Hybrid power engineering machinery control method and system based on operation load identification

Abstract

The invention discloses a hybrid power engineering machinery control method and system based on operation load identification, and belongs to the technical field of engineering machinery. Target torque of a driver and predicted torque of a hydraulic pump at the target moment are calculated; calculating a torque difference value; based on a fuzzy control algorithm, considering the optimal fuel consumption torque interval of the engine and the optimal efficiency torque interval of the ISG motor, and determining the target torque of the engine and the target torque of the ISG motor according to the target torque of the driver, the difference value between the target torque of the driver and the predicted torque of the hydraulic pump and the SOC of the battery; and jointly controlling the pressure of the hydraulic pump by using the target torque of the engine and the target torque of the ISG motor. The method has the advantages that the operation load prediction model is built through Markov, the operation load at the future moment is predicted, the driver target torque and the difference value of the two torque are introduced, the torque distribution fuzzy controller of the engine and the ISG motor is built by considering the SOC state of the battery, and the target torque of the engine and the ISG motor is dynamically adjusted.

Description

Technical Field

[0001] The invention relates to a hybrid power engineering machinery control method and system based on work load identification, belonging to the technical field of engineering machinery. Background Art

[0002] Although pure electric construction machinery has the advantages of low cost and no pollution, it has the obvious disadvantages of short operation time and long recharging time. Hybrid excavators have the advantages of continuous operation of traditional excavators and low cost of pure electric excavators. Therefore, hybrid construction machinery has become an important part of the field of new energy construction machinery.

[0003] Due to the complex and changeable working conditions of excavators, the engine and motor need to respond to frequently changing loads, which seriously affects the efficiency and energy consumption of the motor and engine. In order to ensure that the engine and motor can quickly respond to the working conditions of the excavator, it is necessary to identify the working conditions of the excavator and formulate different engine and motor control strategies according to its working conditions. Therefore, it is a difficult problem to quickly identify the working conditions of the excavator and formulate matching engine and motor control strategies.

[0004] In the prior art, although the working mode of engineering vehicles can be predicted online, the prediction accuracy is limited by the output accuracy of the deep learning model, and there is a large deviation in the identification of working conditions; in the prior art, working condition data is obtained through preset time intervals, and characteristic value analysis is performed on the data of the preset time intervals to determine the working condition of the excavator, but there may be multiple working conditions coexisting within the time interval. If it is identified as a single working condition, it will cause the problem of low identification accuracy; in the prior art, there are also devices such as sensors and gyroscopes that are added to collect the acceleration of engineering vehicles in any direction, which increases costs and does not utilize the application of patents; in existing plans, identification is often performed on the excavation stage and the platform setting condition, ignoring other working conditions of the excavator.

[0005] Therefore, quickly identifying the excavator's operating conditions and formulating matching engine and motor control strategies have become problems that need to be solved urgently. Summary of the invention

[0006] The technical problem to be solved by the present invention is to overcome the defects of the prior art and provide a hybrid power engineering machinery control method and system based on workload identification.

[0007] In order to solve the above technical problems, the present invention is implemented by adopting the following technical solutions.

[0008] On the one hand, in a first aspect, the present invention discloses a hybrid engineering machinery control method based on workload identification, comprising: Calculate the driver's target torque based on the acquired handle opening and opening change rate; According to the currently collected hydraulic pump pressure, the predicted pressure of the hydraulic pump at the target time is predicted, and the predicted torque of the hydraulic pump at the target time is calculated according to the predicted pressure of the hydraulic pump at the target time, wherein the hydraulic pump is used to provide a power source for the hydraulic execution unit of the hydraulic system of the engineering machinery; Calculating a difference between a driver target torque and a hydraulic pump predicted torque according to the driver target torque and the hydraulic pump predicted torque; Based on the fuzzy control algorithm, the engine target torque and the ISG motor target torque are determined according to the driver target torque, the difference between the driver target torque and the hydraulic pump predicted torque, and the battery SOC, taking into account the engine optimal fuel consumption torque range and the ISG motor optimal efficiency torque range; The hydraulic pump pressure is controlled by the engine target torque and the ISG motor target torque.

[0009] Furthermore, the calculation of the driver's target torque according to the acquired handle opening and opening change rate includes: Collect the handle opening k and opening change rate operated by the driver , through the handle opening k and the opening change rate ̇Calculate the driver's target torque.

[0010] Furthermore, the method predicts the predicted pressure of the hydraulic pump at the target time based on the currently collected hydraulic pump pressure, including: The hydraulic pump pressure set under various working conditions is collected, singular values ​​are removed from the hydraulic pump pressure set, and the pressure distribution of the hydraulic pump pressure set after the singular values ​​are removed is statistically analyzed to obtain the overall pressure range [0, p max ], p max Indicates the maximum pressure of the hydraulic pump; The overall pressure interval [0, p max ]According to the discrete interval difference Divide into n discrete intervals, hydraulic pump pressure p It is expressed as: ; The state of the pressure distribution is determined as: ; In the formula, Indicates i The state corresponding to the discrete interval is i= 1,2,3,…, n ; According to the divided pressure distribution state, the transition probability between states is calculated, and the statistics of any state After a change, it moves to another state The probability of Transfer to The transition probability is ; After completing the calculation of all state transition probabilities, the state transition probability matrix composed of all state transition probabilities P is a Markov chain workload prediction model, and the predicted hydraulic pump pressure at the next moment is obtained from the Markov chain workload prediction model according to the currently collected hydraulic pump pressure; ; State transition probability matrix P Satisfy the following properties: .

[0011] Furthermore, the expression for calculating the predicted torque of the hydraulic pump at the target time according to the predicted pressure of the hydraulic pump at the target time is:

[0012] Where: T Predicting the torque of the hydraulic pump at the target time; p f Predicting the pressure of the hydraulic pump at the target time; V is the hydraulic pump displacement; η is the volumetric efficiency of the hydraulic pump.

[0013] Furthermore, the expression for the difference between the driver's target torque and the hydraulic pump predicted torque is: ; Where: Δ is the difference between the driver's target torque and the hydraulic pump's predicted torque, T t is the driver's target torque, T Predict the hydraulic pump torque at the target time.

[0014] Furthermore, the fuzzy control algorithm is based on the engine optimal fuel consumption torque range and the ISG motor optimal efficiency torque range, and the engine target torque and the ISG motor target torque are determined according to the driver target torque, the difference between the driver target torque and the hydraulic pump predicted torque, and the battery SOC, including: Setting the driver target torque The fuzzy subsets of and the corresponding driver target torque The first language variable of each element in the fuzzy subset of ; Setting the driver target torque The difference between the predicted torque of the hydraulic pump and The fuzzy subsets and the corresponding differences The second language variable of the elements in the fuzzy subset of ; Set the fuzzy subset of the battery SOC and the third language variable corresponding to each element in the fuzzy subset of the battery SOC; Setting the engine target torque The fuzzy subsets and the corresponding engine target torque The fourth language variable of each element in the fuzzy subset of ; Set the ISG motor target torque The fuzzy subsets and the corresponding ISG motor target torque The fifth language variable for each element in the fuzzy subset of ; Constructing fuzzy control rules based on first language variables, second language variables, third language variables, fourth language variables and fifth language variables; According to the constructed fuzzy control rules, when the battery SOC is lower than the preset power, the ISG motor target torque is controlled The value of the ISG motor target torque The value can satisfy both the vehicle load and the ISG motor power generation; According to the constructed fuzzy control rules, when the battery SOC is not lower than the preset power, the driver's target torque The difference between the predicted torque of the hydraulic pump and The engine target torque is determined by the subset and ISG motor target torque .

[0015] In a second aspect, the present invention discloses a hybrid engineering machinery control system based on workload identification, comprising: Engine module, ISG motor module, hydraulic system, operating device and control module; The hydraulic system includes a hydraulic pump, a hydraulic valve, a hydraulic actuator and a hydraulic circuit; the hydraulic pump is used to provide a power source for the hydraulic actuator of the hydraulic system of the engineering machinery; the hydraulic valve is used to control the action of the hydraulic actuator; the hydraulic circuit is a hydraulic pipeline connecting the hydraulic pump, the hydraulic valve and the hydraulic actuator; The engine module is used to provide a power source for the engineering machinery and provide power input for the hydraulic pump; The ISG motor is used to provide a power source for the engineering machinery, provide power input for the hydraulic pump, and absorb excess energy of the engine module to charge the battery; The control module includes an engine control unit, an ISG motor control unit, a hydraulic system control unit and a coordination control unit; the engine control unit is used to control the engine to respond to the speed or torque changes of the operator; the ISG motor control unit is used to control the ISG motor to respond to the speed or torque changes of the operator; the hydraulic system control unit is used to control the on and off of the hydraulic system according to the operator's instructions; the coordination control is used for the power or torque distribution of the engine module and the ISG motor module, as well as the coordinated control of the engine module, the ISG motor module and the hydraulic system; The operating device is a mechanism used by the driver to operate the excavator, including a walking operating mechanism, a rotating operating mechanism, and an operating handle.

[0016] In a third aspect, the present invention discloses a hybrid engineering machinery control device based on workload identification, comprising: Compute module for: Calculate the driver's target torque based on the acquired handle opening and opening change rate; According to the currently collected hydraulic pump pressure, the predicted pressure of the hydraulic pump at the target time is predicted, and the predicted torque of the hydraulic pump at the target time is calculated according to the predicted pressure of the hydraulic pump at the target time, wherein the hydraulic pump is used to provide a power source for the hydraulic execution unit of the hydraulic system of the engineering machinery; Calculating a difference between a driver target torque and a hydraulic pump predicted torque according to the driver target torque and the hydraulic pump predicted torque; A fuzzy control module, for determining the engine target torque and the ISG motor target torque based on a fuzzy control algorithm, taking into account the engine optimal fuel consumption torque range and the ISG motor optimal efficiency torque range, according to the driver target torque, the difference between the driver target torque and the hydraulic pump predicted torque, and the battery SOC; The control module is used to control the hydraulic pump pressure by using the engine target torque and the ISG motor target torque.

[0017] In a fourth aspect, the present invention discloses a computer-readable storage medium storing one or more programs, characterized in that the one or more programs include instructions, which, when executed by a computing device, enable the computing device to execute the method of the first aspect.

[0018] In a fifth aspect, the present invention discloses a computer device, characterized in that it includes: One or more processors, a memory, and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, and the one or more programs include instructions for executing the method of the first aspect.

[0019] The beneficial effects achieved by the present invention are: The control method of the present invention can dynamically adjust the target torque of the engine and ISG motor through the driver's target torque, the predicted value of the operating system and the battery SOC, and solve the problem of slow response speed of the engine and ISG motor. The present invention takes into account the problem of lagging operating load, builds an operating load prediction model through Markov, predicts the operating load at future moments, introduces the driver's target torque and the difference between the two to avoid the problem of too low prediction accuracy, builds a fuzzy controller for engine and ISG motor torque distribution considering the battery SOC state, and dynamically adjusts the target torque of the engine and ISG motor. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Figure 1 It is a module schematic diagram of a hybrid excavator control system based on workload identification of the present invention; Figure 2 The invention is a hybrid excavator work load distribution based on work load identification - - Fuzzy rule graph Figure 3 The invention is a hybrid excavator work load distribution based on work load identification - - Fuzzy rule graph Figure 4 The invention is a hybrid excavator work load distribution based on work load identification - - Fuzzy rule graph Figure 5 The invention is a hybrid excavator work load distribution based on work load identification - - Fuzzy rule graph. DETAILED DESCRIPTION

[0021] The present invention will be further described below in conjunction with the accompanying drawings. The following embodiments are only used to more clearly illustrate the technical solution of the present invention, and cannot be used to limit the protection scope of the present invention.

[0022] In the description of the present invention, it should be understood that the terms "center", "longitudinal", "lateral", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside" and the like indicate positions or positional relationships based on the positions or positional relationships shown in the accompanying drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as limiting the present invention. In addition, the terms "first", "second", and the like are only used for descriptive purposes, and cannot be understood as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, features defined as "first", "second", and the like may explicitly or implicitly include one or more of the features. In the description of the present invention, unless otherwise specified, "multiple" means two or more.

[0023] In the description of the present invention, it should be noted that, unless otherwise clearly specified and limited, the terms "installed", "connected", and "connected" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal communication of two components. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood by specific circumstances.

[0024] Embodiment 1: This embodiment introduces a hybrid engineering machinery control method based on workload identification, including: Calculate the driver's target torque based on the acquired handle opening and opening change rate; According to the currently collected hydraulic pump pressure, the predicted pressure of the hydraulic pump at the target time is predicted, and the predicted torque of the hydraulic pump at the target time is calculated according to the predicted pressure of the hydraulic pump at the target time, wherein the hydraulic pump is used to provide a power source for the hydraulic execution unit of the hydraulic system of the engineering machinery; Calculating a difference between a driver target torque and a hydraulic pump predicted torque according to the driver target torque and the hydraulic pump predicted torque; Based on the fuzzy control algorithm, the engine target torque and the ISG motor target torque are determined according to the driver target torque, the difference between the driver target torque and the hydraulic pump predicted torque, and the battery SOC, taking into account the engine optimal fuel consumption torque range and the ISG motor optimal efficiency torque range; The hydraulic pump pressure is controlled by the engine target torque and the ISG motor target torque.

[0025] The method of calculating the driver's target torque according to the acquired handle opening and opening change rate includes: Collect the handle opening k and opening change rate operated by the driver , through the handle opening k and the opening change rate ̇Calculate the driver's target torque.

[0026] The method of predicting the hydraulic pump predicted pressure at the target time according to the currently collected hydraulic pump pressure includes: The hydraulic pump pressure set under various working conditions is collected, singular values ​​are removed from the hydraulic pump pressure set, and the pressure distribution of the hydraulic pump pressure set after the singular values ​​are removed is statistically analyzed to obtain the overall pressure range [0, p max ], p max Indicates the maximum pressure of the hydraulic pump; The overall pressure interval [0, p max ]According to the discrete interval difference Divide into n discrete intervals, hydraulic pump pressure p It is expressed as:

[0027] The state of pressure distribution is:

[0028] In the formula, Indicates i The state corresponding to the discrete interval is i= 1,2,3,…, n ; According to the divided pressure distribution state, the transition probability between states is calculated, and the statistics of any state After a change, it moves to another state The probability of Transfer to The transition probability is ; After completing the calculation of all state transition probabilities, the state transition probability matrix composed of all state transition probabilities P is a Markov chain workload prediction model, and the predicted hydraulic pump pressure at the next moment is obtained from the Markov chain workload prediction model according to the currently collected hydraulic pump pressure;

[0029] State transition probability matrix P Satisfy the following properties: .

[0030] The expression for calculating the predicted torque of the hydraulic pump at the target time according to the predicted pressure of the hydraulic pump at the target time is: Where: T Predicting the torque of the hydraulic pump at the target time; p f Predicting the pressure of the hydraulic pump at the target time; V is the hydraulic pump displacement; η is the volumetric efficiency of the hydraulic pump.

[0031] The expression of the difference between the driver's target torque and the hydraulic pump predicted torque is:

[0032] Where: Δ is the difference between the driver's target torque and the hydraulic pump's predicted torque, T t is the driver's target torque, T Predict the hydraulic pump torque at the target time.

[0033] The fuzzy control algorithm is based on the engine optimal fuel consumption torque range and the ISG motor optimal efficiency torque range, and the engine target torque and the ISG motor target torque are determined according to the driver target torque, the difference between the driver target torque and the hydraulic pump predicted torque, and the battery SOC, including: The driver's target torque The fuzzy subset of , the corresponding language variable is ; The difference between the driver's target torque and the hydraulic pump's predicted torque The fuzzy subset of , the corresponding language variable is ; The fuzzy subset of battery SOC is defined as , the corresponding language variable is ; The engine target torque The fuzzy subset of , the corresponding language variable is ; The ISG motor target torque The fuzzy subset of , the corresponding language variable is ; Based on the engine target torque , the difference between the driver's target torque and the hydraulic pump's predicted torque Dynamically adjust the engine target torque based on different subsets of SOC and ISG motor target torque , the specific rules are as follows: (1) When SOC is or When the fuzzy controller determines that the ISG motor should be in the power generation state, its torque is or Subset, fast compensation of battery power, engine target torque Should be in or The subset must satisfy both the vehicle load and the ISG motor generating torque.

[0034] (2) When SOC is or When the fuzzy controller is based on the engine target torque The difference between the driver's target torque and the hydraulic pump's predicted torque The subset to which it belongs determines the target torque of the engine and ISG motor target torque .when When it is a negative value, it means that the current predicted value is less than the driver's target torque. At this time, the ISG motor torque should be increased to meet the driver's required torque. When it is positive, it means that the predicted value is greater than the driver's target torque, and the ISG motor torque should be reduced. The difference between the driver's target torque and the hydraulic pump predicted torque The size of the ISG motor target torque is adjusted to optimize the prediction model.

[0035] The establishment of specific fuzzy control rules is shown in Table 1: Table 1 ; ;

[0036] Embodiment 2 is based on the same inventive concept as Embodiment 1. This embodiment introduces a hybrid excavator control method based on workload identification, including a workload calculation module, a workload prediction module, and a workload distribution module; (1) Workload prediction module: The workload prediction model is built based on the Markov chain. The steps of the Markov prediction model are generally divided into data sample collection, discretization processing, state transition probability calculation and state transition probability matrix calculation.

[0037] a. Data collection and processing: Collect the operating load data of the hydraulic pump under various working conditions, take the collected sample data as historical data, first analyze the pressure data Perform preprocessing, remove singular values, and calculate pressure Distribution situation.

[0038] b. Data discretization processing: Assume that the pressure interval of the sample data is [0, p max ], the sample data is discretized and the pressure is divided into different pressure intervals. The discrete intervals are , divide the sample data into n The discrete intervals are as follows:

[0039] That is, the state of the sample data is determined as:

[0040] c. State transition probability calculation: According to the divided states, the transition probability between states is calculated, and the statistics of any state are After a change, it moves to another state The probability of one-step state transition is calculated by Transfer to The transition probability is , for example, by the pressure range Jump to pressure range The probability of is 0.2, then ; .

[0041] d. State transition probability matrix calculation: After completing the calculation of all state transition probabilities, the transition probability matrix composed of all state transition probabilities This is the Markov chain workload prediction model that needs to be established. According to the current real-time pressure of the hydraulic pump, the predicted pressure at the next moment can be obtained through the state transfer probability matrix.

[0042]

[0043] The state transition probability matrix P satisfies the following properties:

[0044] e. Pressure prediction: At time t, the real-time pressure of the hydraulic pump is Input into the workload prediction model, the prediction model calculates the predicted pressure at time t+1 according to the state probability transfer matrix, that is: .

[0045] (2) Workload calculation module: a. Target torque calculation: Target torque calculation refers to collecting the handle opening of the driver , through the handle opening and its rate of change Calculate the driver's target torque.

[0046] .

[0047] b. Predicted torque calculation: Predicted torque calculation refers to calculating the required torque of the hydraulic pump through information such as the hydraulic pump mechanical efficiency and displacement. The calculation formula is shown below.

[0048] ; Where: is the predicted torque of the hydraulic pump; The predicted pressure output by the workload prediction module; is the hydraulic pump displacement; is the volumetric efficiency of the hydraulic pump.

[0049] c. Torque difference calculate: Torque difference It is the difference between the driver's target torque and the predicted torque of the hydraulic pump, and is used as the torque distribution basis of the work distribution module. The calculation formula is as follows: .

[0050] (3) Workload distribution module: The load distribution module is mainly used to distribute the torque between the engine and the ISG motor, based on the driver's target torque. , torque difference The fuzzy controller is designed by fuzzy control algorithm, and the engine target torque is calculated in real time by taking the battery SOC as input and the engine target torque and ISG motor target torque as output. and ISG motor target torque .

[0051] The input variable of the fuzzy control algorithm is the driver's target torque , torque difference and battery SOC, the output variables are the engine target torque and the ISG motor target torque. The fuzzy subset of , the corresponding language variable is ; The torque difference The fuzzy subset of , the corresponding language variable is ; Define the fuzzy subset of battery SOC as , the corresponding language variable is ; Set the engine target torque The fuzzy subset of , the corresponding language variable is ; Set the ISG motor target torque The fuzzy subset of , the corresponding language variable is , the established fuzzy control rules are as follows Figure 2-Figure 5 shown.

[0052] The engine target torque output by the fuzzy controller and ISG motor target torque As the torque command of the engine model and the ISG motor model, they jointly drive the hydraulic pump and adjust the hydraulic pump pressure in real time.

[0053] Embodiment 3 is based on the same inventive concept as other embodiments. This embodiment introduces a hybrid excavator control system based on workload identification, including an engine module, an ISG motor module, a hydraulic system, an operating device and a control module.

[0054] The engine is the power source of the excavator and provides power input for the hydraulic pump; The ISG motor is the power source of the excavator. In addition to providing power input for the hydraulic pump, it can also serve as a generator to absorb excess energy from the engine. The hydraulic system includes a hydraulic pump, a hydraulic valve, a hydraulic actuator and a hydraulic circuit, etc. The hydraulic pump refers to the power source of the hydraulic system of the excavator, which provides power for the operation of the excavator; the hydraulic valve refers to the control element of the hydraulic system of the excavator, which is used to control the action of the hydraulic actuator; the hydraulic actuator refers to the actuator of the action of each mechanism of the excavator; the hydraulic circuit refers to the hydraulic pipeline connecting the hydraulic pump, the hydraulic valve and the hydraulic actuator, which is used for the transmission of hydraulic oil; The control module includes the control of the engine, the control of the ISG motor, the control of the hydraulic system and the coordinated control. The engine control refers to the engine responding to the operator's speed or torque changes; the ISG motor control refers to the ISG motor responding to the operator's speed or torque changes; the hydraulic system control refers to the control module controlling the on and off of the hydraulic system according to the operator's instructions to achieve vehicle operation control; the coordinated control refers to the power or torque distribution of the engine and the ISG motor, as well as the coordinated control of the engine, the ISG motor and the hydraulic system.

[0055] The operating device refers to the mechanism used by the driver to operate the excavator, including a walking operating mechanism, a rotating operating mechanism, an operating handle, etc.

[0056] Embodiment 4, based on the same inventive concept as other embodiments, this embodiment introduces a hybrid engineering machinery control device based on workload identification, including: Compute module for: Calculate the driver's target torque based on the acquired handle opening and opening change rate; According to the currently collected hydraulic pump pressure, the predicted pressure of the hydraulic pump at the target time is predicted, and the predicted torque of the hydraulic pump at the target time is calculated according to the predicted pressure of the hydraulic pump at the target time, wherein the hydraulic pump is used to provide a power source for the hydraulic execution unit of the hydraulic system of the engineering machinery; Calculating a difference between a driver target torque and a hydraulic pump predicted torque according to the driver target torque and the hydraulic pump predicted torque; A fuzzy control module, for determining the engine target torque and the ISG motor target torque based on a fuzzy control algorithm, taking into account the engine optimal fuel consumption torque range and the ISG motor optimal efficiency torque range, according to the driver target torque, the difference between the driver target torque and the hydraulic pump predicted torque, and the battery SOC; The control module is used to control the hydraulic pump pressure by using the engine target torque and the ISG motor target torque.

[0057] Embodiment 5 is based on the same inventive concept as other embodiments. This embodiment introduces a computer-readable storage medium storing one or more programs, wherein the one or more programs include instructions, and when the instructions are executed by a computing device, the computing device executes the method described in Embodiment 1.

[0058] Embodiment 6, based on the same inventive concept as other embodiments, this embodiment introduces a computer device, including: One or more processors, a memory, and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, and the one or more programs include instructions for executing the method described in Example 1.

[0059] It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Furthermore, the present invention may take the form of a computer program product implemented 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.

[0060] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0061] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0062] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0063] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the technical principles of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A hybrid engineering machinery control method based on workload identification, characterized in that: include: Calculate the driver's target torque based on the acquired handle opening and opening change rate; According to the currently collected hydraulic pump pressure, the predicted pressure of the hydraulic pump at the target time is predicted, and the predicted torque of the hydraulic pump at the target time is calculated according to the predicted pressure of the hydraulic pump at the target time, wherein the hydraulic pump is used to provide a power source for the hydraulic execution unit of the hydraulic system of the engineering machinery; Calculating a difference between a driver target torque and a hydraulic pump predicted torque according to the driver target torque and the hydraulic pump predicted torque; Based on the fuzzy control algorithm, the engine target torque and the ISG motor target torque are determined according to the driver target torque, the difference between the driver target torque and the hydraulic pump predicted torque, and the battery SOC, taking into account the engine optimal fuel consumption torque range and the ISG motor optimal efficiency torque range; The hydraulic pump pressure is controlled by the engine target torque and the ISG motor target torque.

2. The hybrid engineering machinery control method based on workload identification according to claim 1 is characterized in that: The method of calculating the driver's target torque according to the acquired handle opening and opening change rate includes: Collect the handle opening k and opening change rate operated by the driver , through the handle opening k and the opening change rate ̇Calculate the driver's target torque.

3. The hybrid engineering machinery control method based on workload identification according to claim 1, characterized in that: The method predicts the hydraulic pump pressure at the target time based on the currently collected hydraulic pump pressure, including: The hydraulic pump pressure set under various working conditions is collected, singular values ​​are removed from the hydraulic pump pressure set, and the pressure distribution of the hydraulic pump pressure set after the singular values ​​are removed is statistically analyzed to obtain the overall pressure range [0, p max ], p max Indicates the maximum pressure of the hydraulic pump; The overall pressure interval [0, p max ]According to the discrete interval difference Divide into n discrete intervals, hydraulic pump pressure p It is expressed as: ； The state of the pressure distribution is determined as: ； In the formula, Indicates i The state corresponding to the discrete interval is i= 1,2,3,…, n ; According to the divided pressure distribution state, the transition probability between states is calculated, and the statistics of any state After a change, it moves to another state The probability of Transfer to The transition probability is ; After completing the calculation of all state transition probabilities, the state transition probability matrix composed of all state transition probabilities P is a Markov chain workload prediction model, and the predicted hydraulic pump pressure at the next moment is obtained from the Markov chain workload prediction model according to the currently collected hydraulic pump pressure; ； State transition probability matrix P Satisfy the following properties: 。 4. The hybrid engineering machinery control method based on workload identification according to claim 1, characterized in that: The expression for calculating the predicted torque of the hydraulic pump at the target time according to the predicted pressure of the hydraulic pump at the target time is: ； Where: T Predicting the torque of the hydraulic pump at the target time; p f Predicting the pressure of the hydraulic pump at the target time; V is the hydraulic pump displacement; η is the volumetric efficiency of the hydraulic pump.

5. The hybrid engineering machinery control method based on workload identification according to claim 1, characterized in that: The expression of the difference between the driver's target torque and the hydraulic pump predicted torque is: ； Where: Δ is the difference between the driver's target torque and the hydraulic pump's predicted torque, T t is the driver's target torque, T Predict the hydraulic pump torque at the target time.

6. The hybrid engineering machinery control method based on workload identification according to claim 1, characterized in that: The fuzzy control algorithm is based on the engine optimal fuel consumption torque range and the ISG motor optimal efficiency torque range, and the engine target torque and the ISG motor target torque are determined according to the driver target torque, the difference between the driver target torque and the hydraulic pump predicted torque, and the battery SOC, including: Setting the driver target torque The fuzzy subsets of and the corresponding driver target torque The first language variable of each element in the fuzzy subset of ; Setting the driver target torque The difference between the predicted torque of the hydraulic pump and The fuzzy subsets and the corresponding differences The second language variable of the elements in the fuzzy subset of ; Set the fuzzy subset of the battery SOC and the third language variable corresponding to each element in the fuzzy subset of the battery SOC; Setting the target engine torque The fuzzy subsets and the corresponding engine target torque The fourth language variable of each element in the fuzzy subset of ; Set the ISG motor target torque The fuzzy subsets and the corresponding ISG motor target torque The fifth language variable for each element in the fuzzy subset of ; Constructing fuzzy control rules based on first language variables, second language variables, third language variables, fourth language variables and fifth language variables; According to the constructed fuzzy control rules, when the battery SOC is lower than the preset power, the ISG motor target torque is controlled The value of the ISG motor target torque The value can satisfy both the vehicle load and the ISG motor power generation; According to the constructed fuzzy control rules, when the battery SOC is not lower than the preset power, the driver's target torque The difference between the predicted torque of the hydraulic pump and The engine target torque is determined by the subset and ISG motor target torque .

7. A hybrid engineering machinery control system based on workload identification, characterized in that: include: Engine module, ISG motor module, hydraulic system, operating device and control module; The hydraulic system includes a hydraulic pump, a hydraulic valve, a hydraulic actuator and a hydraulic circuit; the hydraulic pump is used to provide a power source for the hydraulic actuator of the hydraulic system of the engineering machinery; the hydraulic valve is used to control the action of the hydraulic actuator; the hydraulic circuit is a hydraulic pipeline connecting the hydraulic pump, the hydraulic valve and the hydraulic actuator; The engine module is used to provide a power source for the engineering machinery and provide power input for the hydraulic pump; The ISG motor is used to provide a power source for the engineering machinery, provide power input for the hydraulic pump, and absorb excess energy of the engine module to charge the battery; The control module includes an engine control unit, an ISG motor control unit, a hydraulic system control unit and a coordination control unit; the engine control unit is used to control the engine to respond to the speed or torque changes of the operator; the ISG motor control unit is used to control the ISG motor to respond to the speed or torque changes of the operator; the hydraulic system control unit is used to control the on and off of the hydraulic system according to the operator's instructions; the coordination control is used for the power or torque distribution of the engine module and the ISG motor module, as well as the coordinated control of the engine module, the ISG motor module and the hydraulic system; The operating device is a mechanism used by the driver to operate the excavator, including a walking operating mechanism, a rotating operating mechanism, and an operating handle.

8. A hybrid engineering machinery control device based on workload identification, characterized in that: include: Compute module for: Calculate the driver's target torque based on the acquired handle opening and opening change rate; According to the currently collected hydraulic pump pressure, the predicted pressure of the hydraulic pump at the target time is predicted, and the predicted torque of the hydraulic pump at the target time is calculated according to the predicted pressure of the hydraulic pump at the target time, wherein the hydraulic pump is used to provide a power source for the hydraulic execution unit of the hydraulic system of the engineering machinery; Calculating a difference between a driver target torque and a hydraulic pump predicted torque according to the driver target torque and the hydraulic pump predicted torque; A fuzzy control module, for determining the engine target torque and the ISG motor target torque based on a fuzzy control algorithm, taking into account the engine optimal fuel consumption torque range and the ISG motor optimal efficiency torque range, according to the driver target torque, the difference between the driver target torque and the hydraulic pump predicted torque, and the battery SOC; The control module is used to control the hydraulic pump pressure by using the engine target torque and the ISG motor target torque.

9. A computer-readable storage medium storing one or more programs, characterized in that: The one or more programs include instructions which, when executed by a computing device, cause the computing device to perform the method of any one of claims 1 to 6.

10. A computer device, characterized in that: include, One or more processors, a memory, and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, and the one or more programs include instructions for executing any of the methods described in claims 1 to 6.

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