Motor rotor temperature estimation method and device, vehicle and storage medium

Abstract

The invention provides a rotor temperature estimation method and device of a motor, a vehicle and a storage medium, and the method is applied to the field of motor temperature monitoring, and comprises the steps: obtaining the current environment temperature, the current shutdown time, the stator loss and the rotor loss of the motor, determining the initial rotor temperature of the motor according to the current environment temperature and the current shutdown duration, and obtaining the temperature rise of the motor by using a preset thermal network model and a preset double-Kalman filter based on the current environment temperature, the stator loss and the rotor loss; and obtaining the actual rotor temperature of the motor according to the initial rotor temperature and the temperature rise. According to the method, the high-precision estimation of the rotor temperature of the motor is realized by establishing the thermal network model and combining the double Kalman filters, the dependence on a hardware sensor is not needed, the cost is reduced, and the reliability and robustness of the system are improved.

Description

Technical Field

[0001] The present application relates to the field of motor temperature monitoring, and more specifically, to a method, device, vehicle and storage medium for estimating the rotor temperature of a motor in the field of motor temperature monitoring. Background Art

[0002] In motor applications, motor rotor temperature monitoring is crucial to ensure safe operation of the motor and extend its service life. Excessive motor rotor temperature may lead to permanent magnet demagnetization, insulation material aging, mechanical component deformation and other problems, thus affecting the performance and reliability of the motor. Therefore, accurate monitoring of the motor rotor temperature is the key to ensuring efficient and safe operation of the motor.

[0003] In the related art, the monitoring methods of the rotor temperature mainly include local temperature method, back electromotive force estimation method, infrared temperature measurement method and traditional thermal model method to estimate the rotor temperature of the motor.

[0004] However, the above methods have the following defects when monitoring the rotor temperature: (1) Based on the local temperature method, only the local temperature of the motor rotor can be obtained; (2) Based on the back-electromotive force estimation method, it is impossible to obtain a uniform motor rotor temperature, nor is it possible to obtain the hottest point of the motor rotor; (3) Based on the infrared temperature measurement method, only the average temperature of the motor rotor circumference can be obtained, and the temperature of each magnetic pole cannot be distinguished; (4) Based on the traditional thermal model method, the estimation result will cause a cumulative error problem, which will lead to a decrease in the accuracy of temperature estimation, which needs to be solved urgently. Summary of the invention

[0005] The present application provides a method, device, vehicle and storage medium for estimating the rotor temperature of a motor. The method achieves high-precision estimation of the rotor temperature of the motor by establishing a thermal network model and combining it with a dual Kalman filter, without relying on hardware sensors, thereby reducing costs and improving the reliability and robustness of the system.

[0006] In a first aspect, a method for estimating the rotor temperature of a motor is provided, the method comprising: obtaining the current ambient temperature, current downtime duration, stator loss and rotor loss of the motor; determining the initial rotor temperature of the motor according to the current ambient temperature and the current downtime duration, and based on the current ambient temperature, the stator loss and the rotor loss, obtaining the temperature rise of the motor by using a preset thermal network model and a preset dual Kalman filter; obtaining the actual rotor temperature of the motor according to the initial rotor temperature and the temperature rise.

[0007] Through the above technical solution, by establishing a thermal network model and combining it with a dual Kalman filter, a high-precision estimation of the rotor temperature of the motor is achieved without relying on hardware sensors, which reduces costs and improves the reliability and robustness of the system.

[0008] In combination with the first aspect, in some possible implementations, determining the initial rotor temperature of the motor based on the current ambient temperature and the current shutdown duration includes: determining a target shutdown cooling curve based on the current ambient temperature; and obtaining the initial rotor temperature based on the current shutdown duration based on the target shutdown cooling curve.

[0009] Through the above technical solution, the initial rotor temperature of the motor at different temperatures can be obtained based on the target shutdown cooling curve, thereby improving the accuracy of the initial rotor temperature.

[0010] In combination with the first aspect and the above-mentioned implementation methods, in some possible implementation methods, obtaining the stator loss and rotor loss of the motor includes: obtaining the speed, torque and voltage of the motor; inputting the speed, the torque and the voltage into a preset motor loss model to obtain the stator loss and the rotor loss.

[0011] Through the above technical solution, based on the calculation of rotor loss and stator loss, the heat source during the operation of the motor can be obtained, thereby reducing the temperature error based on the calculation of the loss.

[0012] In combination with the first aspect and the above-mentioned implementation method, the temperature rise of the motor is obtained based on the current ambient temperature, the stator loss and the rotor loss using a preset thermal network model and a preset dual Kalman filter, including: inputting the current ambient temperature, the stator loss and the rotor loss into the preset thermal network model to obtain the current stator predicted temperature and the current rotor predicted temperature; obtaining the current model parameters of the preset thermal network model, the stator predicted temperature at the last moment and the rotor predicted temperature at the last moment; based on the stator predicted temperature at the last moment and the rotor predicted temperature at the last moment, the current stator predicted temperature and the current rotor predicted temperature, the temperature rise of the motor is obtained according to the state filter of the preset dual Kalman filter, and the updated parameters of the preset thermal network model are obtained according to the parameter filter of the preset dual Kalman filter.

[0013] Through the above technical solution, based on the predicted temperature at the previous moment, the predicted temperature at the current moment and the preset thermal network model dynamically adjusted through the dual Kalman filter, the predicted temperature of the motor can be continuously corrected after it is obtained to improve the accuracy of the actual rotor temperature of the motor.

[0014] In combination with the first aspect and the above implementation, after obtaining the update parameters of the preset thermal network model according to the parameter filter of the preset dual Kalman filter, it also includes: updating the preset thermal network model using the update parameters.

[0015] Through the above technical solution, the preset thermal network model is updated based on the update parameters, so that the temperature rise is continuously adjusted to improve the accuracy of the actual rotor temperature of the motor.

[0016] In combination with the first aspect and the above implementations, in some possible implementations, the preset thermal network model is:

[0017] θ(n)=[Aθ(n-1)+Bu]Δt+θ(n-1);

[0018] Among them, θ(n-1) is the state quantity at time n-1, u is the input parameter, Δt is the calculation time step, A is the state parameter matrix composed of thermal resistance and heat capacitance parameters, and B is the input parameter matrix composed of thermal resistance and heat capacitance parameters.

[0019] Through the above technical solution, the preset thermal network model helps predict the temperature distribution and temperature rise of the motor under different working conditions, so as to estimate the temperature rise of the motor.

[0020] In combination with the first aspect and the above implementation manner, in some possible implementation manners, the preset dual Kalman filter includes a parameter filter and a state filter, wherein the parameter filter is:

[0021]

[0022] in, is the prior estimated parameter of the parameter filter at the current moment, is the posterior estimated parameter of the parameter filter at the previous moment, S k - is the prior covariance matrix of the parameter prediction module at the current moment, S k-1 + is the posterior covariance matrix of the parameter prediction module at the previous moment, Σ r is the noise of the parameter filter;

[0023] The state filter is:

[0024]

[0025] Among them, A d is the discrete state parameter matrix composed of thermal resistance and thermal capacitance, B d is the discrete input parameter matrix consisting of thermal resistance and thermal capacitance parameters, u k-1 is the input parameter of the previous moment, is the estimated temperature after correction of the state at the previous moment, is the estimated temperature predicted by the current state, Q k-1+ is the posterior covariance matrix of the state filter at the previous moment, Q k - is the prior covariance matrix of the state filter at the current moment, Σ ω is the noise of the state filter.

[0026] Through the above technical solution, the temperature of the motor is obtained more accurately by predicting respectively through the parameter filter and the state filter.

[0027] In a second aspect, a device for estimating rotor temperature of a motor is provided, the device comprising:

[0028] The first acquisition module is used to acquire the current ambient temperature, current downtime duration, stator loss and rotor loss of the motor;

[0029] A second acquisition module is used to determine the initial rotor temperature of the motor according to the current ambient temperature and the current shutdown duration, and obtain the temperature rise of the motor based on the current ambient temperature, the stator loss and the rotor loss by using a preset thermal network model and a preset dual Kalman filter;

[0030] A third acquisition module is used to obtain the actual rotor temperature of the motor according to the initial rotor temperature and the temperature rise.

[0031] In conjunction with the second aspect, in some possible implementations, the second acquisition module includes:

[0032] A determination unit, configured to determine a target shutdown cooling curve according to the current ambient temperature;

[0033] The first acquisition unit is used to obtain the initial rotor temperature according to the current shutdown duration based on the target shutdown cooling curve.

[0034] In combination with the second aspect and the foregoing implementation manner, in some possible implementation manners, the first acquisition module includes:

[0035] A second acquisition unit, used to acquire the speed, torque and voltage of the motor;

[0036] The third acquisition unit is used to input the rotational speed, the torque and the voltage into a preset motor loss model to obtain the stator loss and the rotor loss.

[0037] In combination with the second aspect and the foregoing implementation manner, in some possible implementation manners, the second acquisition module includes:

[0038] a fourth acquisition unit, configured to input the current ambient temperature, the stator loss, and the rotor loss into the preset thermal network model to obtain a current stator predicted temperature and a current rotor predicted temperature;

[0039] A fifth acquisition unit, used to acquire the current model parameters of the preset thermal network model, the predicted stator temperature at the last moment, and the predicted rotor temperature at the last moment;

[0040] The sixth acquisition unit is used to obtain the temperature rise of the motor according to the state filter of the preset double Kalman filter based on the stator predicted temperature at the previous moment and the rotor predicted temperature at the previous moment, the current stator predicted temperature and the current rotor predicted temperature, and obtain the update parameters of the preset thermal network model according to the parameter filter of the preset double Kalman filter.

[0041] In combination with the second aspect and the above implementation, in some possible implementations, after obtaining the update parameters of the preset thermal network model according to the parameter filter of the preset dual Kalman filter, the sixth acquisition unit further includes:

[0042] An updating subunit is used to update the preset thermal network model using the update parameters.

[0043] In combination with the second aspect and the above implementation, in some possible implementations, the preset thermal network model is:

[0044] θ(n)=[Aθ(n-1)+Bu]Δt+θ(n-1);

[0045] Among them, θ(n-1) is the state quantity at time n-1, u is the input parameter, Δt is the calculation time step, A is the state parameter matrix composed of thermal resistance and heat capacitance parameters, and B is the input parameter matrix composed of thermal resistance and heat capacitance parameters.

[0046] In combination with the second aspect and the above implementation, in some possible implementations, the preset dual Kalman filter includes a parameter filter and a state filter, wherein the parameter filter is:

[0047]

[0048] in, is the prior estimated parameter of the parameter filter at the current moment, is the posterior estimated parameter of the parameter filter at the previous moment, S k - is the prior covariance matrix of the parameter prediction module at the current moment, S k-1 + is the posterior covariance matrix of the parameter prediction module at the previous moment, Σr is the noise of the parameter filter;

[0049] The state filter is:

[0050]

[0051] Among them, A d is the discrete state parameter matrix composed of thermal resistance and thermal capacitance, B d is the discrete input parameter matrix consisting of thermal resistance and thermal capacitance parameters, u k-1 is the input parameter of the previous moment, is the estimated temperature after correction of the state at the previous moment, is the estimated temperature predicted by the current state, Q k-1 + is the posterior covariance matrix of the state filter at the previous moment, Q k - is the prior covariance matrix of the state filter at the current moment, Σ ω is the noise of the state filter.

[0052] In a third aspect, a vehicle is provided, comprising the method for estimating the rotor temperature of the motor described in the above embodiment.

[0053] In a fourth aspect, a computer program product is provided, comprising: a computer program code, which, when executed on a computer, enables the computer to execute the method in the first aspect or any possible implementation of the first aspect.

[0054] In a fifth aspect, a computer-readable storage medium is provided, which stores a computer program code. When the computer program code runs on a computer, the computer executes the method in the above-mentioned first aspect or any possible implementation manner of the first aspect. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] Figure 1 A flow chart of a method for estimating the rotor temperature of a motor provided in an embodiment of the present application;

[0056] Figure 2 A schematic diagram of the initial rotor temperature and temperature correction of a motor according to an embodiment of the present application;

[0057] Figure 3 This is a schematic diagram of a target shutdown cooling curve for an embodiment of the present application;

[0058] Figure 4 This is a schematic diagram of a first-order cooling curve fitting according to an embodiment of the present application;

[0059] Figure 5 A schematic diagram of a two-node stator and rotor temperature model of a motor according to an embodiment of the present application;

[0060] Figure 6 A schematic diagram of temperature estimation of a dual Kalman filter according to an embodiment of the present application;

[0061] Figure 7 A schematic diagram of estimating the rotor temperature of a motor according to an embodiment of the present application;

[0062] Figure 8 A block diagram of a device for estimating the rotor temperature of a motor provided in an embodiment of the present application;

[0063] Fig. 9 A schematic diagram of the structure of a vehicle according to an embodiment of the present application. DETAILED DESCRIPTION

[0064] The technical solution in the present application will be described clearly and in detail below in conjunction with the accompanying drawings. In the description of the embodiments of the present application, unless otherwise specified, " / " means or, for example, A / B can mean A or B: "and / or" in the text is only a description of the association relationship of associated objects, indicating that there can be three relationships, for example, A and / or B can mean: A exists alone, A and B exist at the same time, and B exists alone. In addition, in the description of the embodiments of the present application, "multiple" means two or more than two.

[0065] In the following, the terms "first" and "second" are used for descriptive purposes only and are not to be understood as suggesting or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of the features.

[0066] In motor applications, motor rotor temperature monitoring is crucial to ensure safe operation of the motor and extend its service life. Excessive motor rotor temperature may lead to problems such as permanent magnet demagnetization, insulation material aging, and mechanical component deformation, which in turn affects the performance and reliability of the motor. Therefore, accurate monitoring of the motor rotor temperature is the key to ensuring efficient and safe operation of the motor. However, the motor rotor temperature estimation method in the related art still has some limitations, which leads to low accuracy in motor rotor temperature estimation, which is not conducive to ensuring efficient and safe operation of the motor.

[0067] The methods for estimating the motor rotor temperature in the related art mainly include (1) local temperature method: thermocouples are placed on the motor rotor to obtain the temperature of each point of the rotor. Since the motor rotor rotates continuously when the motor is running, the temperature of the thermocouples needs to be transmitted through a wireless transmitter. Although the temperature of each point of the motor rotor can be obtained, only the local temperature of the rotor can be obtained. Due to the high cost of the wireless transmitter, it is not suitable for monitoring the rotor temperature of the motor in engineering; (2) back electromotive force estimation method: there is a linear relationship between the back electromotive force and the rotor temperature of the motor. The rotor temperature of the motor is estimated by estimating the back electromotive force of the motor, but the estimated rotor temperature of the motor is uneven. It can only estimate the average temperature of the motor rotor, but cannot obtain the hottest point of the rotor, and thus cannot determine whether the motor is overheated, which affects the protection effect; (3) Infrared temperature measurement method: By distributing multiple infrared temperature measuring probes in the axial direction of the rotor, it can only obtain the average temperature of the motor rotor circumference, and cannot distinguish the temperature of each magnetic pole, so the scope of application is limited; (9) Traditional thermal model method: By establishing a physical thermal circuit model of the motor, the stator and rotor of the motor are regarded as heat sources, and the radiator is used as a cooling condition, and the temperature distribution of the motor rotor and its various parts is calculated. However, traditional thermal models usually adopt an open-loop structure. As time goes by, the temperature estimation error will gradually accumulate, resulting in a decrease in the final temperature estimation accuracy.

[0068] Therefore, based on the relevant problems existing in the above-mentioned method of estimating the rotor temperature of the motor, the embodiment of the present application achieves high-precision estimation of the rotor temperature of the motor by establishing a thermal network model and combining it with a dual Kalman filter, without relying on hardware sensors, thereby reducing costs and improving the reliability and robustness of the system.

[0069] Figure 1 It is a schematic flow chart of a method for estimating the rotor temperature of a motor provided in an embodiment of the present application.

[0070] For example, Figure 1 As shown, the method includes:

[0071] In step S101 , the current ambient temperature, current shutdown time, stator loss and rotor loss of the motor are obtained.

[0072] Specifically, during vehicle operation, the motor's rotor temperature monitoring is crucial to the motor's overall performance and safe operation. Therefore, accurate monitoring of the motor's rotor temperature is the key to ensuring the motor's efficient and safe operation. In order to address the limitations of the motor's rotor temperature estimation method in the related art, the present application embodiment achieves high-precision estimation of the motor's rotor temperature by establishing a thermal network model and combining a dual Kalman filter, thereby improving the reliability and robustness of the system.

[0073] Therefore, before the motor is started, the initial rotor temperature of the motor needs to be calculated first, so as to accurately estimate the temperature rise during the operation of the motor based on the initial rotor temperature of the motor, so as to finally obtain the actual rotor temperature of the motor.

[0074] In step S102, the initial rotor temperature of the motor is determined according to the current ambient temperature and the current shutdown duration, and based on the current ambient temperature, stator loss and rotor loss, the temperature rise of the motor is obtained using a preset thermal network model and a preset dual Kalman filter.

[0075] Optionally, in one embodiment of the present application, the initial rotor temperature of the motor is determined according to the current ambient temperature and the current shutdown time, including: determining a target shutdown cooling curve according to the current ambient temperature; based on the target shutdown cooling curve, obtaining the initial rotor temperature according to the current shutdown time.

[0076] Among them, the preset thermal network model and the preset dual Kalman filter can be selected by technical personnel in this field according to actual experimental requirements, and are not specifically limited here.

[0077] Specifically, if Figure 2 As shown, since the initial rotor temperature of the motor is mainly affected by the motor temperature stored after the motor was last powered off, the downtime duration and the ambient temperature, when calculating the initial rotor temperature of the motor, it is first necessary to obtain the current ambient temperature of the motor, the motor temperature stored after the motor was last powered off and the current downtime duration of the motor, wherein the motor temperature stored after the motor was last powered off is the actual temperature when the motor was powered off, which is usually recorded and stored by the vehicle controller or other control systems, and the current downtime duration is the time interval from when the motor stops working to when it is restarted. The current ambient temperature of the motor can be measured in real time by a sensor or obtained from historical data, which is not specifically limited here; secondly, Figure 3 and Figure 4 As shown in FIG. 1 , in order to calculate the temperature change of the motor during the current downtime, it is necessary to record the relationship between the motor rotor temperature drop over time under natural cooling conditions at different ambient temperatures (e.g., -20°C to 60°C) during the experiment, with a step size of 10°C. That is, with a step size of 10°C as the ambient temperature interval, based on the current ambient temperature T0, the motor temperature T stored after the motor was last powered off, and the current downtime of the motor T t Construct a target shutdown cooling curve to obtain the temperature change of the motor during the current shutdown time at different ambient temperatures. The target shutdown cooling curve is a cooling curve table, so that the initial rotor temperature of the motor can be obtained by querying the target shutdown cooling curve. The calculation formula is shown in the following formula:

[0078]

[0079] Among them, Tini is the initial rotor temperature of the motor, T is the motor temperature stored after the motor was powered off last time, e is a natural constant, and t is the time after power off, that is, the downtime duration T t , τ is the cooling time constant.

[0080] Furthermore, if the current ambient temperature is not within the range of the above-mentioned target shutdown cooling curve, the initial rotor temperature of the motor can be estimated by linear interpolation, that is, based on the cooling curves at two adjacent known ambient temperatures, the cooling curve at the intermediate temperature can be inferred by linear relationship.

[0081] Optionally, in one embodiment of the present application, obtaining the stator loss and rotor loss of the motor includes: obtaining the speed, torque and voltage of the motor; inputting the speed, torque and voltage into a preset motor loss model to obtain the stator loss and rotor loss.

[0082] Optionally, in one embodiment of the present application, based on the current ambient temperature, stator loss and rotor loss, the temperature rise of the motor is obtained using a preset thermal network model and a preset dual Kalman filter, including: inputting the current ambient temperature, stator loss and rotor loss into the preset thermal network model to obtain the current stator predicted temperature and the current rotor predicted temperature; obtaining the current model parameters of the preset thermal network model, the stator predicted temperature at the last moment and the rotor predicted temperature at the last moment; based on the stator predicted temperature at the last moment and the rotor predicted temperature at the last moment, the current stator predicted temperature and the current rotor predicted temperature, the temperature rise of the motor is obtained according to the state filter of the preset dual Kalman filter, and the updated parameters of the preset thermal network model are obtained according to the parameter filter of the preset dual Kalman filter.

[0083] Optionally, in an embodiment of the present application, after obtaining the update parameters of the preset thermal network model according to the parameter filter of the preset dual Kalman filter, it also includes: updating the preset thermal network model using the update parameters.

[0084] Among them, the preset motor loss model can be selected by technical personnel in this field according to actual experimental requirements and is not specifically limited here.

[0085] Specifically, after calculating the initial rotor temperature of the motor, the embodiment of the present application needs to further calculate the temperature rise of the motor using a preset thermal network model, wherein the stator loss and rotor loss are the main sources of heat generated during the operation of the motor. The heat generated by these losses will be transferred to the various components of the motor (such as the stator, rotor, cooling system, etc.) through heat conduction, convection and radiation, and ultimately affect the temperature distribution of the motor. Therefore, accurately obtaining the loss data of the stator and rotor is the basis for temperature rise calculation. At the same time, due to the continuous changes in stator loss, rotor loss and ambient temperature, the temperature rise calculation needs to be based on stator loss and rotor loss as the main input parameters, and these parameters need to be updated in real time to ensure the accuracy of the results.

[0086] Specifically, first, the speed, torque and voltage of the motor are obtained, and then the speed, torque and voltage are input into the preset motor loss model, and the stator loss and rotor loss are obtained from the preset motor loss model; secondly, the current ambient temperature, stator loss and rotor loss are input into the preset thermal network model, and then the current stator predicted temperature and the current rotor predicted temperature are obtained. The preset thermal network model is used to simulate the temperature changes of various parts of the motor during operation. In order to simplify the calculation and improve real-time performance, a two-node stator and rotor temperature model is usually used for calculation, such as Figure 5 As shown in FIG. 1 , the preset thermal network model regards the stator, rotor and cooling system of the motor as a complex heat transfer system. By establishing the thermal resistance and heat capacity relationship between the stator and the rotor, the heat transfer process inside the motor is described. The main steps include: the thermal resistance network calculation module merges the thermal nodes distributed on the motor, that is, it pays more attention to the special point temperatures of the motor components, such as the stator temperature and the rotor temperature. By reducing the temperature nodes to simplify the preset thermal network model, it is beneficial to reduce the order of the system and is also convenient for running in the single-chip microcomputer. Therefore, under the condition of ensuring the estimation of the temperature of the special key nodes, the preset thermal network model is reduced in order to establish a two-node stator and rotor temperature model of the motor, where θ amb is the coolant (ambient) temperature, R1 is the thermal resistance between the environment and the stator winding, R2 represents the thermal resistance between the stator winding and the rotor, θ cu (t) is the stator temperature, θ rotor (t) is the rotor temperature, C cu is the stator heat capacity, P cu is the stator loss, C rotor is the rotor heat capacity, P rotor is the rotor loss. Based on the above two-node stator and rotor temperature model, the preset thermal network model can be expressed as

[0087]

[0088]

[0089] Among them, θ(t) is the temperature of the motor (stator temperature and rotor temperature), u(t) is the input quantity (ambient temperature, stator loss and rotor loss), A is the state parameter matrix composed of thermal resistance and heat capacitance parameters, and B is the input parameter matrix composed of thermal resistance and heat capacitance parameters.

[0090] It should be noted that, since the temperature change of the motor is a continuous process rather than being completed instantly, the temperature rise calculation of the motor needs to consider the continuity in time, that is, the current stator predicted temperature and the current rotor predicted temperature at the current moment are based on the stator predicted temperature and the rotor predicted temperature at the previous moment. The predicted temperature at the previous moment can ensure a smooth transition of the temperature rise calculation and avoid sudden changes or unreasonable temperature jumps. Therefore, the embodiment of the present application needs to further obtain the current model parameters of the preset thermal network model, the stator predicted temperature at the previous moment, and the rotor predicted temperature at the previous moment, wherein the current model parameters mainly include the stator loss, rotor loss and ambient temperature of the motor, so as to obtain the temperature rise of the motor at the current moment based on the initial rotor temperature of the motor and the current stator predicted temperature and the current rotor predicted temperature, and then make corrections and adjustments based on the stator predicted temperature at the previous moment and the rotor predicted temperature at the previous moment.

[0091] The above preset thermal network model equation is expressed as dθ=Aθ+Bu. In order to facilitate implementation in an embedded system, the above equation can be converted into a differential equation by discretization, that is, [θ(n)-θ(n-1)] / Δt=Aθ(n-1)+Bu, so that the current stator predicted temperature and the current rotor predicted temperature can be calculated using the preset thermal network model. The formula can be expressed as:

[0092] θ(n)=[Aθ(n-1)+Bu]Δt+θ(n-1);

[0093] Among them, θ(n-1) is the state quantity (stator temperature and rotor temperature) at time n-1, u is the input parameter (ambient temperature, stator loss and rotor loss of the motor), Δt is the calculation time step, A is the state parameter matrix composed of thermal resistance and heat capacitance parameters, and B is the input parameter matrix composed of thermal resistance and heat capacitance parameters.

[0094] It should be noted that, in the bench test, in order to fully understand the working characteristics of the motor under different voltage conditions and to verify the robustness and adaptability of the motor in practical applications, the embodiment of the present application selects the highest, lowest and rated bus voltages for testing. At the same time, in order to systematically test the performance of the motor under different working conditions and ensure that the entire working range from low load to high load is covered, the embodiment of the present application sets a fixed speed step of 1000rpm and a torque step of 10Nm for testing.

[0095] Furthermore, since the biggest drawback of the above method is that it adopts an open-loop structure, the error in estimating the rotor temperature of the motor is likely to accumulate over time, making it difficult to ensure the accuracy of its temperature. Therefore, in order to accurately estimate the rotor temperature of the motor, the embodiment of the present application uses a preset dual Kalman filter temperature estimation algorithm for correction based on a preset thermal network model.

[0096] Specifically, Figure 6 As shown in the figure, the preset dual Kalman filter mainly includes two Kalman filters running in parallel, mainly including a state filter and a parameter filter, wherein the state filter is responsible for estimating the stator temperature and rotor temperature (i.e., state variables) of the motor, and correcting the current stator predicted temperature, the current rotor predicted temperature, and the actual measured value (such as the data of the stator temperature sensor) calculated based on the preset thermal network model; the parameter filter is responsible for estimating the parameters in the preset thermal network model, and updating them in real time according to the stator predicted temperature and the current rotor predicted temperature. In this process, the parameter filter uses the temperature estimation from the state filter, and the state filter also uses the parameter estimation from the parameter filter. The two filters are independent of each other but work closely together to ensure the high accuracy and stability of the temperature rise estimation, which mainly includes 4 thermal parameters, namely R1, R2, C cu and C rotor , mainly expressed as:

[0097] ζ=[R1 R2 C Cu C rotor ] T

[0098] Where R1 is the thermal resistance between the environment and the stator winding, R2 represents the thermal resistance between the stator winding and the rotor, C cu is the stator heat capacity, C rotor is the rotor heat capacity.

[0099] Specifically, each of the above-mentioned Kalman filters includes two consecutive steps, mainly including state prediction and a correction module based on the measurement equation, namely, the correction strategy of the parameter filter and the correction strategy of the state filter. Four consecutive steps are executed at each time step k, namely, parameter filter prediction and state filter prediction, followed by parameter filter correction and state filter correction.

[0100] First, in the first step (parameter filter prediction), the parameter prediction is a quasi-constant from one time step k to the next time step k+1. The parameter filter first predicts the parameters at the next moment based on the parameter estimate at the previous moment and the current input parameters (such as loss and current ambient temperature), that is, the prior parameter estimate. Since they change slowly relative to the state prediction, they can be shown as follows:

[0101]

[0102] In order to evaluate the uncertainty of the prediction parameters, the parameter filter also needs to update the prediction error covariance matrix. S(k) is the state covariance matrix of the parameter filter. is the prior estimated parameter of the parameter filter at the current moment, is the posterior estimated parameter of the parameter filter at the previous moment, S k - is the prior covariance matrix of the parameter prediction module at the current moment, S k-1 + is the posterior covariance matrix of the parameter prediction module at the previous moment, Σ r is the noise of the parameter filter.

[0103] Secondly, based on the updated parameters, at each time step k, the state filter first predicts the state at the next moment based on the state at the previous moment and the current input parameters (such as loss and current ambient temperature), that is, predicts the rotor temperature of the motor at the next moment. The state filter equation can be expressed as:

[0104]

[0105] Among them, in order to evaluate the uncertainty of the predicted state, the state filter also needs to update the prediction error covariance matrix. It should be noted that the matrix A d and B d is not constant, but is updated according to the estimated value of the thermal parameter (ζ), where A d is the discrete state parameter matrix composed of thermal resistance and thermal capacitance, Bd is the discrete input parameter matrix consisting of thermal resistance and thermal capacitance parameters, u k-1 is the input at the previous moment (ambient temperature, stator loss and rotor loss of the motor), is the estimated temperature (stator temperature and rotor temperature) after state correction at the previous moment, is the estimated temperature (stator temperature and rotor temperature) predicted by the current state, Q k-1 + is the posterior covariance matrix of the state filter at the previous moment, Q k - is the prior covariance matrix of the state filter at the current moment, Σ ω is the noise of the state filter.

[0106] Again, after obtaining the predicted parameters, the parameter filter corrects the predicted parameters according to the actual measured values ​​to obtain a more accurate posterior parameter estimate, which is calculated as shown in the following formula:

[0107]

[0108] where the superscripts - and + are the quantities evaluated before (a priori parameter estimation) and after (a posteriori parameter estimation) the correction step, respectively, S k is the covariance matrix of the parameter filter, L k is the parameter-related Kalman gain, which is used to determine the weight between the predicted parameters and the measured values. In addition, since the parameter filter and the state filter are based on the same measurement signal (i.e., the installed stator temperature sensor), the measured value θcu of the stator winding is obtained. Therefore, the measurement noise covariance matrix Σ v For parameter filters and state filters, I is the unit matrix, K is the state gain, L is the parameter gain, Q is the k is the covariance, Cx is the matrix of the transformation process, which can be expressed as y(t)=θ Cu (t) = C x θ(t))+ν(t)

[0109] Among them, the parameter filter updates the posterior parameter error covariance matrix to reflect the uncertainty of the corrected parameter estimate, y(t) is the measured stator temperature, θ Cu (t) is the measured stator temperature, C x is the transfer matrix of the transformation process, θ(t) is the motor temperature (stator temperature and rotor temperature), and v(t) is the noise in the prediction process.

[0110] Finally, after obtaining the predicted state, the state filter corrects the predicted state according to the actual measurement value to obtain a more accurate posterior state estimate. Then the state filter updates the posterior error covariance matrix to reflect the uncertainty of the corrected state estimate. The correction module based on the state filter, that is, the correction strategy of the state filter is as follows:

[0111]

[0112] Among them, K k is the Kalman filter gain at the current moment, Q k - is the prior covariance matrix of the state filter at the current moment, is the transfer matrix of the transformation process, for The transposed matrix, Σv is the covariance matrix of the parameter filter, y k is the motor stator temperature measured at the current moment, I2 is the second-order unit matrix, is the estimated temperature (stator temperature and rotor temperature) after state correction at the previous moment, The estimated temperature (stator temperature and rotor temperature) predicted for the current state, is the estimated temperature (stator temperature and rotor temperature) after correction of the current state,

[0113] is the posterior covariance matrix of the state filter at the current moment.

[0114] Therefore, based on the coordinated work of the state filter and the parameter filter, the current rotor predicted temperature and the current rotor predicted temperature obtained at a certain moment are continuously corrected to obtain the temperature rise at the current moment.

[0115] In step S103, the actual rotor temperature of the motor is obtained according to the initial rotor temperature and the temperature rise.

[0116] Specifically, Figure 7 As shown, the embodiment of the present application is based on the coordinated work of the state filter and the parameter filter to correct the current rotor predicted temperature and the current rotor predicted temperature at a certain moment in real time, and dynamically adjust the parameters (such as thermal resistance and thermal capacitance) in the preset thermal network model. Since the state filter and the parameter filter of the dual Kalman filter form a closed-loop feedback mechanism, the current rotor predicted temperature and the current rotor predicted temperature at this moment are more accurate, so that based on the initial rotor temperature obtained initially and the temperature rise at a certain moment, the actual rotor temperature of the motor at this moment is finally obtained, so that the actual rotor temperature of the motor is in a stable state.

[0117] Therefore, based on the discussion of the above specific embodiments, the present application can achieve the following beneficial effects:

[0118] (1) Improve estimation accuracy. By introducing a dual Kalman filter, the system can correct the temperature prediction values ​​of the stator and rotor in real time and dynamically adjust the parameters in the thermal network model (such as thermal resistance and thermal capacitance). This makes the temperature rise estimation more accurate and reduces the cumulative error caused by factors such as model error and measurement noise;

[0119] (2) Closed-loop feedback mechanism: The state filter and parameter filter of the dual Kalman filter form a closed-loop feedback mechanism to ensure that the system's state estimation and parameter estimation can be continuously updated to adapt to changes in actual working conditions;

[0120] (3) Optimize motor control strategy and provide real-time temperature feedback. Through high-precision temperature rise estimation, the system can provide real-time temperature feedback to the motor controller to help optimize the control strategy

[0121] In summary, according to the rotor temperature estimation method of the motor of the embodiment of the present application, the current ambient temperature, current downtime, stator loss and rotor loss of the motor are obtained, and the initial rotor temperature of the motor is determined according to the current ambient temperature and the current downtime, and based on the current ambient temperature, stator loss and rotor loss, the temperature rise of the motor is obtained by using a preset thermal network model and a preset dual Kalman filter; the actual rotor temperature of the motor is obtained according to the initial rotor temperature and the temperature rise. This method achieves high-precision estimation of the rotor temperature of the motor by establishing a thermal network model and combining a dual Kalman filter, without relying on hardware sensors, reducing costs, and improving the reliability and robustness of the system.

[0122] Figure 8 It is a structural schematic diagram of a motor rotor temperature estimation device provided in an embodiment of the present application.

[0123] For example, Figure 8 As shown, the device may include: a first acquisition module 100 , a second acquisition module 200 and a third acquisition module 300 .

[0124] The first acquisition module 100 is used to acquire the current ambient temperature, current downtime duration, stator loss and rotor loss of the motor;

[0125] A second acquisition module 200 is used to determine the initial rotor temperature of the motor according to the current ambient temperature and the current downtime, and obtain the temperature rise of the motor based on the current ambient temperature, stator loss and rotor loss using a preset thermal network model and a preset dual Kalman filter;

[0126] The third acquisition module 300 is used to obtain the actual rotor temperature of the motor according to the initial rotor temperature and the temperature rise.

[0127] Optionally, in one embodiment of the present application, the second acquisition module 200 includes:

[0128] A determination unit, used to determine a target shutdown cooling curve according to the current ambient temperature;

[0129] The first acquisition unit is used to obtain an initial rotor temperature according to the current shutdown duration based on the target shutdown cooling curve.

[0130] Optionally, in one embodiment of the present application, the first acquisition module 100 includes:

[0131] A second acquisition unit is used to acquire the speed, torque and voltage of the motor;

[0132] The third acquisition unit is used to input the rotational speed, torque and voltage into a preset motor loss model to obtain the stator loss and rotor loss.

[0133] Optionally, in one embodiment of the present application, the second acquisition module 200 includes:

[0134] A fourth acquisition unit is used to input the current ambient temperature, stator loss and rotor loss into a preset thermal network model to obtain a current stator predicted temperature and a current rotor predicted temperature;

[0135] A fifth acquisition unit, used to acquire current model parameters of a preset thermal network model, a predicted stator temperature at a previous moment, and a predicted rotor temperature at a previous moment;

[0136] The sixth acquisition unit is used to obtain the temperature rise of the motor according to the state filter of the preset double Kalman filter based on the stator predicted temperature at the previous moment and the rotor predicted temperature at the previous moment, the current stator predicted temperature and the current rotor predicted temperature, and obtain the update parameters of the preset thermal network model according to the parameter filter of the preset double Kalman filter.

[0137] Optionally, in one embodiment of the present application, after obtaining the update parameters of the preset thermal network model according to the parameter filter of the preset dual Kalman filter, the sixth acquisition unit further includes:

[0138] The updating subunit is used to update the preset thermal network model using the updating parameters.

[0139] Optionally, in one embodiment of the present application, the preset thermal network model is:

[0140] θ(n)=[Aθ(n-1)+Bu]Δt+θ(n-1);

[0141] Among them, θ(n-1) is the state quantity at time n-1, u is the input parameter, Δt is the calculation time step, A is the state parameter matrix composed of thermal resistance and heat capacitance parameters, and B is the input parameter matrix composed of thermal resistance and heat capacitance parameters.

[0142] Optionally, in one embodiment of the present application, the preset dual Kalman filter includes a parameter filter and a state filter, wherein the parameter filter is:

[0143]

[0144] in, is the prior estimated parameter of the parameter filter at the current moment, is the posterior estimated parameter of the parameter filter at the previous moment, S k - is the prior covariance matrix of the parameter prediction module at the current moment, S k-1 + is the posterior covariance matrix of the parameter prediction module at the previous moment, Σr is the noise of the parameter filter;

[0145] The state filter is:

[0146]

[0147] Among them, A d is the discrete state parameter matrix composed of thermal resistance and thermal capacitance, B d is the discrete input parameter matrix consisting of thermal resistance and thermal capacitance parameters, u k-1 is the input parameter of the previous moment, is the estimated temperature after correction of the state at the previous moment, is the estimated temperature predicted by the current state, Q k-1 + is the posterior covariance matrix of the state filter at the previous moment, Q k - is the prior covariance matrix of the state filter at the current moment, Σ ω is the noise of the state filter.

[0148] In summary, according to the rotor temperature estimation device of the motor of the embodiment of the present application, the current ambient temperature, current downtime, stator loss and rotor loss of the motor are obtained, and the initial rotor temperature of the motor is determined according to the current ambient temperature and the current downtime, and based on the current ambient temperature, stator loss and rotor loss, the temperature rise of the motor is obtained by using a preset thermal network model and a preset dual Kalman filter; the actual rotor temperature of the motor is obtained according to the initial rotor temperature and the temperature rise. This method achieves high-precision estimation of the rotor temperature of the motor by establishing a thermal network model and combining a dual Kalman filter, without relying on hardware sensors, reducing costs, and improving the reliability and robustness of the system.

[0149] Fig. 9 A schematic structural diagram of a vehicle provided in an embodiment of the present application.

[0150] It should be understood that the above-described method can be applied to Fig. 9 In a vehicle of the structure shown.

[0151] In addition, an embodiment of the present application also protects a device, which may include a memory and a processor, wherein the memory stores an executable program code, and the processor is used to call and execute the executable program code to execute the rotor temperature estimation method of the motor provided in the embodiment of the present application.

[0152] Furthermore, the device also includes: a communication interface 903 for communication between the memory 901 and the processor 902 .

[0153] In this embodiment, the functional modules of the device can be divided according to the above method example. For example, each functional module can be corresponded, or two or more functions can be integrated into one processing module. The above integrated module can be implemented in the form of hardware. It should be noted that the division of modules in this embodiment is schematic and is only a logical function division. There may be other division methods in actual implementation.

[0154] It should be noted that all relevant contents of each step involved in the above method embodiment can be referred to the functional description of the corresponding functional module, which will not be repeated here.

[0155] It should be understood that the device provided in this embodiment is used to execute the above-mentioned method for estimating the rotor temperature of a motor, and thus can achieve the same effect as the above-mentioned implementation method.

[0156] In the case of an integrated unit, the device may include a processing module and a storage module. When the device is applied to a vehicle, the processing module may be used to control and manage the actions of the vehicle. The storage module may be used to support the vehicle in executing mutual program codes, etc.

[0157] The processing module may be a processor 902 or a controller, which may implement or execute various exemplary logic blocks, modules and circuits disclosed in the present application. The processor 902 may also be a combination that implements a computing function, such as a combination of one or more microprocessors, a combination of digital signal processing (DSP) and a microprocessor, etc. The storage module may be a memory 901.

[0158] In addition, the device provided in the embodiments of the present application may specifically be a chip, a component or a module, and the chip may include a connected processor 902 and a memory 901; wherein the memory 901 is used to store instructions, and when the processor calls and executes the instructions, the chip can execute the rotor temperature estimation method of the motor provided in the above embodiments.

[0159] This embodiment also provides a computer-readable storage medium, in which a computer program code is stored. When the computer program code is executed on a computer, the computer executes the above-mentioned related method steps to implement a method for estimating the rotor temperature of a motor provided in the above embodiment.

[0160] This embodiment further provides a computer program product. When the computer program product is run on a computer, the computer is enabled to execute the above-mentioned related steps to implement a method for estimating the rotor temperature of a motor provided in the above-mentioned embodiment.

[0161] Among them, the device, computer-readable storage medium, computer program product or chip provided in this embodiment is used to execute the corresponding method provided above. Therefore, the beneficial effects that can be achieved can refer to the beneficial effects in the corresponding method provided above, and will not be repeated here.

[0162] Through the description of the above implementation methods, technical personnel in the relevant field can 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.

[0163] In the 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.

[0164] The above contents are only specific implementation methods of the present application, but the protection scope of the present application is not limited thereto. Any technician familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which 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 method for estimating the rotor temperature of a motor, characterized in that: The method comprises: Obtain the current ambient temperature, current downtime duration, stator loss, and rotor loss of the motor; Determine the initial rotor temperature of the motor according to the current ambient temperature and the current shutdown duration, and obtain the temperature rise of the motor based on the current ambient temperature, the stator loss and the rotor loss using a preset thermal network model and a preset dual Kalman filter; The actual rotor temperature of the motor is obtained according to the initial rotor temperature and the temperature rise.

2. The method according to claim 1, characterized in that The determining the initial rotor temperature of the motor according to the current ambient temperature and the current shutdown duration includes: Determine a target shutdown cooling curve according to the current ambient temperature; Based on the target shutdown cooling curve, the initial rotor temperature is obtained according to the current shutdown duration.

3. The method according to claim 1, characterized in that The step of obtaining the stator loss and the rotor loss of the motor includes: Obtaining the speed, torque and voltage of the motor; The rotational speed, the torque and the voltage are input into a preset motor loss model to obtain the stator loss and the rotor loss.

4. The method according to claim 1, characterized in that: The method of obtaining the temperature rise of the motor based on the current ambient temperature, the stator loss and the rotor loss by using a preset thermal network model and a preset dual Kalman filter includes: Inputting the current ambient temperature, the stator loss and the rotor loss into the preset thermal network model to obtain a current stator predicted temperature and a current rotor predicted temperature; Obtaining current model parameters of the preset thermal network model, the predicted stator temperature at the last moment, and the predicted rotor temperature at the last moment; Based on the predicted stator temperature at the previous moment and the predicted rotor temperature at the previous moment, the current predicted stator temperature and the current predicted rotor temperature, the temperature rise of the motor is obtained according to the state filter of the preset double Kalman filter, and the updated parameters of the preset thermal network model are obtained according to the parameter filter of the preset double Kalman filter.

5. The method according to claim 4, characterized in that After obtaining the update parameters of the preset thermal network model according to the parameter filter of the preset dual Kalman filter, the method further includes: The preset thermal network model is updated using the update parameters.

6. The method according to claim 4, characterized in that The preset thermal network model is: θ(n)=[Aθ(n-1)+Bu]Δt+θ(n-1); Among them, θ(n-1) is the state quantity at time n-1, u is the input parameter, Δt is the calculation time step, A is the state parameter matrix composed of thermal resistance and heat capacitance parameters, and B is the input parameter matrix composed of thermal resistance and heat capacitance parameters.

7. The method according to claim 4, characterized in that The preset dual Kalman filter includes a parameter filter and a state filter, wherein the parameter filter is: in, is the prior estimated parameter of the parameter filter at the current moment, is the posterior estimated parameter of the parameter filter at the previous moment, S k - is the prior covariance matrix of the parameter prediction module at the current moment, S k-1 + is the posterior covariance matrix of the parameter prediction module at the previous moment, Σ r is the noise of the parameter filter; The state filter is: in, is the discrete state parameter matrix composed of thermal resistance and thermal capacitance, is the discrete input parameter matrix consisting of thermal resistance and thermal capacitance parameters, u k-1 is the input parameter of the previous moment, is the estimated temperature after correction of the state at the previous moment, is the estimated temperature predicted by the current state, Q k-1 + is the posterior covariance matrix of the state filter at the previous moment, Q k - is the prior covariance matrix of the state filter at the current moment, Σ ω is the noise of the state filter.

8. A device for estimating the rotor temperature of a motor, characterized in that: The device comprises: The first acquisition module is used to acquire the current ambient temperature, current downtime duration, stator loss and rotor loss of the motor; A second acquisition module is used to determine the initial rotor temperature of the motor according to the current ambient temperature and the current shutdown duration, and obtain the temperature rise of the motor based on the current ambient temperature, the stator loss and the rotor loss by using a preset thermal network model and a preset dual Kalman filter; A third acquisition module is used to obtain the actual rotor temperature of the motor according to the initial rotor temperature and the temperature rise.

9. A vehicle, characterized in that: The vehicle comprises: a method for estimating the rotor temperature of a motor as described in any one of claims 1 to 7 above.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed, the method according to any one of claims 1 to 7 is implemented.

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