Motor driving method, household appliance and storage medium

Abstract

The invention provides a motor driving method, a household appliance and a storage medium. The method comprises the following steps: acquiring a motor temperature, a motor current and a motor voltage of a motor; calculating a current-stage system state vector of the motor according to the motor temperature, the motor current and the motor voltage; predicting according to the system state vector at the current stage by using a preset PWM control algorithm based on a deep learning model, and generating a PWM control strategy at the next stage; and driving the motor according to the PWM control strategy of the next stage. The method can consider the influence of the motor temperature, and improves the stability and anti-interference capability of the motor.

Description

Technical Field

[0001] The present invention relates to the field of motor technology, and in particular, to a motor driving method, a household appliance applying the motor driving method, and a computer-readable storage medium applying the motor driving method. Background Art

[0002] A brushless DC motor uses electronic commutation technology instead of traditional mechanical brushes. Compared to brushed motors, brushless motors offer higher efficiency and a longer lifespan due to the lack of friction losses, making them suitable for high-load, continuous operation. Furthermore, brushless motors offer high output torque, a wide speed range, fast response, smooth operation, and low noise, making them widely used in new energy vehicle drives, household appliances, and industrial control.

[0003] However, traditional brushless DC motor drives mostly use fixed PWM control strategies, which make it difficult to respond in real time to complex operating conditions during motor operation (such as sudden load changes, voltage fluctuations, temperature changes, etc.), which may lead to insufficient control accuracy and increased energy loss.

[0004] In an existing motor control method, current parameters and position parameters are collected based on the state observer equation to obtain the motor operating state parameters; the motor operating state parameters are input into a five-layer deep neural network to predict the current and speed parameters to obtain the optimal current parameters and speed prediction parameters; a multi-objective function is constructed for the optimal current parameters and speed prediction parameters to obtain the current reference value; the current reference value is input into a cascade PI controller, and the voltage is adjusted through feedforward decoupling control and space vector pulse width modulation technology to obtain a control voltage signal; the control voltage signal is subjected to performance calculation and control parameter optimization to obtain the target control parameters.

[0005] However, this solution does not consider that the motor temperature during operation will affect the stability of the system and cannot meet the temperature control requirements.

[0006] Therefore, a more optimized motor driving method needs to be considered. Summary of the Invention

[0007] A first object of the present invention is to provide a motor driving method that can take into account the influence of motor temperature and improve the stability and anti-interference ability of the motor.

[0008] A second object of the present invention is to provide a household appliance that can take into account the influence of motor temperature and improve the stability and anti-interference ability of the motor.

[0009] A third object of the present invention is to provide a computer-readable storage medium that can take into account the influence of motor temperature and improve the stability and anti-interference ability of the motor.

[0010] In order to achieve the above-mentioned first purpose, the motor driving method provided by the present invention includes: obtaining the motor temperature, motor current and motor voltage of the motor; calculating the current stage system state vector of the motor based on the motor temperature, motor current and motor voltage; using a preset PWM control algorithm based on a deep learning model to predict the current stage system state vector and generate a PWM control strategy for the next stage; driving the motor according to the PWM control strategy for the next stage.

[0011] As can be seen from the above scheme, the motor drive method of the present invention acquires multi-dimensional parameters such as motor temperature, motor current, and motor voltage in real time, converting this multi-dimensional data into a system state vector, achieving a quantitative expression of the correlation between parameters. This enriches the information contained in the sample while maintaining the number of variables in a single sample, providing a better sample basis for accurate predictions by deep learning models. Predicting the PWM control strategy for the next stage based on the current stage system state vector enables forward-looking control and improves the system's anti-interference capability. Furthermore, the introduction of temperature parameters can be directly used for thermal protection and heat dissipation strategy optimization, preventing motor overheating from affecting system stability.

[0012] In a further solution, the step of calculating the current stage system state vector of the motor based on the motor temperature, motor current and motor voltage includes: normalizing the motor temperature, motor current and motor voltage; and calculating the current stage system state vector using the normalized motor temperature, motor current and motor voltage.

[0013] It can be seen that since the dimensions of the three parameters of current, voltage and temperature are different, the scales of the parameter features are different. Using feature normalization can normalize the three parameters to the same scale, improve the performance of the model, and prevent the problem of gradient disappearance or explosion.

[0014] In a further embodiment, the motor current is normalized by the following formula: The motor voltage is normalized by the following formula: Where, I represents the normalized motor current, V represents the normalized motor voltage, i represents the collected motor phase current value, μ i represents the mean value of the collected current samples, σ i represents the standard deviation of the collected current samples, v represents the collected motor phase voltage value, u v Represents the mean value of the collected voltage samples, σ v Indicates the standard deviation of the collected voltage samples.

[0015] It can be seen that due to the sudden changes in current and voltage, the normalization method based on mean and standard deviation is used to normalize the current and voltage, which has high robustness to outliers and improves anti-interference performance.

[0016] In a further solution, the motor temperature is normalized by the following formula: Where, T represents the normalized motor temperature, t is the collected motor temperature value, and T min Indicates the minimum value of the collected motor temperature samples, T max Indicates the maximum value among the collected motor temperature samples.

[0017] It can be seen that the temperature parameters will not change suddenly when the system is running and have a relatively clear distribution range. Therefore, the normalization method based on the minimum-maximum value is adopted, which is simple to implement and saves computing resources.

[0018] In a further solution, the system state vector Obtained by the following formula: Where r represents the system state vector The radial distance, θ represents the system state vector The polar angle, Represents the system state vector azimuth, I represents the normalized motor current, V represents the normalized motor voltage, and T represents the normalized motor temperature.

[0019] As can be seen, the three system constants—motor current, motor voltage, and motor temperature—are placed in the x, y, and z directions of a Cartesian coordinate system, respectively. These three vectors are then transformed and fused into a single system state vector in a spherical coordinate system using the system state vector calculation formula. A single parameter in the system state vector incorporates the characteristics of multiple system parameters, effectively enriching the sample information and improving prediction accuracy.

[0020] In a further solution, the preset PWM control algorithm based on the deep learning model is used to predict the system state vector of the current stage, and the steps of generating the PWM control strategy of the next stage include: using the preset PWM control algorithm to predict the system state vector of the current stage and the historical system state vector to obtain the system state vector of the next stage; generating the PWM control strategy of the next stage according to the system state vector of the next stage.

[0021] It can be seen that by using the current stage system state vector and the historical system state vector to predict the system state vector of the next stage, the historical state information can be combined to predict the evolution trend of the system state, thereby generating the corresponding PWM control strategy and realizing adaptive control.

[0022] In a further solution, after obtaining the motor temperature, motor current and motor voltage of the motor, before calculating the system state vector of the motor in the current stage, it also includes: confirming that the motor temperature, motor current and motor voltage are all within the allowable range of system operation.

[0023] It can be seen from this that the PWM control strategy is only acquired when it is confirmed that the motor temperature, motor current and motor voltage are all within the allowable range of system operation, which can ensure the accuracy of the PWM control strategy.

[0024] In a further solution, after obtaining the motor temperature, motor current and motor voltage of the motor, it also includes: if any one of the motor temperature, motor current and motor voltage is outside the allowable range of system operation, performing a motor protection operation.

[0025] It can be seen from this that if any of the motor temperature, motor current and motor voltage is outside the allowable range of system operation, protection operations need to be given priority to avoid motor damage.

[0026] In order to achieve the second objective of the present invention, the household appliance provided by the present invention includes a processor and a memory, wherein the memory stores a computer program, and when the computer program is executed by the processor, the steps of the above-mentioned motor driving method are implemented.

[0027] In order to achieve the third objective of the present invention, the present invention provides a computer-readable storage medium having a computer program stored thereon, which implements the steps of the above-mentioned motor driving method when executed by a controller. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] Figure 1 The present invention is a circuit block diagram of a motor drive system using the motor drive method of the present invention.

[0029] Figure 2 The present invention is a circuit block diagram of a power supply module in a motor drive system to which the motor drive method of the present invention is applied.

[0030] Figure 3 4 is a flow chart of an embodiment of a motor driving method of the present invention.

[0031] Figure 4 This is a flowchart of the steps for calculating the current stage system state vector of the motor based on the motor temperature, motor current and motor voltage in an embodiment of the motor driving method of the present invention.

[0032] The present invention will be further described below with reference to the accompanying drawings and embodiments. DETAILED DESCRIPTION

[0033] The motor driving method of the present invention is an application program applied in a motor driving system, and is used for driving and controlling the motor.

[0034] Preferably, Figure 1As shown, the motor drive system includes a power module 1, a driver module 2, and an execution module 3. The power module 1 includes multiple DC power outputs, such as +5V, +12V, and +24V, which can power the various circuits in the driver module 2 and the brushless DC (BLDC) motor 31 in the execution module 3. The driver module 2 includes an MCU chip 21, a PWM control module 22, a power driver module 23, an intelligent protection module 24, and a thermal management module 25. The execution module 3 includes a brushless DC motor 31 and a moving component 32 that is mechanically connected to the motor.

[0035] The power drive module 23 uses a half-bridge circuit topology based on GaN power devices to efficiently drive the DC brushless motor 31. GaN power devices have the characteristics of high electron mobility, low on-resistance, low conduction loss and switching loss, which can significantly reduce the power loss of the drive circuit. Through the high-frequency half-bridge circuit topology, higher driving efficiency of the DC brushless motor 31 is achieved. Taking the three-phase DC brushless motor 31 as an example, three groups of half-bridge circuit topologies are used to form the power drive module 23, which are respectively connected to the U, V, and W phases of the DC brushless motor 31. In accordance with the specified sequence, the on and off states of the upper and lower bridges of different phases can be controlled to achieve the driving of the DC brushless motor 31. The GaN power devices used in the power drive module 23 can operate under control signals of different frequencies. Accordingly, the power devices can perform switching operations at multiple frequencies, thereby achieving flexible control of the speed of the DC brushless motor 31.

[0036] In addition, the power module 1 can adopt a well-known switching power supply circuit, such as Figure 2 As shown. The switching power supply circuit includes a switching power supply chip 11, which is a GaN power device. Compared with silicon-based power devices, GaN power devices have faster switching speeds, with switching frequencies reaching over 200kHz, and more sensitive switching responses. The switching power supply chip 11 has a higher power density, which can reduce energy loss during circuit operation. The power supply module 1 has a wide input voltage range of 85VAC to 265VAC, allowing the motor drive system to adapt to various complex voltage environments. At the same time, it is combined with power supply voltage regulator chips such as LM7805, LM7815, and L7824CV to achieve a variety of different DC voltage outputs such as +5V, +15V, and +24V.

[0037] The intelligent protection module 24 integrates a real-time monitoring circuit, providing over-temperature protection, short-circuit protection, and automatic shutdown and recovery functions. The intelligent protection module 24 has an information feedback function, which enables the monitoring circuit status information, including temperature, current, and voltage, to be fed back to the MCU chip 21. The temperature sensing device uses NTC resistors as temperature sensing elements, which are distributed in the switching power supply chip 11, MCU chip 21, GaN-based power devices, and DC brushless motor 31. It can realize real-time temperature monitoring of modules and components with high heat risk in the drive system. The current detection device is respectively arranged at the output end of the switching power supply and the three driving half-bridge circuits of the DC brushless motor 31 to detect the power supply of the power module 1 and the phase current of the DC brushless motor 31.

[0038] Motor driving method embodiment:

[0039] like Figure 3 As shown, in this embodiment, the motor driving method, during operation, first executes step S1 to obtain the motor temperature, motor current, and motor voltage of the motor. Since current and voltage are key parameters of a motor control system, and motor control involves power devices, the temperature of these devices during system operation can affect system stability. Therefore, when driving a motor, the motor temperature, motor current, and motor voltage must be considered for control. The methods for obtaining motor temperature, motor current, and motor voltage are well known to those skilled in the art and will not be elaborated upon here.

[0040] After obtaining the motor temperature, motor current, and motor voltage, step S2 is executed to determine whether the motor temperature, motor current, and motor voltage are all within the system's operating range. To ensure the motor's safe operation, corresponding thresholds must be set for the motor temperature, motor current, and motor voltage to prevent them from exceeding the system's operating range and potentially damaging the motor.

[0041] If the motor temperature, motor current, and motor voltage are all within the system's operating range, step S3 is executed to calculate the motor's current state vector based on the motor temperature, motor current, and motor voltage. If the motor temperature, motor current, and motor voltage are all within the system's operating range, the motor is operating normally. At this point, the motor's PWM control strategy can be optimized. To improve control accuracy, information fusion of the motor temperature, motor current, and motor voltage is required.

[0042] See also Figure 4In this embodiment, when calculating the current-stage system state vector of the motor based on the motor temperature, motor current, and motor voltage, step S11 is first executed to normalize the motor temperature, motor current, and motor voltage. Because the three parameters of current, voltage, and temperature have different dimensions and their parameter features have different scales, feature-based normalization can be used to normalize the three parameters to the same scale, improving the performance of the deep learning model and preventing vanishing or exploding gradients.

[0043] The motor current is normalized by the following formula: The motor voltage is normalized by the following formula: Where, I represents the normalized motor current, V represents the normalized motor voltage, i represents the collected motor phase current value, μ i represents the mean value of the collected current samples, σ i represents the standard deviation of the collected current samples, v represents the collected motor phase voltage value, u v Represents the mean value of the collected voltage samples, σ v Represents the standard deviation of the collected voltage samples. Due to the sudden changes in current and voltage, a normalization method based on mean and standard deviation is used to normalize the current and voltage. This method is more robust to outliers and improves anti-interference performance.

[0044] The motor temperature is normalized by the following formula: Where, T represents the normalized motor temperature, t is the collected motor temperature value, and T min Indicates the minimum value of the collected motor temperature samples, T max Represents the maximum value among the collected motor temperature samples. Temperature parameters do not change suddenly during system operation and have a relatively clear distribution range. Therefore, a minimum-maximum normalization method is used, which is simple to implement and saves computing resources.

[0045] After normalizing the motor temperature, motor current, and motor voltage, step S12 is executed to calculate the system state vector of the current stage using the normalized motor temperature, motor current, and motor voltage. Obtained by the following formula: Where r represents the system state vector The radial distance, θ represents the system state vector The polar angle, Represents the system state vector The azimuth angle is represented by , I represents the normalized motor current, V represents the normalized motor voltage, and T represents the normalized motor temperature. The three system constants (motor current, motor voltage, and motor temperature) are placed in the x, y, and z directions of a Cartesian coordinate system, respectively. Using the system state vector calculation formula, these three Cartesian vectors are transformed and fused into a single system state vector in a spherical coordinate system. A single parameter in the system state vector incorporates the characteristics of multiple system parameters, effectively enriching the sample information.

[0046] It should be noted that r, θ, The combination of parameters I, V, and T in the calculation formula can be set as needed.

[0047] After obtaining the system state vector of the current stage, step S4 is executed, and a preset PWM control algorithm based on a deep learning model is used to make a prediction based on the system state vector of the current stage to generate a PWM control strategy for the next stage. In this embodiment, the deep learning model adopts a model based on a bidirectional long short-term memory network. The deep learning model adopts a bidirectional long short-term memory network Bi-LSTM. This model captures contextual dependencies by simultaneously learning the forward and backward dependencies of the sequence and combining two LSTM layers to capture contextual dependencies. It can capture contextual information more comprehensively and significantly improve the model's understanding and prediction capabilities of time series data. The deep learning model can predict the system state at the next moment through the real-time state vector, adjust the PWM strategy in advance, realize forward control, and achieve the effect of model adaptive prediction.

[0048] In this embodiment, the steps of using a preset PWM control algorithm based on a deep learning model to predict the system state vector of the current stage and generate a PWM control strategy for the next stage include: using the preset PWM control algorithm to predict the system state vector of the current stage and the historical system state vector to obtain the system state vector of the next stage; and generating the PWM control strategy for the next stage based on the system state vector of the next stage. The system state vector of the next stage can be predicted using the current stage system state vector and the historical system state vector, and the historical state information can be combined to predict the evolution trend of the system state, thereby generating a corresponding PWM control strategy, achieving adaptive control, and improving prediction accuracy.

[0049] After obtaining the next-stage PWM control strategy, step S5 is executed to drive the motor according to the next-stage PWM control strategy. The PWM control strategy generated by deep learning includes parameters such as duty cycle and frequency. By adjusting the duty cycle and frequency of the PWM signal in real time, it can accurately match the dynamic requirements of the motor.

[0050] When executing step S2, if any of the motor temperature, motor current, and motor voltage is outside the system's operating range, step S6 is executed to perform a motor protection operation. If any of the motor temperature, motor current, and motor voltage is outside the system's operating range, it indicates that the motor is operating abnormally. In this case, it is necessary to prioritize the protection operation to prevent motor damage. The motor protection operation can be performed according to the motor temperature, motor current, and motor voltage. For example, if the input motor current exceeds the preset maximum current threshold of the drive system, the intelligent protection module cuts off the drive system power supply within 5μs and activates the recovery mode. After the power is cut off for 1 second, the system attempts to reconnect the power supply. If the current detection is normal, the drive system maintains normal operation. If the current detection continues to be abnormal, the power is cut off again and the recovery mode is activated. When the motor temperature is greater than the preset temperature threshold, the PWM control signal is first adjusted to limit the output power to avoid damage to the system hardware. If the motor temperature cannot be less than or equal to the preset temperature threshold after limiting the output power, the power is cut off to protect the system.

[0051] As can be seen from the above, the motor drive method of the present invention acquires multi-dimensional parameters such as motor temperature, motor current, and motor voltage in real time, converting this multi-dimensional data into a system state vector, achieving a quantitative expression of the correlation between parameters. This enriches the information contained in the sample while maintaining the number of variables in a single sample, providing a more optimal sample foundation for accurate predictions by deep learning models. Predicting the PWM control strategy for the next stage based on the current stage system state vector enables forward-looking control and improves the system's anti-interference capability. Furthermore, the introduction of temperature parameters can be directly used for thermal protection and heat dissipation strategy optimization, preventing motor overheating from affecting system stability.

[0052] Household appliances example:

[0053] In this embodiment, household appliances include air conditioners, washing machines, refrigerators and other electrical appliances that require the use of motors.

[0054] The household appliance of this embodiment includes a controller, and the controller implements the steps of the above-mentioned motor driving method embodiment when executing a computer program.

[0055] For example, a computer program may be divided into one or more modules, one or more of which are stored in a memory and executed by a controller to implement the present invention. One or more modules may be a series of computer program instruction segments capable of performing specific functions, and the instruction segments are used to describe the execution process of the computer program in the household appliance.

[0056] The household appliance may include, but is not limited to, a controller and a memory. Those skilled in the art will appreciate that the household appliance may include more or fewer components, or a combination of certain components, or different components. For example, the household appliance may also include input and output devices, network access devices, buses, and the like.

[0057] For example, the controller can be a central processing unit (CPU), other general-purpose controllers, digital signal processors (DSP), application-specific integrated circuits (ASIC), field programmable gate arrays (FPGA), other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose controller can be a microcontroller or any conventional controller. The controller is the control center of the household appliance, connecting the various parts of the entire household appliance using various interfaces and lines.

[0058] The memory can be used to store computer programs and / or modules. The controller implements various functions of the household appliance by running or executing the computer programs and / or modules stored in the memory, and calling the data stored in the memory. For example, the memory may mainly include a program storage area and a data storage area, wherein the program storage area can store an operating system, at least one application required for a function (such as a sound receiving function, a sound-to-text conversion function, etc.); the data storage area can store data created based on the use of the mobile phone (such as audio data, text data, etc.). In addition, the memory can include high-speed random access memory and non-volatile memory, such as a hard disk, internal memory, a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, at least one disk storage device, a flash memory device, or other volatile solid-state storage device.

[0059] Computer readable storage medium embodiment:

[0060] If the module integrated into the household appliance of the above embodiment is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, all or part of the process in the above motor driving method embodiment can also be implemented by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium. When the computer program is executed by a controller, it can implement the steps of the above motor driving method embodiment. The computer program includes computer program code, which can be in source code form, object code form, executable file, or some intermediate form. The storage medium can include: any entity or device capable of carrying computer program code, recording medium, USB flash drive, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electric carrier signal, telecommunication signal, and software distribution medium. It should be noted that the content contained in the computer-readable medium can be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable media does not include electric carrier signal and telecommunication signal.

[0061] It should be noted that the above are only preferred embodiments of the present invention, but the design concept of the invention is not limited thereto. Any non-substantial modifications made to the present invention using this concept also fall within the scope of protection of the present invention.

Claims

1. A motor driving method, characterized in that: include: Get the motor temperature, motor current and motor voltage of the motor; Calculating a current-stage system state vector of the motor according to the motor temperature, the motor current, and the motor voltage; Utilize a preset PWM control algorithm based on a deep learning model to predict the system state vector of the current stage and generate a PWM control strategy for the next stage; The motor is driven according to the PWM control strategy of the next stage.

2. The motor driving method according to claim 1, wherein: The step of calculating the current phase system state vector of the motor according to the motor temperature, the motor current and the motor voltage comprises: performing normalization processing on the motor temperature, the motor current, and the motor voltage; The system state vector of the current stage is calculated using the normalized motor temperature, the motor current, and the motor voltage.

3. The motor driving method according to claim 2, wherein: The motor current is normalized by the following formula: The motor voltage is normalized by the following formula: Wherein, I represents the normalized motor current, V represents the normalized motor voltage, i represents the collected motor phase current value, μ i represents the mean value of the collected current samples, σ i represents the standard deviation of the collected current samples, v represents the collected motor phase voltage value, u v Represents the mean value of the collected voltage samples, σ v Indicates the standard deviation of the collected voltage samples.

4. The motor driving method according to claim 2, wherein: The motor temperature is normalized by the following formula: Where, T represents the normalized motor temperature, t is the collected motor temperature value, and T min Indicates the minimum value of the collected motor temperature samples, T max Indicates the maximum value among the collected motor temperature samples.

5. The motor driving method according to claim 2, wherein: The system state vector Obtained by the following formula: Where r represents the system state vector The radial distance, θ represents the system state vector The polar angle, Represents the system state vector azimuth, I represents the normalized motor current, V represents the normalized motor voltage, and T represents the normalized motor temperature.

6. The motor driving method according to any one of claims 1 to 5, characterized in that: The steps of using a preset PWM control algorithm based on a deep learning model to predict the system state vector of the current stage and generate a PWM control strategy for the next stage include: Using the preset PWM control algorithm to predict the system state vector of the current stage and the historical system state vector to obtain the system state vector of the next stage; The PWM control strategy for the next stage is generated according to the system state vector for the next stage.

7. The motor driving method according to any one of claims 1 to 5, characterized in that: After obtaining the motor temperature, motor current, and motor voltage of the motor, and before calculating the system state vector of the motor at the current stage, the following steps are also included: Confirm that the motor temperature, the motor current, and the motor voltage are all within the allowable range for system operation.

8. The motor driving method according to claim 7, wherein: After obtaining the motor temperature, motor current and motor voltage of the motor, it also includes: If any one of the motor temperature, the motor current and the motor voltage is outside the system operation allowable range, a motor protection operation is performed.

9. A household appliance comprising a processor and a memory, characterized in that: The memory stores a computer program, and when the computer program is executed by the processor, the steps of the motor driving method according to any one of claims 1 to 8 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a controller, the steps of the motor driving method according to any one of claims 1 to 8 are implemented.

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