Vehicle control method, controller, system, related device and vehicle

Abstract

The invention relates to a vehicle control method, a controller, a system, a related device and a vehicle, and the method comprises the step of carrying out rotating speed control or torque control on a motor of the vehicle based on driving information of the vehicle. According to the method, the appropriate control mode needed by the vehicle is judged according to the driving information of the vehicle, smooth switching between the rotating speed control mode and the torque control mode is achieved, complex driving environment and working condition changes can be dealt with, and stable control over the whole vehicle is facilitated.

Description

Technical Field

[0001] This application relates to the field of vehicle technology, and in particular to a vehicle control method, controller, system, related device and vehicle. Background Technology

[0002] With the rapid development of new energy vehicles such as electric vehicles and hybrid vehicles, the vehicle's motor control system plays a crucial role in vehicle performance and energy efficiency. In related technologies, motor control systems typically employ a single control mode, such as fixed torque control or speed control, which is insufficient to adapt to complex and changing driving scenarios and operating conditions, resulting in poor vehicle control performance. Summary of the Invention

[0003] This application provides a vehicle control method, controller, system, related devices, and vehicle to solve the above-mentioned problems.

[0004] To achieve the above objectives, according to a first aspect of this application, a vehicle control method is provided, the method comprising:

[0005] Based on the vehicle's driving information, the vehicle's motor is subjected to speed control or torque control.

[0006] Optionally, the step of controlling the speed or torque of the vehicle's motor based on the vehicle's driving information includes:

[0007] Based on the driving information and the preset fuzzy rule base, a target control mode is determined so as to control the speed or torque of the motor through the target control mode.

[0008] Optionally, determining the target control mode based on the driving signal and a preset fuzzy rule base includes:

[0009] Based on the membership degree of the driving information to the corresponding fuzzy set and the preset fuzzy rules, the membership degree of the control mode required by the vehicle to be used to the preset control mode is obtained, thereby determining the target control mode.

[0010] The preset control mode includes at least one of torque control mode and speed control mode.

[0011] Optionally, determining the target control mode includes:

[0012] Based on the membership degree of the control mode required by the vehicle to belong to the preset control mode, the target control mode is determined by defuzzification rules.

[0013] Optionally, determining the target control mode includes:

[0014] Based on the relationship between the membership degree of the first preset control mode and the preset membership degree threshold, the target control mode is determined to be either the first preset control mode or the second preset control mode.

[0015] Optionally, determining the target control mode as either the first preset control mode or the second preset control mode based on the relationship between the membership degree of the first preset control mode and a preset membership threshold includes:

[0016] If the membership degree of the first preset control mode is greater than or equal to the first preset membership degree threshold, the target control mode is determined to be the first preset control mode.

[0017] Optionally, determining the target control mode as either the first preset control mode or the second preset control mode based on the relationship between the membership degree of the first preset control mode and a preset membership threshold includes:

[0018] If the membership degree of the first preset control mode is less than or equal to the second preset membership degree threshold, the target control mode is determined to be the second preset control mode, wherein the second preset membership degree threshold is less than the first preset membership degree threshold.

[0019] Optionally, the method further includes:

[0020] When the membership degree of the first preset control mode is within the range corresponding to the first preset membership degree threshold and the second preset membership degree threshold, the control mode used by the vehicle at the current moment is maintained.

[0021] Optionally, the method further includes:

[0022] Based on the driving information and the corresponding membership function, the membership degree of the driving information to the corresponding fuzzy set is determined.

[0023] Optionally, the membership function is used to map the driving information to different intervals within the range of the driving information, and each interval corresponds to a fuzzy set.

[0024] Optionally, the driving information includes first driving information, and the method further includes:

[0025] Based on the first driving information, determine the first membership degree of the first driving information to the corresponding first fuzzy set;

[0026] The first driving information is used to indicate the driving status information of the vehicle.

[0027] Optionally, the first driving information includes the degree of wheel slippage, and the first membership degree includes a membership degree indicating that the degree of wheel slippage belongs to a corresponding range of different degrees.

[0028] Optionally, the driving information includes second driving information, and the method further includes:

[0029] Based on the second driving information, determine the second membership degree of the second driving information to the corresponding second fuzzy set;

[0030] The second driving information is used to indicate the driving environment information of the vehicle.

[0031] Optionally, the second driving information includes slope resistance, and the second membership degree includes a membership degree indicating that the slope resistance belongs to a corresponding different resistance range.

[0032] Optionally, the driving information includes third driving information, and the method further includes:

[0033] Based on the third driving information, the third membership degree of the third driving information to the corresponding third fuzzy set is determined;

[0034] The third driving information is used to indicate the driving behavior information of the vehicle.

[0035] Optionally, the third driving information includes pedal depth, and the third membership degree includes membership degrees indicating that the pedal depth belongs to different depth ranges.

[0036] Optionally, the driving information includes first driving information, second driving information, and third driving information.

[0037] The step of obtaining the membership degree of the control mode to be used by the vehicle to a preset control mode based on the membership degree of the corresponding fuzzy set of the driving information and preset fuzzy rules includes:

[0038] Based on the first membership degree corresponding to the first driving information, the second membership degree corresponding to the second driving information, and the third membership degree corresponding to the third driving information, the membership degree of the control mode to be used by the vehicle belonging to the preset control mode is obtained through the preset fuzzy rules.

[0039] Optionally, the preset fuzzy rule is determined in the following way:

[0040] Based on the correlation between sample driving information and sample control mode, multiple sets of fuzzy rules are obtained as the preset fuzzy rules.

[0041] Optionally, the motor is a motor corresponding to the wheel.

[0042] According to a second aspect of this application, embodiments of this application also provide a controller for implementing any of the vehicle control methods described in the embodiments of this application.

[0043] According to a third aspect of this application, embodiments of this application also provide an electronic control system, including the controller and a motor connected to the controller.

[0044] According to a fourth aspect of this application, embodiments of this application also provide an electronic device, comprising:

[0045] A memory on which computer programs are stored;

[0046] A processor is configured to execute the computer program in the memory to implement the steps of any of the methods provided in the embodiments of this application.

[0047] According to a fifth aspect of this application, embodiments of this application also provide a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of any of the methods provided in embodiments of this application.

[0048] According to a sixth aspect of this application, embodiments of this application also provide a computer program product, including a computer program or instructions that, when executed by a processor, implement the steps of any of the methods provided in embodiments of this application.

[0049] According to a seventh aspect of this application, embodiments of this application also provide a vehicle including the controller, or including the vehicle electronic control system, or the electronic device as described, or performing the steps of any of the methods provided in embodiments of this application.

[0050] Some embodiments of this specification include at least the following beneficial effects: by determining the appropriate control mode required by the vehicle through vehicle driving information, a smooth switching between speed control mode and torque control mode can be achieved, which can cope with complex driving environments and changes in operating conditions, and contribute to the stable control of the whole vehicle.

[0051] Other features and advantages of this application will be described in detail in the following detailed description section. Attached Figure Description

[0052] To more clearly illustrate the technical solutions in the embodiments of this application, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0053] To gain a more complete understanding of this application and its beneficial effects, the following description will be provided in conjunction with the accompanying drawings, wherein the same reference numerals in the following description denote the same parts.

[0054] Figure 1 These are application scenario diagrams of vehicle control methods shown in some embodiments of this specification;

[0055] Figure 2 This is an exemplary flowchart of a vehicle control method according to some embodiments of this specification;

[0056] Figure 3 This is an exemplary schematic diagram of yet another vehicle control method according to some embodiments of this specification;

[0057] Figure 4 These are exemplary schematic diagrams illustrating deblurring rules according to some embodiments of this specification;

[0058] Figure 5 These are schematic diagrams of the electronic control system shown in some embodiments of this specification;

[0059] Figure 6 This is a schematic diagram of the structure of an electronic device according to some embodiments of this specification;

[0060] Figure 7 This is an exemplary schematic diagram of a vehicle according to some embodiments of this specification. Detailed Implementation

[0061] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them. All other embodiments obtained by those skilled in the art based on the embodiments of this application without creative effort are within the protection scope of this application.

[0062] To facilitate understanding of the implementation schemes provided in this application, the relevant application background of the vehicle control method provided in this application will be explained first.

[0063] Currently, vehicle motor control systems typically employ a single control mode, such as torque control or speed control. Torque control is primarily used to directly control the motor's output torque to meet the vehicle's acceleration and hill-climbing requirements; while speed control is mainly used to maintain the motor within a specific speed range to optimize energy efficiency and noise levels. However, a single control mode struggles to meet the diverse needs of a vehicle under different driving conditions and cannot achieve precise control in certain situations. For example, considering only wheel torque control prevents the vehicle from quickly getting out of trouble or achieving maximum traction when wheels slip; or considering only wheel speed control fails to address the need for rapid torque response during rapid acceleration and deceleration. Specifically, the torque response speed of wheel-side / hub motors is slower than the speed response speed. In situations such as slippage or wheel lock-up, directly controlling the wheel speed is more timely and accurate. In addition, the estimation error of road surface adhesion coefficient and other parameters is relatively large, and the estimation results are delayed. When driving at high speed on roads with rapidly changing parameters such as bumpy roads, the torque demand of electric vehicles cannot be estimated in a timely and accurate manner, thereby aggravating the vehicle's vibration and reducing the user's driving experience. If only wheel speed control is considered, only the wheel speed can be controlled, which cannot provide good control in situations where the vehicle needs to have greater traction and requires torque control.

[0064] In view of this, some embodiments of this specification provide a vehicle control method based on the principle of fuzzy control. It establishes membership functions according to various operating conditions and maps them to the required speed control mode or torque control mode and their control degree. It can combine road conditions, driving style, driving needs, vehicle driving status information, etc. to determine the appropriate control mode required by the vehicle, realize stepless smooth switching between speed control mode and torque control mode, and help the vehicle stability control under complex extreme conditions such as off-road.

[0065] Figure 1 This is an application scenario diagram of the vehicle control method shown in some embodiments of this specification.

[0066] The vehicle control method provided in this application can be applied to various application scenarios, such as electric vehicles, hybrid vehicles, and autonomous vehicles.

[0067] The implementing entity of the technical solution in this application embodiment can be an electronic device, which can be deployed on a mobile device or connected to the mobile device via wired or wireless means. Of course, the electronic device can also be the mobile device itself. The mobile device can have any appearance, such as a smart vehicle.

[0068] In some embodiments, the electronic device may be an in-vehicle terminal integrated into the vehicle, such as an electronic control unit (ECU), a vehicle control unit (VCU), a microcontroller (MCU), etc., or a device that interacts with the vehicle for data exchange. This application does not limit the specific type of electronic device.

[0069] In some embodiments, the application scenario may also include, for example, networks, storage devices, etc. Networks may include any suitable wired or wireless networks that facilitate the exchange of information and / or data. Storage devices are used to store data, instructions, and / or any other information.

[0070] The following explanation uses a mobile device, specifically an intelligent vehicle (hereinafter referred to as a vehicle), as an example. It is important to note that the application scenarios of the vehicle control method are provided for illustrative purposes only and are not intended to limit the scope of this specification. Those skilled in the art can make various changes and modifications based on the description in this specification. For example, the application scenarios may also include databases, information sources, etc. Furthermore, the application scenarios may be implemented on other devices to achieve similar or different functions. However, these changes and modifications will not depart from the scope of this specification.

[0071] Figure 2 This is an exemplary flowchart illustrating a vehicle control method according to some embodiments of this specification. In some embodiments, process 200 may be performed based on an electronic device. Figure 2 As shown, process 200 includes the following steps.

[0072] Step 210: Based on the vehicle's driving information, perform speed control or torque control on the vehicle's motor.

[0073] Vehicle driving information refers to data related to the vehicle's driving status. For example, vehicle driving information may include vehicle speed, wheel speed, wheel slippage, pedal depth, driving style, road conditions, slope angle, slope resistance, and other data related to vehicle driving.

[0074] Vehicle speed measures the actual speed of a vehicle. Wheel speed measures the rotational speed of each wheel. Wheel slippage determines the adhesion between the wheel and the ground; for example, wheel slippage can be determined based on the ratio of the wheel's linear velocity to the vehicle's actual speed. Pedal depth refers to the degree to which a pedal (e.g., accelerator or brake pedal) is pressed, reflecting the driver's acceleration or braking intention. Driving style refers to information related to the driver's driving habits and style, such as aggressive, smooth, or fuel-efficient. Road surface conditions may include, but are not limited to, the coefficient of friction of the road surface, its slipperiness, and the presence of obstacles. Hill angle measures the angle at which the vehicle travels on a slope, i.e., the degree of inclination of the road. Hill drag measures the resistance a vehicle experiences when traveling on a slope, which may include components of gravity and air resistance.

[0075] Speed ​​control is used to ensure that the output shaft of a motor rotates at a constant or adjustable speed. For example, the motor speed can be maintained at a set value or run according to a predetermined speed curve by adjusting the motor's input voltage or current.

[0076] Torque control is used to ensure that the motor output shaft can provide the required torque to overcome the resistance of the load. For example, the motor output torque can be maintained at a set value or run according to a predetermined torque curve by adjusting the motor's input current.

[0077] In some embodiments, vehicle driving information can be acquired dynamically. For example, the vehicle's electronic devices can periodically acquire vehicle driving information during vehicle operation. For instance, the vehicle's electronic devices can periodically acquire vehicle driving information transmitted by at least one sensor. The at least one sensor includes, but is not limited to, a speed sensor, an acceleration sensor, a wheel speed sensor, a brake pedal sensor, and an accelerator pedal sensor.

[0078] In some embodiments, the vehicle's motor can be controlled by speed or torque in various ways based on the vehicle's driving information. For example, a suitable control mode can be dynamically selected based on the vehicle's driving information using fuzzy control, rule engines, or other algorithms. For instance, if the wheel slippage rate is high and the accelerator pedal depth is large, a torque control mode can be selected to control the torque of the vehicle's motor to reduce slippage; if the vehicle speed is stable and the accelerator pedal depth is small, a speed control mode can be selected to control the speed of the vehicle's motor to optimize energy efficiency.

[0079] In some embodiments, the vehicle's motor may be an electric motor in the vehicle's powertrain system, used to drive the vehicle. For example, the vehicle's motor may be a motor corresponding to one of the vehicle's wheels. Motors include, but are not limited to, permanent magnet synchronous motors, asynchronous motors, switched reluctance motors, DC motors, and brushless DC motors.

[0080] In some embodiments of this specification, the optimal control mode is dynamically selected based on the real-time status of the vehicle and driving needs, thereby improving the vehicle's performance, energy efficiency, and driving safety.

[0081] In some embodiments, based on vehicle driving information, speed control or torque control of the vehicle's motor is performed, including:

[0082] Based on the driving information and the preset fuzzy rule base, the target control mode is determined so as to control the speed or torque of the motor through the target control mode.

[0083] A predefined fuzzy rule base is a set of predefined fuzzy rules used to represent the fuzzy relationship between input variables (driving information) and output variables (the control mode the vehicle needs to use). Fuzzy rules can be expressed in the form of "if-then...". For example:

[0084] Fuzzy rule 1: If the wheel slippage is moderate, the slope resistance is low, and the accelerator pedal depth is shallow, then select the speed control mode.

[0085] Fuzzy rule 2: If the wheel slippage is moderate and the slope resistance is high, then select torque control mode.

[0086] Fuzzy rule 3: If the wheel slippage is moderate, the slope resistance is moderate, and the accelerator pedal depth is shallow, then select the speed control mode.

[0087] The target control mode refers to the optimal control mode determined based on driving information and a fuzzy rule base. The target control mode can be either a speed control mode or a torque control mode, and the specific choice depends on the current driving conditions and driving needs.

[0088] It should be noted that the preset fuzzy rule base can be expanded and updated according to actual usage to adapt to new driving needs and road conditions, giving the vehicle better scalability and maintainability.

[0089] In some embodiments of this specification, the control mode is dynamically adjusted based on different driving information using a fuzzy rule base, enabling the vehicle to better adapt to various complex driving conditions, such as slippery roads, icy roads, and slopes, thereby improving driving safety.

[0090] In some embodiments, determining the target control mode based on driving information and a preset fuzzy rule base includes:

[0091] Based on the membership degree of driving information to the corresponding fuzzy set and preset fuzzy rules, the membership degree of the control mode required by the vehicle to be used to the preset control mode is obtained, thereby determining the target control mode.

[0092] The preset control modes include at least one of torque control mode and speed control mode.

[0093] Fuzzy sets are used to describe a certain degree, level, or state. In some embodiments, each type of driving information (such as wheel slippage, accelerator pedal depth, slope resistance, etc.) can be divided into multiple fuzzy sets based on its characteristics. The specific numerical value of each type of driving information can be mapped to the membership degree of the corresponding fuzzy set through a membership function. The membership degree represents the degree to which the value belongs to a certain fuzzy set, ranging from 0 to 1.

[0094] Preset control mode refers to a predefined control mode.

[0095] In some embodiments, the preset control mode may include a first preset control mode and a second preset control mode, wherein one of the first preset control mode and the second preset control mode is a torque control mode and the other is a speed control mode.

[0096] In some embodiments, matching fuzzy rules are found based on the membership degrees of the input variables; the output membership degrees of all matching fuzzy rules are synthesized to obtain the membership degree of the control mode to be used by the vehicle belonging to the preset control mode; and the target control mode is determined by a defuzzification method. Defuzzification methods may include the centroid method, the maximum membership degree method, etc.

[0097] In some embodiments, determining the target control mode includes:

[0098] Based on the membership degree of the control mode required by the vehicle to belong to the preset control mode, the target control mode is determined by defuzzification rules.

[0099] Defuzzification rules are the process of converting fuzzy output values ​​obtained through reasoning using preset fuzzy rules into precise numerical values.

[0100] It is understandable that defuzzing rules can be a series of predefined rules, or they can be implemented based on defuzzing methods such as centroid method and maximum membership method.

[0101] In some embodiments of this specification, fuzzy reasoning is performed using preset fuzzy rules, which can dynamically select the most suitable control mode based on the current driving information (and its membership degree to the corresponding fuzzy set).

[0102] In some embodiments, determining the target control mode includes:

[0103] Based on the relationship between the membership degree of the first preset control mode and the preset membership degree threshold, the target control mode is determined to be either the first preset control mode or the second preset control mode.

[0104] The preset membership threshold is a critical value used to determine the membership degree of the first preset control mode. The preset membership threshold can be a system default value, an empirical value, a manually preset value, or any combination thereof, and can be set according to actual needs. This manual does not impose any restrictions on it.

[0105] In some embodiments, determining the target control mode as either the first preset control mode or the second preset control mode based on the relationship between the membership degree of the first preset control mode and a preset membership threshold includes:

[0106] If the membership degree of the first preset control mode is greater than or equal to the first preset membership degree threshold, the target control mode is determined to be the first preset control mode.

[0107] The first preset membership threshold is a critical value used to determine whether to switch to the first preset control mode. The first preset membership threshold can be a system default value, an empirical value, a manually preset value, or any combination thereof, and can be set according to actual needs. This manual does not impose any restrictions on this.

[0108] It is understandable that if the membership degree of the first preset control mode is greater than or equal to the first preset membership degree threshold, it means that the membership degree of the first preset control mode is high, and the first preset control mode is tended to be selected as the target control mode. Therefore, the first preset control mode is selected as the target control mode.

[0109] In some embodiments, determining the target control mode as either the first preset control mode or the second preset control mode based on the relationship between the membership degree of the first preset control mode and a preset membership threshold includes:

[0110] If the membership degree of the first preset control mode is less than or equal to the second preset membership degree threshold, the target control mode is determined to be the second preset control mode, wherein the second preset membership degree threshold is less than the first preset membership degree threshold.

[0111] The second preset membership threshold is a critical value used to determine whether to switch to the second preset control mode. The second preset membership threshold can be a system default value, an empirical value, a manually preset value, or any combination thereof, and can be set according to actual needs. This manual does not impose any restrictions on it.

[0112] It is understandable that if the membership degree of the first preset control mode is less than or equal to the second preset membership degree threshold, it means that the membership degree of the first preset control mode is low and insufficient to select the first preset control mode as the target control mode. Therefore, the second preset control mode is selected as the target control mode.

[0113] In some embodiments of this specification, by setting two different preset membership thresholds, more refined decisions can be made within different membership ranges, ensuring that the vehicle's motor control mode can be dynamically adjusted under different driving conditions to optimize vehicle performance.

[0114] In some embodiments, the method further includes:

[0115] When the membership degree of the first preset control mode is within the range corresponding to the first preset membership degree threshold and the second preset membership degree threshold, the control mode used by the vehicle at the current moment is maintained.

[0116] It is understandable that when the membership degree of the first preset control mode is within the range corresponding to the first preset membership degree threshold and the second preset membership degree threshold:

[0117] If the control mode currently in use is the first preset control mode, then the first preset control mode will continue to be used in the next moment.

[0118] If the control mode currently in use is the second preset control mode, then the second preset control mode will continue to be used in the next moment.

[0119] The current moment typically refers to the point in time when the processor acquires, processes, and makes decisions. For example, the current moment could be the point in time when the processor last acquired data, updated control instructions, or made a decision.

[0120] In some embodiments of this specification, by setting two preset membership thresholds and maintaining the current control mode when the membership degree is within the range between the two preset membership thresholds, the frequent switching of control modes can be effectively reduced. In a fuzzy control system, this can improve system stability and driving experience, better handle uncertainty and fuzziness, and ensure that the vehicle maintains its optimal operating state under different working conditions.

[0121] In some embodiments, the method further includes:

[0122] Based on driving information and the corresponding membership function, the membership degree of driving information belonging to the corresponding fuzzy set is determined.

[0123] For any given type of driving information, it corresponds to multiple fuzzy sets, and each of these fuzzy sets corresponds to a membership function. That is, one fuzzy set corresponds to one membership function. Since this type of driving information corresponds to multiple fuzzy sets, it also corresponds to multiple membership functions. By using these multiple membership functions to fuzzify the specific value of this type of driving information, we can obtain the membership degree of this driving information to each of the multiple fuzzy sets it belongs to.

[0124] It is understandable that the sum of the membership degrees of each fuzzy set in the multiple fuzzy sets corresponding to the same type of driving information can be 1.

[0125] In some embodiments, the membership function is used to map driving information to different interval ranges within the range of driving information, and each interval range corresponds to a fuzzy set.

[0126] In some embodiments, among multiple membership functions corresponding to the same type of driving information, each membership function has the same type, but the parameters of each membership function are different, thus ensuring that the same driving information corresponds to multiple membership functions. Membership functions corresponding to different types of driving information can have the same type or different types. Membership function types include triangular membership functions, trapezoidal membership functions, Gaussian membership functions, and bell-shaped membership functions, etc.

[0127] In some embodiments of this specification, determining the membership degree of each type of driving information to the corresponding fuzzy set helps in subsequently determining the most suitable control mode.

[0128] In some embodiments, the driving information includes first driving information, and the method further includes:

[0129] Based on the first driving information, determine the first membership degree of the first driving information to the corresponding first fuzzy set;

[0130] The first driving information is used to indicate the vehicle's driving status information.

[0131] The first driving information may include vehicle speed, wheel speed, wheel slippage, etc.

[0132] The first membership degree indicates the degree to which the first driving information belongs to different first fuzzy sets, ranging from 0 to 1. A first membership degree of 1 indicates that it completely belongs to the first fuzzy set, a first membership degree of 0 indicates that it does not belong to the first fuzzy set at all, and a first membership degree between 0 and 1 indicates that it partially belongs to the first fuzzy set.

[0133] In some embodiments, the specific values ​​of the first driving information can be mapped to the membership degrees of different first fuzzy sets based on the membership function of the first driving information.

[0134] In some embodiments, the first driving information includes the degree of wheel slippage, and the first membership degree includes a membership degree indicating that the degree of wheel slippage belongs to a corresponding range of different degrees.

[0135] In some embodiments, the degree of wheel slippage can be expressed as a numerical value (e.g., slip ratio, rotation ratio, etc.).

[0136] The multiple first fuzzy sets representing the degree of wheel slippage can be different degree ranges. For example, the input range for the degree of wheel slippage is 0-1. Since the three first fuzzy sets corresponding to the degree of wheel slippage are none, medium, and high, and the membership function corresponding to the first fuzzy set "none" is D, the membership function corresponding to the first fuzzy set "medium" is E, and the membership function corresponding to the first fuzzy set "high" is F, the degree of wheel slippage W can be fuzzified using membership function D to obtain the membership degree of wheel slippage W belonging to the first fuzzy set "none"; the degree of wheel slippage W can be fuzzified using membership function E to obtain the membership degree of wheel slippage W belonging to the first fuzzy set "medium"; and the degree of wheel slippage W can be fuzzified using membership function F to obtain the membership degree of wheel slippage W belonging to the first fuzzy set "high". For example, the first membership degree corresponding to the degree of wheel slippage is 50% for none and 50% for medium.

[0137] In some embodiments of this specification, determining the first membership degree corresponding to the degree of wheel slippage helps to analyze the impact of the degree of wheel slippage on the control mode and helps to determine the fuzzy decision quantity in the subsequent process.

[0138] In some embodiments, the driving information includes second driving information, and the method further includes:

[0139] Based on the second driving information, determine the second membership degree of the second driving information to the corresponding second fuzzy set;

[0140] The second driving information is used to indicate the vehicle's driving environment information.

[0141] The second driving information may include road conditions, slope angle, slope resistance, etc.

[0142] The second membership degree indicates the degree to which the second driving information belongs to different second fuzzy sets, ranging from 0 to 1. A second membership degree of 1 indicates that it completely belongs to the second fuzzy set, a second membership degree of 0 indicates that it does not belong to the second fuzzy set at all, and a second membership degree between 0 and 1 indicates that it partially belongs to the second fuzzy set.

[0143] In some embodiments, the specific values ​​of the second driving information can be mapped to the membership degrees of different second fuzzy sets based on the membership function of the second driving information.

[0144] In some embodiments, the second driving information includes ramp resistance, and the second membership degree includes a membership degree indicating that the ramp resistance belongs to a corresponding different resistance range.

[0145] The range of ramp resistance can be divided into different intervals, resulting in multiple second fuzzy sets of ramp resistance, such as multiple resistance ranges. For example, the three second fuzzy sets corresponding to the input range of ramp resistance are small, medium, and large, with the membership function H corresponding to the second fuzzy set "small," I corresponding to the second fuzzy set "medium," and J corresponding to the second fuzzy set "large." Therefore, the ramp resistance U can be fuzzified using the membership function H to obtain the membership degree of ramp resistance U belonging to the second fuzzy set "small"; the membership function I can be fuzzified using the membership function I to obtain the membership degree of ramp resistance U belonging to the second fuzzy set "medium"; and the membership function J can be fuzzified using the membership function J to obtain the membership degree of ramp resistance U belonging to the second fuzzy set "large." For example, the second membership degree corresponding to the ramp resistance is 50% for medium and 50% for large.

[0146] In some embodiments of this specification, determining the second membership degree corresponding to the ramp resistance helps to analyze the impact of ramp resistance on the control mode and facilitates the subsequent determination of fuzzy decision quantities.

[0147] In some embodiments, the driving information includes third driving information, and the method further includes:

[0148] Based on the third driving information, determine the third membership degree of the third driving information to the corresponding third fuzzy set;

[0149] The third driving information is used to indicate the vehicle's driving behavior information.

[0150] Third-party driving information may include driving behavior (accelerator pedal depth, brake pedal depth, etc.) and driving style.

[0151] The third membership degree indicates the degree to which the third driving information belongs to different third fuzzy sets, ranging from 0 to 1. A third membership degree of 1 indicates that it completely belongs to the third fuzzy set, a third membership degree of 0 indicates that it does not belong to the third fuzzy set at all, and a third membership degree between 0 and 1 indicates that it partially belongs to the third fuzzy set.

[0152] In some embodiments, the specific values ​​of the third driving information can be mapped to the membership degrees of different third fuzzy sets based on the membership function of the third driving information.

[0153] In some embodiments, the third driving information includes pedal depth, and the third membership degree includes a membership degree indicating that the pedal depth belongs to a corresponding different depth range.

[0154] For example, the pedal depth is the accelerator pedal depth. The range of the accelerator pedal depth can be divided into different intervals to obtain multiple third fuzzy sets of the accelerator pedal depth, such as multiple depth ranges. For example, the three third fuzzy sets corresponding to the range of the accelerator pedal depth are shallow, medium, and deep. The range of the accelerator pedal depth is 0-4, and the membership function corresponding to the third fuzzy set "shallow" is A, the membership function corresponding to the third fuzzy set "medium" is B, and the membership function corresponding to the third fuzzy set "deep" is C. Therefore, the accelerator pedal depth Y (e.g., Y=1) can be fuzzified using membership function A to obtain a membership degree of 0.5 for the fuzzy set "shallow"; the accelerator pedal depth Y can be fuzzified using membership function B to obtain a membership degree of 0.5 for the fuzzy set "medium"; and the accelerator pedal depth Y can be fuzzified using membership function C to obtain a membership degree of 0 for the fuzzy set "deep". For example, the third membership degree corresponding to the accelerator pedal depth is 50% shallow and 50% medium.

[0155] In some embodiments of this specification, determining the third membership degree corresponding to the pedal depth helps to analyze the impact of the pedal depth on the control mode and facilitates the subsequent determination of fuzzy decision quantities.

[0156] In some embodiments, the driving information includes first driving information, second driving information, and third driving information. Based on the membership degree of the driving information to the corresponding fuzzy set and a preset fuzzy rule, the membership degree of the control mode to be used by the vehicle to the preset control mode is obtained, including:

[0157] Based on the first membership degree corresponding to the first driving information, the second membership degree corresponding to the second driving information, and the third membership degree corresponding to the third driving information, the membership degree of the control mode to be used by the vehicle belonging to the preset control mode is obtained by using preset fuzzy rules.

[0158] In some embodiments, after determining the membership degree of each driving information to its corresponding fuzzy set according to the above steps, fuzzy sets with a membership degree of non-zero are selected from the fuzzy sets corresponding to these driving information to obtain at least one target fuzzy set. Then, the fuzzy sets corresponding to different driving information in the at least one target fuzzy set are combined to obtain a fuzzy set combination result. Subsequently, a fuzzy rule matching the fuzzy set combination is selected from a stored preset fuzzy rule base to obtain a target fuzzy rule.

[0159] For example, the accelerator pedal depth Y has a membership degree of 0.5 in its corresponding fuzzy set "shallow", 0.5 in its corresponding fuzzy set "medium", and 0 in its corresponding fuzzy set "deep". The wheel slippage degree W has a membership degree of 0.5 in its corresponding fuzzy set "none", 0.5 in its corresponding fuzzy set "medium", and 0 in its corresponding fuzzy set "high". The slope resistance U has a membership degree of 0 in its corresponding fuzzy set "small", 0.5 in its corresponding fuzzy set "medium", and 0.5 in its corresponding fuzzy set "large". In other words, the accelerator pedal depth has a membership degree of non-zero in its corresponding fuzzy sets "shallow" and "medium"; the wheel slippage degree has a membership degree of non-zero in its corresponding fuzzy sets "none" and "medium"; and the slope resistance degree has a membership degree of non-zero in its corresponding fuzzy sets "medium" and "large". At this point, the obtained at least one target fuzzy set includes wheel slippage of no degree, wheel slippage of medium degree, accelerator pedal depth of shallow degree, accelerator pedal depth of medium degree, slope resistance of medium degree, and slope resistance of high degree. Then, the fuzzy sets corresponding to different driving information in the at least one target fuzzy set are combined to obtain 8 fuzzy set combination results: wheel slippage of no degree and accelerator pedal depth of shallow degree and slope resistance of medium degree; wheel slippage of no degree and accelerator pedal depth of shallow degree and slope resistance of high degree; wheel slippage of no degree and accelerator pedal depth of medium degree and slope resistance of medium degree; wheel slippage of no degree and accelerator pedal depth of medium degree and slope resistance of high degree; wheel slippage of medium degree and accelerator pedal depth of shallow degree and slope resistance of medium degree; wheel slippage of medium degree and accelerator pedal depth of shallow degree and slope resistance of high degree; wheel slippage of medium degree and accelerator pedal depth of medium degree and slope resistance of medium degree; wheel slippage of medium degree and accelerator pedal depth of medium degree and slope resistance of high degree.

[0160] Then, a fuzzy rule that matches the combination result of the fuzzy set is selected from the stored preset fuzzy rule library as the target fuzzy rule. Based on the membership degree of each driving information to its corresponding fuzzy set, fuzzy logic reasoning is performed on the target fuzzy rule according to the relevant algorithm to obtain the membership degree of the control mode that the vehicle needs to use to the preset control mode, so as to determine the target control mode.

[0161] In some embodiments, the membership degree of the control mode to be used by the vehicle belonging to a preset control mode can be defuzzified using the center of gravity method to obtain the target control mode. Of course, in practical applications, various other methods can be used for defuzzification. For example, the maximum membership degree method, the median method, etc., are not limited to these methods in this application embodiment.

[0162] In some embodiments, the preset fuzzy rule base is shown in Table 1:

[0163] Table 1

[0164]

[0165] In Table 1, S represents the speed control mode. In each column, light, medium, and dark represent the fuzzy sets corresponding to accelerator pedal depth, and in each row, small, medium, and large represent the fuzzy sets corresponding to slope resistance. T represents the torque control mode. For example, considering wheel slippage of 0.5 (no slippage) and 0.5 (medium slippage), accelerator pedal depth of 0.5 (light) and 0.5 (medium), and slope resistance of 0.5 (medium resistance) and 0.5 (high resistance), based on a preset fuzzy rule base, it can be determined that the control mode the vehicle needs to use belongs to the speed control mode with a membership degree of 0.125, and the control mode the vehicle needs to use belongs to the torque control mode with a membership degree of 0.875.

[0166] In some embodiments of this specification, the membership degree of the control mode to be used by the vehicle is determined by the membership degree corresponding to the pedal depth, the slope resistance, and the degree of wheel slippage. This helps to analyze the influence of pedal depth, slope resistance, and the degree of wheel slippage on the control mode and helps to accurately predict the most suitable control mode for the vehicle.

[0167] In some embodiments, the preset fuzzy rules are determined in the following ways:

[0168] Based on the correlation between sample driving information and sample control mode, multiple sets of fuzzy rules are obtained as preset fuzzy rules.

[0169] In some embodiments, a large amount of sample driving information and corresponding sample control modes can be collected. For example, actual driving information and corresponding control modes, including wheel slippage, accelerator pedal depth, vehicle speed, and slope resistance, can be collected from the vehicle's sensors and electronic control units as sample driving information and sample control modes. Another example is generating simulated data through vehicle dynamics models and simulation software. Yet another example is obtaining driving information and corresponding control modes for typical driving scenarios based on expert experience.

[0170] In some embodiments, clustering algorithms (such as K-Means, Fuzzy C-Means, etc.) can be used to cluster the sample driving information to obtain a fuzzy set corresponding to multiple sample driving information; association rule mining algorithms (such as Apriori, FP-Growth, etc.) can be used to determine the degree of influence or association between sample driving information and sample control mode, and generate preset fuzzy rules.

[0171] In some embodiments, pre-defined fuzzy rules can be determined by combining expert experience and knowledge.

[0172] Figure 3 This is an exemplary schematic diagram of yet another vehicle control method according to some embodiments of this specification.

[0173] In some embodiments, such as Figure 3 As shown, based on the characteristics of input variables (driving information) and output variables (controlled object, i.e., control mode), a membership function corresponding to each input variable can be designed to fuzz the input variables to different degrees. The membership functions of the above-mentioned input variables are initially calibrated on the vehicle, including but not limited to the membership functions of accelerator pedal depth in each driving mode, i.e., the range corresponding to shallow, medium, and deep fuzzy sets; the membership function of vehicle speed, i.e., the range corresponding to low, medium, and high fuzzy sets; the membership function of wheel slippage degree, i.e., the range corresponding to none, medium, and high fuzzy sets; and the membership function of road surface adhesion coefficient, i.e., the range corresponding to low, medium, and high fuzzy sets, etc.

[0174] In some embodiments, based on input variables and a preset fuzzy rule base, the membership degree of the control mode to be used by the vehicle to a preset control mode can be calculated to obtain the fuzzy decision quantity. A fuzzy rule base for the membership function of each input variable is designed, i.e., if the wheel slippage is zero, torque control mode is used; if the wheel slippage is not zero, speed control or torque control is determined based on the wheel slippage, accelerator pedal depth, and slope resistance.

[0175] In some embodiments, defuzzification rules can be designed: based on the calculated fuzzy decision quantities, the defuzzification rules yield the control mode to be adopted at time k, i.e., the torque fuzzy decision quantity or the speed fuzzy decision quantity. Further, based on the defuzzification rules, a reference control quantity for the calculated speed fuzzy decision quantity or torque fuzzy decision quantity is obtained.

[0176] In some embodiments, after designing a preset fuzzy rule base, the mapping relationship of the preset fuzzy rule base and the defuzzification rules can be optimized through vehicle calibration under various working conditions, thereby improving the control effect of fuzzy logic.

[0177] In some embodiments, the fuzzy decision quantity α may include the membership degree of the speed control mode or the membership degree of the torque control mode, which can be determined according to the actual situation. For example, the fuzzy decision quantity α can be designed as follows:

[0178]

[0179] Speed ​​control ratio is the membership degree of the speed control mode; torque control ratio is the membership degree of the torque control mode.

[0180] Figure 4 This is an exemplary schematic diagram illustrating deblurring rules according to some embodiments of this specification.

[0181] In some embodiments, such as Figure 4As shown, if the control mode required by the vehicle belongs to the preset control mode with a membership degree of 0.125 for the speed control mode and 0.875 for the torque control mode, then the fuzzy decision quantity α can be determined from the 0.125 speed control mode and the 0.875 torque control mode. Based on the fuzzy decision quantity α at the current time, the target control mode is determined through defuzzification rules: if α∈[0, 0.3), then the speed control mode is selected; if α∈[0.3, 0.6) and if the speed control mode is at time k-1, then the speed control mode is selected; if α∈[0.3, 0.6) and if the torque control mode is at time k-1, then the torque control mode is selected; if α∈[0.6, 1), then the torque control mode is selected. The above defuzzification rules can also be optimized based on the calibration results.

[0182] In some embodiments, the target control mode is a torque control mode, which can obtain a target torque value based on a preset torque value to control the vehicle's motor. The preset torque value can be the design-required torque value or other torque values ​​under the torque control mode. The target torque value can be the design-required torque value, the design-required torque value + a, or the design-required torque value * b; it can also be adjusted according to control requirements. a and b can be preset values ​​or default values, etc. The defuzzification result can include the target control mode and the target parameter values ​​under the target control mode (e.g., target torque value or target speed value, etc.).

[0183] It should be noted that the above description of the process is for illustrative purposes only and does not limit the scope of this specification. Those skilled in the art can make various modifications and changes to the process under the guidance of this specification. However, these modifications and changes remain within the scope of this specification.

[0184] One or more embodiments of this specification also provide a controller for implementing any of the vehicle control methods provided in the embodiments of this application.

[0185] A controller is an electronic device that manages and controls the operation of a motor. For example, a controller can adjust the motor's output voltage to ensure it remains within a set range (e.g., 13.5V-14.5V). Another example is that a controller can dynamically adjust the motor's output power based on the vehicle's power requirements. Yet another example is that a controller can control the motor's speed or torque and monitor its operating status, such as overheating, overload, or short circuit.

[0186] In some embodiments, the controller can communicate with the vehicle controller or other systems to coordinate the operating status of the motor and ensure the normal operation of the vehicle's electronic control system.

[0187] In some embodiments, the controller may be a programmable logic device (PLD), a field programmable gate array (FPGA), a controller, a microcontroller, a microprocessor, or other electronic components.

[0188] Figure 5 This is a schematic diagram of the structure of an electronic control system according to some embodiments of this specification.

[0189] like Figure 5 As shown, one or more embodiments of this specification also provide a structural schematic diagram of a vehicle electronic control system. This vehicle electronic control system may include a controller 510 and a motor 520 connected to the controller.

[0190] The controller 510 can be used to execute the steps in the embodiments of the above vehicle control method. For the specific implementation of these modules and more details, please refer to the corresponding method section. They will not be described in detail here.

[0191] For details on the implementation of each of the above operations, please refer to the previous examples, which will not be repeated here.

[0192] Figure 6 This is a schematic diagram of the structure of an electronic device according to some embodiments of this specification. For example... Figure 6 As shown, the electronic device 600 may include a processor 601 and a memory 602. The electronic device 600 may also include one or more of a multimedia component 603, an input / output (I / O) component 604, and a communication component 605. In this embodiment, the electronic device 600 may be a device that implements the vehicle control method provided in this embodiment.

[0193] The processor 601 controls the overall operation of the electronic device 600 to complete all or part of the steps in the vehicle control method described above. The memory 602 stores various types of data to support the operation of the electronic device 600. This data may include, for example, instructions for any application or method operating on the electronic device 600, and application-related data such as contact data, sent and received messages, pictures, audio, video, etc. The memory 602 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as Static Random Access Memory (SRAM), Electrically Erasable Programmable Read Only Memory (EEPROM), Erasable Programmable Read Only Memory (EPROM), Programmable Read Only Memory (PROM), Read Only Memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk. The multimedia component 603 may include a screen and audio components. The screen may be, for example, a touchscreen, and the audio component is used to output and / or input audio signals. For example, the audio component may include a microphone for receiving external audio signals. The received audio signals may be further stored in memory 602 or transmitted via communication component 605. The audio component also includes at least one speaker for outputting audio signals. I / O component 604 provides an interface between processor 601 and other interface modules, such as a keyboard, mouse, buttons, etc. These buttons may be virtual or physical. Communication component 605 is used for wired or wireless communication between the electronic device 600 and other devices. Wireless communication includes, for example, Wi-Fi, Bluetooth, Near Field Communication (NFC), 2G, 3G, 4G, Narrow Band Internet of Things (NB-IoT), Enhanced Machine Type Communication (eMTC), or other 5G technologies, or combinations thereof, without limitation. Therefore, the corresponding communication component 605 may include: a Wi-Fi module, a Bluetooth module, an NFC module, etc.

[0194] In one exemplary embodiment, the electronic device 600 may be implemented by one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field-programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors, or other electronic components to perform the vehicle control method described above.

[0195] In another exemplary embodiment, a computer-readable storage medium is also provided, on which a computer program is stored, the program instructions of which, when executed by a processor, implement the steps of the vehicle control method described above. For example, the computer-readable storage medium may be the memory 602 described above that includes program instructions, which may be executed by the processor 601 of the electronic device 600 to implement or execute the various methods, steps, and logic block diagrams disclosed in the embodiments of this application;

[0196] Alternatively, when the instructions are executed by a computer, they may be used to implement or execute the methods, steps, and logic diagrams disclosed in the embodiments of this application.

[0197] In another exemplary embodiment, a computer program product is also provided, including a computer program or instructions that, when executed by a processor, implement the steps of the vehicle control method described above. For example, the computer program product may be the aforementioned memory 602 including the computer program, which may be executed by the processor 601 of the electronic device 600 to implement or execute the methods, steps, and logic diagrams disclosed in the embodiments of this application.

[0198] Alternatively, when the instructions are executed by a computer, they may be used to implement or execute the methods, steps, and logic diagrams disclosed in the embodiments of this application.

[0199] Figure 7 This is an exemplary schematic diagram of a vehicle according to some embodiments of this specification.

[0200] like Figure 7As shown, this application also provides a vehicle equipped with the electronic equipment provided in any of the above embodiments. The electronic equipment is used to execute the vehicle control method provided in any of the above embodiments. Alternatively, it may include the controller, the electronic control system, or execute the steps of any of the methods provided in the embodiments of this application. The vehicle may be a gasoline-powered vehicle, a plug-in hybrid electric vehicle, or a new energy vehicle, etc., and this specification does not specifically limit it.

[0201] In one embodiment, the vehicle can be configured for fully or partially autonomous driving. For example, the vehicle can control itself while in autonomous driving mode, and can determine the current state of the vehicle and its surrounding environment through human intervention, determine the possible behaviors of at least one other vehicle in the surrounding environment, and determine the confidence level corresponding to the probability of that other vehicle performing a possible behavior, and control the vehicle based on the determined information. When the vehicle is in autonomous driving mode, it can be configured to operate without human interaction.

[0202] In the description of this application, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Therefore, a feature defined as "first" or "second" may explicitly or implicitly include one or more features. In the description of this application, "multiple" means two or more, unless otherwise explicitly specified.

[0203] The embodiments, implementation methods, and related technical features of this application can be combined and substituted for each other without conflict.

[0204] The above are merely preferred embodiments of this application and are not intended to limit this application in any way. Although the descriptions of each embodiment in this application have different focuses, and the parts not described in detail in a certain embodiment can be referred to the relevant embodiments of other embodiments, any simple modifications, equivalent changes and modifications made to the above embodiments based on the technical essence of this application without departing from the content of the technical solution of this application shall still fall within the scope of the technical solution of this application.

Claims

1. A vehicle control method characterized by, The method comprises: Based on the driving information of the vehicle, the motor of the vehicle is controlled in speed or torque.

2. The method of claim 1, wherein, The motor of the vehicle is controlled in speed or torque based on the driving information of the vehicle, comprising: According to the driving information and the preset fuzzy rule base, a target control mode is determined to control the motor in speed or torque through the target control mode.

3. The method of claim 2, wherein, The target control mode is determined according to the driving information and the preset fuzzy rule base, comprising: Based on the membership of the driving information belonging to the corresponding fuzzy set and the preset fuzzy rule, the membership of the control mode required by the vehicle belonging to the preset control mode is obtained to determine the target control mode, Wherein, the preset control mode includes at least one of the torque control mode and the speed control mode.

4. The method of claim 3, wherein, The target control mode is determined based on the membership of the control mode required by the vehicle belonging to the preset control mode. The target control mode is determined based on the membership of the control mode required by the vehicle belonging to the preset control mode.

5. The method of claim 3, wherein, The target control mode is determined based on the relationship between the membership of the first preset control mode and the preset membership threshold. The target control mode is determined based on the relationship between the membership of the first preset control mode and the preset membership threshold, comprising:

6. The method of claim 5, wherein, In the case that the membership of the first preset control mode is greater than or equal to the first preset membership threshold, the target control mode is determined as the first preset control mode. The target control mode is determined based on the relationship between the membership of the first preset control mode and the preset membership threshold, comprising:

7. The method of claim 6, wherein, In the case that the membership of the first preset control mode is less than or equal to the second preset membership threshold, the target control mode is determined as the second preset control mode, wherein the second preset membership threshold is less than the first preset membership threshold. The method further comprises:

8. The method of claim 7, wherein, When the membership of the first preset control mode is within the interval range corresponding to the first preset membership threshold and the second preset membership threshold, the control mode used by the vehicle at the current time is maintained. The method further comprises:

9. The method of claim 3, wherein, Based on the driving information and the corresponding membership function, the membership of the driving information belonging to the corresponding fuzzy set is determined. The membership function is used to map the driving information to different interval ranges within the range of the driving information, and each interval range corresponds to a fuzzy set.

10. The method of claim 9, wherein, The driving information includes first driving information, and the method further comprises:

11. The method of claim 3, wherein, Based on the first driving information, the first membership of the first driving information belonging to the corresponding first fuzzy set is determined; Wherein, the first driving information is used to indicate the driving state information of the vehicle. The first driving information includes the degree of wheel slip, and the first membership includes the membership indicating that the degree of wheel slip belongs to the corresponding different degree range.

12. The method of claim 11, wherein, The driving information includes second driving information, and the method further comprises:

13. The method of claim 3, wherein, ​ determine, based on the second driving information, a second membership degree of the second driving information belonging to a corresponding second fuzzy set; The second driving information is used to indicate driving environment information of the vehicle.

14. The method of claim 13, wherein, The second driving information includes slope resistance, and the second membership degree includes a membership degree indicating that the slope resistance belongs to a corresponding different resistance range.

15. The method of claim 3, wherein, The driving information includes third driving information, and the method further includes: determine, based on the third driving information, a third membership degree of the third driving information belonging to a corresponding third fuzzy set; The third driving information is used to indicate driving behavior information of the vehicle.

16. The method of claim 15, wherein, The third driving information includes pedal depth, and the third membership degree includes a membership degree indicating that the pedal depth belongs to a corresponding different depth range.

17. The method according to any one of claims 11 to 16, characterized in that, The driving information includes first driving information, second driving information, and third driving information, The membership degree of the control mode needed by the vehicle belonging to the preset control mode is obtained based on the membership degrees of the driving information belonging to the corresponding fuzzy sets and a preset fuzzy rule, and the method includes: The membership degree of the control mode needed by the vehicle belonging to the preset control mode is obtained based on the first membership degree corresponding to the first driving information, the second membership degree corresponding to the second driving information, and the third membership degree corresponding to the third driving information, and the preset fuzzy rule.

18. The method of claim 2, wherein, The preset fuzzy rule is determined by the following method: A plurality of fuzzy rules are obtained as the preset fuzzy rule based on an association relationship between sample driving information and sample control modes.

19. The method according to any one of claims 1 to 18, characterized in that, The motor is a motor corresponding to a wheel.

20. A controller characterized by A vehicle control method according to any one of claims 1 to 19.

21. An electric control system characterized by, A controller according to claim 20 and a motor connected to the controller.

22. An electronic device, comprising: It includes: a memory having a computer program stored thereon; a processor configured to execute the computer program in the memory to implement the steps of the method according to any one of claims 1 to 19.

23. A computer readable storage medium having stored thereon a computer program, characterized in that, The computer program is executed by the processor to implement the steps of the method according to any one of claims 1 to 19.

24. A computer program product, characterised in that, It includes a computer program or instructions, which are executed by the processor to implement the steps of the method according to any one of claims 1 to 19.

25. A vehicle characterized by It includes the controller according to claim 20, or the electronic control system according to claim 21, or the electronic device according to claim 22, or the steps of the method according to any one of claims 1 to 19.

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