Thermal management control method, device, system and equipment and vehicle

Abstract

The invention relates to a thermal management control method, device, system and equipment and a vehicle, and relates to the technical field of new energy vehicles. The method comprises the following steps: acquiring operation data and temperature data of a motor; determining a predicted power loss value of the motor based on the operation data and a power loss prediction model; adjusting the rotating speed of the cooling oil pump based on the predicted power loss value and the temperature data of the motor; the cooling oil pump is used for adjusting temperature of the motor. Therefore, the loss prediction of the motor can be accurately predicted, and the thermal management control of the motor can be accurately carried out.

Description

Technical Field

[0001] The present application relates to the field of new energy vehicle technology, in particular to the field of motor technology, and specifically to a thermal management control method, device, system, equipment and vehicle. Background Art

[0002] With the booming new energy vehicle industry, the drive motor is the core hub of its technical architecture, and its performance excellence and operational stability are crucial. Since the motor loss during operation will inevitably generate a large amount of heat energy, if this heat is not promptly and efficiently channeled and managed, it will directly restrict its working efficiency and accelerate component aging, thereby shortening the overall service life of the motor. Therefore, it is necessary to optimize the thermal management strategy of the new energy vehicle drive motor to ensure that the heat can be dissipated quickly and safely to improve vehicle performance, enhance user experience and extend product life cycle.

[0003] In the related technology CN113619371A, the temperature rise curve is fitted by the initial temperature of the motor, the heat capacity of the stator, the instantaneous current, the ambient temperature and the driving mode, and the oil pump speed is adjusted to dissipate heat. However, this method only considers the copper loss caused by the current, and ignores the iron loss, mechanical loss and stray loss, resulting in limited accuracy of loss calculation and temperature rise fitting, which in turn affects the accuracy of oil pump control.

[0004] Related technology CN115111513A monitors the motor speed and torque in real time, uses a preset heating model (based on individual data, has errors and does not fully consider external factors) to calculate the heat generation, and uses linear interpolation to estimate the loss to control the heat dissipation of the oil cooling circuit, but the accuracy of the model is limited.

[0005] However, the accuracy of motor loss prediction in related technologies is low, making it difficult to perform precise thermal management. Summary of the invention

[0006] The present application provides a thermal management control method, device, system, equipment and vehicle to at least solve the technical problem in the related art that the accuracy of motor loss prediction is low, making it difficult to accurately perform thermal management control. The technical solution of the present application is as follows:

[0007] According to the first aspect provided by the present application, a thermal management control method is provided, including: obtaining operating data and temperature data of a motor; determining a predicted power loss value of the motor based on the operating data and a power loss prediction model; adjusting the speed of a cooling oil pump based on the predicted power loss value and temperature data of the motor; the cooling oil pump is used to regulate the temperature of the motor.

[0008] According to the above technical means, the present application can monitor the operation data and temperature data of the motor in real time, timely discover and adjust the oil pump speed to reduce unnecessary energy loss, and quickly and accurately predict the power loss of the motor based on the operation data and power loss prediction model, so as to accurately control the temperature of the motor.

[0009] In one possible approach, the speed of the cooling oil pump is adjusted based on the predicted power loss value and temperature data of the motor, including: determining a target speed based on the predicted power loss value and temperature data of the motor; and controlling the speed of the cooling oil pump to adjust to the target speed.

[0010] According to the above technical means, the present application can accurately calculate the target speed required by the cooling oil pump by real-time monitoring of the predicted power loss value and temperature data of the motor, thereby realizing accurate control of the cooling oil flow rate. This helps to ensure that the motor can be properly cooled under different working conditions and avoid energy waste caused by overcooling or insufficient cooling.

[0011] In one possible method, the target speed is determined based on the predicted power loss value and temperature data of the motor, including: determining the first speed and the corrected speed of the cooling oil pump according to the predicted power loss value and temperature data of the motor; and correcting the first speed based on the corrected speed to obtain the target speed.

[0012] According to the above technical means, the present application can accurately calculate the cooling demand of the motor by real-time monitoring of the motor temperature data and combining it with the predicted power loss value, thereby setting the appropriate cooling oil pump speed. This helps to ensure that the motor always runs within a safe temperature range and avoids performance degradation or damage caused by overheating.

[0013] In one possible method, the temperature data includes a target temperature; determining a first speed of the cooling oil pump includes: determining a first speed corresponding to a predicted power loss value of the motor and a target temperature based on a mapping relationship; the mapping relationship is a mapping relationship between the power loss value of the motor, the target temperature and the speed.

[0014] According to the above technical means, the present application can more accurately match the current operating conditions and cooling requirements of the motor through a pre-established mapping relationship, thereby reducing errors caused by inaccurate estimates or calculations.

[0015] In one possible approach, the temperature data also includes the current temperature and the historical temperature; determining the corrected speed of the cooling oil pump includes: performing thermal lag compensation on the motor according to the predicted power loss value, historical temperature, and current temperature of the motor to obtain the actual temperature of the motor; and determining the corrected speed based on the temperature difference between the actual temperature and the target temperature.

[0016] According to the above technical means, the present application can compensate for the hysteresis of the thermal effect by considering the historical temperature data, thereby more accurately predicting the actual temperature of the motor, and further determining the corrected speed to correct the first speed. This helps to improve the accuracy of temperature control and ensure that the motor always operates within the target temperature range.

[0017] In one possible approach, multiple sample data are obtained and divided into a training set and a test set; a convolutional neural network model is trained based on the training set to obtain an initial prediction model; the initial prediction model is evaluated based on the training set to obtain an error of the initial prediction model; when the error of the initial prediction model is less than a preset threshold, the initial prediction model is determined as a power loss prediction model.

[0018] According to the above-mentioned technical means, the present application can collect multiple sample data and perform training, and the model can learn the power loss characteristics of the motor under different working conditions, so as to adapt to the prediction of motor power loss under different working conditions.

[0019] In one possible manner, when the error of the initial prediction model is greater than or equal to a preset threshold, the initial prediction model is trained according to a preset algorithm.

[0020] In one possible approach, the predicted power loss value of the motor is determined based on operating data and a power loss prediction model, including: extracting features from the operating data to obtain characteristic parameters of the operating data; and outputting the predicted power loss value of the motor based on the characteristic parameters and the power loss prediction model.

[0021] According to the above technical means, the present application can screen out key parameters that have an important impact on power loss prediction by extracting features from the operating data. These feature parameters can more accurately reflect the operating status and power loss characteristics of the motor, thereby improving the accuracy of the prediction model.

[0022] According to a second aspect provided by the present application, there is provided a thermal management control device, comprising: an acquisition unit, a determination unit and an adjustment unit;

[0023] An acquisition unit is used to acquire the operating data and temperature data of the motor; a determination unit is used to determine the predicted power loss value of the motor based on the operating data and the power loss prediction model; an adjustment unit is used to adjust the speed of the cooling oil pump based on the predicted power loss value and temperature data of the motor; the cooling oil pump is used to adjust the temperature of the motor.

[0024] In one possible manner, the adjustment unit is specifically used to: determine a target speed based on a predicted power loss value and temperature data of the motor; and control the speed of the cooling oil pump to be adjusted to the target speed.

[0025] In one possible manner, the adjustment unit is specifically used to: determine a first speed and a corrected speed of the cooling oil pump according to a predicted power loss value and temperature data of the motor; and correct the first speed based on the corrected speed to obtain a target speed.

[0026] In one possible manner, the adjustment unit is specifically used to: determine a first speed corresponding to a predicted power loss value and a target temperature of the motor according to a mapping relationship; the mapping relationship is a mapping relationship between the power loss value, the target temperature and the speed of the motor.

[0027] In one possible manner, the adjustment unit is specifically used to: perform thermal lag compensation on the motor according to the predicted power loss value, historical temperature, and current temperature of the motor to obtain the actual temperature of the motor; and determine the corrected speed based on the temperature difference between the actual temperature and the target temperature.

[0028] In one possible embodiment, the device also includes: a training unit, an acquisition unit, which is also used to acquire multiple sample data and divide the multiple sample data into a training set and a test set; a training unit, which is used to train the convolutional neural network model based on the training set to obtain an initial prediction model; the training unit is also used to evaluate the initial prediction model based on the training set to obtain an error of the initial prediction model; and a determination unit is also used to determine the initial prediction model as a power loss prediction model when the error of the initial prediction model is less than a preset threshold.

[0029] In a possible manner, the determination unit is further configured to train the initial prediction model according to a preset algorithm when an error of the initial prediction model is greater than or equal to a preset threshold.

[0030] In one possible manner, the determination unit is specifically used to extract features from the operating data to obtain characteristic parameters of the operating data; based on the characteristic parameters and the power loss prediction model, the predicted power loss value of the motor is output.

[0031] According to the third aspect provided by the present application, a thermal management control system is provided, the system comprising: a sensor, a thermal management control device and a cooling oil pump; the sensor is used to collect the operating data and temperature data of the motor, and send the operating data and the temperature data to the thermal management control device; the thermal management control device is used to receive the operating data and the temperature data; the thermal management control device is also used to determine the predicted power loss value of the motor based on the operating data and a power loss prediction model; the thermal management control device is also used to send a speed adjustment instruction to the cooling oil pump based on the predicted power loss value of the motor and the temperature data; the cooling oil pump is used to adjust the speed of the cooling oil pump in response to the speed adjustment instruction to regulate the temperature of the motor.

[0032] According to the fourth aspect provided by the present application, an electronic device is provided, comprising: a processor; a memory for storing processor executable instructions; wherein the processor is configured to execute instructions to implement the method of the above-mentioned first aspect and any possible implementation manner thereof.

[0033] According to the fifth aspect provided by the present application, a computer-readable storage medium is provided. When the instructions in the computer-readable storage medium are executed by a processor of an electronic device, the electronic device is enabled to execute the method in the above-mentioned first aspect and any possible implementation method thereof.

[0034] According to the sixth aspect provided by the present application, a computer program product is provided, the computer program product comprising computer instructions, and when the computer instructions are executed on an electronic device, the electronic device executes the method of the above-mentioned first aspect and any possible implementation manner thereof.

[0035] According to a seventh aspect provided by the present application, a vehicle is provided, comprising the thermal management control system described in the third aspect.

[0036] Therefore, the above technical features of the present application have the following beneficial effects:

[0037] (1) By monitoring the motor’s operating data and temperature data in real time, the oil pump speed can be discovered and adjusted in time to reduce unnecessary energy loss. Based on the operating data and power loss prediction model, the motor’s power loss can be predicted quickly and accurately, thereby accurately controlling the motor’s temperature.

[0038] (2) By monitoring the predicted power loss value and temperature data of the motor in real time, the target speed required by the cooling oil pump can be accurately calculated, thereby achieving precise control of the cooling oil flow rate. This helps ensure that the motor can be properly cooled under different operating conditions and avoid energy waste due to overcooling or insufficient cooling.

[0039] (3) By monitoring the motor’s temperature data in real time and combining it with the predicted power loss value, the required cooling intensity can be accurately calculated, thereby setting the appropriate cooling oil pump speed. This helps ensure that the motor always operates within a safe temperature range and avoids performance degradation or damage caused by overheating.

[0040] (4) The pre-established mapping relationship can more accurately match the current operating conditions and cooling requirements of the motor, reducing errors caused by inaccurate estimation or calculation.

[0041] (5) By collecting multiple sample data and performing training, the model can learn the power loss characteristics of the motor under different working conditions, thereby being able to adapt to the prediction of motor power loss under different working conditions.

[0042] (6) By extracting features from the operating data, key parameters that have an important impact on power loss prediction can be screened out. These characteristic parameters can more accurately reflect the operating status and power loss characteristics of the motor, thereby improving the accuracy of the prediction model.

[0043] It should be noted that the technical effects brought about by any implementation method in the second to seventh aspects can refer to the technical effects brought about by the corresponding implementation method in the first aspect, and will not be repeated here.

[0044] It should be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] The drawings herein are incorporated into the specification and constitute a part of the specification, illustrate embodiments consistent with the present application, and together with the specification are used to explain the principles of the present application, and do not constitute an improper limitation on the present application.

[0046] Figure 1 is a schematic diagram of a vehicle architecture according to an exemplary embodiment;

[0047] Figure 2 is a flow chart showing a thermal management control method according to an exemplary embodiment;

[0048] Figure 3 is a schematic diagram showing a thermal management control process according to an exemplary embodiment;

[0049] Figure 4 is a schematic diagram of a model training process according to an exemplary embodiment;

[0050] Figure 5 is a schematic diagram of a flow chart of determining a target speed according to an exemplary embodiment;

[0051] Figure 6 is a block diagram of a thermal management control device according to an exemplary embodiment;

[0052] Figure 7 It is a block diagram of an electronic device according to an exemplary embodiment. DETAILED DESCRIPTION

[0053] In order to enable ordinary persons in the art to better understand the technical solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the accompanying drawings.

[0054] It should be noted that the terms "first", "second", etc. in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present application. On the contrary, they are merely examples of devices and methods consistent with some aspects of the present application as detailed in the attached claims.

[0055] As the background technology, in order to solve the technical problem that the motor loss prediction accuracy is low in the related technology, making it difficult to accurately perform thermal management, this application provides a thermal management control method, which can obtain the motor's operating data and temperature data, and determine the predicted power loss value of the motor based on the operating data and the power loss prediction model, so as to further adjust the speed of the cooling oil pump based on the predicted power loss value and temperature data of the motor, thereby adjusting the temperature of the motor, avoiding the technical problem that the motor loss prediction accuracy is low in the related technology, making it difficult to accurately perform thermal management. Therefore, this application can accurately predict the motor loss.

[0056] For ease of understanding, the thermal management control method provided in the present application is specifically introduced below with reference to the accompanying drawings.

[0057] Figure 1 is a schematic diagram of the architecture of a vehicle 100 .

[0058] The vehicle 100 may also be referred to as a vehicle, a mobile carrier, an electric vehicle (EV), a hybrid electric vehicle (HEV), a plug-in hybrid electric vehicle (PHEV), a fuel cell vehicle (FCV), an autonomous vehicle, an intelligent and connected vehicle (ICV), a driverless vehicle, etc.

[0059] In the embodiment of the present application, the vehicle 100 may be a sedan, a sport utility vehicle (SUV), a truck, an electric vehicle, a motorcycle, a tricycle, a special vehicle (such as an ambulance, a fire truck, a police car, etc.), an unmanned taxi, an intelligent networked bus, an automatic driving logistics vehicle, an electric truck, etc. In addition, the method is also applicable to various special vehicles, such as agricultural vehicles, mining vehicles, forestry vehicles, airport vehicles, port vehicles, etc. This application does not make specific restrictions on this.

[0060] In one possible manner, the vehicle 100 may include a thermal management control system 101. The thermal management control system 101 may include: a sensor, a thermal management control device, and a cooling oil pump.

[0061] The sensor may be connected to the thermal management control device for communication. The cooling oil pump may be connected to the thermal management control device for communication. The sensor may be integrated into the thermal management control device, or may be a different component from the thermal management control device. This application does not impose any specific restrictions on this.

[0062] Among them, the sensor can be used to collect the operation data and temperature data of the motor, and send the operation data and temperature data to the thermal management control device. The thermal management control device can be used to receive the operation data and temperature data. The thermal management control device can also be used to determine the predicted power loss value of the motor based on the operation data and the power loss prediction model. The thermal management control device can also be used to send a speed adjustment instruction to the cooling oil pump based on the predicted power loss value and temperature data of the motor. The cooling oil pump can be used to adjust the speed of the cooling oil pump in response to the speed adjustment instruction to adjust the temperature of the motor.

[0063] The structure shown in the embodiment of the present application does not constitute a limitation on the vehicle 100. It may include more or fewer components than shown in the figure, or combine some components, or split some components, or arrange the components differently. The components shown in the figure may be implemented in hardware, software, or a combination of software and hardware.

[0064] Figure 2 is a flow chart of a thermal management control method according to an exemplary embodiment. Figure 2 As shown, the thermal management control method includes the following steps: S201-S203.

[0065] S201 . A thermal management control device obtains operation data and temperature data of a motor.

[0066] The operation data includes the motor voltage, motor speed, motor torque, etc. The temperature data includes the current temperature, target temperature and historical temperature of the motor.

[0067] It can be understood that the target temperature of the motor is a temperature determined according to the design, use environment, working conditions and manufacturer's specifications of the motor.

[0068] In one possible approach, the thermal management control device can obtain the voltage of the motor through a voltage sensor or directly from the voltage monitoring interface of the motor controller. The thermal management control device can collect the motor speed through a speed sensor deployed at a fixed position on the non-rotating part of the motor, such as the motor housing or the motor shaft. The thermal management control device can obtain the motor torque through a torque sensor deployed on the output shaft of the motor.

[0069] In another possible manner, the thermal management control device may be deployed on a temperature sensor on the motor to obtain the current temperature of the motor. The thermal management control device may be configured with a storage module. The storage module may store the target temperature and historical temperature of the motor. The speed adjustment module may read the target temperature and historical temperature of the motor from the storage module.

[0070] S202: The thermal management control device determines a predicted power loss value of the motor based on the operating data and the power loss prediction model.

[0071] In one possible approach, the power loss prediction model can be a prediction model trained based on advanced algorithms and a large amount of historical data. It can comprehensively consider multiple factors such as the motor's operating data, environmental conditions, and material properties to accurately predict the motor's power loss.

[0072] Optionally, the power loss prediction model may be a model constructed according to a convolutional neural network algorithm, or a model constructed according to a deep learning algorithm, which is not specifically limited in the present application.

[0073] In one possible manner, the thermal management control device may extract features from the operating data based on the power loss prediction model to obtain feature parameters of the operating data. The power loss prediction model may output a predicted power loss value of the motor based on the feature parameters and the power loss prediction model.

[0074] In one example, when the power loss prediction model is a convolutional neural network model, the power loss prediction model may include multiple layers of convolutional neural networks, and the features of the operating data are extracted by executing the operations of each layer of the convolutional neural network to obtain the predicted power loss value of the motor.

[0075] In one possible manner, the thermal management control device may obtain multiple sample data and divide the multiple sample data into a training set and a test set. The thermal management control device may train a convolutional neural network model based on the training set to obtain an initial prediction model. The thermal management control device may evaluate the initial prediction model based on the training set to obtain an error of the initial prediction model. The thermal management control device may determine the initial prediction model as a power loss prediction model when the error of the initial prediction model is less than a preset threshold. Alternatively, the thermal management control device may train the initial prediction model according to a preset algorithm when the error of the initial prediction model is greater than or equal to a preset threshold.

[0076] In one example, when the power loss prediction model is a convolutional neural network model, the training process of the power loss prediction model may be:

[0077] First, the acquired running data is used as the initial input data of the convolutional neural network. By defining multiple convolution kernels (or filters), the input image is convolved to extract local features in the image and generate convolution feature maps. These feature maps capture key information such as edges and textures in the image.

[0078] Subsequently, a maximum pooling operation is applied to the convolutional feature map. This step reduces the dimension of the feature map by selecting the maximum value from each local neighborhood while retaining important features, reducing the amount of computation and preventing overfitting. The output after pooling is the pooled feature map.

[0079] In order to introduce nonlinear factors and improve the learning ability of the model, a nonlinear activation function is applied to the pooled feature map. This step generates an activated feature map, providing richer feature representation for subsequent layers.

[0080] Fuse the running data with the activation feature map. This can be done by encoding the running data into a form compatible with the feature map (e.g., via an embedding layer), or by directly expanding it to the same spatial dimension as the feature map and then concatenating or adding them.

[0081] The fused features are input into the fully connected layer (also called densely connected layer) for pattern learning and classification / regression. The fully connected layer learns the complex relationship between features through the weight matrix and bias term, and finally outputs the predicted loss value of the motor.

[0082] The thermal management control device can evaluate the performance of the initial prediction model on the test set, that is, quantify the model accuracy by calculating the mean absolute error between the actual loss and the predicted loss. The thermal management control device can determine the initial prediction model as the power loss prediction model when the error of the initial prediction model is less than a preset threshold. Alternatively, the thermal management control device can train the initial prediction model according to a preset algorithm when the error of the initial prediction model is greater than or equal to a preset threshold.

[0083] That is, when the error of the initial prediction model is greater than or equal to the preset threshold, a variety of optimization strategies can be used to adjust the accuracy of the initial prediction model, such as adjusting the network structure (increasing or decreasing the number of layers, changing the size of the convolution kernel), modifying the learning rate, using regularization technology to prevent overfitting, etc. At the same time, the number and diversity of training sets can be increased to train the initial prediction model.

[0084] In one possible manner, the thermal management control device may be trained using operating data and predicted power loss values ​​to obtain an updated power loss prediction model.

[0085] S203 . The thermal management control device adjusts the rotation speed of the cooling oil pump based on the predicted power loss value and temperature data of the motor.

[0086] Among them, the cooling oil pump can be used to adjust the temperature of the motor.

[0087] In one possible manner, the thermal management control device may determine the target speed based on the predicted power loss value and temperature data of the motor. The thermal management control device may control the speed of the cooling oil pump to be adjusted to the target speed. The specific implementation of the thermal management control device adjusting the speed of the cooling oil pump based on the predicted power loss value and temperature data of the motor may refer to S301-S302 below. No further details will be given here.

[0088] In one possible approach, the thermal management control device can increase the speed of the cooling oil pump when the motor temperature is high to enhance the cooling effect and quickly reduce the motor temperature. Conversely, when the motor temperature remains within a safe range, the thermal management control device can appropriately reduce the speed of the cooling oil pump to save energy and extend the life of the equipment. Through this dynamic adjustment process, the thermal management control device can ensure that the motor always operates within the optimal temperature range, thereby ensuring its stable performance and long-term reliability.

[0089] Based on the above Figure 2 The technical solution in this application can monitor the operation data and temperature data of the motor in real time, timely discover and adjust the oil pump speed to reduce unnecessary energy loss, and quickly and accurately predict the power loss of the motor based on the operation data and power loss prediction model, so as to accurately control the temperature of the motor.

[0090] In some embodiments, in order to adjust the rotation speed of the cooling oil pump based on the predicted power loss value and temperature data of the motor, the thermal management control method provided in the present application also includes the following steps: S301-S302.

[0091] S301 . The thermal management control device determines a target rotation speed based on a predicted power loss value and temperature data of the motor.

[0092] In one possible manner, the thermal management control device may determine a first speed and a corrected speed of the cooling oil pump according to the predicted power loss value and temperature data of the motor. The first speed may be a basic speed of the cooling oil pump determined according to the predicted power loss value and temperature data of the motor. The thermal management control device may correct the first speed based on the corrected speed to obtain a target speed, that is, the thermal management control device may correct the basic speed by the corrected speed to obtain a target speed.

[0093] In one example, the thermal management control device may determine the sum of the modified speed and the basic speed as the target speed. The modified speed may be a negative value or a positive value. This application does not impose any specific restrictions on this.

[0094] In one possible manner, in order to determine the first speed of the cooling oil pump, the thermal management control device may determine the first speed corresponding to the predicted power loss value and the target temperature of the motor according to a mapping relationship, wherein the mapping relationship is a mapping relationship between the power loss value, the target temperature and the speed of the motor.

[0095] It should be noted that the mapping relationship can be determined based on a calibration test or a computational fluid dynamics (CFD) simulation.

[0096] In another possible manner, in order to determine the corrected speed of the cooling oil pump, the thermal management control device may perform thermal hysteresis compensation on the motor according to the predicted power loss value, historical temperature, and current temperature of the motor to obtain the actual temperature of the motor. The thermal management control device may determine the corrected speed based on the temperature difference between the actual temperature and the target temperature.

[0097] Specifically, in order to avoid the impact of thermal hysteresis on the motor temperature, the thermal management control device can perform thermal hysteresis compensation on the motor according to the predicted power loss value, historical temperature, and current temperature of the motor to obtain the actual temperature of the motor. The thermal hysteresis compensation satisfies the following formula 1:

[0098]

[0099] Among them, T' can be used to represent the actual temperature. T can be used to represent the current temperature. τ0 can be used to represent the time constant, which can be obtained through actual measurement, and the typical value is generally 1s. It can be used to characterize the heating rate, which is related to motor losses and historical temperature.

[0100] The thermal management control device may determine a temperature difference between the actual temperature and the target temperature. The thermal management control device may determine a modified speed based on a proportional integral (PI) and the temperature difference between the actual temperature and the target temperature.

[0101] S302 : The thermal management control device controls the speed of the cooling oil pump to be adjusted to a target speed.

[0102] In one possible manner, the thermal management control device may adjust the speed of the cooling oil pump based on the target speed to achieve cooling of the motor.

[0103] Based on the above technical solution, the present application can accurately calculate the target speed required by the cooling oil pump by real-time monitoring of the predicted power loss value and temperature data of the motor, thereby realizing accurate control of the cooling oil flow rate. This helps to ensure that the motor can be properly cooled under different working conditions and avoid energy waste caused by overcooling or insufficient cooling.

[0104] In some embodiments, Figure 3 , which is a schematic diagram of a thermal management control process.

[0105] In one possible manner, the thermal management control device can obtain the operating data and temperature data of the motor. The temperature data may include the current temperature and the historical temperature. The thermal management control device can pre-process the operating data, and input the pre-processed data into the convolutional neural network model to obtain a power loss value, that is, input the power loss preset model to obtain the power loss value. The thermal management control device can perform thermal hysteresis correction based on the current temperature and the historical temperature. The thermal management control device can obtain a target speed based on the result obtained by the thermal hysteresis correction and the power loss value. The thermal management control device can adjust the oil pump speed based on the target speed to achieve motor heat dissipation.

[0106] In some embodiments, Figure 4 As shown, this is a schematic diagram of a model training process.

[0107] In one possible manner, the thermal management control device may obtain multiple sample data. The thermal management control device may preprocess the multiple sample data. The thermal management control device may construct a convolutional neural network model. The thermal management control device may input the preprocessed sample data into the convolutional neural network model for model training. The thermal management control device may perform model evaluation on the trained model. The thermal management control device may determine the model as a power loss prediction model when the evaluation result meets the preset conditions. Alternatively, the thermal management control device may reacquire multiple sample data and train the model when the evaluation result does not meet the preset conditions.

[0108] The convolutional neural network model can be used to process sample data (e.g., speed and torque). The convolutional neural network model can process the motor loss map through multiple layers of convolution, for example, convolution layer 1, max pooling layer 1, convolution layer 2, max pooling layer 2, etc. The convolutional neural network model can combine the running data and loss map features. The convolutional neural network model can learn through the fully connected layer and output the results through the output layer.

[0109] In some embodiments, Figure 5 As shown, it is a schematic diagram of a process for determining a target speed.

[0110] In one possible manner, the thermal management control device may determine a first speed corresponding to the predicted power loss value and the target temperature of the motor according to a mapping relationship. The thermal management control device may perform thermal hysteresis compensation on the motor according to the predicted power loss value, the historical temperature, and the current temperature of the motor to obtain the actual temperature of the motor. The thermal management control device may determine a corrected speed based on the temperature difference between the actual temperature and the target temperature. The thermal management control device may correct the first speed based on the corrected speed to obtain the target speed.

[0111] Figure 6 is a block diagram of a thermal management control device according to an exemplary embodiment. Figure 6 The thermal management control device includes: an acquisition unit 401, a determination unit 402, an adjustment unit 403 and a training unit 404.

[0112] In a possible manner, the acquisition unit 401 is used to acquire the operation data and temperature data of the motor.

[0113] In one possible manner, the determination unit 402 is configured to determine a predicted power loss value of the motor based on the operating data and a power loss prediction model.

[0114] In one possible manner, the adjustment unit 403 is used to adjust the rotation speed of the cooling oil pump based on the predicted power loss value and temperature data of the motor; the cooling oil pump is used to adjust the temperature of the motor.

[0115] In one possible manner, the adjustment unit 403 is specifically configured to: determine a target speed based on a predicted power loss value and temperature data of the motor; and control the speed of the cooling oil pump to be adjusted to the target speed.

[0116] In one possible manner, the adjustment unit 403 is specifically used to: determine a first speed and a corrected speed of the cooling oil pump according to the predicted power loss value and temperature data of the motor; and correct the first speed based on the corrected speed to obtain a target speed.

[0117] In one possible manner, the adjustment unit 403 is specifically used to: determine a first speed corresponding to the predicted power loss value and the target temperature of the motor according to a mapping relationship; the mapping relationship is a mapping relationship between the power loss value, the target temperature and the speed of the motor.

[0118] In one possible manner, the adjustment unit 403 is specifically used to: perform thermal lag compensation on the motor according to the predicted power loss value, historical temperature, and current temperature of the motor to obtain the actual temperature of the motor; and determine the corrected rotation speed based on the temperature difference between the actual temperature and the target temperature.

[0119] In a possible manner, the acquisition unit 401 is further configured to acquire a plurality of sample data and divide the plurality of sample data into a training set and a test set.

[0120] In one possible manner, the training unit 404 is used to train the convolutional neural network model based on the training set to obtain an initial prediction model.

[0121] In a possible manner, the training unit 404 is further configured to evaluate the initial prediction model based on the training set to obtain an error of the initial prediction model.

[0122] In a possible manner, the determination unit 402 is further configured to determine the initial prediction model as the power loss prediction model when the error of the initial prediction model is less than a preset threshold.

[0123] In a possible manner, the determination unit 402 is further configured to train the initial prediction model according to a preset algorithm when the error of the initial prediction model is greater than or equal to a preset threshold.

[0124] In one possible manner, the determination unit 402 is specifically configured to extract features from the operating data to obtain feature parameters of the operating data; and output a predicted power loss value of the motor based on the feature parameters and a power loss prediction model.

[0125] Regarding the device in the above embodiment, the specific manner in which each module performs operations has been described in detail in the embodiment of the method, and will not be elaborated here.

[0126] Figure 7 FIG. 1 is a block diagram of an electronic device according to an exemplary embodiment. Figure 7 As shown, the electronic device includes but is not limited to: a processor 501 and a memory 502 .

[0127] The memory 502 is used to store executable instructions of the processor 501. It can be understood that the processor 501 is configured to execute instructions to implement the thermal management control method in the above embodiment.

[0128] It should be noted that those skilled in the art can understand that Figure 7 The electronic device structure shown in the figure does not constitute a limitation on the electronic device, and the electronic device may include Figure 7 More or fewer components may be shown, or certain components may be combined, or the components may be arranged differently.

[0129] The processor 501 is the control center of the electronic device. It uses various interfaces and lines to connect various parts of the entire electronic device. By running or executing software programs and / or modules stored in the memory 502, and calling data stored in the memory 502, it performs various functions of the electronic device and processes data, thereby monitoring the electronic device as a whole. The processor 501 may include one or more processing units. Optionally, the processor 501 may integrate an application processor and a modem processor, wherein the application processor mainly processes the operating system, user interface, and application programs, and the modem processor mainly processes wireless communications. It is understandable that the above-mentioned modem processor may not be integrated into the processor 501.

[0130] The memory 502 can be used to store software programs and various data. The memory 502 can mainly include a program storage area and a data storage area, wherein the program storage area can store an operating system, an application program required by at least one functional module (such as a determination unit, a processing unit, etc.), etc. In addition, the memory 502 can include a high-speed random access memory, and can also include a non-volatile memory, such as at least one disk storage device, a flash memory device, or other volatile solid-state storage devices.

[0131] In an exemplary embodiment, a computer-readable storage medium including instructions is also provided, such as a memory 502 including instructions. The above instructions can be executed by a processor 501 of an electronic device to implement the method in the above embodiment.

[0132] In actual implementation, Figure 6 The functions of the acquisition unit 401, the determination unit 402, the adjustment unit 403 and the training unit 404 in Figure 7The processor 501 in the embodiment calls the computer program stored in the memory 502. The specific execution process can refer to the description of the method part in the above embodiment, which will not be repeated here.

[0133] Optionally, the computer-readable storage medium may be a non-temporary computer-readable storage medium, for example, the non-temporary computer-readable storage medium may be a read-only memory (ROM), a random access memory (RAM), a CD-ROM, a magnetic tape, a floppy disk, an optical data storage device, etc.

[0134] In an exemplary embodiment, the present application also provides a computer program product including one or more instructions, and the one or more instructions can be executed by the processor 501 of the electronic device to complete the method in the above embodiment.

[0135] It should be noted that when the instructions in the above-mentioned computer-readable storage medium or one or more instructions in the computer program product are executed by the processor of the electronic device, the various processes of the above-mentioned method embodiment are implemented, and the same technical effect as the above-mentioned method can be achieved. To avoid repetition, they will not be repeated here.

[0136] Through the description of the above implementation methods, technical personnel in the relevant field can clearly understand that for the convenience and simplicity of description, only the division of the above-mentioned functional modules is used as an example. In actual applications, the above-mentioned functions can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above.

[0137] In the several embodiments provided in the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic, for example, the division of modules or units is only a logical function division, and there may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another device, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0138] The units described as separate components may or may not be physically separated, and the components shown as units may be one physical unit or multiple physical units, that is, they may be located in one place or distributed in multiple different places. Some or all of the units may be selected according to actual needs to achieve the purpose of the present embodiment.

[0139] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0140] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a readable storage medium. Based on this understanding, the technical solution of the embodiment of the present application is essentially or the part that contributes to the prior art or the full classification part or part of the technical solution can be embodied in the form of a software product, which is stored in a storage medium, including a number of instructions to enable a device (which can be a single-chip microcomputer, chip, etc.) or a processor (processor) to execute the full classification part or part of the steps of each embodiment method of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, ROM, RAM, disk or CD and other media that can store program code.

[0141] The above are only specific implementations of the present application, but the protection scope of the present application is not limited thereto, and any changes or substitutions within the technical scope disclosed in the present application should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. A thermal management control method, characterized in that: include: Get the motor's operating data and temperature data; Determining a predicted power loss value of the motor based on the operating data and a power loss prediction model; Based on the predicted power loss value of the motor and the temperature data, the rotation speed of the cooling oil pump is adjusted; the cooling oil pump is used to regulate the temperature of the motor.

2. The method according to claim 1, characterized in that The adjusting the rotation speed of the cooling oil pump based on the predicted power loss value of the motor and the temperature data includes: Determining a target speed based on the predicted power loss value of the motor and the temperature data; The rotation speed of the cooling oil pump is controlled to be adjusted to a target rotation speed.

3. The method according to claim 2, characterized in that The determining the target speed based on the predicted power loss value of the motor and the temperature data includes: Determining a first speed and a modified speed of the cooling oil pump according to the predicted power loss value of the motor and the temperature data; The first rotational speed is corrected based on the corrected rotational speed to obtain the target rotational speed.

4. The method according to claim 3, characterized in that The temperature data includes a target temperature; Determining a first rotation speed of the cooling oil pump includes: Determining, according to the mapping relationship, a first rotation speed corresponding to the predicted power loss value of the motor and the target temperature; The mapping relationship is a mapping relationship between the power loss value of the motor, the target temperature and the rotation speed.

5. The method according to claim 3, characterized in that: The temperature data also includes current temperature and historical temperature; determining the corrected speed of the cooling oil pump includes: Performing thermal hysteresis compensation on the motor according to the predicted power loss value of the motor, the historical temperature, and the current temperature to obtain an actual temperature of the motor; The corrected rotation speed is determined based on a temperature difference between the actual temperature and the target temperature.

6. The method according to any one of claims 1 to 5, characterized in that The method further comprises: Obtain multiple sample data, and divide the multiple sample data into a training set and a test set; Training the convolutional neural network model based on the training set to obtain an initial prediction model; Evaluate the initial prediction model based on the training set to obtain an error of the initial prediction model; When the error of the initial prediction model is less than a preset threshold, the initial prediction model is determined as the power loss prediction model.

7. The method according to claim 6, characterized in that: The method further comprises: When the error of the initial prediction model is greater than or equal to a preset threshold, the initial prediction model is trained according to the preset algorithm.

8. The method according to claim 1, characterized in that: The step of determining the predicted power loss value of the motor based on the operating data and the power loss prediction model comprises: Extracting features from the operating data to obtain feature parameters of the operating data; Based on the characteristic parameters and the power loss prediction model, a predicted power loss value of the motor is output.

9. A thermal management control device, characterized in that: The device comprises: an acquisition unit, a determination unit and an adjustment unit; The acquisition unit is used to acquire the operation data and temperature data of the motor; The determining unit is used to determine the predicted power loss value of the motor based on the operating data and the power loss prediction model; The adjustment unit is used to adjust the rotation speed of the cooling oil pump based on the predicted power loss value of the motor and the temperature data; the cooling oil pump is used to adjust the temperature of the motor.

10. A thermal management control system, characterized in that: The system includes: a sensor, a thermal management control device and a cooling oil pump; The sensor is used to collect operating data and temperature data of the motor and send the operating data and the temperature data to the thermal management control device; The thermal management control device is used to receive the operating data and temperature data; The thermal management control device is further used to determine a predicted power loss value of the motor based on the operating data and the power loss prediction model; The thermal management control device is further used to send a speed adjustment instruction to the cooling oil pump based on the predicted power loss value of the motor and the temperature data; The cooling oil pump is used to adjust the speed of the cooling oil pump in response to the speed adjustment instruction to adjust the temperature of the motor.

11. An electronic device, characterized in that: include: processor; a memory for storing instructions executable by the processor; The processor is configured to execute the instructions to implement the method according to any one of claims 1 to 8.

12. A vehicle, characterized in that: The vehicle comprises the system of claim 10.

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