Hybrid electric vehicle torque control method, system, medium and equipment

Abstract

The invention provides a torque control method, system, medium and equipment for a hybrid electric vehicle, and the method comprises the steps: creating a fuzzy control tool box, taking the total torque of the whole vehicle, the SOC value of a battery and the rotating speed of a motor as input, and taking the output torque of an engine as output; setting fuzzy subsets for the total torque of the whole vehicle, the SOC value of the battery, the rotating speed of the motor and the output torque of the engine, and constructing a membership function; fuzzy rules of the total torque of the whole vehicle, the SOC value of the battery, the rotating speed of the motor and the output torque of the engine are established; constructing a fuzzy rule base, and performing fuzzy reasoning based on a membership function, a fuzzy reasoning algorithm and the fuzzy rule base to obtain a fuzzy quantity; converting the fuzzy quantity into a clear value through a defuzzification algorithm; a motor output torque is calculated based on the clearness value. The torque distribution rule is dynamically adjusted according to the relation between the rotating speed and the efficiency of the motor. The output torque of the motor is reduced at high rotating speed, and the torque is improved at low rotating speed to ensure that the motor is always in a high-efficiency interval.

Description

Technical Field

[0001] The present invention relates to the field of automobile control technology, and in particular to a hybrid electric vehicle torque control method, system, medium and equipment. Background Art

[0002] PHEV energy management strategies already implemented in real vehicles are typically static closed-value methods designed based on engineering experience. These methods offer simple and straightforward control but limited flexibility, failing to fully leverage the performance advantages of hybrid vehicles. More advanced methods, such as dynamic programming and fuzzy control, also have their limitations and hinder their practical application to real-world vehicle control under uncertain operating conditions. Furthermore, methods such as transient optimization and neural networks are limited by the computational performance of current automotive electronic control systems.

[0003] Among existing technologies, traditional rule-based or model-based allocation strategies rely on precise system modeling, making them difficult to adapt to the nonlinear coupling characteristics of dynamic conditions. This results in insufficient real-time performance and high computational complexity. Furthermore, existing algorithms often employ a single-objective optimization framework, which lacks compatibility between robust torque distribution and energy efficiency optimization. This can lead to control lag or oscillation, especially under complex road conditions. Summary of the Invention In view of the deficiencies in the prior art, the present invention aims to provide a torque control method for a hybrid electric vehicle, aiming to solve the technical problems mentioned in the background technology.

[0004] In order to achieve the above object, the present invention is implemented through the following technical solutions: A method for controlling torque of a hybrid electric vehicle comprises the following steps: Create a fuzzy control toolbox, input the required vehicle total torque, battery SOC value and motor speed into the fuzzy control toolbox as input, and use the engine output torque as output; Setting fuzzy subsets for the vehicle total torque, battery SOC value, motor speed, and engine output torque respectively to construct a membership function; Establishing fuzzy rules between the total torque of the vehicle, the battery SOC value, the motor speed and the engine output torque; Constructing a fuzzy rule base according to the fuzzy rules, and performing fuzzy reasoning based on the membership function, the fuzzy reasoning algorithm and the fuzzy rule base in the fuzzy control toolbox to obtain a fuzzy quantity; Converting the fuzzy quantity into a crisp value by a defuzzification algorithm; The motor output torque is calculated based on the clean value of the engine output torque and combined with the total vehicle torque.

[0005] According to one aspect of the above technical solution, the fuzzy control toolbox is created based on MATLAB.

[0006] According to one aspect of the above technical solution, the total torque of the whole vehicle includes 5 fuzzy subsets, the fuzzy set of the total torque of the whole vehicle is {NS, S, ZERO, B, PB}, and the data domain in the fuzzy set of the total torque of the whole vehicle is [-1, 1]; the battery SOC value includes 5 fuzzy subsets, the fuzzy set of the battery SOC value is {VL, L, M, H, VH}, and the data domain in the fuzzy set of the battery SOC value is [0, 1]; the motor speed includes 2 fuzzy subsets, the fuzzy set of the motor speed is {L, H}, and the data domain in the fuzzy set of the motor speed is [0, 1]; the engine output torque includes 5 fuzzy subsets, the fuzzy set of the engine output torque is {VS, S, M, B, VB}, and the data domain in the fuzzy set of the engine output torque is [0, 1.4].

[0007] According to one aspect of the above technical solution, the total vehicle torque, battery SOC value and engine output torque all adopt triangular membership functions, and the motor speed adopts a trapezoidal membership function.

[0008] According to one aspect of the above technical solution, the expression of the fuzzy rule base is: R1: If x in =A1, and y in1 =B1, and y in2 =C1,then=D1 R2: If x in =A2, and y in1 =B2, and y in2 =C2,then=D2 … R i :If x in =A i , and y in1 =B i , and y in2 =C i ,thenz=D i ; Among them, R i represents the fuzzy rule, x in =A i Indicates the total torque of the vehicle as input, y in1 =B i Indicates the battery SOC value as input, y in2 =C i represents the motor speed as input, Di Indicates the engine output torque as output.

[0009] According to one aspect of the above technical solution, the expression of the fuzzy inference algorithm is: ; ; ; Where i∈[1,n].

[0010] According to one aspect of the above technical solution, the specific steps of converting the fuzzy value into a clear value by using a defuzzification algorithm include: The fuzzy value D of each engine output torque i Preset clear representative value u i ; The fuzzy rule R that is triggered under the current input i The trigger strength k i as weight; The clarity value U is calculated by the defuzzification algorithm; ; Where m represents the number of fuzzy rules.

[0011] The present invention also provides a hybrid electric vehicle torque control system, comprising: Create a module: Create a fuzzy control toolbox, input the required vehicle total torque, battery SOC value and motor speed into the fuzzy control toolbox as input, and use the engine output torque as output; A construction module: setting fuzzy subsets for the total torque of the vehicle, the battery SOC value, the motor speed, and the engine output torque respectively to construct a membership function; Establishing module: establishing fuzzy rules between the total torque of the vehicle, the battery SOC value, the motor speed and the engine output torque; Reasoning module: constructing a fuzzy rule base according to the fuzzy rules, and performing fuzzy reasoning based on the membership function, the fuzzy reasoning algorithm and the fuzzy rule base in the fuzzy control toolbox to obtain fuzzy quantities; Defuzzification module: converts the fuzzy value into a clear value through a defuzzification algorithm; Calculation module: Calculates the motor output torque based on the clear value of the engine output torque and the total vehicle torque.

[0012] The present invention also provides a storage medium storing a computer program, which implements the hybrid electric vehicle torque control method as described above when executed by a processor.

[0013] The present invention also provides an electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the hybrid vehicle torque control method as described above is implemented.

[0014] Compared with the prior art, the present invention has the following beneficial effects: A Mamdani-type fuzzy inference system is used to dynamically adjust the torque distribution strategy between the engine and the motor by analyzing the required total vehicle torque, battery SOC value and motor speed in real time. By simplifying the dual output to a single output, the control complexity is reduced, while ensuring the optimization of energy distribution and improving the overall efficiency of the hybrid system. The present invention uses the battery SOC value as a key input variable, and the control target is clearly to maintain the SOC value within the shallow charge and shallow discharge range. This strategy not only avoids the damage to the battery life caused by overcharge and over-discharge, but also ensures the stability of energy reserves, and improves the reliability and service life of the battery system. The present invention dynamically adjusts the torque distribution rules based on the relationship between motor speed and efficiency. The motor output torque is reduced at high speeds and increased at low speeds to ensure that the motor always operates in the high efficiency range. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 Flowchart of the torque control method for a hybrid vehicle according to the first embodiment of the present invention; Figure 2 1 is a flowchart of a torque control method for a hybrid vehicle according to a first embodiment of the present invention; Figure 3 This is a structural block diagram of the fuzzy controller in the first embodiment of the present invention; FIG4( a ) is a schematic diagram of a membership function of a battery SOC value in the first embodiment of the present invention; FIG4( b ) is a schematic diagram of the membership function of the total torque of the vehicle in the first embodiment of the present invention; FIG4( c ) is a schematic diagram of the membership function of the motor speed in the first embodiment of the present invention; FIG4( d ) is a schematic diagram of the membership function of the engine output torque in the first embodiment of the present invention; Figure 5 Schematic diagram of the output surface of the fuzzy controller in the first embodiment of the present invention; Figure 6 This is a structural block diagram of a hybrid vehicle torque control system according to a second embodiment of the present invention; Figure 7 This is a structural block diagram of an electronic device in a third embodiment of the present invention; The following specific embodiments will further illustrate the present invention in conjunction with the above-mentioned drawings. DETAILED DESCRIPTION

[0016] To facilitate understanding of the present invention, the present invention will be described more fully below with reference to the accompanying drawings. The drawings illustrate several embodiments of the present invention. However, the present invention may be implemented in many different forms and is not limited to the embodiments described herein. Rather, these embodiments are provided to provide a more thorough and comprehensive understanding of the present invention.

[0017] It should be noted that when an element is referred to as being "fixed to" another element, it may be directly on the other element or there may be an intermediate element. When an element is referred to as being "connected to" another element, it may be directly connected to the other element or there may be an intermediate element. The terms "vertical," "horizontal," "left," "right," and similar expressions used herein are for illustrative purposes only.

[0018] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one skilled in the art to which this invention pertains. The terms used in this specification of the present invention are for the purpose of describing specific embodiments only and are not intended to limit the present invention. The term "and / or" as used herein includes any and all combinations of one or more of the associated listed items.

[0019] See also Figure 1 , which shows a torque control method for a hybrid vehicle according to a first embodiment of the present invention, comprising the following steps: S10, creating a fuzzy control toolbox, inputting the required vehicle total torque, battery SOC value, and motor speed into the fuzzy control toolbox as input, and using the engine output torque as output; S20, setting fuzzy subsets for the vehicle total torque, battery SOC value, motor speed, and engine output torque respectively to construct a membership function; S30, establishing a fuzzy rule between the total torque of the vehicle, the battery SOC value, the motor speed and the engine output torque; S40, constructing a fuzzy rule base according to the fuzzy rules, and performing fuzzy reasoning based on the membership function, the fuzzy reasoning algorithm and the fuzzy rule base in the fuzzy control toolbox to obtain a fuzzy quantity; S50, converting the fuzzy value into a clear value through a defuzzification algorithm; S60: Calculate the motor output torque based on the clear value of the engine output torque and the total vehicle torque.

[0020] It can be understood that the present invention adopts a Mamdani-type fuzzy inference system, which dynamically adjusts the torque distribution strategy between the engine and the motor by analyzing the required total vehicle torque, battery SOC value and motor speed in real time. By simplifying the dual output to a single output, the control complexity is reduced, while ensuring the optimization of energy distribution and improving the overall efficiency of the hybrid system; the present invention uses the battery SOC value as a key input variable, and the control target is clearly to maintain the SOC value within the shallow charge and shallow discharge range. This strategy not only avoids the damage to the life of the battery caused by overcharging and over-discharging, but also ensures the stability of energy reserves, and improves the reliability and service life of the battery system; the present invention dynamically adjusts the torque distribution rules according to the relationship between the motor speed and efficiency. The motor output torque is reduced at high speeds and increased at low speeds to ensure that the motor always operates in the high efficiency range.

[0021] Specifically, in this embodiment, the fuzzy control toolbox in step S10 is created based on MATLAB. MATLAB provides a complete fuzzy control toolbox for designing, simulating, and optimizing fuzzy control systems.

[0022] It should be noted that if Figure 2 As shown in the figure, the designed fuzzy control strategy is used to optimally distribute engine and motor output torque. The torque variables involved include the vehicle's required torque, engine output torque, and motor output torque. The vehicle's required torque is calculated by the vehicle dynamics model based on parameters such as the required speed and transmission ratio and output to the fuzzy controller. The fuzzy controller then uses fuzzy inference to determine the engine output torque and finally calculates the motor output torque.

[0023] like Figure 3 As shown, the present invention simplifies the output of the fuzzy control toolbox from the dual outputs of engine output torque and motor output torque to a single output of engine output torque, with motor output torque serving as an indirect control variable. Specifically, the vehicle's total torque, battery SOC value, and motor speed are fed into the fuzzy control toolbox as inputs, and the engine output torque is then calculated based on the engine output torque to obtain the motor output torque.

[0024] The battery state of charge (SOC) value is currently commonly used to represent the battery's charge level. Considering that the battery's state of charge not only significantly affects the operating conditions of each power source, but also its service life, the battery pack's energy input and output must adhere to the principle of shallow charge and shallow discharge. Therefore, the fuzzy controller's primary control objective for the battery is to maintain a stable SOC value, controlling it between 0.3 and 0.8.

[0025] As for the motor speed, as the motor speed increases, the high efficiency range of the motor is gradually compressed towards the low output torque direction. Therefore, when the motor speed is high, the output torque of the motor can be lowered, and when the motor speed is low, the output torque of the motor can be higher, thereby improving the working efficiency of the motor to a certain extent.

[0026] Through the above three points, we can construct a flow chart of the hybrid vehicle torque control method based on fuzzy controller, as shown in Figure 2 As shown; the fuzzy controller built using MATLAB's fuzzy control toolbox is as follows Figure 3 As shown, the three inputs are the total vehicle torque T req , battery SOC value and engine speed N m , engine output torque T e For single output.

[0027] Furthermore, in step S20, the total vehicle torque includes five fuzzy subsets, the fuzzy set of the total vehicle torque is {NS, S, ZERO, B, PB}, and the data domain of the fuzzy set of the total vehicle torque is [-1, 1]; wherein NS∈[-1.0, -0.4], S∈[-0.4, -0.2], ZERO∈[-0.2, 0.2], B∈[0.2, 0.4], PB∈[0.4, 1.0]; the membership function of the total vehicle torque is shown in FIG4(b); The battery SOC value includes five fuzzy subsets. The fuzzy set of the battery SOC value is {VL, L, M, H, VH}. The data domain of the fuzzy set of the battery SOC value is [0, 1]; wherein VL (very low) ∈ [0, 0.3]; L (low) ∈ [0.3, 0.5]; M (medium) ∈ [0.5, 0.7]; H (high) ∈ [0.7, 0.9]; VH (very high) ∈ [0.9, 1.0]; the membership function of the battery SOC value is shown in FIG4 (a); The motor speed includes two fuzzy subsets. The fuzzy set of the motor speed is {L, H}, and the data domain of the fuzzy set of the motor speed is [0, 1]; where L (low) ∈ [0, 0.6], H (high) ∈ [0.6, 1.0], and the membership function of the motor speed is shown in FIG4 (c); The engine output torque contains five fuzzy subsets. The fuzzy set of the engine output torque is {VS, S, M, B, VB}. The data domain of the fuzzy set of the engine output torque is [0, 1.4]; among them, VS (very small) ∈ [0, 0.2]; S (small) ∈ [0.2, 0.6]; M (medium) ∈ [0.6, 0.8]; B (large) ∈ [0.8, 1.2]; VB (very large) ∈ [1.2, 1.4]. The membership function of the engine output torque is shown in Figure 4 (d).

[0028] The total torque of the vehicle, the battery SOC value and the engine output torque all adopt a triangular membership function, and the motor speed adopts a trapezoidal membership function.

[0029] It is understandable that the vehicle requires torque T req , battery SOC value and engine output torque T e The membership function of the triangle membership function (trimf) is used, and the motor speed N m The trapezoidal membership function (trapmf) is selected as the membership function. This is because the triangular membership function can be expressed with fewer feature points than other membership functions while maintaining control accuracy, making it more suitable for genetic algorithm optimization. Since the motor speed only contains two fuzzy subsets, the trapezoidal membership function with more feature points is selected to enhance the genetic algorithm's optimization effect. Furthermore, the triangular membership functions must be evenly distributed in the data domain, ensuring the same overlap between different fuzzy subsets.

[0030] Furthermore, in step S30, regarding the establishment of fuzzy rules: The designed fuzzy controller is used for torque distribution of parallel hybrid electric vehicles, so the basic control law of the fuzzy controller is: (1) When the battery SOC value is lower than the low power threshold, when the vehicle's required torque is lower than the engine's maximum torque, the engine does not consider whether it is operating in the high efficiency range. The torque provided must meet the vehicle's power requirements while also having an overflow to drive the electric motor to charge the battery. When the vehicle's required torque is higher than the engine's maximum torque, the engine cannot provide the required torque to charge the battery to the generator. At this time, the engine torque is fully used to meet the vehicle's power requirements. When necessary, the battery must also drive the electric motor to assist.

[0031] (2) When the battery SOC value is within the optimal range and the required torque of the vehicle is within the engine's efficient operating range, the torque used to drive the vehicle should be provided by the engine. When the engine operates in a higher efficiency range and cannot meet the power demand of the vehicle alone, the battery starts to drive the motor to provide part of the torque.

[0032] (3) When the battery SOC value is sufficient, if the vehicle's required torque is higher than the optimal output torque at the engine's current speed, the engine is operated at the optimal operating point, and the remaining required torque is provided by the battery-driven electric motor. This ensures that the engine operates at high efficiency while allowing the battery SOC value to fall back to the optimal range.

[0033] (4) Considering the relationship between motor speed and operating efficiency, when other input control variables (vehicle required torque, battery SOC value) remain the same, the output control variable (engine output torque) takes a lower value when the motor speed is higher than when the speed is lower. Therefore, the control rules of the designed fuzzy controller can be divided into two parts, with the two different fuzzy subsets of motor speed as the boundary.

[0034] According to the above principles, fuzzy rules are formulated as shown in Table 1 and Table 2: Table 1: Control rules for high motor speed (H)

[0035] Table 2: Control rules for low motor speed (L)

[0036] Furthermore, in step S40, the fuzzy inference process is based on the membership function of the input and the fuzzy control rules, and a series of fuzzy logic operations are performed to obtain the output control variable from the input of the fuzzy system. Fuzzy control uses generalized forward reasoning, and the entire inference process is based on the fuzzy inference algorithm and fuzzy rule base selected in the fuzzy controller. A three-input, single-output fuzzy controller is established, and the fuzzy rule base is in the form of: R1: If x in =A1, and y in1 =B1, and y in2 =C1,then=D1 R2: If x in =A2, and y in1 =B2, and y in2 =C2,then=D2 … R i :If x in =A i , and y in1 =B i , and y in2 =C i ,thenz=D i ; Among them, R i represents the fuzzy rule, xin =A i Represents the total torque of the vehicle as input (i.e., the fuzzy subset corresponding to the total torque of the vehicle), y in1 =B i Represents the battery SOC value as input (i.e., the fuzzy subset corresponding to the battery SOC value), y in2 =C i represents the motor speed as input (i.e. the fuzzy subset corresponding to the motor speed), D i Represents the engine output torque as the output (ie, the fuzzy subset corresponding to the engine output torque).

[0037] Furthermore, given that the three input quantities of the fuzzy controller are x in =A i ,y in1 =B i and y in2 =C i Then, the fuzzy quantity D output by the fuzzy controller is calculated by fuzzy inference algorithm. i : ; ; ; Where i∈[1,n].

[0038] The above includes three fuzzy logic operations: 1) "and" operation The "and" operation in fuzzy logic is similar to the AND operation in conventional logic calculations. It is used to express the connection between the conditions of the fuzzy rules, that is, the logical relationship between the inputs of the fuzzy controller, thereby coupling the membership of multiple input variables. The following four operators are commonly used: Minimum operator:

[0039] Algebraic product operator:

[0040] Bounded product operator:

[0041] Direct product operator:

[0042] 2) " "Operation In fuzzy logic operation "The operation stands for composition operation, which means that the fuzzy relationship between the first set and the second set and the fuzzy relationship between the second set and the third set are used to obtain the fuzzy relationship between the first set and the third set. There are four commonly used operators: Maximum operator:

[0043] Algebra and Operators:

[0044] Bounded Sum Operator:

[0045] Direct Sum Operator:

[0046] 3) " "Operation In fuzzy logic operation "The operation stands for implication operation, which is used to characterize the relationship between conditions and conclusions in the fuzzy reasoning process. The membership of the output variable can be obtained based on the membership of the input variable and the implication operator. Select the Mamdani type fuzzy reasoning algorithm, and the corresponding implication operator is as follows: Fuzzy minimum implication operation:

[0047] In the fuzzy controller for torque distribution of hybrid electric vehicles, the minimum operator is selected for the “and” operation. "The maximum value operator is used for the operation," The fuzzy minimum implication operator is used for the operation, and the weighted average method is used for the defuzzification method. The output surface of the fuzzy controller is as follows: Figure 5 shown.

[0048] Furthermore, the fuzzy quantities obtained by the Mamdani fuzzy inference algorithm cannot be directly applied to the control of the controlled system. They need to be converted into clear values ​​before they can be used for actual control. The role of the defuzzification method is to convert the fuzzy quantities into clear values. Common defuzzification methods include the maximum membership method, the center of gravity method, and the weighted average method. The defuzzification method of the Mamdani fuzzy inference in this paper uses the weighted average method. The specific steps of step S50 include: The fuzzy value D of each engine output torque i Preset clear representative value u i ; The fuzzy rule R that is triggered under the current input i The trigger strength k i as weight; The clarity value U is calculated by the defuzzification algorithm; ; Where m represents the number of fuzzy rules.

[0049] Defuzzification uses a weighted average method to convert the fuzzy output Di into a precise control variable U. During the calculation, a clear representative value ui is preset for each output fuzzy subset Di (usually the centroid or typical value of its membership function). The trigger strength ki of each rule Ri triggered under the current input is used as a weight. This is multiplied by the representative value ui of the corresponding conclusion Di, and the sum is then divided by the sum of the trigger strengths to obtain the precise defuzzified output value U.

[0050] Furthermore, in step S60, the formula for the motor output torque is: ; Among them, T m Indicates the motor output torque, T req Represents the total torque of the vehicle, T e Engine output torque.

[0051] In summary, the hybrid vehicle torque control method in the above-mentioned embodiments of the present invention employs a Mamdani-type fuzzy inference system to dynamically adjust the torque distribution strategy between the engine and motor by analyzing the vehicle's required torque, battery SOC, and motor speed in real time. By simplifying the dual outputs into a single output, control complexity is reduced while ensuring optimal energy distribution, thereby improving the overall efficiency of the hybrid system.

[0052] 2. This patent uses the battery SOC value as a key input variable, with the control target clearly defined as maintaining the SOC value within the shallow charge and discharge range of 0.3 to 0.8. This strategy not only avoids the damage to the battery life caused by overcharge and overdischarge, but also ensures the stability of energy storage, thereby improving the reliability and service life of the battery system.

[0053] 3. This patent dynamically adjusts the torque distribution rule based on the relationship between motor speed and efficiency. The motor output torque is reduced at high speeds and increased at low speeds, ensuring that the motor always operates in the high-efficiency range. Please refer to Figure 6 , which shows a hybrid vehicle torque control system in a second embodiment of the present invention, comprising: Creation module 11: creating a fuzzy control toolbox, inputting the required vehicle total torque, battery SOC value, and motor speed into the fuzzy control toolbox as input, and using the engine output torque as output; the fuzzy control toolbox is created based on MATLAB; Construction module 12: Set fuzzy subsets for the total torque of the whole vehicle, the battery SOC value, the motor speed and the engine output torque respectively to construct a membership function; the total torque of the whole vehicle includes 5 fuzzy subsets, the fuzzy set of the total torque of the whole vehicle is {NS, S, ZERO, B, PB}, and the data domain in the fuzzy set of the total torque of the whole vehicle is [-1, 1]; the battery SOC value includes 5 fuzzy subsets, the fuzzy set of the battery SOC value is {VL, L, M, H, VH}, and the data domain in the fuzzy set of the battery SOC value is [ 0, 1]; the motor speed includes two fuzzy subsets, the fuzzy set of the motor speed is {L, H}, and the data domain in the fuzzy set of the motor speed is [0, 1]; the engine output torque includes five fuzzy subsets, the fuzzy set of the engine output torque is {VS, S, M, B, VB}, and the data domain in the fuzzy set of the engine output torque is [0, 1.4]; the total vehicle torque, battery SOC value and engine output torque all use triangular membership functions, and the motor speed uses a trapezoidal membership function; Establishing module 13: establishing fuzzy rules between the total torque of the vehicle, the battery SOC value, the motor speed and the engine output torque; Reasoning module 14: constructs a fuzzy rule base according to the fuzzy rules, and performs fuzzy reasoning based on the membership function, the fuzzy reasoning algorithm and the fuzzy rule base in the fuzzy control toolbox to obtain a fuzzy quantity; the expression of the fuzzy rule base is: R1: If x in =A1, and y in1 =B1, and y in2 =C1,then=D1 R2: If x in =A2, and y in1 =B2, and y in2 =C2,then=D2 … R i :If x in =A i , and y in1 =B i , and y in2 =C i ,thenz=D i ; Among them, R i represents the fuzzy rule, x in =A i Indicates the total torque of the vehicle as input, y in1 =B i Indicates the battery SOC value as input, yin2 =C i represents the motor speed as input, D i represents the engine output torque as output; The expression of the fuzzy inference algorithm is: ; ; ; Where, i∈[1,n]; Defuzzification module 15: converts the fuzzy value into a clear value through a defuzzification algorithm; The defuzzification module 15 is specifically used to: generate the fuzzy value D of each engine output torque i Preset clear representative value u i ; The fuzzy rule R that is triggered under the current input i The trigger strength k i as weight; The clarity value U is calculated by the defuzzification algorithm; ; Where m represents the number of fuzzy rules.

[0054] Calculation module 16: Calculates the motor output torque based on the clear value of the engine output torque and the total vehicle torque.

[0055] The third embodiment of the present invention further provides an electronic device, see Figure 7 , shown is an electronic device in the third embodiment of the present invention, including a memory 10, a processor 20, and a computer program 30 stored in the memory 10 and executable on the processor 20. When the processor 20 executes the computer program 30, the above-mentioned hybrid vehicle torque control method is implemented.

[0056] The memory 10 includes at least one type of storage medium, including flash memory, a hard disk, a multimedia card, a card-type memory (e.g., SD or DX memory), a magnetic memory, a magnetic disk, an optical disk, etc. In some embodiments, the memory 10 may be an internal storage unit of the electronic device, such as the hard disk of the electronic device. In other embodiments, the memory 10 may also be an external storage device, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) card, a flash memory card, etc. Furthermore, the memory 10 may include both an internal storage unit of the electronic device and an external storage device. The memory 10 can be used not only to store application software installed in the electronic device and various types of data, but also to temporarily store data that has been output or is about to be output.

[0057] Among them, in some embodiments, the processor 20 can be an electronic control unit (Electronic Control Unit, abbreviated as ECU, also known as a vehicle computer), a central processing unit (CPU), a controller, a microcontroller, a microprocessor or other data processing chip, used to run the program code stored in the memory 10 or process data, such as executing access restriction programs.

[0058] It should be pointed out that Figure 7 The structure shown does not constitute a limitation to the electronic device. In other embodiments, the electronic device may include fewer or more components than shown in the figure, or combine certain components, or arrange the components differently.

[0059] An embodiment of the present invention further provides a readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the hybrid electric vehicle torque control method as described above is implemented.

[0060] Those skilled in the art will appreciate that the logic and / or steps represented in the flowcharts or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, apparatus, or device (e.g., a computer-based system, a system including a processor, or other system that can fetch and execute instructions from an instruction execution system, apparatus, or device), or in conjunction with such instruction execution system, apparatus, or device. For purposes of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate, or transport a program for use by an instruction execution system, apparatus, or device, or in conjunction with such instruction execution system, apparatus, or device.

[0061] More specific examples (a non-exhaustive list) of computer-readable media include the following: an electrical connection with one or more wires (electronic devices), a portable computer disk cartridge (magnetic devices), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), a fiber optic device, and a portable compact disc read-only memory (CDROM). In addition, the computer-readable medium may even be paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium and then editing, interpreting, or processing it in another suitable manner as necessary, and then storing it in a computer memory.

[0062] It should be understood that various components of the present invention may be implemented using hardware, software, firmware, or a combination thereof. In the aforementioned embodiments, multiple steps or methods may be implemented using software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented using hardware, as in another embodiment, any one or a combination of the following technologies known in the art may be used: a discrete logic circuit having logic gate circuits for implementing logic functions on data signals, an application-specific integrated circuit having suitable combinational logic gate circuits, a programmable gate array (PGA), a field-programmable gate array (FPGA), etc.

[0063] Throughout this specification, reference to terms such as "one embodiment," "some embodiments," "examples," "specific examples," or "some examples" means that a specific feature, structure, material, or characteristic described in conjunction with that embodiment or example is included in at least one embodiment or example of the present invention. In this specification, schematic representations of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.

[0064] The above-described embodiments merely illustrate several implementations of the present invention, and while their descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the present invention. It should be noted that a person skilled in the art would be able to make numerous variations and improvements without departing from the spirit of the present invention, all of which fall within the scope of protection of the present invention. Therefore, the scope of protection of the present invention shall be determined by the appended claims.

Claims

1. A torque control method for a hybrid vehicle, characterized in that: The steps include: Create a fuzzy control toolbox, input the required vehicle total torque, battery SOC value and motor speed into the fuzzy control toolbox as input, and use the engine output torque as output; Setting fuzzy subsets for the vehicle total torque, battery SOC value, motor speed, and engine output torque respectively to construct a membership function; Establishing fuzzy rules between the total torque of the vehicle, the battery SOC value, the motor speed and the engine output torque; Constructing a fuzzy rule base according to the fuzzy rules, and performing fuzzy reasoning based on the membership function, the fuzzy reasoning algorithm and the fuzzy rule base in the fuzzy control toolbox to obtain a fuzzy quantity; Converting the fuzzy quantity into a crisp value by a defuzzification algorithm; The motor output torque is calculated based on the clean value of the engine output torque and combined with the total vehicle torque.

2. The hybrid vehicle torque control method according to claim 1, characterized in that: The fuzzy control toolbox is created based on MATLAB.

3. The hybrid vehicle torque control method according to claim 1, characterized in that: The total torque of the whole vehicle includes 5 fuzzy subsets, the fuzzy set of the total torque of the whole vehicle is {NS, S, ZERO, B, PB}, and the data domain in the fuzzy set of the total torque of the whole vehicle is [-1, 1]; the battery SOC value includes 5 fuzzy subsets, the fuzzy set of the battery SOC value is {VL, L, M, H, VH}, and the data domain in the fuzzy set of the battery SOC value is [0, 1]; the motor speed includes 2 fuzzy subsets, the fuzzy set of the motor speed is {L, H}, and the data domain in the fuzzy set of the motor speed is [0, 1]; the engine output torque includes 5 fuzzy subsets, the fuzzy set of the engine output torque is {VS, S, M, B, VB}, and the data domain in the fuzzy set of the engine output torque is [0, 1.4].

4. The hybrid vehicle torque control method according to claim 1, characterized in that: The total torque of the vehicle, the battery SOC value and the engine output torque all adopt a triangular membership function, and the motor speed adopts a trapezoidal membership function.

5. The hybrid vehicle torque control method according to claim 1, characterized in that: The expression of the fuzzy rule base is: R1:If x in =A1,and y in1 =B1,and y in2 =C1,thenz=D1 R2:If x in =A2,and y in1 =B2,and y in2 =C2,thenz=D2 … R i :If x in =A i ,and y in1 =B i ,and y in2 =C i ,thenz=D i ; Among them, R i represents the fuzzy rule, x in =A i Indicates the total torque of the vehicle as input, y in1 =B i Indicates the battery SOC value as input, y in2 =C i represents the motor speed as input, D i Indicates the engine output torque as output.

6. The hybrid vehicle torque control method according to claim 5, characterized in that: The expression of the fuzzy inference algorithm is: ； ； ； Where i∈[1,n].

7. The hybrid vehicle torque control method according to claim 6, characterized in that: The specific steps of converting the fuzzy value into a clear value by using a defuzzification algorithm include: The fuzzy value D of each engine output torque i Preset clear representative value u i ; The fuzzy rule R that is triggered under the current input i The trigger strength k i as weight; The clarity value U is calculated by the defuzzification algorithm; ； Where m represents the number of fuzzy rules.

8. A hybrid vehicle torque control system, characterized in that: include: Create a module: Create a fuzzy control toolbox, input the required vehicle total torque, battery SOC value and motor speed into the fuzzy control toolbox as input, and use the engine output torque as output; A construction module: setting fuzzy subsets for the total torque of the vehicle, the battery SOC value, the motor speed, and the engine output torque respectively to construct a membership function; Establishing module: establishing fuzzy rules between the total torque of the vehicle, the battery SOC value, the motor speed and the engine output torque; Reasoning module: constructing a fuzzy rule base according to the fuzzy rules, and performing fuzzy reasoning based on the membership function, the fuzzy reasoning algorithm and the fuzzy rule base in the fuzzy control toolbox to obtain fuzzy quantities; Defuzzification module: converts the fuzzy value into a clear value through a defuzzification algorithm; Calculation module: Calculates the motor output torque based on the clear value of the engine output torque and the total vehicle torque.

9. A storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the hybrid vehicle torque control method according to any one of claims 1 to 7 is implemented.

10. An electronic device, characterized in that: The method comprises a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the method for controlling the torque of a hybrid electric vehicle according to any one of claims 1 to 7 is implemented.

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