Load spectrum-based electric drive system design method, device, equipment, medium and product

Abstract

The embodiment of the application provides a load spectrum-based electric drive system design method, device, equipment, medium and product, and is applied to the technical field of motor drive system design. The method comprises the following steps: collecting multi-source heterogeneous data of engineering mechanical equipment, and generating load spectrum data based on the multi-source heterogeneous data; constructing a virtual prototype based on the load spectrum data, wherein the virtual prototype is used for simulating system response of the engineering mechanical equipment under different working conditions; iteratively optimizing the virtual prototype based on a preset optimization target, and generating an electric drive system configuration scheme suitable for the engineering mechanical equipment. The technical effect of reducing the design cost rate is achieved.

Description

Technical Field

[0001] This application relates to the field of motor drive system design technology, and in particular to a method, apparatus, equipment, medium and product for designing electric drive systems based on load spectrum. Background Technology

[0002] Construction machinery is widely used in complex working environments such as mines, construction sites, and ports. This type of equipment typically faces challenges such as drastic load fluctuations, diverse operating modes, and harsh environmental conditions. With the transformation of the energy structure, the electrification of construction machinery has become an inevitable trend in the industry, thus requiring the design of suitable electric drive solutions for various types of construction machinery.

[0003] In existing technologies, the design of electric drive systems for construction machinery mainly relies on the traditional physical prototype development model. Specifically, designers typically conduct preliminary configuration designs based on experience or analogy, and then gradually adjust key parameters such as motor power, transmission ratio, and battery capacity by building physical prototypes and conducting real-vehicle testing.

[0004] Because the design of electric drive systems in the prior art relies on repeated testing and adjustment of physical prototypes, it requires a lot of time and design costs, resulting in the technical problem of high design costs in the prior art. Summary of the Invention

[0005] This application provides a method, apparatus, device, medium, and product for designing electric drive systems based on load spectrum, in order to achieve the technical effect of reducing design costs.

[0006] In a first aspect, embodiments of this application provide a design method for an electric drive system based on a load spectrum, comprising:

[0007] Collect multi-source heterogeneous data from engineering machinery and equipment, and generate load spectrum data based on the multi-source heterogeneous data;

[0008] A virtual prototype is constructed based on load spectrum data. The virtual prototype is used to simulate the system response of engineering machinery equipment under different working conditions.

[0009] The virtual prototype is iteratively optimized based on the preset optimization objectives to generate an electric drive system configuration scheme adapted to engineering machinery equipment.

[0010] In one possible implementation, multi-source heterogeneous data from engineering machinery and equipment are collected, and load spectrum data is generated based on the multi-source heterogeneous data, including:

[0011] Through a distributed sensor network, multi-source heterogeneous data of engineering machinery and equipment in the power domain, motion domain, and hydraulic domain are collected.

[0012] Typical operating conditions are labeled based on multi-source heterogeneous data, and the operating condition labeling results corresponding to the multi-source heterogeneous data are obtained.

[0013] Based on multi-source heterogeneous data and working condition labeling results, load spectrum data is obtained by analyzing from the preset load dimension and preset working condition dimension.

[0014] The preset load dimensions include steady-state components, transient components, and cyclic characteristics, while the preset working condition dimensions include standard excavation cycle, severe load conditions, and composite motion conditions.

[0015] In one possible implementation, a virtual prototype is constructed based on load spectrum data, including:

[0016] Based on load spectrum data processing and working condition screening, peak load spectrum, standard cyclic load spectrum and load requirement parameters are obtained.

[0017] A virtual prototype is constructed based on load demand parameters and equipment information of the construction machinery. The equipment information of the construction machinery consists of key calibration parameters obtained from the reverse engineering of the power system of the prototype, including torque fluctuation coefficient, hydraulic response delay, and mechanical transmission efficiency.

[0018] In one possible implementation, based on load spectrum data processing and load condition screening, the peak load spectrum, standard cyclic load spectrum, and load requirement parameters are obtained, including:

[0019] The load spectrum data is deconstructed based on the preset working condition classification matrix to obtain the load dataset corresponding to each working condition.

[0020] Extreme value prediction is performed based on the load dataset corresponding to each working condition to obtain the peak load spectrum;

[0021] Based on the load dataset corresponding to each working condition, cyclic elements are compiled to obtain the standard cyclic load spectrum.

[0022] Based on the load datasets corresponding to each operating condition, the torque, speed, power range and dynamic characteristics of the load point are clearly identified, and the load demand parameters are obtained.

[0023] In one possible implementation, a virtual prototype is constructed based on load demand parameters and equipment information of the engineering machinery, including:

[0024] Based on load demand parameters and equipment information of engineering machinery, an initial electric drive system configuration is generated.

[0025] For the electric drive system configuration, the transmission architecture and power architecture are optimized to obtain the optimal power architecture, optimal transmission architecture and key component pre-selection parameters corresponding to the initial electric drive system configuration;

[0026] A virtual prototype is constructed based on the optimal power architecture, optimal transmission architecture, and pre-selected parameters of key components.

[0027] In one possible implementation, the virtual prototype is iteratively optimized based on a preset optimization objective to generate an electric drive system configuration scheme adapted to engineering machinery equipment, including:

[0028] The standard cyclic load spectrum is input into the virtual prototype for simulation testing to obtain the simulation results for the current cycle.

[0029] When the simulation results meet the preset optimization objectives, an electric drive system configuration scheme adapted to the engineering machinery equipment is generated based on the pre-selection parameters of the key components corresponding to the virtual prototype.

[0030] When the simulation results do not meet the preset optimization target, the optimization direction corresponding to the simulation results and the preset optimization target is determined; the pre-selected parameters of the key components of the virtual prototype are updated based on the optimization direction, and the next round of simulation testing is initiated.

[0031] Secondly, embodiments of this application provide a design apparatus for an electric drive system based on a load spectrum, comprising:

[0032] The acquisition module is used to collect multi-source heterogeneous data from engineering machinery and equipment, and generate load spectrum data based on the multi-source heterogeneous data;

[0033] The first processing module constructs a virtual prototype based on load spectrum data. The virtual prototype is used to simulate the system response of engineering machinery equipment under different working conditions.

[0034] The second processing module is used to iteratively optimize the virtual prototype based on preset optimization targets and generate an electric drive system configuration scheme adapted to engineering machinery equipment.

[0035] In one possible implementation, the acquisition module is further configured to:

[0036] Through a distributed sensor network, multi-source heterogeneous data of engineering machinery and equipment in the power domain, motion domain, and hydraulic domain are collected.

[0037] Typical operating conditions are labeled based on multi-source heterogeneous data, and the operating condition labeling results corresponding to the multi-source heterogeneous data are obtained.

[0038] Based on multi-source heterogeneous data and working condition labeling results, load spectrum data is obtained by analyzing from the preset load dimension and preset working condition dimension.

[0039] The preset load dimensions include steady-state components, transient components, and cyclic characteristics, while the preset working condition dimensions include standard excavation cycle, severe load conditions, and composite motion conditions.

[0040] In one possible implementation, the first processing module is further configured to:

[0041] Based on load spectrum data processing and working condition screening, peak load spectrum, standard cyclic load spectrum and load requirement parameters are obtained.

[0042] A virtual prototype is constructed based on load demand parameters and equipment information of the construction machinery. The equipment information of the construction machinery consists of key calibration parameters obtained from the reverse engineering of the power system of the prototype, including torque fluctuation coefficient, hydraulic response delay, and mechanical transmission efficiency.

[0043] In one possible implementation, the first processing module is further configured to:

[0044] The load spectrum data is deconstructed based on the preset working condition classification matrix to obtain the load dataset corresponding to each working condition.

[0045] Extreme value prediction is performed based on the load dataset corresponding to each working condition to obtain the peak load spectrum;

[0046] Based on the load dataset corresponding to each working condition, cyclic elements are compiled to obtain the standard cyclic load spectrum.

[0047] Based on the load datasets corresponding to each operating condition, the torque, speed, power range and dynamic characteristics of the load point are clearly identified, and the load demand parameters are obtained.

[0048] In one possible implementation, the first processing module is further configured to:

[0049] Based on load demand parameters and equipment information of engineering machinery, an initial electric drive system configuration is generated.

[0050] For the electric drive system configuration, the transmission architecture and power architecture are optimized to obtain the optimal power architecture, optimal transmission architecture and key component pre-selection parameters corresponding to the initial electric drive system configuration;

[0051] A virtual prototype is constructed based on the optimal power architecture, optimal transmission architecture, and pre-selected parameters of key components.

[0052] In one possible implementation, the second processing module is further configured to:

[0053] The standard cyclic load spectrum is input into the virtual prototype for simulation testing to obtain the simulation results for the current cycle.

[0054] When the simulation results meet the preset optimization objectives, an electric drive system configuration scheme adapted to the engineering machinery equipment is generated based on the pre-selection parameters of the key components corresponding to the virtual prototype.

[0055] When the simulation results do not meet the preset optimization target, the optimization direction corresponding to the simulation results and the preset optimization target is determined; the pre-selected parameters of the key components of the virtual prototype are updated based on the optimization direction, and the next round of simulation testing is initiated.

[0056] Thirdly, embodiments of this application provide an electronic device, including: a memory and a processor;

[0057] The memory stores the instructions that the computer executes;

[0058] The processor executes computer execution instructions stored in memory, causing the processor to perform the first aspect above and various possible implementations of the first aspect.

[0059] Fourthly, embodiments of this application provide a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, are used to implement the first aspect and various possible implementations thereof.

[0060] Fifthly, embodiments of this application provide a computer program product, including a computer program that, when executed by a processor, implements the first aspect and various possible implementations thereof.

[0061] This application provides a method, apparatus, device, medium, and product for designing electric drive systems based on load spectra. The method generates load spectrum data corresponding to the engineering machinery equipment by collecting multi-source heterogeneous data. Further data processing and analysis are performed based on the load spectrum data to generate a virtual prototype for simulation testing. The virtual prototype is iteratively optimized using preset optimization objectives to generate an electric drive system configuration scheme corresponding to the engineering machinery equipment. Compared with existing technologies, this application determines the final design scheme adapted to the engineering machinery equipment through an iterative approach using virtual prototypes when designing electric drive system configuration schemes. This allows for rapid iteration of the virtual prototype and efficient modification of relevant design parameters, avoiding repeated testing and modification of the physical machine, thus achieving the technical effect of reducing design costs. Attached Figure Description

[0062] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0063] Figure 1 A flowchart illustrating the load spectrum-based electric drive system design method provided in this application. Figure 1 ;

[0064] Figure 2A flowchart illustrating the load spectrum-based electric drive system design method provided in this application. Figure 2 ;

[0065] Figure 3 A multi-domain coupled topology diagram of a virtual prototype of an electric drive system for engineering machinery provided in this application embodiment;

[0066] Figure 4 A schematic diagram of the structure of the electric drive system design device based on load spectrum provided in this application;

[0067] Figure 5 A schematic diagram of the structure of the electronic device provided in this application.

[0068] The accompanying drawings illustrate specific embodiments of this application, which will be described in more detail below. These drawings and descriptions are not intended to limit the scope of the concept in any way, but rather to illustrate the concepts of this application to those skilled in the art through reference to particular embodiments. Detailed Implementation

[0069] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numbers in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this application. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this application as detailed in the appended claims.

[0070] First, let me explain the terms used in this application:

[0071] Proportional-Integral-Derivative (PID) control algorithm: This refers to a closed-loop control algorithm. In electric drives, this algorithm can be used for motor speed control, motor torque control, battery charging and discharging current control, and temperature closed-loop adjustment, etc.

[0072] In existing technologies, the design approach for electric drive systems of construction machinery involves initial configuration design based on experience or analogy, followed by the fabrication of a physical prototype and subsequent testing. Based on the results of each test, key parameters such as motor power, transmission ratio, and battery capacity are gradually adjusted until the physical prototype meets the design requirements of the electric drive system for the construction machinery, thus generating the corresponding electric drive system design scheme.

[0073] However, the design of electric drive systems in the existing technology requires repeated testing and adjustment of physical prototypes, which consumes a lot of time and design costs in each test and adjustment. At the same time, the lack of guiding design methods in the iterative testing process leads to high R&D costs, resulting in the technical problem of high design costs in the existing technology.

[0074] To address the aforementioned technical problems, this application proposes the following technical concept: By collecting load spectrum data of engineering machinery equipment, a virtual prototype of the equipment is constructed; the electric drive system configuration scheme is generated through iterative optimization of the virtual prototype. Compared with existing technologies, this avoids repeated testing using physical machines, instead using virtual prototypes for iterative optimization, significantly reducing costs. Furthermore, the iteration efficiency of virtual prototypes is far higher than that of physical machines, further reducing time costs, thereby achieving the technical effect of reducing design costs.

[0075] The technical solution of this application and how the technical solution of this application solves the above-mentioned technical problems are described in detail below with specific embodiments. These specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described again in some embodiments. The embodiments of this application will now be described with reference to the accompanying drawings.

[0076] Figure 1 A flowchart illustrating the load spectrum-based electric drive system design method provided in this application. Figure 1 ,like Figure 1 As shown, the method includes:

[0077] S101. Collect multi-source heterogeneous data of engineering machinery and equipment, and generate load spectrum data based on the multi-source heterogeneous data.

[0078] In this step, multi-source heterogeneous data refers to real-time monitoring data from physical domains such as the power domain, motion domain, and hydraulic domain; load spectrum data refers to load data presented in the form of a spectrum or matrix, including information such as load amplitude, frequency of occurrence, and operating condition attributes.

[0079] Alternatively, one possible implementation for obtaining multi-source heterogeneous data to generate load spectrum data is as follows:

[0080] S1011. Collect multi-source heterogeneous data of engineering machinery equipment in the power domain, motion domain and hydraulic domain through a distributed sensor network.

[0081] In this step, the distributed sensor network refers to a network system that deploys different types of sensors at key force transmission nodes and working domains of engineering machinery equipment to achieve synchronous acquisition of multi-dimensional parameters.

[0082] For example, sensors are deployed in the excavator's power domain (engine crankshaft), motion domain (traverse mechanism), and hydraulic domain (bucket hydraulic cylinder) to form a distributed sensor network.

[0083] The corresponding multi-source heterogeneous data can be crankshaft torque data (unit N·m) in the power domain, travel speed data (unit km / h) in the motion domain, and hydraulic cylinder pressure data (unit MPa) in the hydraulic domain.

[0084] It should be noted that the power domain, motion domain, and hydraulic domain are the three core working domains of engineering machinery equipment, corresponding to power output, mechanical motion execution, and hydraulic drive functions, respectively.

[0085] For example, the power domain can be the output of a diesel engine or an electric motor, with the core parameters being torque, power, and speed.

[0086] The motion domain can be: the traveling wheel, the rotating platform, or the working device, and the core parameters to be collected are velocity, acceleration, and displacement.

[0087] The hydraulic domain can include: hydraulic pumps, hydraulic cylinders, and hydraulic valves. The core parameters to be acquired are pressure, flow rate, and response time.

[0088] Alternatively, the methods for acquiring multi-source heterogeneous data can be:

[0089] a1. Select a prototype machine for the electric drive system configuration design, construct a three-level topology model consisting of the prime mover, transmission system, and actuator, and clarify the power transmission path.

[0090] In this step, the prototype can be selected from diesel-powered construction machinery with typical industry characteristics, such as a 20-ton excavator or a 5-ton loader.

[0091] a2. Deploy sensors for the prototype, using a distributed sensor network.

[0092] In this step, the sensors are deployed according to the force transmission nodes of the three-level topology model, forming a distributed sensor network. For the power domain, crankshaft torque sensors and engine speed sensors can be deployed. For the motion domain, photoelectric laser speed sensors can be deployed to measure the speed of the traveling wheels, and acceleration sensors can be deployed to measure the acceleration and deceleration of the working device. For the hydraulic domain, high-frequency pressure sensors and flow sensors can be deployed, with the flow sensor measuring the hydraulic oil flow rate.

[0093] a3. Based on a distributed sensor network, monitoring data from multiple sensors are collected synchronously to obtain multi-source heterogeneous data.

[0094] For example, using a 20-ton diesel excavator as a prototype, the distributed sensor network can be deployed as follows: A torque sensor is installed at the end of the engine crankshaft, with a sampling range of 0-2000 N·m and a sampling frequency of 10 kHz. Photoelectric laser speed sensors are installed on the left and right travel wheel axles, with a sampling range of 0-300 rpm. A displacement sensor is installed on the boom cylinder piston rod, with a sampling range of 0-1500 mm. A high-frequency pressure sensor is installed at the main hydraulic pump outlet, with a sampling range of 0-35 MPa and a sampling frequency of 5 kHz. Data from the excavator's full-condition operation in the mine is continuously collected for 48 hours, resulting in approximately 1.728 × 10^9 raw data points forming multi-source heterogeneous data. This multi-source heterogeneous data covers the complete operational process, including excavation, slewing, traveling, and lifting.

[0095] S1012. Based on multi-source heterogeneous data, typical operating conditions are labeled to obtain the operating condition labeling results corresponding to the multi-source heterogeneous data.

[0096] In this step, typical working condition labeling refers to automatically classifying data fragments into corresponding working conditions by identifying characteristic patterns in multi-source heterogeneous data through algorithms, thereby binding data with working conditions. The working condition labeling result refers to structured information containing the time interval, working condition type, and working condition characteristic parameters of the data fragments.

[0097] For example, continuously collected excavator data is automatically labeled as operating condition segments such as standard digging cycle, swing braking, compound action, and idling standby. The resulting labeling result is: 2024-05-20 10:05:30-10:06:15, standard digging cycle, average digging torque 850 N·m, cycle duration 45 seconds.

[0098] Alternatively, typical operating condition marking can be implemented as follows:

[0099] b1. Extract operating condition identification features from multi-source heterogeneous data.

[0100] For example, the extracted working condition identification features can be:

[0101] Excavation conditions: peak torque ≥ 600 N·m, hydraulic pressure ≥ 25 MPa, boom displacement change rate ≥ 5 mm / s;

[0102] Slewing braking condition: speed drops from ≥5 rpm to 0, braking torque ≥300 N·m, duration ≤3 seconds;

[0103] Composite motion conditions: walking speed ≥ 1 km / h and boom displacement change rate ≥ 3 mm / s.

[0104] b2. Input the collected multi-source heterogeneous data into the trained working condition identification model. The working condition identification model identifies the working condition type through feature matching and outputs a labeled result containing time interval, working condition type, and feature parameters.

[0105] For example, the training method for the work condition recognition model used in this step can be as follows: develop a work pattern recognition algorithm based on CAN bus data, adopt the support vector machine algorithm, and train the work condition recognition model using more than 5,000 manually labeled work condition segments as the training set.

[0106] b3. Randomly check a portion of the labeling results, correct mislabeling, optimize the model parameters of the working condition identification model, and ensure that the accuracy of the model labeling is not lower than the preset threshold.

[0107] In this step, 10% of the labeling results can be randomly sampled for error correction. The preset threshold can be set to 98% to ensure that the accuracy of the labeling model is greater than or equal to 98%.

[0108] S1013. Based on multi-source heterogeneous data and working condition labeling results, load spectrum data is obtained by analyzing from the preset load dimension and preset working condition dimension.

[0109] In this step, the preset load dimensions include steady-state components, transient components, and cyclic characteristics, and the preset working condition dimensions include standard excavation cycle, severe load conditions, and composite motion conditions.

[0110] Among them, the steady-state component refers to the load reference value when the equipment is running stably, with small fluctuations and a long duration; for example, the torque load when the excavator is moving at a constant speed, and the standby pressure of the hydraulic system.

[0111] Transient components refer to the peak load when the operating conditions of equipment change abruptly. They are short in duration but large in amplitude; for example, the impact torque when the excavator bucket suddenly contacts hard rock, or the instantaneous braking torque during slewing braking.

[0112] Cyclic characteristics refer to the load variation pattern of equipment during repeated operations, which manifests as periodic fluctuations; for example, the torque variation curve and hydraulic pressure fluctuation cycle in the cycle of excavator digging, lifting, unloading, and resetting.

[0113] A standard excavation cycle refers to a typical excavation process of equipment under normal operating conditions, with a relatively stable load; for example, the complete cycle of shoveling, lifting, rotating, and unloading loose soil.

[0114] Severe load conditions refer to the operating conditions of equipment under extreme working conditions, with large load amplitude and drastic fluctuations; for example: excavating hard rock, overload operation.

[0115] Composite motion conditions refer to conditions in which equipment performs two or more motions simultaneously, with complex load coupling; for example, an excavator may simultaneously lift its boom and tilt its bucket while traveling.

[0116] Optionally, load spectrum data can be obtained by analyzing multi-source heterogeneous data from preset load and preset operating condition dimensions as follows:

[0117] c1. Perform wavelet denoising on multi-source heterogeneous data to remove abnormal noise such as sensor errors and electromagnetic interference, while retaining the effective payload characteristics to obtain standardized data.

[0118] c2. Analysis from the perspective of preset load: Extract steady-state components, transient components, and cyclic features from the data segments marked for each working condition in the standardized data.

[0119] c3. Analysis from the perspective of preset working conditions: The analysis results of preset load dimensions are classified into standard mining cycle, severe load conditions, and compound motion conditions, and the load dimension analysis results of each working condition are collected.

[0120] c4. Based on the load dimension analysis results of each working condition, construct a four-dimensional load feature matrix that includes working condition type, load dimension, load parameters, and frequency of occurrence, forming structured load spectrum data.

[0121] c5. Output load spectrum data in ASCII format, including a data header, load condition information, and load parameter table, for easy reading by subsequent simulation software. The data header includes the device model, acquisition time, and sampling frequency.

[0122] S102. Construct a virtual prototype based on load spectrum data.

[0123] In this step, the virtual prototype is used to simulate the system response of engineering machinery equipment under different operating conditions.

[0124] Alternatively, one possible implementation of constructing a virtual prototype based on load spectrum data is as follows:

[0125] S1021. Based on load spectrum data processing and working condition screening, the peak load spectrum, standard cyclic load spectrum, and load requirement parameters are obtained.

[0126] In this step, the peak load spectrum refers to the spectrum of load limits and distribution patterns of the equipment under extreme operating conditions, including the +3σ extreme value distribution confidence interval, used for the ultimate strength design of key components of the equipment. The standard cyclic load spectrum refers to a standardized set of cyclic cells formed by compressing and fusing load data from various operating conditions. It can characterize the typical load history throughout the entire life cycle of the equipment and is used for simulation testing. Load requirement parameters refer to mapping the load spectrum data to the design input parameters of the electrified equipment, clarifying the torque, speed, power range, and dynamic characteristics at the load point, and serving as the direct basis for the configuration design of the electric drive system.

[0127] S1022. Based on load requirement parameters and equipment information of engineering machinery, construct a virtual prototype.

[0128] In this step, the equipment information for the engineering machinery consists of key calibration parameters obtained from the prototype machine through reverse engineering of the power system, including torque fluctuation coefficient, hydraulic response delay, and mechanical transmission efficiency. A virtual prototype refers to a digital model built in a simulation platform that can simulate the physical characteristics and operational response of the equipment's electric drive system, supporting multi-configuration modeling and simulation testing.

[0129] Among these, the torque fluctuation coefficient refers to the degree of fluctuation in the output torque of the prototype engine. For example, the torque fluctuation coefficient of the excavator prototype is 0.08. Hydraulic response delay refers to the time delay from command input to actuator action in the hydraulic system; for example, the hydraulic response delay for boom lifting is 0.3 seconds. Mechanical transmission efficiency refers to the efficiency of power transmission from the prime mover to the actuator; for example, the mechanical transmission efficiency of the prototype's drivetrain is 82%.

[0130] It should be noted that the virtual prototype construction in this step is based on the following... Figure 2 Further explanation will be provided in the embodiments shown, and will not be repeated here.

[0131] S103. Based on the preset optimization target, iteratively optimize the virtual prototype to generate an electric drive system configuration scheme that is suitable for engineering machinery equipment.

[0132] In this step, the preset optimization target refers to the pre-determined indicators based on the operational requirements of engineering machinery and the constraints of electrification technology, which are used to measure the overall performance of the electric drive system.

[0133] For example, the preset optimization objectives include power matching degree, energy efficiency index, minimum total cost of ownership (TCO), and system reliability and integrity. Power matching degree refers to the degree to which the output of the electric drive system matches the load requirements of the equipment, ensuring sufficient power for operation. Energy efficiency index refers to the system's energy utilization efficiency, used to reduce energy loss. Total cost of ownership (TCO) covers the entire lifecycle cost of equipment purchase, operating energy consumption, and maintenance. System reliability verification refers to ensuring long-term operational stability through risk point identification and verification.

[0134] The load spectrum-based electric drive system design method provided in this application generates load spectrum data for engineering machinery equipment by collecting multi-source heterogeneous data. Further data processing and analysis are performed on the load spectrum data to generate a virtual prototype for simulation testing. The virtual prototype is iteratively optimized using preset optimization objectives to generate an electric drive system configuration scheme for the engineering machinery equipment. Compared with existing technologies, this application determines the final design scheme adapted to the engineering machinery equipment through an iterative approach using a virtual prototype. This allows for rapid iteration of the virtual prototype and efficient modification of relevant design parameters, avoiding repeated testing and modification of the physical machine, thus achieving the technical effect of reducing design costs.

[0135] Figure 2 A flowchart illustrating the load spectrum-based electric drive system design method provided in this application. Figure 2 ,like Figure 2 As shown, the method includes:

[0136] S201. Deconstruct the load spectrum data based on the preset working condition classification matrix to obtain the load dataset corresponding to each working condition.

[0137] In this step, the preset operating condition classification matrix refers to a matrix constructed with operating condition types as rows and classification parameters as columns, used by the system to classify equipment operating modes. The load dataset corresponding to each operating condition refers to the load data set collected under each operating condition after deconstructing the operating condition classification matrix, containing all load characteristic parameters under that operating condition.

[0138] For example, the preset working condition classification matrix can be constructed as follows: based on the functional characteristics and typical application scenarios of engineering machinery and equipment, road conditions, driving speed, work intensity, and load fluctuation range are selected as classification parameters to construct the working condition classification matrix. Among them, road conditions include: smooth or rugged; driving speed includes: 0, low, medium, and high; work intensity includes: light, medium, and heavy; and load fluctuation range includes: small, medium, and large.

[0139] In this step, the load spectrum data can be deconstructed by using a data clustering analysis algorithm based on the load condition classification matrix to perform clustering deconstruction on the load spectrum data.

[0140] Accordingly, the load dataset can be generated by matching the clustering results with the working condition type to generate a load dataset corresponding to each working condition. Each dataset contains load parameters, time series, and feature statistics.

[0141] For example, the preset load condition classification matrix is ​​a 3×4 matrix, with 3 rows representing 3 load condition categories and 4 columns representing 4 classification parameters. Based on the preset load condition classification matrix and using the K-means clustering algorithm, the load spectrum data is clustered and deconstructed, with the number of clusters set to 3. The clustering results are matched with the load condition types to obtain a standard cyclic load dataset, a severe load condition dataset, and a composite motion condition dataset. Each dataset contains torque, speed, pressure, and displacement parameters.

[0142] S202. Based on the load dataset corresponding to each working condition, extreme value prediction is performed to obtain the peak load spectrum.

[0143] In this step, extreme value prediction refers to calculating the possible load limit values ​​during the entire life cycle of the equipment based on the statistical characteristics of the load datasets for each working condition, combined with engineering experience and mathematical models, in order to avoid the defect that the measured data does not cover the extreme working conditions.

[0144] Alternatively, one possible implementation for generating the peak load spectrum is as follows:

[0145] S2021. Extract load peak samples from the load datasets of each working condition, remove outliers by wavelet denoising, and retain effective extreme value features.

[0146] S2022. Establish a two-parameter Weibull distribution load extreme value prediction model, and solve the shape parameters and scale parameters of each working condition by the maximum likelihood estimation method.

[0147] S2023. Introduce a dynamic safety factor matrix to perform boundary calibration on the extreme values ​​predicted by the model, balancing accuracy and safety.

[0148] For example, the dynamic safety factor matrix includes: standard operating condition 1.2, severe operating condition 1.5, and combined operating condition 1.3.

[0149] S2024. Calculate the mean and standard deviation of the load peak sequence, and construct the extreme value distribution confidence interval within three times the standard deviation to ensure coverage of the vast majority of extreme scenarios.

[0150] S2025. The results are integrated according to the working condition type to form the peak load spectrum of each working condition, which includes key information such as load amplitude, frequency of occurrence, and confidence interval.

[0151] S203. Based on the load dataset corresponding to each working condition, cyclic elements are compiled to obtain the standard cyclic load spectrum.

[0152] In this step, the cyclic unit compilation refers to extracting representative load change segments from the load datasets of each working condition and compiling them into a complete cyclic unit consisting of start-up, steady state, and stop states, which is used to characterize the core load characteristics of a single working condition.

[0153] Alternatively, one possible implementation for generating a standard cyclic load spectrum is as follows:

[0154] S2031. Use the rainflow counting method to compress the load data for each working condition load dataset, retain the key cycles with a damage contribution ratio of ≥0.01%, remove redundant data, and ensure damage equivalence ≥98%.

[0155] S2032. The compressed load datasets for each working condition are weighted and fused with the design parameter spectrum according to the preset weights to compile standardized cyclic units.

[0156] S2033. Based on the proportion of operating conditions throughout the entire life cycle of the equipment, combine the cyclic units to form a preliminary spectrum.

[0157] For example, the proportion of operating conditions throughout the equipment's life cycle is as follows: standard cycle accounts for 60%, compound motion accounts for 30%, and harsh operating conditions account for 10%.

[0158] S2034. Using the fatigue cumulative damage theory, calculate the conversion factor k = total life duration / accelerated test duration × load strengthening factor.

[0159] For example, the original standard excavation cycle dataset consisted of 100,000 data points, which were compressed to 30,000 data points using rainflow counting, achieving a damage equivalence of 98.5%. The weights of the load datasets for each working condition were 0.7, and the design parameter spectrum weight was 0.3. These two weighted fusions were used to compile a 50-second standard excavation cycle unit. The standard excavation cycle units corresponding to each working condition were combined into a 90-second comprehensive cycle, consisting of 60% standard cycle + 30% composite motion + 10% severe load. The accelerated conversion coefficient k = 10000 / 2000 × 1.3 = 6.5. After adjusting the cycle frequency, 2000 hours of accelerated testing can be equivalent to a full-lifetime load.

[0160] S204. Based on the load dataset corresponding to each working condition, analyze and clarify the torque, speed, power range and dynamic characteristics of the load point to obtain the load demand parameters.

[0161] In this step, dynamic characteristics refer to the way the load at the load point changes over time, including load type, fluctuation frequency, and fluctuation amplitude.

[0162] Alternatively, one possible way to obtain the load demand parameters is as follows:

[0163] S2041. Extract static parameters, specifically: from the load dataset of each working condition, statistically analyze the maximum, minimum, and average values ​​of torque, speed, and power at the load point, and clarify the parameter range.

[0164] S2042. Analyze dynamic characteristics, specifically: identify load type, fluctuation frequency, and fluctuation amplitude through time-frequency domain analysis.

[0165] S2043, Diesel-to-Electric Mapping: Specifically, based on the mechanical transmission efficiency of the prototype, the diesel power load parameters are converted into the load requirements of the electrified equipment. For example, diesel torque × transmission efficiency = electric torque requirement.

[0166] S2044. Integrate static range and dynamic characteristics to obtain a set of load demand parameters, ensuring coverage of load characteristics under all operating conditions.

[0167] S205. Based on the load demand parameters and the equipment information of the engineering machinery, generate the initial electric drive system configuration.

[0168] In this step, the initial electric drive system configuration refers to the overall architecture scheme of the electric drive system that is initially determined based on load demand parameters and equipment information, including the power architecture, transmission architecture, and the initial selection of core component types.

[0169] For example, the initial configuration of the excavator is: parallel hybrid power architecture + dual-motor torque coupling transmission architecture + permanent magnet synchronous motor + lithium iron phosphate battery.

[0170] Alternatively, one possible implementation of the initial electric drive system configuration is as follows:

[0171] S2051. Based on load demand parameters and equipment information, clarify the electrification performance targets.

[0172] S2052. By comparing four architectures—series hybrid, parallel hybrid, series-parallel hybrid, and pure electric drive—candidate power architectures were initially selected based on load fluctuation characteristics.

[0173] In this step, the series hybrid architecture consists of an engine → generator → motor → load, suitable for light loads and long driving range conditions. The parallel hybrid architecture consists of an engine + motor that can independently or jointly drive the load, suitable for frequent impact loads. The series-parallel hybrid architecture combines the characteristics of both series and parallel systems, offering more flexible power source combinations but at a higher cost. The pure electric drive architecture refers to an architecture that only includes a motor and battery drive, suitable for environmentally friendly and low-noise scenarios.

[0174] S2053. For candidate power architectures, three transmission schemes are matched: centralized single motor + transfer case, dual motor torque coupling, and multi-motor distributed drive, to ensure that transmission efficiency matches load requirements.

[0175] In this step, centralized single motor + transfer case refers to an architecture where a single motor distributes power to multiple loads through a transfer case; it is simple in structure and low in cost. Dual-motor torque coupling refers to an architecture where two motors work together to output power through a coupling mechanism, suitable for high torque requirements. Multi-motor distributed drive refers to an architecture where each load corresponds to an independent motor; it has high transmission efficiency but higher cost.

[0176] S2054. Preliminarily determine the motor type, such as a permanent magnet synchronous motor or an asynchronous motor; the battery type, such as a lithium iron phosphate battery or a ternary lithium battery; and the reducer type, such as a planetary gear reducer or a fixed-axis gear reducer. It should be noted that determining the motor type at this stage does not require specific parameters.

[0177] S206. Optimize the transmission architecture and power architecture for the electric drive system configuration to obtain the optimal power architecture, optimal transmission architecture, and pre-selection parameters of key components corresponding to the initial electric drive system configuration.

[0178] In this step, the optimal power architecture and optimal transmission architecture refer to the architecture combination that is selected from the candidate schemes of the initial configuration through multi-dimensional comparative simulation, based on its optimal technical feasibility, economic efficiency, and adaptability. The pre-selection parameters of key components refer to the key technical parameters of core components initially determined based on the optimal architecture and load requirement parameters; these are the core inputs for building the virtual prototype.

[0179] For example, the pre-selected parameters for the motor can be: peak power 120kW, continuous power 60kW, and peak torque 2400N·m; the pre-selected parameters for the battery can be: capacity 80kWh and energy density 180Wh / kg.

[0180] Optionally, the optimal powertrain and transmission architectures can be determined as follows: Simplified models of each candidate configuration are built in a simulation platform, a standard cyclic load spectrum is loaded, and key indicators such as power margin, transmission efficiency, energy consumption, and cost are output. The analytic hierarchy process (AHP) is used, with decision dimension weights set (e.g., power margin 0.3, transmission efficiency 0.2, cost increase 0.3, operating cost reduction 0.2) to comprehensively score the candidate architectures. The architecture combination with the highest comprehensive score, such as parallel hybrid + dual-motor torque coupling, is selected as the optimal architecture. Based on the principles of continuous power ≥ original average power and peak power ≥ highest operating condition requirements, and combined with the characteristics of the optimal architecture, the pre-selection parameters for the motor, battery, and reducer are determined.

[0181] S207, a virtual prototype is built based on the optimal power architecture, optimal transmission architecture and pre-selected parameters of key components.

[0182] In this step, the virtual prototype can be built in the following ways:

[0183] S2071. Based on the preset simulation platform and the optimal power architecture, build models of core components such as the motor, battery, engine, and generator. Based on the optimal transmission architecture, build models of the reducer, drive shaft, and transfer case, and set parameters such as transmission ratio and transmission efficiency. Build strategy models for PID control, energy recovery, and power source coordinated control, and define initial control strategy combinations.

[0184] In this step, the preset simulation platform can be a co-simulation platform or a dedicated electric drive simulation platform, ensuring that the platform has multi-domain coupled simulation capabilities. The control strategies in the initial control strategy combination are determined based on the specific configuration of the virtual prototype. When the virtual prototype is a hybrid configuration, the coordinated efficiency of the power source should be prioritized. Selectable control strategies include: single-point power start-stop strategy, multi-point power demand strategy, and dynamic power following strategy. When the virtual prototype is a pure electric configuration, energy utilization efficiency should be prioritized. Selectable control strategies include: regenerative braking energy recovery strategy, vehicle control unit (VCU) based coordinated control strategy, and battery management system (BMS) based optimization strategy.

[0185] S2072. Assign the pre-selected parameters of key components and equipment information to the model.

[0186] S2073. Compare the prototype vehicle test data with the virtual prototype simulation results, correct the model parameters, and ensure that the simulation confidence level is ≥95%.

[0187] S208. Input the standard cyclic load spectrum into the virtual prototype for simulation testing to obtain the simulation results for the current cycle.

[0188] In this step, simulation testing refers to using the standard cyclic load spectrum generated in step S203 above as input to drive the virtual prototype to run, simulating the operating state of the equipment under typical operating conditions throughout its entire life cycle, and verifying the performance of the current configuration scheme. The simulation results of the current round refer to the key performance data output by the simulation, including efficiency, energy consumption, battery state of charge (SOC), dynamic response time, power operation status, etc., which are the core basis for iterative optimization.

[0189] For example, the simulation results for the current round are: average transmission efficiency 88%, excavation cycle energy consumption 85kWh, battery SOC decay 0.14% / hour, dynamic response time 0.4 seconds, and no overload risk.

[0190] S209. When the simulation results meet the preset optimization objectives, generate an electric drive system configuration scheme adapted to the engineering machinery equipment based on the pre-selected parameters of the key components corresponding to the virtual prototype.

[0191] In this step, the electric drive system configuration scheme refers to the engineering implementation scheme formed based on the optimal pre-selected parameters and simulation verification results, which includes hardware configuration, architecture configuration, control strategy, and performance verification report.

[0192] For example, the electric drive configuration of the excavator is: parallel hybrid architecture + dual motor torque coupling transmission, with a motor of 120kW, a battery of 80kWh, a reducer transmission ratio of 3.5, and the control strategy is VCU power distribution and regenerative braking.

[0193] S210. When the simulation results do not meet the preset optimization target, determine the optimization direction corresponding to the simulation results and the preset optimization target; update the pre-selected parameters of the key components of the virtual prototype based on the optimization direction, and enter the next round of simulation testing.

[0194] In this step, "optimization direction" refers to identifying the performance indicators that need improvement and the corresponding parameter adjustment directions based on the deviation between the simulation results and the preset targets. "Parameter update" refers to adjusting the pre-selected parameters of key components according to the optimization direction to generate new parameter combinations for the next round of simulation.

[0195] For example, the simulation results of the first round are: power matching degree 92%, less than the preset optimization target of 95%; energy efficiency index 83%, less than the preset optimization target of 85%. The reasons for the deviation are: insufficient peak motor torque of 2200 N·m and unreasonable transmission ratio of 3.2. The optimization direction is: increase the peak motor torque to 2400 N·m and adjust the transmission ratio to 3.5. Lower the energy recovery trigger threshold from 2 km / h to 1.5 km / h. Based on the multi-objective genetic algorithm, 8 sets of candidate parameters are generated, and the optimal combination is selected as: motor 120kW, transmission ratio 3.5, PID proportional coefficient 0.7, and recovery threshold 1.5 km / h. It is identified that the increase in motor torque may cause overload, and the overload protection threshold is corrected from 2400 N·m to 2600 N·m. After updating the parameters, the second round of simulation is started. The simulation results obtained at this time are: power matching degree 96% and energy efficiency index 87%, which meet the target.

[0196] Based on the above embodiments, this application provides a schematic diagram of the structural configuration of a virtual prototype. Figure 3 A multi-domain coupled topology diagram of a virtual prototype of an electric drive system for engineering machinery provided in this application embodiment, such as... Figure 3 As shown, the virtual prototype includes: ASC, Cockpit, Battery H, E-Motor01~E-Motor12, Engine, Generator, Load04~Load13, Final Drive, Rear Differential, Vehicle: Front, and Vehicle: Rear.

[0197] Among them, ASC (Anti-Slip Control) refers to an anti-slip control system, which is used to monitor the output torque of each motor and the adhesion of the wheels to prevent slippage during operation or driving and ensure the stability of power transmission.

[0198] Cockpit: refers to the driving control command input module, which simulates actual operating scenarios and provides operating condition trigger signals for the virtual prototype.

[0199] Battery H: refers to the energy storage unit, such as the power battery pack, which is the energy source of the electric drive system. It corresponds to the "auxiliary power source" in the pre-selection of key components, and its parameters need to match the load demand parameters.

[0200] E-Motor01~E-Motor12: refers to multiple drive motors, covering different power levels and layout positions. They can be flexibly combined into single-motor drive, dual-motor torque coupling, multi-motor distributed drive and other architectures to meet the simulation needs of different power configurations such as series hybrid, parallel hybrid and pure electric.

[0201] Engine: refers to the engine, which is only used in hybrid configurations. It is the auxiliary power source corresponding to parallel or series-parallel hybrid architectures, working in conjunction with the generator to supplement energy under high load conditions.

[0202] Generator: refers to a generator, which is used in conjunction with an engine to convert the engine's mechanical energy into electrical energy to charge the power battery or directly power the drive motor, thus achieving coordinated energy distribution in a hybrid configuration.

[0203] Load04~Load13: These refer to the execution-end load modules, corresponding to the working devices and traveling mechanisms of construction machinery. Each Load is matched with a dedicated E-Motor to accurately simulate the load requirements of different execution ends. Figure 2 In the illustrated embodiment, the load requirement parameters in step S204 correspond one-to-one.

[0204] Final Drive: This refers to the power transmission end component, connecting the drive motor and the load. It is responsible for reducing speed and increasing torque; its transmission ratio parameters are derived from the above. Figure 2 The key component pre-selection in step S206 of the illustrated embodiment must meet the optimization goal of improving transmission efficiency.

[0205] Rear Differential: This refers to the rear axle differential, which is used to adapt to the differential requirements of the running gear, ensuring that the speed of the wheels on both sides is coordinated when turning or on uneven roads, and avoiding stress concentration in the transmission system.

[0206] Vehicle: Front refers to the front functional module in a virtual prototype of an electric drive system for engineering machinery built on a simulation platform. It integrates the power drive components, load execution components, and transmission components at the front of the equipment, simulating the power transmission and load response logic of the front working device or front traveling mechanism of the engineering machinery. It is a key component of the multi-domain coupled virtual prototype. Vehicle: Front and Vehicle: Rear form a symmetrical synergy, together constituting the complete power transmission and load execution system of the equipment.

[0207] Figure 4 A schematic diagram of the structure of the load spectrum-based electric drive system design device provided in this application is shown below. Figure 4 As shown, the load spectrum-based electric drive system design device provided in this embodiment includes:

[0208] The acquisition module 401 is used to collect multi-source heterogeneous data of engineering machinery and equipment, and generate load spectrum data based on the multi-source heterogeneous data.

[0209] The first processing module 402 constructs a virtual prototype based on load spectrum data. The virtual prototype is used to simulate the system response of engineering machinery equipment under different working conditions.

[0210] The second processing module 403 is used to iteratively optimize the virtual prototype based on a preset optimization target and generate an electric drive system configuration scheme adapted to engineering machinery equipment.

[0211] Alternatively, in one possible implementation, the acquisition module 401 is further configured to:

[0212] Through a distributed sensor network, multi-source heterogeneous data of engineering machinery equipment in the power domain, motion domain, and hydraulic domain are collected.

[0213] Typical operating conditions are labeled based on multi-source heterogeneous data, and the operating condition labeling results corresponding to the multi-source heterogeneous data are obtained.

[0214] Based on multi-source heterogeneous data and operating condition labeling results, load spectrum data is obtained by analyzing the preset load dimension and preset operating condition dimension.

[0215] The preset load dimensions include steady-state components, transient components, and cyclic characteristics, while the preset working condition dimensions include standard excavation cycle, severe load conditions, and composite motion conditions.

[0216] Optionally, in one possible implementation, the first processing module 402 is further configured to:

[0217] Based on load spectrum data processing and operating condition screening, peak load spectrum, standard cyclic load spectrum, and load requirement parameters are obtained.

[0218] A virtual prototype is constructed based on load demand parameters and equipment information of the construction machinery. The equipment information of the construction machinery consists of key calibration parameters obtained from the reverse engineering of the power system of the prototype, including torque fluctuation coefficient, hydraulic response delay, and mechanical transmission efficiency.

[0219] Optionally, in one possible implementation, the first processing module 402 is further configured to:

[0220] The load spectrum data is deconstructed based on the preset working condition classification matrix to obtain the load dataset corresponding to each working condition.

[0221] Extreme value prediction is performed based on the load dataset corresponding to each working condition to obtain the peak load spectrum.

[0222] Based on the load dataset corresponding to each working condition, cyclic elements are compiled to obtain the standard cyclic load spectrum.

[0223] Based on the load datasets corresponding to each operating condition, the torque, speed, power range and dynamic characteristics of the load point are clearly identified, and the load demand parameters are obtained.

[0224] Optionally, in one possible implementation, the first processing module 402 is further configured to:

[0225] Based on load demand parameters and equipment information of engineering machinery, an initial electric drive system configuration is generated.

[0226] For the electric drive system configuration, the transmission architecture and power architecture are optimized to obtain the optimal power architecture, optimal transmission architecture and key component pre-selection parameters corresponding to the initial electric drive system configuration.

[0227] A virtual prototype is constructed based on the optimal power architecture, optimal transmission architecture, and pre-selected parameters of key components.

[0228] Optionally, in one possible implementation, the second processing module 403 is further configured to:

[0229] The standard cyclic load spectrum is input into the virtual prototype for simulation testing to obtain the simulation results for the current cycle.

[0230] When the simulation results meet the preset optimization objectives, an electric drive system configuration scheme adapted to the engineering machinery equipment is generated based on the pre-selection parameters of the key components corresponding to the virtual prototype.

[0231] When the simulation results do not meet the preset optimization target, the optimization direction corresponding to the simulation results and the preset optimization target is determined; the pre-selected parameters of the key components of the virtual prototype are updated based on the optimization direction, and the next round of simulation testing is initiated.

[0232] The apparatus provided in this embodiment can execute the method provided in the above method embodiment. Its implementation principle and technical effect are similar, and will not be described in detail here.

[0233] Figure 5 A schematic diagram of the structure of the electronic device provided in this application. Figure 5 As shown, the electronic device provided in this embodiment includes at least one processor 501 and a memory 502. Optionally, the device further includes a communication component 503. The processor 501, memory 502, and communication component 503 are connected via a bus 504.

[0234] In the specific implementation process, at least one processor 501 executes computer execution instructions stored in memory 502, causing at least one processor 501 to execute the above-described load spectrum-based electric drive system design method or approach.

[0235] The specific implementation process of processor 501 can be found in the above method embodiments, and its implementation principle and technical effect are similar. It will not be repeated here.

[0236] In the above embodiments, it should be understood that the processor can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), etc. The general-purpose processor can be a microprocessor or any conventional processor. The steps of the method disclosed in this invention can be directly implemented by a hardware processor, or implemented by a combination of hardware and software modules within the processor.

[0237] The memory may include random access memory (RAM) and may also include non-volatile memory (NVM), such as at least one disk storage device.

[0238] The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus, etc. Buses can be categorized as address buses, data buses, control buses, etc. For ease of illustration, the buses shown in the accompanying drawings are not limited to a single bus or a single type of bus.

[0239] This application also provides a computer program product, including a computer program that, when executed by a processor, implements the above-described method.

[0240] This application also provides a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, implement the above-described method.

[0241] The aforementioned readable storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk. The readable storage medium can be any available medium accessible to a general-purpose or special-purpose computer.

[0242] An exemplary readable storage medium is coupled to a processor, enabling the processor to read information from and write information to the readable storage medium. Of course, the readable storage medium can also be a component of the processor. The processor and the readable storage medium can reside in an Application Specific Integrated Circuit (ASIC). Alternatively, the processor and the readable storage medium can exist as discrete components in the device.

[0243] The division of units is merely a logical functional division; in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be indirect coupling or communication connection through some interfaces, devices, or units, and may be electrical, mechanical, or other forms.

[0244] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0245] In addition, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0246] If a function is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0247] Those skilled in the art will understand that all or part of the steps of the above-described method embodiments can be implemented by hardware related to program instructions. The aforementioned program can be stored in a computer-readable storage medium. When executed, the program performs the steps of the above-described method embodiments; and the aforementioned storage medium includes various media capable of storing program code, such as ROM, RAM, magnetic disks, or optical disks.

[0248] Finally, it should be noted that other embodiments of the invention will readily occur to those skilled in the art upon consideration of the specification and practice of the invention disclosed herein. This invention is intended to cover any variations, uses, or adaptations of the invention that follow the general principles of the invention and include common knowledge or customary techniques in the art not disclosed herein, and is not limited to the precise structures described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of the invention is limited only by the appended claims.

Claims

1. A design method for an electric drive system based on load spectrum, characterized in that, include: Collect multi-source heterogeneous data from engineering machinery and equipment, and generate load spectrum data based on the multi-source heterogeneous data; A virtual prototype is constructed based on the load spectrum data, and the virtual prototype is used to simulate the system response of the engineering machinery equipment under different working conditions. The virtual prototype is iteratively optimized based on the preset optimization objectives to generate an electric drive system configuration scheme adapted to the engineering machinery equipment.

2. The method according to claim 1, characterized in that, The process of collecting multi-source heterogeneous data from engineering machinery and generating load spectrum data based on the multi-source heterogeneous data includes: The engineering machinery equipment is collected from multiple sources and heterogeneous data in the power domain, motion domain, and hydraulic domain through a distributed sensor network. Typical operating conditions are labeled based on the multi-source heterogeneous data to obtain the operating condition labeling results corresponding to the multi-source heterogeneous data. Based on the multi-source heterogeneous data and the operating condition labeling results, the load spectrum data is obtained by analyzing from the preset load dimension and the preset operating condition dimension. The preset load dimensions include steady-state components, transient components, and cyclic characteristics, while the preset working condition dimensions include standard excavation cycles, severe load conditions, and composite motion conditions.

3. The method according to claim 2, characterized in that, The construction of the virtual prototype based on the load spectrum data includes: Based on the load spectrum data processing and working condition screening, the peak load spectrum, standard cyclic load spectrum, and load requirement parameters are obtained. Based on the load requirement parameters and the equipment information of the engineering machinery, the virtual prototype is constructed; wherein, the equipment information of the engineering machinery consists of key calibration parameters obtained by reverse engineering the power system of the prototype, including torque fluctuation coefficient, hydraulic response delay, and mechanical transmission efficiency.

4. The method according to claim 3, characterized in that, The process of data processing and load condition screening based on the load spectrum data yields the peak load spectrum, standard cyclic load spectrum, and load requirement parameters, including: The load spectrum data is deconstructed based on a preset working condition classification matrix to obtain the load dataset corresponding to each working condition. Extreme value prediction is performed based on the load datasets corresponding to each working condition to obtain the peak load spectrum; Based on the load datasets corresponding to each working condition, cyclic units are compiled to obtain the standard cyclic load spectrum. Based on the load datasets corresponding to each operating condition, the torque, speed, power range, and dynamic characteristics of the load point are analyzed to obtain the load demand parameters.

5. The method according to claim 3, characterized in that, The process of constructing the virtual prototype based on the load demand parameters and the equipment information of the engineering machinery includes: Based on the load requirement parameters and the equipment information of the engineering machinery, an initial electric drive system configuration is generated; The transmission architecture and power architecture of the electric drive system configuration are optimized to obtain the optimal power architecture, optimal transmission architecture, and pre-selection parameters of key components corresponding to the initial electric drive system configuration. The virtual prototype is constructed based on the optimal power architecture, optimal transmission architecture, and pre-selected parameters of key components.

6. The method according to claim 5, characterized in that, The iterative optimization of the virtual prototype based on a preset optimization objective to generate an electric drive system configuration scheme adapted to the engineering machinery equipment includes: The standard cyclic load spectrum is input into the virtual prototype for simulation testing to obtain the simulation results for the current cycle. When the simulation results meet the preset optimization target, an electric drive system configuration scheme adapted to the engineering machinery equipment is generated based on the pre-selection parameters of the key components corresponding to the virtual prototype. When the simulation results do not meet the preset optimization target, the optimization direction corresponding to the simulation results and the preset optimization target is determined; the key component pre-selection parameters of the virtual prototype are updated based on the optimization direction, and the next round of simulation testing is initiated.

7. A design device for an electric drive system based on load spectrum, characterized in that, include: The acquisition module is used to collect multi-source heterogeneous data from engineering machinery and equipment, and generate load spectrum data based on the multi-source heterogeneous data; The first processing module constructs a virtual prototype based on the load spectrum data. The virtual prototype is used to simulate the system response of the engineering machinery equipment under different working conditions. The second processing module is used to iteratively optimize the virtual prototype based on a preset optimization target, and generate an electric drive system configuration scheme that is adapted to the engineering machinery equipment.

8. An electronic device, characterized in that, include: Memory, processor; The memory stores computer-executed instructions; The processor executes computer execution instructions stored in the memory, causing the processor to perform the method as described in any one of claims 1-6.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer-executable instructions, which, when executed by a processor, are used to implement the method as described in any one of claims 1-6.

10. A computer program product, characterized in that, Includes a computer program that, when executed by a processor, implements the method described in any one of claims 1-6.

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