Method and device for testing integrated electric drive system for forklift

Abstract

The invention provides an integrated electric drive system test method and device for a forklift, and the method comprises the following steps: obtaining the factory parameters of an electric drive system of a to-be-tested forklift, and inputting the factory parameters of the electric drive system into a pre-trained adaptive physical model in combination with a test condition to obtain simulated electric drive parameters; and acquiring the simulated electric driving parameters of multiple groups of to-be-tested forklifts under different test working conditions as an evaluation data set, and inputting the evaluation data set into a pre-trained performance evaluation model to obtain the working temperature interval, the load upper limit and the maximum running speed of the to-be-tested forklifts. By fusing a self-adaptive multi-body dynamics physical model and a multi-mode forklift performance evaluation model, an electric drive system parameterized virtual test system is constructed, and full-working-condition efficient test without real vehicle movement and real environment simulation is achieved.

Description

Technical Field

[0001] The present application relates to the field of machine learning, and particularly to a test method and device for an integrated electric drive system for forklifts. Background Art

[0002] In the current field of forklift electric drive system testing, traditional technologies mainly rely on physical testing of real vehicles under real working conditions. It is necessary to build diverse test environments such as slopes, loads, and different road surface friction coefficients, and directly collect the operating data of the motor, braking system, and kinetic energy recovery system through sensors. This method not only requires a large amount of manpower and material resources to build the test scenario, but also is limited by site conditions and difficult to reproduce extreme working conditions (such as -30°C low temperature, 15° full-load climbing, etc.). At the same time, frequent real vehicle testing will cause hardware losses such as motor winding insulation aging and brake pad wear, and the cost of a single test is as high as tens of thousands of yuan.

[0003] Although some solutions introduce physical models based on multi-body dynamics for simulation, existing models mostly use fixed parameterization modeling, ignoring the electromagnetic-thermal coupling effect of the motor, the non-linear characteristics of the braking hydraulic system, and the bi-directional energy flow characteristics during kinetic energy recovery, and cannot adaptively correct key parameters such as friction coefficient and damping coefficient through real-time data feedback, resulting in a simulation error of more than 15% under complex working conditions.

[0004] In addition, existing AI evaluation models are mostly based on traditional algorithms such as BP neural networks, rely on limited working condition data manually labeled (usually only covering 60%-70% of standard working conditions) for training, have insufficient feature extraction ability for extreme scenarios such as non-steady braking and sudden load mutation, the evaluation accuracy is generally lower than 85%, and cannot form a data closed-loop with the physical model, making it difficult to meet the requirements of rapid verification of multiple working conditions and early warning of design defects during the R & D stage of the electric drive system, resulting in the cycle from R & D to mass production of new forklift drive systems generally exceeding 18 months, seriously restricting the technical iteration efficiency of the industry. Summary of the Invention

[0005] The embodiments of the present application provide a test method and device for an integrated electric drive system for forklifts. By integrating an adaptive multi-body dynamics physical model and a multi-modal forklift performance evaluation model, a parametric virtual test system for the electric drive system is constructed to achieve efficient full-condition testing without real vehicle movement and real environment simulation.

[0006] In a first aspect, the embodiments of the present application provide a test method for an integrated electric drive system for forklifts, the method including: Obtain the factory parameters of the electric drive system of the forklift to be tested, and input the factory parameters of the electric drive system combined with the test conditions into the pre-trained adaptive physical model to obtain simulated electric drive parameters. Among them, the test conditions include road surface gradient, ambient temperature, and load weight. The simulated electric drive parameters are the electric drive system parameters of the forklift to be tested under the test conditions. The electric drive system includes a battery, a motor, and a kinetic energy recovery device; Obtain multiple groups of simulated electric drive parameters of the forklift to be tested under different test conditions as an evaluation data set, and input the evaluation data set into the pre-trained performance evaluation model to obtain the operating temperature range, load capacity limit, and maximum driving speed of the forklift to be tested.

[0007] In a second aspect, an integrated electric drive system test device for a forklift provided by an embodiment of the present application includes: A simulation module, configured to obtain the factory parameters of the electric drive system of the forklift to be tested, and input the factory parameters of the electric drive system combined with the test conditions into the pre-trained adaptive physical model to obtain simulated electric drive parameters. Among them, the test conditions include road surface gradient, ambient temperature, and load weight. The simulated electric drive parameters are the electric drive system parameters of the forklift to be tested under the test conditions. The electric drive system includes a battery, a motor, and a kinetic energy recovery device; An evaluation module, configured to obtain multiple groups of simulated electric drive parameters of the forklift to be tested under different test conditions as an evaluation data set, and input the evaluation data set into the pre-trained performance evaluation model to obtain the operating temperature range, load capacity limit, and maximum driving speed of the forklift to be tested.

[0008] In a third aspect, an electronic device provided by an embodiment of the present application includes a memory and a processor. A computer program is stored in the memory, and the processor is configured to run the computer program to execute a method for testing an integrated electric drive system for a forklift.

[0009] In a fourth aspect, an embodiment of the present application provides a readable storage medium. A computer program is stored in the readable storage medium. The computer program includes program code for controlling a process to execute the process. When the program code is executed by a processor, a method for testing an integrated electric drive system for a forklift is implemented.

[0010] The main contributions and innovations of the present invention are as follows: This solution inputs the factory parameters of the electric drive system of the forklift to be tested, combined with test conditions such as road surface gradient, ambient temperature, and load weight, into a pre-trained adaptive physical model (including battery, motor, and kinetic energy recovery simulation models) to obtain simulated electric drive parameters. After preprocessing and dimension unification to form an evaluation data set, it is input into a performance evaluation model (through structures such as multi-branch parallel connection, feature fusion, and multi-head self-attention mechanism), which can accurately predict the working temperature range, load capacity limit, and maximum driving speed. It has the advantages of combining the advantages of physical models and deep learning models, being able to comprehensively evaluate multiple objectives by integrating multi-dimensional parameters, being trainable and optimizable through historical data, and being applicable to different test conditions, providing an efficient and comprehensive solution for the performance test of forklift electric drive systems.

[0011] Details of one or more embodiments of this application are presented in the following drawings and description to make other features, objectives, and advantages of this application more concise and understandable. Brief Description of the Drawings

[0012] The drawings described herein are used to provide a further understanding of this application, form a part of this application, and the illustrative embodiments and descriptions thereof are used to explain this application and do not constitute an improper limitation to this application. In the drawings: Figure 1 is a flowchart of a method for testing an integrated electric drive system for forklifts according to an embodiment of this application; Figure 2 is a schematic diagram of the mutual coupling of a kinetic energy recovery simulation model, a motor simulation model, and a battery simulation model according to an embodiment of this application; Figure 3 is a schematic structural diagram of a performance evaluation model according to an embodiment of this application; Figure 4 is a structural block diagram of a testing device for an integrated electric drive system for forklifts according to an embodiment of this application; Figure 5 is a schematic hardware structure diagram of an electronic device according to an embodiment of this application. Detailed Embodiments

[0013] Here, the exemplary embodiments will be described in detail, and the examples are shown in the drawings. When the following description refers to the drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with one or more embodiments of this specification. On the contrary, they are only examples of devices and methods that are consistent with some aspects of one or more embodiments of this specification as detailed in the appended claims.

[0014] It should be noted that: In other embodiments, the steps of the corresponding method are not necessarily executed in the order shown and described in this specification. In some other embodiments, the steps included in the method may be more or less than those described in this specification. In addition, a single step described in this specification may be decomposed into multiple steps for description in other embodiments; and multiple steps described in this specification may also be combined into a single step for description in other embodiments.

[0015] Embodiment 1 The embodiment of the present application provides a test method for an integrated electric drive system for a forklift. By integrating an adaptive multi-body dynamics physical model and a multi-modal forklift performance evaluation model, a parametric virtual test system for the electric drive system is constructed to achieve efficient full-condition testing without real vehicle movement and real environment simulation. Specifically, referring to Figure 1 , the method includes: Obtain the factory parameters of the electric drive system of the forklift to be tested, and input the factory parameters of the electric drive system combined with the test conditions into the pre-trained adaptive physical model to obtain simulated electric drive parameters. Among them, the test conditions include road surface gradient, ambient temperature, and load weight. The simulated electric drive parameters are the electric drive system parameters of the forklift to be tested under the test conditions. The electric drive system includes a battery, a motor, and a kinetic energy recovery device; Obtain multiple sets of simulated electric drive parameters of the forklift to be tested under different test conditions as an evaluation data set, and input the evaluation data set into the pre-trained performance evaluation model to obtain the working temperature range, load capacity limit, and maximum driving speed of the forklift to be tested.

[0016] In some specific embodiments, the electric drive system in this solution is mainly composed of a battery, a motor, and a kinetic energy recovery device. Therefore, the factory parameters of the electric drive system include the factory parameters of the battery, the factory parameters of the motor, and the factory parameters of the kinetic energy recovery device.

[0017] Specifically, the factory parameters of the battery include rated capacity, internal resistance, OSC-OCV curve, charge and discharge rate, and heat capacity coefficient.

[0018] Specifically, the factory parameters of the motor include peak torque, rated power, efficiency MAP diagram, winding thermal resistance, and magnetic saturation characteristic curve.

[0019] Specifically, the factory parameters of the kinetic energy recovery include maximum recovery power, recovery efficiency curve, and mechanical-electric energy conversion coefficient.

[0020] Specifically, batteries, motors, and kinetic energy recovery devices are generally tested before leaving the factory, and the test results are written in the technical manual or the manufacturer's official website. By querying the technical manual or official website, the corresponding battery factory parameters, motor factory parameters, and kinetic energy recovery factory parameters can be obtained.

[0021] In some specific embodiments, the adaptive physical model includes a battery simulation model, a motor simulation model, and a kinetic energy recovery simulation model, and the battery simulation model, the motor simulation model, and the kinetic energy recovery simulation model are mathematical models, and MATLAB is used to train and solve the adaptive physical model.

[0022] Specifically, this solution obtains historical electric drive system test data under different test conditions as training data, and uses MATLAB to train the adaptive physical model based on the training data. During the training process, the adaptive physical model learns the impact of different working conditions on different parameters in the adaptive physical model to complete the training.

[0023] Furthermore, the formula of the battery simulation model is expressed as:

[0024] in, is the battery terminal voltage, is the open circuit voltage-state of charge function, I is the operating current, is the battery's internal resistance in ohms, To reflect the electrochemical polarization internal resistance, To reflect the polarization internal resistance of deep polarization, 、 is the time constant, and t is the duration of the current acting on the battery.

[0025] Specifically, the parameters for constructing the battery simulation model are provided by the battery factory parameters. For example, during the training process of the battery simulation model, MATLAB is used to calculate the influence of ambient temperature on the open circuit voltage-state of charge function, the influence of ambient temperature on the battery ohmic internal resistance, the influence of ambient temperature on the polarization internal resistance reflecting electrochemistry, the polarization internal resistance reflecting deep polarization, and the influence of load and slope on the operating current. In the battery simulation model, the time constant is not affected by any working conditions.

[0026] For example, it can be seen from the calculation results of MATLAB that the temperature correction formula of the open circuit voltage-state of charge function is: ,in, is the battery open circuit voltage, is the open circuit voltage at 25°C, is the reference temperature, k is the temperature coefficient, is the difference between the actual ambient temperature and the reference temperature; the ohmic internal resistance of the battery and the polarization internal resistance reflecting electrochemistry decrease as the ambient temperature increases; the polarization internal resistance reflecting deep polarization is affected by the state of charge SOC; the working current is positively correlated with the load / gradient.

[0027] Further, the formula of the motor simulation model is expressed as:

[0028] where, is the electromagnetic torque generated by the motor, is the number of pole pairs of the motor, is the permanent magnet flux linkage, is the d-axis inductance, is the q-axis inductance, is the current component on the d-axis, is the current component on the q-axis, is the instantaneous change rate of the angular velocity, is the load torque, is the damping torque caused by viscous friction, is the moment of inertia.

[0029] Specifically, the parameters for constructing the motor simulation model are provided by the motor factory parameters. Exemplarily, during the training process of the motor simulation model, MATLAB is used to calculate the influence of the ambient temperature on the permanent magnet flux linkage, the influence of the current on the d-axis inductance and q-axis inductance, the influence of the ambient temperature on the damping torque caused by viscous friction, and the influence of the load torque on the load and gradient. The remaining parameters remain fixed.

[0030] Exemplarily, it can be known from the calculation results of MATLAB that the permanent magnet flux linkage decreases as the ambient temperature increases, the d-axis inductance and q-axis inductance decrease as the current increases, the damping torque caused by viscous friction slightly increases as the ambient temperature increases, and the load torque is directly calculated from the load and gradient.

[0031] Further, the formula of the kinetic energy recovery simulation model is expressed as:

[0032] where, is the electric power of kinetic energy recovery, is the energy recovery efficiency of kinetic energy recovery, m is the total weight of the forklift, v is the driving speed of the forklift, is the kinetic energy of the forklift, is the minimum speed threshold of kinetic energy recovery.

[0033] Specifically, the parameters for constructing the kinetic energy recovery simulation model are provided by the kinetic energy recovery factory parameters. Exemplarily, during the training process of the kinetic energy recovery simulation model, MATLAB is used to calculate the environmental temperature, the impact on the energy recovery efficiency of kinetic energy recovery, and the impact of the load on the vehicle mass.

[0034] Exemplarily, it can be learned from the calculation results of MATLAB that the lower the environmental temperature, the lower the energy recovery efficiency of kinetic energy recovery.

[0035] In some specific embodiments, the kinetic energy recovery simulation model, the motor simulation model, and the battery simulation model in this solution are coupled to each other, that is, the parameters in the kinetic energy recovery simulation model, the motor simulation model, and the battery simulation model will affect each other. A schematic diagram of the mutual coupling of the kinetic energy recovery simulation model, the motor simulation model, and the battery simulation model is as Figure 2 shown.

[0036] That is to say, in this solution, the battery factory parameters, the motor factory parameters, and the kinetic energy recovery factory parameters are obtained. The motor simulation model is used to perform simulation based on the motor factory parameters and the test conditions to obtain the simulated motor parameters. The battery address model is used to perform simulation based on the battery factory parameters, the simulated motor parameters, and the test conditions to obtain the simulated battery parameters. The kinetic energy recovery simulation model is used to perform simulation based on the kinetic energy recovery factory parameters, the test conditions, and the simulated motor parameters to obtain the simulated recovery parameters. The simulated electric drive parameters are composed of the simulated motor parameters, the simulated battery parameters, and the simulated recovery parameters.

[0037] Specifically, during the process of simulating the battery, the output current and power of the battery are determined by the motor. The greater the heavy load of the forklift and the greater the slope, the higher the speed of the motor. Then, as the power supply, the battery needs to transmit a larger current. Therefore, during the process of simulating the battery, the simulated motor parameters need to be obtained first. Similarly, the simulation of the kinetic energy recovery device also needs to obtain the operating conditions of the motor to obtain the running speed of the forklift. Therefore, during the process of simulating the kinetic energy recovery device, the simulated motor parameters also need to be obtained first.

[0038] In some specific embodiments, the simulated motor parameters include the motor peak temperature, the rated power, the speed range, the efficiency curve, the winding resistance, and the continuous output torque. The simulated battery parameters are the battery operating voltage, the capacity, the internal resistance, the open circuit voltage (OCV), and the temperature coefficient. The simulated recovery parameters include the kinetic energy recovery efficiency, the recovery torque threshold, the energy recovery power upper limit, and the braking distribution coefficient.

[0039] Specifically, through simulation in MATLAB, simulation parameters of the motor, battery, and kinetic energy recovery device under test conditions can be obtained based on a pre-defined simulation model. Thus, by analyzing the simulation parameters, simulated motor parameters, simulated battery parameters, and simulated recovery parameters can be obtained.

[0040] In some specific embodiments, the simulated electric drive parameters of multiple forklifts to be tested under different test conditions are preprocessed and dimensionally unified to obtain an evaluation dataset. Specifically, through preprocessing and dimensional unification, physical quantity features with different units can be brought to the same dimension, facilitating the understanding and analysis by the performance evaluation model.

[0041] Specifically, preprocessing is completed by means of data cleaning and normalization.

[0042] In some embodiments, the structure of the performance evaluation model is as Figure 3 shown. The first layer of the performance evaluation model is composed of a battery feature branch, a motor feature branch, and a kinetic energy recovery feature branch in parallel. The battery feature branch extracts features from all simulated battery parameters in the evaluation dataset through a convolutional layer to obtain battery features. The motor feature branch extracts features from all simulated motor parameters in the evaluation dataset to obtain motor features. The kinetic energy recovery branch extracts features from all simulated recovery parameters in the evaluation dataset to obtain kinetic energy recovery features. The second layer of the performance evaluation model is a feature fusion layer, which fuses the battery features, motor features, and kinetic energy recovery features to obtain a feature fusion result. The third layer of the performance evaluation model is composed of a first multi-head self-attention mechanism, a second multi-head self-attention mechanism, and a third multi-head self-attention mechanism in parallel. The first multi-head self-attention mechanism assigns weights to different types of features in the feature fusion result with the goal of predicting the working temperature range to obtain a first multi-head self-attention result. The second multi-head self-attention mechanism assigns weights to different types of features in the feature fusion result with the goal of predicting the load capacity limit to obtain a second multi-head self-attention result. The third multi-head self-attention mechanism assigns weights to different types of features in the feature fusion result with the goal of predicting the maximum driving speed to obtain a third multi-head self-attention result. The fourth layer of the performance evaluation model is composed of a temperature fully connected layer, a load fully connected layer, and a speed fully connected layer in parallel. The temperature fully connected layer makes a prediction based on the first multi-head self-attention result to obtain the working temperature range. The load fully connected layer makes a prediction based on the second multi-head self-attention result to obtain the load capacity limit. The speed fully connected layer makes a prediction based on the third multi-head self-attention result to obtain the maximum driving speed.

[0043] Specifically, in this solution, historical test data of the forklift under different working conditions is used as training samples to train the performance evaluation model, and the corresponding historical test results are used to correct the prediction results of the performance evaluation model.

[0044] Specifically, since the input of the performance evaluation model in this solution is a multi-modal data set and the output is not a single prediction result, different multi-head self-attention layers are used in this solution to allocate rights for different prediction targets respectively. That is to say, when predicting the working temperature range, the first multi-head self-attention mechanism will automatically learn and highlight the features closely related to temperature changes, such as the correlation weights of parameters such as the motor peak temperature and the battery temperature coefficient; when predicting the load capacity limit, the second multi-head self-attention mechanism will strengthen the analysis of the feature dimensions affecting the load capacity, such as the rated power, continuous output torque, and battery capacity; similarly, the third multi-head self-attention mechanism will focus on the parameter weight distribution directly related to the speed performance, such as the speed range and the upper limit of the energy recovery power.

[0045] Specifically, this differential feature weight allocation mechanism for different prediction targets enables the model to more accurately capture the information dimensions most relevant to the specific output target in the input data, thus obtaining better prediction results.

[0046] Specifically, in addition to the electric drive system parameters mentioned in this solution, those skilled in the art can use the same method to simulate other electric drive system parameters to obtain the desired test results.

[0047] Specifically, this solution can obtain simulated electric drive parameters by constructing a front-end page and inputting the test working conditions on the front-end page.

[0048] Embodiment 2 Based on the same concept, referring to Figure 4 , this application also proposes a test device for an integrated electric drive system of a forklift, including: A simulation module for obtaining the factory parameters of the electric drive system of the forklift to be tested, and inputting the factory parameters of the electric drive system combined with the test working conditions into a pre-trained adaptive physical model to obtain simulated electric drive parameters. Among them, the test working conditions include the road surface slope, the ambient temperature, and the load weight. The simulated electric drive parameters are the electric drive system parameters of the forklift to be tested under the test working conditions. The electric drive system includes a battery, a motor, and a kinetic energy recovery device; An evaluation module for obtaining multiple groups of simulated electric drive parameters of the forklift to be tested under different test working conditions as an evaluation data set, and inputting the evaluation data set into a pre-trained performance evaluation model to obtain the working temperature range, the load capacity limit, and the maximum driving speed of the forklift to be tested.

[0049] Embodiment 3 This embodiment also provides an electronic device. Referring to Figure 5 , it includes a memory 404 and a processor 402. A computer program is stored in the memory 404, and the processor 402 is configured to run the computer program to execute the steps in any one of the above method embodiments.

[0050] Specifically, the above-mentioned processor 402 may include a central processing unit (CPU), or an application specific integrated circuit (ASIC), or may be configured as one or more integrated circuits implementing the embodiments of the present application.

[0051] Among them, the memory 404 may include a mass memory 404 for data or instructions. By way of example and not limitation, the memory 404 may include a hard disk drive (HDD), a floppy disk drive, a solid state drive (SSD), a flash memory, an optical disk, a magneto-optical disk, a magnetic tape, or a universal serial bus (USB) drive, or a combination of two or more of these. In a suitable case, the memory 404 may include removable or non-removable (or fixed) media. In a suitable case, the memory 404 may be inside or outside the data processing device. In a specific embodiment, the memory 404 is a non-volatile memory. In a specific embodiment, the memory 404 includes a read-only memory (ROM) and a random access memory (RAM). In a suitable case, the ROM may be a mask-programmed ROM, a programmable ROM (PROM), an erasable PROM (EPROM), an electrically erasable PROM (EEPROM), an electrically alterable ROM (EAROM), or a flash memory (FLASH), or a combination of two or more of these. In a suitable case, the RAM may be a static random access memory (SRAM) or a dynamic random access memory (DRAM), where the DRAM may be a fast page mode dynamic random access memory (FPMDRAM), an extended data output dynamic random access memory (EDODRAM), a synchronous dynamic random access memory (SDRAM), etc.

[0052] The memory 404 can be used to store or cache various data files required for processing and / or communication, as well as possible computer program instructions executed by the processor 402.

[0053] The processor 402 reads and executes the computer program instructions stored in the memory 404 to implement any one of the forklift integrated electric drive system test methods in the above embodiments.

[0054] Optionally, the above electronic device may further include a transmission device 406 and an input / output device 408. Among them, the transmission device 406 is connected to the above processor 402, and the input / output device 408 is connected to the above processor 402.

[0055] The transmission device 406 can be used to receive or send data via a network. Specific examples of the above network may include wired or wireless networks provided by the communication provider of the electronic device. In one example, the transmission device includes a network adapter (Network Interface Controller, abbreviated as NIC), which can be connected to other network devices through a base station and thus communicate with the Internet. In one example, the transmission device 406 can be a radio frequency (RF) module, which is used to communicate with the Internet wirelessly.

[0056] The input / output device 408 is used to input or output information. In this embodiment, the input information can be the factory parameters of the electric drive system, test conditions, etc., and the output information can be simulated electric drive parameters, etc.

[0057] Optionally, in this embodiment, the above processor 402 can be set to execute the following steps through a computer program: Obtain the factory parameters of the electric drive system of the forklift to be tested, and input the factory parameters of the electric drive system combined with the test conditions into the pre-trained adaptive physical model to obtain simulated electric drive parameters. Among them, the test conditions include road surface gradient, ambient temperature, and load weight. The simulated electric drive parameters are the electric drive system parameters of the forklift to be tested under the test conditions. The electric drive system includes a battery, a motor, and a kinetic energy recovery device; Obtain multiple groups of simulated electric drive parameters of the forklift to be tested under different test conditions as an evaluation data set, and input the evaluation data set into the pre-trained performance evaluation model to obtain the operating temperature range, load capacity limit, and maximum driving speed of the forklift to be tested.

[0058] It should be noted that the specific examples in this embodiment can refer to the examples described in the above embodiments and optional implementation manners, and this embodiment will not be elaborated here.

[0059] In general, various embodiments can be implemented in hardware or dedicated circuits, software, logic, or any combination thereof. Some aspects of the present invention can be implemented in hardware, while other aspects can be implemented by firmware or software executed by a controller, microprocessor, or other computing device, but the present invention is not limited thereto. Although various aspects of the present invention may be shown and described as block diagrams, flowcharts, or using some other graphical representation, it should be understood that, by way of non-limiting example, the blocks, devices, systems, techniques, or methods described herein can be implemented in hardware, software, firmware, dedicated circuits or logic, general hardware or a controller or other computing device, or some combination thereof.

[0060] Embodiments of the present invention can be implemented by computer software that is executable by a data processor of a mobile device, such as in a processor entity, or by hardware, or by a combination of software and hardware. A computer software or program (also referred to as a program product), including software routines, applets, and / or macros, can be stored in any device-readable data storage medium, and they include program instructions for performing specific tasks. The computer program product can include one or more computer-executable components that are configured to perform the embodiments when the program runs. One or more computer-executable components can be at least one software code or a part thereof. Additionally, in this regard, it should be noted that any box in the logical flow, as Figure 5 described, can represent a program step, or interconnected logic circuits, boxes, and functions, or a combination of program steps and logic circuits, boxes, and functions. The software can be stored on physical media such as memory chips or storage blocks implemented within the processor, magnetic media such as hard disks or floppy disks, and optical media such as, for example, DVDs and their data variants, CDs. The physical media are non-transitory media.

[0061] Those skilled in the art should understand that the technical features of the above embodiments can be combined arbitrarily. For the sake of brevity of description, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, it should be considered as falling within the scope described in this specification.

[0062] The above embodiments only represent several implementation manners of the present application, and their descriptions are relatively specific and detailed, but should not be construed as limiting the scope of the present application. It should be noted that for those of ordinary skill in the art, without departing from the concept of the present application, several modifications and improvements can still be made, and these all belong to the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the appended claims.

Claims

1. An integrated electric drive system test method for a forklift, characterized in that, Including the following steps: Obtain the factory parameters of the electric drive system of the forklift to be tested, and input the factory parameters of the electric drive system combined with the test conditions into the pre-trained adaptive physical model to obtain simulated electric drive parameters. Among them, the test conditions include road surface gradient, ambient temperature, and load weight. The simulated electric drive parameters are the electric drive system parameters of the forklift to be tested under the test conditions. The electric drive system includes a battery, a motor, and a kinetic energy recovery device; Obtain multiple groups of simulated electric drive parameters of the forklift to be tested under different test conditions as an evaluation data set, and input the evaluation data set into the pre-trained performance evaluation model to obtain the operating temperature range, load capacity limit, and maximum driving speed of the forklift to be tested.

2. The testing method of an integrated electric drive system for a forklift according to claim 1, wherein The adaptive physical model includes a battery simulation model, a motor simulation model, and a kinetic energy recovery simulation model. The battery simulation model, the motor simulation model, and the kinetic energy recovery simulation model are mathematical models. Obtain historical electric drive system test data as training data, and obtain historical electric drive system test data under different test conditions as training data. Use MATLAB to train the adaptive physical model based on the training data. During the training process, the adaptive physical model learns the influence of different working conditions on different parameters in the adaptive physical model to complete the training.

3. The test method for an integrated electric drive system for a forklift according to claim 2, characterized in that, The formula of the battery simulation model is expressed as: ； wherein, is the battery terminal voltage, is the open circuit voltage - state of charge function, I is the working current, is the battery ohmic internal resistance, is the polarization internal resistance reflecting electrochemistry, is the polarization internal resistance reflecting deep polarization, and, are the time constants, and t is the duration of the current acting on the battery.

4. The testing method for an integrated electric drive system for a forklift according to claim 2, characterized in that, The formula of the motor simulation model is expressed as: ； Among them, is the electromagnetic torque generated by the motor, is the number of pole pairs of the motor, is the permanent magnet flux linkage, is the d-axis inductance, is the q-axis inductance, is the current component on the d-axis, is the current component on the q-axis, is the instantaneous change rate of the angular velocity, is the load torque, is the damping torque caused by viscous friction, is the moment of inertia.

5. The test method for an integrated electric drive system for a forklift according to claim 2, characterized in that, The formula of the kinetic energy recovery simulation model is expressed as: ； Among them, is the electric power for kinetic energy recovery, is the energy recovery efficiency of kinetic energy recovery, m is the total weight of the forklift, v is the driving speed of the forklift, is the kinetic energy of the forklift, is the minimum speed threshold for kinetic energy recovery.

6. The testing method for an integrated electric drive system for a forklift according to claim 1, wherein Obtain the battery factory parameters, motor factory parameters, and kinetic energy recovery factory parameters. Use the motor simulation model to perform simulation based on the motor factory parameters and test conditions to obtain simulated motor parameters. Use the battery simulation model to perform simulation based on the battery factory parameters, simulated motor parameters, and test conditions to obtain simulated battery parameters. Use the kinetic energy recovery simulation model to perform simulation based on the kinetic energy recovery factory parameters, test conditions, and simulated motor parameters to obtain simulated recovery parameters. The simulated electric drive parameters are composed of the simulated motor parameters, the simulated battery parameters, and the simulated recovery parameters.

7. A test method for an integrated electric drive system for a forklift according to claim 1, characterized in that The first layer of the performance evaluation model is composed of a battery feature branch, a motor feature branch, and a kinetic energy recovery feature branch in parallel. The battery feature branch extracts features from all the simulated battery parameters in the evaluation data set through a convolutional layer to obtain battery features. The motor feature branch extracts features from all the simulated motor parameters in the evaluation data set to obtain motor features. The kinetic energy recovery branch extracts features from all the simulated recovery parameters in the evaluation data set to obtain kinetic energy recovery features. The second layer of the performance evaluation model is a feature fusion layer, and the feature fusion layer fuses the battery features, the motor features, and the kinetic energy recovery features to obtain a feature fusion result; The third layer of the performance evaluation model is composed of a first multi-head self-attention mechanism, a second multi-head self-attention mechanism, and a third multi-head self-attention mechanism in parallel. The first multi-head self-attention mechanism assigns weights to different types of features in the feature fusion result with the goal of predicting the working temperature range to obtain a first multi-head self-attention result. The second multi-head self-attention mechanism assigns weights to different types of features in the feature fusion result with the goal of predicting the load capacity limit to obtain a second multi-head self-attention result. The third multi-head self-attention mechanism assigns weights to different types of features in the feature fusion result with the goal of predicting the maximum driving speed to obtain a third multi-head self-attention result. The fourth layer of the performance evaluation model is composed of a temperature fully connected layer, a load fully connected layer, and a speed fully connected layer in parallel. The temperature fully connected layer makes a prediction based on the first multi-head self-attention result to obtain the working temperature range. The load fully connected layer makes a prediction based on the second multi-head self-attention result to obtain the load capacity limit. The speed fully connected layer makes a prediction based on the third multi-head self-attention result to obtain the maximum driving speed.

8. An integrated electric drive system test device for a forklift, comprising: A simulation module, configured to obtain the factory parameters of the electric drive system of the forklift to be tested, and input the factory parameters of the electric drive system combined with the test working conditions into a pre-trained adaptive physical model to obtain simulated electric drive parameters. Wherein, the test working conditions include road surface gradient, ambient temperature, and load weight. The simulated electric drive parameters are the electric drive system parameters of the forklift to be tested under the test working conditions. The electric drive system includes a battery, a motor, and a kinetic energy recovery device; An evaluation module, configured to obtain multiple groups of simulated electric drive parameters of the forklift to be tested under different test working conditions as an evaluation data set, and input the evaluation data set into a pre-trained performance evaluation model to obtain the working temperature range, load capacity limit, and maximum driving speed of the forklift to be tested.

9. An electronic device, comprising a memory and a processor, characterized in that, A computer program is stored in the memory, and the processor is configured to run the computer program to execute an integrated electric drive system test method for a forklift according to any one of claims 1-7.

10. A readable storage medium, characterized in that, A computer program is stored in the readable storage medium. The computer program includes program codes for controlling a process to execute the process. When the program codes are executed by a processor, an integrated electric drive system test method for a forklift according to any one of claims 1-7 is implemented.

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