An engine torque control method, system, and hybrid engineering machinery

Abstract

This application discloses an engine torque control method, system, and hybrid engineering machinery. The method includes: a dual-layer optimization control based on hybrid system speed and engine torque. The outer layer uses a Markov chain algorithm to build a hybrid system speed fluctuation prediction model to predict the hybrid system speed fluctuation at the next moment. The inner layer uses the speed fluctuation prediction result and combines the engine torque difference and the engine's optimal torque range to establish an engine compensation torque fuzzy control model. The engine torque is adjusted in real time according to the hybrid system speed fluctuation to maintain the hybrid system speed stability and effectively suppress the problem of decreased working efficiency caused by hybrid system speed fluctuation.

Description

Technical Field

[0001] This application relates to an engine torque control method, system, and hybrid engineering machinery, belonging to the field of hybrid engineering machinery technology. Background Technology

[0002] Currently, new energy construction machinery includes two types: pure electric and hybrid. Due to the problems of long charging time and limited charging facilities, pure electric construction machinery, although currently using battery swapping, has a high cost, has become the mainstream new energy construction machinery in the market.

[0003] Since hybrid-powered construction machinery has both an engine and an electric motor as drive mechanisms, how to rationally allocate and adjust the power of these two drive mechanisms has become a key research topic. Conventional control strategies usually only consider single control of speed or torque, which often leads to engine speed fluctuations, causing changes in hydraulic system flow and seriously affecting the operating efficiency of the construction machinery. Summary of the Invention

[0004] Objective: In view of at least one of the above technical problems, in order to solve the problem of reduced operating efficiency caused by speed fluctuations in hybrid engineering machinery, this application provides an engine torque control method, system, and hybrid engineering machinery. Based on two-layer optimization, the hybrid system speed fluctuation is used as the outer optimization target and the engine compensation torque is used as the inner optimization target. A hybrid system speed fluctuation prediction model and an engine compensation torque fuzzy control model are built. While ensuring that the hybrid system speed fluctuation is minimized, the engine torque is optimized to keep it always in the optimal working efficiency range, thereby reducing the energy consumption of the whole vehicle.

[0005] The technical solution adopted in this application is as follows:

[0006] In a first aspect, this application provides an engine torque control method, including:

[0007] Obtain the required speed and total required torque of the hybrid system;

[0008] Subtract the actual motor torque from the total required torque to obtain the engine required torque;

[0009] The difference between the engine's required torque and actual torque is obtained by comparing the engine's required torque and actual torque.

[0010] The optimal efficiency coefficient of the engine is obtained based on the engine's required torque and the current engine speed.

[0011] The hybrid system speed fluctuation is calculated based on the required speed and the actual speed of the hybrid system; based on the hybrid system speed fluctuation, the hybrid system speed fluctuation at the next moment is predicted using the hybrid system speed fluctuation prediction model;

[0012] Based on the predicted hybrid system speed fluctuation, the difference between the engine demand torque and the actual torque, and the engine's optimal efficiency coefficient, the engine compensation torque and compensation coefficient are obtained using the engine compensation torque control model.

[0013] The engine torque is calculated based on the engine demand torque, engine torque, and compensation coefficient.

[0014] The engine is controlled based on the calculated engine torque.

[0015] Secondly, this application provides an engine torque control system, including a processor and a storage medium;

[0016] The storage medium is used to store instructions;

[0017] The processor is configured to operate according to the instructions to execute the method according to the first aspect.

[0018] Thirdly, this application provides a hybrid engineering machinery, including the aforementioned engine torque control system.

[0019] Fourthly, this application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the method described in the first aspect.

[0020] Beneficial Effects: The engine torque control method, system, and hybrid engineering machinery provided in this application are based on a dual-layer optimization control method for hybrid system speed and engine torque. The outer layer uses a Markov chain algorithm to build a hybrid system speed fluctuation prediction model to predict the hybrid system speed fluctuation at the next moment. The inner layer uses the speed fluctuation prediction results, combined with the engine torque difference and the engine's optimal torque range, to establish an engine compensation torque fuzzy control model. The engine torque is adjusted in real time according to the hybrid system speed fluctuation to maintain stable hybrid system speed, effectively suppressing the problem of decreased working efficiency caused by hybrid system speed fluctuation. This application also considers engine fuel economy, controlling the engine to always operate within the optimal torque range, thereby improving overall vehicle fuel efficiency while reducing speed fluctuations in the hybrid system. Attached Figure Description

[0021] Figure 1 This is a schematic flowchart illustrating the engine torque control principle according to an embodiment of this application;

[0022] Figure 2 To compensate for engine torque according to one embodiment of this application - - Schematic diagram of fuzzy control rules;

[0023] Figure 3To compensate for engine torque according to one embodiment of this application - - Schematic diagram of fuzzy control rules;

[0024] Figure 4 The compensation coefficient according to one embodiment of this application - - Schematic diagram of fuzzy control rules. Detailed Implementation

[0025] The present application will be further described below with reference to the accompanying drawings and embodiments. The following embodiments are only used to more clearly illustrate the technical solutions of the present application, and should not be used to limit the scope of protection of the present application.

[0026] In the description of this application, "several" means one or more, "multiple" means two or more, "greater than," "less than," and "exceeding" are understood to exclude the stated number, while "above," "below," and "within" are understood to include the stated number. The use of "first" and "second" in the description is merely for distinguishing technical features and should not be construed as indicating or implying relative importance, or implicitly indicating the number of indicated technical features, or implicitly indicating the order of the indicated technical features.

[0027] In the description of this application, the terms "one embodiment," "some embodiments," "illustrative embodiment," "example," "specific example," or "some examples," etc., refer to specific features, structures, materials, or characteristics described in connection with that embodiment or example, which are included in at least one embodiment or example of this application. 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.

[0028] The term "and / or" simply describes the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A alone, A and B simultaneously, or B alone. Additionally, the character " / " generally indicates that the preceding and following related objects have an "or" relationship.

[0029] Example 1: This example provides an engine torque control method, such as... Figure 1 As shown, it includes:

[0030] S1. Obtain the required speed of the hybrid system. and total demand torque ;

[0031] S2, Total required torque Subtract the actual torque of the motor to obtain the required torque of the engine. ;

[0032] S3. Obtain the difference between the engine demand torque and the actual engine torque based on the engine demand torque and the engine actual torque. ;

[0033] S4. Obtain the optimal engine efficiency coefficient based on the engine's required torque and the current engine speed. ;

[0034] S5, based on the required speed of the hybrid system. and the actual speed of the hybrid system Calculate the speed fluctuation of the hybrid system According to the speed fluctuation of the hybrid system The hybrid system speed fluctuation prediction model is used to predict the hybrid system speed fluctuation at the next moment.

[0035] S6. Based on the predicted speed fluctuation of the hybrid system The difference between the engine's required torque and the actual torque Engine optimal efficiency coefficient The engine compensation torque is obtained using the engine compensation torque control model. and compensation coefficient ;

[0036] S7. Calculate the engine torque based on the engine demand torque, engine torque, and compensation coefficient.

[0037] S8. Control the engine based on the calculated engine torque.

[0038] In some embodiments, in step S1, the required speed of the hybrid system is obtained. and total demand torque Specifically, it includes:

[0039] According to the gear signal and operating handle opening signal Determine the required speed of the hybrid system and total demand torque The calculation method is as follows:

[0040]

[0041]

[0042] The opening value of the operating handle Defined as The linear scaling is performed based on the physical position of the handle.

[0043] In some embodiments, the engine torque is calculated based on the engine demand torque, engine torque, and compensation coefficient, and is expressed as:

[0044] ;

[0045] in, For engine torque, To meet the engine's torque requirements, To compensate for engine torque, This is the compensation coefficient.

[0046] The speed fluctuation prediction model mainly uses the Markov chain algorithm to build a speed fluctuation state transition matrix to predict the speed fluctuation of the hybrid system in real time at the next moment.

[0047] In some embodiments, step S5, the method for constructing the hybrid system speed fluctuation prediction model, includes:

[0048] Obtain the required speed of the hybrid system at various times within a historical period. and the actual speed of the hybrid system According to the required speed of the hybrid system at the corresponding time and the actual speed of the hybrid system Calculate the speed fluctuation of the hybrid system at each time point. And determine the speed fluctuation range [ ], To minimize the speed fluctuation, This represents the maximum speed fluctuation; for the speed fluctuation of the hybrid system Discretization is performed to... and As the boundary, with Given a discrete range of x speed fluctuation intervals, a discrete model of the speed fluctuation of the hybrid system is established, which is expressed as:

[0049] ;

[0050] Obtain the state transition of the hybrid system's speed fluctuation over a historical period. Based on the state transition of the hybrid system's speed fluctuation over all time periods, statistically analyze the speed fluctuation of the hybrid system from the i-th speed fluctuation interval. Shift to the j-th speed fluctuation range Number of times and the i-th speed fluctuation range Total number of times shifted to other speed fluctuation ranges According to the number of times Total number of times The maximum likelihood estimation method is used to calculate the value of the i-th speed fluctuation range. Shift to the j-th speed fluctuation range probability ;

[0051] ;

[0052] Based on all probabilities of each speed fluctuation range Thus, the state transition probability matrix of the speed fluctuation of the hybrid system is obtained. That is, to obtain a prediction model for the speed fluctuation of the hybrid system;

[0053] .

[0054] This application divides the hybrid system into x speed fluctuation ranges. Therefore, it is necessary to calculate the state transition probability from each speed fluctuation range to other speed fluctuation ranges. The specific calculation steps are as follows:

[0055] a) Set the number of transitions from each hybrid system speed fluctuation range to other speed error ranges as follows: , , , , and initialize it to an initial value of 0, where x is the hybrid system speed fluctuation range number;

[0056] b) Statistically analyze the number of transitions within the speed fluctuation range for each hybrid system, for example, when... (i, j) = 1 and When (i, j+1) = x, ,express By repeating the above steps, the number of state transitions in all speed fluctuation ranges of the hybrid system can be calculated.

[0057] c) Count the number of times each hybrid system's speed fluctuation range shifts to other speed fluctuation ranges, for example... By repeating the above calculation steps, the number of times each hybrid system speed fluctuation range transitions to other speed fluctuation ranges can be calculated, and the state transition probability of each hybrid system speed fluctuation range to other speed fluctuation ranges can be calculated according to the state transition probability calculation formula.

[0058] d) Using the state transition probability from each speed fluctuation range of the hybrid system to other speed fluctuation ranges calculated in the above steps as input, calculate the state transition probability matrix of the speed fluctuation range of the hybrid system according to the formula for calculating the state transition probability matrix, and then obtain the speed fluctuation prediction model of the hybrid system.

[0059] Prediction of the hybrid system speed fluctuation range at the next moment: The current moment's hybrid system speed fluctuation range (k) As input, the hybrid system speed fluctuation prediction model takes the interval corresponding to its maximum state transition probability value as the hybrid system speed fluctuation interval for the next time step k+1. By continuously repeating this prediction step, the speed fluctuation range of the hybrid system at the next moment can be predicted in real time.

[0060] In some embodiments, in step S6, the engine compensation torque control model is constructed using a fuzzy control algorithm, specifically including:

[0061] The input variable for the fuzzy control algorithm is the speed fluctuation predicted by the speed fluctuation prediction model. The difference between the engine's required torque and the actual torque Engine optimal efficiency coefficient The output variable is the engine compensation torque. and compensation coefficient ;

[0062] The engine compensation torque control model stores the engine compensation torque. With speed fluctuation The difference between the engine's required torque and the actual torque Engine optimal efficiency coefficient The correspondence between them, and the compensation coefficient With speed fluctuation The difference between the engine's required torque and the actual torque Engine optimal efficiency coefficient The correspondence between them.

[0063] In this embodiment, in the engine compensation torque control model,

[0064] The speed fluctuation predicted by the speed fluctuation prediction model The fuzzy subset is defined as The corresponding language variable is ;

[0065] Difference between engine required torque and actual torque Fuzzy subset The corresponding language variable is ;

[0066] Engine optimal efficiency coefficient The fuzzy subset is defined as The corresponding language variable is ;

[0067] Engine compensation torque The fuzzy subset is The corresponding language variable is ;

[0068] Compensation coefficient The fuzzy subset is defined as The corresponding language variable is The established fuzzy control rules for engine compensation torque are as follows: Figure 2 , Figure 3 , Figure 4 As shown.

[0069] Since the engine and the motor are rigidly connected, the engine speed is equal to the actual speed of the motor.

[0070] In some embodiments, step S4, obtaining the engine's optimal efficiency coefficient based on the engine's required torque and current engine speed, includes:

[0071] The optimal torque range at each engine speed is determined based on the engine's universal characteristic curve.

[0072] The optimal torque range is determined based on the current engine speed (actual motor speed); the optimal torque range includes the maximum and minimum values ​​of the engine's optimal torque range.

[0073] Calculate the engine's optimal efficiency coefficient based on the engine's required torque and optimal torque range. , is represented as:

[0074] ;

[0075] In the formula, This is the torque required by the engine; This represents the maximum value within the engine's optimal torque range. This represents the minimum value within the engine's optimal torque range. This represents the engine's optimal efficiency coefficient. .

[0076] This application presents an engine torque control method based on a dual-layer optimization control approach for hybrid system speed and engine torque. The outer layer uses a Markov chain algorithm to build a hybrid system speed fluctuation prediction model to predict the next moment's hybrid system speed fluctuation. The inner layer uses the speed fluctuation prediction results, combined with the engine torque difference and the engine's optimal torque range, to establish an engine compensation torque fuzzy control model. This model adjusts the engine torque in real time according to the hybrid system speed fluctuations to maintain stable hybrid system speed, effectively suppressing the problem of decreased operating efficiency caused by hybrid system speed fluctuations. This application also considers engine fuel economy, controlling the engine to always operate within the optimal torque range, thereby improving overall vehicle fuel efficiency while reducing speed fluctuations in the hybrid system.

[0077] Example 2: Based on Example 1, this example provides an engine torque control system, including a processor and a storage medium;

[0078] The storage medium is used to store instructions;

[0079] The processor is configured to operate according to the instructions to execute the method according to Embodiment 1.

[0080] In some specific embodiments, the control system includes a demand instruction parsing module, a two-layer optimization control module, and a hybrid system.

[0081] The demand instruction parsing module mainly includes gear position instruction and operating handle opening instruction. The gear position instruction is used to control the speed state of the hybrid system module, and the operating handle opening instruction is used to control the torque state of the hybrid system module.

[0082] The dual-layer optimization control module mainly includes a speed fluctuation prediction model and an engine compensation torque fuzzy control model. The speed fluctuation prediction model mainly uses a Markov chain algorithm to build a speed fluctuation state transition matrix to predict the speed fluctuation of the hybrid system in real time at the next moment. The engine torque compensation fuzzy control model mainly uses the speed fluctuation of the hybrid system at the next moment output by the speed fluctuation prediction model as input to build fuzzy control rules to calculate the engine compensation torque and dynamically adjust the speed of the hybrid system to maintain the stable operation of the hybrid system.

[0083] The hybrid system includes a motor and an engine. The motor uses speed control to meet the flow requirements of the hydraulic system, and the engine uses torque control to meet the power requirements of the hydraulic system.

[0084] In this embodiment, an ISG motor is used.

[0085] Example 3: Based on Example 2, this example provides a hybrid engineering machinery, including the aforementioned engine torque control system.

[0086] Example 4: Based on Example 1, this example provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the method described in Example 1.

[0087] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0088] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, as well as combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0089] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0090] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0091] The above description is only a preferred embodiment of this application. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of this application, and these improvements and modifications should also be considered within the scope of protection of this application.

Claims

1. An engine torque control method characterized by, include: Obtain the required speed and total required torque of the hybrid system; Subtract the actual motor torque from the total required torque to obtain the engine required torque; The difference between the engine's required torque and actual torque is obtained by comparing the engine's required torque with the engine's actual torque. The optimal efficiency coefficient of the engine is obtained based on the engine's required torque and the current engine speed. The hybrid system speed fluctuation is calculated based on the required speed and the actual speed of the hybrid system; based on the hybrid system speed fluctuation, the hybrid system speed fluctuation at the next moment is predicted using the hybrid system speed fluctuation prediction model; Based on the predicted hybrid system speed fluctuation, the difference between the engine demand torque and the actual torque, and the engine's optimal efficiency coefficient, the engine compensation torque and compensation coefficient are obtained using the engine compensation torque control model. The engine torque is calculated based on the engine demand torque, engine torque, and compensation coefficient. The engine is controlled based on the calculated engine torque.

2. The method according to claim 1, characterized in that, The method for constructing a speed fluctuation prediction model for hybrid systems includes: Obtain the required speed of the hybrid system at various times within a historical period. and the actual speed of the hybrid system According to the required speed of the hybrid system at the corresponding time and the actual speed of the hybrid system Calculate the speed fluctuation of the hybrid system at each time point. And determine the speed fluctuation range [ ], To minimize the speed fluctuation, This represents the maximum speed fluctuation; for the speed fluctuation of the hybrid system Discretization is performed to... and As the boundary, with Given a discrete range of x speed fluctuation intervals, a discrete model of the speed fluctuation of the hybrid system is established, which is expressed as: ； Obtain the state transition of the hybrid system's speed fluctuation over a historical period. Based on the state transition of the hybrid system's speed fluctuation over all time periods, statistically analyze the speed fluctuation of the hybrid system from the i-th speed fluctuation interval. Shift to the j-th speed fluctuation range Number of times and the i-th speed fluctuation range Total number of times shifted to other speed fluctuation ranges According to the number of times Total number of times The maximum likelihood estimation method is used to calculate the value of the i-th speed fluctuation range. Shift to the j-th speed fluctuation range probability ; ； Based on all probabilities of each speed fluctuation range Thus, the state transition probability matrix of the speed fluctuation of the hybrid system is obtained. That is, to obtain a prediction model for the speed fluctuation of the hybrid system; 。 3. The method according to claim 1, characterized in that, Based on the engine's required torque and current engine speed, the optimal engine efficiency coefficient is obtained, including: The optimal torque range at each engine speed is determined based on the engine's universal characteristic curve. The optimal torque range is determined based on the current engine speed; wherein the optimal torque range includes the maximum and minimum values ​​of the engine's optimal torque range. The optimal efficiency coefficient of the engine is calculated based on the engine's required torque and the optimal torque range.

4. The method according to claim 3, characterized in that, The optimal efficiency coefficient of the engine is calculated based on the engine's required torque and the optimal torque range, and is expressed as follows: ； In the formula, This represents the engine's optimal efficiency coefficient. This is the torque required by the engine; This represents the maximum value within the engine's optimal torque range. This represents the minimum value within the engine's optimal torque range.

5. The method according to claim 1, characterized in that, The engine compensation torque control model is constructed using a fuzzy control algorithm, specifically including: The input variable for the fuzzy control algorithm is the predicted speed fluctuation of the hybrid system. The difference between the engine's required torque and the actual torque Engine optimal efficiency coefficient The output variable is the engine compensation torque. and compensation coefficient ; The engine compensation torque control model stores the engine compensation torque. With speed fluctuation The difference between the engine's required torque and the actual torque Engine optimal efficiency coefficient The correspondence between them, and the compensation coefficient With speed fluctuation The difference between the engine's required torque and the actual torque Engine optimal efficiency coefficient The correspondence between them.

6. The method according to claim 5, characterized in that, In the engine compensation torque control model The speed fluctuation predicted by the speed fluctuation prediction model The fuzzy subset is defined as ; Difference between engine required torque and actual torque Fuzzy subset ; Engine optimal efficiency coefficient The fuzzy subset is defined as ; Engine compensation torque The fuzzy subset is ; compensation coefficient The fuzzy subset is defined as .

7. The method according to claim 1, characterized in that, Based on the engine demand torque, engine torque, and compensation coefficient, the engine torque is calculated, including: ； in, For engine torque, To meet the engine's torque requirements, To compensate for engine torque, This is the compensation coefficient.

8. An engine torque control system, characterized in that, Including processor and storage media; The storage medium is used to store instructions; The processor is configured to operate according to the instructions to perform the method according to any one of claims 1 to 7.

9. A hybrid engineering machinery, characterized in that, Includes the engine torque control system as described in claim 8.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the method according to any one of claims 1 to 7.

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