Motor control method, controller, vehicle, storage medium and program product

Abstract

The invention relates to a motor control method, a controller, a vehicle, a storage medium and a program product, and relates to the technical field of motor control. In the application, firstly, in the aspect of power output of the vehicle, the current power demand of the vehicle is determined by acquiring the demand torque in real time; in the aspect of battery heating, the set target battery temperature is obtained to determine the heating requirement of the battery. Then, the target winding current is generated according to the required torque of the motor and the set target battery temperature, so that the target winding current is used for controlling the motor, the motor can meet the current required torque so as to enable a vehicle to run normally, the requirement for heating the battery can be met, and waste heat generated by the motor is effectively utilized.

Description

Technical Field

[0001] The present application relates to the technical field of motor control, and in particular, to a motor control method, a controller, a vehicle, a storage medium, and a program product. Background Art

[0002] With the rapid development of new energy vehicles, the performance and lifespan of power batteries have become key factors restricting the reliable operation of vehicles in low-temperature environments. Under low-temperature conditions (such as below 0°C), the charge and discharge efficiency of power batteries significantly decreases, and the internal resistance increases sharply, resulting in a shortened driving range and limited power output. Therefore, it is usually necessary to heat the battery pack.

[0003] Currently, the commonly used battery pack heating methods mainly rely on external heating devices, such as PTC (Positive Temperature Coefficient) heaters, or use a liquid heating system. Although the above methods can effectively increase the temperature of the battery pack, they all require additional consumption of battery energy, thereby reducing the energy efficiency of the entire vehicle. To improve energy utilization efficiency, it is possible to try to use the waste heat generated by the vehicle itself to heat the power battery. For example, during the process of the motor driving the vehicle, the waste heat generated when the motor is running.

[0004] However, the motor needs to provide power for the vehicle during driving. How to heat the battery pack using the waste heat generated by the motor while ensuring the normal operation of the motor is an urgent problem to be solved currently. Summary of the Invention

[0005] Embodiments of the present application provide a motor control method, which can heat the battery pack using the waste heat generated by the motor while ensuring the normal operation of the motor, so as to at least partially solve the above technical problems.

[0006] To achieve the above object, according to the first aspect of the present application, a motor control method is provided, including:

[0007] Generating a target winding current of the motor according to the required torque of the motor and a set target battery temperature to control the operation of the motor.

[0008] Optionally, the target winding current includes a target direct-axis current and a target quadrature-axis current.

[0009] Optionally, the generating the target winding current of the motor according to the required torque of the motor and a set target battery temperature includes:

[0010] Obtaining a constraint model according to the required torque and the target battery temperature;

[0011] Generating the target winding current according to the constraint model.

[0012] Optionally, obtaining a constraint model based on the required torque and the target battery temperature includes:

[0013] Establishing a temperature error function according to a preset battery temperature prediction function and the target battery temperature;

[0014] Establishing a torque error function according to a preset torque function and the required torque;

[0015] Constructing the constraint model according to the temperature error function and the torque error function.

[0016] Optionally, before establishing the temperature error function according to the preset battery temperature prediction function and the target battery temperature, it further includes:

[0017] Obtaining a battery heat generation function;

[0018] Obtaining a motor heat generation function;

[0019] Establishing a heat conduction model between the motor and the battery according to the battery heat generation function, the motor heat generation function, and a preset heat conduction coefficient to obtain the battery temperature prediction function;

[0020] Wherein, the heat conduction coefficient is used to characterize the correlation between the temperature of the battery and the temperature of the motor.

[0021] Optionally, obtaining the battery heat generation function includes:

[0022] Generating a correspondence between battery attribute parameters and battery heat based on the difference between a battery heat conduction function and a battery heat dissipation function to obtain the battery heat generation function;

[0023] Wherein, the battery heat conduction function is a correspondence between battery attribute parameters and generated heat; the battery heat dissipation function is a correspondence between battery attribute parameters and dissipated heat.

[0024] Optionally, the motor heat generation function includes the variation relationship between the motor winding current and the motor heat.

[0025] Optionally, establishing the heat conduction model between the motor and the battery according to the battery heat generation function, the motor heat generation function, and a preset heat conduction coefficient to obtain the battery temperature prediction function includes:

[0026] Establishing a correlation relationship between the battery heat generation function, the heat conduction coefficient, and the motor heat generation function to obtain the heat conduction model;

[0027] Fitting the heat conduction model according to the historical data of the motor winding current and the historical data of the battery temperature change amount to obtain the battery temperature change function;

[0028] According to the current battery temperature and the battery temperature change function, obtain the battery temperature prediction function.

[0029] Optionally, the battery temperature prediction function is the correspondence between the motor winding current and the battery heat.

[0030] Optionally, obtaining the constraint model according to the temperature error function and the torque error function includes:

[0031] Obtain a constraint weight value;

[0032] According to the constraint weight value, perform weighted fusion on the temperature error function and the torque error function to obtain the constraint model;

[0033] Wherein, the constraint model is used to express the functional relationship between the motor winding current and the error value, and the error value is the weighted sum value of the output value of the torque error function and the output value of the temperature error function.

[0034] Optionally, the motor winding current includes the direct-axis current and the quadrature-axis current of the motor; generating the target winding current according to the constraint model includes:

[0035] Obtain a preset descent step size;

[0036] According to the descent step size, perform iterative update on the direct-axis current and the quadrature-axis current by the gradient descent method, and respectively determine the direct-axis current and the quadrature-axis current corresponding to when the error value of the constraint model converges as the target direct-axis current and the target quadrature-axis current.

[0037] Optionally, it further includes:

[0038] Obtain a preset motor temperature prediction function;

[0039] Input the target direct-axis current and the target quadrature-axis current into the motor temperature prediction function to generate a predicted motor temperature;

[0040] When the predicted motor temperature exceeds the motor temperature threshold, adjust the target direct-axis current and the target quadrature-axis current.

[0041] Optionally, adjusting the target direct-axis current and the target quadrature-axis current includes:

[0042] According to the motor temperature prediction function and the motor temperature threshold, obtain a current constraint value;

[0043] Determine a reduction ratio according to the current constraint value, the target direct-axis current, and the target quadrature-axis current.

[0044] Adjust the target direct-axis current and the target quadrature-axis current according to the reduction ratio.

[0045] According to a second aspect of the present application, there is provided a controller on which a computer program is stored, and when the computer program is executed by a processor, the steps of the above method are implemented.

[0046] According to a third aspect of the present application, there is provided a vehicle including the above controller.

[0047] According to a fourth aspect of the present application, there is provided a computer-readable storage medium on which a computer program is stored, and when the computer program is executed by a processor, the steps of the above method are implemented.

[0048] According to a fifth aspect of the present application, there is provided a computer program product including a computer program or instruction, and when the computer program or instruction is executed by a processor, the steps of the above method are implemented.

[0049] In summary, in the motor control method of the embodiments of the present application, first, in terms of vehicle power output, by obtaining the required torque in real time, the current power demand of the vehicle is clarified. In terms of battery heating, by obtaining the set target battery temperature, the heating demand of the battery is clarified. Then, according to the required torque of the motor and the set target battery temperature, a target winding current is generated, and thus the motor is controlled by using the target winding current, which can enable the motor to not only meet the current required torque to enable the vehicle to drive normally, but also meet the heating demand of the battery, and effectively utilize the waste heat generated by the motor.

[0050] Other features and advantages of the present application will be described in detail in the subsequent specific implementation part. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the following drawings are only some embodiments of the present application, and those skilled in the art can also obtain other drawings without creative efforts based on these drawings.

[0052] In order to more completely understand the present application and its beneficial effects, the following will be described in conjunction with the drawings, where the same reference numerals represent the same parts in the following description.

[0053] Figure 1It is a flowchart of a method for constructing a constraint model provided in an exemplary embodiment of the present disclosure;

[0054] Figure 2 It is a flowchart of a method for obtaining a constraint model through weighted fusion provided in an exemplary embodiment of the present disclosure;

[0055] Figure 3 It is a flowchart of a method for generating a battery temperature prediction function provided in an exemplary embodiment of the present disclosure;

[0056] Figure 4 It is a flowchart of a method for fitting a battery temperature prediction function provided in an exemplary embodiment of the present disclosure;

[0057] Figure 5 It is a flowchart of a method for controlling the motor temperature provided in an exemplary embodiment of the present disclosure;

[0058] Figure 6 It is a flowchart of a method for adjusting the target direct-axis current and the target quadrature-axis current provided in an exemplary embodiment of the present disclosure. Specific Embodiments

[0059] Next, the technical solutions in the embodiments of the present application will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative efforts shall fall within the protection scope of the present application.

[0060] According to a first aspect of the present application, the present disclosure provides a motor control method, which may include: generating a target winding current of the motor according to the required torque of the motor and a set target battery temperature to control the operation of the motor.

[0061] Among them, the target winding current includes a target direct-axis current and a target quadrature-axis current.

[0062] As an example, the dq coordinate system is a rotating coordinate system, and its coordinate axes are perpendicular to the three-phase power supply in the circuit, thereby helping to convert the relatively complex AC motor control into DC motor control. In the dq coordinate system, the current is decomposed into a direct-axis component (d-axis component) and a quadrature-axis component (q-axis component), corresponding to the direct axis and the quadrature axis directions of the motor respectively.

[0063] Among them, the quadrature-axis current is related to the direction of the magnetic flux on the quadrature axis of the motor. When the quadrature-axis current changes, it will change the distribution of the stator magnetic field of the motor, and then enhance or weaken the interaction between the stator magnetic field and the permanent magnet magnetic field. Therefore, by adjusting the magnitude and phase of the quadrature-axis current, the torque of the motor can be adjusted. The direct-axis current mainly affects the heating condition and magnetic flux distribution of the motor. By adjusting the magnitude of the direct-axis current, the magnetic saturation effect, demagnetization phenomenon, and the distribution and magnitude of resistance loss and magnetic loss of the motor will be changed, thus affecting the heat generation of the motor.

[0064] As an example, the demanded torque is the torque value required for the motor to meet the vehicle power demand. The demanded torque can be generated according to the driving mode or the throttle opening controlled by the driver. The faster the vehicle travels, the greater the demanded torque. The target battery temperature is the temperature value that the battery is expected to reach. The target battery temperature can be a fixed value, which is set according to the optimal operating temperature of the battery.

[0065] As an example, in cold weather, the battery performance may decline. At this time, by adjusting the quadrature-axis current and direct-axis current according to the demanded torque of the motor and the set target battery temperature to obtain the target quadrature-axis current and target direct-axis current, the heat generated by the motor can appropriately heat the battery while the motor meets the power demand.

[0066] In the above embodiments, first, in terms of vehicle power output, by obtaining the demanded torque in real time, the current power demand of the vehicle can be clarified. In terms of battery heating, by obtaining the set target battery temperature, the heating demand of the battery can be clarified. Then, according to the demanded torque of the motor and the set target battery temperature, the target winding current is generated, and thus by controlling the motor with the target winding current, the motor can not only meet the current demanded torque to enable the vehicle to travel normally, but also meet the heating demand of the battery, effectively utilizing the waste heat generated by the motor.

[0067] In some embodiments, generating the target quadrature-axis current and target direct-axis current according to the demanded torque of the motor and the set target battery temperature may include steps S10 - S20, which will be introduced in detail below.

[0068] Step S10: Obtain a constraint model according to the demanded torque and the target battery temperature.

[0069] Step S20: Generate the target winding current according to the constraint model.

[0070] In the above embodiments, by establishing a constraint model according to the demanded torque and the target battery temperature, and then generating the target winding current according to the constraint model, the target winding current can be obtained more quickly through the constraint model, so that the motor can respond in real time, provide the demanded torque and meet the heating demand.

[0071] Refer toFigure 1 , in some embodiments, step S10 includes steps S11 - S13, which are introduced in detail below.

[0072] Step S11: Establish a temperature error function according to a preset battery temperature prediction function and a target battery temperature.

[0073] As an example, the battery temperature prediction function can be constructed based on the heat generation of the motor and the thermal characteristics of the battery itself, and is used to predict the change of the battery temperature. The temperature error function can be the square of the difference between the preset battery temperature prediction function and the target battery temperature.

[0074] Step S12: Establish a torque error function according to a preset torque function and a required torque.

[0075] Among them, the preset torque function can be expressed as where i s = [i d , i q T , i d represents the direct-axis current; i q represents the quadrature-axis current. L d and L q are the d-axis and q-axis inductances respectively; ψ p = [ψ f , 0] T ; n p represents the number of pole pairs of the motor; ψp represents the magnetic flux in the dq coordinate system; ψf represents a specific magnetic flux value.

[0076] As an example, the torque error function can be the square of the difference between the preset torque function and the required torque.

[0077] Step S13: Construct a constraint model according to the temperature error function and the torque error function.

[0078] Referring to Figure 2 , in some embodiments, step S13 may include steps S31 - S32, which are introduced in detail below.

[0079] Step S31: Obtain a constraint weight value.

[0080] Step S32: According to the constraint weight value, perform weighted fusion on the temperature error function and the torque error function to obtain a constraint model.

[0081] Among them, the constraint model is used to express the functional relationship between the motor winding current and the error value, and the error value is the weighted sum value of the output value of the torque error function and the output value of the temperature error function. ​

[0082] As an example, the constraint model can be expressed as min J = λ([T b ref k -[T b k ) 2 +(1 - λ)([T e ref k -[T e k ) 2 ; where λ represents the constraint weight value; [T b ref k represents the target battery temperature at time k, and [T b k represents the prediction result of the battery temperature prediction function at time k; ([T b ref k -[T b k ) 2 represents the temperature error function. [T e ref k represents the demanded torque at time k; [T e ref k represents the demanded torque at time k, [T e k represents the prediction result of the preset torque function at time k, ([T e ref k -[T e k ) 2 represents the torque error function.

[0083] As an example, since the main task of the motor is still to provide power for the vehicle, the constraint weight value can be set to a relatively small value, such as 0.03. In this case, the constraint model is min J = 0.03([T b ref k -[T b k ) 2 +0.97([T e ref k -[T e k ) 2 ​​​​​​​​​​​​​​​​​Under different working conditions, the constraint weight value can also be adjusted. For example, when the difference between the battery temperature and the target battery temperature is large, the performance of the battery may be severely affected. At this time, the constraint weight value can be appropriately increased.

[0084] In the above embodiments, first, a temperature error function is established according to the preset battery temperature prediction function and the target battery temperature, which reflects the deviation between the predicted temperature and the target battery temperature. Secondly, a torque error function is established according to the preset torque function and the required torque to reflect the deviation between the actual output torque and the required torque. Through the temperature error function and the torque error function, the requirements for the torque provided by the motor and the heat provided are fused. Then, the relative importance of the temperature error and the torque error in constructing the constraint model is reflected by the constraint weight value, so that the constraint model can be flexibly constructed according to actual needs, and the generated target quadrature-axis current and target direct-axis current can better control the battery temperature while meeting the motor torque requirements, achieving the effect of simultaneously considering the requirements of battery temperature management and motor power output.

[0085] Refer to Figure 3 , in some embodiments, before step S13, steps S21 - S23 are further included, which are introduced in detail below.

[0086] Step S21: Obtain the battery heat generation function.

[0087] Step S22: Obtain the motor heat generation function.

[0088] In some embodiments, the motor heat generation function includes the variation relationship between the motor winding current and the motor heat. When the motor is working, heat is generated when there is current passing through the winding. When the motor winding current increases, the heat generated by the motor will increase accordingly. And the motor winding current includes the direct-axis current and the quadrature-axis current, that is, the changes in the magnitudes of the direct-axis current and the quadrature-axis current will both affect the heat generation of the motor.

[0089] As an example, the motor heat generation function can be expressed as P motor =α×I^2×R×t; where P motor represents the motor heat, α is the heat generation coefficient and α < 1, I represents the motor winding current, R represents the stator resistance of the motor, and t is the time.

[0090] Step S23: According to the battery heat generation function, the motor heat generation function, and the preset heat conduction coefficient, establish a heat conduction model between the motor and the battery to obtain the battery temperature prediction function.

[0091] Among them, the heat conduction coefficient is used to characterize the correlation between the temperature of the battery and the temperature of the motor. When heat is transferred between the motor and the battery, heat loss will occur, and the heat conduction system represents the loss during the process of heat transfer between the battery and the motor.

[0092] In some embodiments, step S21 may include: generating a correspondence between battery attribute parameters and battery heat based on the difference between the battery heat conduction function and the battery heat dissipation function, so as to obtain the battery heat generation function.

[0093] Among them, the battery heat conduction function is the correspondence between battery attribute parameters and generated heat; the battery heat dissipation function is the correspondence between battery attribute parameters and dissipated heat.

[0094] As an example, the battery attribute parameters include the battery thermal conductivity constant, the battery thermal conduction area, the battery thermal conduction thickness, and the battery self-heat dissipation coefficient. The battery heat conduction function mainly describes the temperature change amount outside the battery pack that is conducted into the battery and causes temperature change. The battery heat dissipation function mainly describes the heat dissipation speed of the battery, which is related to the battery self-heat dissipation coefficient. The larger the battery self-heat dissipation coefficient, the faster the battery temperature drops. The battery heat conduction function can be expressed as P1 = ΔTb × λb × Sb × t / Lb; where P1 is the heat absorbed by the battery, ΔTb represents the battery temperature change amount; λb is the battery thermal conductivity constant, Sb is the battery thermal conduction area, t is the time, Lb is the battery thermal conduction thickness, and Kb is the battery self-heat dissipation coefficient. The battery heat dissipation function can be expressed as P2 = Kb × t; where Kb is the battery self-heat dissipation coefficient.

[0095] As an example, the battery heat generation function can be expressed as: P battery = P1 - P2 = ΔTb × λb × Sb × t / Lb - Kb × t = β × ΔTb × t - Kb × t. For the convenience of expression, let β = λb × Sb / Lb, then the battery heat generation function can be expressed as P battery = β × ΔTb × t - Kb × t.

[0096] In the above embodiments, the battery heat generation function can be expressed as the difference between the battery heat conduction function and the battery heat dissipation function, that is, the actual heat accumulation of the battery is the difference between the generated heat and the dissipated heat, so that the obtained battery heat generation function can accurately reflect the change of the internal heat of the battery, so as to more accurately establish the heat conduction model between the motor and the battery subsequently.

[0097] Refer to Figure 4 , in some embodiments, step S23 may include steps S231 - S233, which will be introduced in detail below.

[0098] Step S231: Establish the correlation between the battery heat generation function, the heat conduction coefficient, and the motor heat generation function to obtain the heat conduction model.

[0099] As an example, the heat conduction coefficient is used to represent the heat loss during the conduction process from the motor heat to the battery heat, and the heat conduction coefficient is less than 1. The heat conduction model can be expressed as P motor = μ × P battery ; where μ represents the heat conduction coefficient. Combining the battery heat generation function and the motor heat generation function, the heat conduction model can be further expressed as α × I^2 × R × t = μ × (β × ΔTb × t - Kb × t). Since the final heat conduction model needs to obtain the battery temperature change, the heat conduction model can be sorted out as: ΔTb = α × R / μ × β × I^2 + Kb / β = Ab × I^2 + Bb.

[0100] Step S232: Fit the heat conduction model based on the historical data of the motor winding current and the historical data of the battery temperature change to obtain the battery temperature change function.

[0101] As an example, the historical data of the motor winding current and the historical data of the battery temperature change should be in a one-to-one correspondence relationship, reflecting the current of the motor in different working states and the heat transfer situation to the battery.

[0102] As an example, after fitting the heat conduction model, the heat transfer fitting parameters of the heat conduction model are generated, and the battery temperature change function is obtained according to the heat transfer fitting parameters. Since the battery property parameters such as the battery thermal conductivity, the battery thermal area, the battery thermal thickness, and the battery self-cooling coefficient cannot be directly obtained, Ab = α × R / μ × β and Bb = Kb / β can be set, then the heat conduction model is simplified to the change relationship between the motor winding current and the battery temperature change, that is, ΔTb = Ab × I^2 + Bb, where Ab and Bb represent the heat transfer fitting parameters. For example, the heat transfer fitting parameters can be calculated by the least squares method, that is, the values of Ab and Bb. And the heat transfer fitting parameters can be fitted online or offline.

[0103] Step S233: Obtain the battery temperature prediction function according to the current battery temperature and the battery temperature change function.

[0104] In some embodiments, the battery temperature prediction function is the correspondence between the motor winding current and the battery heat. Among them, the motor winding current includes the direct-axis current and the quadrature-axis current.

[0105] As an example, the battery temperature prediction function can be expressed as:

[0106] [Tb] k =[Tb] k-1+ΔTb = [Tb] k-1 +Ab×I^2 + Bb;

[0107] where, [Tb] k represents the battery temperature at time k, [Tb] k-1 represents the battery temperature at time k - 1, I represents the motor winding current, and Ab and Bb are heat transfer fitting parameters. That is, if the motor winding current and the current battery temperature are obtained at time k - 1, the battery temperature at time k can be predicted.

[0108] In the above embodiments, the battery temperature prediction function is the corresponding relationship between the motor winding current and the battery heat, and the motor winding current includes the direct-axis current and the quadrature-axis current. Then, through the battery temperature prediction function, the change of the battery heat can be predicted according to the magnitudes of the direct-axis current and the quadrature-axis current, so that the battery temperature can be predicted based on the direct-axis current and the quadrature-axis current of the motor. Therefore, in motor control, the direct-axis current and the quadrature-axis current can be adjusted according to the target battery temperature, so as to effectively control the battery temperature.

[0109] In some embodiments, the motor winding current includes the motor direct-axis current and the motor quadrature-axis current; step S20 may include steps S41 - S42, which will be introduced in detail below.

[0110] Step S41: Obtain a preset step size for descent.

[0111] Step S42: According to the step size for descent, iteratively update the direct-axis current and the quadrature-axis current by the gradient descent method, and respectively determine the direct-axis current and the quadrature-axis current corresponding to when the error value of the constraint model converges as the target direct-axis current and the target quadrature-axis current.

[0112] As an example, the gradient descent method gradually adjusts the direct-axis current and the quadrature-axis current according to the gradient direction of the constraint model to make the constraint model meet the convergence condition. The convergence condition can be set in advance, that is, an error threshold is set. When the error value is less than the preset error threshold, it is determined that the error value of the constraint model converges. For example, assume that the initial direct-axis current is Id0, the quadrature-axis current is Iq0, and the preset step size for descent is α. According to the gradient of the constraint model through the iterative formula where n represents the number of iterations, so as to continuously update the direct-axis current and the quadrature-axis current. When the error value of the constraint model converges, the corresponding direct-axis current and quadrature-axis current are respectively determined as the target direct-axis current and the target quadrature-axis current, so that the target quadrature-axis current and the target direct-axis current that meet the requirements of the constraint model can be effectively determined. Furthermore, in the optimal control of the motor, while accurately providing the required torque, the control of the battery temperature can be taken into account.

[0113] In the above embodiments, the direct-axis current and the quadrature-axis current are iteratively updated by introducing the gradient descent method. The gradient descent method gradually adjusts the direct-axis current and the quadrature-axis current along the steepest descent direction of the error function through a preset descent step size, which can gradually reduce the error value of the constraint model and make the error value converge. Thus, it ensures that while the motor outputs the required torque, the heating effect of the current is maximally utilized to heat the battery.

[0114] Referring to Figure 5 , in some embodiments, the motor control method further includes step S51 - step S53, which will be introduced in detail below.

[0115] Step S51: Obtain a preset motor temperature prediction function.

[0116] As an example, similar to the battery temperature prediction function, there is also a variation relationship between the predicted motor temperature and the motor winding current, which is described by the temperature prediction function.

[0117] As an example, the preset motor temperature prediction function can be expressed as:

[0118] [Tm] k =[Tm] k-1 +ΔTm=[Tm] k-1 +Am×I^2 + Bm;

[0119] Wherein, [Tm] k represents the motor temperature at time k, [Tm] k-1 represents the motor temperature at time k - 1, I represents the motor winding current, and Am and Bm are motor fitting parameters. The motor fitting parameters are similar to the above heat transfer fitting parameters, that is, by obtaining the historical data of the motor temperature change amount and the historical data of the motor winding current for fitting to obtain the motor fitting parameters.

[0120] Step S52: Input the target direct-axis current and the target quadrature-axis current into the motor temperature prediction function to generate the predicted motor temperature.

[0121] Step S53: When the predicted motor temperature exceeds the motor temperature threshold, adjust the target direct-axis current and the target quadrature-axis current.

[0122] As an example, the motor temperature threshold can be set according to the actual operating temperature range of the motor. For example, the motor temperature threshold can be set to 110 degrees Celsius. For example, an inequality relationship can be established: [Tm] k-1 +Am×I^2 + Bm ≤ 110 to determine whether the predicted motor temperature meets the condition of the motor temperature threshold.

[0123] In the above embodiments, after obtaining the preset motor temperature prediction function, the target direct-axis current and the target quadrature-axis current are input into the motor temperature prediction function to generate the predicted motor temperature. The motor temperature prediction function reflects the variation relationship between the target direct-axis current, the target quadrature-axis current, and the motor temperature. When the predicted motor temperature exceeds the motor temperature threshold, it is necessary to adjust the target direct-axis current and the target quadrature-axis current. Thus, the influence of excessive motor temperature on the performance and lifespan of the motor is avoided to ensure the normal operation of the motor.

[0124] Referring to Figure 6 , in some embodiments, step S53 may include steps S531 - S533, which will be introduced in detail below.

[0125] Step S531: Obtain a current constraint value according to the motor temperature prediction function and the motor temperature threshold.

[0126] As an example, the current constraint value is the maximum allowable current synthesis value at the motor temperature threshold. The current synthesis value is the synthesis amplitude of the quadrature-axis current and the direct-axis current, which represents the vector length of the quadrature-axis current and the direct-axis current. For example, when the motor temperature threshold is 110 degrees Celsius, the current constraint value can be 70A.

[0127] Step S532: Determine a reduction ratio according to the current constraint value, the target direct-axis current, and the target quadrature-axis current.

[0128] As an example, taking the pseudo target direct-axis current as -20A and the target quadrature-axis current as 80A as an example. At this time, the current synthesis value of the target direct-axis current and the target quadrature-axis current is 82.46A. At this time, determine the ratio by which the current synthesis value exceeds the current constraint value, that is, 70A ÷ 82.46A ≈ 0.85, and at this time, the reduction ratio is determined to be 85%.

[0129] Step S533: Adjust the target direct-axis current and the target quadrature-axis current according to the reduction ratio.

[0130] As an example, synchronously reduce the target direct-axis current and the target quadrature-axis current with a reduction ratio of 85%: the adjusted direct-axis current is approximately -20A × 0.85 ≈ -17A, and the adjusted quadrature-axis current: 80A × 0.85 ≈ 68A, thereby realizing the adjustment of the target direct-axis current and the target quadrature-axis current.

[0131] According to the second aspect of the present application, there is provided a controller on which a computer program is stored, and when the computer program is executed by a processor, the steps of the above method are implemented.

[0132] According to the third aspect of the present application, there is provided a vehicle including the above controller.

[0133] Among them, the vehicle can be a fuel vehicle, a plug-in hybrid vehicle, a new energy vehicle, etc., and the present disclosure does not make specific limitations thereon.

[0134] According to a fourth aspect of the present application, there is provided a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the steps of the above method are implemented.

[0135] Those skilled in the art should understand that the embodiments of the present application can be provided as a method, a system, or a computer program product. Therefore, the present application can adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present application can adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0136] The present application is described with reference to the flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present application. It should be understood that each flow and / or block in the flowchart and / or block diagram can be implemented by computer program instructions, and the combination of the flows and / or blocks in the flowchart and / or block diagram can also be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing devices generate a device for implementing the specified functions in one Figure 1 one process or multiple processes and / or blocks Figure 1 one block or multiple blocks.

[0137] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer-readable memory generate a manufactured product including an instruction device, and the instruction device implements the specified functions in one Figure 1 one process or multiple processes and / or blocks Figure 1 one block or multiple blocks.

[0138] These computer program instructions can also be loaded onto a computer or other programmable data processing device, so that a series of operation steps are executed on the computer or other programmable device to generate a computer-implemented process, and thus the instructions executed on the computer or other programmable device provide steps for implementing the specified functions in one Figure 1 one process or multiple processes and / or blocks Figure 1 one block or multiple blocks.

[0139] In a typical configuration, a computing device includes one or more processors (CPUs), an input / output interface, a network interface, and memory.

[0140] The memory may include non-permanent memory in the form of computer-readable media, random access memory (RAM), and / or non-volatile memory such as read-only memory (ROM) or flash memory (flash RAM). The memory is an example of computer-readable media.

[0141] Computer-readable media includes permanent and non-permanent, removable and non-removable media that can store information by any method or technology. The information can be computer-readable instructions, data structures, program modules, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, compact disc read-only memory (CD-ROM), digital versatile disc (DVD) or other optical storage, magnetic cassette tapes, magnetic disk storage or other magnetic storage devices, or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer-readable media does not include transitory media such as modulated communication signals and carrier waves.

[0142] According to a fifth aspect of the present application, there is provided a computer program product including a computer program or instructions that, when executed by a processor, implement the steps of the above-described method.

[0143] In the description of the present application, the terms "first" and "second" are used only for descriptive purposes and should not be construed as indicating or implying relative importance or implicitly specifying the quantity of the indicated technical features. Thus, features defined with "first" and "second" may explicitly or implicitly include one or more features. In the description of the present application, "a plurality of" means two or more unless otherwise specifically defined.

[0144] In the above embodiments, the descriptions of the various embodiments have their own emphases. For parts not detailed in a certain embodiment, reference may be made to the relevant descriptions of other embodiments.

[0145] The embodiments, implementation manners, and related technical features of the present application can be combined and replaced with each other without conflict.

[0146] The above are only the preferred embodiments of the present application and do not impose any formal restrictions on the present application. However, any simple modifications, equivalent changes, and decorations made to the above embodiments based on the technical essence of the present application without departing from the content of the technical solution of the present application still fall within the scope of the technical solution of the present application.

Claims

1. A motor control method, characterized in that: include: A target winding current of the motor is generated according to a required torque of the motor and a set target battery temperature to control the operation of the motor.

2. The motor control method according to claim 1, characterized in that: The target winding current includes a target direct-axis current and a target quadrature-axis current.

3. The motor control method according to claim 2, characterized in that: The step of generating a target winding current of the motor according to the required torque of the motor and a set target battery temperature includes: Obtaining a constraint model according to the required torque and the target battery temperature; The target winding current is generated according to the constraint model.

4. The motor control method according to claim 3, characterized in that: The step of obtaining a constraint model according to the required torque and the target battery temperature includes: Establishing a temperature error function according to a preset battery temperature prediction function and the target battery temperature; Establishing a torque error function according to a preset torque function and the required torque; The constraint model is constructed according to the temperature error function and the torque error function.

5. The motor control method according to claim 4, characterized in that: Before establishing the temperature error function according to the preset battery temperature prediction function and the target battery temperature, the method further includes: Get the battery heat generation function; Get the motor heat generation function; Establishing a heat conduction model between the motor and the battery according to the battery heat generation function, the motor heat generation function and a preset heat conduction coefficient to obtain the battery temperature prediction function; The thermal conductivity coefficient is used to characterize the correlation between the temperature of the battery and the temperature of the motor.

6. The motor control method according to claim 5, characterized in that: The obtaining of the battery heat generation function comprises: Based on the difference between the battery heat conduction function and the battery heat dissipation function, a corresponding relationship between the battery property parameter and the battery heat is generated to obtain the battery heat generation function; The battery heat conduction function is the correspondence between the battery property parameters and the generated heat; the battery heat dissipation function is the correspondence between the battery property parameters and the dissipated heat.

7. The motor control method according to claim 6, characterized in that: The motor heat generation function includes the changing relationship between the motor winding current and the motor heat.

8. The motor control method according to claim 7, characterized in that: The step of establishing a heat conduction model between the motor and the battery according to the battery heat generation function, the motor heat generation function and a preset heat conduction coefficient to obtain the battery temperature prediction function includes: Establishing a correlation between the battery heat generation function, the thermal conductivity coefficient and the motor heat generation function to obtain the thermal conduction model; Fitting the heat conduction model according to the historical data of the motor winding current and the historical data of the battery temperature change to obtain the battery temperature change function; The battery temperature prediction function is obtained according to the current battery temperature and the battery temperature variation function.

9. The motor control method according to claim 8, characterized in that: The battery temperature prediction function is the corresponding relationship between the motor winding current and the battery heat.

10. The motor control method according to claim 4, characterized in that: The step of obtaining the constraint model according to the temperature error function and the torque error function comprises: Get constraint weight value; According to the constraint weight value, weighted fusion is performed on the temperature error function and the torque error function to obtain the constraint model; The constraint model is used to express the functional relationship between the motor winding current and the error value, and the error value is the weighted sum of the output value of the torque error function and the output value of the temperature error function.

11. The motor control method according to claim 10, characterized in that: The motor winding current includes a motor direct-axis current and a motor quadrature-axis current; and generating the target winding current according to the constraint model includes: Get the preset descending step length; According to the descent step size, the direct-axis current and the quadrature-axis current are iteratively updated by a gradient descent method, and the direct-axis current and the quadrature-axis current corresponding to when the error value of the constraint model converges are respectively determined as the target direct-axis current and the target quadrature-axis current.

12. The motor control method according to claim 10, characterized in that: Also includes: Get the preset motor temperature prediction function; Inputting the target direct-axis current and the target quadrature-axis current into the motor temperature prediction function to generate a motor predicted temperature; When the predicted motor temperature exceeds a motor temperature threshold, the target direct-axis current and the target quadrature-axis current are adjusted.

13. The motor control method according to claim 12, characterized in that: The target direct-axis current and the target quadrature-axis current are adjusted, including: Obtaining a current constraint value according to the motor temperature prediction function and the motor temperature threshold; determining a reduction ratio according to the current constraint value, the target direct-axis current, and the target quadrature-axis current; The target direct-axis current and the target quadrature-axis current are adjusted according to the reduction ratio.

14. A controller having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 13 are implemented.

15. A vehicle, characterized in that: Comprising a controller as claimed in claim 14.

16. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 13 are implemented.

17. A computer program product, characterized in that The method comprises a computer program or instructions, which implement the steps of the method according to any one of claims 1 to 13 when executed by a processor.

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