A method, system, medium, and device for torque control of hybrid electric vehicles

Abstract

This invention provides a torque control method, system, medium, and device for hybrid electric vehicles, comprising: creating a fuzzy control toolbox, taking the total vehicle torque, battery SOC value, and motor speed as inputs, and engine output torque as output; setting fuzzy subsets for the total vehicle torque, battery SOC value, motor speed, and engine output torque, and constructing membership functions; establishing fuzzy rules for the total vehicle torque, battery SOC value, motor speed, and engine output torque; constructing a fuzzy rule library, performing fuzzy inference based on the membership functions, fuzzy inference algorithm, and fuzzy rule library to obtain fuzzy quantities; converting the fuzzy quantities into clear values ​​through a defuzzification algorithm; and calculating the motor output torque based on the clear values. This invention dynamically adjusts the torque distribution rules according to the relationship between motor speed and efficiency. At high speeds, the motor output torque is reduced, and at low speeds, the torque is increased, ensuring that the motor always operates within its high-efficiency range.

Description

Technical Field

[0001] This invention relates to the field of automotive control technology, and in particular to a torque control method, system, medium, and device for hybrid electric vehicles. Background Technology

[0002] The energy management strategies for PHEVs already applied in real vehicles are typically static closed-value methods designed based on engineering experience. These methods are simple and direct, but lack flexibility and cannot fully leverage the performance advantages of hybrid vehicles. More advanced methods, such as dynamic programming and fuzzy control, also have their limitations and are difficult to apply to the uncertain operating conditions of real vehicles. Transient optimization and neural networks are limited by the computational performance of current automotive electronic control systems.

[0003] In existing technologies, traditional rule-based or model-based allocation strategies rely on accurate system modeling, which is difficult to adapt to the nonlinear coupling characteristics under dynamic operating conditions, resulting in insufficient real-time performance and high computational complexity. Furthermore, existing algorithms mostly adopt a single-objective optimization framework, which lacks robustness to torque allocation and compatibility with energy efficiency optimization, and is prone to control lag or oscillation phenomena, especially under complex road conditions. Summary of the Invention

[0004] In view of the shortcomings of the prior art, the purpose of this invention is to provide a torque control method for hybrid electric vehicles, which aims to solve the technical problems mentioned in the background art.

[0005] To achieve the above objectives, the present invention is implemented through the following technical solution:

[0006] A torque control method for a hybrid electric vehicle includes the following steps:

[0007] A fuzzy control toolbox is created, with the required total vehicle torque, battery SOC value, and motor speed as inputs, and the engine output torque as the output.

[0008] Fuzzy subsets are set for the total torque of the vehicle, the battery SOC value, the motor speed and the engine output torque respectively, in order to construct membership functions;

[0009] Establish fuzzy rules between the total vehicle torque, battery SOC value, electric motor speed, and engine output torque;

[0010] A fuzzy rule base is constructed based on the fuzzy rules, and fuzzy inference is performed based on the membership function, the fuzzy inference algorithm in the fuzzy control toolbox, and the fuzzy rule base to obtain fuzzy quantities;

[0011] The blurred value is converted into a clear value using a deblurring algorithm;

[0012] The output torque of the electric motor is calculated based on the clear value of the engine output torque and in combination with the total torque of the vehicle.

[0013] According to one aspect of the above technical solution, the fuzzy control toolbox is created based on MATLAB.

[0014] According to one aspect of the above technical solution, the total vehicle torque includes 5 fuzzy subsets, the fuzzy set of the total vehicle torque is {NS, S, ZERO, B, PB}, and the data field of the fuzzy set of the total vehicle torque 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 field of 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 field of 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 field of the fuzzy set of the engine output torque is [0, 1, 4].

[0015] According to one aspect of the above technical solution, the total torque of the vehicle, the battery SOC value, and the engine output torque all adopt triangular membership functions, while the motor speed adopts a trapezoidal membership function.

[0016] According to one aspect of the above technical solution, the expression of the fuzzy rule base is:

[0017] R1: If x in =A1, and y in1 =B1, and y in2 =C1, thenz=D1

[0018] R2: If x in =A2, and y in1 =B2, and y in2 =C2, thenz=D2

[0019] …

[0020] R i If x in =A i , and y in1 =B i , and y in2 =C i ,thenz=D i ;

[0021] Among them, R iRepresenting fuzzy rules, x in =A i Indicates the total torque of the vehicle as input, y in1 =B i Indicates that the battery SOC value is used as input, y in2 =C i D represents the motor speed as input. i This indicates that the engine output torque is used as the output.

[0022] According to one aspect of the above technical solution, the expression of the fuzzy inference algorithm is:

[0023] ;

[0024] ;

[0025] ;

[0026] Where i∈[1,n].

[0027] According to one aspect of the above technical solution, the specific steps of converting the blurred value into a sharp value using a deblurring algorithm include:

[0028] The fuzzy quantity D of the output torque for each of the engines i Preset clear representative value u i ;

[0029] The fuzzy rule R triggered under the current input i Trigger strength k i As weight;

[0030] The sharp value U is calculated using a defuzzification algorithm;

[0031] ;

[0032] Where m represents the number of fuzzy rules.

[0033] The present invention also provides a torque control system for a hybrid electric vehicle, comprising:

[0034] Create a module: Create a fuzzy control toolbox, take the required total vehicle torque, battery SOC value and motor speed as inputs to the fuzzy control toolbox, and take the engine output torque as the output;

[0035] Construction module: Set fuzzy subsets for the total vehicle torque, battery SOC value, motor speed and engine output torque respectively to construct membership functions;

[0036] Establishment module: Establish fuzzy rules between the total vehicle torque, battery SOC value, motor speed and engine output torque;

[0037] Inference module: Constructs a fuzzy rule base based on the fuzzy rules, and performs fuzzy inference based on the membership function, the fuzzy inference algorithm in the fuzzy control toolbox, and the fuzzy rule base to obtain fuzzy quantities;

[0038] Deblurring module: Converts the blurred value into a clear value using a deblurring algorithm;

[0039] Calculation module: Based on the clear value of the engine output torque and combined with the total torque of the vehicle, calculate the electric motor output torque.

[0040] The present invention also provides a storage medium having a computer program stored thereon, which, when executed by a processor, implements the torque control method for hybrid electric vehicles as described above.

[0041] The present invention also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the torque control method for a hybrid electric vehicle as described above.

[0042] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0043] This invention employs a Mamdani-type fuzzy inference system to dynamically adjust the torque distribution strategy between the engine and motor by analyzing the total vehicle torque, battery SOC value, and motor speed in real time. By simplifying the dual-output system to a single output, control complexity is reduced while ensuring optimal energy distribution, thus improving the overall efficiency of the hybrid system. This invention uses the battery SOC value as a key input variable, with the control objective clearly defined as maintaining the SOC value within the shallow charge / discharge range. This strategy avoids the damage to battery life caused by overcharging and over-discharging, while ensuring the stability of energy reserves, thereby improving the reliability and lifespan of the battery system. Furthermore, this invention dynamically adjusts the torque distribution rules based on the relationship between motor speed and efficiency. At high speeds, the motor output torque is reduced, while at low speeds, the torque is increased, ensuring that the motor always operates within its high-efficiency range. Attached Figure Description

[0044] Figure 1 This is a flowchart of the torque control method for a hybrid electric vehicle in the first embodiment of the present invention;

[0045] Figure 2 This is a flowchart of the torque control method for a hybrid electric vehicle in the first embodiment of the present invention;

[0046] Figure 3This is a structural block diagram of the fuzzy controller in the first embodiment of the present invention;

[0047] Figure 4(a) is a schematic diagram of the membership function of the battery SOC value in the first embodiment of the present invention;

[0048] Figure 4(b) is a schematic diagram of the membership function of the total torque of the vehicle in the first embodiment of the present invention;

[0049] Figure 4(c) is a schematic diagram of the membership function of the motor speed in the first embodiment of the present invention;

[0050] Figure 4(d) is a schematic diagram of the membership function of the engine output torque in the first embodiment of the present invention;

[0051] Figure 5 This is a schematic diagram of the output surface of the fuzzy controller in the first embodiment of the present invention;

[0052] Figure 6 This is a structural block diagram of the hybrid electric vehicle torque control system in the second embodiment of the present invention;

[0053] Figure 7 This is a structural block diagram of the electronic device in the third embodiment of the present invention;

[0054] The following detailed description, in conjunction with the accompanying drawings, will further illustrate the present invention. Detailed Implementation

[0055] To facilitate understanding of the present invention, a more complete description will be given below with reference to the accompanying drawings. Several embodiments of the invention are illustrated in the drawings. However, the invention can be implemented in many different forms and is not limited to the embodiments described herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete.

[0056] It should be noted that when a component is said to be "fixed to" another component, it can be directly on the other component or there may be an intervening component. When a component is said to be "connected to" another component, it can be directly connected to the other component or there may be an intervening component. The terms "vertical," "horizontal," "left," "right," and similar expressions used in this document are for illustrative purposes only.

[0057] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains. The terminology used herein in the description of the invention is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. The term "and / or" as used herein includes any and all combinations of one or more of the associated listed items.

[0058] Please see Figure 1 The figure shows a torque control method for a hybrid electric vehicle according to the first embodiment of the present invention, which includes the following steps:

[0059] S10, Create a fuzzy control toolbox, input the required total vehicle torque, battery SOC value and motor speed as inputs to the fuzzy control toolbox, and use the engine output torque as the output;

[0060] S20, set fuzzy subsets for the total torque of the vehicle, the SOC value of the battery, the speed of the electric motor and the output torque of the engine respectively, in order to construct the membership function;

[0061] S30, establish fuzzy rules between the total vehicle torque, battery SOC value, motor speed and engine output torque;

[0062] S40, construct a fuzzy rule base according to the fuzzy rules, and perform fuzzy inference based on the membership function, the fuzzy inference algorithm in the fuzzy control toolbox and the fuzzy rule base to obtain fuzzy quantities;

[0063] S50, the blurred value is converted into a clear value through a deblurring algorithm;

[0064] S60, based on the clear value of the engine output torque and combined with the total torque of the vehicle, calculate the electric motor output torque.

[0065] Understandably, this invention employs a Mamdani-type fuzzy inference system to dynamically adjust the torque distribution strategy between the engine and 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 configuration, control complexity is reduced while ensuring optimal energy distribution, thus improving the overall efficiency of the hybrid system. This invention uses the battery SOC value as a key input variable, with the control objective clearly defined as maintaining the SOC value within the shallow charge / discharge range. This strategy avoids damage to battery life caused by overcharging and over-discharging, while ensuring the stability of energy reserves, thereby improving the reliability and lifespan of the battery system. Based on the relationship between motor speed and efficiency, this invention dynamically adjusts the torque distribution rules. At high speeds, the motor output torque is reduced, while at low speeds, the torque is increased, ensuring that the motor always operates within its high-efficiency range.

[0066] 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.

[0067] It should be noted that, as Figure 2As shown, the designed fuzzy control strategy is used to optimally allocate the engine and motor torques. The torque variables involved include the vehicle's required torque, the engine's output torque, and the motor's output torque. The vehicle's required torque is calculated by the vehicle dynamics model based on parameters such as vehicle speed requirement and transmission ratio, and then output to the fuzzy controller. The fuzzy controller obtains the engine's output torque through fuzzy inference and finally calculates the motor's output torque.

[0068] like Figure 3 As shown, this invention simplifies the output of the fuzzy control toolbox from a dual output of engine output torque and motor output torque to a single output of engine output torque, with the motor output torque serving as an indirect control quantity. Specifically, by inputting the total vehicle torque, battery SOC value, and motor speed into the fuzzy control toolbox, the engine output torque is obtained as the output, and then the motor output torque is calculated based on the engine output torque.

[0069] For battery state of charge (SOC), the SOC value is currently commonly used to represent battery capacity. However, considering that the battery's SOC value not only significantly impacts the operating conditions of various power sources but also affects battery lifespan, the energy input and output process of the battery pack must adhere to the principle of shallow charging and discharging. Therefore, the fuzzy controller's control objective for the battery is primarily to maintain a stable SOC value, keeping it between 0.3 and 0.8.

[0070] Regarding motor speed, as the motor speed increases, the high-efficiency range of the motor gradually compresses towards the direction of low output torque. Therefore, when the motor speed is high, the output torque of the motor can be lower, 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.

[0071] Based on the above three points, a flowchart of a torque control method for hybrid electric vehicles based on a fuzzy controller can be constructed, as follows: Figure 2 As shown; a 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 It is a single output.

[0072] Furthermore, in step S20, the total vehicle torque includes 5 fuzzy subsets, and 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]; where 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 Figure 4(b);

[0073] The battery SOC value contains 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]. Among them, 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 Figure 4(a).

[0074] The motor speed comprises 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 Figure 4(c);

[0075] The engine output torque comprises 5 fuzzy subsets, and 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]; where 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).

[0076] The total torque of the vehicle, the battery SOC value, and the engine output torque are all calculated using triangular membership functions, while the motor speed is calculated using a trapezoidal membership function.

[0077] Understandable, the vehicle requires torque T req Battery SOC value and engine output torque T e The membership functions are all triangular membership functions (trimf), and the motor speed N mThe membership function chosen is the trapezoidal membership function (trapmf). This is because, while meeting control accuracy requirements, the triangular membership function can be expressed with fewer feature points compared to other types of membership functions, which is beneficial for the genetic algorithm to optimize the membership function. Since the motor speed only contains two fuzzy subsets, selecting the trapezoidal membership function, which has more feature points, allows the genetic algorithm to optimize it more effectively. Simultaneously, the triangular membership function needs to be evenly distributed across the data domain according to the requirement that the overlap rate between different fuzzy subsets is the same.

[0078] Furthermore, in step S30, regarding the establishment of fuzzy rules:

[0079] The designed fuzzy controller is used for torque distribution in parallel hybrid electric vehicles; therefore, the basic control law of the fuzzy controller is as follows:

[0080] (1) When the battery SOC value is lower than the low charge threshold, and the vehicle's required torque is lower than the engine's maximum torque, the engine, regardless of whether it is operating in the high-efficiency range, must provide torque that not only meets the vehicle's power requirements but also has an excess 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 the generator to charge the battery. At this time, the engine torque is used entirely to meet the vehicle's power requirements, and if necessary, the battery also needs to drive the electric motor for assistance.

[0081] (2) When the battery SOC value is within a relatively 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. However, when the engine is operating in a higher efficiency range and cannot meet the power demand of the vehicle alone, the battery begins to drive the motor to provide part of the torque.

[0082] (3) When the battery SOC value is very sufficient, if the torque required by the vehicle is higher than the optimal output torque of the engine at the current speed, the engine will operate at the optimal operating point, and the remaining torque required will be provided by the battery-driven electric motor. This ensures that the engine operates at high efficiency while the battery SOC value falls back to the optimal range.

[0083] (4) Considering the relationship between motor speed and working efficiency, when other input control variables (vehicle demand torque, battery SOC value) are in the same state, the output control variable (engine output torque) takes a lower value when the motor speed is higher than when the motor 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.

[0084] Based on the above principles, the fuzzy rules shown in Table 1 and Table 2 are as follows:

[0085] Table 1: Control rules when the motor speed is high (H)

[0086]

[0087] Table 2: Control rules when the motor speed is low (L)

[0088]

[0089] Furthermore, in step S40, the fuzzy inference process, based on the membership function of the input quantity and the fuzzy control rules, undergoes a series of fuzzy logic operations to obtain the output control quantity from the input of the fuzzy system. The fuzzy control employs generalized forward inference, 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 following form:

[0090] R1: If x in =A1, and y in1 =B1, and y in2 =C1, thenz=D1

[0091] R2: If x in =A2, and y in1 =B2, and y in2 =C2, thenz=D2

[0092] …

[0093] R i If x in =A i , and y in1 =B i , and y in2 =C i ,thenz=D i ;

[0094] Among them, R i Representing fuzzy rules, x in =A i Indicates that the total torque of the vehicle is used as input (i.e., the fuzzy subset corresponding to the total torque of the vehicle), y in1 =B i This indicates that the battery SOC value is used as input (i.e., the fuzzy subset corresponding to the battery SOC value), y in2 =C i D represents the motor speed as input (i.e., the fuzzy subset corresponding to the motor speed). i This represents the engine output torque as the output (i.e., the fuzzy subset corresponding to the engine output torque).

[0095] 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 using a fuzzy inference algorithm. i :

[0096] ;

[0097] ;

[0098] ;

[0099] Where i∈[1,n].

[0100] The above includes three types of fuzzy logic operations:

[0101] 1) "and" operation

[0102] The "AND" operation in fuzzy logic is similar to the AND operation in conventional logic calculations. It is used to represent the connection relationship between the conditions of fuzzy rules, that is, the logical relationship between the inputs of the fuzzy controller, thereby coupling the membership degrees of multiple input variables. The following four operators are commonly used:

[0103] Minimum operator:

[0104]

[0105] Algebraic product operator:

[0106]

[0107] Bounded product operator:

[0108]

[0109] Direct product operator:

[0110]

[0111] 2) "operation"

[0112] Fuzzy logic operations "The operation represents composition, which refers to obtaining the fuzzy relation between the first and third sets from the fuzzy relation between the first and second sets, and the fuzzy relation between the second and third sets. The four commonly used operators are as follows:"

[0113] Maximum value operator:

[0114]

[0115] Algebra and Operators:

[0116]

[0117] Bounded sum operators:

[0118]

[0119] Direct sum operator:

[0120]

[0121] 3) "operation"

[0122] Fuzzy logic operations "The implication operation represents the relationship between conditions and conclusions in fuzzy inference. The membership degree of the output variable can be obtained based on the membership degree of the input variable and the implication operator. The Mamdani-type fuzzy inference algorithm is selected, and the corresponding implication operator is as follows:"

[0123] Fuzzy minimum implication operation:

[0124]

[0125] In the established fuzzy controller for torque distribution in hybrid electric vehicles, the minimum value operator is selected for the "and" operation. "The operation uses the maximum value operator," "The operation employs the fuzzy minimum implication operator, and the defuzzification method uses the weighted average method. The output surface of the fuzzy controller is as follows..." Figure 5 As shown.

[0126] Furthermore, the fuzzy quantities obtained through the Mamdani-type fuzzy inference algorithm cannot be directly applied to the control of the controlled system. They need to be converted into sharp values ​​before they can be used for actual control. The role of the defuzzification method is to convert the fuzzy quantities into sharp values. Commonly used defuzzification methods include the maximum membership method, the centroid method, and the weighted average method. This paper selects the weighted average method for the defuzzification of the Mamdani-type fuzzy inference. The specific steps of step S50 include:

[0127] The fuzzy quantity D of the output torque for each of the engines i Preset clear representative value u i ;

[0128] The fuzzy rule R triggered under the current input i Trigger strength k i As weight;

[0129] The sharp value U is calculated using a defuzzification algorithm;

[0130] ;

[0131] Where m represents the number of fuzzy rules.

[0132] Defuzzification employs a weighted average method to convert the fuzzy output Di into a precise control quantity U. Specifically, for each fuzzy subset Di, a clear representative value ui is pre-defined (usually the centroid or typical value of its membership function). The trigger intensity ki of each rule Ri triggered under the current input is used as a weight, multiplied by the corresponding representative value ui of the conclusion Di, summed, and then divided by the sum of the trigger intensities to obtain the final defuzzified precise output value U.

[0133] Furthermore, in step S60, the formula for the motor output torque is:

[0134] ;

[0135] Among them, T m T represents the output torque of the electric motor. req T represents the total torque of the vehicle. e Engine output torque.

[0136] In summary, the hybrid electric vehicle torque control method in the above embodiments of the present invention 1. employs a Mamdani-type fuzzy inference system to dynamically adjust the torque distribution strategy between the engine and the electric motor by analyzing the vehicle's required torque, battery SOC value, and electric motor speed in real time. By simplifying the dual-output to a single-output configuration, the control complexity is reduced, while ensuring optimal energy distribution and improving the overall efficiency of the hybrid system.

[0137] 2. This patent uses the battery's State of Charge (SOC) value as a key input variable, with the clear control objective of maintaining the SOC value within the shallow charge / discharge range of 0.3 to 0.8. This strategy avoids the damage to battery life caused by overcharging and over-discharging, while ensuring the stability of energy reserves, thus improving the reliability and lifespan of the battery system.

[0138] 3. This patent dynamically adjusts the torque distribution rules based on the relationship between motor speed and efficiency. It reduces the motor's output torque at high speeds and increases the torque at low speeds, ensuring the motor always operates within its high-efficiency range.

[0139] Please refer to Figure 6 The figure shows a hybrid electric vehicle torque control system according to a second embodiment of the present invention, comprising:

[0140] Module 11: Create a fuzzy control toolbox, taking the required total vehicle torque, battery SOC value, and motor speed as inputs, and using the engine output torque as the output; the fuzzy control toolbox is created based on MATLAB.

[0141] Construction Module 12: Fuzzy subsets are set for the total vehicle torque, battery SOC value, motor speed, and engine output torque to construct membership functions; the total vehicle torque contains 5 fuzzy subsets, and the fuzzy set of the total vehicle torque is {NS, S, ZERO, B, PB}, with a data field of [-1, 1] in the fuzzy set of the total vehicle torque; the battery SOC value contains 5 fuzzy subsets, and the fuzzy set of the battery SOC value is {VL, L, M, H, VH}, with a data field of [...]. [0, 1]; The motor speed contains two fuzzy subsets, the fuzzy set of the motor speed is {L, H}, and the data field of the fuzzy set of the motor speed is [0, 1]; The engine output torque contains five fuzzy subsets, the fuzzy set of the engine output torque is {VS, S, M, B, VB}, and the data field of the fuzzy set of the engine output torque is [0, 1, 4]; The total vehicle torque, battery SOC value and engine output torque all adopt triangular membership functions, and the motor speed adopts trapezoidal membership functions;

[0142] Module 13: Establish fuzzy rules between the total vehicle torque, battery SOC value, motor speed and engine output torque;

[0143] Inference module 14: Constructs a fuzzy rule base based on the fuzzy rules, and performs fuzzy inference based on the membership function, the fuzzy inference algorithm in the fuzzy control toolbox, and the fuzzy rule base to obtain fuzzy quantities; the expression of the fuzzy rule base is:

[0144] R1: If x in =A1, and y in1 =B1, and y in2 =C1, thenz=D1

[0145] R2: If x in =A2, and y in1 =B2, and y in2 =C2, thenz=D2

[0146] …

[0147] R i If x in =A i , and y in1=B i , and y in2 =C i ,thenz=D i ;

[0148] Among them, R i Representing fuzzy rules, x in =A i Indicates the total torque of the vehicle as input, y in1 =B i Indicates that the battery SOC value is used as input, y in2 =C i D represents the motor speed as input. i This indicates that the engine's output torque is used as the output.

[0149] The expression for the fuzzy inference algorithm is:

[0150] ;

[0151] ;

[0152] ;

[0153] Where i∈[1,n];

[0154] Deblurring module 15: Converts the blurred value into a clear value using a deblurring algorithm;

[0155] The unfuzzing module 15 is specifically used to: determine the fuzzy amount D for each of the engine output torques. i Preset clear representative value u i ;

[0156] The fuzzy rule R triggered under the current input i Trigger strength k i As weight;

[0157] The sharp value U is calculated using a defuzzification algorithm;

[0158] ;

[0159] Where m represents the number of fuzzy rules.

[0160] Calculation module 16: Calculates the output torque of the electric motor based on the clear value of the engine output torque and in combination with the total torque of the vehicle.

[0161] The third embodiment of the present invention also proposes an electronic device, please refer to [link / reference]. Figure 7The image shows an electronic device according to a 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, it implements the above-described torque control method for hybrid electric vehicles.

[0162] The memory 10 includes at least one type of storage medium, such as flash memory, hard disk, multimedia card, card-type memory (e.g., SD or DX memory), magnetic memory, magnetic disk, optical disk, etc. In some embodiments, the memory 10 can be an internal storage unit of an electronic device, such as the hard disk of the electronic device. In other embodiments, the memory 10 can be an external storage device, such as a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, etc. Furthermore, the memory 10 can include both internal and external storage units of the electronic device. The memory 10 can be used not only to store application software and various types of data installed in the electronic device, but also to temporarily store data that has been output or will be output.

[0163] In some embodiments, the processor 20 may be an electronic control unit (ECU), a central processing unit (CPU), a controller, a microcontroller, a microprocessor, or other data processing chip, used to run program code stored in the memory 10 or process data, such as executing access restriction programs.

[0164] It should be pointed out that, Figure 7 The structure shown does not constitute a limitation on the electronic device. In other embodiments, the electronic device may include fewer or more components than shown, or combine certain components, or have different component arrangements.

[0165] This invention also proposes a readable storage medium storing a computer program that, when executed by a processor, implements the hybrid vehicle torque control method described above.

[0166] Those skilled in the art will understand that the logic and / or steps represented in the flowcharts or otherwise described herein, for example, can be considered as a ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-included system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can mean any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device.

[0167] More specific examples of computer-readable media (a non-exhaustive list) include: electrical connections (electronic devices) having one or more wires, portable computer disk drives (magnetic devices), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Furthermore, computer-readable media can even be paper or other suitable media on which the program can be printed, because the program can be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, interpreting, or otherwise processing as necessary, and then stored in computer memory.

[0168] It should be understood that various parts of the present invention can be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0169] In the description of this specification, references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the invention. In this specification, the illustrative expressions 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 one or more embodiments or examples.

[0170] The embodiments described above are merely illustrative of several implementations of the present invention, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of the present invention. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of the present invention, and these modifications and improvements all fall within the scope of protection of the present invention. Therefore, the scope of protection of this patent should be determined by the appended claims.

Claims

1. A torque control method for a hybrid electric vehicle, characterized in that, Includes the following steps: A fuzzy control toolbox is created, with the required total vehicle torque, battery SOC value, and motor speed as inputs, and the engine output torque as the output. Fuzzy subsets are set for the total torque of the vehicle, the battery SOC value, the motor speed and the engine output torque respectively, in order to construct membership functions; Establish fuzzy rules between the total vehicle torque, battery SOC value, electric motor speed, and engine output torque; A fuzzy rule base is constructed based on the fuzzy rules, and fuzzy inference is performed based on the membership function, the fuzzy inference algorithm in the fuzzy control toolbox, and the fuzzy rule base to obtain fuzzy quantities; The blurred value is converted into a clear value using a deblurring algorithm; Based on the clear value of the engine output torque and combined with the total torque of the vehicle, the electric motor output torque is calculated; The total vehicle torque comprises 5 fuzzy subsets, the fuzzy set of the total vehicle torque is {NS, S, ZERO, B, PB}, and the data field of the fuzzy set of the total vehicle torque is [-1, 1]; the battery SOC value comprises 5 fuzzy subsets, the fuzzy set of the battery SOC value is {VL, L, M, H, VH}, and the data field of the fuzzy set of the battery SOC value is [0, 1]; the motor speed comprises 2 fuzzy subsets, the fuzzy set of the motor speed is {L, H}, and the data field of the fuzzy set of the motor speed is [0, 1]; the engine output torque comprises 5 fuzzy subsets, the fuzzy set of the engine output torque is {VS, S, M, B, VB}, and the data field of the fuzzy set of the engine output torque is [0, 1.4]; When the fuzzy set of the motor speed is H, the following control rules apply: When the fuzzy set of the motor speed is L, the following control rules apply: The total torque of the vehicle, the battery SOC value, and the engine output torque are all calculated using triangular membership functions, while the motor speed is calculated using a trapezoidal membership function.

2. The torque control method for hybrid electric vehicles according to claim 1, characterized in that, The fuzzy control toolbox was created based on MATLAB.

3. The torque control method for hybrid electric vehicles according to claim 1, characterized in that, The expression for 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 Representing fuzzy rules, x in =A i Indicates the total torque of the vehicle as input, y in1 =B i Indicates that the battery SOC value is used as input, y in2 =C i D represents the motor speed as input. i This indicates that the engine output torque is used as the output.

4. The torque control method for hybrid electric vehicles according to claim 3, characterized in that, The expression for the fuzzy inference algorithm is: R i =(A i and B i and C i )→D i ; Where i∈[1,n].

5. The torque control method for hybrid electric vehicles according to claim 4, characterized in that, The specific steps for converting the blurred value into a clear value using the deblurring algorithm include: The fuzzy quantity D of the output torque for each of the engines i Preset clear representative value u i ; The fuzzy rule R triggered under the current input i Trigger strength k i As weight; The sharp value U is calculated using a defuzzification algorithm; Where m represents the number of fuzzy rules.

6. A torque control system for a hybrid electric vehicle, characterized in that, include: Create a module: Create a fuzzy control toolbox, take the required total vehicle torque, battery SOC value and motor speed as inputs to the fuzzy control toolbox, and take the engine output torque as the output; Construction module: Set fuzzy subsets for the total vehicle torque, battery SOC value, motor speed and engine output torque respectively to construct membership functions; Establishment module: Establish fuzzy rules between the total vehicle torque, battery SOC value, motor speed and engine output torque; Inference module: Constructs a fuzzy rule base based on the fuzzy rules, and performs fuzzy inference based on the membership function, the fuzzy inference algorithm in the fuzzy control toolbox, and the fuzzy rule base to obtain fuzzy quantities; Deblurring module: Converts the blurred value into a clear value using a deblurring algorithm; Calculation module: Based on the clear value of the engine output torque and combined with the total torque of the vehicle, calculate the electric motor output torque; The total vehicle torque comprises 5 fuzzy subsets, the fuzzy set of the total vehicle torque is {NS, S, ZERO, B, PB}, and the data field of the fuzzy set of the total vehicle torque is [-1, 1]; the battery SOC value comprises 5 fuzzy subsets, the fuzzy set of the battery SOC value is {VL, L, M, H, VH}, and the data field of the fuzzy set of the battery SOC value is [0, 1]; the motor speed comprises 2 fuzzy subsets, the fuzzy set of the motor speed is {L, H}, and the data field of the fuzzy set of the motor speed is [0, 1]; the engine output torque comprises 5 fuzzy subsets, the fuzzy set of the engine output torque is {VS, S, M, B, VB}, and the data field of the fuzzy set of the engine output torque is [0, 1.4]; When the fuzzy set of the motor speed is H, the following control rules apply: When the fuzzy set of the motor speed is L, the following control rules apply: The total torque of the vehicle, the battery SOC value, and the engine output torque are all calculated using triangular membership functions, while the motor speed is calculated using a trapezoidal membership function.

7. A storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the torque control method for hybrid electric vehicles as described in any one of claims 1-5.

8. An electronic device, characterized in that, The device includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor, when executing the computer program, implements the torque control method for a hybrid electric vehicle as described in any one of claims 1-5.

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