Transmission efficiency prediction method based on friction pair mesh and lubrication medium properties

Abstract

This invention belongs to the field of transmission efficiency prediction technology, specifically relating to a transmission efficiency prediction method based on friction pair mesh and lubrication medium characteristics. This method achieves representation of the overall state with a small number of samples by quantifying the macroscopic operating condition distribution, identifying major friction pairs, mesh segmentation and state merging, actual measurement and function fitting of friction coefficients, weight allocation, and efficiency calculation. It solves the problems of incomplete generalization and high cost in existing technologies, achieving accurate, fast, and low-cost transmission efficiency prediction, providing support for lubrication medium selection and optimization, and is applicable to the design and performance optimization of mechanical equipment transmission systems.

Description

Technical Field

[0001] This invention belongs to the field of mechanical equipment transmission system performance testing technology, specifically relating to a transmission efficiency prediction method based on friction pair mesh and lubrication medium characteristics. Background Technology

[0002] Transmission devices are a core component of mechanical equipment, and transmission efficiency directly affects the energy utilization rate and service life of the equipment. Existing technologies for predicting transmission efficiency mainly fall into two categories: one uses a typical fixed-value friction testing machine to select representative parameters such as surface pressure and linear velocity from a global distribution for testing; the other uses typical fixed-value bench tests for evaluation. However, under actual working conditions, the typical parameters of mechanical equipment exhibit a wide range of distributions. The surface pressure, linear velocity, and surface curvature of different friction pairs change dynamically with the working conditions, and even the parameters of the same friction pair are subject to dynamic fluctuations. Using typical fixed-value testing ignores a large distribution area far from the typical values. If these areas have a high weight, the predicted results will be biased and fail to reflect the true transmission efficiency. Bench tests, on the other hand, are costly and time-consuming, and can only perform relative comparisons of lubricating media, failing to provide optimization directions for the lubricating media.

[0003] Based on the above problems, there is an urgent need for a transmission efficiency prediction technology that can comprehensively cover the working conditions and dynamic parameters of friction pairs, and is accurate and low-cost. Summary of the Invention

[0004] The purpose of this invention is to overcome the shortcomings of existing technologies and propose a transmission efficiency prediction method based on the characteristics of friction pair mesh and lubrication medium, comprising the following steps: S1: Quantify the macroscopic working condition distribution of mechanical equipment and identify the main friction pairs that generate energy loss in mechanical equipment; S2: The main friction pair is divided into time grid and spatial grid. Finite element analysis and Hertz analysis physical models are used to accurately calculate the deformation and stress distribution of the friction pair grid by grid. The relationship between the three elements of the grid state and the macroscopic working condition is analyzed. The three elements of the grid state are surface pressure, linear velocity and surface curvature. S3: Merge the mesh states of different main friction pairs under different macroscopic working conditions to obtain the global distribution and weight of the mesh states; S4: The coefficient of friction is tested using a sliding friction tester with adjustable contact point surface pressure, linear velocity, and surface curvature. S5: Select a test lattice in the global distribution of the grid state, and measure the friction coefficient of the test lattice using a sliding friction testing machine; S6: Regression analysis of the functional relationship between the friction coefficient and the three elements of the mesh state; S7: Add a calculation lattice to the test lattice spacing, and derive the friction coefficient of the calculation lattice based on the functional relationship obtained in S6; S8: Based on the global distribution and weights of the grid state, combined with the grid area, surface pressure, linear velocity, and friction coefficient, calculate the friction loss and transmission efficiency.

[0005] Preferably, the macroscopic operating condition distribution of the quantified mechanical equipment is specifically as follows: collect the operating data of the mechanical equipment, and fit the distribution relationship of the input or output parameters of the equipment, such as speed, torque, flow rate, air pressure, temperature, voltage and current, based on the operating data to form macroscopic operating condition distribution data.

[0006] Further preferred methods for identifying the main friction pairs that generate energy loss in mechanical equipment include: examining the transmission gears, transmission chains, transmission belts, pistons, guide rails, and load-bearing bearings in the transmission system, where the friction loss power of the main friction pairs accounts for the highest proportion of the total loss; and also including secondary friction pairs, which include braking structures, differential structures, reciprocating motion structures, and frequently opening and closing valves.

[0007] Further preferred, dividing the main friction pair into time grids and spatial grids specifically involves: performing grid differentiation on time and space, analyzing differential surface pressure and differential linear velocity, and globally merging and statistically analyzing the global distribution and weights of the grid states.

[0008] More preferably, the structure of the sliding friction testing machine is as follows: one side is a plane with a fixed tangential direction at the contact point, and the other side is a rotating body. The linear velocity at the contact point is controlled by adjusting the rotational speed of the rotating body. The loading direction of the sliding friction testing machine is the normal direction at the contact point. When the rotating body is a sphere, a fixed curvature point contact is formed. When it is a cylinder, a fixed curvature line contact is formed. When it is an ellipse that rotates around its own arbitrary axis and sweeps across the area, the surface curvature is smoothly transformed by changing the angle between the axis on one side of the rotating body and the plane.

[0009] In a further preferred embodiment, the selection of the test point array is as follows: within the parameter range of the global distribution of the grid state, the test point array is selected at intervals, and each test point array is repeatedly tested multiple times by a sliding friction testing machine. The average value of the multiple test results is taken as the friction coefficient of the test point array. During the test, multiple ambient temperature gradients are set, and data is collected after the system has been running stably for a preset time at each temperature.

[0010] Further preferred, the regression analysis of the functional relationship between the friction coefficient and the three elements of the grid state specifically involves: identifying discrete points or inflection points in the data, and processing these discrete points or inflection points using local functions or piecewise functions; the functional relationship covers the fitting equations corresponding to the three friction states: solid lubrication, boundary-mixed lubrication, and fluid lubrication.

[0011] In a further preferred embodiment, the observation equation is constructed based on the deviation between the measured friction coefficient and the calculated friction coefficient, and a corresponding confidence threshold is set during the weight calculation process; points with a global distribution weight of less than a preset threshold in the grid state are not included in the calculation of friction loss and transmission efficiency.

[0012] Further optimized, the specific method for merging the grid states of different main friction pairs under different macroscopic working conditions is as follows: statistical averaging is performed according to the distribution of the corresponding standard working conditions, and grid states with a weight ratio lower than the preset threshold are removed during the merging process. After merging, a block-shaped global differential distribution is formed, and multiple friction coefficient sampling lines are designed to run through the block, with a corresponding number of sampling points set on each sampling line.

[0013] In a further preferred embodiment, the test piece used in the sliding friction testing machine has the same material and surface roughness as the actual equipment. After the test piece is wetted by the target lubricating medium, the friction coefficient is tested. When calculating the friction loss and transmission efficiency, the coupled finite element analysis module accurately calculates the deformation of the friction pair and the local stress of the contact surface, and outputs point-by-point efficiency data and transmission efficiency comparison data of different lubricating media under different load conditions.

[0014] The technical effects include: The core innovation of this invention lies in achieving comprehensive coverage of dynamic working conditions and friction pair parameters through grid segmentation, state merging, point-based measurement, function fitting, and weight adjustment. It solves the major problems of existing technologies, such as incomplete generalization and high cost. It eliminates the need for complex bench tests, accurately characterizing the overall state with only a small number of friction coefficient samples. Simultaneously, it provides a clear direction for lubrication medium optimization, enabling rapid, low-cost, and high-precision transmission efficiency prediction. Attached Figure Description

[0015] Figure 1 Flowchart of a transmission efficiency prediction method based on friction pair mesh and lubrication medium properties; Figure 2 Figure 1.019 of the driving condition curves for light-duty passenger vehicles in China (GB / T38146.1-2019); Figure 3 Weighted distribution diagram of the operating state of the main motor speed-torque in new energy vehicles; Figure 4 A graph showing the shift in the ratio of the meshing line at the meshing end face of a single tooth of a first-stage reduction gear. Figure 5 Curve of surface pressure and linear velocity shift at the meshing end face of a single tooth of a first-stage reduction gear; Figure 6 Cloud map of the meshing line ratio distribution of the entire contact surface of the gear under the working condition of 3000rpm / 30Nm; Figure 7 Cloud map of surface pressure distribution on the entire contact surface of the gear under operating conditions of 3000rpm / 30Nm; Figure 8 Linear velocity distribution cloud map of the entire contact surface of the gear under operating conditions of 3000rpm / 30Nm; Figure 9 Distribution of linear velocity-surface pressure weight in mesh segmentation for gear operation at 3000 rpm / 30 Nm; Figure 10 Gear CLTC-P driving condition mesh segmentation linear velocity-surface pressure weight distribution diagram; Figure 11 Mind map of regression analysis of friction coefficient and three elements of grid state (Part 1); Figure 12 Mind map of regression analysis of friction coefficient and three elements of grid state (Part 2); Figure 13 Fitted curve of smooth change in friction coefficient throughout the entire single-tooth meshing process; Figure 14 Powertrain transmission efficiency prediction distribution map under multiple operating conditions; Figure 15 Deviation distribution diagram between predicted and measured values ​​of powertrain transmission efficiency. Detailed Implementation

[0016] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0017] Existing technologies use typical fixed values ​​to test transmission efficiency, which cannot cover dynamically changing operating conditions and friction pair parameters, resulting in incomplete or biased results. Furthermore, bench testing is costly and cannot provide direction for optimizing lubrication media.

[0018] To address this technical problem, this invention provides a method for predicting transmission efficiency based on friction pair mesh and lubrication medium characteristics. The method first quantifies the macroscopic operating condition distribution of the mechanical equipment, then identifies the main friction pairs that generate energy loss in the equipment. Based on this, subsequent core operations are performed: dividing the main friction pairs into temporal and spatial meshes; analyzing the correlation between the three elements of the mesh state and the macroscopic operating conditions (surface pressure, linear velocity, and surface curvature); and merging the mesh states of different main friction pairs under different macroscopic operating conditions to obtain the global distribution of the mesh states. The friction coefficient is tested using a sliding friction testing machine with adjustable contact point surface pressure, linear velocity, and surface curvature. A test lattice is selected within the global distribution of the grid state. The friction coefficient of the test lattice is measured using the sliding friction testing machine. Regression analysis is performed to determine the functional relationship between the friction coefficient and the three elements of the grid state. A calculation lattice is added between the test lattice points, and the friction coefficient of this lattice is calculated using the functional relationship. Based on the global distribution and weights of the grid state, combined with the grid area, surface pressure, linear velocity, and friction coefficient, friction loss and transmission efficiency are calculated, achieving representation of the overall state with a small number of friction coefficient samples. Please refer to [link / reference]. Figure 1 , Figure 1 This is a flowchart of the transmission efficiency prediction method based on friction pair mesh and lubrication medium characteristics according to this application. It shows the overall process of the transmission efficiency prediction method based on friction pair mesh and lubrication medium characteristics of this invention. The method achieves comprehensive coverage of dynamic working conditions and friction pair parameters through the orderly connection and coordinated cooperation of each step. At the same time, it completes the characterization of the overall state through a small number of samples, thus taking into account both the accuracy and economy of transmission efficiency prediction.

[0019] This method is applicable to the prediction of transmission efficiency for various mechanical devices, demonstrating high practicality and accuracy in scenarios such as new energy pure electric vehicles, wind turbines, and piston air compressors. The following will provide a complete and detailed explanation of the implementation process of this method, combining actual measurements from these scenarios and data from a friction testing machine. First, let's take the new energy pure electric vehicle scenario as an example. Under common structures and power configurations, the working logic of the power transmission system of a new energy pure electric vehicle is as follows: the electric motor obtains electrical energy from the battery, converts it into high-speed, low-torque mechanical energy, and then, through a multi-stage reduction gear pair, converts the high-speed, low-torque mechanical energy into low-speed, high-torque mechanical energy, ultimately transmitting it to the tires to generate the effective work required to propel the vehicle forward. The conventional method for calculating the overall efficiency of the transmission system in this scenario is the ratio of the effective mechanical energy transmitted to the tires to the electrical energy input to the electric motor. This overall efficiency has a corresponding typical range. The main sources of energy loss include electromagnetic losses from the electric motor, friction losses from the electric motor, friction losses from the multi-stage gear pair, friction losses from the differential, bearing losses from the electric motor, and bearing friction losses from the multi-stage gear pair. The total loss directly related to friction also has a corresponding typical range. In this scenario, the key macroscopic variables of the transmission system are vehicle speed and vehicle forward resistance. To describe the implementation process of this method in detail, the first-level tooth friction pair is selected as the core analysis object. The first-level tooth friction pair is mostly made of involute helical gears, whose tooth surface contact shape is adapted to the force state in the actual transmission process.

[0020] Based on the relevant requirements of the Chinese national standard for vehicle driving conditions, real-vehicle driving tests were conducted on mainstream new energy pure electric vehicle models from major domestic brands currently on the market. High-precision sensors were installed at key locations in the vehicle's power transmission system to continuously collect the speed and torque data of the power unit. The massive amount of collected data was statistically analyzed into corresponding state distributions. According to professional vehicle dynamics calculations, the speed and torque state distributions of the power unit in different models from other brands under typical vehicle condition parameters showed a high degree of similarity. To clearly illustrate this method, specific values ​​from the typical distribution were selected as the speed and torque parameters under the corresponding operating conditions. Considering the actual working environment of the first-stage gear friction pair, most models on the market are designed with a reduction ratio of around 3. The vehicle models selected for this real-vehicle test also have a reduction ratio of around 3. Other gear size parameters are also representative of mainstream models on the market, and this embodiment also uses these parameters for detailed explanation.

[0021] The core implementation process of this technical solution requires close integration with the actual working characteristics of the transmission system. Firstly, a mesh segmentation operation is performed on the main friction pairs. The time mesh setting specifications must precisely match the dynamic change cycle of the friction pair parameters to ensure that continuous fluctuations in the parameters over time can be captured. The spatial mesh setting specifications must comprehensively cover the entire contact area of ​​the friction pairs to ensure that no parameter change areas are missed. The analysis of the three elements of the mesh state needs to be based on the dynamic changes of macroscopic working conditions. In the powertrain of new energy pure electric vehicles, real-time changes in assembly speed and torque directly lead to increases or decreases in the surface pressure and linear velocity of the gear pairs. By establishing the mapping relationship between the three elements of the mesh state and speed and torque, the parameter fluctuation patterns of the friction pairs under different working conditions can be accurately captured, laying a data foundation for subsequent accurate calculations. The mesh state merging process needs to be carried out using a weighted summation method. Based on the actual occurrence frequency of different working conditions and the energy loss proportion of different friction pairs, corresponding weights are assigned to each mesh state. The final global distribution of the mesh states can comprehensively and realistically reflect the overall working state of the transmission system. The parameters of the sliding friction testing machine must be strictly adjusted to match the actual value range of the three elements of the grid state, ensuring a high degree of consistency between the measured friction coefficient and the friction coefficient under actual working conditions, and avoiding calculation errors caused by the disconnect between test parameters and actual working conditions. The function fitting between the friction coefficient and the three elements of the grid state is completed using the least squares method. A nonlinear functional relationship between the two is established through effective data of the measured point matrix. The supplementary settings of the calculation point matrix can significantly reduce the workload of actual testing, while ensuring the complete coverage of friction coefficient data across the entire parameter range.

[0022] Gears are among the most complex mechanical components designed to transmit torque. In actual transmission, only a small number of teeth are engaged at any given moment, with all teeth continuously and alternately participating in meshing; no single tooth continuously bears the transmission load. During meshing, a single tooth exhibits elastic line contact. As meshing progresses, the contact line moves continuously from the tooth root to the tooth tip or vice versa, ultimately forming a continuous contact surface in a macroscopic state. Taking the small tooth in this embodiment as an example, the probability that any possible contact line is exactly the actual contact line at any given moment is equal for the corresponding number of tooth surfaces. Therefore, only the energy loss generated during single-tooth meshing needs to be calculated, and the total energy loss of the entire gear can be calculated based on the number of single-tooth meshings per unit time. At the corresponding rotational speed in this embodiment, a corresponding number of single-tooth meshings occur per second. Through professional methods such as finite element analysis and Hertzian analysis, a complete analysis result of the surface pressure, linear velocity, and instantaneous meshing rate distribution on the contact surface in a macroscopic state during single-tooth meshing at the exact meshing moment can be obtained—that is, the spatial distribution analysis result of the surface pressure and linear velocity of the actual contact surface. Transforming this distribution in physical space into a state-space weight distribution with linear velocity on the horizontal axis and surface pressure on the vertical axis clearly shows the weight ratio of different combinations of surface pressure linear velocity parameters, providing accurate and comprehensive basic data for subsequent mesh state merging operations.

[0023] The macroscopic operating condition distribution step for quantifying mechanical equipment requires collecting operational data from the core locations of the transmission system. The focus of this data collection is capturing the dynamic changes in assembly speed and torque. The massive amounts of collected data are then processed using professional statistical analysis methods to generate macroscopic operating condition distribution data covering the entire operating range of the transmission system. The core purpose of this step is to provide a solid foundation for subsequent friction pair state analysis, ensuring that all actual operating conditions of the transmission system are considered and avoiding inaccurate predictions due to omissions of key operating conditions. In the scenario of new energy pure electric vehicles, operating condition data collected through actual vehicle driving can comprehensively cover all operating states, including vehicle start-up, acceleration, constant speed, deceleration, and stopping. This ensures that the final macroscopic operating condition distribution highly matches the actual usage scenario of the vehicle, guaranteeing the authenticity and reliability of subsequent calculation results from the data source. Please refer to [link / reference]. Figure 2 , Figure 2 This figure illustrates the standard driving conditions for passenger vehicles in China as described in "China Automotive Driving Conditions Part 1: Light Vehicles (GB / T38146.1-2019)". These conditions provide a standardized basis for the actual vehicle testing and condition data collection in this invention, covering typical driving states of vehicles under different road types, including urban, suburban, and highway conditions. Please refer to [link / reference]. Figure 3 , Figure 3 For a mainstream new energy vehicle Figure 2The weight distribution of the main motor's operating state collected during the actual vehicle test under the driving conditions is shown in the figure. The figure clearly presents the weight ratio of the operating conditions under different speed and torque combinations, reflecting the actual operating condition distribution characteristics under the actual vehicle road test. The collected speed and torque data will be processed by multinomial fitting to establish the distribution function between the two, clarify the actual occurrence probability of different speed and torque combinations, and provide a quantitative basis for subsequent grid state weight allocation.

[0024] Identifying key friction pairs requires a comprehensive review of all potential energy loss points and friction pairs within the mechanical equipment's transmission system. Through professional bench tests or simulation analysis, the actual friction loss power of each friction pair is measured, and the load-bearing capacity and energy loss percentage of each pair are analyzed. This identifies the core transmission gears, differentials, and bearings with the highest load as the primary sources of energy loss. Simultaneously, all energy loss points corresponding to main friction pairs, secondary friction pairs, minor friction pairs, and agitation, iron loss, and copper loss are included in the analysis to ensure comprehensive coverage of energy loss sources. This approach focuses on the precise analysis of core friction pairs without overlooking any secondary loss points. In the context of new energy pure electric vehicles, real-vehicle testing and simulation analysis have verified that the friction loss percentages of primary gears, secondary gears, differentials, and corresponding high-load bearings are the highest. These friction pairs are classified as primary friction pairs and subjected to refined meshing and parameter calculations. For other energy loss points, a simplified calculation method is adopted, effectively improving overall prediction efficiency while maintaining prediction accuracy, achieving a balance between precision and calculation efficiency.

[0025] The main friction pair is divided into temporal and spatial grids. Precise grid specifications need to be set based on the structural characteristics and motion patterns of the friction pair. By performing grid differentiation operations in both time and space, the continuously changing surface pressure and linear velocity are decomposed into discrete grid element parameters, i.e., differential surface pressure and differential linear velocity. Then, global merging statistics are used to obtain the global distribution and weights of the grid states. This operation can accurately capture the dynamic fluctuations of parameters in the time dimension and the differences in their spatial distribution. During the meshing process of the gear pair, the changes in surface pressure and linear velocity at different meshing positions can be characterized one by one through independent grid elements, achieving a microscopic and precise description of the friction pair parameters, laying a solid foundation for subsequent accurate calculations. Please refer to [link / reference]. Figure 4 , Figure 4 This figure illustrates the shift in the meshing line ratio of the first-stage reduction gear in the powertrain of this new energy vehicle during single-tooth meshing. It visually demonstrates the continuous shift in the meshing line ratio of the first-stage friction pair as the end-face position changes during single-tooth meshing. Please refer to [link / reference]. Figure 5 , Figure 5This figure illustrates the face pressure and velocity shift of the first-stage reduction gear in the powertrain of this new energy vehicle during single-tooth meshing. It visually presents the two-dimensional distribution of face pressure and linear velocity along the meshing line after mesh differentiation of the first-stage gear friction pair, clearly demonstrating the dynamic changes in face pressure and linear velocity as the meshing position advances. During global merging and statistical analysis, the mesh states under different macroscopic operating conditions need to be comprehensively summarized. Weighted calculations are performed based on operating condition weights and mesh weights to ultimately form a global distribution of mesh states and corresponding weights that reflects all operating conditions. In the scenario of new energy pure electric vehicles, the spatial mesh of the first-stage gear under different operating conditions will exhibit corresponding face pressure and linear velocity distributions. During the merging process, the mesh state under this operating condition needs to be organically combined with the mesh states under all other operating conditions to comprehensively reflect the parameter variation patterns of the first-stage gear under all operating conditions. This provides refined basic data for subsequent friction coefficient testing and transmission efficiency calculation, ensuring accurate data support for every step of the calculation process.

[0026] Merging the mesh states of different major friction pairs under different macroscopic operating conditions requires weighted merging of the state parameters of all mesh cells based on the frequency of occurrence of macroscopic operating conditions and the proportion of energy loss of friction pairs, ultimately obtaining the global distribution of mesh states and their corresponding weights. The merging process employs an overlay calculation method, merging mesh states with the same parameter range, accurately calculating the total weight of that range, and ultimately forming a block-shaped global differential distribution, rather than the line-segment distribution of traditional methods. This ensures comprehensive parameter coverage from a global perspective and effectively simplifies subsequent computational complexity. The merging process requires statistical averaging according to the distribution of the corresponding standard operating conditions. Mesh states with a weight proportion below a preset threshold are removed during the merging process. The resulting block-shaped global differential distribution clearly presents the value range and distribution density of the three elements of the mesh state. Based on this distribution, multiple friction coefficient sampling lines are designed and run through all blocks, with a corresponding number of sampling points set on each sampling line to ensure that the sampling points cover all key parameter areas. Please refer to [link to relevant documentation]. Figure 6 , Figure 6 This figure shows the meshing line ratio distribution of the gear at 3000 rpm and 30 Nm of the main motor, with red approaching the MAX value and green approaching the MIN value. This reveals the spatial distribution characteristics of the meshing line ratio during gear meshing and demonstrates the differences in meshing line ratio among different grid cells. Please refer to [link to relevant documentation]. Figure 7 , Figure 7 This figure shows the surface pressure distribution of the gear at 3000 rpm and 30 Nm under the influence of the main motor. Red indicates the maximum value, and green indicates the minimum value. The figure presents the surface pressure values ​​and distribution of each grid cell, visually reflecting the differences in load on different locations on the gear surface. Please refer to [link / reference]. Figure 8 , Figure 8The figure shows the linear velocity distribution of the gear across the entire contact surface at 3000 rpm and 30 Nm on the main motor. Red indicates the MAX value and green indicates the MIN value. This figure represents the linear velocity distribution pattern of each grid cell. Figures 6 to 8 Together, these characteristics constitute the grid state distribution features of the gear friction pair under a single working condition, providing a precise basis for subsequent test point selection. Please refer to... Figure 9 , Figure 9 This figure shows the linear velocity and surface pressure weights of the gear at the full contact surface after meshing under a main motor operating at 3000 rpm and 30 Nm. It transforms the spatial mesh information of the gear contact surface into a weight distribution in the state space, clearly presenting the weight ratio of different combinations of surface pressure and linear velocity parameters under a single operating condition. In the scenario of new energy pure electric vehicles, the stacking of different macroscopic operating conditions and different friction pairs forms a complete parameter range. This range comprehensively covers all parameter combinations of friction pairs under all vehicle operating conditions, providing a complete and accurate parameter boundary for subsequent friction coefficient measurement and function fitting, ensuring the relevance and effectiveness of the measurement and fitting work. Please refer to... Figure 10 , Figure 10 The figure shows the linear velocity and surface pressure weight of the gear after the full contact surface mesh is divided under the CLTC-P driving condition. The figure presents the global distribution of the mesh state after merging under all working conditions, reflecting the comprehensive state space weight characteristics after the superposition of weights under different working conditions.

[0027] When testing the coefficient of friction using a sliding friction testing machine, a machine capable of freely adjusting the contact point surface pressure, linear velocity, and surface curvature must be selected. One side of the machine is a plane with a fixed tangential direction at the contact point, while the other side is a rotating body. The linear velocity at the contact point is precisely controlled by adjusting the rotational speed of the rotating body, and the surface pressure is precisely adjusted by applying a load to the normal direction of the contact point. When the rotating body is a sphere, the machine forms a fixed curvature point contact, suitable for testing point contact friction pairs. When the rotating body is a cylinder, the machine forms a fixed curvature line contact, suitable for testing line contact friction pairs such as gear pairs and bearings. When the rotating body is an ellipse that rotates one revolution along any axis of its own, the surface curvature can be smoothly changed by altering the angle between the axis on one side of the rotating body and the plane, covering the testing requirements of point contact friction pairs with different curvatures. During the test, a small test piece with the same material and surface roughness as the actual equipment must be used. The test piece is thoroughly wetted with the target lubricating medium before the coefficient of friction test to ensure the authenticity and validity of the test data. In the scenario of new energy pure electric vehicles, the test piece uses the same material and surface roughness as the gear pair of the real vehicle, and is wetted using the lubricating medium actually installed in the vehicle. This ensures that the test environment is highly consistent with the actual operating environment of the real vehicle. The friction coefficient data obtained from the test can be directly adapted to the calculation requirements of the real vehicle operating conditions without the need for additional parameter conversion or correction, which greatly improves the calculation efficiency.

[0028] In the process of selecting test points in the global distribution of the grid state, the test points must be selected at uniform intervals based on the parameter range and weight distribution of the global distribution. This ensures that the selected test points can fully cover all key parameter ranges without omitting any core parameter areas. The selected test points need to be tested using the sliding friction testing machine to obtain the friction coefficient data corresponding to each point, providing real and reliable basic data support for subsequent function fitting. Each test point is repeatedly tested using the sliding friction testing machine, and the average value of the multiple test results is taken as the final friction coefficient of the test point. This effectively reduces the impact of random errors on the test results and improves the stability and reliability of the data. During the test, multiple ambient temperature gradients are set, and the testing machine is allowed to run stably for a preset time at each temperature before data acquisition. This ensures that the friction pair and lubrication medium reach a stable state before data acquisition, avoiding test data distortion caused by not reaching a stable state. In the scenario of new energy pure electric vehicles, in order to examine the distribution law of friction coefficient in the parameter region formed by stacking, based on Figures 6 to 10 The grid distribution is selected to correspond to the actual measurement points. The actual measurement points uniformly cover the core area and boundary area of ​​the parameter range to ensure that the measured data can fully reflect the friction characteristics of the lubricating medium under all working conditions. The friction coefficient of typical lubricating media is measured by the friction testing machine, which provides reliable and comprehensive basic data for subsequent function fitting work.

[0029] In the regression analysis of the functional relationship between the friction coefficient and the three elements of the mesh state, professional fitting methods such as the least squares method are used. Based on the measured friction coefficient data of the lattice and the corresponding surface pressure, linear velocity, and surface curvature parameters, a nonlinear functional relationship between the two is established. Please refer to [link / reference]. Figure 11 and Figure 12 , Figure 11 To guide the thinking process of regression analysis of the functional relationship between friction coefficient and the three elements of grid state Figure 1 , Figure 12 To guide the thinking process of regression analysis of the functional relationship between friction coefficient and the three elements of grid state Figure 2Two mind maps comprehensively outline the entire logical chain of regression analysis, from data acquisition and processing, data classification and identification, construction of fitting equations, to weight adjustment. They clearly present the fitting equation system corresponding to the three friction states—solid lubrication, boundary-mixed lubrication, and fluid lubrication—and the technical path of inflection point identification and piecewise function processing. During the analysis, discrete points or inflection points in the data are accurately identified. These identified discrete points or inflection points are processed using local functions or piecewise functions to ensure that the established functional relationship accurately reflects the variation law of friction coefficient under different friction states. The functional relationship must comprehensively cover the fitting equations corresponding to the three friction states—solid lubrication, boundary-mixed lubrication, and fluid lubrication—ensuring fitting accuracy across the entire lubrication range. Please refer to [link to relevant documentation]. Figure 13 , Figure 13 This image presents a smooth variation of the friction coefficient throughout the entire single-tooth meshing process, predicted through regression fitting. It visually illustrates the continuous and smooth change in friction coefficient as the meshing position advances, accurately reflecting the characteristics of friction coefficient variation at different positions during gear meshing. Subsequently, a calculation lattice is added to the spacing of the test point lattice. The friction coefficient of this calculation lattice is calculated using a defined functional relationship, and weighting and prediction corrections are applied based on the deviation of adjacent measured points. This achieves comprehensive coverage of friction coefficient data across the entire parameter range, significantly reducing the workload of actual measurements while ensuring data integrity. In the scenario of new energy pure electric vehicles, the friction coefficient obtained from the spatial distribution fitted by the measured results can fully represent the friction coefficient distribution on the meshing surface, accurately reflecting the variation of friction coefficient at different positions during gear meshing. This provides precise point-by-point data support for subsequent friction loss calculations, enabling microscopic and accurate calculation of friction losses.

[0030] The step of calculating friction loss and transmission efficiency based on the global distribution and weighting of the grid state requires combining the weight, grid area, surface pressure, linear velocity, and friction coefficient of each point in the grid. A specialized formula is used to calculate the friction loss power of each grid cell. Then, by summing the friction loss power of all grid cells and combining it with the input power of the transmission system, the transmission efficiency is calculated. In this step, the friction loss power of a single grid cell is calculated using the formula... The calculation and derivation of this formula are based on the dual theoretical foundations of tribology and the law of conservation of energy. From a tribological perspective, the magnitude of frictional force is directly and definitely related to the normal force and the coefficient of friction. The magnitude of the normal force is equal to the product of the surface pressure and the contact area, i.e. The formula for calculating friction is: Combining the classical definition of power By substituting the formulas for normal force and friction into the power definition, the formula for calculating the frictional loss power of a single mesh element can be derived. This represents the frictional loss power of a single grid cell, measured in watts. This parameter directly reflects the degree of energy loss in that grid region, and its value accurately reflects the energy loss at that location of the friction pair during actual operation. The surface pressure at the mesh location is expressed in Pascals. This parameter is obtained through mesh differentiation and mechanical analysis. The calculation process must fully consider various factors such as the force distribution of the friction pair and the elastic deformation of the material. For example, during gear meshing, the surface pressure fluctuates significantly with changes in the meshing position. Spatial meshing can accurately capture this fluctuation, making the surface pressure parameter more realistic. The friction coefficient is dimensionless and its value is determined by various factors such as the characteristics of the lubricating medium, the materials of the friction pair, and the surface roughness. The friction coefficient under different mesh conditions needs to be obtained through actual measurement or function calculation to ensure that this parameter is highly matched with the actual working conditions. The linear velocity at the grid point is expressed in meters per second. This parameter is directly related to the motion of the friction pair. The linear velocity for rotational motion can be accurately calculated using the rotational speed and radius, while for reciprocating motion, it needs to be calculated by combining the motion stroke and period to ensure the accuracy of the linear velocity parameter. The area of ​​a single grid cell is expressed in square meters. This parameter is determined by the precision of the spatial grid division; the finer the grid division, the smaller the area parameter, and the higher the accuracy of the calculation results. The grid division precision can be adjusted according to actual calculation needs. The core innovation of this formula lies in breaking the limitation of traditional transmission efficiency calculations that use fixed parameters to calculate friction loss. By combining the microscopic parameters after grid differentiation, it achieves precise point-by-point calculation of each grid cell of the friction pair, which can truly reflect the energy loss differences at different locations of the friction pair. This changes the status quo of traditional methods that can only calculate the overall average loss, making the calculation of friction loss more targeted and accurate. At the same time, this formula can be combined with the global distribution and weights of the grid state to achieve friction loss calculation from the microscopic grid to the macroscopic whole, completing the organic combination of microscopic parameters and macroscopic results.

[0031] After calculating the frictional loss power of a single grid cell, the total frictional loss power of the transmission system is obtained by summing the frictional loss power of all grid cells. Combined with the input power of the transmission system Through formula The transmission efficiency of the transmission system is calculated. The derivation of this formula is based on the fundamental definition of transmission efficiency, which is the ratio of the effective output power to the input power of the transmission system. In a transmission system, after deducting all frictional losses from the input power, the remaining power is the effective output power capable of performing work. Therefore, by subtracting the total frictional losses from the input power and then comparing it with the input power, the accurate transmission efficiency can be obtained. Transmission efficiency is a dimensionless value ranging from 0 to 1. The closer the value is to 1, the higher the energy utilization efficiency of the transmission system and the smaller the transmission loss. This refers to the input power of the transmission system, measured in watts. This parameter is a core power parameter of the transmission system and can be obtained through actual sensor measurements or calculations based on the rated parameters of the power source and operating conditions. This is the sum of frictional power losses across all meshes, expressed in watts. This parameter is obtained by applying a parameter to all mesh cells. The values ​​are summed to obtain the total power loss, which is determined by the weights of each grid cell to ensure that the total power loss reflects the actual power loss under all operating conditions. This formula requires accurate calculation of the frictional power loss of each grid cell, followed by weighted summation to obtain the total power loss. The calculation process is coupled with a finite element analysis module, inputting data such as the three elements of the grid state and the friction coefficient into the finite element analysis model. Simulation calculations further verify and optimize the calculation results, ultimately outputting point-by-point efficiency data and transmission efficiency comparison data for different lubricating media under different load conditions. The core innovation of this formula lies in combining the microscopic calculation results of each grid cell with the macroscopic transmission efficiency calculation, while introducing the concept of weights. This allows the transmission efficiency calculation results to truly reflect the efficiency performance of the transmission system under actual operating conditions, rather than the fixed efficiency under a single operating condition as in traditional methods. Furthermore, this formula can directly compare the transmission efficiency performance of different lubricating media, providing an intuitive and accurate quantitative basis for the selection and optimization of lubricating media, solving the technical problem that traditional methods cannot provide a clear direction for lubricating media optimization. Please refer to [link to relevant documentation]. Figure 14 , Figure 14 To predict the transmission efficiency of the powertrain under different operating conditions after considering the losses of all major friction pairs, this figure fully presents the distribution characteristics of the powertrain transmission efficiency calculated by this method across all operating conditions, intuitively demonstrating the efficiency variation law under different speed and torque combinations. In the scenario of new energy pure electric vehicles, this formula can be used to calculate the transmission loss of the first-level tooth friction pair under the corresponding operating condition. Further integrating the weights of multiple operating conditions yields the comprehensive loss value of this friction pair. Then, by further integrating the loss values ​​of other major friction pairs, the total friction loss of the entire transmission system can be obtained, providing a complete and accurate representation of the overall friction loss of the transmission system and offering precise data support for the optimized design of the transmission system of new energy pure electric vehicles. Please refer to [link / reference]. Figure 15 , Figure 15 The figure shows the deviation between the predicted efficiency and the measured efficiency of the powertrain. It directly compares the transmission efficiency predicted by the method of this invention with the measured efficiency on the bench. The deviation range is extremely small, which fully verifies the accuracy and reliability of the method and proves its effectiveness in practical engineering applications.

[0032] By rewriting the weighting steps of the test and calculation point lattices, it is necessary to construct a state equation based on the dynamic changes of the three elements of the grid state and an observation equation based on the deviation between the measured and calculated friction coefficients. The Kalman filter prediction and update process is completed through the synergistic effect of these two equations. Finally, each point is assigned a reasonable weight, achieving the effect of accurately representing the overall state with a small number of sampled points, significantly reducing the experimental workload while ensuring prediction accuracy. The Kalman filter state equation used in this step is: ; The observation equation is The derivation of the two equations is based on optimal estimation theory. Addressing the systematic and random errors present in the friction coefficient testing and calculation process, a dual-step approach of state prediction and observation updating is used to dynamically optimize the grid state weights, ensuring that the test results from a small number of sampling points can reflect the overall friction coefficient distribution to the greatest extent possible. Specifically, in the state equation... This is the mesh state vector at time k. This vector contains three core parameters: surface pressure, linear velocity, and surface curvature, which can comprehensively characterize the state features of the mesh at time k. This is the state transition matrix, determined based on the dynamic changes of the three elements of the grid state. It accurately reflects the changing relationships of the grid state at different times, ensuring the accuracy of state prediction. To control the input matrix, The input at time k-1 is the control input. The combination of these two can reflect the influence of external control factors on the grid state. This parameter represents process noise and follows a Gaussian distribution. Its variance is determined based on the accuracy of the test system and can effectively characterize the systematic error in the state prediction process. In the observation equation... The observed value at time k, i.e., the friction coefficient obtained by actual measurement using a sliding friction testing machine, is the core data of the observation process. This is the observation matrix, determined based on the functional relationship between the friction coefficient and the three elements of the mesh state. It can establish a precise mapping relationship between the mesh state and the friction coefficient. To observe the noise, this parameter also follows a Gaussian distribution, with its variance determined by the test error, and can characterize the random error in the actual measurement process.

[0033] The implementation process of this set of formulas consists of two core steps: prediction and update. In the prediction step, the grid state vector at time k-1 is used... Combined with the state transition matrix Control input matrix Control input at time k-1 The prior estimate of the grid state at time k is predicted, and the process noise is also considered. Determine the prior estimated error covariance matrix, and in the update step, use the measured friction coefficient at time k. Compared with prior estimates and observation matrices The predicted observations are compared to obtain the observation bias, which is then combined with the observation noise. The prior estimates are corrected to obtain the posterior estimates of the grid state at time k, and the error covariance matrix is ​​updated accordingly. Finally, each lattice is assigned a corresponding weight based on the posterior estimates. A confidence threshold is set during the weight calculation process; only lattices with a confidence level higher than this threshold are assigned higher weights to participate in the calculation of friction loss and transmission efficiency. At the same time, lattices with a global distribution weight lower than the preset threshold are removed, as these lattices have minimal impact on the overall calculation results. Removing them effectively reduces the computational load and avoids interference from invalid data. The core innovation of this set of formulas lies in the first-time introduction of Kalman filtering, the optimal estimation method, into the field of transmission efficiency prediction. Addressing the technical pain points of dynamic changes in friction pair parameters and measurement errors, it achieves dynamic optimization of grid state weights through the synergistic effect of state equations and observation equations. This solves the technical problems of large sampling amounts and inaccurate sampling point representation in traditional methods, allowing a small number of sampling points to accurately represent the overall state of the friction pair. This significantly reduces the workload of measuring the friction coefficient and lowers testing costs. Simultaneously, the error correction mechanism of Kalman filtering effectively reduces errors in the testing and calculation processes, improving the accuracy of weight allocation and further ensuring the accuracy of transmission efficiency prediction results. In the scenario of new energy pure electric vehicles, after assigning reasonable weights to the test and calculation point matrices using this set of formulas, only a small number of measured point matrices are needed to accurately predict transmission efficiency under all operating conditions, significantly shortening the testing cycle and reducing testing costs. This is more suitable for the rapid R&D and iteration needs of the new energy vehicle industry.

[0034] The implementation process of the method of this invention in the wind turbine power generation scenario generally follows... Figure 1The eight core steps outlined describe the transmission system of a wind turbine, which consists primarily of the hub gearbox, main shaft bearings, and generator connecting gear pairs. Key macroscopic operating variables are wind speed and impeller speed. Impeller speed and output torque exhibit wide-range dynamic variations under different wind speeds. Quantifying the macroscopic operating condition distribution involves collecting main shaft speed and torque data across different wind speed ranges and fitting corresponding operating condition distribution data. Identifying the main friction pairs identifies the gear pairs at each stage of the gearbox and the heavy-duty main shaft bearings as the core friction pairs, while also covering energy loss points such as churning losses and bearing seal friction losses within the turbine nacelle. When meshing the main friction pairs, the meshing characteristics of the gearbox gears and the rolling characteristics of the main shaft bearings are combined to set appropriate temporal and spatial grid specifications. The relationships between surface pressure, linear velocity, surface curvature, wind speed, and impeller speed are analyzed. When merging grid states, weights are assigned to each grid state based on the probability of occurrence at different wind speeds, forming a global distribution of grid states. When using a sliding friction testing machine, the test piece is made of the same material and has the same surface roughness as the gearbox gear and main shaft bearing of the wind turbine. It is wetted with a wind power-specific lubricating medium. The subsequent selection of test points, function fitting, friction loss calculation and Kalman filter weight configuration are all carried out with reference to the aforementioned core method. This method can accurately calculate the transmission efficiency of the wind turbine transmission system under different wind speed conditions, providing data support for the selection of wind power lubricating medium and the optimization of gearbox structure.

[0035] The implementation of the method of this invention in the scenario of a reciprocating air compressor also follows the same principle. Figure 1 The core process shown illustrates that the transmission system of a reciprocating air compressor includes a crankshaft connecting rod mechanism, piston ring and cylinder wall friction pairs, flywheel gear pairs, and various support bearings. The key macroscopic operating variables are exhaust pressure and spindle speed, with dynamic changes in spindle torque and speed under different exhaust pressures. To quantify the macroscopic operating condition distribution, spindle operating data under different exhaust pressures is collected, and the distribution relationship between speed and torque is fitted. When identifying the main friction pairs, the piston ring and cylinder wall friction pairs, crankshaft connecting rod bearings, and flywheel gear pairs are identified as the core sources of energy loss, while also covering gas stirring losses within the cylinder and seal friction losses. When meshing the main friction pairs, a suitable temporal and spatial grid is set based on the reciprocating friction characteristics of the piston ring and cylinder wall. The relationship between surface pressure, linear velocity, surface curvature, exhaust pressure, and spindle speed is analyzed. When merging the grid states, weights are allocated based on the proportion of working time under different exhaust pressures. During the sliding friction testing machine test, the test piece is made of the same material as the piston ring and cylinder wall, and is tested with a special lubricating medium for air compressors. Subsequently, the transmission efficiency is accurately predicted by actual measurement of the test point matrix, function fitting, and grid-by-grid friction loss calculation. This method can effectively capture the dynamic parameter changes of the reciprocating friction pair of the piston air compressor, solve the problem that traditional fixed value testing cannot reflect the real working conditions, and provide a quantitative basis for energy-saving optimization of air compressors and selection of lubricating media.

[0036] In practical applications, the method of this invention has been tested on actual test benches to measure the comprehensive efficiency of multiple major friction pairs and other minor energy losses under different macroscopic working conditions. The measured results are highly consistent with the comprehensive efficiency trend calculated by this method, fully verifying the accuracy and reliability of the method. By quantitatively calculating the transmission losses of equipment under specific lubricating medium characteristics using this method, the thermal load of transmission system materials can be accurately assessed at the engineering design level, providing a precise quantitative basis for the energy allocation of the transmission system. This avoids equipment failure due to excessive thermal load or energy waste caused by unreasonable energy allocation. At the lubricating medium selection level, this method can accurately predict the transmission efficiency performance of different lubricating media under actual working conditions, providing a clear and quantitative basis for selecting lubricating media more suitable for the operating environment. In the scenario of new energy pure electric vehicles, this method was also used to evaluate the transmission efficiency of another lubricating medium under multiple operating conditions. This medium outperformed the preferred lubricating medium under low-speed, high-load conditions, but its efficiency was poor under high-speed, low-load conditions. Considering the weighting of all operating conditions, the friction loss value corresponding to this medium was higher. Therefore, the medium could be quickly eliminated through rapid evaluation using the sliding friction testing machine, eliminating the high cost of bench testing. In the scenarios of wind turbine generators and piston air compressors, this method was used to predict and screen the efficiency of multiple lubricating media, quickly obtaining lubricating medium selection schemes suitable for actual operating conditions, significantly reducing bench testing costs. Simultaneously, it provided accurate friction loss data for the structural optimization of the transmission system, effectively improving the transmission efficiency of the equipment.

[0037] The above are merely preferred embodiments of the present invention and are not intended to limit the present invention in any other way. Any person skilled in the art may make changes or modifications to the above-disclosed technical content to create equivalent embodiments that can be applied to other fields. However, any simple modifications, equivalent changes, and modifications made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the protection scope of the present invention.

Claims

1. A method for predicting transmission efficiency based on friction pair mesh and lubrication medium characteristics, characterized in that, Includes the following steps: S1: Quantify the macroscopic working condition distribution of mechanical equipment and identify the main friction pairs that generate energy loss in mechanical equipment; S2: The main friction pair is divided into time grid and spatial grid. Finite element analysis and Hertz analysis physical models are used to accurately calculate the deformation and stress distribution of the friction pair grid by grid. The relationship between the three elements of the grid state and the macroscopic working condition is analyzed. The three elements of the grid state are surface pressure, linear velocity and surface curvature. S3: Merge the mesh states of different main friction pairs under different macroscopic working conditions to obtain the global distribution and weight of the mesh states; S4: The coefficient of friction is tested using a sliding friction tester with adjustable contact point surface pressure, linear velocity, and surface curvature. S5: Select a test lattice in the global distribution of the grid state, and measure the friction coefficient of the test lattice using a sliding friction testing machine; S6: Regression analysis of the functional relationship between the friction coefficient and the three elements of the mesh state; S7: Add a calculation lattice to the test lattice spacing, and derive the friction coefficient of the calculation lattice based on the functional relationship obtained in S6; S8: Based on the global distribution and weights of the grid state, combined with the grid area, surface pressure, linear velocity, and friction coefficient, calculate the friction loss and transmission efficiency.

2. The transmission efficiency prediction method based on friction pair mesh and lubrication medium characteristics according to claim 1, characterized in that, The specific process of quantifying the macroscopic operating condition distribution of mechanical equipment is as follows: collect the operating data of the mechanical equipment, and fit the distribution relationship of the input or output parameters of the equipment, such as speed, torque, flow rate, air pressure, temperature, voltage and current, based on the operating data to form macroscopic operating condition distribution data.

3. The transmission efficiency prediction method based on friction pair mesh and lubrication medium characteristics according to claim 2, characterized in that, The main friction pairs that generate energy loss in the mechanical equipment include: transmission gears, transmission chains, transmission belts, pistons, guide rails, and load-bearing bearings in the transmission system, with the friction loss power of the main friction pairs accounting for the highest proportion of the total loss; it also includes secondary friction pairs, including braking structures, differential structures, reciprocating motion structures, and frequently opening and closing valves.

4. The transmission efficiency prediction method based on friction pair mesh and lubrication medium characteristics according to claim 3, characterized in that, The main friction pairs are divided into time grids and spatial grids. Specifically, by differentiating the time and space grids, the differential surface pressure and differential linear velocity are analyzed, and the global distribution and weight of the grid states are globally merged and statistically analyzed.

5. The transmission efficiency prediction method based on friction pair mesh and lubrication medium characteristics according to claim 4, characterized in that, The structure of the sliding friction testing machine is as follows: one side is a plane with a fixed tangential direction at the contact point, and the other side is a rotating body. The linear velocity at the contact point is controlled by adjusting the rotational speed of the rotating body. The loading direction of the sliding friction testing machine is the normal direction at the contact point. When the rotating body is a sphere, a fixed curvature point contact is formed. When it is a cylinder, a fixed curvature line contact is formed. When it is an ellipse that rotates around its own arbitrary axis and sweeps across the area, the surface curvature is smoothly transformed by changing the angle between the axis on one side of the rotating body and the plane.

6. The transmission efficiency prediction method based on friction pair mesh and lubrication medium characteristics according to claim 5, characterized in that, The selection of test points is as follows: within the parameter range of the global distribution of the grid state, test points are selected at intervals. Each test point is repeatedly tested by a sliding friction testing machine, and the average value of the multiple test results is taken as the friction coefficient of the test point. During the test, multiple ambient temperature gradients were set, and data was collected after the system had been running stably for a preset time at each temperature.

7. The transmission efficiency prediction method based on friction pair mesh and lubrication medium characteristics according to claim 6, characterized in that, The regression analysis of the functional relationship between the friction coefficient and the three elements of the grid state is specifically as follows: identify discrete points or inflection points in the data, and process the discrete points or inflection points using local functions or piecewise functions; the functional relationship covers the fitting equations corresponding to the three friction states of solid lubrication, boundary-mixed lubrication, and fluid lubrication.

8. The transmission efficiency prediction method based on friction pair mesh and lubrication medium characteristics according to claim 7, characterized in that, The observation equation is constructed based on the deviation between the measured friction coefficient and the calculated friction coefficient, and a corresponding confidence threshold is set during the weight calculation process; Points with a global distribution weight in the grid state that are lower than a preset threshold are not included in the calculation of friction loss and transmission efficiency.

9. The transmission efficiency prediction method based on friction pair mesh and lubrication medium characteristics according to claim 8, characterized in that, The specific steps for merging the grid states of different main friction pairs under different macroscopic working conditions are as follows: statistical average is performed according to the distribution of the corresponding standard working conditions. During the merging process, grid states with a weight ratio lower than the preset threshold are removed. After merging, a block-shaped global differential distribution is formed. Multiple friction coefficient sampling lines are designed to run through the block, and a corresponding number of sampling points are set on each sampling line.

10. The transmission efficiency prediction method based on friction pair mesh and lubrication medium characteristics according to claim 9, characterized in that, The test pieces used in the sliding friction testing machine are made of the same material and have the same surface roughness as the actual equipment. After the test pieces are wetted by the target lubricating medium, the friction coefficient is tested. When calculating friction loss and transmission efficiency, the coupled finite element analysis module accurately calculates the deformation of the friction pair and the local stress of the contact surface, and outputs point-by-point efficiency data and transmission efficiency comparison data of different lubricating media under different load conditions.

Images