adjustment and coupled adjustment, the dynamic balance problems of independent degrees of freedom and multi-degrees of freedom coupling can be solved respectively. At the same time, the simplified model reduces the calculation complexity by ignoring unimportant factors and simplifying the control algorithm, improves the real-time response capability of the system, and ensures the stability and safety of the vehicle under variable road conditions.

[0026] Based on the three-dimensional simulation model and the simplified adjustment mechanism, supervised training of the balance adjustment model is performed.

[0027] Furthermore, the present application also includes the following steps: According to the three-dimensional simulation model, supervised training of the imbalance decision unit is performed, wherein the whole machine imbalance decision is performed based on the sideslip angle of the center of mass and the yaw angular velocity.

[0028] Specifically, in the balance adjustment system of the electric tricycle, the imbalance decision unit is one of the core modules, which is mainly used to judge and adjust the balance state of the vehicle according to the driving state data of the vehicle. According to the three-dimensional simulation model, the center of mass side slip angle and yaw angular velocity are used to make the whole machine imbalance decision, and supervise the training of the imbalance decision unit, wherein the center of mass side slip angle is an important indicator of the vehicle's driving state, which describes the deviation of the center of mass relative to the longitudinal trajectory during the vehicle's driving process. The size of the center of mass side slip angle directly affects the lateral stability of the vehicle. A larger deviation angle usually means that the vehicle is unbalanced or skidding. The yaw angular velocity describes the rate of lateral rotation of the vehicle when turning. It reflects the dynamic response of the vehicle during the turning process. If the yaw angular velocity is too high, it means that the vehicle may be at risk of over-turning or poor lateral stability.

[0029] The goal of the imbalance decision unit is to monitor the driving state of the vehicle in real time and predict and correct the imbalance state according to the side slip angle and yaw rate of the center of mass of the vehicle, that is, to judge whether the vehicle is at risk of imbalance according to the changing trend of the side slip angle and yaw rate. For example, when the side slip angle of the center of mass is greater than a certain threshold and the yaw rate exceeds the safe range, the decision unit will determine it as an imbalance state. Once it is determined that the vehicle is at risk of imbalance, the decision unit will send a balance adjustment instruction by adjusting the wheel hub motor, suspension and other systems, and make corresponding corrections and adjustments to ensure that the vehicle is restored to stability. Among them, the imbalance decision unit can be used by using supervised learning algorithms (such as support vector machines, random forests or deep neural networks, etc.) and continuously optimizing the loss function to train the model, so that the imbalance decision unit can accurately predict and adjust the balance state of the vehicle. During the training process, the model learns according to the input features (side slip angle and yaw rate) and the output label (whether the vehicle is unbalanced), and gradually adjusts the decision rules.

[0030] Through supervised training of the imbalance decision unit based on a three-dimensional simulation model, combined with important dynamic characteristics such as the sideslip angle of the center of mass and the yaw angular velocity, the vehicle's balance state can be judged in real time and precise adjustments can be made. This method can effectively solve the problems of insufficient real-time response and dynamic adaptability in traditional control methods, thereby improving the driving stability, controllability and safety of electric tricycles.

[0031] For the straight-ahead mode, a first simplified adjustment mechanism is determined, and the first adjustment branch is supervised and trained; Furthermore, the present application also includes the following steps: Based on the driving mode, sample division is performed to determine a first sample and a second sample; for the first sample, instability adjustment clustering is performed by coupling the road surface excitation and the load mass to determine N cluster clusters; the N cluster clusters are traversed to determine the weight distribution, and the training learning rate is configured, wherein the extreme value difference within the cluster is positively correlated with the weight value, and the weight distribution is positively correlated with the training learning rate; the N cluster clusters are mapped to the training learning rate, and the first adjustment branch is supervised for training.

[0032] Specifically, a first simplified adjustment mechanism is determined for the straight-ahead mode. The adjustment mechanism for the straight-ahead mode is mainly based on the requirements for lateral and longitudinal balance of the vehicle during straight-ahead driving. The simplified adjustment mechanism focuses on the stability adjustment of the vehicle during straight-ahead driving. The core control variables of the first simplified adjustment mechanism include longitudinal acceleration, sideslip angle of the center of mass, and vehicle speed, and the training of the first adjustment branch is supervised.

[0033] First, based on the driving mode (straight driving and turning), sample division is performed to determine the first sample and the second sample, wherein the first sample is the straight driving sample data, which includes the data of the vehicle during straight driving, and mainly focuses on the longitudinal acceleration, vehicle speed, center of mass sideslip angle and other parameters of the vehicle. During straight driving, the main imbalance factors of the vehicle usually come from the longitudinal dynamics (such as acceleration, deceleration) and the balance control of the vehicle; the second sample is the turning sample data, which includes the data of the vehicle during turning. In addition to considering the longitudinal acceleration and vehicle speed, the influence of lateral force (such as lateral acceleration, yaw angular velocity, etc.) also needs to be considered. During turning, the stability of the vehicle is affected by the lateral force, center of mass sideslip angle and wheel lateral force distribution, so more complex dynamic control is required.

[0034] Next, for the first sample, the instability adjustment clustering is performed by coupling the road surface excitation and the load mass. In the clustering process, the road surface excitation (such as road undulation, vibration, etc.) and the load mass (i.e., the change of vehicle weight) are coupled as key factors to identify the instability characteristics under different conditions. The changes of road surface excitation and load mass will significantly affect the longitudinal, lateral and yaw motion of the vehicle, so they are the key factors for determining vehicle instability. By using common clustering algorithms such as K-means and DBSCAN, the unstable driving state data (such as longitudinal acceleration, lateral acceleration, yaw angular velocity, etc.) are combined with road surface excitation and load mass for clustering, and N clusters are obtained, each cluster representing a specific instability mode.

[0035] Then, for each cluster, the difference between the maximum and minimum values ​​within the cluster (i.e., volatility or instability within the cluster) is calculated. If the volatility within a cluster is large, it means that the instability mode represented by the cluster is more severe, and more attention needs to be paid during control and adjustment. The difference between the extreme values ​​within the cluster is positively correlated with the weight value of the cluster. The more volatile the cluster is, the greater the instability impact of the mode is, and more adjustment resources are required. For this reason, the weight values ​​of these clusters are increased so that the control system pays more attention to these clusters during training. Then, the N clusters are traversed to determine the weight distribution and configure the training learning rate. The learning rate controls the speed and accuracy of model training. When the volatility of a cluster is large, increasing the learning rate can help the control system adjust and respond to the instability needs of these high-volatility clusters more quickly. The higher the learning rate, the faster the adjustment response to the cluster. According to the weight value distribution of the cluster and the volatility within the cluster, the training learning rate is adjusted. For clusters with large volatility, the learning rate is increased to adapt to changes more quickly; for clusters with small volatility, the learning rate is appropriately reduced to prevent over-adjustment.

[0036] Further, the N clusters are associated with their corresponding training learning rates through mapping relationships, that is, each cluster is assigned a different training learning rate according to the volatility of its internal distribution (i.e., the extreme value difference), so as to guide the adjustment range during the training process. Through supervised learning, the first adjustment branch, i.e., the balance adjustment strategy in the straight driving state, is trained using the above-mapped clusters and corresponding training learning rates. Among them, the training process includes, first, inputting sample data after clustering and weight assignment to ensure that each sample data is associated with the corresponding cluster and training learning rate; then, based on the clusters and their weights, learning rates and other parameters, the adjustment strategy is generated. These strategies will dynamically adjust the balance state of the vehicle according to different road and load conditions; then, the training process is adjusted through the feedback mechanism to ensure that the adjustment strategy is continuously optimized during the training process to improve the stability of the vehicle. By combining road excitation and load mass for instability adjustment clustering, and configuring weights and learning rates according to the volatility within the clusters, the training process of balance adjustment can be effectively optimized.

[0037] According to the turning mode, a second simplified adjustment mechanism is determined, and a second adjustment branch is supervised and trained; the first adjustment branch and the second adjustment branch are parallelly connected to the imbalance decision unit, and the balance adjustment model is generated.

[0038] Specifically, the second simplified adjustment mechanism is determined for the turning mode. During the turning process, the vehicle's motion characteristics change significantly, especially in the mechanical response in the lateral and yaw directions. Based on these changes, a balance adjustment mechanism different from that during straight driving needs to be designed, namely the second simplified adjustment mechanism, to deal with the imbalance problem during turning. The second simplified adjustment mechanism includes lateral adjustment and yaw adjustment. Since the vehicle is greatly affected by the lateral force during turning, a lateral adjustment mechanism is required. By real-time monitoring of the lateral acceleration, yaw angle and wheel load distribution, the risk of instability during turning can be effectively predicted. During the turning process, the change in yaw angular velocity is the key adjustment object. By adjusting the torque output of the wheel, the yaw torque generated during the turning process is balanced to prevent instability caused by excessive yaw. Then, the second adjustment branch is trained by the second sample supervision using the same method as the first adjustment branch to obtain the second adjustment branch.

[0039] Finally, the first adjustment branch and the second adjustment branch are connected in parallel to the imbalance decision unit to generate the balance adjustment model, that is, the imbalance decision unit is responsible for judging the current vehicle state according to the real-time driving state (straight or turning), and deciding whether to use the first adjustment branch or the second adjustment branch for balance adjustment, and the training results of the first adjustment branch and the second adjustment branch are connected in parallel to the imbalance decision unit to generate the final balance adjustment model. The model can intelligently select the appropriate adjustment branch according to the real-time driving state of the vehicle and the changes in the external environment, thereby achieving precise balance adjustment.

[0040] Step 3: interacting with the driving status data of the electric vehicle, wherein the driving status data includes road excitation, load mass and driving mode, and the driving mode includes straight driving and turning.

[0041] Specifically, the driving status data of the interactive electric vehicle, wherein the driving status data includes road surface excitation, load mass and driving mode. Road surface excitation refers to the impact of road conditions on electric vehicles, including road surface unevenness, slippery or soft road surface, ramps or slope changes, etc.; load mass refers to the load condition of the electric vehicle, including the weight of the items on the vehicle, etc.; the driving mode includes straight driving and turning.

[0042] Step 4: Transmit the driving state data to the balance adjustment model, make a balance adjustment decision, determine a multi-wheel balance adjustment strategy, respond the multi-wheel balance adjustment strategy to the wheel hub motor, and perform balance adjustment management.

[0043] Furthermore, step 4 of this application also includes: The driving state data is received, and based on the balance adjustment model, a first center of mass sideslip angle and a second yaw angular velocity of the electric vehicle are evaluated; based on the first center of mass sideslip angle and the second yaw angular velocity, an instability judgment is made on the electric vehicle, and an instability judgment result is determined; if the instability judgment result is no, the balance adjustment model terminates processing; if the instability judgment result is yes, a target adjustment branch based on the driving mode is triggered, and multiple rounds of balance adjustment decisions are made, wherein the target adjustment branch is the first adjustment branch or the second adjustment branch.

[0044] Specifically, the driving state data of the electric vehicle is received, including information such as road excitation, load mass and driving mode. Then, based on the received driving state data, the first center of mass sideslip angle and the second yaw rate of the electric vehicle are evaluated through the balance adjustment model. The first center of mass sideslip angle represents the deviation angle of the center of mass of the vehicle relative to its driving trajectory during the driving process; the second yaw rate represents the rotation speed of the vehicle around the vertical axis, that is, the lateral swing speed of the vehicle when turning. Then, based on the first center of mass sideslip angle and the second yaw rate, the electric vehicle is judged to be unstable and the result of the instability judgment is determined. For example, an excessively large sideslip angle usually means that the lateral stability of the vehicle is poor, which may cause loss of control; an excessively high yaw rate may indicate that the vehicle is not stable enough when turning, and it is easy to slip or roll over.

[0045] If the instability determination result is no, it means that the driving state of the electric vehicle is within the stable range. At this time, no balance adjustment is required, the system terminates the processing, and the vehicle continues to operate in the normal driving mode. If the instability determination result is yes, it means that the motion state of the electric vehicle is in an unstable area and may face the risk of losing control. Balance adjustment is required to restore stability. At this time, the target adjustment branch based on the driving mode is triggered. According to different driving modes (such as straight driving or turning), the system selects the corresponding adjustment branch for adjustment, that is, in the straight driving state, the first adjustment branch is called to adjust the longitudinal stability of the vehicle according to the adjustment strategy in the straight driving state; in the turning state, the second adjustment branch is called to adjust the lateral stability and yaw angle of the vehicle according to the adjustment strategy in the turning state. And use the adjustment branch to make multiple rounds of balance adjustment decisions, that is, according to the actual state of the vehicle and the target adjustment branch, the system will make multiple rounds of dynamic adjustment decisions. Each round of decision-making will adjust the control parameters (such as the torque output of the hub motor) based on the latest vehicle state and road environment data to restore the stability of the vehicle as much as possible; after each round of adjustment, the system will evaluate the adjustment effect and continue to make the next round of decisions until a stable state is reached. This process realizes instability judgment based on the dynamic state of the vehicle (such as the sideslip angle of the center of mass and the yaw angular velocity), and selects the appropriate adjustment branch and adjustment strategy based on the judgment result. The stability of the electric vehicle is restored through multi-round balance adjustment decisions, thereby improving driving smoothness and safety.

[0046] Furthermore, the present application also includes the following steps: If the instability judgment result is yes, a correction decision is made on the first center of mass sideslip angle and the second yaw angular velocity, and a first whole machine balance angle and a second whole machine balance angular velocity are determined, wherein a correction decision is made based on a center of mass sideslip angle threshold and a yaw angular velocity threshold under critical stability conditions; according to the driving mode, a multi-wheel balance sharing decision is made on the first whole machine balance angle and the second whole machine balance angular velocity, and the multi-wheel balance adjustment strategy is determined.

[0047] Specifically, if the instability determination result is yes, that is, it is confirmed that the vehicle is at risk of instability (such as too large center of mass sideslip angle, too high yaw rate, etc.), and a correction decision and further balance adjustment are required. Next, a correction decision is made for the first center of mass sideslip angle and the second yaw rate, wherein the correction decision is made based on the center of mass sideslip angle threshold and the yaw rate threshold under critical stability conditions. For example, when the center of mass sideslip angle of the vehicle reaches a certain threshold, the lateral stability of the vehicle decreases, which may cause the vehicle to lose control. By monitoring the center of mass sideslip angle, it is determined whether it exceeds the safety threshold. If it exceeds, correction is required; similarly, when the yaw rate exceeds a certain critical value, it indicates that the vehicle may be at risk of slipping or steering out of control. By detecting the yaw rate, it is determined whether adjustment is required. The first whole-machine balance angle and the second whole-machine balance angular velocity are determined based on the correction decision result. The first whole-machine balance angle refers to the corrected sideslip angle of the vehicle's center of mass, and the vehicle will be restored to a stable driving state by adjusting the angle; the second whole-machine balance angular velocity refers to the corrected yaw angular velocity, and the angular velocity is adjusted to restore the vehicle's lateral stability and prevent the vehicle from losing control.

[0048] Then, according to the driving mode, multiple rounds of balance sharing decisions are made for the first whole-machine balance angle and the second whole-machine balance angular velocity, that is, multiple judgments and adjustments are made according to the status of the vehicle at different times and under different road conditions. Each round of decision-making will take into account the current dynamic state, road conditions and load conditions of the vehicle, so as to adopt the most appropriate adjustment strategy. For example, when driving straight, the longitudinal stability of the vehicle is more critical, and the system will give priority to adjusting the sideslip angle of the center of mass to keep the vehicle driving in a straight line and avoid deviation; when turning, the lateral stability of the vehicle becomes particularly important, and the system will focus on adjusting the yaw angular velocity to ensure that the vehicle remains stable when turning and avoids oversteering or rollover; after the multiple rounds of balance sharing decisions, the multiple rounds of balance adjustment strategy are determined, including adjusting the specific values ​​of the first whole-machine balance angle and the second whole-machine balance angular velocity of the vehicle, and ensuring that the vehicle always maintains a stable driving state in each round of adjustment.

[0049] Through correction decisions based on stability thresholds, the vehicle's balance angle and yaw rate are dynamically adjusted, and multi-round adjustment strategies are implemented according to different driving modes (straight driving or turning) to ensure that the electric vehicle always remains stable and avoid safety problems caused by instability. This process improves the dynamic stability of the electric vehicle, allowing it to drive stably even in complex road conditions or extreme driving conditions.

[0050] Furthermore, the present application also includes the following steps: Based on the correspondence between the multi-wheel balancing adjustment strategy and the hub motor, each hub motor is controlled to perform balancing adjustment management; balancing adjustment tracking is performed synchronously, and the balancing adjustment trend is determined based on the adjustment data stream; the response delay judgment and trend off-axis judgment of the balancing adjustment trend are performed, and feedback adjustment compensation of the balancing adjustment is performed.

[0051] Specifically, the multi-wheel balancing adjustment strategy is responded to the hub motor. First, the multi-wheel balancing adjustment strategy is matched with the hub motor to determine the motion response of each hub motor under different adjustment states. For example, the speed, torque and other parameters of each hub motor are determined according to the current center of mass sideslip angle and yaw angular velocity of the vehicle to achieve the balance adjustment of the vehicle. The control signal will be transmitted to each hub motor to perform the corresponding adjustment action. Among them, the control of each hub motor is not limited to the movement of a single tire, but also takes into account the overall stability requirements of the vehicle. The balance state of the vehicle is coordinated by adjusting the speed and torque of the hub motor in real time.

[0052] Then, the balance adjustment tracking is carried out synchronously, that is, the balance state and adjustment process of the vehicle are continuously monitored. By tracking the data flow of the adjustment process (such as motor control signal, tire state, body posture, etc.), the system can evaluate in real time whether the current adjustment achieves the expected effect and make adjustments as needed. Through the continuous analysis of the adjustment data flow (such as the output of the hub motor, the change of the balance angle of the vehicle, etc.) during the adjustment process, the trend of the current adjustment can be judged. If the balance state of the vehicle is gradually restored, the adjustment trend will be stable or gradually tend to be stable; if the balance state cannot be improved, the system needs to optimize or adjust the current adjustment strategy. Further, the response delay judgment and trend off-axis judgment are performed on the balance adjustment trend. Among them, since the response of the system may have a certain delay (for example, the response speed of the hub motor, the dynamic feedback of the body, etc.), it is necessary to judge whether there is a response delay in the adjustment. If the response of the balance adjustment exceeds the predetermined time range, it may be necessary to speed up the adjustment rate or adjust the control strategy; if the balance trend of the vehicle deviates during the balance adjustment process, the system needs to immediately detect and determine the trend off-axis, that is, when the adjustment trend deviates from the expectation, it may indicate that the current control strategy is no longer applicable or the adjustment is insufficient, and compensation must be made. When a time delay or a tendency to deviate from the axis is detected, feedback adjustment compensation will be initiated. The goal of feedback adjustment compensation is to correct the deviation through further adjustment to ensure that the balance of the vehicle is restored to a stable state. For example, when a time delay or a tendency to deviate from the axis is detected, the control signal of the hub motor is increased or adjusted (such as increasing torque output, adjusting motor response speed, etc.) to accelerate the balance recovery. Through these measures, the balance adjustment system can maintain the stability of the electric vehicle in real time in a changing driving environment, improving driving safety and comfort.

[0053] In summary, the multi-directional balance adjustment method of an electric vehicle wheel hub provided by the present application has the following technical effects: By acquiring the component information of the electric vehicle and determining the three-track degrees of freedom, wherein the component information at least includes the basic information of the wheel hub motor, suspension and wheels of the electric vehicle, and the three-track degrees of freedom include the movement degrees of freedom of three tires, and the movement degrees of freedom include the degrees of freedom of rotation, lateral, longitudinal and yaw movement; then based on the component information and the three-track degrees of freedom, dynamic modeling is performed to determine the balance adjustment model; then the driving state data of the electric vehicle is interacted, wherein the driving state data includes road excitation, load mass and driving mode, and the driving mode includes straight driving and turning; finally, the driving state data is transmitted to the balance adjustment model, a balance adjustment decision is made, a multi-wheel balance adjustment strategy is determined, and the multi-wheel balance adjustment strategy is responded to the wheel hub motor to perform balance adjustment management; that is, through dynamic control, real-time feedback and multi-wheel adjustment strategy, the control accuracy of the vehicle can be significantly improved, ensuring that the vehicle maintains balance and stability under various complex road conditions, so as to achieve the technical effect of improving vehicle stability, optimizing driving experience and safety.

[0054] Embodiment 2: Based on the multi-directional balance adjustment method of an electric vehicle hub in the above embodiment, the present application also provides a multi-directional balance adjustment system for an electric vehicle hub, as shown in the attached Figure 2 ,include: The electric vehicle information acquisition module 11 is used to obtain the component information of the electric vehicle and determine the three-track degrees of freedom, wherein the component information at least includes the basic information of the wheel hub motor, suspension and wheels of the electric vehicle, and the three-track degrees of freedom include the movement degrees of freedom of three tires, and the movement degrees of freedom include the degrees of freedom of rotation, lateral, longitudinal and yaw movement; the balance adjustment model determination module 12 is used to perform dynamic modeling based on the component information and the three-track degrees of freedom and determine the balance adjustment model; the driving state data interaction module 13 is used to interact with the driving state data of the electric vehicle, wherein the driving state data includes road excitation, load mass and driving mode, and the driving mode includes straight driving and turning; the balance adjustment management module 14 is used to transmit the driving state data to the balance adjustment model, make a balance adjustment decision, determine the multi-wheel balance adjustment strategy, respond the multi-wheel balance adjustment strategy to the wheel hub motor, and perform balance adjustment management.

[0055] Furthermore, the multi-directional balance adjustment system of the electric vehicle wheel hub is also used to: perform three-dimensional modeling based on the component information to determine the three-dimensional simulation model; determine the balance adjustment mechanism based on the three-track degrees of freedom, and determine the simplified adjustment mechanism by simplifying the mechanism elements, wherein the balance adjustment mechanism includes a single-track adjustment mechanism and a coupling adjustment mechanism; supervise the training of the balance adjustment model based on the three-dimensional simulation model and the simplified adjustment mechanism.

[0056] Furthermore, the multi-directional balance adjustment system for the wheel hub of an electric vehicle is also used for: supervising the training of an imbalance decision unit according to the three-dimensional simulation model, wherein the imbalance decision of the whole machine is made based on the sideslip angle of the center of mass and the yaw angular velocity; for a straight-ahead mode, determining a first simplified adjustment mechanism and supervising the training of a first adjustment branch; for a turning mode, determining a second simplified adjustment mechanism and supervising the training of a second adjustment branch; and running the first adjustment branch and the second adjustment branch in parallel, and post-connecting them to the imbalance decision unit to generate the balance adjustment model.

[0057] Furthermore, the multi-directional balance adjustment system of the electric vehicle wheel hub is also used to: based on the driving mode, perform sample division to determine the first sample and the second sample; for the first sample, perform instability adjustment clustering by coupling the road excitation and load mass to determine N cluster clusters; traverse the N cluster clusters to determine the weight distribution, and configure the training learning rate, wherein the extreme value difference within the cluster is positively correlated with the weight value, and the weight distribution is positively correlated with the training learning rate; map the N cluster clusters and the training learning rate, and supervise the training of the first adjustment branch.

[0058] Furthermore, the multi-directional balance adjustment system of the electric vehicle wheel hub is also used to: receive the driving status data, and evaluate the first center of mass sideslip angle and the second yaw angular velocity of the electric vehicle based on the balance adjustment model; perform instability judgment on the electric vehicle based on the first center of mass sideslip angle and the second yaw angular velocity, and determine the instability judgment result; if the instability judgment result is no, the balance adjustment model terminates processing; if the instability judgment result is yes, trigger a target adjustment branch based on the driving mode, and make multi-round balance adjustment decisions, wherein the target adjustment branch is the first adjustment branch or the second adjustment branch.

[0059] Furthermore, the multi-directional balance adjustment system of the electric vehicle wheel hub is also used for: if the instability judgment result is yes, making a correction decision on the first center of mass sideslip angle and the second yaw angular velocity, and determining the first whole machine balance angle and the second whole machine balance angular velocity, wherein the correction decision is made based on the center of mass sideslip angle threshold and the yaw angular velocity threshold under critical stability conditions; according to the driving mode, making a multi-wheel balance sharing decision on the first whole machine balance angle and the second whole machine balance angular velocity, and determining the multi-wheel balance adjustment strategy.

[0060] Furthermore, the multi-directional balance adjustment system of the electric vehicle wheel hub is also used to: control each hub motor to perform balance adjustment management based on the corresponding relationship between the multi-wheel balance adjustment strategy and the hub motor; synchronously perform balance adjustment tracking, and determine the balance adjustment trend based on the adjustment data stream; perform response delay judgment and trend off-axis judgment on the balance adjustment trend, and perform feedback adjustment compensation for the balance adjustment.

[0061] The various embodiments in this specification are described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The multi-directional balancing adjustment method and specific examples of an electric vehicle hub in the aforementioned embodiment 1 are also applicable to a multi-directional balancing adjustment system of an electric vehicle hub in this embodiment. Through the aforementioned detailed description of the multi-directional balancing adjustment method of an electric vehicle hub, those skilled in the art can clearly understand the multi-directional balancing adjustment system of an electric vehicle hub in this embodiment, so for the sake of brevity of the specification, it will not be described in detail here. For the system disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant parts can be referred to the method part description.

[0062] The above description of the disclosed embodiments enables those skilled in the art to implement or use the present application. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application will not be limited to the embodiments shown herein, but will conform to the widest scope consistent with the principles and novel features disclosed herein.

[0063] Obviously, those skilled in the art can make various changes and modifications to the present application without departing from the spirit and scope of the present application. Thus, if these modifications and variations of the present application belong to the scope of the present application and its equivalent technology, the present application is also intended to include these modifications and variations.

Claims

1. A multi-directional balance adjustment method for an electric vehicle wheel hub, characterized in that: Methods include: Acquire component information of the electric vehicle and determine three-track degrees of freedom, wherein the component information at least includes basic information of the wheel hub motor, suspension and wheels of the electric vehicle, and the three-track degrees of freedom include the degrees of freedom of movement of three tires, and the degrees of freedom of movement include the degrees of freedom of rotation, lateral, longitudinal and yaw movement; Based on the component information and the three-track degrees of freedom, dynamic modeling is performed to determine a balance adjustment model; Interactive electric vehicle driving state data, wherein the driving state data includes road excitation, load mass and driving mode, and the driving mode includes straight driving and turning; The driving state data is transmitted to the balance adjustment model, a balance adjustment decision is made, a multi-wheel balance adjustment strategy is determined, and the multi-wheel balance adjustment strategy is responded to the wheel hub motor to perform balance adjustment management.

2. A multi-directional balance adjustment method for an electric vehicle wheel hub as claimed in claim 1, characterized in that: Perform kinetic modeling to determine the equilibrium regulation model, including: Based on the component information, three-dimensional modeling is performed to determine a three-dimensional simulation model; Taking the three-track degrees of freedom as a reference, determining a balance adjustment mechanism, and determining a simplified adjustment mechanism by simplifying the mechanism elements, wherein the balance adjustment mechanism includes a single-track adjustment mechanism and a coupling adjustment mechanism; Based on the three-dimensional simulation model and the simplified adjustment mechanism, supervised training of the balance adjustment model is performed.

3. A multi-directional balance adjustment method for an electric vehicle wheel hub as claimed in claim 2, characterized in that: Based on the three-dimensional simulation model and the simplified adjustment mechanism, supervised training of the balance adjustment model includes: According to the three-dimensional simulation model, supervise the training of the imbalance decision unit, wherein the whole machine imbalance decision is made based on the sideslip angle of the center of mass and the yaw angular velocity; For the straight-ahead mode, a first simplified adjustment mechanism is determined, and the first adjustment branch is supervised and trained; According to the turning mode, a second simplified adjustment mechanism is determined, and the second adjustment branch is supervised and trained; The first regulating branch and the second regulating branch are operated in parallel and are post-connected to the imbalance decision unit to generate the balance regulating model.

4. A multi-directional balance adjustment method for an electric vehicle wheel hub as claimed in claim 3, characterized in that: The supervised training first adjustment branch includes: Based on the driving mode, performing sample division to determine a first sample and a second sample; For the first sample, performing instability adjustment clustering by coupling the road surface excitation and the load mass, and determining N clusters; Traversing the N clusters to determine the weight distribution, and configuring the training learning rate, wherein the extreme value difference within the cluster is positively correlated with the weight value, and the weight distribution is positively correlated with the training learning rate; Mapping the N clusters to the training learning rate to supervise the training of the first regulation branch.

5. The multi-directional balance adjustment method of an electric vehicle wheel hub as claimed in claim 3, characterized in that: The making of a balance adjustment decision includes: receiving the driving state data, and evaluating a first center of mass sideslip angle and a second yaw angular velocity of the electric vehicle based on the balance adjustment model; Based on the first center of mass sideslip angle and the second yaw angular velocity, performing instability determination on the electric vehicle to determine an instability determination result; If the instability determination result is negative, the balance adjustment model terminates processing; If the instability determination result is yes, a target adjustment branch based on the driving mode is triggered to make a multi-round balance adjustment decision, wherein the target adjustment branch is the first adjustment branch or the second adjustment branch.

6. A multi-directional balance adjustment method for an electric vehicle wheel hub as claimed in claim 5, characterized in that: If the instability determination result is yes, including: If the instability determination result is yes, a correction decision is made on the first center of mass sideslip angle and the second yaw angular velocity, and a first whole machine balance angle and a second whole machine balance angular velocity are determined, wherein the correction decision is made based on a center of mass sideslip angle threshold and a yaw angular velocity threshold under critical stability conditions; According to the driving mode, a multi-wheel balancing allocation decision is made for the first whole-machine balancing angle and the second whole-machine balancing angular velocity to determine the multi-wheel balancing adjustment strategy.

7. The multi-directional balance adjustment method of an electric vehicle wheel hub as claimed in claim 1, characterized in that: The balance adjustment management includes: Based on the correspondence between the multi-wheel balancing adjustment strategy and the wheel hub motor, each wheel hub motor is controlled to perform balancing adjustment management; Simultaneously track the balance adjustment and determine the balance adjustment trend based on the adjustment data flow; The response delay judgment and trend off-axis judgment are performed on the balance adjustment trend, and feedback adjustment compensation of the balance adjustment is performed.

8. A multi-directional balance adjustment system for an electric vehicle wheel hub, characterized in that: The steps for implementing the multi-directional balance adjustment method of an electric vehicle wheel hub according to any one of claims 1 to 7 include: An electric vehicle information acquisition module, used to acquire component information of the electric vehicle and determine three-track degrees of freedom, wherein the component information at least includes basic information of the wheel hub motor, suspension and wheels of the electric vehicle, and the three-track degrees of freedom include the degrees of freedom of movement of three tires, and the degrees of freedom of movement include the degrees of freedom of rotation, lateral, longitudinal and yaw movement; A balance adjustment model determination module, used for performing dynamic modeling based on the component information and the three-track degrees of freedom to determine a balance adjustment model; A driving state data interaction module, used to interact with the driving state data of the electric vehicle, wherein the driving state data includes road excitation, load mass and driving mode, and the driving mode includes straight driving and turning; The balance adjustment management module is used to transmit the driving state data to the balance adjustment model, make balance adjustment decisions, determine the multi-wheel balance adjustment strategy, respond the multi-wheel balance adjustment strategy to the wheel hub motor, and perform balance adjustment management.

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