Cooling regulation and control method, system and framework of motor bearing
By performing model prediction and temperature trend analysis on motor bearing data, a cooling control strategy is generated to dynamically adjust the oil flow rate, solving the problem of insufficient dynamic adjustment capability of oil-cooled motor bearings and achieving precise cooling and extended lifespan.
Patent Information
- Application Number
- CN202511664627.1
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-11-13
- Publication Date
- 2026-02-10
AI Technical Summary
In the existing technology, the cooling method of oil-cooled motor bearings is passive, which cannot adjust the cooling strategy in advance according to the temperature trend, resulting in the bearing life being less than expected and insufficient dynamic adjustment capability of cooling.
By acquiring motor bearing data, performing model predictions, dynamically predicting temperature change trends, generating cooling control strategies based on vehicle operating parameters, and adjusting oil flow to achieve precise cooling.
It achieves precise optimization of the cooling oil volume for motor bearings, improves the dynamic adjustment capability of cooling, and enhances the service life and performance of the bearings.
Smart Images
Figure CN121497737A_ABST
Abstract
Description
Technical Field
[0001] This application relates to the field of bearing cooling, specifically to a cooling control method, system, and architecture for motor bearings. Background Technology
[0002] To meet the power performance and range requirements of vehicles, motors need to operate at high power and high speeds. This causes the motor bearings to generate a large amount of heat during operation. If not effectively cooled, the bearing temperature will become too high, affecting its performance and lifespan.
[0003] In the existing technology, the bearing cooling method of oil-cooled motors is mostly passive cooling, which cannot adjust the cooling strategy in advance according to the temperature trend and cannot adapt to complex and ever-changing working conditions, resulting in the bearing life being less than expected and causing the problem of insufficient dynamic adjustment capability of oil-cooled motor bearing cooling. Summary of the Invention
[0004] In view of this, this application provides a cooling control method, system, and architecture for motor bearings, which enables the cooling oil volume of motor bearings to be predicted according to different vehicle operating parameters, thereby achieving the goal of accurately controlling the cooling oil volume of motor bearings. This solves the technical problem of insufficient dynamic adjustment capability of oil-cooled motor bearings, thereby achieving the technical effect of accurately optimizing the cooling oil volume of motor bearings and improving the dynamic adjustment capability of motor bearing cooling.
[0005] To achieve the above objectives, this application provides the following technical solution: acquiring motor bearing data and performing model prediction on the motor bearing data to obtain the temperature change trend of the motor bearing over a preset time period; if the temperature change trend of the motor bearing over the preset time period meets preset cooling parameters, generating a corresponding cooling control strategy based on vehicle operating parameters; the vehicle operating parameters include one or more of the following: motor bearing operating parameters, motor operating parameters, and vehicle driving operating parameters; adjusting the current oil flow rate of the motor bearing according to the cooling control strategy to perform cooling control on the motor bearing.
[0006] In one embodiment of this application, the vehicle operating parameters include the operating parameters of the motor bearing, which include the temperature rise rate of the motor bearing. Based on the vehicle operating parameters, a corresponding cooling control strategy is generated, including: when the temperature rise rate is greater than a preset temperature rise rate threshold, a first cooling control strategy is generated, which is to increase the current oil flow rate of the motor bearing by a first preset value.
[0007] In one embodiment of this application, the vehicle operating parameters include the motor operating parameters, which include the motor vibration energy. Based on the vehicle operating parameters, a corresponding cooling control strategy is generated, including: when the increase in the motor vibration energy within a preset time is greater than a preset energy surge threshold, a second cooling control strategy is generated. The second cooling control strategy is to increase the current oil flow rate of the motor bearing by a second preset value, where the second preset value is less than a first preset value.
[0008] In one embodiment of this application, the vehicle operating parameters include the vehicle driving parameters, which include the vehicle driving gradient. Based on the vehicle operating parameters, a corresponding cooling control strategy is generated, including: when the increase value of the vehicle driving gradient is greater than a preset gradient increase threshold, a third cooling control strategy is generated. The third cooling control strategy is to increase the current oil flow rate of the motor bearing by a third preset value, which is less than a second preset value.
[0009] In one embodiment of this application, before generating the corresponding cooling control strategy, the method includes: if multiple motor bearings are co-cooled, obtaining the priority of the multiple motor bearings; configuring the oil flow rate of the multiple motor bearings based on the priority to generate a cooling control strategy for each motor bearing.
[0010] In one embodiment of this application, before adjusting the current oil flow rate of the motor bearing according to the cooling control strategy, the method includes: obtaining the current remaining oil volume of the motor oil pump; if the current remaining oil volume does not meet the preset flow rate value required by the cooling control strategy, adjusting the preset flow rate value; if the current remaining oil volume meets the preset flow rate value required by the cooling control strategy, adjusting the current oil flow rate of the motor bearing according to the cooling control strategy.
[0011] In one embodiment of this application, model prediction is performed on motor bearing data to obtain the temperature change trend of the motor bearing over a preset time period. This includes: inputting the motor bearing data into a thermal simulation model for training to obtain the thermal scale of the motor bearing at the current moment; and inputting the thermal scale at the current moment into a thermal prediction model for prediction to obtain the temperature change trend of the motor bearing over a preset time period.
[0012] In one embodiment of this application, after cooling the motor bearing according to the cooling control strategy, the method includes: predicting the temperature of the motor bearing after cooling control based on the temperature change trend over a preset time period and the cooling control strategy, to obtain a predicted bearing temperature; actually collecting the temperature of the motor bearing after cooling control to obtain an actual bearing temperature; if the absolute value of the difference between the predicted bearing temperature and the actual bearing temperature is less than a preset value, confirming that the confidence evaluation result of the thermal prediction model meets the preset standard; if the absolute value of the difference between the predicted bearing temperature and the actual bearing temperature is not less than the preset value, automatically adjusting the thermal prediction model, and verifying and optimizing the adjusted model.
[0013] As a second aspect of this application, this application also provides a cooling control system for an electric motor bearing. The system includes: a model prediction module for acquiring electric motor bearing data and performing model prediction on the data to obtain the temperature change trend of the electric motor bearing over a preset time period; a strategy generation module for generating a corresponding cooling control strategy based on vehicle operating parameters if the temperature change trend of the electric motor bearing over the preset time period meets preset cooling parameters; the vehicle operating parameters include one or more of the following: operating parameters of the electric motor bearing, operating parameters of the motor, and operating parameters of vehicle operation; and a cooling control module for adjusting the current oil flow rate of the electric motor bearing according to the cooling control strategy to regulate the cooling of the electric motor bearing.
[0014] As a third aspect of this application, this application also provides a cooling control architecture for motor bearings, which includes: a local layer, comprising sensors and a cooling system, wherein the sensors can collect motor bearing data, and the cooling system can adjust the current oil flow rate of the motor bearing based on a cooling control strategy to control the cooling of the motor bearing; a cloud layer, comprising historical data and model training, wherein the model includes a thermal simulation model and a thermal prediction model, and the model training can output the temperature change trend of the motor bearing over a preset time period; the local layer and the cloud layer transmit data bidirectionally with encryption through a transmission protocol.
[0015] The cooling control method for motor bearings provided in this application acquires motor bearing data and performs model prediction on the motor bearing data to obtain the temperature change trend of the motor bearing over a preset time period. If the temperature change trend of the motor bearing over the preset time period meets preset cooling parameters, a corresponding cooling control strategy is generated based on vehicle operating parameters. The vehicle operating parameters include one or more of the following: motor bearing operating parameters, motor operating parameters, and vehicle driving operating parameters. The current oil flow rate of the motor bearing is adjusted according to the cooling control strategy to regulate the cooling of the motor bearing. It is noteworthy that by dynamically predicting the temperature change trend of the motor bearing over a future period based on motor bearing data, and then generating a corresponding cooling control strategy based on vehicle operating parameters when the temperature change trend meets preset cooling parameters, the current oil flow rate of the motor bearing is adjusted to regulate the cooling of the motor bearing. This achieves the goal of accurately controlling the amount of cooling oil in the motor bearing based on the predicted cooling demand according to different vehicle operating parameters, thus solving the technical problem of insufficient dynamic adjustment capability of oil-cooled motor bearings. This achieves the technical effect of accurately optimizing the amount of cooling oil in the motor bearing, thereby improving the dynamic adjustment capability of motor bearing cooling. Attached Figure Description
[0016] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of this application. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.
[0017] Figure 1 The diagram shows a flowchart of a cooling control method for motor bearings provided in an embodiment of this application.
[0018] Figure 2 The diagram shows a flowchart of a cooling strategy for a motor bearing provided in an embodiment of this application.
[0019] Figure 3 The diagram shown is a flowchart of the verification process for a thermal prediction model provided in an embodiment of this application.
[0020] Figure 4 The diagram shown is a schematic diagram of a digital twin structure model provided in an embodiment of this application.
[0021] Figure 5 The diagram shown is a schematic diagram of a motor bearing cooling control system provided in an embodiment of this application.
[0022] Figure 6 The diagram shown is a schematic diagram of a cooling control architecture for a motor bearing provided in an embodiment of this application. Detailed Implementation
[0023] To meet the power performance and range requirements of vehicles, motors need to operate at high power and high speeds. This causes the motor bearings to generate a large amount of heat during operation. If not effectively cooled, the bearing temperature will become too high, affecting its performance and service life. Current oil-cooled motor bearing cooling methods are mostly passive, unable to adjust the cooling strategy in advance according to temperature trends, and unable to adapt to complex and changing operating conditions. This results in shorter-than-expected bearing life, highlighting the insufficient dynamic adjustment capability of oil-cooled motor bearing cooling.
[0024] The inventors of this application, through research, propose a method to dynamically predict the temperature change trend of a motor bearing over a future period based on motor bearing data. When the temperature change trend meets preset cooling parameters, a corresponding cooling control strategy is generated based on vehicle operating parameters to adjust the current oil flow rate of the motor bearing. This achieves the goal of accurately controlling the amount of cooling oil in the motor bearing by predicting cooling needs based on different vehicle operating parameters. This solves the technical problem of insufficient dynamic adjustment capability of oil-cooled motor bearings, thereby achieving precise optimization of the cooling oil quantity and improving the dynamic adjustment capability of motor bearing cooling.
[0025] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.
[0026] As a first aspect of this application, this application provides a method for cooling regulation of motor bearings. Figure 1 The diagram shown is a flowchart of a cooling control method for motor bearings provided in an embodiment of this application. Figure 1 As shown, the method includes the following steps: S101, acquire motor bearing data, and perform model prediction on the motor bearing data to obtain the temperature change trend of the motor bearing over a preset time period. Specifically, the motor bearing data includes, but is not limited to, the temperature of the motor bearing, the vibration rate of the motor, and the flow rate of cooling oil flowing through the motor bearing.
[0027] Specifically, the temperature of the motor bearing can be collected using a temperature sensor, the vibration energy of the motor can be collected using a vibration sensor, and the flow rate of the cooling oil flowing through the motor bearing can be collected using a flow sensor.
[0028] Generally, in order to adjust the cooling strategy of the motor bearing in advance according to the temperature change of the motor bearing, it is necessary to collect motor bearing data related to the temperature change of the motor bearing, such as the temperature of the motor bearing, the vibration energy of the motor, and the flow rate of the cooling oil flowing through the motor bearing.
[0029] The aforementioned preset time period can be used to represent a pre-defined future time period, such as 5 minutes or 10 minutes in the future. There is no specific limitation on the preset time period, which can be adjusted according to the actual situation.
[0030] In one optional embodiment, after collecting motor bearing data through sensors, the collected motor bearing data can be used for model training, so that the model outputs the temperature change trend of the motor bearing over a preset time period. This allows for the determination of the temperature trend of the motor bearing over a future time period, i.e., whether the temperature change trend meets the cooling requirements of the motor bearing.
[0031] Specifically, when training a model using the collected motor bearing data, machine learning or deep learning algorithms, such as linear regression, support vector machines, and neural networks, can be used to train the model based on historical data. This allows the model to learn the complex nonlinear relationship between input parameters and temperature changes, thereby enabling the output of the motor bearing temperature change trend by collecting motor bearing data in real time and inputting it into the prediction model.
[0032] S102, if the temperature change trend of the motor bearing within a preset time period meets the preset cooling parameters, a corresponding cooling control strategy is generated based on the vehicle operating parameters; the vehicle operating parameters include one or more of the following: the operating parameters of the motor bearing, the operating parameters of the motor, and the operating parameters of the vehicle driving. Specifically, the aforementioned preset cooling parameters can be used to represent the data indicating the pre-set temperature of the motor bearing when it reaches a certain level of cooling. For example, it can be 80℃ or 95℃. There is no specific limitation on the preset cooling parameters here, and they can be adjusted according to the actual situation.
[0033] The aforementioned cooling control strategy specifically controls the cooling of the motor bearing by adjusting the current oil flow rate. Generally, the current oil flow rate of the motor bearing can be adjusted in real time based on vehicle operating parameters.
[0034] Generally, in controlling the current oil flow rate of the motor bearing, correlation analysis can be performed based on vehicle operating parameters. For example, the aforementioned vehicle operating parameters include one or more of the following: motor bearing operating parameters, motor operating parameters, and vehicle driving operating parameters. Specifically, the motor bearing operating parameters can be the motor bearing temperature, the motor operating parameters can be the motor's vibration energy, and the vehicle driving operating parameters can be the angle at which the vehicle climbs a slope during driving, etc.
[0035] In one optional embodiment, if the temperature rise rate of the motor bearing is detected to be too high during vehicle operation, the current oil flow rate of the motor bearing needs to be increased to cool the motor bearing; if the vibration energy of the motor is detected to increase suddenly in a short period of time, and the temperature of the motor bearing rises, the current oil flow rate of the motor bearing needs to be increased to cool the motor bearing; if the angle of the incline increases during vehicle driving, and the temperature of the motor bearing rises, the current oil flow rate of the motor bearing needs to be increased to cool the motor bearing, etc.
[0036] S103, adjust the current oil flow rate of the motor bearing according to the cooling control strategy to control the cooling of the motor bearing.
[0037] Specifically, after generating the corresponding cooling control strategy based on the vehicle operating parameters, the current oil flow rate can be adjusted based on the adjustment value of the current oil flow rate of the motor bearing required by the cooling control strategy, thereby achieving cooling control of the motor bearing during the adjustment process.
[0038] Generally, compared to traditional motor bearing cooling methods such as air cooling and water cooling, oil cooling utilizes lubricating oil, which has a higher heat capacity and thermal conductivity, effectively absorbing and conducting the heat generated by the bearing. Simultaneously, the lubricating oil also acts as a lubricant, reducing bearing friction and further decreasing heat generation, achieving the dual functions of lubrication and cooling. Furthermore, oil cooling systems can be flexibly designed to suit different motor structures and operating conditions. For example, dedicated oil circuits can be installed inside the motor, or oil spray nozzles can be used to precisely spray cooling oil onto the bearing, improving cooling efficiency. In addition, oil cooling systems can be combined with other motor cooling methods (such as water cooling) to form composite cooling systems, further enhancing cooling efficiency. Moreover, oil cooling systems are relatively enclosed, making them less susceptible to external environmental influences such as dust and moisture. This allows oil cooling systems to maintain good performance in various harsh environments, improving the reliability of the motor bearing cooling system.
[0039] The cooling control method for motor bearings provided in this application acquires motor bearing data and performs model prediction on the motor bearing data to obtain the temperature change trend of the motor bearing over a preset time period. If the temperature change trend of the motor bearing over the preset time period meets preset cooling parameters, a corresponding cooling control strategy is generated based on vehicle operating parameters. The vehicle operating parameters include one or more of the following: motor bearing operating parameters, motor operating parameters, and vehicle driving operating parameters. The current oil flow rate of the motor bearing is adjusted according to the cooling control strategy to regulate the cooling of the motor bearing. It is noteworthy that by dynamically predicting the temperature change trend of the motor bearing over a future period based on motor bearing data, and then generating a corresponding cooling control strategy based on vehicle operating parameters when the temperature change trend meets preset cooling parameters, the current oil flow rate of the motor bearing is adjusted to regulate the cooling of the motor bearing. This achieves the goal of accurately controlling the amount of cooling oil in the motor bearing based on the predicted cooling demand according to different vehicle operating parameters, thus solving the technical problem of insufficient dynamic adjustment capability of oil-cooled motor bearings. This achieves the technical effect of accurately optimizing the amount of cooling oil in the motor bearing, thereby improving the dynamic adjustment capability of motor bearing cooling.
[0040] In one embodiment of this application, the vehicle operating parameters include the operating parameters of the motor bearing, which include the temperature rise rate of the motor bearing. Based on the vehicle operating parameters, a corresponding cooling control strategy is generated, including: when the temperature rise rate is greater than a preset temperature rise rate threshold, a first cooling control strategy is generated, which is to increase the current oil flow rate of the motor bearing by a first preset value.
[0041] Specifically, the aforementioned temperature rise rate can be obtained by subtracting the motor bearing temperature at the current moment from the motor shaft temperature at the next moment, and then dividing the difference by the time difference between the next moment and the current moment.
[0042] The aforementioned preset temperature rise rate threshold can be used to represent the preset temperature rise rate of the motor bearing. For example, it can be 5°C or 6°C. The preset temperature rise rate threshold is not specifically limited here and can be adjusted according to the actual situation.
[0043] The aforementioned first preset value can be used to represent a pre-set first increase value for adjusting the current oil flow rate. For example, it can be 50%. The first preset value is not specifically limited here and can be adjusted according to the actual situation.
[0044] In one optional embodiment, when the vehicle operating parameters include the operating parameters of the motor bearing, and the operating parameters of the motor bearing include the temperature rise rate of the motor bearing, a corresponding cooling control strategy can be determined by the temperature rise rate of the motor bearing. Specifically, when the temperature rise rate of the motor bearing is greater than a preset temperature rise rate threshold, it indicates that the temperature of the motor bearing has risen in a short period of time. A first cooling control strategy can be generated accordingly, and the current oil flow rate of the motor bearing can be increased by a first preset value based on the first cooling control strategy. That is, the flow rate is increased by a first preset value on the basis of the current oil flow rate. In this way, after increasing the current oil flow rate of the motor bearing, the temperature of the motor bearing can be reduced, thereby achieving cooling control of the motor bearing.
[0045] In one embodiment of this application, the vehicle operating parameters include the motor operating parameters, which include the motor vibration energy. Based on the vehicle operating parameters, a corresponding cooling control strategy is generated, including: when the increase in the motor vibration energy within a preset time is greater than a preset energy surge threshold, a second cooling control strategy is generated. The second cooling control strategy is to increase the current oil flow rate of the motor bearing by a second preset value, where the second preset value is less than a first preset value.
[0046] Specifically, the aforementioned preset time can be used to represent the pre-set time for monitoring the vibration energy of the motor. It can be 2 seconds, 3 seconds, etc. There is no specific limitation on the preset time here, and it can be adjusted according to the actual situation.
[0047] The aforementioned preset energy surge threshold can be used to represent the value of a sudden increase in motor vibration energy within a preset time period. It can be 20% or 21%. There is no specific limitation on the preset energy surge threshold here, and it can be adjusted according to the actual situation.
[0048] The aforementioned second preset value can be used to represent a pre-set second increase value for adjusting the current oil flow rate, and the second preset value is less than the first preset value. For example, it can be 30%, but the second preset value is not specifically limited here and can be adjusted according to the actual situation.
[0049] In one optional embodiment, when the vehicle operating parameters include the motor's operating parameters, and the motor's operating parameters include the motor's vibration energy, a corresponding cooling control strategy can be determined through the motor's vibration energy. Specifically, when the increase in the motor's vibration energy within a preset time exceeds a preset energy surge threshold, it indicates a sudden rise in the motor bearing temperature. A second cooling control strategy can then be generated, and based on this strategy, the current oil flow rate of the motor bearing is increased by a second preset value. That is, the flow rate is increased by a second preset value on top of the current flow rate. By increasing the current oil flow rate of the motor bearing, the temperature of the motor bearing can decrease, thereby achieving cooling control of the motor bearing.
[0050] In one embodiment of this application, the vehicle operating parameters include the vehicle driving parameters, which include the vehicle driving gradient. Based on the vehicle operating parameters, a corresponding cooling control strategy is generated, including: when the increase value of the vehicle driving gradient is greater than a preset gradient increase threshold, a third cooling control strategy is generated. The third cooling control strategy is to increase the current oil flow rate of the motor bearing by a third preset value, which is less than a second preset value.
[0051] Specifically, the aforementioned preset gradient rise threshold can be used to represent the preset gradient rise value of the vehicle's driving. For example, it can be 10% or 11%. The preset gradient rise threshold is not specifically limited here and can be adjusted according to the actual situation.
[0052] The aforementioned third preset value can be used to represent a pre-set third increase value for adjusting the current oil flow rate, and the third preset value is less than the second preset value. For example, it can be 20%, but the third preset value is not specifically limited here and can be adjusted according to the actual situation.
[0053] In one optional embodiment, when the vehicle operating parameters include vehicle travel parameters, and the vehicle travel parameters include the vehicle's gradient, a corresponding cooling control strategy can be determined based on the vehicle's gradient. Specifically, when the gradient increase is greater than a preset gradient increase threshold, it indicates that the vehicle is currently climbing. At this time, the increased motor speed causes the motor bearing temperature to rise rapidly. A third cooling control strategy can be generated accordingly, and the current oil flow rate of the motor bearing is increased by a third preset value based on the third cooling control strategy. That is, the flow rate is increased by a third preset value on the basis of the current oil flow rate. After increasing the current oil flow rate of the motor bearing, the temperature of the motor bearing can be reduced, thereby achieving cooling control of the motor bearing.
[0054] It should be noted that the above optional embodiments provide illustrative examples of the vehicle operating parameters, including the operating parameters of the motor bearing, the operating parameters of the motor, and the operating parameters of vehicle travel. Similarly, when the vehicle operating parameters include multiple of the operating parameters of the motor bearing, the operating parameters of the motor, and the operating parameters of vehicle travel, a comprehensive analysis can be performed on the operating parameters of the motor bearing, the operating parameters of the motor, and the operating parameters of vehicle travel, and a corresponding cooling control strategy can be determined based on the comprehensive analysis results.
[0055] For example, if the temperature rise rate of the motor bearing is detected to be greater than the preset temperature rise rate threshold, and the increase value of the vibration energy of the motor within a preset time is greater than the preset energy surge threshold, a fourth cooling control strategy can be generated, and the current oil flow rate of the motor bearing can be increased to a fourth preset value according to the fourth cooling control strategy. If the temperature rise rate of the motor bearing is detected to be greater than the preset temperature rise rate threshold, and the increase value of the slope of the vehicle is greater than the preset slope increase threshold, a fifth cooling control strategy can be generated, and the current oil flow rate of the motor bearing can be increased to a fifth preset value according to the fifth cooling control strategy. If the temperature rise rate of the motor bearing is detected to be greater than the preset temperature rise rate threshold, and the increase in vibration energy of the motor within a preset time is greater than the preset energy surge threshold, and the increase in the slope of the vehicle is greater than the preset slope increase threshold, a sixth cooling control strategy can be generated, and the current oil flow rate of the motor bearing can be increased to a sixth preset value according to the sixth cooling control strategy.
[0056] In one embodiment of this application, before generating the corresponding cooling control strategy, the method includes: if multiple motor bearings are co-cooled, obtaining the priority of the multiple motor bearings; configuring the oil flow rate of the multiple motor bearings based on the priority to generate a cooling control strategy for each motor bearing.
[0057] Specifically, multiple bearings work together during vehicle operation, and inevitably multiple motor bearings need to be cooled together. Therefore, before generating the corresponding cooling control strategy, the priority of multiple motor bearings can be obtained, and the oil flow of multiple motor bearings can be configured based on the priority. That is, the oil flow of the motor bearing with the highest priority is configured first based on the priority ranking result, and then the flow of other motor bearings is configured to generate a cooling control strategy for each motor bearing, thereby realizing orderly cooling control of multiple motor bearings.
[0058] Determining the priority of multiple motor bearings in a vehicle requires comprehensive consideration of several factors, including the severity of the bearing failure, its impact on vehicle operational safety, and the bearing's operating status. For example, regarding the severity of the failure, if the temperature of a particular motor bearing is significantly higher than that of other bearings and approaches or exceeds its maximum permissible temperature (e.g., 95°C for rolling bearings, 80°C for sliding bearings), then that bearing has a higher priority. Vibration sensors monitor bearing vibration; if the vibration rate of a particular bearing is significantly higher than that of other bearings, this may indicate a bearing failure that requires priority handling. If the vehicle's monitoring system issues a bearing failure alarm, especially a high-level alarm (e.g., a Level II alarm), then that bearing has a higher priority. Further details are omitted here.
[0059] In one embodiment of this application, before adjusting the current oil flow rate of the motor bearing according to the cooling control strategy, the method includes: obtaining the current remaining oil volume of the motor oil pump; if the current remaining oil volume does not meet the preset flow rate value required by the cooling control strategy, adjusting the preset flow rate value; if the current remaining oil volume meets the preset flow rate value required by the cooling control strategy, adjusting the current oil flow rate of the motor bearing according to the cooling control strategy.
[0060] Specifically, since the capacity of the motor oil pump is constant, before adjusting the current oil flow rate of the motor bearing according to the cooling control strategy, it is necessary to determine whether the current oil volume of the motor oil pump is sufficient to support the cooling control of the motor bearing. Therefore, the current remaining oil volume of the motor oil pump can be obtained, and it can be determined whether the current remaining oil volume of the motor oil pump meets the preset flow rate value required by the cooling control strategy. For example, vehicle operating parameters, including the operating parameters of the motor bearing, and the operating parameters of the motor bearing, including the temperature rise rate of the motor bearing, can be used as an example. It is necessary to determine whether the current remaining oil volume of the motor oil pump meets the first preset value required by the cooling control strategy. If the current remaining oil volume does not meet the preset flow rate value required by the cooling control strategy, the preset flow rate value is adjusted, that is, the above-mentioned first preset value needs to be reduced. For example, the first preset value can be reduced from 50% to 45%, etc. Conversely, if the current remaining oil volume meets the preset flow rate value required by the cooling control strategy, the current oil flow rate of the motor bearing is adjusted according to the cooling control strategy, that is, the current oil flow rate of the motor bearing is increased by 50% according to the first preset value.
[0061] It is important to note that during the entire cooling control process, the current remaining oil level in the oil pump needs to be fed back in real time. This allows for dynamic adjustment of the flow rate preset value required by the cooling control strategy based on the current remaining oil level in the motor oil pump, ensuring that the overall cooling control process of the motor bearing is not affected.
[0062] Figure 2 The diagram shows a flowchart of a cooling strategy for a motor bearing according to an embodiment of this application. Figure 2 As shown, the cooling strategy is as follows: S201, Input the prediction result; The temperature change trend of the motor bearing over a preset time period is input into the model for judgment.
[0063] S202, determine if there is a cooling requirement; if yes, proceed to S203; otherwise, jump to S201. This means determining whether the temperature change trend of the motor bearing within a preset time period meets the preset cooling parameters.
[0064] S203, Bearing priority determination; S204, main bearing cooling; S205, Determine the threshold; This involves threshold judgment of vehicle operating parameters. Specifically, for example, in S205a, it determines whether the temperature rise rate of the motor bearing is greater than the preset temperature rise rate threshold of 5℃ / min. If so, the current oil flow rate of the motor bearing is increased by a first preset value of 50%. For example, in S205b, it is determined whether the increase in the vibration energy of the motor within a preset time is greater than the preset energy surge threshold of 20%. If so, the current oil flow rate of the motor bearing is increased by a second preset value of 30%. For example, in S205c, it is determined whether the increase in the slope of the vehicle is greater than the preset slope increase threshold of 10%. If so, the current oil flow rate of the motor bearing is increased by the third preset value of 20%.
[0065] S206, Real-time traffic data feedback; This means providing real-time feedback on the current remaining oil level in the oil pump.
[0066] S207, Oil pump remaining capacity confirmed; That is, determine whether the remaining oil volume of the oil pump is sufficient to cool and regulate the remaining motor bearings. If yes, execute S208; otherwise, jump to S205 to adjust the preset flow rate value.
[0067] S208, secondary bearing cooling; S209, real-time traffic data feedback.
[0068] In one embodiment of this application, model prediction is performed on motor bearing data to obtain the temperature change trend of the motor bearing over a preset time period. This includes: inputting the motor bearing data into a thermal simulation model for training to obtain the thermal scale of the motor bearing at the current moment; and inputting the thermal scale at the current moment into a thermal prediction model for prediction to obtain the temperature change trend of the motor bearing over a preset time period.
[0069] Specifically, the aforementioned thermal simulation model is used to simulate the input motor bearing data and output the thermal index of the motor bearing at the current moment.
[0070] The aforementioned thermal prediction model is used to predict the thermal gradient at the current moment and output the temperature change trend of the motor bearing over a preset time period.
[0071] In one optional embodiment, during the process of predicting the temperature change trend of the motor bearing over a preset time period using model prediction based on motor bearing data, the motor bearing data can be input into a thermal simulation model for training. Specifically, the thermal simulation model simulates the input data such as motor bearing temperature, motor vibration energy, and coolant flow rate through the motor bearing to obtain the thermal distribution of the motor bearing at the current moment, specifically, the thermal distribution of the motor bearing within the current 1 minute. During the thermal simulation training of the motor bearing data, historical data can be incorporated into the calculation. That is, by obtaining the correspondence between motor bearing data and thermal distribution in historical data, the thermal distribution of the motor bearing at the current moment can be obtained by calculating the currently input motor bearing data using this correspondence.
[0072] Furthermore, in order to respond to the temperature change trend of the motor bearing in the future, this application can predict the temperature change trend of the motor bearing in a preset time period. That is, after obtaining the thermodynamic index of the motor bearing at the current moment, the thermodynamic index at the current moment can be input into the thermodynamic prediction model for prediction to obtain the temperature change trend of the motor bearing in the preset time period. For example, the temperature change trend of the motor bearing after 60 seconds can be predicted. After obtaining the temperature change trend of the motor bearing in the preset time period, it can be determined whether the motor bearing needs to be cooled down based on the temperature change trend. If so, a corresponding cooling control strategy is generated according to the vehicle operating parameters.
[0073] In one embodiment of this application, after cooling the motor bearing according to the cooling control strategy, the method includes: predicting the temperature of the motor bearing after cooling control based on the temperature change trend over a preset time period and the cooling control strategy, to obtain a predicted bearing temperature; actually collecting the temperature of the motor bearing after cooling control to obtain an actual bearing temperature; if the absolute value of the difference between the predicted bearing temperature and the actual bearing temperature is less than a preset value, confirming that the confidence evaluation result of the thermal prediction model meets the preset standard; if the absolute value of the difference between the predicted bearing temperature and the actual bearing temperature is not less than the preset value, automatically adjusting the thermal prediction model, and verifying and optimizing the adjusted model.
[0074] Specifically, the aforementioned preset value can be used to represent the absolute value of the difference between the preset predicted bearing temperature and the actual bearing temperature. For example, it can be 4℃ or 3℃. The preset value is not specifically limited here and can be adjusted according to the actual situation.
[0075] The aforementioned preset standard can be used to describe the confidence assessment result of the pre-set thermal prediction model. For example, it can be 90% or 91%. The preset standard is not specifically limited here and can be adjusted according to the actual situation.
[0076] In one optional embodiment, to improve the prediction accuracy of the above-mentioned thermal prediction model, a confidence assessment can also be performed on the thermal prediction model. Specifically, after obtaining the temperature change trend of the motor bearing over a preset time period and determining that the motor bearing needs cooling regulation based on the temperature change trend over the preset time period, the temperature of the motor bearing after cooling regulation can be predicted to obtain the predicted bearing temperature. For example, if the temperature change trend of the motor bearing reaches 95°C after 60 seconds, then cooling regulation of the motor bearing is required. That is, by increasing the flow rate of the coolant flowing through the motor bearing, the motor bearing can be cooled down, and the predicted temperature of the motor bearing after cooling regulation can be 89°C. At the same time, the actual bearing temperature after cooling regulation can also be collected to obtain the actual bearing temperature, which can be 87°C. By judging the absolute value of the difference between the predicted bearing temperature and the actual bearing temperature, the confidence assessment result of the thermal prediction model can be obtained. Specifically, if the absolute value of the difference between the predicted bearing temperature and the actual bearing temperature is less than the preset value, the confidence assessment result of the thermal prediction model is confirmed to meet the preset standard. That is, the absolute value of the difference between the predicted bearing temperature of 89℃ and the actual bearing temperature of 87℃ is 2℃, which satisfies the condition that the absolute value of the difference of 2℃ is less than the preset value of 4℃. Therefore, it can be determined that the confidence assessment result of the thermal prediction model meets the preset standard, that is, the confidence assessment result of the thermal prediction model is greater than 90%.
[0077] Conversely, if the absolute value of the difference between the predicted bearing temperature and the actual bearing temperature is not less than the preset value, it indicates that the confidence assessment result of the thermal prediction model does not meet the above preset standard. If the thermal prediction model is continued to be used to predict the temperature change trend of the motor bearing in the future, the prediction result will deviate significantly, affecting the subsequent generation of cooling control strategies and thus reducing the dynamic adjustment capability of the motor bearing cooling. To avoid the above problems, the thermal prediction model needs to be automatically adjusted, and the adjusted model needs to be verified and optimized.
[0078] Specifically, during the automatic adjustment of the thermal prediction model, it can automatically switch to proportional-integral-derivative control, that is, automatically adjust the proportional gain, integral time and derivative time of the original thermal prediction model respectively, so as to obtain the adjusted thermal prediction model. By performing model verification and optimization on the adjusted thermal prediction model, the confidence evaluation result of the adjusted thermal prediction model meets the preset standard.
[0079] Figure 3 The diagram shown is a flowchart illustrating the verification process of a thermal prediction model provided in an embodiment of this application. Figure 3 As shown, the verification process includes the following steps: S301, Data Input; The current thermal distribution is input into the thermal prediction model for prediction.
[0080] S302, Model Startup; This means activating the thermal prediction model.
[0081] S303, Data Preprocessing; This involves preprocessing the input thermal distribution data at the current moment.
[0082] S304, Feature Extraction; That is, to extract features from the thermal distribution at the current moment.
[0083] S305, Model Training; S306, Model Validation; S307, Trend Forecasting; That is, using a thermal prediction model to predict the thermal distribution at the current moment.
[0084] S308, determine if the confidence level assessment is greater than 90%; if yes, output the prediction result; if no, proceed to S309. That is, to determine whether the confidence level assessment result of the thermal prediction model is greater than 90%.
[0085] S309, switch to PID control; This involves automatically adjusting the proportional gain, integral time, and derivative time of the original thermal prediction model.
[0086] S310, Model Validation; The adjusted thermal prediction model is validated. If the validation result is normal, proceed to S302. If the validation result is abnormal, the adjusted thermal prediction model is optimized.
[0087] Figure 4 The diagram shown is a schematic representation of a digital twin structural model provided in an embodiment of this application. Figure 4As shown, the structure is distributed as follows: The physical model includes sensors and a cooling system; the sensors include at least temperature sensors, vibration sensors, and flow sensors; the cooling system can adjust the current oil flow rate of the motor bearing based on a cooling control strategy to regulate the cooling of the motor bearing. Virtual models include 3D models and thermal simulation models; the thermal simulation model is used to simulate the input motor bearing data and output the thermal distribution of the motor bearing at the current moment; the 3D model is used to optimize the thermal simulation model. The data model includes filtering and prediction algorithms. The filtering algorithm is used to preprocess the thermodynamic index of the input motor bearing at the current moment. The prediction algorithm, namely the thermodynamic prediction model mentioned above, is used to predict the thermodynamic index of the input at the current moment and output the temperature change trend of the motor bearing over a preset time period.
[0088] As a second aspect of this application, this application also provides a cooling control system for motor bearings. Figure 5 The diagram shown is a schematic of a cooling control system for a motor bearing provided in an embodiment of this application. Figure 5 As shown, the system includes: The model prediction module 51 is used to acquire motor bearing data and perform model prediction on the motor bearing data to obtain the temperature change trend of the motor bearing over a preset time period. The strategy generation module 52 is used to generate a corresponding cooling control strategy based on vehicle operating parameters if the temperature change trend of the motor bearing within a preset time period meets the preset cooling parameters. The vehicle operating parameters include one or more of the following: the operating parameters of the motor bearing, the operating parameters of the motor, and the operating parameters of the vehicle driving. The cooling control module 53 is used to adjust the current oil flow rate of the motor bearing according to the cooling control strategy in order to control the cooling of the motor bearing.
[0089] The motor bearing cooling control system provided in this application dynamically predicts the temperature change trend of the motor bearing over a future period based on motor bearing data. When the temperature change trend meets preset cooling parameters, it generates a corresponding cooling control strategy based on vehicle operating parameters to adjust the current oil flow rate of the motor bearing. This achieves the goal of accurately controlling the amount of cooling oil in the motor bearing by predicting cooling needs based on different vehicle operating parameters. It solves the technical problem of insufficient dynamic adjustment capability of oil-cooled motor bearings, thus achieving precise optimization of the cooling oil quantity and improving the dynamic adjustment capability of motor bearing cooling.
[0090] Optionally, the cooling control module 53 is specifically used to generate a first cooling control strategy when the temperature rise rate is greater than a preset temperature rise rate threshold. The first cooling control strategy is to increase the current oil flow rate of the motor bearing by a first preset value.
[0091] Optionally, the cooling control module 53 is specifically used to generate a second cooling control strategy when the increase in the vibration energy of the motor within a preset time is greater than a preset energy surge threshold. The second cooling control strategy is to increase the current oil flow rate of the motor bearing by a second preset value, which is less than a first preset value.
[0092] Optionally, the cooling control module 53 is specifically used to generate a third cooling control strategy when the increase in the slope of the vehicle is greater than a preset slope increase threshold. The third cooling control strategy is to increase the current oil flow rate of the motor bearing by a third preset value, which is less than a second preset value.
[0093] Optionally, the system further includes: a priority determination module, specifically used to obtain the priority of multiple motor bearings if multiple motor bearings are co-cooled; and to configure the oil flow rate of multiple motor bearings based on the priority to generate a cooling control strategy for each motor bearing.
[0094] Optionally, the system further includes: an oil quantity determination module, specifically used to obtain the current remaining oil quantity of the motor oil pump; if the current remaining oil quantity does not meet the preset flow rate value required by the cooling control strategy, the preset flow rate value is adjusted; if the current remaining oil quantity meets the preset flow rate value required by the cooling control strategy, the current oil flow rate of the motor bearing is adjusted according to the cooling control strategy.
[0095] Optionally, the model prediction module 51 is specifically used to input the motor bearing data into the thermal simulation model for training to obtain the thermal distribution of the motor bearing at the current moment; and to input the thermal distribution at the current moment into the thermal prediction model for prediction to obtain the temperature change trend of the motor bearing over a preset time period.
[0096] Optionally, the system further includes: a confidence assessment module, specifically used to predict the temperature of the motor bearing after cooling control based on the temperature change trend and cooling control strategy over a preset time period, to obtain the predicted bearing temperature; to actually collect the temperature of the motor bearing after cooling control, to obtain the actual bearing temperature; if the absolute value of the difference between the predicted bearing temperature and the actual bearing temperature is less than a preset value, the confidence assessment result of the thermal prediction model is confirmed to meet the preset standard; if the absolute value of the difference between the predicted bearing temperature and the actual bearing temperature is not less than the preset value, the thermal prediction model is automatically adjusted, and the adjusted model is verified and optimized.
[0097] As a third aspect of this application, this application also provides a cooling control architecture for motor bearings. Figure 6 The diagram shown is a schematic of a cooling control architecture for a motor bearing according to an embodiment of this application. Figure 6 As shown, the architecture includes: The local layer includes sensors and a cooling system. The sensors can collect data on the motor bearings, and the cooling system can adjust the current oil flow rate of the motor bearings based on a cooling control strategy to regulate the cooling of the motor bearings. The cloud layer includes historical data and model training. The model includes thermal simulation model and thermal prediction model. After model training, the temperature change trend of motor bearings over a preset time period can be output. The local layer and the cloud layer transmit data bidirectionally with encryption through a transmission protocol.
[0098] The local layer sensors collect data on the motor bearings and upload this data to the cloud layer for model training via data amplification (encrypted data transmission protocol TLS1.3). The cloud layer's thermal simulation model and thermal prediction model, combined with historical data, predict the input motor bearing data to obtain the predicted temperature change trend of the motor bearings over a preset time period. This predicted result is then transmitted back to the local layer via data amplification. The local layer performs edge computing on the received predicted result and determines whether cooling regulation of the motor bearings is needed based on the edge results. If so, it determines the corresponding cooling regulation strategy based on vehicle operating data and transmits the cooling regulation strategy to the local layer's cooling system. This allows the cooling system to adjust the current oil flow rate of the motor bearings based on the cooling regulation strategy to achieve cooling regulation of the motor bearings.
[0099] The methods in this application can be implemented, in whole or in part, by software, hardware, firmware, or any combination thereof. When implemented in software, they can be implemented, in whole or in part, as a computer program product. This computer program product includes one or more computer programs or instructions that, when loaded and executed on a computer, perform, in whole or in part, the processes or functions described in this application. The computer can be a general-purpose computer, a special-purpose computer, a computer network, network equipment, user equipment, core network equipment, OAM (Operational Information Management), or other programmable devices.
[0100] The computer program product can be written in any combination of one or more programming languages to perform the operations of the embodiments of this application. The programming languages include object-oriented programming languages such as Java and C++, as well as conventional procedural programming languages such as C or similar languages. The program code can be executed entirely on the user's computing device, partially on the user's computing device, as a standalone software package, partially on the user's computing device and partially on a remote computing device, or entirely on a remote computing device or server.
[0101] The computer program or instructions may be stored in a computer-readable storage medium or transferred from one computer-readable storage medium to another. For example, the computer program or instructions may be transferred from one website, computer, server, or data center to another website, computer, server, or data center via wired or wireless means. The computer-readable storage medium may be any available medium that a computer can access, or a data storage device such as a server or data center that integrates one or more available media. The available medium may be a magnetic medium, such as a floppy disk, hard disk, or magnetic tape; or an optical medium, such as a digital video optical disc; or a semiconductor medium, such as a solid-state drive. The computer-readable storage medium may be a volatile or non-volatile storage medium, or may include both volatile and non-volatile types of storage media.
[0102] Furthermore, embodiments of this application may also be storage media storing a computer program, which is executed by a processor of the steps in a method for controlling the cooling of an electric motor bearing as described in any of the above embodiments of this specification.
[0103] For the foregoing method embodiments, in order to simplify the description, they are all described as a series of actions. However, those skilled in the art should understand that this application is not limited to the described order of actions, because according to this application, some steps can be performed in other orders or simultaneously. Furthermore, those skilled in the art should also understand that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily essential to this application.
[0104] It should be noted that the various embodiments in this specification are described in a progressive manner, with each embodiment focusing on the differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably. For apparatus embodiments, since they are basically similar to method embodiments, the description is relatively simple; relevant parts can be referred to the descriptions in the method embodiments.
[0105] The steps in the methods of the various embodiments of this application can be adjusted, combined, or deleted according to actual needs, and the technical features described in each embodiment can be replaced or combined. The apparatuses in the various embodiments of this application can be combined, divided, or deleted according to actual needs.
[0106] Those skilled in the art will further recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of both. To clearly illustrate the interchangeability of hardware and software, the components and steps of the various examples have been generally described in terms of functionality in the foregoing description. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.
[0107] The steps of the methods or algorithms described in conjunction with the embodiments disclosed herein can be implemented directly by hardware, a software unit executed by a processor, or a combination of both. The software unit can be located in random access memory (RAM), main memory, read-only memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, hard disk, removable disk, CD-ROM, or any other form of storage medium known in the art.
[0108] Finally, it should be noted that in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.
[0109] The above description of the disclosed embodiments enables those skilled in the art to make or use this application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of this application. Therefore, this application is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
Claims
1. A method for controlling the cooling of an electric motor bearing, characterized in that, The method includes: Acquire motor bearing data and perform model prediction on the motor bearing data to obtain the temperature change trend of the motor bearing over a preset time period; If the temperature change trend of the motor bearing within a preset time period meets the preset cooling parameters, a corresponding cooling control strategy is generated based on the vehicle operating parameters; the vehicle operating parameters include one or more of the following: the operating parameters of the motor bearing, the operating parameters of the motor, and the operating parameters of the vehicle driving. The current oil flow rate of the motor bearing is adjusted according to the cooling control strategy to control the cooling of the motor bearing.
2. The cooling control method for motor bearings according to claim 1, characterized in that, The vehicle operating parameters include the operating parameters of the motor bearing, which include the temperature rise rate of the motor bearing. The generation of a corresponding cooling control strategy based on these vehicle operating parameters includes: When the temperature rise rate is greater than a preset temperature rise rate threshold, a first cooling control strategy is generated, wherein the first cooling control strategy is to increase the current oil flow rate of the motor bearing by a first preset value.
3. The cooling control method for motor bearings according to claim 1, characterized in that, The vehicle operating parameters include the motor operating parameters, which include the motor's vibration energy. The step of generating a corresponding cooling control strategy based on the vehicle operating parameters includes: When the increase in vibration energy of the motor within a preset time exceeds a preset energy surge threshold, a second cooling control strategy is generated. The second cooling control strategy is to increase the current oil flow rate of the motor bearing by a second preset value, which is less than a first preset value.
4. The cooling control method for motor bearings according to claim 1, characterized in that, The vehicle operating parameters include vehicle driving parameters, including the vehicle driving gradient. The step of generating a corresponding cooling control strategy based on these vehicle operating parameters includes: When the gradient increase of the vehicle exceeds a preset gradient increase threshold, a third cooling control strategy is generated. The third cooling control strategy is to increase the current oil flow rate of the motor bearing by a third preset value, which is less than a second preset value.
5. The cooling control method for motor bearings according to claim 1, characterized in that, Before generating the corresponding cooling control strategy, the method includes: If multiple motor bearings are used for coordinated cooling, the priority of each motor bearing is determined. The oil flow rate of multiple motor bearings is configured based on the aforementioned priority to generate a cooling control strategy for each motor bearing.
6. The cooling control method for motor bearings according to claim 1, characterized in that, Before adjusting the current oil flow rate of the motor bearing according to the cooling control strategy, the method includes: Get the current remaining oil level of the motor oil pump; If the current remaining oil level does not meet the preset flow rate required by the cooling control strategy, the preset flow rate is adjusted. If the current remaining oil volume meets the preset flow rate required by the cooling control strategy, the current oil flow rate of the motor bearing is adjusted according to the cooling control strategy.
7. The cooling control method for motor bearings according to claim 1, characterized in that, The step of performing model prediction on the motor bearing data to obtain the temperature change trend of the motor bearing over a preset time period includes: The motor bearing data is input into a thermal simulation model for training to obtain the thermal index of the motor bearing at the current moment. The current thermodynamic index is input into the thermodynamic prediction model for prediction, and the temperature change trend of the motor bearing over a preset time period is obtained.
8. The cooling control method for motor bearings according to claim 7, characterized in that, After cooling the motor bearing according to the cooling control strategy, the method includes: Based on the temperature change trend over the preset time period and the cooling control strategy, the temperature of the motor bearing after cooling control is predicted to obtain the predicted bearing temperature. The actual bearing temperature was obtained by collecting actual data after the motor bearing was cooled and regulated. If the absolute value of the difference between the predicted bearing temperature and the actual bearing temperature is less than a preset value, the confidence evaluation result of the thermal prediction model is confirmed to meet the preset standard. If the absolute value of the difference between the predicted bearing temperature and the actual bearing temperature is not less than the preset value, the thermal prediction model is automatically adjusted, and the adjusted model is verified and optimized.
9. A cooling control system for an electric motor bearing, characterized in that, The system includes: The model prediction module is used to acquire motor bearing data and perform model prediction on the motor bearing data to obtain the temperature change trend of the motor bearing over a preset time period. The strategy generation module is used to generate a corresponding cooling control strategy based on vehicle operating parameters if the temperature change trend of the motor bearing within a preset time period meets preset cooling parameters; the vehicle operating parameters include one or more of the following: motor bearing operating parameters, motor operating parameters, and vehicle driving operating parameters. A cooling control module is used to adjust the current oil flow rate of the motor bearing according to the cooling control strategy, so as to control the cooling of the motor bearing.
10. A cooling control architecture for an electric motor bearing, characterized in that, The architecture includes: The local layer includes sensors and a cooling system. The sensors can collect data on the motor bearings, and the cooling system can adjust the current oil flow rate of the motor bearings based on a cooling control strategy to regulate the cooling of the motor bearings. The cloud layer includes historical data and model training. The model includes a thermal simulation model and a thermal prediction model. After model training, the temperature change trend of the motor bearing over a preset time period can be output. The local layer and the cloud layer transmit data bidirectionally with encryption via a transmission protocol.