Motor-based prediction method and device, storage medium and electronic equipment

Abstract

The invention discloses a motor-based prediction method and device, a storage medium and electronic equipment, and relates to the technical field of data processing, and the method comprises the steps: collecting the operation data of a plurality of indexes corresponding to a vehicle motor in a target time period, and forming a plurality of operation data sets; target operation data meeting a preset condition is determined from the multiple operation data sets; inputting the target operation data into a prediction model, and predicting a target insulation aging life value corresponding to the target time period in the prediction model based on an association relationship between a plurality of operation indexes and the insulation aging life value of the vehicle motor; and generating an overall insulation aging life value corresponding to the vehicle motor based on the target insulation aging life value. Compared with the prior art, the method has the advantages that the trained prediction model is used for prediction based on the incidence relation between the multiple operation indexes and the insulation aging life value of the vehicle motor, the insulation aging life value can be accurately predicted, the prediction mode is simple and convenient, and the operability of the method is improved.

Description

Technical Field

[0001] The present application relates to the technical field of data processing, and particularly to a prediction method, device, storage medium and electronic device based on an electric motor. Background Art

[0002] The insulation aging life of a vehicle motor refers to the expected service life of the insulation material used inside the motor under specific working conditions until its performance deteriorates to the point where it can no longer meet the design requirements. The aging of the insulation material is caused by various factors, including but not limited to heat, electric field, mechanical stress, chemical substances, and environmental conditions, etc.

[0003] Currently, mainly by online monitoring the partial discharge situation of the motor insulation system and predicting the insulation aging degree based on the partial discharge situation, it plays a role in preventing failures and improves the operation safety of motors and transformers.

[0004] However, using this prediction method, due to the influence of external interference on the vehicle motor during operation, it is difficult to accurately monitor the partial discharge data, which will result in a low prediction accuracy rate of the insulation aging degree. Summary of the Invention

[0005] In view of this, the present application provides a prediction method, device, storage medium and electronic device based on an electric motor, mainly aiming to improve the technical problem that in the current existing technology, due to the influence of external interference on the vehicle motor during operation, it is difficult to accurately monitor the partial discharge data, which will result in a low prediction accuracy rate of the insulation aging degree.

[0006] In a first aspect, the present application provides a prediction method based on an electric motor, including: Collect the operation data of multiple indicators corresponding to the vehicle motor within a target time period to form multiple operation data sets; Respectively determine the target operation data that meets the predetermined conditions from the multiple operation data sets; Input the target operation data into a prediction model, and based on the correlation relationship between the multiple operation indicators and the insulation aging life value of the vehicle motor in the prediction model, predict the target insulation aging life value corresponding to the target time period; Generate the overall insulation aging life value corresponding to the vehicle motor based on the target insulation aging life value.

[0007] Optionally, the multiple indicators include a voltage indicator, a frequency indicator, and a temperature indicator; The step of collecting the operation data of multiple indicators corresponding to the vehicle motor within a target time period to form multiple operation data sets includes: Collect the DC bus voltage data, DC bus current data, line voltage frequency data of the vehicle motor in real time, and the winding temperature data of the preset winding position in the vehicle motor; Input the DC bus voltage and the DC bus current into a preset voltage model to determine the line voltage data of the vehicle motor during operation, and use the line voltage data under each operating condition within the target time period as the voltage operation data of the vehicle motor and form a voltage operation data set; Use the line voltage frequency data under each operating condition within the target time period as the frequency operation data of the vehicle motor and form a frequency operation data set; Input the winding temperature data into a preset temperature model to determine the target winding temperature data of the target winding position of the vehicle motor during operation, and use the target winding temperature data under each operating condition within the target time period as the temperature operation data of the vehicle motor and form a temperature operation data set, where the target winding position is the position with the highest temperature withstand of the insulation in the vehicle motor.

[0008] Optionally, the determining the target operation data that meets the predetermined conditions from the multiple operation data sets includes: Select the maximum voltage operation data, maximum frequency operation data, and maximum temperature operation data from the voltage operation data set, the frequency operation data set, and the temperature operation data set; Determine the maximum voltage operation data, the maximum frequency operation data, and the maximum temperature operation data as the target voltage data, target frequency data, and target temperature data respectively.

[0009] Optionally, the inputting the target operation data into a prediction model and predicting the target insulation aging life value corresponding to the target time period based on the correlation relationship between the multiple operation indicators and the insulation aging life value of the vehicle motor includes: Input the target voltage data, target frequency data, and target temperature data corresponding to each operating condition into the prediction model respectively; Based on the correlation relationship between the voltage index, frequency index, temperature index and the insulation aging life value of the vehicle motor in the prediction model, predict the insulation aging life values of all operating conditions within the target time period; Perform a superposition process on the insulation aging life values of all operating conditions to obtain the target insulation aging life value.

[0010] Optionally, the generating the overall insulation aging life value corresponding to the vehicle motor based on the target insulation aging life value includes: Determine the insulation aging life value to be updated of the vehicle motor before the target time period; Determine the sum of the to-be-updated insulation aging life value and the target insulation aging life value as the overall insulation aging life value of the vehicle motor in the target time period.

[0011] Optionally, after generating the overall insulation aging life value corresponding to the vehicle motor based on the target insulation aging life value, the method further includes: Compare the overall insulation aging life value with an insulation aging life threshold, where the insulation aging life threshold is determined based on a standard insulation aging life value predicted by a prediction model for the standard voltage, standard frequency, and standard indicators of the vehicle motor.

[0012] Generate an alarm message for the vehicle motor when it is determined that the overall insulation aging life value is greater than or equal to the insulation aging life threshold.

[0013] In a second aspect, the present application provides a prediction device based on a motor, including: An acquisition module configured to acquire operation data of a plurality of indicators corresponding to a vehicle motor in a target time period to form a plurality of operation data sets; A determination module configured to respectively determine target operation data that meets a predetermined condition from the plurality of operation data sets; A prediction module configured to input the target operation data into a prediction model, and predict a target insulation aging life value corresponding to the target time period based on the association relationship between the plurality of operation indicators and the insulation aging life value of the vehicle motor in the prediction model; A generation module configured to generate an overall insulation aging life value corresponding to the vehicle motor based on the target insulation aging life value.

[0014] In a third aspect, the present application provides a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, it implements the prediction method based on a motor described in the first aspect.

[0015] In a fourth aspect, the present application provides an electronic device, including a storage medium, a processor, and a computer program stored on the storage medium and executable on the processor, and when the processor executes the computer program, it implements the prediction method based on a motor described in the first aspect.

[0016] In a fifth aspect, the present application provides a computer program product, on which a computer program is stored, and when the computer program product is executed by a processor, it implements the prediction method based on a motor described in the first aspect.

[0017] With the above technical solution, a prediction method, device, storage medium and electronic device based on a motor provided by the present application, wherein the method includes: collecting operation data of a plurality of indicators corresponding to a vehicle motor within a target time period to form a plurality of operation data sets; respectively determining target operation data that meets a predetermined condition from the plurality of operation data sets; inputting the target operation data into a prediction model, and predicting a target insulation aging life value corresponding to the target time period in the prediction model based on the correlation between the plurality of operation indicators and the insulation aging life value of the vehicle motor; generating an overall insulation aging life value corresponding to the vehicle motor based on the target insulation aging life value. Compared with the current existing technologies, the present application collects operation data of a plurality of indicators corresponding to a vehicle motor within a target time period to form a plurality of operation data sets, and respectively determines target operation data that meets a predetermined condition from the plurality of operation data sets; inputs the target operation data into a prediction model, and predicts a target insulation aging life value corresponding to the target time period in the prediction model based on the correlation between the plurality of operation indicators and the insulation aging life value of the vehicle motor; generates an overall insulation aging life value corresponding to the vehicle motor based on the target insulation aging life value, which can associate the motor operation indicators with the insulation system aging life, and perform cumulative calculation of the aging life through the data corresponding to the indicators, so that the present application avoids the situation of inaccurate prediction of the insulation aging degree caused by the difficulty in monitoring partial discharge conditions. Moreover, the present application uses a trained prediction model to perform prediction based on the correlation between a plurality of operation indicators and the insulation aging life value of the vehicle motor, and can accurately predict the insulation aging life value; in addition, the index parameters used in the present application are relatively conventional and easy to collect, making the prediction method of the present application simple and convenient, and improving the operability of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] The accompanying drawings herein are incorporated into the specification and form a part of the specification, showing embodiments consistent with the present application and, together with the specification, are used to explain the principles of the present application.

[0019] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, for those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0020] Figure 1 FIG. shows a schematic flow chart of a prediction method based on a motor provided by an embodiment of the present application; Figure 2 FIG. shows a schematic flow chart of a prediction method based on a motor provided by an embodiment of the present application; Figure 3Shows a schematic flowchart of an example provided by an embodiment of the present application; Figure 4 Shows a schematic flowchart of another example provided by an embodiment of the present application; Figure 5 Shows a schematic structural diagram of a prediction device based on a motor provided by an embodiment of the present application; Figure 6 Shows a schematic structural diagram of an electronic device provided by an embodiment of the present application. Detailed implementation manners

[0021] Embodiments of the present application will be described in more detail below with reference to the accompanying drawings. It should be noted that, without conflict, the embodiments in the present application and the features in the embodiments may be combined with each other.

[0022] In order to improve the technical problem that in the prior art, due to the influence of external interference during the operation of a vehicle motor, it is difficult to accurately monitor partial discharge data, which will lead to a low prediction accuracy of the insulation aging degree. This embodiment provides a prediction method based on a motor, as Figure 1 shown, the method includes: Step 101, collect the operation data of multiple indicators corresponding to the vehicle motor within a target time period to form multiple operation data sets.

[0023] In the embodiment of the present application, the target time period may be any time period during the use of the vehicle motor.

[0024] In some examples, the vehicle motor may specifically be a new energy vehicle motor. Specifically, the vehicle motor, especially the drive motor in an electric vehicle, is one of the core components of modern transportation tools. They convert electrical energy into mechanical energy to provide power for the vehicle. With the development of technology, the design and performance of vehicle motors are also constantly improving to meet higher efficiency, power density, and reliability requirements.

[0025] For this embodiment, the main types of vehicle motors may include but are not limited to: AC Induction Motor, Permanent Magnet Synchronous Motor (PMSM), and so on.

[0026] Exemplarily, the key technical parameters of the vehicle motor may include but are not limited to: 1. Power density: It refers to the power output by the motor per unit volume or weight, which directly affects the vehicle's power performance and driving range. 2. Efficiency: An efficient motor can convert energy more effectively, reduce energy loss, and improve the vehicle's economy. 3. Speed range: A wide operating speed range allows the motor to operate efficiently under different driving conditions. 4. Cooling method: Effective heat dissipation design is crucial for maintaining the optimal operating temperature of the motor. Common cooling methods include air cooling, liquid cooling, etc.

[0027] In the embodiments of the present application, the multiple indicators of the vehicle motor may be voltage, frequency, and temperature. It should be noted that the multiple operation data sets in the embodiments of the present application may be the operation data sets corresponding to each indicator respectively. Exemplarily, if the multiple indicators in the present application include a voltage indicator, a frequency indicator, and a temperature indicator, then the voltage indicator corresponds to the collected operation data set, the frequency indicator corresponds to the collected operation data set, and the temperature indicator also corresponds to the collected operation data set.

[0028] Step 102: Determine the target operation data that meets the predetermined conditions from multiple operation data sets respectively.

[0029] In some examples, the predetermined conditions may be set based on the vehicle motor, including but not limited to selecting the maximum, selecting the median, selecting the average, etc.

[0030] It should be noted that if the multiple indicators in the present application include a voltage indicator, a frequency indicator, and a temperature indicator, then the voltage indicator corresponds to the collected operation data set, and the target operation data corresponding to the voltage indicator can be determined from the operation data set corresponding to the voltage indicator; correspondingly, the frequency indicator corresponds to the collected operation data set, and the target operation data corresponding to the frequency indicator can be determined from the operation data set corresponding to the frequency indicator; correspondingly, the temperature indicator also corresponds to the collected operation data set, and the target operation data corresponding to the temperature indicator can be determined from the operation data set corresponding to the temperature indicator.

[0031] Step 103: Input the target operation data into the prediction model, and predict the target insulation aging life value corresponding to the target time period based on the correlation between the multiple operation indicators and the insulation aging life value of the vehicle motor in the prediction model.

[0032] In the embodiments of the present application, the prediction model may be a pre-trained model. Specifically, the prediction model may be trained based on the historical operation data and historical insulation aging life values corresponding to multiple indicators.

[0033] In some examples, the prediction model in the embodiments of the present application can capture the correlation between multiple operating metrics and the insulation aging life, and further predict the target insulation aging life value corresponding to the target operating data.

[0034] Step 104: Generate the overall insulation aging life value corresponding to the vehicle motor based on the target insulation aging life value.

[0035] In the embodiments of the present application, the target insulation aging life value is specifically the insulation aging life value within the target time period, and the overall insulation aging life value corresponding to the vehicle motor needs to be determined based on the superposition value of the overall insulation aging life value before the target time period and the insulation aging life value within the target time period.

[0036] Exemplarily, if the target insulation aging life value is 2 and the overall insulation aging life value of the vehicle motor based on before the target time period is 16, then the overall insulation aging life value corresponding to the vehicle motor can be the sum of the target insulation aging life value and the overall insulation aging life value based on before the target time period, that is, 18.

[0037] Compared with the current existing technologies, in this embodiment, by collecting the operation data of multiple metrics corresponding to the vehicle motor within the target time period to form multiple operation data sets, the target operation data that meets the predetermined conditions is determined from each of the multiple operation data sets; the target operation data is input into the prediction model, and based on the correlation between the multiple operation metrics and the insulation aging life value of the vehicle motor in the prediction model, the target insulation aging life value corresponding to the target time period is predicted; the overall insulation aging life value corresponding to the vehicle motor is generated based on the target insulation aging life value, which can associate the motor operation metrics with the aging life of the insulation system, and achieve the cumulative calculation of the aging life through the data corresponding to the metrics, so that this embodiment avoids the situation of inaccurate prediction of the insulation aging degree caused by the difficulty in monitoring the partial discharge situation. Moreover, in this embodiment, the trained prediction model is used to make a prediction based on the correlation between the multiple operation metrics and the insulation aging life value of the vehicle motor, and the insulation aging life value can be accurately predicted; in addition, the index parameters used in this embodiment are relatively conventional and easy to collect, making the prediction method in this embodiment simple and convenient, and improving the operability of this embodiment.

[0038] As a refinement and extension of the above embodiment, the multiple metrics in the embodiments of the present application include voltage metrics, frequency metrics, and temperature metrics; correspondingly, when performing "collecting the operation data of multiple metrics corresponding to the vehicle motor within the target time period to form multiple operation data sets", the following method can be adopted but is not limited to this, such as Figure 2 As shown, the method includes: Step 201: Collect the DC bus voltage data, DC bus current data, line voltage frequency data of the vehicle motor, and the winding temperature data of the preset winding position in the vehicle motor in real time.

[0039] In the embodiment of the present application, the DC bus voltage refers to the voltage level in the high-voltage DC circuit connecting the inverter and the motor. This voltage is usually provided by the battery pack and adjusted to the voltage range suitable for the inverter operation through one or more DC-DC converters. For electric vehicles, the typical DC bus voltage can be from several hundred volts to higher. For example, systems of 400V or 800V or even higher are becoming more and more common. Increasing the DC bus voltage helps reduce the current load, thus allowing the use of thinner wires, reducing the vehicle weight, and lowering the energy loss.

[0040] In some examples, the DC bus current refers to the current intensity flowing on the DC bus. It reflects the power demand of the motor under the current operating state. When the vehicle accelerates or climbs a slope, a greater torque output is required, which usually leads to an increase in the current on the DC bus. On the contrary, during constant-speed driving or deceleration (regenerative braking), the current may decrease or flow in the reverse direction to charge the battery.

[0041] For this embodiment, the preset winding position can be the winding position set according to requirements, and the specific position of the preset winding position on the winding is not limited in the embodiment of the present application.

[0042] Step 202: Input the DC bus voltage and the DC bus current into the preset voltage model to determine the line voltage data of the vehicle motor during operation, and use the line voltage data under each operating condition within the target time period as the voltage operating data of the vehicle motor and form a voltage operating data set.

[0043] In the embodiment of the present application, the expression of the preset voltage model can specifically be Formula 1, and Formula 1 is specifically as follows: U nm =U dc +L*di / dt (Formula 1) In Formula 1, U nm is the voltage borne by the insulation system, that is, the line voltage of the motor, namely the line voltage data in the embodiment of the present application; U dc is the DC bus voltage measured in real time; L is the stray inductance of the controller, which is related to the design of the controller. Once the controller is selected, this parameter is fixed; di / dt is the current rising rate, which is related to the magnitude of the current I dc collected in real time. Different currents correspond to different di / dt, which can be obtained through static tests.

[0044] Step 203: Use the line voltage frequency data under each operating condition within the target time period as the frequency operating data of the vehicle motor and form a frequency operating data set.

[0045] As an alternative, in a vehicle motor, especially in the motor system of an electric vehicle and a hybrid vehicle, the line voltage frequency is a key parameter. It refers to the frequency of the three-phase alternating current supplied to the motor, and this frequency determines the speed (RPM) of the motor rotor.

[0046] Step 204: Input the winding temperature data into a preset temperature model to determine the target winding temperature data of the target winding position during the operation of the vehicle motor. Use the target winding temperature data under each operating condition within the target time period as the temperature operating data of the vehicle motor and form a temperature operating data set.

[0047] Among them, the target winding position is the position where the insulation of the vehicle motor bears the highest temperature.

[0048] It should be noted that different vehicle motors in the embodiments of the present application correspond to different temperature models, which are mainly related to the cooling design scheme and winding design scheme of the vehicle motor.

[0049] As an alternative, the target winding position can be the position where the insulation of the vehicle motor bears the highest temperature. Since the vehicle motor includes multiple windings and the positions of each winding are different, for example, in the slot or the welding end, etc. Among them, the temperatures of windings in different positions are different. For example, the end is always in the oil injection state, resulting in a lower temperature. The temperature in the slot is higher because the oil temperature cannot directly cool it. During the acquisition process, a temperature sensor needs to be installed at a fixed position of the winding (i.e., the preset winding position in the embodiments of the present application), and the temperature of this fixed position (i.e., the preset winding position in the embodiments of the present application) is collected through the temperature sensor to obtain the winding temperature data in the embodiments of the present application, and then the target temperature data of the position where the insulation of the winding bears the highest temperature is determined using the temperature model corresponding to the vehicle motor.

[0050] Exemplarily, as Figure 3 shown, the process of collecting data for the vehicle motor can be specifically as follows: Use the voltage back-off function module to measure the battery DC bus voltage U dc and current I dc in real time, and use the voltage calculation model (i.e., the voltage model in the embodiments of the present application) to control the system to automatically calculate the line voltage U nm during the operation of the motor; Use the resolver to collect the line voltage frequency f nm; Embedded a temperature sensor at a certain position of the stator winding to measure the temperature of the winding at that place in real time during motor operation. The control system automatically calculates the maximum temperature of the stator at a certain moment during motor operation through the stator temperature model (i.e., the temperature model in the embodiments of the present application), and takes it as the temperature T borne by the insulation system at that moment nm ; Transmit the above test data to the control system and perform data analysis to capture the highest working voltage U within a certain unit time period t nmax 、frequency f nmax 、temperature T nmax .

[0051] In the embodiments of the present application, the operating conditions of the vehicle motor refer to various operating states and environmental conditions experienced by the motor under actual driving conditions. These factors directly affect the working efficiency, performance, and service life of the motor. Specifically, the operating conditions of the vehicle motor can include the following aspects but are not limited to: 1. Vehicle driving mode: Acceleration: When the driver accelerates, the motor needs to provide a high torque output to quickly increase the vehicle speed. At this time, the motor usually operates at a relatively high power state. Constant speed driving: When driving at a stable speed, the power demand of the motor is relatively low, mainly used to overcome air resistance and rolling resistance to maintain the current speed. Deceleration / Braking: Modern electric vehicles are usually equipped with a regenerative braking system. During deceleration or braking, the motor transforms into a generator, converting the vehicle's kinetic energy into electrical energy and feeding it back to the battery, thereby improving energy utilization efficiency. 2. Environmental conditions: Temperature: Extreme temperatures (too cold or too hot) will affect the working efficiency of the motor and its control system. Low temperature may cause an increase in the viscosity of the lubricating oil, affecting the operation of mechanical components; high temperature may cause overheating of electronic components and reduce their lifespan. Humidity and waterproofness: The motor design needs to consider the moisture-proof and waterproof capabilities, especially when used in humid or rainy areas, to prevent moisture from entering and causing short circuits or other electrical faults. Altitude: As the altitude increases, the air density decreases, which poses a challenge to motors that rely on air cooling because the heat dissipation efficiency may decrease. 3. Driving habits: Different driving styles will cause the motor to bear different degrees of stress. For example, frequent hard acceleration and emergency braking not only consume more energy but also exacerbate the wear of the motor and other power systems. 4. Motor load: Load change: Depending on factors such as the number of passengers and the weight of luggage, the power required by the motor will also vary. Under full load conditions, the motor needs a greater torque to start and climb slopes. Road conditions: A flat road requires less of the motor compared to a rough or sloped road. When climbing a slope, the motor needs to generate a greater traction force, which requires a higher current input and thus increases the working load of the motor. 5. Maintenance status: Regular inspection and maintenance can ensure that the motor is in the best working condition. This includes but is not limited to cleaning the outside of the motor, checking whether the wire connections are tight, and lubricating necessary mechanical components, etc.

[0052] Exemplarily, if there are three operating conditions, namely operating condition 1, operating condition 2, and operating condition 3, within the target time period, it is necessary to respectively determine the voltage data, frequency operating data, and temperature operating data corresponding to operating condition 1, the voltage data, frequency operating data, and temperature operating data corresponding to operating condition 2, and the voltage data, frequency operating data, and temperature operating data corresponding to operating condition 3; the prototype data of the three conditions constitute the operating dataset.

[0053] Optionally, when performing "respectively determining the target operating data that meets the predetermined conditions from multiple operating datasets", the method of this embodiment may specifically include: selecting the maximum voltage operating data, maximum frequency operating data, and maximum temperature operating data from the voltage operating dataset, frequency operating dataset, and temperature operating dataset; determining them as the target voltage data, target frequency data, and target temperature data corresponding to this operating condition.

[0054] Exemplarily, based on the example in step 202, it is necessary to select the maximum voltage operating data 1, maximum frequency operating data, and maximum temperature operating data from the voltage operating dataset, frequency operating dataset, and temperature operating dataset collected within the target time period as the target voltage data, target frequency data, and target temperature data.

[0055] Optionally, when performing "inputting the target operating data into the prediction model and predicting the target insulation aging life value corresponding to the target time period based on the correlation between multiple operating indicators and the insulation aging life value of the vehicle motor" in the prediction model, the method of this embodiment may specifically include: respectively inputting the target voltage data, target frequency data, and target temperature data corresponding to each operating condition into the prediction model; predicting the insulation aging life values of all operating conditions within the target time period based on the correlation between the voltage index, frequency index, temperature index and the insulation aging life value of the vehicle motor in the prediction model; performing a superposition process on the insulation aging life values of all operating conditions to obtain the target insulation aging life value.

[0056] In the embodiment of the present application, the correlation between multiple operating indicators and the insulation aging life value of the vehicle motor in the prediction model can be specifically represented by Formula Two, and Formula Two is specifically as follows: L(U, f, T)=C*U -n *T -m / f (Formula Two) In Formula Two, L(U, f, T) represents the insulation system aging life, which is mainly related to the voltage, frequency, and temperature borne by the insulation system; U represents the voltage borne by the insulation system, with the unit of V; f represents the voltage frequency borne by the insulation system, with the unit of Hz; T represents the temperature borne by the insulation system, with the unit of °C; C, n, m represent coefficients related to the insulation material and test parameters.

[0057] Exemplarily, based on the above embodiments, the embodiments of the present application need to input the maximum voltage operation data, the maximum frequency operation data, and the maximum temperature operation data into the prediction model, and predict the insulation aging life value L1 corresponding to the target time period 1 based on the correlation shown in Formula 2.

[0058] Optionally, when performing "generating the overall insulation aging life value corresponding to the vehicle motor based on the target insulation aging life value", the method of this embodiment may specifically include: determining the insulation aging life value to be updated before the target time period of the vehicle motor; and determining the sum of the insulation aging life value to be updated and the target insulation aging life value as the overall insulation aging life value of the vehicle motor in the target time period.

[0059] Exemplarily, based on the above example, the present application also needs to determine the insulation aging life value to be updated before the target time period of the vehicle motor, for example, L (before the target time period), and obtain the overall insulation aging life value of the vehicle motor in the target time period based on the sum of L (before the target time period) and L1 (target time period).

[0060] Optionally, after performing "generating the overall insulation aging life value corresponding to the vehicle motor based on the target insulation aging life value", the method of this embodiment further includes: comparing the overall insulation aging life value with the insulation aging life threshold; and generating an alarm message for the vehicle motor when it is determined that the overall insulation aging life value is greater than or equal to the insulation aging life threshold.

[0061] Wherein, the insulation aging life threshold is determined based on the standard insulation aging life value predicted by the prediction model for the standard voltage, standard frequency, and standard index of the vehicle motor.

[0062] In the embodiments of the present application, the insulation aging threshold may be the product of the standard insulation aging life value predicted by the prediction model for the standard voltage, standard frequency, and standard temperature of the vehicle motor and a predetermined insulation aging coefficient, where the predetermined insulation aging coefficient can be set according to different vehicle motors or different usage requirements.

[0063] It should be noted that, in the embodiments of the present application, the smaller the target time period, the more accurate the cumulative measured life, and the more accurate the prediction of the insulation system aging life.

[0064] It should be noted that the aging life of the insulation system is related to the safe operation of the motor. At present, the online monitoring of the aging life of the insulation system has been realized in the industrial motor and transformer industries. For example, by online monitoring the partial discharge of the insulation system to predict the degree of insulation aging, it plays a role in preventing failures and improves the operation safety of motors and transformers. However, in the drive motors of new energy vehicles, there is currently no example of online monitoring of the aging life of the insulation system. Therefore, this application studies a real-time monitoring method for the aging life of the insulation system, aiming to give an alarm reminder before the insulation system fails and avoid insulation failure during the vehicle operation. Since everyone's driving habits are different, the formed driving road spectrum and voltage distribution spectrum are different, and the impact on the aging life of the insulation system is also different. Therefore, for the same insulation system design, the insulation system life caused by different driving habits is also different, which is manifested in different driving mileage of the vehicle. At present, in the industry, to evaluate whether the aging life of the insulation system meets the design requirements, it is evaluated according to a typical road spectrum and voltage distribution spectrum, as long as the customer's mileage design requirements are met. This method does not make personalized life evaluations according to different users' driving habits and cannot real-time predict the actual aging life after exceeding the design mileage. The purpose of the embodiments of this application is to establish an aging life model of the insulation system, real-time calculate the operation life of the insulation system corresponding to different driving habits, and establish an alarm mechanism to remind users before insulation failure and avoid safety accidents caused by insulation failure.

[0065] Compared with the current existing technologies, in this embodiment, by collecting the operation data of multiple indicators corresponding to the vehicle motor within a target time period to form multiple operation data sets, the target operation data meeting the predetermined conditions are respectively determined from the multiple operation data sets; the target operation data is input into the prediction model, and based on the correlation between multiple operation indicators and the insulation aging life value of the vehicle motor in the prediction model, the target insulation aging life value corresponding to the target time period is predicted; based on the target insulation aging life value, the overall insulation aging life value corresponding to the vehicle motor is generated, which can associate the motor operation indicators with the aging life of the insulation system, and realize the cumulative calculation of the aging life through the data corresponding to the indicators, so that this embodiment avoids the situation of inaccurate prediction of the insulation aging degree caused by the difficulty of local discharge monitoring. Moreover, in this embodiment, the trained prediction model is used to make a prediction based on the correlation between multiple operation indicators and the insulation aging life value of the vehicle motor, and the insulation aging life value can be accurately predicted; in addition, the index parameters used in this embodiment are relatively conventional and easy to collect, making the prediction method of this embodiment simple and convenient and improving the operability of this embodiment.

[0066] To illustrate the specific implementation process of this embodiment, the following specific application examples are given, such as Figure 4 shown, but not limited thereto: Step 1: Establish the voltage-frequency-thermal combined aging life model of the designed insulation system through static electrical aging life tests: L(U, f, T) = C * U -n * T -m / f, where: L(U, f, T) represents the aging life of the insulation system, which is mainly related to the voltage, frequency, and temperature borne by the insulation system; U represents the voltage borne by the insulation system, with the unit of V; f represents the voltage frequency borne by the insulation system, with the unit of Hz; T represents the temperature borne by the insulation system, with the unit of °C; C, n, and m represent coefficients related to the insulation material and test parameters.

[0067] Step 2: Obtain the electrical aging life L0 of the insulation system at the standard voltage U0, frequency f0, and temperature T0 through static electrical aging life tests.

[0068] Step 3: The line voltages U 11 、U 12 …… U 1m , frequencies f 11 、f 12 ……f 1m , and winding temperatures T 11 、T 12 ……T 1m during a certain unit time period t when the motor is running are collected in real time by the control system, and their maximum values U 1max 、f 1max 、T 1max are taken. Through the combined aging model established in Step 1, the insulation aging life in this unit operation time t is converted to the insulation aging life L 01 at the standard voltage U0, frequency f0, and temperature T0, and the conversion formula is as shown in Formula 3 below: (Formula 3) It should be noted that the purpose of the conversion is to convert the operation time under different voltages, temperatures, and frequencies into the insulation aging life at the standard voltage U0, frequency f0, and temperature T0, so as to compare with the aging life L0 standard of the insulation system under standard parameters and establish the standard of the automatic alarm mechanism.

[0069] Similarly, the conversion of the insulation system aging life under different working conditions within any unit time period during driving can be realized, such as L 02 、L 03 ……L 0n . The control system automatically calculates the cumulative aging life of the insulation system L = L 01 + L 02 + L 03 +……+ L 0n . When L = 0.9 * L0, the system automatically alarms to indicate that the life of the motor insulation system has reached the limit.

[0070] It should be noted that the smaller the unit time period t is, the more accurate the cumulative measured life is, and the more accurate the prediction of the insulation system aging life is.

[0071] Compared with the current existing technologies, in this embodiment, by collecting the operation data of multiple indicators corresponding to the vehicle motor within the target time period to form multiple operation data sets, the target operation data that meets the predetermined conditions is respectively determined from the multiple operation data sets; the target operation data is input into the prediction model, and based on the correlation between the multiple operation indicators and the insulation aging life value of the vehicle motor in the prediction model, the target insulation aging life value corresponding to the target time period is predicted; based on the target insulation aging life value, the overall insulation aging life value corresponding to the vehicle motor is generated, which can associate the motor operation indicators with the insulation system aging life, and realize the cumulative calculation of the aging life through the data corresponding to the indicators, so that this embodiment avoids the inaccurate prediction of the insulation aging degree caused by the difficulty of monitoring the basic discharge situation, and in this embodiment, the trained prediction model is used to predict based on the correlation between the multiple operation indicators and the insulation aging life value of the vehicle motor, and the insulation aging life value can be accurately predicted; in addition, the index parameters used in this embodiment are relatively conventional and easy to collect, so that the prediction method of this embodiment is simple and convenient, and the operability of this embodiment is improved.

[0072] Further, as Figure 1 a specific implementation of the method shown, this embodiment provides a prediction device based on a motor, as Figure 5 shown. The device includes: a collection module 31, a determination module 32, a prediction module 33, and a generation module 34.

[0073] The collection module 31 is configured to collect the operation data of multiple indicators corresponding to the vehicle motor within the target time period to form multiple operation data sets; The determination module 32 is configured to respectively determine the target operation data that meets the predetermined conditions from the multiple operation data sets; The prediction module 33 is configured to input the target operation data into the prediction model, and based on the correlation between the multiple operation indicators and the insulation aging life value of the vehicle motor in the prediction model, predict the target insulation aging life value corresponding to the target time period; The generation module 34 is configured to generate the overall insulation aging life value corresponding to the vehicle motor based on the target insulation aging life value.

[0074] In some examples of this embodiment, the multiple metrics include a voltage metric, a frequency metric, and a temperature metric; correspondingly, the acquisition module 31 is specifically configured to collect in real time the DC bus voltage data, DC bus current data, line voltage frequency data of the vehicle motor, and the winding temperature data of a preset winding position in the vehicle motor; input the DC bus voltage and the DC bus current into a preset voltage model to determine the line voltage data of the vehicle motor during operation, and use the line voltage data under each operating condition within the target time period as the voltage operation data of the vehicle motor and form a voltage operation data set; use the line voltage frequency data under each operating condition within the target time period as the frequency operation data of the vehicle motor and form a frequency operation data set; input the winding temperature data into a preset temperature model to determine the target winding temperature data of the target winding position of the vehicle motor during operation, and use the target winding temperature data under each operating condition within the target time period as the temperature operation data of the vehicle motor and form a temperature operation data set, where the target winding position is the position in the vehicle motor with the highest insulation temperature tolerance.

[0075] In some examples of this embodiment, the determination module 32 is specifically configured to select the maximum voltage operation data, maximum frequency operation data, and maximum temperature operation data from the voltage operation data set, the frequency operation data set, and the temperature operation data set; and determine the maximum voltage operation data, the maximum frequency operation data, and the maximum temperature operation data as the target voltage data, target frequency data, and target temperature data respectively.

[0076] In some examples of this embodiment, the prediction module 33 is specifically configured to input the target voltage data, target frequency data, and target temperature data corresponding to each operating condition into the prediction model respectively; based on the correlation relationship between the voltage metric, frequency metric, temperature metric and the insulation aging life value of the vehicle motor in the prediction model, predict the insulation aging life values of all operating conditions within the target time period; and perform a superposition process on the insulation aging life values of all operating conditions to obtain the target insulation aging life value.

[0077] In some examples of this embodiment, the generation module 34 is specifically configured to determine the insulation aging life value to be updated of the vehicle motor before the target time period; and determine the sum of the insulation aging life value to be updated and the target insulation aging life value as the overall insulation aging life value of the vehicle motor during the target time period.

[0078] In some examples of this embodiment, the generation module 34 is further specifically configured to compare the overall insulation aging life value with an insulation aging life threshold, where the insulation aging life threshold is determined based on a standard insulation aging life value predicted by a prediction model for the standard voltage, standard frequency, and standard indicators of the vehicle motor. In the case where it is determined that the overall insulation aging life value is greater than or equal to the insulation aging life threshold, alarm information for the vehicle motor is generated.

[0079] Correspondingly, this embodiment also provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the above-mentioned method as Figure 1 shown is implemented.

[0080] Based on such an understanding, the technical solution of this application can be embodied in the form of a software product. The software product can be stored in a non-volatile storage medium (which can be a CD-ROM, a USB flash drive, a mobile hard disk, etc.), and includes several instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods in various implementation scenarios of this application.

[0081] As Figure 6 shown is a schematic hardware structure diagram of an electronic device according to the present invention, including: At least one processor 401; and, A memory 402 communicatively connected to at least one of the processors 401; wherein, The memory 402 stores instructions executable by at least one of the processors. The instructions are executed by at least one of the processors so that at least one of the processors can execute the prediction method based on the motor as described above.

[0082] Figure 6 Taking one processor 401 as an example.

[0083] The electronic device may further include: an input device 403 and a display device 404.

[0084] The processor 401, the memory 402, the input device 403, and the display device 404 may be connected through a bus or other means, Figure 6 Taking connection through a bus as an example.

[0085] The memory 402, as a non-volatile computer-readable storage medium, can be used to store non-volatile software programs, non-volatile computer-executable programs, and modules, such as program instructions / modules corresponding to the prediction method based on the motor in the embodiments of this application. For example, Figure 1The method flow shown. The processor 401 executes various functional applications and data processing by running non-volatile software programs, instructions, and modules stored in the memory 402, that is, implements the motor-based prediction method in the above embodiments.

[0086] The memory 402 may include a program storage area and a data storage area. Among them, the program storage area can store an operating system and application programs required for at least one function; the data storage area can store data created according to the use of the motor-based prediction method, etc. In addition, the memory 402 may include high-speed random access memory, and may also include non-volatile memory, such as at least one disk storage device, a flash memory device, or other non-volatile solid-state storage devices. In some embodiments, the memory 402 may optionally include a memory remotely provided with respect to the processor 401, and these remote memories can be connected to the device executing the motor-based prediction method through a network. Examples of the above network include but are not limited to the Internet, an enterprise intranet, a local area network, a mobile communication network, and combinations thereof.

[0087] The input device 403 can receive input user clicks and generate signal inputs related to user settings and function controls of the motor-based prediction method. The display device 404 may include a display screen and other display devices.

[0088] When the one or more modules are stored in the memory 402 and run by the one or more processors 401, they execute the motor-based prediction method in any of the above method embodiments.

[0089] Optionally, the above physical device may further include a user interface, a network interface, a camera, a radio frequency (RF) circuit, sensors, an audio circuit, a WI-FI module, and so on. The user interface may include a display screen (Display), an input unit such as a keyboard (Keyboard), etc. Optionally, the user interface may further include a USB interface, a card reader interface, etc. The network interface may optionally include a standard wired interface, a wireless interface (such as a WI-FI interface), etc.

[0090] Those skilled in the art can understand that the above physical device structure provided in this embodiment does not constitute a limitation on the physical device, and may include more or fewer components, or combine certain components, or have different component arrangements.

[0091] The storage medium may further include an operating system and a network communication module. The operating system is a program that manages the hardware and software resources of the above physical device and supports the operation of information processing programs and other software and / or programs. The network communication module is used to implement communication between components inside the storage medium, as well as communication between other hardware and software in the information processing physical device.

[0092] Through the description of the above embodiments, those skilled in the art can clearly understand that the present application can be implemented by means of software plus a necessary general hardware platform, or can also be implemented by hardware. By applying the solution of this embodiment, compared with the current existing technologies, in this embodiment, the operation data of multiple indicators corresponding to the vehicle motor in a target time period are collected to form multiple operation data sets, and target operation data meeting a predetermined condition are respectively determined from the multiple operation data sets; the target operation data is input into a prediction model, and based on the correlation relationship between multiple operation indicators and the insulation aging life value of the vehicle motor in the prediction model, the target insulation aging life value corresponding to the target time period is predicted; based on the target insulation aging life value, an overall insulation aging life value corresponding to the vehicle motor is generated, which can associate the motor operation indicators with the insulation system aging life, and the aging life cumulative calculation is realized through the data corresponding to the indicators, so that this embodiment avoids the situation that the prediction of the insulation aging degree is inaccurate due to the difficulty in monitoring the partial discharge situation. Moreover, in this embodiment, the trained prediction model is used to make a prediction based on the correlation relationship between multiple operation indicators and the insulation aging life value of the vehicle motor, and the insulation aging life value can be accurately predicted; in addition, the index parameters used in this embodiment are relatively conventional and easy to collect, so that the prediction method of this embodiment is simple and convenient, and the operability of this embodiment is improved. It should be noted that in this article, relational terms such as "first" and "second" are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the term "comprising", "including" or any other variant thereof is intended to cover a non-exclusive inclusion, so that a process, method, article or device including a series of elements not only includes those elements, but also includes other elements not expressly listed, or further includes elements inherent to such process, method, article or device. Without further limitation, an element defined by the statement "including one..." does not exclude the existence of additional identical elements in the process, method, article or device including the element.

[0093] The above are only specific embodiments of the present application, enabling those skilled in the art to understand or implement 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 described herein, but will conform to the widest scope consistent with the principles and novel features claimed herein.

Claims

1. A prediction method based on an electric machine, characterized in that Including: Collecting operation data of multiple indicators corresponding to a vehicle motor within a target time period to form multiple operation data sets; Respectively determining target operation data that meets predetermined conditions from the multiple operation data sets; Inputting the target operation data into a prediction model, and predicting a target insulation aging life value corresponding to the target time period in the prediction model based on the correlation relationship between multiple indicators and the insulation aging life value of the vehicle motor, where the prediction model is trained based on historical operation data and historical insulation aging life values corresponding to multiple indicators; Generating an overall insulation aging life value corresponding to the vehicle motor based on the target insulation aging life value.

2. The method according to claim 1, wherein The multiple indicators include a voltage indicator, a frequency indicator, and a temperature indicator; The collecting operation data of multiple indicators corresponding to a vehicle motor within a target time period to form multiple operation data sets includes: Real-time collecting the DC bus voltage data, DC bus current data, line voltage frequency data of the vehicle motor, and the winding temperature data at a preset winding position in the vehicle motor; Inputting the DC bus voltage and the DC bus current into a preset voltage model to determine the line voltage data of the vehicle motor during operation, and taking the line voltage data under each operation condition within the target time period as the voltage operation data of the vehicle motor and forming a voltage operation data set; Taking the line voltage frequency data under each operation condition within the target time period as the frequency operation data of the vehicle motor and forming a frequency operation data set; Inputting the winding temperature data into a preset temperature model to determine the target winding temperature data at a target winding position of the vehicle motor during operation, and taking the target winding temperature data under each operation condition within the target time period as the temperature operation data of the vehicle motor and forming a temperature operation data set, where the target winding position is the position in the vehicle motor where the insulation bears the highest temperature.

3. The method according to claim 2, wherein The respectively determining target operation data that meets predetermined conditions from the multiple operation data sets includes: Selecting the maximum voltage operation data, maximum frequency operation data, and maximum temperature operation data from the voltage operation data set, the frequency operation data set, and the temperature operation data set; Respectively determining the maximum voltage operation data, the maximum frequency operation data, and the maximum temperature operation data as target voltage data, target frequency data, and target temperature data.

4. The method according to claim 3, wherein The inputting the target operation data into a prediction model, and predicting a target insulation aging life value corresponding to the target time period in the prediction model based on the correlation relationship between the multiple indicators and the insulation aging life value of the vehicle motor includes: Respectively inputting the target voltage data, target frequency data, and target temperature data corresponding to each operation condition into the prediction model; In the prediction model, predicting the insulation aging life values of all operation conditions within the target time period based on the correlation relationship between the voltage indicator, the frequency indicator, the temperature indicator, and the insulation aging life value of the vehicle motor. Superpose the insulation aging life values of all the operating conditions to obtain the target insulation aging life value.

5. The method according to claim 4, wherein Generating the overall insulation aging life value corresponding to the vehicle motor based on the target insulation aging life value includes: Determine the insulation aging life value to be updated for the vehicle motor before the target time period; Determine the sum of the insulation aging life value to be updated and the target insulation aging life value as the overall insulation aging life value of the vehicle motor in the target time period.

6. The method according to claim 5, wherein After generating the overall insulation aging life value corresponding to the vehicle motor based on the target insulation aging life value, the method further includes: Compare the overall insulation aging life value with an insulation aging life threshold, which is determined based on a standard insulation aging life value predicted by a prediction model for the standard voltage, standard frequency, and standard indicators of the vehicle motor; Generate an alarm message for the vehicle motor when it is determined that the overall insulation aging life value is greater than or equal to the insulation aging life threshold.

7. A prediction device based on a motor, characterized in that, Includes: An acquisition module configured to acquire the operation data of multiple indicators corresponding to the vehicle motor in a target time period to form multiple operation data sets; A determination module configured to respectively determine target operation data that meets a predetermined condition from the multiple operation data sets; A prediction module configured to input the target operation data into a prediction model, and predict the target insulation aging life value corresponding to the target time period based on the correlation between multiple indicators and the insulation aging life value of the vehicle motor in the prediction model. The prediction model is trained based on the historical operation data and historical insulation aging life values corresponding to multiple indicators; A generation module configured to generate the overall insulation aging life value corresponding to the vehicle motor based on the target insulation aging life value.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that, The computer program, when executed by a processor, implements the method according to any one of claims 1 to 6.

9. An electronic device, comprising a storage medium, a processor, and a computer program stored on the storage medium and executable on the processor, characterized in that, The processor, when executing the computer program, implements the method according to any one of claims 1 to 6.

10. A computer program product, on which a computer program is stored, characterized in that, The computer program product, when executed by a processor, implements the method according to any one of claims 1 to 6.

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