Intelligent electric control system and method for new energy loading machine

Abstract

The invention provides an intelligent electric control system and method of a new energy loader, and relates to the technical field of intelligent electric control, the operation parameters of a driving motor of a target new energy loader are collected, parameter identification is carried out according to the operation parameters, and a power parameter set is obtained; performing disturbance observation according to the historical measurement data and the power parameter set to obtain a load disturbance observation value, performing frequency spectrum extraction on the load disturbance observation value to obtain a power density spectrum, and determining driving operation characteristics according to the power density spectrum; performing working condition matching according to each historical working condition and the driving operation characteristics to obtain a current operation mode, and determining a compensation response moment according to the current operation mode and the load disturbance observation value; and generating a driving motor response torque instruction of the target new energy loader according to the compensation response torque. Accurate working condition identification and real-time compensation control can be realized based on the motor operation parameters and the load disturbance characteristics, and the response speed and the operation stability of the new energy loader driving motor are improved.

Description

Technical Field

[0001] This application relates to the field of intelligent electronic control technology, and more specifically, to an intelligent electronic control system and method for a new energy loader. Background Technology

[0002] New energy loaders typically use electric motors as their power source, with a motor control system driving the walking and working devices. In existing technologies, motor control systems generally rely on collecting motor operating parameters and combining them with mathematical or empirical models to estimate the motor's state, thus achieving basic control over motor torque and speed. Simultaneously, loaders experience various working conditions during operation, including loading, lifting, transporting, and unloading, resulting in frequent load changes. Control systems typically analyze historical working condition data, typical load characteristics, and motor response to construct working condition identification models or load prediction models, assisting in achieving relatively stable power output. In the control field of electric drive loaders, common solutions involve observing disturbances and identifying parameters based on motor state variables to better characterize the actual stress on the motor, and extracting load characteristics through frequency domain analysis methods, thereby providing a basis for optimizing motor control strategies.

[0003] In existing technologies, under complex and frequently changing load environments, it is difficult to accurately obtain the motor power characteristics of new energy loaders, resulting in insufficient accuracy in operating condition identification and often delayed load disturbance compensation. This leads to unstable motor response and low energy utilization efficiency. Therefore, how to achieve accurate operating condition identification and real-time compensation control based on motor operating parameters and load disturbance characteristics, and improve the response speed and operational stability of the drive motor of new energy loaders, has become a challenge for the industry. Summary of the Invention

[0004] This application provides an intelligent electronic control system and method for a new energy loader, which can achieve accurate working condition identification and real-time compensation control based on motor operating parameters and load disturbance characteristics, thereby improving the response speed and operational stability of the drive motor of the new energy loader.

[0005] In a first aspect, this application provides an intelligent electronic control method for a new energy loader, the intelligent electronic control method comprising the following steps: The operating parameters of the drive motor of the target new energy loader are collected, and parameter identification is performed based on the operating parameters to obtain a set of power parameters; Historical measurement data of the drive motor is acquired, and disturbance observation is performed based on the historical measurement data and the power parameter set to obtain load disturbance observation values. The power density spectrum is extracted from the load disturbance observation values, and then the drive operation characteristics of the target new energy loader are determined based on the power density spectrum. The historical operating conditions of the target new energy loader are obtained, and the operating conditions are matched with the driving operation characteristics to obtain the current operating mode of the target new energy loader. The compensation response torque of the drive motor of the target new energy loader is determined according to the current operating mode and the load disturbance observation value. The drive motor response torque command for the target new energy loader is generated based on the compensation response torque.

[0006] In this embodiment, the operating parameters of the drive motor of the target new energy loader are collected by sending a preset torque command sequence to the drive motor controller, and then synchronously collecting the operating parameters of the drive motor via the CAN bus.

[0007] In this embodiment, the parameter identification based on the operating parameters to obtain the power parameter set specifically includes: The angular acceleration sequence is obtained by numerical differentiation based on the operating parameters. Determine the pre-identification parameter vector based on the angular acceleration sequence and the operating parameters; The pre-identified parameter vector is inversely solved to obtain the dynamic parameter set.

[0008] In this embodiment, the disturbance observation obtained by performing disturbance observation based on the historical measurement data and the power parameter set specifically includes: The values ​​of internal state variables are determined based on the historical measurement data and the set of dynamic parameters; The load disturbance observations are determined using the internal state variable values ​​and the set of dynamic parameters.

[0009] In this embodiment, the spectral extraction of the load disturbance observations to obtain the power density spectrum specifically includes: Initialize the window function sequence; The data is windowed based on the window function sequence and the load disturbance observations to obtain each windowed observation; The power density spectrum is determined based on the observations of each windowed area.

[0010] In this embodiment, determining the driving operation characteristics of the target new energy loader based on the power density spectrum specifically includes: Initialize the boundary values ​​for each frequency; The original feature vector is determined based on the power density spectrum and each frequency boundary value; The driving operation characteristics of the target new energy loader are determined based on the original feature vector.

[0011] In this embodiment, determining the compensation response torque of the drive motor of the target new energy loader based on the current operating mode and the load disturbance observation specifically includes: Based on the current operating mode, load disturbance observations are filtered to obtain the load disturbance signal; The basic feedforward compensation amount is determined based on the load disturbance signal; The compensation response torque of the drive motor of the target new energy loader is determined by the basic feedforward compensation amount.

[0012] Secondly, this application provides an intelligent electronic control system for a new energy loader to execute an intelligent electronic control method for a new energy loader, the intelligent electronic control system comprising: The parameter identification module is used to collect the operating parameters of the drive motor of the target new energy loader, and perform parameter identification based on the operating parameters to obtain a set of power parameters; The disturbance observation module is used to acquire historical measurement data of the drive motor, perform disturbance observation based on the historical measurement data and the power parameter set to obtain load disturbance observation values, extract the spectrum of the load disturbance observation values ​​to obtain the power density spectrum, and then determine the drive operation characteristics of the target new energy loader based on the power density spectrum. The compensation response module is used to acquire various historical operating conditions of the target new energy loader, perform operating condition matching based on various historical operating conditions and the drive operation characteristics to obtain the current operating mode of the target new energy loader, and determine the compensation response torque of the drive motor of the target new energy loader based on the current operating mode and the load disturbance observation value. The instruction determination module is used to generate the drive motor response torque instruction of the target new energy loader based on the compensation response torque.

[0013] Thirdly, this application provides a computer device, which includes a memory and a processor. The memory is used to store a computer program, and the processor is used to call and run the computer program from the memory, so that the computer device executes the above-described intelligent electronic control method for a new energy loader.

[0014] Fourthly, this application provides a computer-readable storage medium storing instructions or code that, when executed on a computer, cause the computer to implement the aforementioned intelligent electronic control method for a new energy loader.

[0015] The technical solutions provided by the embodiments disclosed in this application have the following beneficial effects: The system collects the operating parameters of the drive motor of the target new energy loader, identifies parameters based on these parameters, and obtains a power parameter set. It acquires historical measurement data of the drive motor, performs disturbance observation based on the historical measurement data and the power parameter set, obtains load disturbance observation values, extracts the power density spectrum from the load disturbance observation values, and then determines the drive operation characteristics of the target new energy loader based on the power density spectrum. It acquires various historical operating conditions of the target new energy loader, performs operating condition matching based on these historical operating conditions and the drive operation characteristics, obtains the current operating mode of the target new energy loader, determines the compensation response torque of the drive motor of the target new energy loader based on the current operating mode and the load disturbance observation values, and generates a drive motor response torque command for the target new energy loader based on the compensation response torque.

[0016] Therefore, in this application, firstly, by collecting and identifying the operating parameters of the drive motor, a set of power parameters accurately characterizing the motor's dynamic characteristics can be obtained, enabling the control system to promptly grasp the actual performance state of the motor and providing reliable data support for subsequent disturbance observation, operating condition matching, and compensation control; secondly, by acquiring historical measurement data of the drive motor and combining it with the power parameter set for disturbance observation, the load changes experienced by the motor during actual operation can be accurately identified. Spectrum extraction of the observed load disturbance values ​​and generation of a power density spectrum can extract typical load characteristics of the new energy loader under various operating conditions from a frequency domain perspective. Based on the power density spectrum, the drive operation characteristics can be determined, enabling an accurate characterization of the loader's actual operating mode and providing highly reliable characteristic evidence for subsequent operating condition matching and compensation control, thus contributing to improving the intelligence and refinement of the overall control strategy; then, by acquiring By matching the historical operating conditions of the new energy loader with its driving operation characteristics, the current operating mode can be accurately identified and typical load behaviors can be quantified. Based on this current operating mode and combined with real-time load disturbance observations, a compensation response torque can be calculated and generated. This enables targeted feedforward compensation control, which significantly reduces torque errors and speed fluctuations caused by operating mode switching or sudden loads, improves the tracking accuracy and response speed of the drive motor, and enhances the robustness and operating efficiency of the drive motor under complex operating conditions. Finally, by converting the calculated compensation response torque into the drive motor's response torque command, the compensation strategy based on disturbance observation and operating mode matching can be implemented in real time as an executable control quantity. This ensures that the motor controller can immediately take corresponding feedforward closed-loop actions to offset load disturbances, reducing torque tracking errors and response delays, thereby improving the power response accuracy, energy utilization efficiency, and operational reliability of the new energy loader.

[0017] In summary, the technical solution adopted in this application can achieve accurate working condition identification and real-time compensation control based on motor operating parameters and load disturbance characteristics, thereby improving the response speed and operational stability of the drive motor of the new energy loader. Attached Figure Description

[0018] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0019] Figure 1 This is an exemplary flowchart of an intelligent electronic control method for a new energy loader provided in this application; Figure 2 This is an exemplary flowchart for determining the drive operation characteristics of a target new energy loader according to the present application; Figure 3 This is an exemplary flowchart for determining the compensation response torque of the drive motor of a target new energy loader, based on the information provided in this application. Figure 4 This is a modular structure diagram of the intelligent electronic control system of the new energy loader provided in this application; Figure 5 This is a schematic diagram of the structure of a computer device for implementing an intelligent electronic control method for a new energy loader, according to the present application. Detailed Implementation

[0020] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0021] This application provides an intelligent electronic control system and method for a new energy loader. The core of the system involves: collecting the operating parameters of the drive motor of the target new energy loader; identifying parameters based on these operating parameters to obtain a power parameter set; acquiring historical measurement data of the drive motor; performing disturbance observation based on the historical measurement data and the power parameter set to obtain load disturbance observation values; extracting the power density spectrum from the load disturbance observation values ​​to obtain a power density spectrum; determining the drive operation characteristics of the target new energy loader based on the power density spectrum; acquiring various historical operating conditions of the target new energy loader; matching operating conditions with the drive operation characteristics to obtain the current operating mode of the target new energy loader; determining the compensation response torque of the drive motor of the target new energy loader based on the current operating mode and the load disturbance observation values; and generating a drive motor response torque command for the target new energy loader based on the compensation response torque. This approach enables accurate operating condition identification and real-time compensation control based on motor operating parameters and load disturbance characteristics, improving the response speed and operational stability of the new energy loader's drive motor.

[0022] Example 1: To better understand the above technical solution, the following will provide a detailed description of the technical solution in conjunction with the accompanying drawings and specific implementation methods. (Refer to...) Figure 1 As shown in the figure, this is an exemplary flowchart of an intelligent electronic control method for a new energy loader according to this embodiment of the present application. The intelligent electronic control method includes the following steps: In step S1, the operating parameters of the drive motor of the target new energy loader are collected, and parameter identification is performed based on the operating parameters to obtain a set of power parameters.

[0023] In this embodiment, the operating parameters of the drive motor of the target new energy loader are collected by sending a preset torque command sequence to the drive motor controller, and then synchronously collecting the operating parameters of the drive motor through the CAN bus. In specific implementation, a preset torque command sequence can be sent to the drive motor controller, and the timestamp, torque command, current speed and current torque can be read and recorded by listening to the CAN bus, so that the timestamp, torque command, current speed and current torque can be used as the operating parameters of the drive motor.

[0024] In this embodiment, the process of identifying the power parameter set based on the operating parameters can be achieved through the following steps: The angular acceleration sequence is obtained by numerical differentiation based on the operating parameters. Determine the pre-identification parameter vector based on the angular acceleration sequence and the operating parameters; The pre-identified parameter vector is inversely solved to obtain the dynamic parameter set.

[0025] In practical implementation, firstly, the angular acceleration sequence can be obtained by numerical differentiation based on the operating parameters. That is, the current rotational speed in the operating parameters can be low-pass filtered using a first-order inertial filter to obtain the filtered rotational speed sequence. The angular acceleration sequence can then be obtained by the following formula:

[0026]

[0027] in, This represents the angular acceleration at time k; This represents the current rotational speed at time k+1; This represents the current rotational speed at time k-1; The sampling period is represented to obtain the angular acceleration sequence. Then, the pre-identification parameter vector can be determined based on the angular acceleration sequence and the operating parameters. That is, the angular acceleration sequence, the current speed, and the torque command can be timestamped to obtain a time length of N, timestamped angular acceleration sequence, current speed sequence, and torque command sequence. The angular acceleration sequence and the current speed sequence are constructed into an N-row, 3-column matrix, with each row corresponding to a timestamp, such as [α[k], ω[k], 1]. Then, the torque command sequence is transposed into an N-row, 1-column column vector to obtain a data matrix and a column vector. The pre-identification parameter vector can then be obtained by the following formula:

[0028]

[0029] in, This represents the pre-identification parameter vector; Represents a data matrix; Indicates transpose; Represents a column vector; This represents the inverse of the matrix; finally, the pre-identified parameter vector can be inversely solved to obtain the dynamic parameter set. That is, the three elements in the pre-identified parameter vector can be assigned values ​​respectively, with the first element as the moment of inertia, the second element as the viscous friction coefficient, and the third element as the Coulomb friction torque, thus obtaining the vector as the dynamic parameter set.

[0030] It should be noted that by collecting the operating parameters of the drive motor and performing parameter identification, a set of power parameters that accurately characterize the motor's dynamic characteristics can be obtained. This enables the control system to promptly grasp the actual performance status of the motor, effectively reducing the deviation between the model and the actual operating conditions, and providing reliable basic data support for subsequent disturbance observation, operating condition matching, and compensation control.

[0031] In step S2, historical measurement data of the drive motor is acquired, and disturbance observation is performed based on the historical measurement data and the power parameter set to obtain load disturbance observation values. The power density spectrum is extracted from the load disturbance observation values, and then the drive operation characteristics of the target new energy loader are determined based on the power density spectrum.

[0032] In practice, historical measurement data of the drive motor can be obtained. That is, historical operating parameters can be extracted from the historical database and used as historical measurement data of the drive motor.

[0033] In this embodiment, obtaining the load disturbance observation value based on the historical measurement data and the power parameter set can be achieved through the following steps: The values ​​of internal state variables are determined based on the historical measurement data and the set of dynamic parameters; The load disturbance observations are determined using the internal state variable values ​​and the set of dynamic parameters.

[0034] In practice, firstly, the values ​​of the internal state variables can be determined based on historical measurement data and the set of dynamic parameters. That is, the values ​​of the internal state variables can be obtained by the following formula:

[0035]

[0036] in, This represents the value of the internal state variable at the current time k; This represents the value of the internal state variable at time k-1; Indicates the sampling period; The observer gain can be preset based on historical experience. Indicates the moment of inertia; Indicates the coefficient of viscous friction; This represents the Coulomb friction torque; This represents the current rotational speed at time k-1; This represents the current torque at time k-1. It should be noted that the internal state variable values ​​are a set of minimal variables used to describe the internal dynamics of the system. Then, the load disturbance observations can be determined using the internal state variable values ​​and the set of dynamic parameters; that is, the load disturbance observations can be obtained using the following formula:

[0037]

[0038] in, This represents the observed load disturbance value at time k. This represents the value of the internal state variable at the current time k; Indicates the observer gain; Indicates the moment of inertia; This represents the current rotational speed at time k. It should be noted that the load disturbance observation refers to the real-time estimate of the generalized load torque acting on the system.

[0039] In this embodiment, the power density spectrum is obtained by extracting the spectrum from the load disturbance observations, which can be achieved through the following steps: Initialize the window function sequence; The data is windowed based on the window function sequence and the load disturbance observations to obtain each windowed observation; The power density spectrum is determined based on the observations of each windowed area.

[0040] In practice, firstly, a window function sequence can be initialized, i.e., a Hanning window can be initialized to obtain a window function sequence with a time length of N. Then, the data can be windowed based on the window function sequence and the load disturbance observations to obtain individual windowed observations. That is, the load disturbance observations with the same timestamp in the same time length N can be multiplied with the window function sequence, and the result can be used as the windowed observations, thus obtaining individual windowed observations in the time length N. It should be noted that the windowed observations are signal values ​​that have been processed for spectral analysis. Finally, the power density spectrum can be determined based on the individual windowed observations. That is, a fast Fourier transform can be performed on the individual windowed observations, and then the periodogram can be calculated to convert the periodogram into a one-sided power spectral density, thereby outputting the power density spectrum.

[0041] Preferably, in this embodiment, reference Figure 2 As shown, this figure is an exemplary flowchart for determining the driving operation characteristics of the target new energy loader in an embodiment of this application. In this embodiment, determining the driving operation characteristics of the target new energy loader based on the power density spectrum can be achieved by the following steps: In step S21, the boundary values ​​of each frequency are initialized; In step S22, the original feature vector is determined based on the power density spectrum and the boundary values ​​of each frequency. In step S23, the driving operation characteristics of the target new energy loader are determined based on the original feature vector.

[0042] In practical implementation, firstly, the boundary values ​​of each frequency can be initialized. That is, the boundary values ​​of each frequency can be determined based on experimental data analysis, such as: low-frequency boundary: (0.1Hz, 1Hz); mid-frequency boundary: (1Hz, 3Hz); high-frequency boundary: (3Hz, 10Hz). Then, the original feature vector can be determined based on the power density spectrum and the boundary values ​​of each frequency. That is, based on all frequency points in the power density spectrum that are at each frequency boundary value, the frequency values ​​of all frequency points at the low-frequency boundary are summed to obtain the low-frequency energy, the frequency values ​​of all frequency points at the mid-frequency boundary are summed to obtain the mid-frequency energy, and the frequency values ​​of all frequency points at the high-frequency boundary are summed to obtain the high-frequency energy. Thus, the low-frequency energy, mid-frequency energy, and high-frequency energy are combined as vector elements to form the original feature vector. The initial feature vector is used to determine the driving operation characteristics of the target new energy loader. Specifically, the low-frequency energy, mid-frequency energy, and high-frequency energy are summed to obtain the total frequency energy. The low-frequency energy is then divided by the total frequency energy, and the result is used as the low-frequency operation characteristic. Similarly, the mid-frequency energy is divided by the total frequency energy, and the result is used as the mid-frequency operation characteristic. Finally, the high-frequency energy is divided by the total frequency energy, and the result is used as the high-frequency operation characteristic. These low-frequency, mid-frequency, and high-frequency operation characteristics are then combined into a single vector, which is used as the driving operation characteristics of the target new energy loader. It should be noted that the driving operation characteristics are a set of numerical features used to quantitatively describe and uniquely identify each operating mode of the new energy loader.

[0043] It should be noted that by acquiring historical measurement data of the drive motor and combining it with the power parameter set for disturbance observation, the load changes experienced by the motor during actual operation can be accurately identified. The observed load disturbance values ​​are then subjected to spectral extraction to generate a power density spectrum. This allows for the mining of typical load characteristics of the loader under different operating conditions from a frequency domain perspective. Based on the power density spectrum, the drive operation characteristics can be determined, enabling an accurate characterization of the loader's actual operating mode. This provides a highly reliable feature basis for subsequent operating condition matching and compensation control, and helps to improve the intelligence and refinement of the overall control strategy.

[0044] In step S3, the historical operating conditions of the target new energy loader are obtained, and the operating conditions are matched according to the historical operating conditions and the drive operation characteristics to obtain the current operating mode of the target new energy loader. The compensation response torque of the drive motor of the target new energy loader is determined according to the current operating mode and the load disturbance observation value.

[0045] In practice, to obtain the historical operating conditions of the target new energy loader, the labels of each operating condition and their corresponding drive operation features can be extracted from the historical operating condition sample library. Thus, the labels of each operating condition and their corresponding drive operation features are used as the historical operating conditions of the target new energy loader.

[0046] In this embodiment, the current operating mode of the target new energy loader is obtained by matching the operating conditions based on the historical operating conditions and the driving operation characteristics. In specific implementation, the driving operation characteristics can be matched with the historical operating conditions. When the current driving operation characteristics are within the range of driving operation characteristics corresponding to the operating condition labels in the historical operating conditions, the corresponding operating condition label is taken as the current operating mode of the target new energy loader.

[0047] Preferably, in this embodiment, reference Figure 3 As shown, this figure is an exemplary flowchart for determining the compensation response torque of the drive motor of the target new energy loader in an embodiment of this application. In this embodiment, the determination of the compensation response torque of the drive motor of the target new energy loader based on the current operating mode and the load disturbance observation value can be achieved by the following steps: In step S31, load disturbance observations are filtered according to the current operating mode to obtain the load disturbance signal; In step S32, the basic feedforward compensation amount is determined based on the load disturbance signal; In step S33, the compensation response torque of the drive motor of the target new energy loader is determined by the basic feedforward compensation amount.

[0048] In practical implementation, firstly, load disturbance observations can be filtered according to the current operating mode to obtain the load disturbance signal. That is, a filtering method can be selected from a preset strategy library based on the current operating mode, such as: low-pass filtering for heavy-load shoveling; band-pass filtering for lifting and leveling; low-pass filtering for light-load transport; and high-pass filtering for high-speed travel. Based on the selected filtering method, the load disturbance observations are digitally filtered to obtain the load disturbance signal. Then, the basic feedforward compensation amount can be determined based on the load disturbance signal. That is, the feedforward gain can be preset according to the current operating mode. It should be noted that the feedforward gain is a gain value used to determine the compensation intensity. Each corresponding feedforward gain is preset according to each operating mode. Then, the load disturbance signal at time k is multiplied by the feedforward gain, and the result is used as the basic feedforward compensation amount at time k. The basic feedforward compensation amount is used to offset the load disturbance. The initial torque command required for the movement is obtained; finally, the compensation response torque of the drive motor of the target new energy loader can be determined by the basic feedforward compensation amount. That is, the system torque limit, i.e., the maximum driving torque and the minimum braking torque, can be obtained from the background system of the drive motor of the target new energy loader. Then, the maximum compensation amount is obtained by subtracting the current torque from the maximum driving torque, and the minimum compensation amount (negative value) is obtained by subtracting the current torque from the minimum braking torque. When the feedforward compensation amount is greater than the maximum compensation amount, the maximum compensation amount is used as the compensation response torque. When the feedforward compensation amount is less than the minimum compensation amount, the minimum compensation amount is used as the compensation response torque. When the feedforward compensation amount is greater than the minimum compensation amount but less than the maximum compensation amount, the feedforward compensation amount is used as the compensation response torque. Thus, the compensation response torque of the drive motor of the target new energy loader is obtained. It should be noted that the compensation response torque refers to the additional torque calculated to offset external load disturbances.

[0049] It should be noted that by collecting historical operating conditions of new energy loaders and matching them with previously extracted drive operation characteristics, the current operating mode can be accurately identified and typical load behavior can be quantified. Based on the current operating mode and combined with real-time load disturbance observations, a compensation response torque can be calculated and generated, thereby achieving targeted feedforward compensation control. This significantly reduces torque errors and speed fluctuations caused by operating condition switching or sudden loads, improves the tracking accuracy and response speed of the drive motor, extends the life of the motor and transmission system, and enhances the robustness and operating efficiency of the whole machine under complex operating conditions.

[0050] In step S4, the drive motor response torque command of the target new energy loader is generated based on the compensation response torque.

[0051] In this embodiment, the drive motor response torque command of the target new energy loader is generated according to the compensation response torque. In specific implementation, the compensation response torque can be added to the current torque of the drive motor of the target new energy loader, and the result can be used as the drive motor response torque command of the target new energy loader. Then, the response torque command is sent to the drive motor controller to complete the intelligent electronic control of the new energy loader. It should be noted that the response torque command refers to the final torque command after compensation response.

[0052] It should be noted that by converting the calculated compensation response torque into the response torque command of the drive motor, the compensation strategy based on disturbance observation and operating condition matching can be implemented in real time as an executable control quantity. This ensures that the motor controller can immediately take corresponding feedforward closed-loop actions to offset load disturbances, thereby reducing torque tracking error and response delay, and thus improving the vehicle's power response accuracy, energy utilization efficiency and operational reliability.

[0053] Therefore, in this application, firstly, by collecting and identifying the operating parameters of the drive motor, a set of power parameters accurately characterizing the motor's dynamic characteristics can be obtained, enabling the control system to promptly grasp the actual performance state of the motor and providing reliable data support for subsequent disturbance observation, operating condition matching, and compensation control; secondly, by acquiring historical measurement data of the drive motor and combining it with the power parameter set for disturbance observation, the load changes experienced by the motor during actual operation can be accurately identified. Spectrum extraction of the observed load disturbance values ​​and generation of a power density spectrum can extract typical load characteristics of the new energy loader under various operating conditions from a frequency domain perspective. Based on the power density spectrum, the drive operation characteristics can be determined, enabling an accurate characterization of the loader's actual operating mode and providing highly reliable characteristic evidence for subsequent operating condition matching and compensation control, thus contributing to improving the intelligence and refinement of the overall control strategy; then, by acquiring By matching the historical operating conditions of the new energy loader with its driving operation characteristics, the current operating mode can be accurately identified and typical load behaviors can be quantified. Based on this current operating mode and combined with real-time load disturbance observations, a compensation response torque can be calculated and generated. This enables targeted feedforward compensation control, which significantly reduces torque errors and speed fluctuations caused by operating mode switching or sudden loads, improves the tracking accuracy and response speed of the drive motor, and enhances the robustness and operating efficiency of the drive motor under complex operating conditions. Finally, by converting the calculated compensation response torque into the drive motor's response torque command, the compensation strategy based on disturbance observation and operating mode matching can be implemented in real time as an executable control quantity. This ensures that the motor controller can immediately take corresponding feedforward closed-loop actions to offset load disturbances, reducing torque tracking errors and response delays, thereby improving the power response accuracy, energy utilization efficiency, and operational reliability of the new energy loader.

[0054] In summary, the technical solution adopted in this application can achieve accurate working condition identification and real-time compensation control based on motor operating parameters and load disturbance characteristics, thereby improving the response speed and operational stability of the drive motor of the new energy loader.

[0055] Example 2: This application provides a reference intelligent electronic control system for a new energy loader. Figure 4 As shown in the figure, this is a modular structure diagram of the intelligent electronic control system of the new energy loader according to this embodiment of the present application. The intelligent electronic control system of the new energy loader includes: The parameter identification module 100 is used to collect the operating parameters of the drive motor of the target new energy loader, and perform parameter identification based on the operating parameters to obtain a set of power parameters; The disturbance observation module 200 is used to acquire historical measurement data of the drive motor, perform disturbance observation based on the historical measurement data and the power parameter set to obtain load disturbance observation values, extract the spectrum of the load disturbance observation values ​​to obtain the power density spectrum, and then determine the drive operation characteristics of the target new energy loader based on the power density spectrum. The compensation response module 300 is used to acquire various historical operating conditions of the target new energy loader, perform operating condition matching based on various historical operating conditions and the drive operation characteristics to obtain the current operating mode of the target new energy loader, and determine the compensation response torque of the drive motor of the target new energy loader based on the current operating mode and the load disturbance observation value. The instruction determination module 400 is used to generate the drive motor response torque instruction of the target new energy loader based on the compensation response torque.

[0056] The foregoing detailed an example of an intelligent electronic control system and method for a new energy loader provided in this application. It is understood that the corresponding device, in order to achieve the above functions, includes hardware structures and / or software modules corresponding to the execution of each function. Those skilled in the art should readily recognize that, in conjunction with the units and algorithm steps of the examples described in the embodiments disclosed herein, this application can be implemented in hardware or a combination of hardware and computer software. Whether a function is executed by hardware or by computer software driving hardware depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0057] In embodiment three, this application also provides a computer device, which includes a memory and a processor. The memory is used to store a computer program, and the processor is used to call and run the computer program from the memory, so that the computer device executes the above-described intelligent electronic control method for a new energy loader.

[0058] In this embodiment, reference Figure 5 The dashed lines in the figure indicate that the unit or module is optional. This figure is a structural schematic diagram of the computer equipment of an intelligent electronic control system for a new energy loader according to an embodiment of this application. The intelligent electronic control method for a new energy loader in the above embodiment can be achieved through... Figure 5 The computer device shown is used to implement this, and the computer device includes at least one processor 501, a memory 502 and at least one communication unit 505. The computer device may be a terminal device, a server or a chip.

[0059] Processor 501 can be a general-purpose processor or a special-purpose processor. For example, processor 501 can be a central processing unit (CPU), which can be used to control computer devices, execute software programs, and process data from software programs. The computer device may also include a communication unit 505 for receiving and transmitting signals.

[0060] For example, the computer device may be a chip, the communication unit 505 may be the input and / or output circuit of the chip, or the communication unit 505 may be the communication interface of the chip, and the chip may be a component of a terminal device, network device or other device.

[0061] For example, the computer device may be a terminal device or a server, and the communication unit 505 may be a transceiver of the terminal device or the server, or the communication unit 505 may be a transceiver circuit of the terminal device or the server.

[0062] The computer device may include one or more memories 502 storing a program 504. The program 504 can be executed by a processor 501 to generate instructions 503, causing the processor 501 to perform the methods described in the above method embodiments according to the instructions 503. Optionally, the memory 502 may also store data (such as a target audit model). Optionally, the processor 501 may also read data stored in the memory 502, which may be stored at the same storage address as the program 504, or the data may be stored at a different storage address than the program 504.

[0063] The processor 501 and memory 502 can be configured separately or integrated together, for example, integrated on the system-on-chip (SOC) of the terminal device.

[0064] It should be understood that each step of the above method embodiment can be completed by hardware logic circuits or software instructions in processor 501. Processor 501 can be a central processing unit, digital signal processor (DSP), application specific integrated circuit (ASIC), field programmable gate array (FPGA), or other programmable logic device, such as discrete gate, transistor logic device, or discrete hardware component.

[0065] 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.

[0066] In embodiment four, this application also provides a computer-readable storage medium storing instructions or code that, when executed on a computer, cause the computer to implement the above-described intelligent electronic control method for a new energy loader.

[0067] Although preferred embodiments of this application have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments as well as all changes and modifications falling within the scope of this application.

[0068] Obviously, those skilled in the art can make various modifications and variations to this application without departing from the spirit and scope of the invention. Therefore, if these modifications and variations fall within the scope of the claims of this application and their equivalents, this application also intends to include these modifications and variations.

Claims

1. An intelligent electric control method of a new energy loader, characterized in that, The intelligent electronic control method includes: The operating parameters of the drive motor of the target new energy loader are collected, and parameter identification is performed based on the operating parameters to obtain a set of power parameters; Historical measurement data of the drive motor is acquired, and disturbance observation is performed based on the historical measurement data and the power parameter set to obtain load disturbance observation values. The power density spectrum is extracted from the load disturbance observation values, and then the drive operation characteristics of the target new energy loader are determined based on the power density spectrum. The historical operating conditions of the target new energy loader are obtained, and the operating conditions are matched with the driving operation characteristics to obtain the current operating mode of the target new energy loader. The compensation response torque of the drive motor of the target new energy loader is determined according to the current operating mode and the load disturbance observation value. The drive motor response torque command for the target new energy loader is generated based on the compensation response torque.

2. The intelligent electric control method of a new energy loader as claimed in claim 1, characterized in that, The operating parameters of the drive motor of the target new energy loader are collected by sending a preset torque command sequence to the drive motor controller, and then synchronously collecting the operating parameters of the drive motor via the CAN bus.

3. The intelligent electric control method of a new energy loader as claimed in claim 1, characterized in that, Based on the operating parameters, parameter identification is performed to obtain the power parameter set, which specifically includes: The angular acceleration sequence is obtained by numerical differentiation based on the operating parameters. Determine the pre-identification parameter vector based on the angular acceleration sequence and the operating parameters; The pre-identified parameter vector is inversely solved to obtain the dynamic parameter set.

4. The intelligent electronic control method for a new energy loader as described in claim 1, characterized in that, Based on the historical measurement data and the dynamic parameter set, disturbance observations are performed to obtain load disturbance observation values, specifically including: The values ​​of internal state variables are determined based on the historical measurement data and the set of dynamic parameters; The load disturbance observations are determined using the internal state variable values ​​and the set of dynamic parameters.

5. The intelligent electronic control method for a new energy loader as described in claim 1, characterized in that, The power density spectrum is obtained by performing spectral extraction on the load disturbance observations, specifically including: Initialize the window function sequence; The data is windowed based on the window function sequence and the load disturbance observations to obtain each windowed observation; The power density spectrum is determined based on the observations of each windowed area.

6. The intelligent electronic control method for a new energy loader as described in claim 1, characterized in that, Determining the driving operation characteristics of the target new energy loader based on the power density spectrum specifically includes: Initialize the boundary values ​​for each frequency; The original feature vector is determined based on the power density spectrum and each frequency boundary value; The driving operation characteristics of the target new energy loader are determined based on the original feature vector.

7. The intelligent electronic control method for a new energy loader as described in claim 1, characterized in that, The compensation response torque of the drive motor of the target new energy loader is determined based on the current operating mode and the load disturbance observation value, specifically including: Based on the current operating mode, load disturbance observations are filtered to obtain the load disturbance signal; The basic feedforward compensation amount is determined based on the load disturbance signal; The compensation response torque of the drive motor of the target new energy loader is determined by the basic feedforward compensation amount.

8. An intelligent electronic control system for a new energy loader, used to execute the intelligent electronic control method for a new energy loader as described in any one of claims 1 to 7, characterized in that, The intelligent electronic control system includes: The parameter identification module is used to collect the operating parameters of the drive motor of the target new energy loader, and perform parameter identification based on the operating parameters to obtain a set of power parameters; The disturbance observation module is used to acquire historical measurement data of the drive motor, perform disturbance observation based on the historical measurement data and the power parameter set to obtain load disturbance observation values, extract the spectrum of the load disturbance observation values ​​to obtain the power density spectrum, and then determine the drive operation characteristics of the target new energy loader based on the power density spectrum. The compensation response module is used to acquire various historical operating conditions of the target new energy loader, perform operating condition matching based on various historical operating conditions and the drive operation characteristics to obtain the current operating mode of the target new energy loader, and determine the compensation response torque of the drive motor of the target new energy loader based on the current operating mode and the load disturbance observation value. The instruction determination module is used to generate the drive motor response torque instruction of the target new energy loader based on the compensation response torque.

9. A computer device, characterized in that, The computer device includes a memory and a processor. The memory is used to store computer programs, and the processor is used to call and run the computer programs from the memory, so that the computer device executes the intelligent electronic control method for a new energy loader according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores instructions or code that, when executed on a computer, cause the computer to implement the intelligent electronic control method for a new energy loader as described in any one of claims 1 to 7.

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