Automobile braking control method and device, computer device and storage medium

Abstract

The embodiment of the application belongs to the field of automobile brake control, is applied to the field of electric vehicles, and relates to an automobile brake control method, which comprises the following steps: acquiring brake intensity of the electric vehicle, determining brake working conditions of the electric vehicle based on the brake intensity; constructing a target brake power boundary based on the brake working conditions; introducing the brake intensity and the target brake power boundary into a preset brake energy efficiency model for processing to obtain target brake power of the electric vehicle; and performing brake control on the electric vehicle based on the target brake power. The application further provides an automobile brake control device, a computer device and a storage medium. The application constructs a brake energy efficiency model, searches for an optimal combination of front and rear axle motor brake torques within a dynamic constraint boundary by using a QPSO algorithm, so as to maximize energy recovery efficiency and optimize comprehensive performance under a motor brake priority strategy, and provides safe and stable automobile brake control capability for electric vehicles.

Description

Automotive braking control methods, devices, computer equipment and storage media Technical Field

[0001] This application relates to the field of automotive braking control technology, and in particular to automotive braking control methods, devices, computer equipment, and storage media. Background Technology

[0002] In dual-motor driven electric vehicles, braking requires balancing energy recovery efficiency, vehicle ride comfort, and braking stability. However, the braking capacity of the motors is limited by the maximum torque of the motors, the battery charging capacity, and the state of charge (SOC). Furthermore, the front-to-rear axle braking force distribution must meet certain proportional boundary constraints. Under medium-to-high intensity braking or low-traction road surface conditions, it is often difficult to achieve efficient energy recovery and smooth braking force output without exceeding these constraints. In addition, with the vehicle control unit (VCU) control cycle shortening to the millisecond level, higher demands are placed on the real-time performance and accuracy of torque distribution. The timing, compensation magnitude, and response speed of mechanical braking intervention in electromechanical combined braking also directly affect the vehicle's braking performance and driving comfort.

[0003] To address the aforementioned issues, existing solutions primarily employ the following methods: First, a fixed-ratio allocation or rule-based mapping approach simplifies control logic to meet basic braking force requirements; second, methods based on linear programming or convex optimization use energy recovery rate or braking smoothness as optimization objectives; third, intelligent optimization methods such as particle swarm optimization (PSO) and genetic algorithms (GA) are used to search for torque between the front and rear axles; and fourth, a master-slave control strategy prioritizes motor braking while supplementing it with mechanical braking, achieving overall braking through compensation. These methods, to a certain extent, improve energy recovery rate, ensure basic braking safety, and achieve coordination between motor and mechanical braking.

[0004] However, existing solutions using fixed ratios or linear programming struggle to dynamically adapt to nonlinear constraints under complex operating conditions; algorithms such as PSO and GA have limited convergence speeds, making it difficult to meet real-time requirements within a 1ms control cycle; and master-slave control lacks flexible scheduling, leading to torque discontinuities and shocks during electromechanical switching, reducing braking smoothness and stability. Therefore, current technologies cannot simultaneously achieve energy recovery efficiency, braking regulatory compliance, smooth electromechanical switching, and real-time optimization under high-speed control cycles within the constraints of braking force distribution boundaries. Summary of the Invention

[0005] The purpose of this application is to provide an automotive braking control method, device, computer equipment, and storage medium to solve the technical problem of achieving efficient energy recovery and balancing real-time performance and comfort in dual-motor torque distribution.

[0006] To address the aforementioned technical problems, this application provides a vehicle braking control method applied to electric vehicles, employing the following technical solution:

[0007] The braking intensity of the electric vehicle is obtained, and the braking condition of the electric vehicle is determined based on the braking intensity;

[0008] Construct the target braking force boundary based on the described braking conditions;

[0009] The braking intensity and the target braking force boundary are imported into a preset braking efficiency model for processing to obtain the target braking force of the electric vehicle.

[0010] The electric vehicle is braked based on the target braking force.

[0011] Furthermore, the braking conditions of the electric vehicle include a first braking condition and a second braking condition, and the step of determining the braking condition of the electric vehicle based on the braking intensity specifically includes:

[0012] If the braking intensity of the electric vehicle is less than or equal to a preset braking intensity coefficient threshold, then the braking condition of the electric vehicle is determined to be the first braking condition.

[0013] If the braking intensity of the electric vehicle is greater than or equal to a preset braking intensity coefficient threshold, then the braking condition of the electric vehicle is determined to be the second braking condition.

[0014] Furthermore, the step of constructing the target braking force boundary based on the braking condition specifically includes:

[0015] Obtain the dynamic parameters of the electric vehicle;

[0016] When the braking condition is the second braking condition, the dynamic parameters are combined with the preset maximum and minimum wheel-end braking force constraints of the front and rear axles to obtain the target braking force boundary.

[0017] Furthermore, before the step of importing the braking intensity and the target braking force boundary into a preset braking efficiency model for processing to obtain the target braking force of the electric vehicle, the method further includes:

[0018] Obtain energy recovery efficiency data for the front and rear axles of the electric motor in the electric vehicle;

[0019] The energy recovery efficiency data of the front and rear axles of the motor are used to construct a function by a linear interpolator to obtain the efficiency interpolation model of the front and rear axle motors.

[0020] The preset quantum particle swarm optimization algorithm is imported into the front and rear axle motor efficiency interpolation model for fusion to obtain the preset braking energy efficiency model.

[0021] Furthermore, the step of importing the braking intensity and the target braking force boundary into a preset braking efficiency model for processing to obtain the target braking force of the electric vehicle specifically includes:

[0022] The braking intensity is calculated using the preset braking efficiency model to obtain a set of energy recovery efficiencies.

[0023] The target braking force of the electric vehicle is obtained by applying boundary constraints to the energy recovery efficiency set using the target braking force boundary.

[0024] Furthermore, the step of applying boundary constraints to the energy recovery efficiency set through the target braking force boundary to obtain the target braking force of the electric vehicle specifically includes:

[0025] The energy recovery efficiency set is optimized and searched using the preset quantum particle swarm optimization algorithm to obtain the initial braking force;

[0026] The initial braking force is constrained based on the target braking force boundary to obtain the target braking force.

[0027] Furthermore, prior to the step of controlling the braking of the electric vehicle based on the target braking force, the method further includes:

[0028] Detect whether the target braking force meets the braking force requirements of the current electric vehicle;

[0029] If the required braking force is greater than the target braking force, hydraulic braking force will be activated to compensate for the target braking force and update the target braking force.

[0030] To address the aforementioned technical problems, this application also provides an automotive braking control device applied to electric vehicles, employing the following technical solution:

[0031] An acquisition module is used to acquire the braking intensity of the electric vehicle and determine the braking condition of the electric vehicle based on the braking intensity.

[0032] The construction module is used to construct the target braking force boundary based on the braking condition;

[0033] The processing module is used to import the braking intensity and the target braking force boundary into a preset braking energy efficiency model for processing, so as to obtain the target braking force of the electric vehicle.

[0034] A braking module is used to control the braking of the electric vehicle based on the target braking force.

[0035] To address the aforementioned technical problems, this application also provides a computer device that employs the following technical solution:

[0036] A computer device includes a memory and a processor, the memory storing computer-readable instructions, the processor executing the computer-readable instructions to implement the steps of the vehicle braking control method.

[0037] To address the aforementioned technical problems, this application also provides a computer-readable storage medium, employing the technical solution described below:

[0038] A computer-readable storage medium, characterized in that the computer-readable storage medium stores computer-readable instructions, which, when executed by a processor, implement the steps of the vehicle braking control method.

[0039] Compared with the prior art, the embodiments of this application have the following main advantages:

[0040] This application embodiment obtains the braking intensity of the electric vehicle, determines the braking condition of the electric vehicle based on the braking intensity, constructs a target braking power boundary based on the braking condition, imports the braking intensity and the target braking power boundary into a preset braking energy efficiency model for processing, and obtains the target braking power of the electric vehicle. Based on the target braking power, the electric vehicle is braked and controlled. By constructing a braking energy efficiency model, the QPSO algorithm is used to search for the optimal combination of braking torque of the front and rear axle motors within the dynamic constraint boundary, so as to maximize energy recovery efficiency and achieve optimal comprehensive performance under the motor braking priority strategy, thereby providing the electric vehicle with safe and stable vehicle braking control capabilities. Attached Figure Description

[0041] To more clearly illustrate the solutions in this application, the accompanying drawings used in the description of the embodiments of this application will be briefly introduced below. Obviously, the accompanying drawings described below are some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0042] Figure 1 is an exemplary architecture diagram in which this application can be applied;

[0043] Figure 2 is a flowchart of an embodiment of the vehicle braking control method according to this application;

[0044] Figure 3 is a schematic diagram of an embodiment of the vehicle braking control device according to this application;

[0045] Figure 4 is a schematic diagram of the structure of an embodiment of a computer device according to this application;

[0046] Figure 5 is a flowchart of the front and rear axle braking torque distribution according to an embodiment of the vehicle braking control method of this application;

[0047] Figure 6 is a flowchart of mechanical braking dynamic compensation according to an embodiment of the vehicle braking control method of this application. Detailed Implementation

[0048] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains; the terminology used herein in the specification of the application is for the purpose of describing particular embodiments only and is not intended to be limiting of the application; the terms "comprising" and "having," and any variations thereof, in the specification, claims, and foregoing drawings of this application, are intended to cover non-exclusive inclusion. The terms "first," "second," etc., in the specification, claims, or foregoing drawings of this application are used to distinguish different objects, not to describe a particular order.

[0049] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of this application. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0050] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings.

[0051] As shown in Figure 1, the system architecture 100 of the vehicle braking control system may include a terminal device 101, a network 102, and a server 103. The terminal device 101 may be an in-vehicle computer 1011, a tablet computer 1012, or a mobile phone 1013. The network 102 is used as a medium to provide a communication link between the terminal device 101 and the server 103. The network 102 may include various connection types, such as wired or wireless communication links or fiber optic cables.

[0052] Users can use terminal device 101 to interact with server 103 via network 102 to receive or send messages, etc. Various communication client applications can be installed on terminal device 101, such as web browser applications, shopping applications, search applications, instant messaging tools, email clients, social media platform software, etc.

[0053] Terminal device 101 can be various electronic devices with a display screen and support web browsing. In addition to in-vehicle computer 1011, tablet computer 1012 or mobile phone 1013, terminal device 101 can also be e-book reader, MP3 player (Moving Picture Experts Group Audio Layer III), MP4 player (Moving Picture Experts Group Audio Layer IV), laptop computer and desktop computer, etc.

[0054] Server 103 can be a server that provides various services, such as a backend server that provides support for the pages displayed on terminal device 101.

[0055] It should be noted that the vehicle braking control method provided in this application embodiment is generally executed by a server / terminal device, and correspondingly, the vehicle braking control device is generally located in the server / terminal device.

[0056] It should be understood that the number of terminal devices, networks, and servers shown in Figure 1 is merely illustrative. Depending on implementation needs, any number of terminal devices, networks, and servers can be included.

[0057] Referring again to Figure 2, a flowchart of one embodiment of the vehicle braking control method according to this application is shown. The vehicle braking control method includes the following steps:

[0058] Step S201: Obtain the braking intensity of the electric vehicle and determine the braking condition of the electric vehicle based on the braking intensity.

[0059] In this embodiment, the aforementioned vehicle braking control method can be deployed in a vehicle braking control platform. This platform can be constructed using a server or server cluster. The server or server cluster can be any electronic device with data transmission and data storage functions. The electronic device on which the vehicle braking control method runs (e.g., the server / terminal device shown in Figure 1) can receive the braking intensity of the electric vehicle via a wired or wireless connection. It should be noted that the aforementioned wireless connection methods may include, but are not limited to, 3G / 4G / 5G connections, WiFi connections, Bluetooth connections, WiMAX connections, Zigbee connections, UWB (ultra-wideband) connections, and other currently known or future-developed wireless connection methods.

[0060] In this embodiment, the electric vehicle can be a pure electric passenger vehicle or commercial vehicle driven by dual motors, or a hybrid vehicle with regenerative braking function.

[0061] The aforementioned braking intensity can be a parameter representing the current braking demand, obtained by measuring brake pedal travel, brake pedal force, vehicle deceleration, or calculations by the electronic control system. This parameter may include, but is not limited to, the output value of the brake pedal displacement sensor, the signal from the brake master cylinder pressure sensor, the signal from the vehicle longitudinal acceleration sensor, the braking request value comprehensively estimated by the brake control unit (EBS / VCU), and the braking demand factor corrected by combining road adhesion conditions and vehicle load status, thereby comprehensively reflecting the driver's braking intention and the actual braking demand under the current operating conditions.

[0062] The aforementioned braking conditions can be braking states determined comprehensively based on factors such as braking intensity, vehicle speed, and road surface adhesion coefficient. These conditions can include light to moderate braking conditions and medium to high-intensity braking conditions, and are used to dynamically adjust the braking torque distribution between the front and rear axle motors and whether to intervene in mechanical braking compensation under different conditions.

[0063] Specifically, the detailed implementation process of determining the braking conditions of an electric vehicle based on braking intensity will be further described in subsequent specific embodiments of this application, and will not be elaborated on here.

[0064] Step S202: Construct the target braking force boundary based on the braking conditions.

[0065] In this embodiment, the target braking force boundary can be the upper and lower limit range of the allowable braking force of the front and rear axles dynamically calculated based on the vehicle's longitudinal dynamic parameters (including vehicle mass, wheelbase, center of gravity height, front and rear axle load distribution, etc.) and the I-curve and M-curve of the ECE R13H regulations. Alternatively, it can be a feasible solution space determined comprehensively based on the motor braking capacity, battery charging capacity, and road adhesion coefficient, used to limit the maximum and minimum values ​​of the front and rear axle motor braking torque and mechanical braking compensation torque, thereby ensuring that the braking distribution results meet the regulatory safety and vehicle stability requirements during the optimization process.

[0066] Among them, the I-curve (Ideal Curve) of the ECE R13H regulation refers to the "ideal braking force distribution curve" specified in the international vehicle braking regulations. It specifies the ideal braking force ratio between the front and rear axles based on the dynamic axle load transfer of the vehicle during braking, in order to ensure that the vehicle maintains longitudinal stability under different braking intensities and avoids wheel lock-up or sideslip.

[0067] The aforementioned M-curve (Minimum Curve) refers to the "minimum braking force distribution curve" specified in regulations. It represents the minimum limit of the front and rear axle braking force distribution under various adhesion coefficients and braking intensities, and is used to ensure that the vehicle still has sufficient braking safety redundancy and stability during emergency or high-intensity braking.

[0068] Specifically, the I curve mentioned above defines the ideal distribution, and the M curve mentioned above defines the minimum permissible distribution. These two curves together constitute the regulatory boundary of vehicle braking force distribution, which is used to check whether the braking ratio of the front and rear axles is compliant and to serve as a constraint condition for optimization calculation.

[0069] More specifically, the specific implementation process of constructing the target braking force boundary based on the braking conditions will be described in more detail in the subsequent specific embodiments of this application, and will not be elaborated on here.

[0070] Step S203: The braking intensity and target braking force boundary are imported into a preset braking efficiency model for processing to obtain the target braking force of the electric vehicle.

[0071] In this embodiment, the aforementioned preset braking energy efficiency model can be a multi-dimensional interpolation model constructed based on the energy recovery efficiency data of the front and rear axle motors of electric vehicles under different torque conditions, and combined with the improved quantum particle swarm optimization (QPSO) algorithm to form a comprehensive energy efficiency solution model framework, which is used to dynamically calculate the optimal torque distribution under regulatory constraints.

[0072] The aforementioned target braking force can be the result of a combination of the braking torque of the front and rear axle motors and their necessary mechanical braking compensation, obtained by optimizing the current braking intensity using the aforementioned braking energy efficiency model and target braking force boundary. This reflects the comprehensive braking force output that maximizes energy recovery efficiency and vehicle longitudinal stability while meeting regulatory safety constraints.

[0073] Specifically, the implementation process of importing the braking intensity and target braking force boundary into a preset braking efficiency model to obtain the target braking force of the electric vehicle will be described in more detail in subsequent specific embodiments of this application, and will not be elaborated on here.

[0074] Step S204: Perform braking control on the electric vehicle based on the target braking force.

[0075] In one possible embodiment, the vehicle braking control of an electric vehicle can distribute the target braking force obtained from the aforementioned optimized calculations to the motor control unit and / or hydraulic braking module, respectively. By adjusting the braking torque of the front and rear axle motors of the electric vehicle and the necessary mechanical braking compensation torque, the corresponding braking force output can be achieved. Under light to moderate braking conditions, the motor braking is prioritized to meet the vehicle's braking needs and recover energy. Under medium to high-intensity braking conditions, when the motor braking reaches its upper limit, mechanical braking is automatically introduced for collaborative compensation to ensure the vehicle's braking safety and stability. The entire control process operates in a closed loop within a millisecond-level control cycle to track the target vehicle speed and braking demand in real time.

[0076] Through the above braking process, on the one hand, the optimal torque distribution between the front and rear axles can be dynamically achieved under the constraints of ECE R13H regulations, maximizing the use of electric motor braking for energy recovery and improving vehicle economy; on the other hand, the electromechanical braking can be smoothly switched according to operating conditions to avoid torque abrupt changes and braking shocks, thereby improving vehicle longitudinal stability and ride comfort; at the same time, it can quickly compensate for insufficient braking force during high-intensity braking or emergency braking, ensuring vehicle braking safety redundancy and significantly improving the real-time performance, stability and robustness of the entire vehicle braking system.

[0077] This application obtains the braking intensity of an electric vehicle and determines its braking conditions based on that intensity. It then constructs a target braking power boundary based on the braking conditions. The braking intensity and target braking power boundary are imported into a preset braking efficiency model for processing to obtain the target braking power of the electric vehicle. Based on this target braking power, the application performs braking control on the electric vehicle. By constructing a braking efficiency model and utilizing the QPSO algorithm within dynamic constraint boundaries, the optimal combination of braking torques for the front and rear axle motors is searched to maximize energy recovery efficiency and achieve optimal overall performance under a motor braking priority strategy, thus providing the electric vehicle with safe and stable braking control capabilities.

[0078] In some alternative implementations, step S201 includes the following steps:

[0079] If the current braking intensity of the electric vehicle is less than or equal to the preset braking intensity coefficient threshold, then the braking condition of the electric vehicle is determined to be the first braking condition.

[0080] If the braking intensity of the current electric vehicle is greater than or equal to the preset braking intensity coefficient threshold, then the braking condition of the electric vehicle is determined to be the second braking condition.

[0081] In this embodiment, the braking conditions of the electric vehicle include a first braking condition and a second braking condition. The first braking condition can be the light to moderate braking condition described in step 201, and the second braking condition can be the medium to high intensity braking condition described in step 201.

[0082] In this embodiment, the aforementioned preset braking intensity coefficient threshold can be a dividing value determined comprehensively based on the vehicle's longitudinal dynamic parameters, vehicle curb weight, road surface adhesion coefficient, and driving comfort requirements. This threshold is used to distinguish between light to moderate braking conditions and medium to high-intensity braking conditions. This threshold can be obtained through simulation tests, road tests, or regulatory requirements (such as the typical dividing value of Z=1.5 in ECE R13H). It can also be dynamically adjusted, for example, automatically calibrated according to different vehicle models, load states, or brake pedal sensitivity. Thus, it serves as a key parameter in the control strategy for triggering mechanical braking collaborative compensation and motor braking torque optimization.

[0083] In one possible embodiment, firstly based on the braking intensity coefficient Determine the current braking condition:

[0084] If the current At this time, the braking condition is light to moderate, and the vehicle can rely entirely on the electric motor to achieve the braking process.

[0085] At this time, the maximum wheel-end braking force of the front axle It can be represented as:

[0086]

[0087] Where G represents the current weight of the electric vehicle, and b represents the distance from the vehicle's center of gravity to the rear axle. This indicates the current center of gravity height of the electric vehicle, and L indicates the current wheelbase of the electric vehicle, which is the distance between the front and rear axle centers.

[0088] Maximum wheel-end braking force of the rear axle It can be represented as:

[0089]

[0090] Where 'a' represents the distance from the vehicle's center of gravity to the front axle (axle load distribution parameter), used to characterize the longitudinal center of gravity position.

[0091] Minimum wheel-end braking force of front axle It can be represented as:

[0092]

[0093] Minimum wheel-end braking force of the rear axle It can be represented as:

[0094]

[0095] Maximum motor braking torque of front axle It can be represented as:

[0096]

[0097] Where r represents the wheel radius, used to convert the braking force into braking torque (T = F· r), and i represents the gear ratio of the reducer (i.e. the speed ratio between the motor and the wheel), used to convert the wheel end torque to the motor end.

[0098] The maximum braking torque of the rear axle motor can be expressed as:

[0099]

[0100] Front axle minimum motor braking torque It can be represented as:

[0101]

[0102] Minimum motor braking torque of the rear axle It can be represented as:

[0103]

[0104] If the current At this point, the braking condition enters the medium-to-high intensity braking stage. Electric vehicles are at risk of motor braking saturation, so mechanical braking compensation needs to be introduced.

[0105] At this moment, the maximum wheel-end braking force of the front axle is:

[0106]

[0107] Maximum wheel-end braking force of the rear axle:

[0108]

[0109] Minimum wheel-end braking force of the front axle:

[0110]

[0111] Minimum wheel-end braking force of the rear axle:

[0112]

[0113] Maximum braking torque of the front axle motor:

[0114]

[0115] Maximum braking torque of the rear axle motor:

[0116]

[0117] Minimum motor braking torque for front axle:

[0118]

[0119] Minimum permissible motor braking torque for the rear axle:

[0120]

[0121] In some alternative implementations, step S202 includes the following steps:

[0122] Obtain the dynamic parameters of the electric vehicle;

[0123] When the braking condition is the second braking condition, the dynamic parameters are combined with the preset maximum and minimum wheel-end braking force constraints of the front and rear axles to obtain the target braking force boundary.

[0124] In this embodiment, the aforementioned dynamic parameters can be key data characterizing the longitudinal motion characteristics and load distribution of the vehicle, including but not limited to the total vehicle mass, front and rear axle load distribution, wheelbase, vehicle center of gravity height, wheel radius, tire adhesion coefficient, brake pedal force, and brake line pressure, etc., used to calculate the dynamic axle load transfer and torque boundary during braking; the aforementioned preset maximum and minimum wheel-end braking force constraints of the front and rear axles can be the upper and lower limit ranges of allowable braking forces at the wheel ends of the front and rear axles dynamically constructed based on the I curves and M curves of the ECE R13H regulations and vehicle dynamic parameters, used to limit the feasible solution space of braking force distribution, and as a constraint condition for the braking torque optimization model, ensuring that the braking distribution of the front and rear axles meets regulatory requirements and vehicle stability.

[0125] In one possible embodiment, the maximum and minimum wheel-end braking force constraints of the front and rear axles are constructed based on the ECE R13H regulation I curve (slip ratio limit line) and M curve (braking force distribution line). This refers to the aforementioned preset maximum and minimum wheel-end braking force constraints for the front and rear axles, combined with vehicle geometric parameters (wheelbase). Center of mass height Front and rear axle distance That is, the aforementioned dynamic parameters, and mass The theoretical lower and upper limits of the braking torque of the front and rear axle motors were calculated. This refers to the target braking force boundary, which serves as the feasible solution space for subsequent optimization searches.

[0126] In some alternative implementations, the following steps are included before step S203:

[0127] Obtain energy recovery efficiency data for the front and rear axles of electric motors in electric vehicles;

[0128] A linear interpolator is used to construct a function from the energy recovery efficiency data of the front and rear axles of the motor, resulting in an interpolation model for the efficiency of the front and rear axle motors.

[0129] The preset quantum particle swarm optimization algorithm is imported into the front and rear axle motor efficiency interpolation model for fusion to obtain the preset braking energy efficiency model.

[0130] In this embodiment, the energy recovery efficiency data of the front and rear axles of the electric motor can be obtained by testing on a test bench or by testing the entire vehicle under road conditions. This data can be used to reflect the energy recovery efficiency curves or discrete data points of the front and rear axle motors under different torque, speed and battery charge states.

[0131] The aforementioned linear interpolator can be a multidimensional interpolation function established based on the discrete efficiency data points, such as using piecewise linear interpolation or gridded interpolation methods, to quickly calculate the corresponding energy recovery efficiency at any torque point;

[0132] The above-mentioned front and rear axle motor efficiency interpolation model can be a dual-input efficiency model composed of the front axle motor efficiency function and the rear axle motor efficiency function. It aims to maximize the comprehensive energy recovery efficiency of the two motors and provides a basis for fitness calculation for subsequent optimization algorithms.

[0133] The aforementioned preset quantum particle swarm optimization algorithm can be an improved QPSO (Quantum-behaved ParticleSwarm Optimization) algorithm. By introducing mechanisms such as boundary legalization initialization, quantum jump update mechanism, boundary projection correction and dynamic convergence factor, it can improve global search capability and convergence efficiency while meeting regulatory constraints, thereby achieving optimal allocation of braking torque between the front and rear axle motors.

[0134] In one possible embodiment, energy recovery efficiency data for the front and rear axles of the electric motor under different braking torques are collected separately, and an efficiency function is established using a gridded interpolator. and , used for particle fitness calculation. Where:

[0135] This indicates the braking torque of the front axle motor;

[0136] This indicates the braking torque of the rear axle motor;

[0137] Overall efficiency To optimize the objective function.

[0138] In some alternative implementations, step S203 includes the following steps:

[0139] The braking intensity is calculated using a pre-defined braking efficiency model to obtain a set of energy recovery efficiencies.

[0140] The target braking force of an electric vehicle is obtained by applying boundary constraints to the energy recovery efficiency set through the target braking force boundary.

[0141] In this embodiment, the aforementioned energy recovery efficiency set can refer to a set of dual-motor comprehensive energy recovery efficiency data calculated by the aforementioned efficiency interpolation model under different combinations of front axle motor braking torque and rear axle motor braking torque. This set not only includes the efficiency value corresponding to a single torque point, but also includes the efficiency results corresponding to multiple torque combinations within a certain range, which are used to constitute the search space and fitness evaluation basis of the optimization algorithm, thereby providing a complete energy recovery performance reference for the subsequent quantum particle swarm optimization process.

[0142] In one possible embodiment, the vehicle braking control system first acquires the braking intensity and dynamic parameters of the front and rear axles of the current electric vehicle, and imports this input data into a braking efficiency model constructed based on the front and rear axle energy recovery efficiency interpolation model. The model calculates or solves the dual-motor comprehensive energy recovery efficiency point by point or in batches for each torque combination according to the current braking torque range of the front and rear axle motors. All the calculated efficiency values ​​are arranged according to the front and rear axle torques to form a multidimensional dataset, i.e., the energy recovery efficiency set. This set fully reflects the energy recovery performance corresponding to different torque distribution schemes, providing data support for the subsequent quantum particle swarm optimization algorithm to select the optimal torque combination.

[0143] Specifically, the above-mentioned implementation process of applying boundary constraints to the energy recovery efficiency set through the target braking power boundary to obtain the target braking power of the electric vehicle will be further described in detail in subsequent specific embodiments of this application, and will not be elaborated on here.

[0144] In some alternative implementations, the step "applying boundary constraints to the energy recovery efficiency set through the target braking force boundary to obtain the target braking force of the electric vehicle" includes the following steps:

[0145] The initial braking force is obtained by optimizing the energy recovery efficiency set through a preset quantum particle swarm optimization algorithm.

[0146] The initial braking force is constrained based on the target braking force boundary to obtain the target braking force.

[0147] In this embodiment, the initial braking force can refer to the front and rear axle braking force distribution result obtained by first searching under the condition of not applying target braking force boundary constraints after inputting the above-mentioned braking intensity and energy recovery efficiency set into a preset braking energy efficiency model and combining it with the quantum particle swarm optimization algorithm. The initial braking force can include the front axle motor braking torque, the rear axle motor braking torque, and the mechanical braking compensation torque when necessary, as the basis value for subsequent constraint optimization and correction based on the target braking force boundary, in order to accelerate the convergence speed and improve the real-time performance and stability of the braking distribution calculation.

[0148] In one possible embodiment, the QPSO optimization search process and constraint flow can be as follows:

[0149] I. Particle Initialization:

[0150] In the two-dimensional torque space, the boundary lines of the braking torque of the front and rear axle motors are used as the basis;

[0151] All initial particles are connected. Uniform initialization is performed to ensure that the initial population satisfies the regulatory linear constraints.

[0152] Position is represented as The aforementioned position refers to the coordinate value of each particle in the search space in the quantum particle swarm optimization algorithm, which is used to describe the front and rear axle braking force distribution scheme represented by the current particle.

[0153] in, It represents the braking torque of the front axle motor corresponding to the i-th particle at the beginning of the iteration. "1f" can be understood as "1st (first dimension), front (front axle)", which represents the value of the front axle torque dimension in the particle search space. It represents the braking torque of the rear axle motor corresponding to the i-th particle at the beginning of the iteration. "2r" can be understood as "2nd (second dimension)" or "rear (rear axle)", representing the value of the rear axle torque dimension in the particle search space.

[0154] II. Fitness Function Calculation:

[0155] The efficiency of each particle is the sum of the two current torque values ​​on the interpolation function of the front and rear axle motor efficiency interpolation model, as shown below:

[0156] ;

[0157] Update individual optimal with global optimal The location and efficiency values ​​are shown below;

[0158]

[0159]

[0160] in, The total energy recovery efficiency (fitness value) of the i-th particle; The function is the interpolation function for the front axle motor efficiency, with the front axle torque as input. The corresponding energy recovery efficiency value is then output. The function is the interpolation function for the efficiency of the rear axle motor, with the rear axle torque as input. The corresponding energy recovery efficiency value is then output. Let be the historical best position (personal best) of the i-th particle, representing the best torque combination coordinates searched for this particle so far; To find the position with the highest energy recovery efficiency eff(i), update it to the new optimal position for the individual; The global best solution represents the coordinates of the position with the highest efficiency value among all particles; The goal is to select the solution with the highest efficiency value from all individual optimal solutions as the global optimal solution.

[0161] III. QPSO Particle Position Update:

[0162] The following quantum jump update formula is used:

[0163]

[0164]

[0165] in:

[0166] The mean of the historical best positions of all individuals;

[0167] These are uniformly distributed random numbers;

[0168] The iteration control factor decreases linearly between 1.0 and 0.6.

[0169] Δ is the quantum random step size, which determines the amplitude of the particle's next "jump" based on distance and randomness;

[0170] To obtain new candidate solutions for the particle by adding or subtracting Δ based on the amplitude controlled by β;

[0171] To represent the current position of the particle in the search space (which can be the coordinate vector of the front axle torque or the rear axle torque);

[0172] The updated position needs to be mapped back to the physically feasible solution set through the boundary projection function. If the updated position exceeds the boundary, it should be mapped back to the physically feasible region through the projection function to ensure that the optimization process meets the regulations and physical constraints.

[0173] IV. Boundary projection constraint handling:

[0174] To prevent particles from exceeding the legal domain of the ECE braking force distribution, a linear mapping method is used to reproject the particles back onto the boundary line segment, ensuring the updated... Always meet the distribution restrictions stipulated by regulations.

[0175]

[0176] Among them, the above The projection operator represents the projection of the particle's position. Projected onto the legal boundary line that complies with ECE regulations.

[0177] In some alternative implementations, the following steps are included before step S204:

[0178] The test determines whether the target braking force meets the braking force requirements of the current electric vehicle.

[0179] If the required braking force is greater than the target braking force, hydraulic braking force will be activated to compensate for the target braking force and update the target braking force.

[0180] In this embodiment, the aforementioned required braking force refers to the theoretical braking force requirement value calculated by the electric vehicle under the current driving conditions based on the driver's braking intention, the vehicle deceleration target, the road surface adhesion conditions, and the vehicle dynamics model. It is used to reflect the total braking force level that the vehicle needs to achieve at that moment. The aforementioned hydraulic braking force refers to the supplementary braking force provided by the vehicle's hydraulic braking system (mechanical brake or hydraulic brake actuator) when the motor braking is insufficient or reaches its upper limit. This includes the actual braking force output by the front and rear axle brake calipers, brake drums, or other hydraulic actuators. It is used to compensate for the insufficient braking capacity of the motor and ensure braking safety and regulatory compliance.

[0181] If the current At this point, the braking condition enters the medium-to-high intensity braking stage. Electric vehicles are at risk of motor braking saturation, so mechanical braking compensation needs to be introduced.

[0182] In one embodiment, when the optimization result or When the maximum permissible motor braking limit is exceeded (set at 2500 Nm), the excess portion is defined as the mechanical braking compensation torque:

[0183]

[0184]

[0185] This method transfers the insufficient braking capacity caused by the limitation of the motor to the hydraulic system for compensation, thereby avoiding the risk of motor braking output overload and meeting the safety redundancy design requirements for medium and high intensity braking.

[0186] After QPSO completes all iterations, it outputs the optimal solution:

[0187] Motor braking torque output: ;

[0188] Mechanical brake output: ;

[0189] Overall Energy Recovery Efficiency ;

[0190] This embodiment achieves comprehensive optimization of the dual-motor drive braking system for electric vehicles in terms of energy recovery efficiency, regulatory compliance, speed tracking accuracy, and braking safety by integrating ECE regulatory constraints, improving the QPSO algorithm, and the vehicle speed error feedback mechanism.

[0191] Referring again to Figure 5, which is a flowchart of the front and rear axle braking torque distribution of an embodiment of the vehicle braking control method.

[0192] The front and rear axle braking torque distribution process includes the following steps: First, initialize the parameters of the interpolator and the improved quantum particle swarm optimization (QPSO) algorithm, and determine the search boundary according to the ECE regulation braking force distribution range; uniformly generate initial particles within the front and rear axle torque boundaries and assign them initial positions; calculate the fitness of each particle (energy recovery efficiency obtained based on the front and rear axle motor efficiency interpolation model), and update the individual optimal position and the global optimal position; further calculate the average position of the particle swarm and update the convergence coefficient; during the iteration process, update the particle positions through the quantum jump formula, and use the boundary projection function to project particles that exceed the regulatory boundary back to the feasible region; when the number of iterations reaches a preset threshold, output the optimal combination of front and rear axle motor braking torque and mechanical braking compensation torque, and simultaneously calculate the comprehensive energy recovery efficiency to obtain the optimal braking distribution scheme.

[0193] The process begins by initializing the contraction factor and QPSO parameters. First, based on ECE braking regulations and vehicle dynamics, it determines the legal front / rear axle braking force distribution range and uniformly generates initial particles along the boundary lines. During iteration, the overall energy efficiency fitness is calculated based on the particle's current position, updating the individual optimal and global optimal. New quantum transition positions are calculated based on the average of the individual historical optimal values ​​(mbest), and the iteration convergence factor β is updated simultaneously. If a particle goes out of bounds, it is mapped back to the feasible region by "projecting onto the legal boundary line." Then, the iteration count is incremented, and it is determined whether the maximum number of iterations has been reached. Upon termination, the braking torque of the front / rear axle motors and the mechanical braking force of the front / rear axles corresponding to the optimal solution are output, and the final overall energy recovery efficiency is calculated.

[0194] This process enables real-time optimal distribution of braking force between the front and rear axles under ECE regulatory constraints, and can efficiently search for the optimal torque combination within a millisecond-level control cycle.

[0195] On the one hand, it maximizes the energy recovery efficiency of the dual motors and improves the overall vehicle economy;

[0196] On the other hand, particle projection and convergence factor adjustment are introduced in the algorithm iteration to ensure that the search process always meets regulatory requirements and avoids torque mutation, thereby improving the safety, smoothness and robustness of the braking process. The final control strategy can be directly applied to the embedded control system of electric vehicles, providing technical support for high-precision and high-real-time braking energy management.

[0197] Referring again to Figure 6, which is a flowchart of dynamic compensation for mechanical braking in an embodiment of the vehicle braking control method.

[0198] This embodiment describes the process from inputting the actual vehicle speed. With target speed The process begins by initializing the parameters of the improved QPSO and generating a particle swarm. The fitness of each particle is calculated, and the individual optimal (pBest) is initialized, while the global optimal (gBest) is selected. During iteration, mbest = mean(pBest) is first calculated, and then the particle positions are updated based on the average optimal position, with physical boundary constraints and projections performed after each update. Subsequently, the vehicle speed is predicted, and the fitness is recalculated; if the fitness is better, the corresponding particle's position is updated. Repeat the iteration until the maximum number of iterations is reached, and then output the compensating mechanical braking torque as the final control quantity.

[0199] In one possible embodiment, to make the predicted speed of the electric vehicle Approaching target speed The following prediction model is introduced:

[0200] (1) Predictive velocity modeling:

[0201] Assuming the braking process is approximately a linear uniform deceleration model, within the control time window Inside, predict vehicle speed for:

[0202]

[0203] Among them, braking deceleration The control factor brake and a fixed mapping ratio Decide:

[0204]

[0205] (2) Definition of error term:

[0206] Error between predicted velocity and target velocity:

[0207]

[0208] (3) Design of penalty items:

[0209] To avoid excessively large values ​​for `brake` causing abrupt braking, an exponential time penalty function is added:

[0210]

[0211] in, This represents an exponentially decaying function; the larger 'a' is, the smaller the exponential term. It represents the penalty coefficient or penalty value, used to limit or correct a certain optimization variable or error.

[0212] (4) The final form of the fitness function:

[0213]

[0214] in, The fitness function value reflects the quality of the current braking allocation solution;

[0215] The error term is the braking error term, which usually represents the deviation between the predicted vehicle speed and the target vehicle speed, or the difference between the actual braking force and the required braking force.

[0216] The penalty term is used to constrain illegal solutions (such as exceeding the boundary or failing to meet ECE regulatory constraints) or to suppress excessive deviations. As mentioned earlier, its value increases smoothly with the deviation and saturates at a certain value.

[0217] The objective is:

[0218]

[0219] This means finding an optimal braking control solution within the braking intensity range [0,100] that minimizes the fitness function f(brake).

[0220] In one possible embodiment, the QPSO optimization process is detailed as follows:

[0221] (1) Initialization phase

[0222] Particle swarm size ;

[0223] Maximum number of iterations ;

[0224] Control variable dimensions ;

[0225] brake range: ;

[0226] The initial position matrix is ​​generated randomly as follows:

[0227]

[0228] in, Indicates the span of the search space;

[0229] This indicates the generation of a random number matrix within the range [0, ub-lb].

[0230] This indicates that the random matrix is ​​shifted to the range [lb, ub].

[0231]

[0232] Individual Optimum with group optimal Initialize it to the one with the smallest fitness;

[0233] (2) Main Iteration Loop

[0234] right arrive Perform the following steps:

[0235] a. Calculate the current individual fitness

[0236]

[0237] b. Update the individual optimal solution

[0238] like If the current solution is used, then the historical best for that individual is updated.

[0239] c. Calculate the center location mbest

[0240]

[0241] d. Quantum behavior updates particle position

[0242] For each particle, update its position. :

[0243]

[0244] in: (Experience points); ; It is the currently optimal individual globally.

[0245] e. Boundary handling

[0246] all Must meet Those exceeding the boundary will be forcibly truncated:

[0247]

[0248] f. Update the global optimal solution

[0249] If the optimal fitness is found in the current population If so, then update the global optimum.

[0250] (3) Output the optimal solution. After the iteration is complete, return:

[0251]

[0252] This value represents the optimal mechanical braking compensation strength, with an accuracy of [value missing]. .

[0253] This process incorporates a closed loop of the vehicle's target speed and real-time speed into QPSO optimization, ensuring that the solution for mechanical braking compensation not only meets regulatory and physical constraints but also aligns with real-time vehicle speed control requirements. Through average optimal position, boundary projection, and fitness update mechanisms, the algorithm's convergence speed and accuracy are improved while ensuring the feasibility and stability of the solution. Ultimately, the optimal mechanical braking compensation torque can be obtained quickly, effectively compensating for insufficient electric motor braking and improving braking smoothness, energy recovery rate, and overall vehicle longitudinal stability.

[0254] It is understood that in the specific embodiments of this application, data related to the braking intensity of electric vehicles are involved. When the embodiments in this application are applied to specific products or technologies, user permission or consent is required, and the collection, use and processing of related data must comply with the relevant laws, regulations and standards of the relevant countries and regions.

[0255] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by instructing related hardware through computer-readable instructions. These computer-readable instructions can be stored in a computer-readable storage medium. When the program is executed, it can include the processes of the embodiments of the above methods. The aforementioned storage medium can be a non-volatile storage medium such as a magnetic disk, optical disk, or read-only memory (ROM), or random access memory (RAM).

[0256] It should be understood that although the steps in the flowcharts of the accompanying figures are shown sequentially as indicated by the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the accompanying figures may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily completed at the same time, but can be executed at different times, and their execution order is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the sub-steps or stages of other steps.

[0257] Referring further to FIG3, as an implementation of the method shown in FIG2, this application provides an embodiment of an automotive braking control device, which corresponds to the method embodiment shown in FIG2, and the device can be specifically applied to various electronic devices.

[0258] As shown in Figure 3, the automotive braking control device 300 described in this embodiment includes: an acquisition module 301, a construction module 302, a processing module 303, and a braking module 304, and is applied to an electric vehicle, wherein:

[0259] The acquisition module 301 is used to acquire the braking intensity of the electric vehicle and determine the braking condition of the electric vehicle based on the braking intensity.

[0260] Construction module 302 is used to construct the target braking force boundary based on the braking condition;

[0261] The processing module 303 is used to import the braking intensity and the target braking force boundary into a preset braking energy efficiency model for processing, so as to obtain the target braking force of the electric vehicle.

[0262] Braking module 304 is used to perform braking control on the electric vehicle based on the target braking force.

[0263] The acquisition module 301 includes:

[0264] The first determining submodule is used to determine the braking condition of the electric vehicle as the first braking condition if the braking intensity of the electric vehicle is less than or equal to a preset braking intensity coefficient threshold.

[0265] The second determining submodule is used to determine the braking condition of the electric vehicle as the second braking condition if the braking intensity of the electric vehicle is greater than or equal to a preset braking intensity coefficient threshold.

[0266] The construction module 302 includes:

[0267] The acquisition submodule is used to acquire the dynamic parameters of the electric vehicle;

[0268] The import submodule is used to import the dynamic parameters into the preset maximum and minimum wheel-end braking force constraints of the front and rear axles when the braking condition is the second braking condition, and combine them to obtain the target braking force boundary.

[0269] Prior to the processing module 303, the following is also included:

[0270] The model data acquisition module is used to acquire the energy recovery efficiency data of the front and rear axles of the electric motor of the electric vehicle;

[0271] The model building module is used to construct a function from the energy recovery efficiency data of the front and rear axles of the motor using a linear interpolator, so as to obtain the efficiency interpolation model of the front and rear axle motors.

[0272] The fusion module is used to import the preset quantum particle swarm optimization algorithm into the front and rear axle motor efficiency interpolation model for fusion to obtain the preset braking energy efficiency model.

[0273] The processing module 303 includes:

[0274] The first processing submodule is used to perform energy efficiency calculation on the braking intensity using the preset braking energy efficiency model to obtain a set of energy recovery efficiencies.

[0275] The second processing submodule is used to perform boundary constraint processing on the energy recovery efficiency set through the target braking power boundary to obtain the target braking power of the electric vehicle.

[0276] The second processing submodule includes:

[0277] The processing unit is used to perform optimization search processing on the energy recovery efficiency set through the preset quantum particle swarm optimization algorithm to obtain the initial braking force;

[0278] A constraint unit is used to constrain the initial braking force based on the target braking force boundary to obtain the target braking force.

[0279] Prior to the braking module 304, the following is also included:

[0280] The detection module is used to detect whether the target braking force meets the braking force requirements of the current electric vehicle.

[0281] The update module is used to activate hydraulic braking power to compensate for the target braking power if the required braking power is greater than the target braking power, and update the target braking power.

[0282] In this embodiment, the braking intensity of the electric vehicle is obtained, and the braking condition of the electric vehicle is determined based on the braking intensity. A target braking power boundary is constructed based on the braking condition. The braking intensity and the target braking power boundary are imported into a preset braking energy efficiency model for processing to obtain the target braking power of the electric vehicle. The electric vehicle is braked based on the target braking power. By constructing a braking energy efficiency model, the QPSO algorithm is used to search for the optimal combination of braking torque of the front and rear axle motors within the dynamic constraint boundary to achieve maximum energy recovery efficiency and optimal comprehensive performance under the motor braking priority strategy, thereby providing the electric vehicle with safe and stable vehicle braking control capabilities.

[0283] In this embodiment, the operations performed by the above-mentioned units or modules correspond one-to-one with the steps of the vehicle braking control method of the above-described embodiments, and will not be repeated here.

[0284] To address the aforementioned technical problems, this application also provides a computer device. Please refer to Figure 4 for details; Figure 4 is a basic structural block diagram of the computer device according to this embodiment.

[0285] The computer device 4 includes a memory 41, a processor 42, and a network interface 43 that are interconnected via a system bus. It should be noted that only the computer device 4 with components 41-43 is shown in the figure; however, it should be understood that it is not required to implement all the shown components, and more or fewer components can be implemented alternatively. Those skilled in the art will understand that the computer device described here is a device capable of automatically performing numerical calculations and / or information processing according to pre-set or stored instructions, and its hardware includes, but is not limited to, microprocessors, application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), digital signal processors (DSPs), embedded devices, etc.

[0286] The computer device can be a desktop computer, laptop, handheld computer, or cloud server, etc. The computer device can interact with the user via a keyboard, mouse, remote control, touchpad, or voice control.

[0287] The memory 41 includes at least one type of readable storage medium, including flash memory, hard disk, multimedia card, card-type memory (e.g., SD or DX memory), random access memory (RAM), static random access memory (SRAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), programmable read-only memory (PROM), magnetic memory, magnetic disk, optical disk, etc. In some embodiments, the memory 41 may be an internal storage unit of the computer device 4, such as the hard disk or memory of the computer device 4. In other embodiments, the memory 41 may also be an external storage device of the computer device 4, such as a plug-in hard disk, smart media card (SMC), secure digital (SD) card, flash card, etc., equipped on the computer device 4. Of course, the memory 41 may also include both the internal storage unit and its external storage device of the computer device 4. In this embodiment, the memory 41 is typically used to store the operating system and various application software installed on the computer device 4, such as computer-readable instructions for automobile braking control methods. In addition, the memory 41 can also be used to temporarily store various types of data that have been output or will be output.

[0288] In some embodiments, the processor 42 may be a central processing unit (CPU), controller, microcontroller, microprocessor, or other data processing chip. The processor 42 is typically used to control the overall operation of the computer device 4. In this embodiment, the processor 42 is used to execute computer-readable instructions stored in the memory 41 or to process data, for example, to execute computer-readable instructions for the vehicle braking control method.

[0289] The network interface 43 may include a wireless network interface or a wired network interface, which is typically used to establish communication connections between the computer device 4 and other electronic devices.

[0290] This application also provides another embodiment, namely, providing a computer-readable storage medium storing computer-readable instructions that can be executed by at least one processor to cause the at least one processor to perform the steps of the automobile braking control method described above.

[0291] 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 a storage medium (such as ROM / RAM, magnetic disk, optical disk) and includes several instructions to cause a terminal device (which may be a mobile phone, computer, server, air conditioner, or network device, etc.) to execute the methods described in the various embodiments of this application.

[0292] Obviously, the embodiments described above are only some embodiments of this application, not all embodiments. The accompanying drawings show preferred embodiments of this application, but do not limit the patent scope of this application. This application can be implemented in many different forms; rather, the purpose of providing these embodiments is to provide a more thorough and comprehensive understanding of the disclosure of this application. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing specific embodiments, or make equivalent substitutions for some of the technical features. Any equivalent structures made using the content of this application's specification and drawings, directly or indirectly applied to other related technical fields, are similarly within the scope of patent protection of this application.

Claims

1. A vehicle braking control method, applied to electric vehicles, characterized in that, The process includes the following steps: obtaining the braking intensity of the electric vehicle; determining the braking condition of the electric vehicle based on the braking intensity; constructing a target braking force boundary based on the braking condition. The target braking force boundary is the upper and lower limit range of the allowable braking force of the front and rear axles dynamically calculated based on the vehicle's longitudinal dynamic parameters, the ideal braking force distribution curve, and the minimum braking force distribution curve, or a feasible solution space determined comprehensively based on the motor braking capacity, battery charging capacity, and road surface adhesion coefficient. This boundary is used to limit the maximum and minimum values ​​of the front and rear axle motor braking torque and mechanical braking compensation torque. Before the step of processing the braking intensity and the target braking force boundary into a preset braking efficiency model to obtain the target braking force of the electric vehicle, the method further includes: acquiring the energy recovery efficiency data of the front and rear axles of the electric motor of the electric vehicle; constructing a function from the energy recovery efficiency data of the front and rear axles of the electric motor using a linear interpolator to obtain a front and rear axle motor efficiency interpolation model; and fusing a preset quantum particle swarm optimization algorithm into the front and rear axle motor efficiency interpolation model to obtain the preset braking efficiency model. Specifically, the step of processing the braking intensity and the target braking force boundary into the preset braking efficiency model to obtain the target braking force of the electric vehicle includes: performing energy efficiency calculation processing on the braking intensity using the preset braking efficiency model to obtain an energy recovery efficiency set; and performing boundary constraint processing on the energy recovery efficiency set using the target braking force boundary to obtain the target braking force of the electric vehicle.

2. The vehicle braking control method according to claim 1, characterized in that, The braking conditions of the electric vehicle include a first braking condition and a second braking condition. The step of determining the braking condition of the electric vehicle based on the braking intensity specifically includes: if the current braking intensity of the electric vehicle is less than or equal to a preset braking intensity coefficient threshold, then the braking condition of the electric vehicle is determined to be the first braking condition; if the current braking intensity of the electric vehicle is greater than or equal to the preset braking intensity coefficient threshold, then the braking condition of the electric vehicle is determined to be the second braking condition.

3. The vehicle braking control method according to claim 2, characterized in that, The step of constructing the target braking force boundary based on the braking condition specifically includes: obtaining the dynamic parameters of the electric vehicle; when the braking condition is the second braking condition, combining the dynamic parameters with preset maximum and minimum wheel-end braking force constraints of the front and rear axles to obtain the target braking force boundary.

4. The vehicle braking control method according to claim 1, characterized in that, The step of performing boundary constraint processing on the energy recovery efficiency set through the target braking power boundary to obtain the target braking power of the electric vehicle specifically includes: performing optimization search processing on the energy recovery efficiency set through the preset quantum particle swarm optimization algorithm to obtain the initial braking power; and constraining the initial braking power based on the target braking power boundary to obtain the target braking power.

5. The vehicle braking control method according to claim 1, characterized in that, Before the step of braking control of the electric vehicle based on the target braking force, the method further includes: detecting whether the target braking force meets the current braking force required by the electric vehicle; if the required braking force is greater than the target braking force, then hydraulic braking force will be activated to perform power compensation processing on the target braking force and update the target braking force.

6. An apparatus employing the vehicle braking control method as described in any one of claims 1-5, applied to an electric vehicle, characterized in that, include: An acquisition module is used to acquire the braking intensity of the electric vehicle and determine the braking condition of the electric vehicle based on the braking intensity. The system includes a construction module for constructing a target braking force boundary based on the braking conditions; a processing module for importing the braking intensity and the target braking force boundary into a preset braking efficiency model for processing to obtain the target braking force of the electric vehicle; and a braking module for performing braking control on the electric vehicle based on the target braking force.

7. A computer device, characterized in that, The system includes a memory and a processor, wherein the memory stores computer-readable instructions, and the processor executes the computer-readable instructions to implement the steps of the vehicle braking control method as described in any one of claims 1 to 5.

8. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer-readable instructions, which, when executed by a processor, implement the steps of the vehicle braking control method as described in any one of claims 1 to 5.

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