Efficiency map prediction method based on electric vehicle reducer efficiency test

Abstract

The application particularly relates to an efficiency MAP prediction method based on electric vehicle reducer efficiency test, which comprises the following steps: analyzing the relationship between efficiency and torque at a fixed rotating speed based on electric vehicle reducer efficiency test data, and constructing an efficiency and torque rotating speed relationship prediction model; obtaining a plurality of torque rotating speed working condition points in the electric vehicle reducer external characteristic range; inputting the torque rotating speed working condition points in the electric vehicle reducer external characteristic range into the constructed relationship prediction model, and outputting corresponding predicted efficiency; and drawing the efficiency MAP of the electric vehicle reducer external characteristic range based on the predicted efficiency of each torque rotating speed working condition point in the electric vehicle reducer external characteristic range. The application can realize electric vehicle reducer working condition point efficiency prediction in all working ranges, and can effectively generate the electric vehicle reducer external characteristic range efficiency MAP, so as to improve the effectiveness of electric vehicle reducer efficiency acquisition and the comprehensiveness of efficiency MAP generation.

Description

Technical Field

[0001] This invention relates to the field of experimental data analysis and prediction, specifically to an efficiency MAP prediction method based on electric vehicle reducer efficiency tests. Background Technology

[0002] Currently, with the continuous development of society and economy, new energy pure electric vehicles are attracting more and more attention. The electric drive system is one of the core components of pure electric vehicles, consisting of a motor controller, drive motor, and reducer. Its operating efficiency is a key concern for many companies and users, as the efficiency of the electric drive system directly affects the economy of the vehicle.

[0003] The reducer is a core transmission component of the electric drive system in electric vehicles. Its efficiency significantly impacts the vehicle's energy consumption and driving range, thus attracting increasing attention from manufacturers and users. Obtaining an efficiency map (MAP) of the electric vehicle reducer allows for a direct analysis and evaluation of high-efficiency and low-efficiency regions and their ranges, providing direction for efficiency optimization. It is a crucial tool for analyzing, evaluating, improving, and optimizing the efficiency of electric vehicle reducers.

[0004] Bench efficiency testing of electric vehicle reducers is the primary means of obtaining reducer efficiency, enabling the measurement of efficiency at certain operating points under specific torque and speed conditions. However, when there are numerous operating points, bench efficiency testing suffers from being time-consuming, labor-intensive, and inefficient. Furthermore, due to the limitations of the measurement range of bench efficiency testing equipment, it typically can only measure the efficiency of a portion of the operating points, failing to achieve efficiency measurement across the entire operating range (i.e., the external characteristic range), and thus unable to obtain an efficiency MAP for the entire operating range. Therefore, designing a method capable of predicting electric vehicle reducer efficiency at all operating points and effectively generating an efficiency MAP for electric vehicle reducers is a pressing technical problem that needs to be solved. Summary of the Invention

[0005] To address the shortcomings of the existing technologies, the technical problem to be solved by this invention is: how to provide an efficiency MAP prediction method based on electric vehicle reducer efficiency testing, which can predict the efficiency of electric vehicle reducers at all operating points and effectively generate an efficiency MAP for the external characteristic range of electric vehicle reducers, thereby improving the effectiveness of obtaining the efficiency at the operating points of electric vehicle reducers and the comprehensiveness of efficiency MAP generation, and providing important guidance for the development, analysis and optimization of electric vehicle reducer efficiency.

[0006] To solve the above-mentioned technical problems, the present invention adopts the following technical solution:

[0007] The efficiency MAP prediction method based on electric vehicle reducer efficiency test includes:

[0008] S1: Based on the test data of electric vehicle reducer efficiency, analyze the relationship between efficiency and torque at a fixed speed, analyze the relationship with speed, and construct a predictive model for the relationship between efficiency, torque and speed;

[0009] S2: Obtain torque and speed operating points for several electric vehicle reducers within their external characteristic range;

[0010] S3: Input the torque and speed operating points of the electric vehicle reducer's external characteristic range into the constructed relational prediction model, and output the corresponding prediction efficiency;

[0011] S4: Plot the efficiency MAP of the electric vehicle reducer's external characteristic range based on the predicted efficiency at torque-speed operating points within the external characteristic range of each electric vehicle reducer.

[0012] Preferably, the relationship prediction model is represented by the following formula:

[0013]

[0014] In the formula: e represents the predicted efficiency of the electric vehicle reducer; n i T represents the input speed of the electric vehicle reducer. i This indicates the input torque of the electric vehicle reducer; a ni b ni c ni (i = 0, 1, 2) represents the fixed speed operating point n. i Below, the fitting coefficients of the quadratic, linear, and constant terms of the efficiency and torque quadratic polynomials are respectively related to the rotational speed n. i The fitting coefficients for the quadratic, linear, and constant terms are as follows.

[0015] Preferably, for torque lower than the maximum speed n max For the low-torque segment corresponding to the torque, the relationship prediction model is as follows:

[0016]

[0017] In the formula: e l This indicates the predicted efficiency of the electric vehicle reducer in the low torque range; a nli b nli c nli (i = 0, 1, 2) represents the fixed speed operating point n. i Below, the fitting coefficients of the quadratic, linear, and constant terms of the torque quadratic polynomial for efficiency in the low torque range are respectively related to the rotational speed n. iThe fitting coefficients for the quadratic, linear, and constant terms;

[0018] For torque greater than or equal to the maximum speed n max For the high torque segment corresponding to the torque, the relationship prediction model is as follows:

[0019]

[0020] In the formula: e h This indicates the predicted efficiency of the electric vehicle reducer in the high torque range; a nhi b nhi c nhi (i = 0, 1, 2) represents the fixed speed operating point n. i Below, the fitting coefficients of the quadratic, linear, and constant terms of the high-torque range efficiency and the torque quadratic polynomial are respectively related to the rotational speed n. i The fitting coefficients for the quadratic, linear, and constant terms are as follows.

[0021] Preferably, a predictive model for the relationship between efficiency and torque-speed is constructed through the following steps:

[0022] S101: Test torque-speed operating point designed for bench efficiency testing of electric vehicle reducers;

[0023] S102: Efficiency tests are conducted using an electric vehicle reducer efficiency test bench based on various test torque and speed operating points to obtain test data on speed, torque, and efficiency at the operating points.

[0024] S103: Perform data quality checks on efficiency test data;

[0025] S104: Classify the efficiency test data that have passed the data quality check according to the same speed to obtain the efficiency and torque data corresponding to each fixed speed.

[0026] S105: Analyze the relationship between efficiency and torque at fixed speeds based on efficiency and torque data corresponding to various fixed speeds, and establish a corresponding relationship prediction model.

[0027] Preferably, in step S101, the maximum speed n of the drive motor of the electric vehicle reducer is first determined. max Rated speed n r Rated torque T r Plot the external characteristic curve of the drive motor of the electric vehicle reducer; then, based on the testing capabilities of the electric vehicle reducer efficiency test bench, divide the operating points, i.e., the speed from 0 to n. max The range is divided into equal intervals, with each interval being n. x Divide the operating points by speed, n x≤1000rpm; similarly, torque from 0-T r Range per interval T x Set the torque operating point, T x ≤50N.m; Finally, the test torque and speed operating points for the efficiency test of the electric vehicle reducer bench are generated.

[0028] Preferably, in step S102, the test torque-speed operating point is used as the working condition of the electric vehicle reducer on the electric vehicle reducer efficiency test bench. Under the preset temperature conditions, the loaded power at the two output ends and the power at the input end of the electric vehicle reducer are measured respectively, and then the efficiency at the corresponding test torque-speed operating point is calculated by combining the following formula.

[0029]

[0030] In the formula: e1 represents the efficiency of the electric vehicle reducer; P ol P represents the power output of the left side of the electric vehicle reducer. or P represents the power output of the right side of the electric vehicle reducer. i Indicates the input power of the electric vehicle reducer; n ol Indicates the rotational speed at the left output terminal; T ol Indicates the torque at the left output terminal; n or Indicates the rotational speed at the right output terminal; T or Indicates the torque at the right output terminal; n i Indicates the input rotational speed; T i This indicates the input torque.

[0031] Preferably, in step S103, the data quality check includes checking the linearity and bias of the corresponding data of the torque measurement system.

[0032] Preferably, Minitab software is used to perform linearity and bias analysis of the torque measurement range based on torque measurement data, so as to check the linearity and bias of the torque measurement system.

[0033] Preferably, in step S105:

[0034] 1) At each fixed speed, the efficiency and torque data are calculated based on the highest speed n. max The corresponding torque is used as a critical point to divide the range into low torque and high torque segments, and quadratic polynomial fitting is performed on each segment to obtain the relationship between efficiency and torque at each fixed speed:

[0035]

[0036] In the formula: e T n represents the fixed speed operating point i Efficiency under T; ia represents the input torque of the electric vehicle reducer. ni b ni c ni (i = 0, 1, 2) represents the fixed speed operating point n. i Below, the fitting coefficients of the quadratic, linear, and constant terms of the efficiency and torque quadratic polynomials are respectively related to the rotational speed n. i The fitting coefficients for the quadratic, linear, and constant terms;

[0037] 2) Compare the fitting coefficients of efficiency and torque in the low-torque and high-torque ranges, respectively. and Each with its corresponding rotational speed n i Perform a quadratic polynomial fitting to obtain the fitting coefficients a. nli b nl c nl and a nhi b nhi c nhi This leads to the generation of a predictive model for the relationship between efficiency and torque-speed.

[0038] Preferably, the operating points are divided by equal intervals of speed and torque. When the speed is less than the rated speed, the maximum torque is set to the rated torque. When the speed is greater than the rated speed, the maximum torque is calculated based on the constant power curve, thereby generating several torque-speed operating points with different external characteristic ranges.

[0039] The efficiency MAP prediction method based on electric vehicle reducer efficiency test in this invention has the following advantages compared with the prior art:

[0040] This invention analyzes the relationship between efficiency and torque of an electric vehicle reducer at a fixed speed, and constructs a predictive model for the relationship between efficiency and torque-speed. This model enables the prediction of efficiency at all torque-speed operating points within the external characteristic range. On one hand, this invention utilizes data mining and other methods to uncover the characteristics and patterns of efficiency and torque-speed data, and establishes a predictive model. This allows for the prediction of efficiency across the entire operating range based on existing experimental data of electric vehicle reducer operating points, thereby improving the effectiveness of obtaining electric vehicle reducer operating point efficiency data. On the other hand, this invention obtains the torque-speed operating points within the external characteristic range of the electric vehicle reducer and predicts the efficiency at each torque-speed operating point using the predictive model. This enables the effective generation of an external characteristic range efficiency MAP for the electric vehicle reducer based on the predictive model, thereby improving the comprehensiveness and accuracy of the generated electric vehicle reducer efficiency MAP and providing important guidance for the development, analysis, and optimization of electric vehicle reducer efficiency. Attached Figure Description

[0041] To make the objectives, technical solutions, and advantages of the invention clearer, the invention will now be described in further detail with reference to the accompanying drawings, wherein:

[0042] Figure 1 This is a logic block diagram of the efficiency MAP prediction method based on the efficiency test of electric vehicle reducer;

[0043] Figure 2 The efficiency MAP is the external characteristic range of the electric vehicle reducer.

[0044] Figure 3 A schematic diagram showing the division of operating points;

[0045] Figure 4 This is a schematic diagram of the external characteristic curves and operating points of the drive motor.

[0046] Figure 5 For efficiency test results;

[0047] Figure 6 This is a schematic diagram of linearity and bias analysis;

[0048] Figure 7 A schematic diagram illustrating the predicted efficiency at operating points;

[0049] Figure 8 This is a schematic diagram illustrating the efficiency prediction error at the operating point. Detailed Implementation

[0050] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. The components of the embodiments of the present invention described and shown in the accompanying drawings can generally be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but only to illustrate selected embodiments of the invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without inventive effort are within the scope of protection of the present invention.

[0051] It should be noted that similar reference numerals and letters in the following figures indicate similar items. Therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures. In the description of this invention, it should be noted that the terms "center," "upper," "lower," "left," "right," "vertical," "horizontal," "inner," and "outer," etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the figures, or the orientation or positional relationship commonly used when the product is in use. They are only for the convenience of describing the invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on the invention. Furthermore, the terms "first," "second," and "third," etc., are only used to distinguish descriptions and should not be construed as indicating or implying relative importance. In addition, the terms "horizontal," "vertical," etc., do not mean that the component is required to be absolutely horizontal or suspended, but can be slightly tilted. For example, "horizontal" only means that its direction is more horizontal than "vertical," and does not mean that the structure must be completely horizontal, but can be slightly tilted. In the description of this invention, it should also be noted that, unless otherwise explicitly specified and limited, the terms "set," "install," "connect," and "link" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal connection of two components. Those skilled in the art can understand the specific meaning of the above terms in this invention based on the specific circumstances.

[0052] The following detailed explanation illustrates the specific implementation methods:

[0053] Example:

[0054] This embodiment discloses an efficiency MAP prediction method based on electric vehicle reducer efficiency test.

[0055] The applicant found that current research on the efficiency of electric vehicle reducers focuses on efficiency testing methods and efficiency design optimization. For example, Meng Qingyong et al. (from Meng Qingyong, Deng Baoqing, Jiang Liqin. Research on Efficiency Test of Electric Drive System for Electric Vehicles Based on ISO 21782 Standard) studied the test conditions for the efficiency of electric drive systems based on the ISO 21782 standard, proposed optimized test conditions for the efficiency of electric drive systems, and conducted experimental verification; Zhao Qian et al. (from Zhao Qian, Qu Jingyao, Sun Xudong, Zhao Lijin, Ma Yongzhi, Yang Lianghui. Efficiency Test and Comprehensive Efficiency Analysis of Reducer for Pure Electric Passenger Vehicles) studied the commonly used single-speed reducer for pure electric passenger vehicles and its efficiency bench test method and comprehensive efficiency calculation method; Zhan Rui et al. (from Zhan Rui, Cheng Huaguo, Xu Kang, Jia Jiyong. Research on Test Bench and Method for Integrated Electric Vehicle Reducer for Light Commercial Vehicles) developed a test bench for integrated electric vehicle reducers for light commercial vehicles, and established a test method suitable for integrated electric vehicle reducers by referring to traditional drive axle and motor test methods; Devesh Pareek et al. (from Pareek, D., Performance & Efficiency Improvement of Electric Vehicle Power) The study investigated the efficiency optimization of subsystems related to the powertrain of electric vehicles, improving efficiency through controller strategy improvements. Shi Junxu et al. (from Shi Junxu, Chen Zhichu, Fu Minli et al. Efficiency Optimization Method for Permanent Magnet Synchronous Motors in Electric Vehicles under NEDC Conditions) analyzed the distribution of energy consumption points under NEDC road spectrum conditions for passenger vehicles, introducing the concept of an energy efficiency center point to achieve rapid assessment of motor energy consumption and efficiency. Peng Pengfeng et al. (from Peng Pengfeng, Xu Xinquan, Zeng Jieqiong. Research on Parameter Matching of Pure Electric Vehicle Transmission System Based on Motor Efficiency) conducted a comparative study of three-speed transmission schemes for pure electric vehicles to improve motor efficiency. Liu Xianghuan et al. (from Liu Xianghuan, Sun Yincheng, Liu Ping et al. Efficiency Optimization Design Method for Electric Vehicle Powertrain System Based on Vehicle Conditions) analyzed the wheel-side parameters of the whole vehicle under NEDC conditions to obtain the output characteristics of the motor at each condition point, and then analyzed the motor energy consumption at each condition point to identify the concentrated area of ​​motor energy consumption.

[0056] Research on the application of data mining technology in experimental data analysis and processing is scarce. Zhang Jianbin et al. (from Zhang Jianbin, Zhu Lanjuan. Application of Data Mining in Tire Uniformity Test Data) used feature selection to analyze the quality factors and weights affecting the uniformity of tires of various specifications, based on uniformity detection data extracted from the MES data warehouse from the tire quality inspection section. Wang Wenzheng et al. (from Wang Wenzheng, Zheng Kunpeng, Chen Gong, He Kaifeng. Application Research of Data Mining Technology in Flight Test Data Analysis and Aerodynamic Parameter Identification) used data mining technology to establish an aerodynamic mathematical model based on flight test data, compared it with ground test results, and provided the prediction error for ground tests.

[0057] However, there is currently no method in the art to predict the efficiency of an electric vehicle reducer across all operating ranges based on existing efficiency test data at operating points. Therefore, this patent application proposes the following technical solution.

[0058] like Figure 1 As shown, the efficiency MAP prediction method based on electric vehicle reducer efficiency test includes:

[0059] S1: Based on the test data of electric vehicle reducer efficiency, analyze the relationship between efficiency and torque at a fixed speed, analyze the relationship with speed, and construct a predictive model for the relationship between efficiency, torque and speed;

[0060] S2: Obtain torque and speed operating points for several electric vehicle reducers within their external characteristic range;

[0061] S3: Input the torque and speed operating points of the electric vehicle reducer's external characteristic range into the constructed relational prediction model, and output the corresponding prediction efficiency;

[0062] S4: Plot the efficiency MAP of the electric vehicle reducer's external characteristic range based on the predicted efficiency at torque-speed operating points within the external characteristic range of each electric vehicle reducer.

[0063] In this embodiment, the external characteristic range is the entire working range, which refers to the external characteristics of the drive motor, that is, the maximum range of the torque and speed characteristics of the drive motor (all working areas or conditions), which can be unified as the entire working range.

[0064] This invention analyzes the relationship between efficiency and torque of an electric vehicle reducer at a fixed speed, and constructs a predictive model for the relationship between efficiency and torque-speed. This model enables the prediction of efficiency at all torque-speed operating points within the external characteristic range. On one hand, this invention utilizes data mining and other methods to uncover the characteristics and patterns of efficiency and torque-speed data, and establishes a predictive model. This allows for the prediction of efficiency across the entire operating range based on existing experimental data of electric vehicle reducer operating points, thereby improving the effectiveness of obtaining electric vehicle reducer operating point efficiency data. On the other hand, this invention obtains the torque-speed operating points within the external characteristic range of the electric vehicle reducer and predicts the efficiency at each torque-speed operating point using the predictive model. This enables the effective generation of an external characteristic range efficiency MAP for the electric vehicle reducer based on the predictive model, thereby improving the comprehensiveness and accuracy of the generated electric vehicle reducer efficiency MAP and providing important guidance for the development, analysis, and optimization of electric vehicle reducer efficiency.

[0065] In practical applications, experiments can only obtain the efficiency of some operating points. This invention establishes a relationship prediction model based on the efficiency of experimental operating points, which can predict the efficiency of any operating point, obtain the efficiency of all working ranges (external characteristic ranges), and draw an efficiency MAP diagram. This allows for intuitive analysis and evaluation of high-efficiency regions and their ranges, as well as low-efficiency regions and their ranges, and also points the way for optimizing the efficiency of electric vehicle reducers.

[0066] In the specific implementation process, the operating points are divided by equal intervals of speed and torque. When the speed is less than the rated speed, the maximum torque is set to the rated torque. When the speed is greater than the rated speed, the maximum torque is calculated based on the constant power curve, thereby generating several torque and speed operating points in the external characteristic range.

[0067] Specifically, Python programming is used to divide the operating points for each external characteristic range and predict efficiency, and a MAP (Mean Assigned to) of the external characteristic range efficiency of the electric vehicle reducer is plotted. Figure 2 As shown, the curves in the figure are iso-efficiency curves. From Figure 2 It can be seen that the efficiency of electric vehicle reducers is generally high, with a high efficiency range of 97% or higher in the 4000rpm-9000rpm and 120N.m-270N.m range. The efficiency is relatively low in the low speed and low torque, low speed and high torque, and high speed and low torque regions, especially the lowest efficiency at high speed and low torque.

[0068] This invention divides the operating points by equal intervals of speed and torque. When the speed is less than the rated speed, the maximum torque is set as the rated torque. When the speed is greater than the rated speed, the maximum torque is calculated based on the constant power curve. This allows for the effective acquisition of the torque-speed operating points within the external characteristic range of the electric vehicle reducer. This, in turn, enables the better generation of the external characteristic range efficiency MAP of the electric vehicle reducer, thereby solving the technical problem of improving and optimizing the efficiency of the electric vehicle reducer.

[0069] In practice, the relationship prediction model is represented by the following formula:

[0070]

[0071] In the formula: e represents the predicted efficiency of the electric vehicle reducer; n i T represents the input speed of the electric vehicle reducer. i This indicates the input torque of the electric vehicle reducer; a ni b ni c ni (f = 0, 1, 2) represents the fixed speed operating point n. i Below, the fitting coefficients of the quadratic, linear, and constant terms of the efficiency and torque quadratic polynomials are respectively related to the rotational speed n. i The fitting coefficients for the quadratic, linear, and constant terms are as follows.

[0072] This invention constructs a predictive model for the relationship between efficiency and torque-speed as shown in the above formula, enabling the prediction of efficiency at various torque-speed operating points based on the predictive model. This allows for the prediction of electric vehicle reducer efficiency across all operating ranges based on existing operating point efficiency test data, thereby improving the effectiveness of obtaining electric vehicle reducer operating point efficiency.

[0073] For torque below the maximum speed n max For the low-torque segment corresponding to the torque, the relationship prediction model is as follows:

[0074]

[0075] In the formula: e l This indicates the predicted efficiency of the electric vehicle reducer in the low torque range; a nl b nli c nl (i = 0, 1, 2) represents the fixed speed operating point n. i Below, the fitting coefficients of the quadratic, linear, and constant terms of the torque quadratic polynomial for efficiency in the low torque range are respectively related to the rotational speed n. i The fitting coefficients for the quadratic, linear, and constant terms;

[0076] For torque greater than or equal to the maximum speed n max For the high torque segment corresponding to the torque, the relationship prediction model is as follows:

[0077]

[0078] In the formula: e h This indicates the predicted efficiency of the electric vehicle reducer in the high torque range; a nhi b nhi c nhi (i = 0, 1, 2) represents the fixed speed operating point n. i Below, the fitting coefficients of the quadratic, linear, and constant terms of the high-torque range efficiency and the torque quadratic polynomial are respectively related to the rotational speed n. i The fitting coefficients for the quadratic, linear, and constant terms are as follows.

[0079] The applicant discovered in actual research that the efficiency changes at different rates in the "low torque range" and the "high torque range," making it difficult to describe using the same formula. Therefore, this invention uses speeds below the maximum speed n... max Torque is defined as the low-torque range, which is higher than or equal to the maximum speed n. max The torque is defined as the high torque segment, and then corresponding relationship prediction models are constructed based on the low torque segment and the high torque segment respectively, so that high fitting accuracy can be obtained in both the low torque segment and the high torque segment, thereby further improving the comprehensiveness and accuracy of electric vehicle reducer efficiency prediction.

[0080] In the specific implementation process, the following steps are used to construct a predictive model for the relationship between efficiency and torque-speed:

[0081] S101: Test torque-speed operating point designed for bench efficiency testing of electric vehicle reducers;

[0082] S102: Efficiency tests are conducted using an electric vehicle reducer efficiency test bench based on various test torque and speed operating points to obtain test data on speed, torque, and efficiency at the operating points.

[0083] S103: Perform data quality checks on efficiency test data;

[0084] S104: Classify the efficiency test data that have passed the data quality check according to the same speed to obtain the efficiency and torque data corresponding to each fixed speed.

[0085] S105: Analyze the relationship between efficiency and torque at fixed speeds based on efficiency and torque data corresponding to various fixed speeds, and establish a corresponding relationship prediction model.

[0086] This invention constructs a predictive model of the relationship between efficiency and torque-speed through the above steps. It can use data mining and other methods to mine the characteristics and patterns of efficiency and torque-speed data and establish a predictive model of the relationship. In this way, it can predict the efficiency of electric vehicle reducers in all working ranges based on existing operating point efficiency test data, thereby further improving the effectiveness of obtaining the operating point efficiency of electric vehicle reducers.

[0087] Bench testing is currently the primary method for testing the efficiency of electric vehicle reducers. Major manufacturers have made significant improvements to the transmission efficiency test items in "QC / T1022-2015 Technical Conditions for Reducer Assemblies for Pure Electric Passenger Vehicles." These improvements mainly stem from the increasing torque-speed range of electric vehicle reducers, leading to more detailed efficiency test operating points. Typically, operating points are divided into equal or non-equal intervals within the maximum speed and rated torque range at a certain temperature. A schematic diagram of these operating point divisions is then created using Python programming, as shown below. Figure 3 As shown. In principle, the smaller the torque-speed interval, the more operating points there are, which makes it easier to fully understand the efficiency of the electric vehicle reducer. However, this will greatly increase the workload. In practical applications, non-equal interval operating points are often used.

[0088] In this embodiment, the maximum speed n of the electric vehicle reducer drive motor is first determined according to the parameters. max Rated speed n r Rated torque T r Drawing as Figure 4 The external characteristic curve of the electric vehicle reducer drive motor is shown; then, based on the testing capabilities of the electric vehicle reducer efficiency test bench, the operating points are divided, i.e., the speed from 0 to n. max The range is divided into equal intervals, with each interval being n. x Defining the operating points at various speeds, typically n x ≤1000rpm; similarly, torque from 0-T r Range per interval T x Setting the torque operating point, typically T x ≤50N.m; Finally, the test torque and speed operating points for the efficiency test of the electric vehicle reducer bench are generated.

[0089] In the specific implementation process, such as Figure 6 The electric vehicle reducer efficiency test bench shown uses the test torque and speed operating point as the working condition of the electric vehicle reducer. Under the preset temperature conditions, the loaded power at the two output ends and the power at the input end of the electric vehicle reducer are measured respectively, and then the efficiency at the corresponding test torque and speed operating point is calculated by combining the following formula.

[0090]

[0091] In the formula: e1 represents the efficiency of the electric vehicle reducer; P ol This indicates the power output of the left side of the electric vehicle reducer (in this embodiment, the positive Y-direction of the vehicle coordinate system represents the left; the negative Y-direction represents the right); P or P represents the power output of the right side of the electric vehicle reducer. i Indicates the input power of the electric vehicle reducer; n ol Indicates the rotational speed at the left output terminal; T ol Indicates the torque at the left output terminal; n or Indicates the rotational speed at the right output terminal; T or Indicates the torque at the right output terminal; n i Indicates the input rotational speed; T i This indicates the input torque.

[0092] Since temperature has a significant impact on efficiency, it is usually necessary to control the temperature during testing. In this invention, the applicant discovered through research that conducting efficiency tests on electric vehicle reducers between 80 degrees Celsius and 60 degrees Celsius can better improve the accuracy of efficiency measurements, thereby further ensuring the performance of the constructed relationship prediction model.

[0093] The measured efficiency at each operating point was plotted as a scatter plot using Python programming, as shown below. Figure 5 As shown.

[0094] In practice, data quality checks include verifying the linearity and bias of the torque measurement system's data. Minitab software is used to perform linearity and bias analysis of the torque measurement range based on the torque measurement data to verify the linearity and bias of the torque measurement system.

[0095] Before conducting feature analysis and mining of experimental data, it is necessary to check the quality of the experimental data. In efficiency tests, efficiency is calculated by measuring torque and speed. The experimental data shows that the speed measurement accuracy is very high and very stable, while the quality of torque test data determines the quality of efficiency data. Therefore, this paper takes input torque measurement data as an example to introduce the method of checking experimental data quality. First, the torque measurement data at the same torque operating point are grouped together. To ensure consistency in the number of data points, five measurement points are taken at each operating point from 30 N·m to 360 N·m, and from 1000 rpm to 5000 rpm, for a total of 60 data points for analysis.

[0096] Then, the linearity and bias of the torque sensor measurement system were checked. Minitab software was used to perform linearity and bias analysis on the torque measurement range, and the results are as follows: Figure 6 As shown in Table 1. From Figure 6It can be seen that the bias increases with the increase of the measurement range, showing an approximately linear relationship, and all average biases are within the 95% confidence interval. Furthermore, the specific hypothesis test results in Table 1 also show that the p-values ​​for the average bias hypothesis tests of torque measurements at each operating point are all greater than 0.05, indicating that there is no significant bias. The slope and intercept p-values ​​of the linear regression equation are both less than 0.05, indicating that the linear regression equation is effective and has a significant linear relationship.

[0097] Table 1 Results of Linearity and Bias Analysis

[0098]

[0099] This invention improves the quality of torque test data by examining the linearity and bias of torque measurement system efficiency test data, thereby enabling the construction of a more accurate relationship prediction model and further enhancing the effectiveness of obtaining the operating point efficiency of electric vehicle reducers.

[0100] In the specific implementation process, the test data are classified according to the test method, and classified according to the same torque and the same speed respectively, to obtain the efficiency data corresponding to different fixed torques and fixed speeds. The relationship curve between efficiency and speed or torque is plotted using Python programming.

[0101] The relationship curves between efficiency and torque and speed show no clear pattern. Generally, at a fixed speed, efficiency increases rapidly with increasing torque, reaches its maximum value, and then decreases slowly; the curves are relatively dispersed at different fixed speeds. At a fixed torque, efficiency also tends to increase first and then decrease with increasing speed, but the curves are more concentrated at different fixed torques, especially at medium to high torques. Because the concentrated curves make it difficult to accurately identify patterns and characteristics, the efficiency-torque relationship curve at a fixed speed is selected for further analysis of the efficiency curve characteristics and patterns.

[0102] Based on the relationship curves, it is inferred that at a fixed speed, the efficiency-torque relationship curve consists of two parabolic segments, one rising and one falling. These two parabolic segments intersect in the range of 120 N·m-150 N·m, where efficiency is relatively high. Therefore, based on the experimental data, the curve is divided into low-torque and high-torque segments with 120 N·m as the critical point. Quadratic polynomial fitting is performed on each segment. However, considering the external characteristics of electric vehicle reducers, the high-torque, high-speed segment is unusable and therefore cannot be tested. Due to limited experimental conditions, data from the high-torque, low-speed portion (1000 rpm-5000 rpm) is used for fitting analysis. The fitting coefficients and goodness of fit for the low-torque segment are shown in Table 2, and those for the high-torque segment are shown in Table 3, obtained using Python programming. Tables 2 and 3 show that the goodness of fit is greater than 0.98, with the first-order coefficient being much larger for the low-torque segment than for the high-torque segment. Therefore, it can be concluded that at a fixed speed, efficiency first increases rapidly parabolically with increasing torque, and then decreases slowly parabolically.

[0103] Table 2 Fitting coefficients and goodness of fit for the low torque range

[0104] engine speed / rpm <![CDATA[Quadratic coefficient a Tl > <![CDATA[The first-order coefficient b Tl > <![CDATA[Constant c Tl > <![CDATA[Goodness of fit R 2 > 1000 -0.00020 0.043953 93.89027 0.980254 2000 -0.00027 0.054642 93.75478 0.987721 3000 -0.00028 0.058816 93.81471 0.986997 4000 -0.00029 0.060213 93.97756 0.983623 5000 -0.00028 0.057948 94.27753 0.980753 6000 -0.00037 0.078642 93.05766 0.990256 7000 -0.00049 0.106270 91.30937 0.993147 8000 -0.00063 0.130149 90.18275 0.992011 9000 -0.00064 0.136484 89.58845 0.988492 10000 -0.00073 0.158325 88.21605 0.990171 11000 -0.00077 0.164785 87.59054 0.987758 12000 -0.00098 0.213099 84.61022 0.989516

[0105] Table 3 Fitting coefficients and goodness of fit for the high torque range

[0106] engine speed / rpm <![CDATA[Quadratic coefficient a Th > <![CDATA[The first-order coefficient b Th > <![CDATA[Constant c Th > <![CDATA[Goodness of fit R 2 > 1000 -1.4E-05 0.00397 96.02068 0.998112 2000 -1.3E-05 0.003797 96.24903 0.997771 3000 -1.1E-05 0.003411 96.55975 0.999234 4000 -1E-05 0.002868 96.88362 0.984367 5000 -8.4E-06 0.001961 97.20097 0.996651

[0107] Tables 2 and 3 initially identified the relationship between efficiency and torque. However, rotational speed also significantly impacts efficiency. Closer observation of Tables 2 and 3 reveals that the fitting coefficients at various speeds in both the low-torque and high-torque ranges show an increasing or decreasing trend, but not a linear one. Therefore, a quadratic parabolic fitting was further performed on each coefficient and constant. Using Python programming, the fitting coefficients and goodness-of-fit are shown in Table 4. Table 4 shows that the goodness-of-fit is above 0.97, indicating that regardless of whether it's the low-torque or high-torque range, the efficiency and torque fitting coefficients at each speed increase or decrease parabolically with increasing rotational speed.

[0108] Table 4. Efficiency and Torque Fitting Coefficients, Speed ​​Fitting Coefficients, and Goodness of Fit

[0109] Efficiency and torque fitting coefficients <![CDATA[Quadratic coefficient a nl > <![CDATA[First-order coefficient b nl > <![CDATA[Constant c nl > <![CDATA[Goodness-of-fit R 2 > Low torque range second-order coefficient -4.9E-12 -3.7E-09 -0.00021 0.971183 Low torque range first-order coefficient 1.14E-09 -2.4E-07 0.045034 0.974579 Low torque range constant -9.7E-08 0.000451 93.52671 0.973594 Efficiency and torque fitting coefficients <![CDATA[Quadratic coefficient a nh > <![CDATA[The first-order coefficient b nh > <![CDATA[Constant c nh > <![CDATA[Goodness-of-fit R 2 > Second-order coefficient in high torque range 1.33E-13 5.75E-10 -1.5E-05 0.984719 High torque range first-order coefficient -1.2E-10 2.02E-07 0.003873 0.998769 High torque range constant 1.37E-08 0.000218 95.77983 0.999084

[0110] Therefore, this patent application concludes as follows:

[0111] 1) At each fixed speed, the efficiency and torque data are calculated based on the highest speed n. max The corresponding torque is used as the critical point to divide the system into low-torque and high-torque segments, and quadratic polynomial fitting is performed on each segment. (For the high-torque segment, due to power limitations, the operating speed cannot be too high; a speed less than or equal to the rated speed n is used.)r (By fitting some of the data), the relationship between efficiency and torque in the low-torque and high-torque ranges at each fixed speed was obtained:

[0112]

[0113] In the formula: e T n represents the fixed speed operating point i Efficiency under T; i a represents the input torque of the electric vehicle reducer. ni b ni c ni (f = 0, 1, 2) represents the fixed speed operating point n. i Below, the fitting coefficients of the quadratic, linear, and constant terms of the efficiency and torque quadratic polynomials are respectively related to the rotational speed n. i The fitting coefficients for the quadratic, linear, and constant terms;

[0114] 2) Compare the fitting coefficients of efficiency and torque in the low-torque and high-torque ranges, respectively. and Each with its corresponding rotational speed n i Perform a quadratic polynomial fitting to obtain the fitting coefficients a. nli b nli C nli and a nhi b nhi c nhi This leads to the generation of a predictive model for the relationship between efficiency and torque-speed.

[0115] To better illustrate the effectiveness of the relationship prediction model in this patent application, the following verification was performed in this embodiment.

[0116] Based on the relationship prediction model between low torque and high torque ranges, the efficiency test was conducted at various operating points. Figure 4 Efficiency prediction was performed using Python programming, and the predicted efficiency results at the operating point were obtained as shown below. Figure 7 As shown, the error between the predicted efficiency and the experimental measurement efficiency at the operating point is as follows: Figure 8 As shown.

[0117] Depend on Figure 7 It can be seen that the scatter plots of the predicted efficiency at the operating point and the experimental efficiency have the same trend and shape, and at the same time, according to Figure 8 It can be seen that the error between the prediction efficiency and the experimental efficiency at each working point is within 1.5%, and most errors are within 0.5%, which verifies the effectiveness of the prediction model.

[0118] In summary, this invention can utilize data mining and other methods to uncover the characteristics and patterns of efficiency and torque-speed data and establish a relationship prediction model. This enables the prediction of electric vehicle reducer efficiency across all operating ranges based on existing operating point efficiency test data, thereby further improving the effectiveness of obtaining electric vehicle reducer operating point efficiency.

[0119] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit the technical solutions. Those skilled in the art should understand that any modifications or equivalent substitutions to the technical solutions of the present invention without departing from the spirit and scope of the present invention should be covered within the scope of the claims of the present invention.

Claims

1. An efficiency MAP prediction method based on electric vehicle reducer efficiency tests, characterized in that, include: S1: Based on the test data of electric vehicle reducer efficiency, analyze the relationship between efficiency and torque at a fixed speed, analyze the relationship with speed, and construct a predictive model for the relationship between efficiency, torque and speed; A predictive model for the relationship between efficiency and torque-speed is constructed using the following steps: S101: Test torque-speed operating point designed for bench efficiency testing of electric vehicle reducers; S102: Efficiency tests are conducted using an electric vehicle reducer efficiency test bench based on various test torque and speed operating points to obtain test data on speed, torque, and efficiency at the operating points. S103: Perform data quality checks on efficiency test data; S104: Classify the efficiency test data that have passed the data quality check according to the same speed to obtain the efficiency and torque data corresponding to each fixed speed. S105: Analyze the relationship between efficiency and torque at fixed speeds based on efficiency and torque data corresponding to various fixed speeds, and establish a corresponding relationship prediction model; In step S105: 1) At each fixed speed, the efficiency and torque data are calculated based on the highest speed. The corresponding torque is used as a critical point to divide the range into low torque and high torque segments, and quadratic polynomial fitting is performed on each segment to obtain the relationship between efficiency and torque at each fixed speed: ； In the formula: Indicates the fixed speed operating point The efficiency of the lower; This indicates the input torque of the electric vehicle reducer. , , Indicates the fixed speed operating point Below, the fitting coefficients of the quadratic, linear, and constant terms of the efficiency and torque quadratic polynomials are respectively related to the rotational speed. The fitting coefficients for the quadratic, linear, and constant terms; 2) Compare the fitting coefficients of efficiency and torque in the low-torque and high-torque ranges, respectively. , and Each and its corresponding rotational speed Perform a quadratic polynomial fitting to obtain the fitting coefficients. , , and , , This leads to the generation of a predictive model for the relationship between efficiency and torque-speed. For torque below maximum speed For the low-torque segment corresponding to the torque, the relationship prediction model is as follows: ； In the formula: This indicates the predicted efficiency of the electric vehicle reducer in the low torque range. , , Indicates the fixed speed operating point Below, the fitting coefficients of the quadratic, linear, and constant terms of the torque quadratic polynomial for efficiency in the low torque range are respectively related to the rotational speed. The fitting coefficients for the quadratic, linear, and constant terms; This indicates the input torque of the electric vehicle reducer; For torque greater than or equal to the maximum speed For the high torque segment corresponding to the torque, the relationship prediction model is as follows: ； In the formula: This indicates the predicted efficiency of the electric vehicle reducer in the high torque range. , , Indicates the fixed speed operating point Below, the fitting coefficients of the quadratic, linear, and constant terms of the torque quadratic polynomial for high torque efficiency are respectively related to the rotational speed. The fitting coefficients for the quadratic, linear, and constant terms; S2: Obtain torque and speed operating points for several electric vehicle reducers within their external characteristic range; S3: Input the torque and speed operating points of the electric vehicle reducer's external characteristic range into the constructed relational prediction model, and output the corresponding prediction efficiency; S4: Plot the efficiency MAP of the electric vehicle reducer's external characteristic range based on the predicted efficiency at torque-speed operating points within the external characteristic range of each electric vehicle reducer.

2. The efficiency MAP prediction method based on electric vehicle reducer efficiency test as described in claim 1, characterized in that, In step S1, the relationship prediction model is represented by the following formula: ； In the formula: This indicates the predicted efficiency of the electric vehicle reducer. Indicates the input speed of the electric vehicle reducer; This indicates the input torque of the electric vehicle reducer; , , Indicates the fixed speed operating point Below, the fitting coefficients of the quadratic, linear, and constant terms of the efficiency and torque quadratic polynomials are respectively related to the rotational speed. The fitting coefficients for the quadratic, linear, and constant terms are as follows.

3. The efficiency MAP prediction method based on electric vehicle reducer efficiency test as described in claim 1, characterized in that, In step S101, the maximum speed of the drive motor driven by the electric vehicle reducer is first determined according to the parameters. Rated speed Rated torque Plot the external characteristic curve of the drive motor of the electric vehicle reducer; then, based on the testing capabilities of the electric vehicle reducer efficiency test bench, divide the operating points, i.e., the speed from 0- The range is divided into equal intervals, each interval Divide the operating points of the speed range. Similarly, torque from 0- Range per interval Set the torque operating point. Finally, the test torque and speed operating points for the efficiency test of the electric vehicle reducer bench are generated.

4. The efficiency MAP prediction method based on electric vehicle reducer efficiency test as described in claim 1, characterized in that: In step S102, the electric vehicle reducer operates under the test torque-speed condition as the working condition of the electric vehicle reducer on the electric vehicle reducer efficiency test bench. Under the preset temperature conditions, the power loaded at the two output ends and the power at the input end of the electric vehicle reducer are measured respectively. Then, the efficiency at the corresponding test torque-speed condition is calculated by combining the following formula. ； In the formula: Indicates the efficiency of the electric vehicle reducer; Power at the left output end of the electric vehicle reducer; This indicates the power output of the right side of the electric vehicle reducer. This indicates the input power of the electric vehicle reducer; Indicates the rotational speed at the left output terminal; Indicates the torque at the left output terminal; Indicates the rotational speed at the right output terminal; Indicates the torque at the right output terminal; Indicates the input rotational speed; This indicates the input torque.

5. The efficiency MAP prediction method based on electric vehicle reducer efficiency test as described in claim 1, characterized in that: In step S103, the data quality check includes checking the linearity and bias of the corresponding data of the torque measurement system.

6. The efficiency MAP prediction method based on electric vehicle reducer efficiency test as described in claim 5, characterized in that: Minitab software is used to perform linearity and bias analysis of the torque measurement range based on torque measurement data in order to check the linearity and bias of the torque measurement system.

7. The efficiency MAP prediction method based on electric vehicle reducer efficiency test as described in claim 1, characterized in that, In step S2, operating points are divided at equal intervals for speed and torque. When the speed is less than the rated speed, the maximum torque is set to the rated torque. When the speed is greater than the rated speed, the maximum torque is calculated based on the constant power curve, thereby generating several torque-speed operating points with different external characteristic ranges.

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