Method and System for Predicting Deterioration of Dynamic Balance of Axial Flux Motor for Electric Tricycle

Abstract

The present invention provides a method and system for predicting the deterioration of dynamic balance of an axial-flux motor for electric tricycles, which relates to the technical field of predicting the deterioration of dynamic balance and includes: placing a target electric tricycle in a predetermined fixed base state for dynamic balance detection to obtain a fixed base detection record; extracting a first record at a first vehicle speed, introducing a dynamic balance fusion discrimination model for analysis to obtain a first dynamic balance index; reading a predetermined road surface excitation state for dynamic balance detection to obtain a road surface excitation detection record; obtaining a road surface excitation coefficient; extracting a second record at the first vehicle speed to obtain a second dynamic balance index; comparing to obtain a first dynamic balance deterioration degree; obtaining a target dynamic balance deterioration trend line according to a first mapping relationship; and performing prediction of the deterioration of dynamic balance. The present invention solves the technical problem that the analysis of the dynamic balance state in the prior art is limited to the fixed base detection mode and lacks a systematic analysis for dynamic working conditions, resulting in a low accuracy of predicting the deterioration of dynamic balance.

Description

Technical Field

[0001] The present invention relates to the technical field of dynamic balance deterioration prediction, and particularly to a method and system for predicting the dynamic balance deterioration of an axial-flux motor for electric tricycles. Background Art

[0002] As a core component of the drive system, the dynamic balance performance of the axial-flux motor for electric tricycles directly affects the running stability of the whole vehicle. The axial-flux motor is a motor with a compact structure and high efficiency. However, due to its design and operating characteristics, there are also some special dynamic balance problems. For example, during long-term use, due to factors such as mechanical wear, external excitation, and vehicle speed changes, the dynamic balance performance of the motor may gradually deteriorate, resulting in increased vibration, decreased efficiency, and even possible motor failures in severe cases.

[0003] Some existing technologies detect the vibration condition of electric tricycles by using sensors and vibration monitoring technologies to identify potential dynamic balance problems in a timely manner. However, these methods usually rely on a single detection index, such as vibration acceleration, vibration frequency, etc., lacking a comprehensive analysis of the overall dynamic balance state of the vehicle under different working conditions, and it is difficult to accurately reflect the dynamically changing road conditions and motor performance. Especially in the face of complex road conditions or long-term use, the dynamic changes of dynamic balance deterioration are difficult to accurately predict. Summary of the Invention

[0004] This application provides a method and system for predicting the dynamic balance deterioration of an axial-flux motor for electric tricycles, aiming to solve the technical problem that the existing technology's analysis of the dynamic balance state is limited to a fixed basic detection mode and lacks a systematic analysis method for dynamic working conditions, resulting in a low accuracy of dynamic balance deterioration prediction.

[0005] In the first aspect disclosed in this application, a method for predicting the deterioration of dynamic balance of an axial flux motor for electric tricycles is provided. The method includes: placing the target electric tricycle in a predetermined fixed base state for dynamic balance detection to obtain a fixed base detection record; extracting a first record at a first vehicle speed from the fixed base detection record, and introducing a dynamic balance fusion discrimination model to analyze the first record to obtain a first dynamic balance index; reading a predetermined road surface excitation state, and placing the target electric tricycle in the predetermined road surface excitation state for dynamic balance detection to obtain a road surface excitation detection record; performing feature extraction on the road surface excitation detection record to obtain road surface excitation features, and performing variation weighted calculation on the road surface excitation features to obtain a road surface excitation coefficient; extracting a second record at the first vehicle speed from the road surface excitation detection record, and analyzing the second record according to the dynamic balance fusion discrimination model to obtain a second dynamic balance index; comparing the first dynamic balance index with the second dynamic balance index to obtain a first dynamic balance deterioration degree of the target electric tricycle at the first vehicle speed; obtaining a target dynamic balance deterioration trend line according to a first mapping relationship between the first vehicle speed and the first dynamic balance deterioration degree; and predicting the deterioration of the dynamic balance of the target axial flux motor of the target electric tricycle according to the target dynamic balance deterioration trend line.

[0006] The second aspect disclosed in this application provides a dynamic balance deterioration prediction system for an axial flux motor used in an electric tricycle. The system is used for the dynamic balance deterioration prediction method of the above-mentioned axial flux motor for electric tricycles. The system includes: a first dynamic balance detection module, which is used to place the target electric tricycle in a predetermined fixed base state for dynamic balance detection to obtain a fixed base detection record; a first record analysis module, which is used to extract the first record at the first vehicle speed from the fixed base detection record and introduce a dynamic balance fusion discrimination model to analyze the first record to obtain a first dynamic balance index; a second dynamic balance detection module, which is used to read a predetermined road surface excitation state and place the target electric tricycle in the predetermined road surface excitation state for dynamic balance detection to obtain a road surface excitation detection record; a variation weighted calculation module, which is used to extract road surface excitation features from the road surface excitation detection record and perform variation weighted calculation on the road surface excitation features to obtain a road surface excitation coefficient; a second record analysis module, which is used to extract the second record at the first vehicle speed from the road surface excitation detection record and analyze the second record according to the dynamic balance fusion discrimination model to obtain a second dynamic balance index; a dynamic balance deterioration degree acquisition module, which is used to compare the first dynamic balance index with the second dynamic balance index to obtain the first dynamic balance deterioration degree of the target electric tricycle at the first vehicle speed; a deterioration trend line acquisition module, which is used to obtain a target dynamic balance deterioration trend line according to the first mapping relationship between the first vehicle speed and the first dynamic balance deterioration degree; a dynamic balance deterioration prediction module, which is used to predict the dynamic balance deterioration of the target axial flux motor of the target electric tricycle according to the target dynamic balance deterioration trend line.

[0007] One or more technical solutions provided in this application have at least the following beneficial effects:

[0008] By performing dynamic balance detection on the electric tricycle in a predetermined fixed base state, a fixed base detection record is obtained, which is the benchmark data for subsequent analysis. This process ensures that all detections and evaluations are carried out under known standard conditions, providing an accurate reference for subsequent comparison and analysis. By introducing a dynamic balance fusion discrimination model, the data in the fixed base detection record is analyzed to obtain the first dynamic balance index, which quantifies the dynamic balance performance of the electric tricycle in the base state and provides a quantitative index for subsequent performance analysis. Dynamic balance detection is performed under a predetermined road surface excitation state to obtain a road surface excitation detection record. This step simulates different excitation conditions that the electric tricycle will encounter during actual driving and can evaluate the dynamic balance performance of the electric tricycle in the actual road environment. By extracting the characteristics of the road surface excitation detection record and performing variable weighted calculation, a road surface excitation coefficient is obtained. This process synthesizes the influence of road surface conditions on dynamic balance performance and can more accurately evaluate the influencing factors encountered by the vehicle during actual driving. The second record is extracted at the first vehicle speed and analyzed through the same dynamic balance fusion discrimination model to obtain the second dynamic balance index. This process reflects the change in dynamic balance of the electric tricycle at the same vehicle speed under the influence of road surface excitation and provides a basis for subsequent deterioration degree calculation. By comparing the first dynamic balance index with the second dynamic balance index, the first dynamic balance deterioration degree is calculated. This deterioration degree can quantify the degree of dynamic balance loss of the electric tricycle during actual driving due to road surface excitation and provides an evaluation basis for the health status of the vehicle. By establishing a mapping relationship between the first vehicle speed and the dynamic balance deterioration degree, a target dynamic balance deterioration trend line is obtained. This trend line can reflect the relationship between vehicle speed and dynamic balance deterioration and provides data support for further dynamic balance prediction, enabling more accurate prediction of the future dynamic balance performance of the vehicle. Based on the target dynamic balance deterioration trend line, dynamic balance deterioration prediction is performed on the axial flux motor of the electric tricycle. This prediction realizes early warning of possible dynamic balance deterioration of the electric tricycle during future use, thus providing a scientific basis for vehicle maintenance and repair.

[0009] The above description is only an overview of the technical solution of this application. In order to be able to understand the technical means of this application more clearly, it can be implemented according to the content of the specification. And in order to make the above and other purposes, features and advantages of this application more obvious and understandable, the specific embodiments of this application are specifically exemplified below. Brief Description of the Drawings

[0010] Figure 1 It is a schematic flowchart of the method for predicting dynamic balance deterioration of the axial flux motor for electric tricycles provided by the embodiments of this application.

[0011] Figure 2 It is a schematic structural diagram of the system for predicting dynamic balance deterioration of the axial flux motor for electric tricycles provided by the embodiments of this application.

[0012] Description of reference numerals: The first dynamic balance detection module 10, the first recording and analysis module 20, the second dynamic balance detection module 30, the variation weighted calculation module 40, the second recording and analysis module 50, the dynamic balance deterioration degree acquisition module 60, the deterioration trend line acquisition module 70, and the dynamic balance deterioration prediction module 80. Specific implementation manners

[0013] In the embodiments of the present application, by providing a method and system for predicting the dynamic balance deterioration of an axial flux motor for electric tricycles, the technical problem in the prior art that the analysis of the dynamic balance state is limited to a fixed basic detection mode and lacks a systematic analysis method for dynamic working conditions, resulting in low accuracy of dynamic balance deterioration prediction, is solved.

[0014] After introducing the basic principle of the present application, various non-limiting implementation manners of the present application will be specifically introduced below in conjunction with the accompanying drawings of the specification. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0015] Embodiment 1, as Figure 1 shown, the embodiments of the present application provide a method for predicting the dynamic balance deterioration of an axial flux motor for electric tricycles, and the method includes:

[0016] Place the target electric tricycle in a predetermined fixed basic state for dynamic balance detection to obtain a fixed basic detection record.

[0017] Place the target electric tricycle in a predetermined fixed basic state, where the predetermined fixed basic state means that the wheels do not touch the ground, or the wheels are fixed by a special support device (such as a bracket) to ensure that the vehicle body is in a stationary state. At this time, the electric tricycle will not be affected by external road excitations and can separately reflect the dynamic balance performance of the motor and its system. In this state, a comprehensive detection is performed through dynamic balance detection equipment, such as an accelerometer, a vibration sensor, a current sensor, etc., and all relevant data are recorded to form a fixed basic detection record, which provides a basis for subsequent analysis.

[0018] Extract the first record at the first vehicle speed from the fixed basic detection record, and introduce a dynamic balance fusion discrimination model to analyze the first record to obtain a first dynamic balance index.

[0019] During the fixed basic detection process, detection records at different vehicle speeds are included. At this time, the first record at the first vehicle speed is extracted, and the first vehicle speed is any one of the different vehicle speeds. This random extraction facilitates subsequent traversal analysis of all vehicle speeds.

[0020] Introduce a dynamic balance fusion discrimination model. The dynamic balance fusion discrimination model is a comprehensive analysis model that can combine various types of data, such as vibration data, electrical data, and torque data, to make a comprehensive judgment and obtain an evaluation of the dynamic balance state. Input the extracted first recorded data into the dynamic balance fusion discrimination model. Based on these data, the model calculates the first dynamic balance index, which reflects the dynamic balance performance of the electric tricycle at this vehicle speed. The higher the value, the better the dynamic balance state of the vehicle. Conversely, it may indicate a potential risk of imbalance or damage.

[0021] Read the predetermined road surface excitation state, and place the target electric tricycle in the predetermined road surface excitation state for dynamic balance detection to obtain a road surface excitation detection record.

[0022] Read the predetermined road surface excitation state. The road surface excitation state refers to the road surface characteristics and environmental factors that an electric tricycle will encounter during operation. For example, the unevenness, potholes, fluctuations, ruggedness, etc. of the road surface can be regarded as road surface excitations, which affect the dynamic balance state of the tricycle. The predetermined road surface excitation state refers to the pre-set road surface conditions, such as the unevenness degree of a specific road section obtained through measurement or calculation.

[0023] To simulate the operation of the electric tricycle on the actual road, the electric tricycle needs to be placed on a road surface with a predetermined road surface excitation state. The road surface excitation can be controlled by a road vibration table, a simulated vibration test device, etc. When the electric tricycle operates in this specific road surface state, it will be exposed to different vibrations, torques, accelerations, etc. Under the predetermined road surface excitation state, perform dynamic balance detection on the target electric tricycle, including measuring the vibration conditions of the wheels, body, frame, and motor, and simultaneously monitoring electrical signals such as the current and voltage of the motor and mechanical responses. Obtain the detection data of the electric tricycle under road surface excitation through sensors and generate a road surface excitation detection record.

[0024] Extract features from the road surface excitation detection record to obtain road surface excitation features, and perform variation-weighted calculation on the road surface excitation features to obtain a road surface excitation coefficient.

[0025] From the data obtained from the road excitation detection record, road excitation characteristics are extracted, such as road unevenness, crankshaft speed, vibration signal, etc. During the processing, in order to better reflect the different impacts of different road excitations, a variation-weighting strategy is adopted, which means that different road excitation characteristics will be given different weights according to their importance and degree of change. For example, for some specific road excitation characteristics (such as vibrations with a higher frequency), a higher weight can be given because it has a greater impact on the dynamic balance state. Combine the extracted road excitation characteristics with the results after variation-weighting, and obtain the road excitation coefficient through weighted calculation. This coefficient is used to characterize the degree of influence on the dynamic balance performance of the electric tricycle under specific road excitation conditions. A higher road excitation coefficient means that parts such as the wheels and the body will be more affected, and there is a risk of imbalance.

[0026] Extract the second record at the first vehicle speed from the road excitation detection record, and analyze the second record according to the dynamic balance fusion discrimination model to obtain the second dynamic balance index.

[0027] Extract the second record corresponding to the first vehicle speed from the road excitation detection record. This record contains data such as vibrations, torques, and electrical signals of the electric tricycle under a predetermined road excitation state at the first vehicle speed. Input the extracted second record into the dynamic balance fusion discrimination model, and the model calculates the second dynamic balance index according to the input data. This index reflects the dynamic balance performance of the electric tricycle at the first vehicle speed under the action of road excitation.

[0028] Compare the first dynamic balance index with the second dynamic balance index to obtain the first dynamic balance deterioration degree of the target electric tricycle at the first vehicle speed.

[0029] Compare the first dynamic balance index with the second dynamic balance index to evaluate the dynamic balance deterioration degree of the electric tricycle at the first vehicle speed. Specifically, by calculating the difference between the first dynamic balance index and the second dynamic balance index, and then dividing the difference by the first dynamic balance index, the first dynamic balance deterioration degree is calculated. This value reflects the difference between the dynamic balance performance of the electric tricycle under the influence of road excitation and the fixed-base state. The larger the ratio, the more serious the dynamic balance deterioration, and vice versa, indicating that the dynamic balance state of the electric tricycle is maintained well.

[0030] Obtain the target dynamic balance deterioration trend line according to the first mapping relationship between the first vehicle speed and the first dynamic balance deterioration degree.

[0031] Establish a first mapping relationship between the first vehicle speed and the first dynamic balance deterioration degree. This mapping relationship represents the relationship between the dynamic balance deterioration degree and the vehicle speed at a specific first vehicle speed. In order to obtain a more comprehensive trend line, this mapping relationship needs to be extended to multiple vehicle speeds. For each vehicle speed, through corresponding dynamic balance detection and analysis, the corresponding dynamic balance deterioration degree is obtained. The relationships between these vehicle speeds and the dynamic balance deterioration degrees form multiple mapping relationships.

[0032] Connect these data points to form a complete trend line. In this process, the vehicle speed is used as the abscissa and the dynamic balance deterioration degree is used as the ordinate. Each vehicle speed and its corresponding dynamic balance deterioration degree will form a data point in the coordinate system. Connect all the data points to finally obtain the dynamic balance deterioration trend line. This trend line shows how the dynamic balance deterioration degree changes within the entire vehicle speed range of the electric tricycle. This trend line will provide a key basis for further prediction of dynamic balance deterioration.

[0033] Perform dynamic balance deterioration prediction on the target axial flux motor of the target electric tricycle according to the target dynamic balance deterioration trend line.

[0034] The electric tricycle uses an axial flux motor. This means that the dynamic balance deterioration trend line can not only be used to evaluate the dynamic balance state of the overall vehicle, but also be particularly applied to the performance evaluation of the motor. By analyzing the trend line, it is possible to predict the dynamic balance deterioration of the axial flux motor of the electric tricycle in future use. For example, as the usage time increases, the motor may gradually experience a decline in its dynamic balance performance due to the accumulation of vibration and imbalance. By using the trend line for dynamic balance deterioration prediction, it is possible to predict the dynamic balance deterioration degree of the motor in different future usage scenarios, thereby giving early warnings or taking necessary maintenance measures.

[0035] Furthermore, extract the first record at the first vehicle speed from the fixed base detection record and introduce a dynamic balance fusion discrimination model to analyze the first record to obtain a first dynamic balance index, including:

[0036] Extract the first electrical data record from the first record, where the first electrical data record includes a first current and a first voltage; extract the first operation data record from the first record, where the first operation data record includes a first overturning moment and a first internal bending moment; extract the first vibration time series signal from the first record, and extract the first vibration characteristic data of the first vibration time series signal, where the first vibration characteristic data includes a first vibration acceleration, a first vibration velocity, and a first vibration displacement; input the first current, the first voltage, the first overturning moment, the first internal bending moment, the first vibration acceleration, the first vibration velocity, and the first vibration displacement into the dynamic balance fusion discrimination model to obtain the first dynamic balance index.

[0037] Extract the first electrical data record in the first record. The first electrical data record includes the operating parameters of the motor, including the first current and the first voltage. Among them, the first current is the current value of the motor during operation, which reflects the load condition of the motor, power consumption, and the overall electrical state of the electric tricycle. The change in current indicates the working load of the motor, which in turn affects the dynamic balance. The first voltage is the input voltage of the motor. Voltage fluctuations or instability may mean there are problems in the electrical system, indirectly affecting the dynamic balance of the vehicle. These electrical data are collected by sensors, including current sensors, voltage sensors, etc.

[0038] Extract the first operating data record in the first record. The operating data is usually related to the physical movement and mechanical state of the vehicle, including the first tipping moment and the first internal bending moment. Among them, the first tipping moment refers to the moment acting on the electric tricycle, which may cause the vehicle to tilt or overturn. This moment may be caused by unbalanced loads, design problems of the vehicle itself, or uneven road surfaces. By monitoring the tipping moment, stability problems of the vehicle can be identified, which in turn affects the dynamic balance assessment. The first internal bending moment refers to the moment at which the vehicle frame or body bends due to internal forces during operation. The existence of the internal bending moment usually means that stress concentration has occurred during vehicle operation, which may be caused by imbalance or component loosening, etc. These moment data can be obtained through devices such as moment sensors, accelerometers, and gyroscopes, which are usually installed on key components of the vehicle, such as the frame, wheels, or motor, in order to monitor and record the changes in moments in real time.

[0039] Extract the first vibration time series signal in the first record. The first vibration time series signal is the vibration data recorded during the movement of the vehicle, usually collected by an accelerometer or a vibration sensor. It is the time series data of vibration, which can reflect the vibration state and intensity of the vehicle at different time points. Extract the key information of the vibration from the vibration time series signal as the first vibration characteristic data. These vibration characteristics are usually extracted from the vibration time series signal through Fourier transform or wavelet transform, which can convert the time domain signal into frequency domain characteristics in order to analyze the frequency components of the vibration and their corresponding vibration intensities.

[0040] The first vibration characteristic data includes the first vibration acceleration, the first vibration velocity, and the first vibration displacement. Among them, the first vibration acceleration reflects the intensity and frequency of vibration. Acceleration usually represents the change in the velocity of an object per unit time. The larger it is, the greater the vibration amplitude. Vibration acceleration is very important for the dynamic balance of a vehicle, especially in high-frequency vibrations. The first vibration velocity represents the change in the velocity of vibration and reflects the dynamic characteristics of vibration. Through the vibration velocity, the propagation characteristics of vibration and whether there are resonance problems caused by imbalance can be evaluated. The first vibration displacement represents the displacement amount generated by the object due to vibration. Through the displacement, the impact of vibration on the structural parts of the vehicle, such as the offset of the frame or wheels, can be evaluated.

[0041] The extracted different types of characteristic data are input into the dynamic balance fusion discrimination model. The dynamic balance fusion discrimination model is a multi-input model that can perform comprehensive analysis based on the input values of different characteristics. Through the analysis of the above data, the model calculates the first dynamic balance index, which can quantify the dynamic balance status of the vehicle at the first vehicle speed.

[0042] Furthermore, inputting the first current, the first voltage, the first overturning moment, the first internal bending moment, the first vibration acceleration, the first vibration velocity, and the first vibration displacement into the dynamic balance fusion discrimination model to obtain the first dynamic balance index includes:

[0043] Obtain the initial discrimination layer in the dynamic balance fusion discrimination model; analyze the first current and the first voltage through the first discrimination model in the initial discrimination layer to obtain the first discrimination index; analyze the first overturning moment and the first internal bending moment through the second discrimination model in the initial discrimination layer to obtain the second discrimination index; analyze the first vibration acceleration, the first vibration velocity, and the first vibration displacement through the third discrimination model in the initial discrimination layer to obtain the third discrimination index; obtain the meta-discrimination layer in the dynamic balance fusion discrimination model; analyze the first discrimination index, the second discrimination index, and the third discrimination index through the meta-discrimination layer to obtain the first dynamic balance index.

[0044] The initial discrimination layer is a key component in the dynamic balance fusion discrimination model. Its role is to perform preliminary analysis and classification on the input multiple characteristic data, evaluate the impact of different types of sensor data on the dynamic balance state, and calculate the corresponding preliminary discrimination index. This layer includes multiple discrimination models, and each discrimination model is responsible for analyzing a specific type of input data.

[0045] The first discrimination model in the initial discrimination layer is specifically designed to process the two input data of the first current and the first voltage. Current and voltage are key electrical parameters of an electric tricycle, which are usually closely related to the load of the motor, power output, and the operating state of the vehicle.

[0046] Through the first discrimination model, the correlation analysis of the first current and the first voltage is carried out. The changes in current and voltage reflect the working conditions of the electric motor. If there are large fluctuations or instability in the current and voltage at a certain vehicle speed, it indicates that there is a problem with the motor system of the electric tricycle, which in turn affects the dynamic balance of the vehicle. For example, if the motor works unevenly, it may cause abnormal fluctuations in current and voltage. The first discrimination model analyzes based on the characteristic values of current and voltage, such as amplitude, frequency, fluctuation conditions, etc. By comparing historical data, normal working modes, and abnormal modes, the model can judge the impact of the current and voltage data on dynamic balance and calculate the first discrimination index. This index quantifies the impact degree of the electrical system of the electric tricycle on dynamic balance at the first vehicle speed. For example, if the fluctuations of current and voltage are large, the first discrimination index is low, which means that there may be an imbalance in the motor system; if the current and voltage are stable, the first discrimination index is high, indicating that the electrical system is normal and the dynamic balance may be good.

[0047] The second discrimination model in the initial discrimination layer is specifically used to analyze the parameters related to vehicle mechanics and stability. The first overturning moment and the first internal bending moment are input into this model for analysis. These data describe the mechanical behavior of the vehicle during operation due to external excitation or imbalance.

[0048] The second discrimination model analyzes based on the characteristics of the first overturning moment and the first internal bending moment, and evaluates their impact on the dynamic balance of the vehicle. For example, if the overturning moment is too large, it indicates that the vehicle may face a large imbalance, affecting the overall stability. The model combines historical data to analyze the change trend and fluctuation range of the moment, and thus calculates the second discrimination index. The second discrimination index is a quantitative value representing the impact of the vehicle's mechanical behavior on dynamic balance. If this index is high, it means that the moment data indicates that there may be an abnormality in the mechanical state of the vehicle, affecting the dynamic balance of the vehicle; if this index is low, it means that the vehicle performs well in terms of stability.

[0049] The third discrimination model in the initial discrimination layer is responsible for analyzing the data related to vehicle vibration, including vibration acceleration, vibration velocity, and vibration displacement. Vibration analysis is crucial for dynamic balance assessment because the vibration state of the vehicle directly reflects its dynamic balance performance.

[0050] The third discrimination model analyzes by combining the data of the first vibration acceleration, the first vibration velocity, and the first vibration displacement, and evaluates their impact on the overall dynamic balance of the vehicle. Generally, larger or irregular vibration data indicates a problem with the dynamic balance and requires further repair. The output third discrimination index is a quantified result that reflects the impact of vibration factors on the vehicle's dynamic balance. A higher index usually means larger vibrations, possible damage to the dynamic balance, and the vehicle may have asymmetric or unstable vibration modes.

[0051] The meta-discrimination layer is an advanced level in the dynamic balance fusion discrimination model, responsible for comprehensively analyzing multiple discrimination indices obtained in the primary discrimination layer. By integrating multiple discrimination indices, it further optimizes the evaluation result of the dynamic balance. Through the integration of the meta-discrimination layer, the model can more accurately reflect the dynamic balance state of the electric tricycle, avoid biases caused by a single data source, and improve the accuracy of the evaluation.

[0052] The first discrimination index, the second discrimination index, and the third discrimination index of the primary discrimination layer are used as inputs and transmitted to the meta-discrimination layer. The meta-discrimination layer comprehensively analyzes these discrimination indices. For example, methods such as weighted averaging are used to optimize according to the importance of each discrimination index. Finally, the integrated first dynamic balance index is calculated. This index is a comprehensive evaluation value that reflects the dynamic balance status of the electric tricycle in multiple dimensions. A higher value indicates better dynamic balance performance of the electric tricycle, while a lower value may indicate imbalance problems, affecting the stability and safety of the vehicle.

[0053] Furthermore, the road surface excitation characteristics at least include road surface unevenness and crankshaft speed. Among them, the road surface unevenness refers to the excitation detection characteristic value of the predetermined road surface excitation state calculated through a predetermined road surface unevenness function. The expression of the predetermined road surface unevenness function is as follows:

[0054] ;

[0055] Among them, refers to the road surface unevenness per unit length , refers to the road surface height per unit length , refers to the average road surface height, refers to the length of the measurement section, refers to the amplitude of the predetermined road surface unevenness function, and refers to the attenuation factor, and

[0056] Specifically, the expression of the predetermined road surface unevenness function is as follows:

[0057] ;

[0058] This function represents the road surface roughness characteristics within a unit length and reflects the degree of change in the road surface height at this position. A larger indicates a larger road surface roughness, and when the vehicle is driving, it will be subjected to a stronger excitation, affecting the dynamic balance of the vehicle. Among them, refers to the road surface height within a unit length , is the average height of the entire section of the road surface. By calculating the square of the difference between and , it reflects the impact of the road surface height change on the vehicle. The greater the road surface roughness, the more significant the change and fluctuation of the road surface; refers to the length of the measurement section, which is set in advance; the amplitude controls the overall strength of the roughness function and determines the magnitude of the calculated road surface roughness. The closer the value of is to 1, the greater the road surface roughness and the more significant the impact. The closer the value of is to 0, it indicates that the road surface is relatively flat and the excitation on the vehicle is smaller; the attenuation factor is used to adjust the impact of the road surface roughness on the vehicle. As

[0059] increases, the impact of the road surface roughness gradually weakens, and vice versa, making its impact more significant. The role of the attenuation factor is to simulate that in actual situations, the impact of road surface roughness on different vehicles may vary under certain conditions. Generally speaking, this formula can be used to reflect the impact of road surface roughness on dynamic balance during vehicle driving by describing the calculation method of road surface roughness, combining different physical parameters and adjustment coefficients.

[0060] Furthermore, after extracting the road surface excitation characteristics from the road surface excitation detection record and performing a variation-weighted calculation on the road surface excitation characteristics to obtain the road surface excitation coefficient, it further includes:

[0061] Extract the environmental excitation features from the road surface excitation detection records. The environmental excitation features refer to external environmental factors that are independent of the road surface excitation state but affect the vehicle's dynamic balance. They usually include environmental temperature, humidity, air pressure, etc. These factors may indirectly affect the dynamic balance by influencing the road surface conditions or the performance of vehicle components. These environmental features are collected by external sensors, such as temperature and humidity sensors, and stored together with the road surface excitation data for subsequent analysis.

[0062] Variant weighting means assigning different weights according to the fluctuations or changes in environmental temperature and humidity. Different environmental factors may have different degrees of influence on the vehicle's dynamic balance. Therefore, weighting processing is required according to their changes during analysis. Specifically, the changes in environmental temperature and humidity can affect the mechanical components or electrical systems of the vehicle, changing their operating characteristics. Therefore, different weights need to be assigned to these changes. The weighting calculation adjusts the coefficient according to the assigned weights. The purpose is to obtain an environmental feedback coefficient based on the actual changes in the environment to reflect the comprehensive influence of the external environment on the dynamic balance.

[0063] The road surface excitation coefficient reflects the influence of the vehicle's dynamic balance under specific road surface conditions. It is calculated based on the excitation feature data measured under the road surface excitation state. According to the environmental feedback coefficient, the road surface excitation coefficient is adjusted. For example, at a higher temperature, the battery and motor of an electric tricycle may be more easily affected by temperature changes, resulting in changes in vibration characteristics. Therefore, the road surface excitation coefficient needs to be adjusted with the corresponding weight according to the environmental feedback coefficient. The adjusted road surface excitation coefficient can more accurately reflect the dynamic balance state of the electric tricycle under specific environmental conditions.

[0064] Furthermore, comparing the first dynamic balance index with the second dynamic balance index to obtain the first dynamic balance deterioration degree of the target electric tricycle at the first vehicle speed includes:

[0065] Comparing the first dynamic balance index with the second dynamic balance index to obtain a first index difference; taking the first ratio of the first index difference to the first dynamic balance index, denoted as the first dynamic balance deterioration degree.

[0066] Comparing the first dynamic balance index with the second dynamic balance index. These two dynamic balance indexes reflect the dynamic balance states of the electric tricycle under different conditions. The first dynamic balance index represents the ideal balance state of the vehicle without external excitation, while the second dynamic balance index represents the actual balance state of the vehicle when subjected to road surface excitation. The first index difference is the difference between these two indexes, indicating the change in the dynamic balance state of the vehicle under different excitation conditions. It reflects the influence of external excitation on the vehicle's dynamic balance. The larger the difference, the more significant the influence of external excitation on the dynamic balance.

[0067] Calculate the ratio between the first exponential difference and the first dynamic balance index. This ratio is the first dynamic balance deterioration degree, which represents the degree of dynamic balance deterioration of the electric tricycle under specific vehicle speeds and excitation conditions. When the ratio is relatively large, it indicates that the dynamic balance performance of the vehicle deteriorates severely when subjected to road surface excitation; when the ratio is relatively small, it indicates that the dynamic balance of the vehicle is maintained well.

[0068] Furthermore, performing dynamic balance deterioration prediction on the target axial flux motor of the target electric tricycle according to the target dynamic balance deterioration trend line includes:

[0069] Perform polynomial regression processing on the target dynamic balance deterioration trend line to obtain a target fitting polynomial; obtain a target speed, and combine the target polynomial to obtain a target predicted dynamic balance deterioration degree.

[0070] Performing polynomial regression processing on the target dynamic balance deterioration trend line. The purpose of this process is to obtain a mathematical model that can predict future dynamic balance deterioration, namely the target fitting polynomial, by fitting the data points of the target dynamic balance deterioration trend line. Specifically, polynomial regression is a statistical analysis method used to establish the relationship between input variables (vehicle speed) and output variables (dynamic balance deterioration degree). Here, the regression model will fit a polynomial model based on the relationship between vehicle speed and dynamic balance deterioration degree. For example, assuming the relationship between dynamic balance deterioration degree and vehicle speed is a quadratic curve, a quadratic polynomial can be used for regression to obtain the target fitting polynomial. Through polynomial regression processing, an accurate mathematical model is obtained, which can better reflect the relationship between vehicle speed and dynamic balance deterioration degree and provide a basis for subsequent prediction and analysis.

[0071] Obtain the target speed. The target speed refers to the vehicle speed when it is desired to predict the dynamic balance deterioration degree of the electric tricycle during actual use or future applications. Substitute the target speed into the target fitting polynomial obtained through polynomial regression to calculate the corresponding target predicted dynamic balance deterioration degree. This is the predicted value of the dynamic balance deterioration degree of the electric tricycle at the target speed and is used to predict the performance of the vehicle at a specific speed.

[0072] In summary, the dynamic balance deterioration prediction method for the axial flux motor of the electric tricycle provided by the embodiments of the present application has the following technical effects:

[0073] The dynamic balance detection is carried out by placing the electric tricycle in a predetermined fixed base state, and a fixed base detection record is obtained, which is the reference data for subsequent analysis. This process ensures that all detections and evaluations are carried out under known standard conditions, providing an accurate reference for subsequent comparison and analysis. By introducing a dynamic balance fusion discrimination model, the data in the fixed base detection record is analyzed to obtain a first dynamic balance index, which quantifies the dynamic balance performance of the electric tricycle in the base state and provides a quantitative index for subsequent performance analysis. The dynamic balance detection is carried out under a predetermined road surface excitation state to obtain a road surface excitation detection record. This step simulates different excitation conditions that the electric tricycle will encounter during actual driving and can evaluate the dynamic balance performance of the electric tricycle in the actual road environment. By extracting the characteristics of the road surface excitation detection record and performing variation weighted calculation, a road surface excitation coefficient is obtained. This process synthesizes the influence of road surface conditions on the dynamic balance performance and can more accurately evaluate the influencing factors encountered by the vehicle during actual driving. The second record is extracted at the first vehicle speed and analyzed through the same dynamic balance fusion discrimination model to obtain a second dynamic balance index. This process reflects the change in the dynamic balance of the electric tricycle at the same vehicle speed under the influence of road surface excitation and provides a basis for subsequent deterioration degree calculation. By comparing the first dynamic balance index with the second dynamic balance index, the first dynamic balance deterioration degree is calculated. This deterioration degree can quantify the degree of dynamic balance loss of the electric tricycle during actual driving due to road surface excitation and provides an evaluation basis for the health state of the vehicle. By establishing a mapping relationship between the first vehicle speed and the dynamic balance deterioration degree, a target dynamic balance deterioration trend line is obtained. This trend line can reflect the relationship between the vehicle speed and the dynamic balance deterioration and provides data support for further dynamic balance prediction, enabling more accurate prediction of the future dynamic balance performance of the vehicle. Based on the target dynamic balance deterioration trend line, the dynamic balance deterioration prediction of the axial flux motor of the electric tricycle is carried out. This prediction realizes the early warning of the possible dynamic balance deterioration of the electric tricycle during future use, thus providing a scientific basis for the maintenance and servicing of the vehicle.

[0074] Embodiment 2, based on the same inventive concept as the method for predicting the dynamic balance deterioration of the axial flux motor for electric tricycles in the foregoing embodiment, as Figure 2 shown, the embodiment of the present application provides a system for predicting the dynamic balance deterioration of the axial flux motor for electric tricycles, and the system includes:

[0075] The first dynamic balance detection module 10 is used to place the target electric tricycle in a predetermined fixed base state for dynamic balance detection to obtain a fixed base detection record; the first record analysis module 20 is used to extract the first record at the first vehicle speed from the fixed base detection record and introduce a dynamic balance fusion discrimination model to analyze the first record to obtain a first dynamic balance index; the second dynamic balance detection module 30 is used to read a predetermined road surface excitation state and place the target electric tricycle in the predetermined road surface excitation state for dynamic balance detection to obtain a road surface excitation detection record; the variation weighted calculation module 40 is used to extract road surface excitation features from the road surface excitation detection record and perform variation weighted calculation on the road surface excitation features to obtain a road surface excitation coefficient; the second record analysis module 50 is used to extract the second record at the first vehicle speed from the road surface excitation detection record and analyze the second record according to the dynamic balance fusion discrimination model to obtain a second dynamic balance index; the dynamic balance deterioration degree acquisition module 60 is used to compare the first dynamic balance index with the second dynamic balance index to obtain a first dynamic balance deterioration degree of the target electric tricycle at the first vehicle speed; the deterioration trend line acquisition module 70 is used to obtain a target dynamic balance deterioration trend line according to a first mapping relationship between the first vehicle speed and the first dynamic balance deterioration degree; the dynamic balance deterioration prediction module 80 is used to predict the dynamic balance deterioration of the target axial flux motor of the target electric tricycle according to the target dynamic balance deterioration trend line.

[0076] Furthermore, the first record analysis module 20 includes:

[0077] The first electrical data record extraction unit is used to extract the first electrical data record from the first record, where the first electrical data record includes a first current and a first voltage; the first operation data record extraction unit is used to extract the first operation data record from the first record, where the first operation data record includes a first tipping moment and a first internal bending moment; the first vibration time series signal extraction unit is used to extract the first vibration time series signal from the first record and extract first vibration characteristic data of the first vibration time series signal, where the first vibration characteristic data includes a first vibration acceleration, a first vibration velocity, and a first vibration displacement; the first dynamic balance index acquisition unit is used to input the first current, the first voltage, the first tipping moment, the first internal bending moment, the first vibration acceleration, the first vibration velocity, and the first vibration displacement into the dynamic balance fusion discrimination model to obtain the first dynamic balance index.

[0078] Furthermore, the first dynamic balance index acquisition unit includes:

[0079] The initial discrimination layer acquisition channel is used to acquire the initial discrimination layer in the dynamic balance fusion discrimination model; the first analysis channel is used to analyze the first current and the first voltage through the first discrimination model in the initial discrimination layer to obtain a first discrimination index; the second analysis channel is used to analyze the first overturning moment and the first internal bending moment through the second discrimination model in the initial discrimination layer to obtain a second discrimination index; the third analysis channel is used to analyze the first vibration acceleration, the first vibration velocity and the first vibration displacement through the third discrimination model in the initial discrimination layer to obtain a third discrimination index; the meta-discrimination layer acquisition channel is used to acquire the meta-discrimination layer in the dynamic balance fusion discrimination model; the fourth analysis channel is used to analyze the first discrimination index, the second discrimination index and the third discrimination index through the meta-discrimination layer to obtain the first dynamic balance index.

[0080] Furthermore, the road surface excitation characteristics at least include road surface unevenness and crankshaft speed. Among them, the road surface unevenness refers to the excitation detection characteristic value of the predetermined road surface excitation state calculated through a predetermined road surface unevenness function. The expression of the predetermined road surface unevenness function is as follows:

[0081] ;

[0082] Among them, refers to the road surface unevenness per unit length within, refers to the road surface height per unit length within, refers to the average road surface height, refers to the length of the measurement section, refers to the amplitude of the predetermined road surface unevenness function, and , refers to the attenuation factor, and .

[0083] Furthermore, the variation weighted calculation module 40 includes:

[0084] The environmental excitation feature extraction unit is used to extract the environmental excitation features in the road surface excitation detection record; the variation weighted calculation unit is used to perform variation weighted calculation on the environmental temperature and environmental humidity in the environmental excitation features to obtain an environmental feedback coefficient; the adjustment unit is used to adjust the road surface excitation coefficient with the environmental feedback coefficient as the weight.

[0085] Furthermore, the dynamic balance deterioration degree acquisition module 60 includes:

[0086] The first index difference obtaining unit is configured to obtain a first index difference by comparing the first dynamic balance index and the second dynamic balance index; the first dynamic balance deterioration degree obtaining unit is configured to take a first ratio of the first index difference to the first dynamic balance index, and record it as the first dynamic balance deterioration degree.

[0087] Furthermore, the dynamic balance deterioration prediction module 80 includes:

[0088] The polynomial regression processing unit is configured to perform polynomial regression processing on the target dynamic balance deterioration trend line to obtain a target fitting polynomial; the target predicted dynamic balance deterioration degree obtaining unit is configured to obtain a target speed and combine the target polynomial to obtain a target predicted dynamic balance deterioration degree.

[0089] Through the foregoing detailed description of the dynamic balance deterioration prediction method for the axial flux motor for electric tricycles in this specification, those skilled in the art can clearly know the dynamic balance deterioration prediction system for the axial flux motor for electric tricycles in this embodiment. Since it corresponds to the method disclosed in the embodiment, the description is relatively simple. For related parts, refer to the description in the method part.

[0090] The above description of the disclosed embodiments enables those skilled in the art to implement or use the present application. Various modifications to these embodiments will be obvious to those skilled in the art, and the general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application will not be limited to these embodiments shown herein, but rather to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for predicting dynamic balance degradation of an axial flux motor for an electric three-wheeled vehicle, characterized in that: The method comprises: The target electric tricycle is placed in a predetermined fixed foundation state for dynamic balancing detection to obtain a fixed foundation detection record; Extracting a first record at a first vehicle speed from the fixed basic detection record, and introducing a dynamic balance fusion discrimination model to analyze the first record to obtain a first dynamic balance index; Reading a predetermined road surface excitation state, and placing the target electric tricycle in the predetermined road surface excitation state for dynamic balance detection, to obtain a road surface excitation detection record; Extracting features from the road surface excitation detection records to obtain road surface excitation features, and performing variation weighted calculation on the road surface excitation features to obtain road surface excitation coefficients; Extracting a second record at the first vehicle speed from the road surface excitation detection record, and analyzing the second record according to the dynamic balance fusion discrimination model to obtain a second dynamic balance index; Comparing the first dynamic balance index with the second dynamic balance index to obtain a first dynamic balance degradation degree of the target electric three-wheeler at the first vehicle speed; obtaining a target dynamic balance degradation trend line according to a first mapping relationship between the first vehicle speed and the first dynamic balance degradation degree; Predicting dynamic balance degradation of a target axial flux motor of the target electric three-wheeler according to the target dynamic balance degradation trend line; Extracting a first record at a first vehicle speed from the fixed basic detection record, and introducing a dynamic balance fusion discrimination model to analyze the first record to obtain a first dynamic balance index, including: extracting a first electrical data record from the first record, wherein the first electrical data record includes a first current and a first voltage; Extracting a first operation data record from the first record, wherein the first operation data record includes a first overturning moment and a first internal bending moment; Extracting a first vibration time sequence signal from the first record, and extracting first vibration characteristic data of the first vibration time sequence signal, wherein the first vibration characteristic data includes a first vibration acceleration, a first vibration velocity, and a first vibration displacement; The first current, the first voltage, the first overturning moment, the first internal bending moment, the first vibration acceleration, the first vibration velocity, and the first vibration displacement are input into the dynamic balance fusion discrimination model to obtain the first dynamic balance index.

2. The method for predicting dynamic balance degradation of an axial flux motor for an electric three-wheeled vehicle according to claim 1, characterized in that: Inputting the first current, the first voltage, the first overturning moment, the first internal bending moment, the first vibration acceleration, the first vibration velocity, and the first vibration displacement into the dynamic balance fusion discrimination model to obtain the first dynamic balance index includes: Obtaining the initial discrimination layer in the dynamic balance fusion discrimination model; Analyze the first current and the first voltage by using the first discriminant model in the initial discriminant layer to obtain a first discriminant index; Analyze the first overturning moment and the first internal bending moment by using the second discriminant model in the initial discriminant layer to obtain a second discriminant index; Analyzing the first vibration acceleration, the first vibration velocity, and the first vibration displacement by using a third discriminant model in the initial discriminant layer to obtain a third discriminant index; Obtaining a meta-discriminant layer in the dynamic balance fusion discriminant model; The first discriminant index, the second discriminant index and the third discriminant index are analyzed by the meta-discriminant layer to obtain the first dynamic balance index.

3. The method for predicting dynamic balance degradation of an axial flux motor for an electric three-wheeled vehicle according to claim 1, characterized in that: The road surface excitation feature at least includes road surface roughness and crankshaft speed, wherein the road surface roughness refers to the excitation detection characteristic value of the predetermined road surface excitation state calculated by a predetermined road surface roughness function, and the expression of the predetermined road surface roughness function is as follows: ; in, The unit length The road surface roughness within The unit length The road surface height inside is the average road height. is the length of the measuring section, refers to the amplitude of the predetermined road roughness function, and , is the attenuation factor, and .

4. The method for predicting dynamic balance degradation of an axial flux motor for an electric three-wheeled vehicle according to claim 3, characterized in that: After extracting features from the road surface excitation detection record to obtain road surface excitation features, and performing variation weighted calculation on the road surface excitation features to obtain road surface excitation coefficients, the method further includes: Extracting environmental excitation features from the road surface excitation detection record; Performing a weighted calculation on the ambient temperature and ambient humidity in the environmental excitation characteristics to obtain an environmental feedback coefficient; The road surface excitation coefficient is adjusted by taking the environmental feedback coefficient as a weight.

5. The method for predicting dynamic balance degradation of an axial flux motor for an electric three-wheeled vehicle according to claim 1, characterized in that: Comparing the first dynamic balance index with the second dynamic balance index to obtain a first dynamic balance degradation degree of the target electric three-wheeler at the first vehicle speed includes: Comparing the first dynamic balance index with the second dynamic balance index to obtain a first index difference; A first ratio of the first index difference to the first dynamic balance index is taken as the first dynamic balance degradation degree.

6. The method for predicting dynamic balance degradation of an axial flux motor for an electric three-wheeled vehicle according to claim 1, characterized in that: Predicting the dynamic balance degradation of the target axial flux motor of the target electric three-wheeler according to the target dynamic balance degradation trend line includes: Performing polynomial regression processing on the target dynamic balance degradation trend line to obtain a target fitting polynomial; The target speed is obtained, and the target predicted dynamic balance degradation degree is obtained in combination with the target polynomial.

7. A dynamic balance degradation prediction system for an axial flux motor for an electric three-wheeled vehicle, characterized in that: A method for predicting dynamic balance degradation of an axial flux motor for an electric three-wheeled vehicle according to any one of claims 1 to 6, the system comprising: A first dynamic balance detection module is used to place the target electric tricycle in a predetermined fixed base state for dynamic balance detection to obtain a fixed base detection record; A first record analysis module is used to extract a first record at a first vehicle speed from the fixed basic detection record, and introduce a dynamic balance fusion discrimination model to analyze the first record to obtain a first dynamic balance index; A second dynamic balance detection module is used to read a predetermined road surface excitation state, and place the target electric tricycle in the predetermined road surface excitation state to perform dynamic balance detection, and obtain a road surface excitation detection record; A variation weighted calculation module, used for extracting features from the road surface excitation detection record to obtain road surface excitation features, and performing variation weighted calculation on the road surface excitation features to obtain road surface excitation coefficients; A second record analysis module is used to extract a second record at the first vehicle speed from the road excitation detection record, and analyze the second record according to the dynamic balance fusion discrimination model to obtain a second dynamic balance index; a dynamic balance degradation degree acquisition module, configured to compare the first dynamic balance index with the second dynamic balance index to obtain a first dynamic balance degradation degree of the target electric three-wheeler at the first vehicle speed; a degradation trend line acquisition module, configured to obtain a target dynamic balance degradation trend line according to a first mapping relationship between the first vehicle speed and the first dynamic balance degradation degree; A dynamic balance degradation prediction module is used to predict the dynamic balance degradation of the target axial flux motor of the target electric three-wheeler according to the target dynamic balance degradation trend line.

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