Electrically-driven disconnecting mechanism load spectrum generation method, device and equipment and storage medium

Abstract

The invention discloses an electric drive disconnecting mechanism load spectrum generation method, device and equipment and a storage medium. The method comprises the following steps: constructing a whole vehicle simulation model integrated with a preset electric drive disconnecting mechanism control strategy; inputting a preset driving scene database into the whole vehicle simulation model to output data of each disconnection and combination of an electric drive disconnection mechanism and electric drive working condition data during disconnection and combination as durability data; and performing accelerated processing on the endurance data based on a damage equivalent manner to generate an electric drive disconnect mechanism endurance load spectrum. The endurance load spectrum closely associated with the real user working condition and the whole vehicle parameters can be generated, so that the accuracy of the endurance test of the disconnecting mechanism is remarkably improved, the test period is shortened, and the development cost is reduced.

Description

Technical Field

[0001] This invention relates to the field of new energy vehicle technology, and in particular to a method, apparatus, device and storage medium for generating load spectrum of an electric drive disconnection mechanism. Background Technology

[0002] With the rapid development of new energy vehicle technology, the electric drive system has become a key factor influencing vehicle energy consumption and range. The electric drive disconnect mechanism, as a core component capable of intelligently controlling the drive shaft connection status, can quickly separate the electric drive output shaft from the wheel-end half-shafts when the vehicle does not require motor drive. This effectively reduces drag torque loss and unnecessary energy waste, playing a crucial role in improving overall vehicle energy efficiency and extending driving range. Especially during high-speed cruising, coasting, or braking conditions, its intelligent disconnect function can further optimize the vehicle's energy management strategy and improve overall system efficiency.

[0003] To ensure the reliability and durability of the disconnection mechanism throughout the vehicle's entire lifecycle, thorough durability verification is necessary during the design and development phase. Traditional durability verification methods primarily rely on engineering experience or simplified load assumptions to generate test load spectra. These methods are often disconnected from actual vehicle operating conditions, user habits, and the specific vehicle model's dynamic parameters. This results in test spectra that fail to accurately reflect the true loads the disconnection mechanism experiences in real-world applications, potentially leading to increased costs due to over-design or reliability risks due to under-design. Furthermore, traditional methods do not adequately consider the direct impact of intelligent control strategies on the mechanism's action frequency and load state during load spectrum generation, and lack load acceleration mechanisms based on damage equivalence theory, resulting in long testing cycles, high costs, and low efficiency.

[0004] Therefore, how to generate the durability load spectrum of the electrically driven disconnection mechanism is a technical problem that urgently needs to be solved. Summary of the Invention

[0005] The main objective of this invention is to provide a method, apparatus, device, and storage medium for generating load spectra of an electric drive disconnect mechanism. This method can generate a durability load spectrum that is closely related to real user operating conditions and vehicle parameters, thereby significantly improving the accuracy of durability testing of the disconnect mechanism, shortening the test cycle, reducing development costs, and effectively avoiding over-design or under-design caused by traditional empirical methods.

[0006] In a first aspect, this application provides a method for generating the load spectrum of an electrically driven disconnecting mechanism, wherein the method includes the following steps: Construct a vehicle simulation model integrating a preset control strategy for the electric drive disconnection mechanism; The preset driving scenario database is input into the vehicle simulation model to output the data of each disconnection and engagement of the electric drive disconnection mechanism, as well as the electric drive operating condition data during disconnection and engagement, as durability data. The durability data is accelerated using a damage equivalence approach to generate a durability load spectrum for the electrically driven disconnect mechanism.

[0007] In conjunction with the first aspect mentioned above, as an optional implementation method, all disconnection and engagement events are identified and summarized from the durability data, and the number of occurrences of the events in different motor torque ranges, different motor speed ranges, and different vehicle speed ranges is counted to obtain the original durability data statistics table based on disconnection and engagement events. Actions with torque values ​​lower than a preset baseline torque threshold in the original durability data statistics table are defined as low-damage operating conditions. Actions with torque values ​​equal to or higher than a preset target torque threshold are defined as high-damage operating conditions.

[0008] The number of actions under the low-damage condition is equivalently converted to the number of actions under the high-damage condition using the damage equivalence method, and finally the durability load spectrum of the electrically driven disconnection mechanism is generated.

[0009] In conjunction with the first aspect mentioned above, as an optional implementation method, according to the formula: Calculate the number of actions after the transformation, where, This represents the number of disconnections and reconnections corresponding to the first torque range. This represents the average torque corresponding to the first torque range. This represents the maximum torque for electric drive. In conjunction with the first aspect mentioned above, as an optional implementation method, based on a simulation platform, a physical model of the electric drive disconnecting mechanism is constructed according to the parameters of the disconnecting mechanism drive motor, the stroke parameters of the shift fork, the shift fork lever ratio, the material parameters of the dog tooth synchronizer synchronization ring, the cone angle parameters, and the inner and outer diameter parameters. Based on the preset electric drive disconnection mechanism control strategy, an electric drive disconnection mechanism control model is constructed. The control strategy includes: intelligently deciding the disconnection and engagement actions of the electric drive disconnection mechanism under different operating conditions based on the battery SOC state, vehicle speed, accelerator pedal opening and motor efficiency MAP. A vehicle dynamics model is constructed based on vehicle parameters, front and rear electric drive parameters, battery parameters, accelerator and brake pedal parameters, and driving scenario parameters. The physical model of the electric drive disconnection mechanism, the control model of the electric drive disconnection mechanism, and the vehicle dynamics model are integrated to obtain the vehicle simulation model.

[0010] In conjunction with the first aspect mentioned above, as an optional implementation method, a driving scenario database is constructed. The database includes: mountain driving scenarios, urban driving scenarios, highway driving scenarios, suburban driving scenarios, extreme hill climbing driving scenarios, and extreme differential speed driving scenarios, as well as the number of cycles, mileage, torque distribution strategy, and vehicle weight corresponding to each scenario in the simulation. The vehicle simulation model is driven by the constructed driving scenario database to simulate the vehicle's driving data under various working conditions. From the driving data, obtain the disconnection and engagement events of the electric drive disconnection mechanism under various operating conditions, as well as the torque and speed data of the electric drive at the time of each disconnection and engagement event, and use the disconnection and engagement events and the torque and speed data of the electric drive as durability data.

[0011] In conjunction with the first aspect mentioned above, as an optional implementation method, the speed difference distribution data is obtained by combining the speed difference of all electrically driven disconnecting mechanisms in the statistical simulation at the time of the event. The speed difference distribution data is scaled according to a preset ratio.

[0012] In conjunction with the first aspect mentioned above, as an optional implementation method, based on vehicle big data analysis, the proportion of environmental temperature distribution experienced by the disconnection mechanism during the vehicle's life cycle is determined; The number of disconnections and reconnections of the electrically driven disconnection mechanism is allocated based on the ambient temperature distribution ratio.

[0013] Secondly, this application provides a load spectrum generation device for an electrically driven disconnecting mechanism, the device comprising: The architecture module is used to build a vehicle simulation model that integrates a preset electric drive disconnection mechanism control strategy; The output module is used to input a preset driving scenario database into the vehicle simulation model to output data on each disconnection and engagement of the electric drive disconnection mechanism, as well as electric drive operating condition data during disconnection and engagement, as durability data. The processing module is used to accelerate the processing of the durability data based on damage equivalence to generate a durability load spectrum for the electrically driven disconnect mechanism.

[0014] Thirdly, this application also provides an electronic device, the electronic device comprising: a processor; and a memory storing computer-readable instructions, which, when executed by the processor, implement the method described in any one of the first aspects.

[0015] Fourthly, this application also provides a computer-readable storage medium storing computer program instructions that, when executed by a computer, cause the computer to perform the method described in any of the first aspects.

[0016] This application provides a method, apparatus, device, and storage medium for generating a load spectrum of an electric drive disconnect mechanism. The method includes the following steps: constructing a vehicle simulation model integrating a preset electric drive disconnect mechanism control strategy; inputting a preset driving scenario database into the vehicle simulation model to output data on each disconnection and engagement of the electric drive disconnect mechanism, as well as electric drive operating condition data during disconnection and engagement, as durability data; and performing accelerated processing on the durability data based on damage equivalence to generate a durability load spectrum for the electric drive disconnect mechanism. This application can generate a durability load spectrum closely related to real user operating conditions and vehicle parameters, thereby significantly improving the accuracy of disconnect mechanism durability testing, shortening the testing cycle, reducing development costs, and effectively avoiding over-design or under-design caused by traditional empirical methods.

[0017] It should be understood that the above general description and the following detailed description are merely exemplary and do not limit the invention. Attached Figure Description

[0018] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with the invention and, together with the description, serve to explain the principles of the invention.

[0019] Figure 1 This is a flowchart of a load spectrum generation method for an electrically driven disconnection mechanism provided in an embodiment of this application; Figure 2 This is a schematic diagram of a load spectrum generation device for an electrically driven disconnection mechanism provided in an embodiment of this application; Figure 3 This is a schematic diagram of the hardware structure of the load spectrum generation device for the electrically driven disconnection mechanism involved in the embodiments of this application. Detailed Implementation

[0020] 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 the present invention. Rather, they are merely examples of apparatuses and methods consistent with some aspects of the invention as detailed in the appended claims.

[0021] Furthermore, the accompanying drawings are merely illustrative of this disclosure and are not necessarily drawn to scale. Some of the block diagrams shown in the drawings represent functional entities and do not necessarily correspond to physically or logically independent entities.

[0022] The embodiments of this application will be further described in detail below with reference to the accompanying drawings.

[0023] In a first aspect, this application provides a method for generating the load spectrum of an electrically driven disconnecting mechanism.

[0024] Reference Figure 1 , Figure 1 The diagram shown is a flowchart of a load spectrum generation method for an electrically driven disconnecting mechanism provided by the present invention. Figure 1 As shown, the method includes the following steps: Step S101: Construct a vehicle simulation model that integrates a preset electric drive disconnection mechanism control strategy.

[0025] Specifically, based on the simulation platform, a physical model of the electric drive disconnection mechanism is constructed according to the parameters of the disconnection mechanism drive motor, the fork stroke parameters, the fork lever ratio, the synchronizer synchronizer ring material parameters, the cone angle parameters, and the inner and outer diameter parameters. Based on the preset electric drive disconnection mechanism control strategy, an electric drive disconnection mechanism control model is constructed. The control strategy includes: intelligently deciding the disconnection and engagement actions of the electric drive disconnection mechanism under different operating conditions based on the battery SOC state, vehicle speed, accelerator pedal opening and motor efficiency MAP. A vehicle dynamics model is constructed based on vehicle parameters, front and rear electric drive parameters, battery parameters, accelerator and brake pedal parameters, and driving scenario parameters. The physical model of the electric drive disconnection mechanism, the control model of the electric drive disconnection mechanism, and the vehicle dynamics model are integrated to obtain the vehicle simulation model.

[0026] To facilitate understanding and provide examples, we establish a physical model of the disconnecting mechanism, including parameters of the drive motor, fork stroke, fork lever ratio, dog tooth synchronizer synchronization ring material, cone angle, and inner / outer diameter.

[0027] The control model for the electric drive disconnection mechanism is as follows: 1. Based on the battery SOC state, if SOC>90%, the regenerative braking of the front electric drive is turned off, and the disconnection mechanism is disconnected during vehicle braking or coasting; (if SOC<80%, regenerative braking is allowed), vehicle speed (if>15 km / h, regenerative braking is effective), and brake pedal depth (if the brake pedal is pressed deeply, regenerative braking is forced).

[0028] 2. Efficiency optimization: Select the optimal switching point based on the motor efficiency MAP (efficiency at different speeds / torques) (e.g., prioritize recycling when efficiency is >90%).

[0029] 3. During the high-speed cruising phase (vehicle speed greater than 100km / h), the accelerator pedal is less than 30%. At this time, the torque output of the rear-drive motor is sufficient to meet the power requirements of the vehicle's high-speed cruising. The front-drive disconnect mechanism intelligently disconnects the power transmission of the front drive, thereby reducing mechanical friction and energy waste in the transmission system under high-speed conditions.

[0030] 4. When the front-wheel drive is intelligently disengaged at low speeds (less than 10 km / h) and with light throttle (less than 10% throttle pedal depth), the number of rotating parts in the transmission system is reduced, which helps to reduce in-vehicle noise and vibration levels, further improving driving comfort and NVH performance.

[0031] Based on the battery SOC state, vehicle speed, accelerator pedal opening, and motor efficiency MAP, the system intelligently decides on the disconnection and engagement actions of the electric drive disconnection mechanism under different operating conditions.

[0032] The vehicle dynamics model includes: driving scenario input model, driver model, driving mode selection model, front electric drive assembly model, rear electric drive assembly model, battery model, body and tire model, and data processing and analysis model.

[0033] Based on the vehicle parameters, a body and tire model is constructed. The vehicle parameters include: full load weight, unloaded weight, wheelbase, drive mode, front and rear axle load distribution, center of gravity height, tire radius, tire rolling friction coefficient, tire sliding friction coefficient, and frontal area.

[0034] Based on the front electric drive parameters, a front electric drive assembly model is constructed. The front electric drive parameters include: peak torque, rated torque, maximum speed, rotor moment of inertia, external characteristic curve, efficiency curve, and power generation characteristic curve of the front motor, as well as the speed ratio, moment of inertia of each gear, and transmission efficiency of the front gearbox.

[0035] Based on the rear electric drive parameters, a rear electric drive assembly model is constructed. The rear electric drive parameters include: peak torque, rated torque, maximum speed, rotor moment of inertia, external characteristic curve, efficiency curve, and power generation characteristic curve of the rear motor, as well as the speed ratio, moment of inertia of each gear, and transmission efficiency of the rear gearbox.

[0036] Based on the battery parameters, construct a battery model, where the battery parameters include at least: battery capacity, electromotive force, internal resistance, and SOC characteristic curve.

[0037] A driving mode selection model is constructed based on the accelerator and brake pedal parameters, which include: accelerator pedal response speed, brake pedal response speed, accelerator pedal opening slope, and brake pedal opening slope.

[0038] Based on the driving scenario database, a driving scenario input model is constructed. The driving scenario database includes: the number of cycles corresponding to each driving scenario, the vehicle weight corresponding to the simulation, the mileage corresponding to each driving scenario, and the torque distribution strategy corresponding to each driving scenario. The driving scenarios specifically include: mountain driving scenario, urban driving scenario, highway driving scenario, suburban driving scenario, extreme hill climbing driving scenario, and extreme differential driving scenario.

[0039] Among them, the driving scenario input model is used to receive data from the driving scenario database and transmit it to the driver model.

[0040] The driver model is used to automatically generate acceleration or braking commands based on the difference between the target vehicle speed and the actual vehicle speed; The driving mode selection model determines the driving intensity by adjusting the opening slope of the accelerator and brake pedals, including Eco mode, Standard mode, and Sport mode. The front electric drive assembly model is used to execute torque commands and monitor the front electric drive status; The rear electric drive assembly model is used to execute torque commands and monitor the rear electric drive status; The battery model is used to monitor battery status and provide data support for the driving mode selection model; The vehicle body and tire models are used to comprehensively calculate information related to the vehicle's motion and to enable data interaction between models; The data processing and analysis model is used to process real-time data from the front and rear electric drive assembly models and to perform algorithmic processing on the real-time data.

[0041] It is understandable that a vehicle simulation model with a disconnection mechanism is built based on the Matlab / Simulink simulation platform.

[0042] Step S102: Input the preset driving scenario database into the vehicle simulation model to output the data of each disconnection and engagement of the electric drive disconnection mechanism, as well as the electric drive operating condition data during disconnection and engagement, as durability data.

[0043] Specifically, a driving scenario database is constructed, which includes: mountain driving scenario, urban driving scenario, highway driving scenario, suburban driving scenario, extreme hill climbing driving scenario and extreme differential speed driving scenario, as well as the number of cycles, mileage, torque distribution strategy and vehicle weight corresponding to each scenario. The vehicle simulation model is driven by the constructed driving scenario database to simulate the vehicle's driving data under various working conditions. From the driving data, obtain the disconnection and engagement events of the electric drive disconnection mechanism under various operating conditions, as well as the torque and speed data of the electric drive at the time of each disconnection and engagement event, and use the disconnection and engagement events and the torque and speed data of the electric drive as durability data.

[0044] To illustrate this, a vehicle simulation model with a disconnect mechanism is run. Based on different vehicle operating conditions, different operating conditions and the corresponding number of cycles for each condition are input, and the simulation model runs automatically in sequence. The disconnection-engagement data of the disconnect mechanism for all operating conditions is obtained, and the motor torque and motor speed at the corresponding disconnection-engagement times are recorded. The disconnection-engagement events of the disconnect mechanism are statistically summarized, and the motor speed and motor torque are statistically summarized for different intervals, resulting in the statistical results in Tables 1 and 2.

[0045] Table 1

[0046] Table 2

[0047] Step S103: Accelerate the durability data based on damage equivalence to generate a durability load spectrum for the electrically driven disconnect mechanism.

[0048] Specifically, all disconnection and engagement events are identified and summarized from the durability data, and the number of occurrences of the events in different motor torque ranges, different motor speed ranges, and different vehicle speed ranges is counted to obtain the original durability data statistics table based on disconnection and engagement events; In the original durability data statistics table, actions with torque values ​​below a preset benchmark torque threshold are defined as low-damage conditions; actions with torque values ​​equal to or higher than a preset target torque threshold are defined as high-damage conditions. The number of actions under low-damage conditions is equivalently converted to the number of actions under high-damage conditions using a damage equivalence method, ultimately generating the durability load spectrum of the electrically driven disconnect mechanism.

[0049] According to the formula: Calculate the number of actions after the transformation, where, This represents the number of disconnections and reconnections corresponding to the first torque range. This represents the average torque corresponding to the first torque range. This represents the maximum torque for electric drive.

[0050] For ease of understanding and illustration, a database of operating conditions containing multiple preset driving scenarios is input into the constructed vehicle simulation model, and the simulation is run automatically in sequence. The system collects and records the disconnection and engagement events of the disconnection mechanism under all operating conditions, as well as the motor torque and speed at the time of each event, to generate a raw durability data statistics table (refer to Tables 1 and 2). Actions with torque values ​​lower than a preset benchmark torque threshold in the raw durability data statistics table are defined as low-damage operating conditions; actions with torque values ​​equal to or higher than a preset target torque threshold are defined as high-damage operating conditions. According to the formula: The number of actions in the low torque range is equivalently converted to the high torque range, and finally the durability load spectrum of the electric drive disconnection mechanism is generated.

[0051] Before finally generating the durability load spectrum of the electric drive disconnection mechanism, the process includes: statistically analyzing the speed difference of all electric drive disconnection mechanisms in the simulation at the time of the event to obtain speed difference distribution data; and scaling the speed difference distribution data according to a preset ratio.

[0052] The following distribution is obtained based on the speed difference distribution of the vehicle disconnection mechanism: 20% zero differential; 60% intermediate differential; 20% maximum differential (design allowed).

[0053] Understandably, replicating 1000 engagements exactly as they were performed on a test bench, with each engagement having a different speed difference, would be extremely complex, time-consuming, and difficult to control. Moreover, many low-differential-speed conditions contribute very little to the wear of the synchronizing ring, but consume a significant amount of time.

[0054] Therefore, it is necessary to perform engineering representativeness condensation, which is distribution statistics and scaling. The goal is to use fewer but more representative typical working conditions to equivalently simulate the damage caused to the mechanism by 1000 complex and varied real working conditions.

[0055] In one embodiment, based on vehicle big data analysis, the proportion of ambient temperature distribution experienced by the disconnection mechanism throughout the vehicle's lifecycle is determined; the number of disconnections and reconnections of the electrically driven disconnection mechanism is allocated based on the proportion of ambient temperature distribution. For example, if the number of disconnections and reconnections is 1000, then 100 times are allocated at -30℃, 700 times at room temperature, and 200 times at 100℃.

[0056] Taking a motor with a maximum torque of 210 Nm as an example, the final load spectrum after acceleration is shown in Tables 3 and 4.

[0057] Table 3

[0058] Table 4

[0059] In summary, this application constructs a virtual simulation model of a vehicle with a disconnection mechanism. A database of operating conditions containing various preset driving scenarios is input into the constructed vehicle simulation model, and the simulation runs automatically in sequence. The motor speed and torque are collected and recorded for each disconnection and engagement event of the disconnection mechanism under all operating conditions, serving as durability data. From the durability data, all disconnection and engagement events are identified and summarized, and the frequency of these events in different motor torque and speed ranges is counted. The obtained torque-frequency data is then subjected to damage equivalence transformation to generate a load spectrum. Durability testing of the electric drive structure is performed using the load spectrum. After each operating condition test is completed, the disconnection mechanism is disassembled and analyzed to detect and analyze the wear of specific components.

[0060] A more detailed explanation of the principle: 1. Build a complete vehicle simulation model. This involves assembling a complete virtual electric vehicle using mathematical formulas and parameters. This vehicle includes not only the body, tires, battery, and front and rear motors, but also a highly detailed mechanical model of the disconnect mechanism (containing detailed parameters of all levers, forks, and dog teeth). By inputting driving commands, the system calculates how the vehicle moves and the forces, torques, displacements, and state changes experienced by each component within the disconnect mechanism.

[0061] 2. Develop and integrate a control strategy for the disconnect mechanism. This means making the disconnect mechanism of the virtual vehicle work like a real product, rather than being manually controlled. This ensures the accuracy of the mechanism's actuation data (when it moves and under what operating conditions) collected in subsequent simulations.

[0062] It's understandable that 1) involves building a virtual vehicle, and 2) developing and integrating control strategies for the disconnection mechanism, enabling the virtual test vehicle to operate autonomously and intelligently. This virtual test vehicle is then used to continuously run on simulated, reinforced roads, recording detailed data from each disconnection mechanism operation.

[0063] 3: Simulation of the whole vehicle model based on durability conditions.

[0064] The pre-built database of driving scenarios (urban, highway, mountainous, etc.) is input into the model, and then the model runs these scenarios sequentially. This is equivalent to having the virtual car run 300,000 kilometers of various road conditions on a laboratory computer at hundreds or thousands of times the efficiency. Each simulation run generates massive amounts of time-series data.

[0065] 4: Obtain event-based disconnect mechanism durability data.

[0066] From the massive data stream generated in step 3, data analysis is performed to identify each disconnection and engagement event of the disconnecting mechanism. For each event, the key operating conditions at that time are recorded: motor speed and torque. Then, preliminary statistics are performed: within which torque range and speed range, how many times did the disconnecting mechanism actuate?

[0067] 5: Generation and acceleration of durability load spectrum based on damage equivalence.

[0068] Equivalent compression and reinforcement aim to cause the same amount of damage in a shorter time.

[0069] Specifically, torque acceleration involves using damage mechanics formulas to transform multiple light-load actions under low torque into a few heavy-load actions under high torque. This significantly reduces the total number of tests.

[0070] Specifically, the rotational speed difference during engagement is statistically analyzed and its distribution scaled, while retaining the proportions of key operating conditions (such as 0 differential, medium differential, and maximum differential, each accounting for a certain proportion). In other words, without changing the main failure mechanism, the real-life load is compressed into a short-term accelerated test load.

[0071] 6. Introduce environmental stress by considering ambient temperature. The mechanism is not only subject to mechanical loads but also to temperature (material properties, lubrication conditions). This step assigns corresponding ambient temperature conditions to the mechanical load spectrum generated in step 5 based on the big data of temperatures the vehicle may actually experience (such as the proportion of extremely cold, normal temperature, and high temperature).

[0072] 7. Durability Load Spectrum Compilation. This involves compiling the accelerated torque, speed, and differential loads into a clear and standardized durability load spectrum. This durability load spectrum is then input into the control system of the electric dynamometer test bench. The test bench automatically and accurately reproduces the most severe operating conditions experienced by the mechanism throughout its lifespan, verifying its durability in a short time.

[0073] Secondly, this application provides a load spectrum generation device for an electrically driven disconnection mechanism.

[0074] Reference Figure 2 , Figure 2 The diagram shown is a schematic of a load spectrum generation device for an electrically driven disconnecting mechanism provided by the present invention. Figure 2 As shown, the device includes: Architecture Module 201: It is used to build a whole vehicle simulation model that integrates a preset electric drive disconnection mechanism control strategy.

[0075] Output module 202: It is used to input the preset driving scenario database into the vehicle simulation model, so as to output the data of each disconnection and engagement of the electric drive disconnection mechanism and the electric drive operating condition data during disconnection and engagement, as durability data.

[0076] Processing module 203: It is used to accelerate the processing of the durability data based on damage equivalence to generate the durability load spectrum of the electrically driven disconnect mechanism.

[0077] Furthermore, in one possible implementation, the processing module is also used to identify and summarize all disconnection and engagement events from the durability data, and to count the number of occurrences of the events in different motor torque ranges, different motor speed ranges, and different vehicle speed ranges, so as to obtain a raw durability data statistics table based on disconnection and engagement events. Actions with torque values ​​lower than a preset baseline torque threshold in the original durability data statistics table are defined as low-damage operating conditions. Actions with torque values ​​equal to or higher than a preset target torque threshold are defined as high-damage operating conditions.

[0078] The number of actions under the low-damage condition is equivalently converted to the number of actions under the high-damage condition using the damage equivalence method, and finally the durability load spectrum of the electrically driven disconnection mechanism is generated.

[0079] Furthermore, in one possible implementation, the processing module is also configured to process according to the formula: Calculate the number of actions after the transformation, where, This represents the number of disconnections and reconnections corresponding to the first torque range. This represents the average torque corresponding to the first torque range. This represents the maximum torque for electric drive. Furthermore, in one possible implementation, the building module is also used to construct a physical model of the electric drive disconnecting mechanism based on a simulation platform, according to the parameters of the disconnecting mechanism drive motor, the fork stroke parameters, the fork lever ratio, the synchronizer synchronizer ring material parameters, the cone angle parameters, and the inner and outer diameter parameters. Based on the preset electric drive disconnection mechanism control strategy, an electric drive disconnection mechanism control model is constructed. The control strategy includes: intelligently deciding the disconnection and engagement actions of the electric drive disconnection mechanism under different operating conditions based on the battery SOC state, vehicle speed, accelerator pedal opening and motor efficiency MAP. A vehicle dynamics model is constructed based on vehicle parameters, front and rear electric drive parameters, battery parameters, accelerator and brake pedal parameters, and driving scenario parameters. The physical model of the electric drive disconnection mechanism, the control model of the electric drive disconnection mechanism, and the vehicle dynamics model are integrated to obtain the vehicle simulation model.

[0080] Furthermore, in one possible implementation, the output module is also used to construct a driving scenario database, which includes: mountain driving scenarios, urban driving scenarios, highway driving scenarios, suburban driving scenarios, extreme hill climbing driving scenarios and extreme differential speed driving scenarios, as well as the number of cycles, mileage, torque distribution strategy and simulated vehicle weight for each scenario. The vehicle simulation model is driven by the constructed driving scenario database to simulate the vehicle's driving data under various working conditions. From the driving data, obtain the disconnection and engagement events of the electric drive disconnection mechanism under various operating conditions, as well as the torque and speed data of the electric drive at the time of each disconnection and engagement event, and use the disconnection and engagement events and the torque and speed data of the electric drive as durability data.

[0081] Furthermore, in one possible implementation, the processing module is also used to statistically analyze the speed difference of all electrically driven disconnecting mechanisms in the simulation when the combined event occurs, and obtain speed difference distribution data. The speed difference distribution data is scaled according to a preset ratio.

[0082] Furthermore, in one possible implementation, the processing module is also used to determine the proportion of environmental temperature distribution experienced by the disconnection mechanism during the vehicle's life cycle based on vehicle big data analysis. The number of disconnections and reconnections of the electrically driven disconnection mechanism is allocated based on the ambient temperature distribution ratio.

[0083] Thirdly, embodiments of this application provide an electric drive disconnection mechanism load spectrum generation device, which may be a device with data processing capabilities.

[0084] Reference Figure 3 , Figure 3 This is a schematic diagram of the hardware structure of the load spectrum generation device for the electrically driven disconnecting mechanism involved in the embodiments of this application. In the embodiments of this application, the load spectrum generation device for the electrically driven disconnecting mechanism may include a processor, a memory, a communication interface, and a communication bus.

[0085] The communication bus can be of any type and is used to interconnect the processor, memory, and communication interface.

[0086] The communication interface includes input / output (I / O) interfaces, physical interfaces, and logical interfaces used for interconnecting devices within the electrically driven disconnect mechanism load spectrum generation device, as well as interfaces used for interconnecting the electrically driven disconnect mechanism load spectrum generation device with other devices (such as other computing devices or user equipment). Physical interfaces can be Ethernet interfaces, fiber optic interfaces, ATM interfaces, etc.; user equipment can be displays, keyboards, etc.

[0087] Memory can be various types of storage media, such as random access memory (RAM), read-only memory (ROM), non-volatile RAM (NVRAM), flash memory, optical storage, hard disk, programmable ROM (PROM), erasable PROM (EPROM), electrically erasable PROM (EEPROM), etc.

[0088] The processor can be a general-purpose processor, which can call the load spectrum generation program for the electrically driven disconnecting mechanism stored in the memory and execute the load spectrum generation method for the electrically driven disconnecting mechanism provided in the embodiments of this application. For example, the general-purpose processor can be a central processing unit (CPU). The method executed when the load spectrum generation program for the electrically driven disconnecting mechanism is called can be referred to in the various embodiments of the load spectrum generation method for the electrically driven disconnecting mechanism of this application, and will not be repeated here.

[0089] Those skilled in the art will understand that Figure 3 The hardware structure shown does not constitute a limitation of this application and may include more or fewer components than shown, or combine certain components, or have different component arrangements.

[0090] Fourthly, embodiments of this application also provide a computer-readable storage medium.

[0091] The present application stores an electrically driven disconnect mechanism load spectrum generation program on a computer-readable storage medium, wherein when the electrically driven disconnect mechanism load spectrum generation program is executed by a processor, it implements the steps of the electrically driven disconnect mechanism load spectrum generation method as described above.

[0092] The method implemented when the load spectrum generation program of the electric drive disconnection mechanism is executed can be referred to in various embodiments of the load spectrum generation method of the electric drive disconnection mechanism of this application, and will not be repeated here.

[0093] It should be noted that the sequence numbers of the embodiments in this application are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.

[0094] The terms "comprising" and "having," and any variations thereof, in the specification, claims, and accompanying drawings of this application are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or apparatus that includes a series of steps or units is not limited to the listed steps or units, but may optionally include steps or units not listed, or may optionally include other steps or units inherent to such process, method, product, or apparatus. The terms "first," "second," and "third," etc., are used to distinguish different objects, etc., and do not indicate a sequence, nor do they limit "first," "second," and "third" to different types.

[0095] In the description of the embodiments of this application, terms such as "exemplary," "for example," or "for instance" are used to indicate examples, illustrations, or explanations. Any embodiment or design described as "exemplary," "for example," or "for instance" in the embodiments of this application should not be construed as being more preferred or advantageous than other embodiments or designs. Specifically, the use of terms such as "exemplary," "for example," or "for instance" is intended to present the relevant concepts in a concrete manner.

[0096] In the description of the embodiments of this application, unless otherwise stated, " / " means "or". For example, A / B can mean A or B. The "and / or" in the text is merely a description of the relationship between related objects, indicating that there can be three relationships. For example, A and / or B can mean: A exists alone, A and B exist simultaneously, and B exists alone. In addition, in the description of the embodiments of this application, "multiple" means two or more.

[0097] In some processes described in the embodiments of this application, multiple operations or steps are included in a specific order. However, it should be understood that these operations or steps may not be executed in the order they appear in the embodiments of this application, or they may be executed in parallel. The sequence number of the operation is only used to distinguish different operations, and the sequence number itself does not represent any execution order. In addition, these processes may include more or fewer operations, and these operations or steps may be executed sequentially or in parallel, and these operations or steps may be combined.

[0098] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) as described above, and includes several instructions to cause a terminal device to execute the methods described in the various embodiments of this application.

[0099] The above are merely preferred embodiments of this application and do not limit the patent scope of this application. Any equivalent structural or procedural transformations made using the content of this application's specification and drawings, or direct or indirect applications in other related technical fields, are similarly included within the patent protection scope of this application.

Claims

1. A method for generating a load spectrum of an electrically driven disconnecting mechanism, characterized in that, include: Construct a vehicle simulation model integrating a preset control strategy for the electric drive disconnection mechanism; The preset driving scenario database is input into the vehicle simulation model to output the data of each disconnection and engagement of the electric drive disconnection mechanism, as well as the electric drive operating condition data during disconnection and engagement, as durability data. The durability data is accelerated using a damage equivalence approach to generate a durability load spectrum for the electrically driven disconnect mechanism.

2. The method according to claim 1, characterized in that, The accelerated processing of the durability data based on damage equivalence to generate the durability load spectrum of the electrically driven disconnect mechanism includes: All disconnection and engagement events are identified and summarized from the durability data, and the number of occurrences of the events in different motor torque ranges, different motor speed ranges, and different vehicle speed ranges is counted to obtain the original durability data statistics table based on disconnection and engagement events. Actions with torque values ​​lower than a preset baseline torque threshold in the original durability data statistics table are defined as low-damage operating conditions. Actions with torque values ​​equal to or higher than a preset target torque threshold are defined as high-damage operating conditions. The number of actions under the low-damage condition is equivalently converted to the number of actions under the high-damage condition using the damage equivalence method, and finally the durability load spectrum of the electrically driven disconnection mechanism is generated.

3. The method according to claim 2, characterized in that, include: According to the formula: Calculate the number of actions after the transformation, where, This represents the number of disconnections and reconnections corresponding to the first torque range. This represents the average torque corresponding to the first torque range. This represents the maximum torque for electric drive.

4. The method according to claim 1, characterized in that, The construction of the vehicle simulation model integrating the preset electric drive disconnection mechanism control strategy includes: Based on the simulation platform, a physical model of the electric drive disconnection mechanism is constructed according to the parameters of the drive motor, the stroke parameters of the shift fork, the lever ratio of the shift fork, the material parameters of the synchronizer ring of the dog tooth synchronizer, the cone angle parameters, and the inner and outer diameter parameters. Based on the preset electric drive disconnection mechanism control strategy, an electric drive disconnection mechanism control model is constructed. The control strategy includes: intelligently deciding the disconnection and engagement actions of the electric drive disconnection mechanism under different operating conditions based on the battery SOC state, vehicle speed, accelerator pedal opening and motor efficiency MAP. A vehicle dynamics model is constructed based on vehicle parameters, front and rear electric drive parameters, battery parameters, accelerator and brake pedal parameters, and driving scenario parameters. The physical model of the electric drive disconnection mechanism, the control model of the electric drive disconnection mechanism, and the vehicle dynamics model are integrated to obtain the vehicle simulation model.

5. The method according to claim 1, characterized in that, The step of inputting a preset driving scenario database into the vehicle simulation model to output data on each disconnection and engagement of the electric drive disconnection mechanism, as well as electric drive operating condition data during disconnection and engagement, as durability data, includes: A driving scenario database is constructed, which includes: mountain driving scenario, urban driving scenario, highway driving scenario, suburban driving scenario, extreme hill climbing driving scenario and extreme differential speed driving scenario, as well as the number of cycles, mileage, torque distribution strategy and simulated vehicle weight for each scenario; The vehicle simulation model is driven by the constructed driving scenario database to simulate the vehicle's driving data under various working conditions. From the driving data, obtain the disconnection and engagement events of the electric drive disconnection mechanism under various operating conditions, as well as the torque and speed data of the electric drive at the time of each disconnection and engagement event, and use the disconnection and engagement events and the torque and speed data of the electric drive as durability data.

6. The method according to claim 1, characterized in that, Before finally generating the durability load spectrum of the electrically driven disconnect mechanism, the following steps are included: In the statistical simulation, the speed difference distribution data of all electrically driven disconnecting mechanisms is obtained by combining the speed difference at the time of the event. The speed difference distribution data is scaled according to a preset ratio.

7. The method according to claim 1, characterized in that, Before generating the final durability load spectrum of the electrically driven disconnect mechanism, the process also includes: Based on vehicle big data analysis, the proportion of environmental temperature distribution experienced by the disconnection mechanism during the vehicle's life cycle is determined. The number of disconnections and reconnections of the electrically driven disconnection mechanism is allocated based on the ambient temperature distribution ratio.

8. A load spectrum generation device for an electrically driven disconnecting mechanism, characterized in that, include: The architecture module is used to build a vehicle simulation model that integrates a preset electric drive disconnection mechanism control strategy; The output module is used to input a preset driving scenario database into the vehicle simulation model to output data on each disconnection and engagement of the electric drive disconnection mechanism, as well as electric drive operating condition data during disconnection and engagement, as durability data. The processing module is used to accelerate the processing of the durability data based on damage equivalence to generate a durability load spectrum for the electrically driven disconnect mechanism.

9. An electronic device, characterized in that, The electronic device includes: processor; A memory storing computer-readable instructions that, when executed by the processor, implement the method as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, It stores computer program instructions that, when executed by a computer, cause the computer to perform the method according to any one of claims 1 to 7.

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