Method, system and storage medium for controlling a permanent magnet synchronous motor

Abstract

This disclosure provides a method, system, and storage medium for controlling a permanent magnet synchronous motor, relating to the field of motor control technology. The method includes: acquiring battery parameters characterizing the current transient characteristics of the battery; acquiring the current control command value used by the current loop in the motor controller during the current control cycle; predicting the expected bus current on the DC side of the inverter based on the current control command value; predicting the actual available bus voltage of the motor controller in the next control cycle based on the battery parameters and the expected bus current; adjusting the anti-integral saturation limit of the PI regulator in the current loop based on the actual available bus voltage to perform predictive anti-saturation limiting processing on the PI regulator output; and adjusting the modulation region switching threshold of the SVPWM module based on the actual available bus voltage to perform predictive overmodulation control on the SVPWM module. This method employs a prediction + active adjustment mechanism, which can effectively avoid the deterioration of control performance under high current demand conditions.

Description

Technical Field

[0001] This disclosure belongs to the field of motor control technology, specifically relating to a method, system, and storage medium for controlling a permanent magnet synchronous motor. Background Technology

[0002] Permanent magnet synchronous motors (PMSMs) are widely used in electric vehicle drive systems due to their compact structure, low maintenance costs, and high power density. The control functions of a permanent magnet synchronous motor are implemented by a motor controller (MCU).

[0003] In related technologies, the motor controller uses the real-time measured DC bus voltage This serves as the upper limit of the modulation voltage. However, under high-current demand conditions such as rapid acceleration during cold starts in winter, the battery's internal resistance... This causes a significant instantaneous voltage drop. The motor controller locally samples... Much higher than the actual usable bus voltage of the inverter power bridge arm . This is typically acquired before a voltage drop occurs or when the drop is small. This results in the voltage command output by the motor controller. Unconsciously exceeding the inverter's requirements The voltage synthesis capability leads to passive overmodulation in the system.

[0004] Furthermore, in the event of passive overmodulation, the actual output voltage of the MCU cannot follow the voltage command value of the current loop PI regulator. The deviation between the actual output voltage and the voltage command value causes the integral term of the PI regulator to accumulate continuously, resulting in integral saturation. This integral saturation will lead to a significant deterioration in current tracking performance, current waveform distortion, increased torque ripple, and reduced system dynamic response performance. Summary of the Invention

[0005] This disclosure provides a method, system, and storage medium for controlling a permanent magnet synchronous motor, aiming to at least partially solve the technical problem in related technologies where the lack of consideration for battery transient characteristics leads to unavoidable deterioration in control performance caused by voltage drops under high current demand conditions.

[0006] At least one embodiment of this disclosure provides a method for controlling a permanent magnet synchronous motor, including:

[0007] Obtain battery parameters to characterize the current transient characteristics of the battery, wherein the battery powers a permanent magnet synchronous motor; Obtain the current control command value used by the current loop in the motor controller during the current control cycle; The expected bus current on the DC side of the inverter in the motor controller is predicted based on the current control command value. Based on the battery parameters and the expected bus current, the actual available bus voltage of the motor controller in the next control cycle of the current control cycle is predicted. Adjusting the anti-integral saturation limit of the PI regulator in the current loop based on the actual available bus voltage, to perform predictive anti-saturation limiting processing on the output of the PI regulator; and, Based on the actual available bus voltage, the modulation region switching threshold of the SVPWM module in the motor controller is adjusted to perform predictive overmodulation control on the SVPWM module.

[0008] The above solution offers the following technical advantages: It provides a predictive motor control method based on battery transient characteristics, employing a predictive + active adjustment mechanism. This allows the motor controller to proactively and smoothly enter an overmodulation control state when a voltage drop is imminent, or to reasonably limit the voltage command output of the PI controller by adjusting the anti-integral saturation limit of the PI controller in the current loop in advance. This enables the PI controller to detect potential integral saturation early, thereby suppressing PI integral saturation and ensuring control stability under high dynamic operating conditions. The advantages of this method are mainly reflected in the following four aspects. First, compared to related technologies, this method does not directly use the actual collected bus voltage for limiting the output of the PI regulator. Instead, it uses the predicted actual available bus voltage for the next control cycle. Based on the advance prediction of battery parameters and dynamic adjustment of the anti-integral saturation limit, the PI regulator can detect potential integral saturation in advance, suppressing the growth of the PI integral term in the current loop or directly limiting the current command of the motor controller. This significantly suppresses the integral saturation problem caused by voltage mismatch. By changing the control strategy from post-event anti-saturation compensation to dynamic adjustment based on the advance prediction of battery parameters and the anti-integral saturation limit, the PI regulator operates within the predicted actual available bus voltage limit, thus suppressing integral saturation caused by voltage mismatch. Second, this method improves the transient response and stability of the motor control. By utilizing battery parameters that characterize the current transient characteristics of the battery, the transient characteristics of the battery are introduced into the fast current loop of the motor controller in real time and in a feedforward manner, improving transient response and stability. Especially under high current impact conditions such as rapid acceleration, it avoids control delay and oscillation caused by passive saturation, resulting in more accurate current tracking, smoother torque output, and faster response, greatly improving the driving experience and system robustness. Third, this method achieves smooth and controllable overmodulation. By using the predictively dynamically changing actual available bus voltage as the control limit, instead of using the lagging parameter of the bus voltage acquired at the current moment as the control limit in unrelated technologies, it synchronously updates the anti-integral saturation limit of the PI regulator and the modulation region switching threshold of the SVPWM module. This allows the controller to actively and smoothly enter the overmodulation region, which not only avoids the harm of passive saturation but also maximizes the utilization of the reduced bus voltage, achieving a large torque / power output capability under high load conditions. Fourth, this method improves the safety of motor control. By avoiding the huge current distortion and torque ripple caused by passive overmodulation, it reduces the impact on the motor, inverter, and drive system, improving the reliability and safety of the motor control system.

[0009] In at least one embodiment of the method provided in this disclosure, the battery parameters include battery voltage and battery internal resistance, wherein the battery voltage includes at least one of battery terminal voltage and battery open-circuit voltage.

[0010] The above solution has the following technical effect: it effectively integrates the transient characteristics of the battery.

[0011] In at least one embodiment of the method provided in this disclosure, obtaining battery parameters for characterizing the current transient characteristics of the battery includes: A command is sent to the battery management system connected to the battery to obtain battery parameters, so that the battery management system can estimate the current battery parameters; and, The battery management system receives the estimated battery parameters in response to the acquisition command.

[0012] The above solution has the following technical effects: it utilizes the battery management system's ability to estimate the battery's internal resistance, and introduces the battery's transient characteristics into the motor controller's current loop in real time and in an advanced manner.

[0013] In at least one embodiment of the method provided in this disclosure, predicting the expected bus current on the DC side of the inverter in the motor controller based on the current control command value includes: The current control command value is input into a preset motor power consumption prediction model to obtain the expected power consumption of the permanent magnet synchronous motor, wherein the motor power consumption prediction model is configured to generate the expected power consumption based on the current control command value; and, The expected power consumption and the battery parameters are input into a preset DC-side bus current prediction model to obtain the expected bus current on the DC side of the inverter in the permanent magnet synchronous motor. The DC-side bus current prediction model is configured to generate the expected bus current based on the expected power consumption and the battery parameters.

[0014] The above solution has the following technical effect: it enables accurate prediction of the expected bus current.

[0015] In at least one embodiment of the method provided in this disclosure, predicting the actual available bus voltage of the motor controller in the next control cycle based on the battery parameters and the expected bus current includes: The expected bus current and the battery parameters are input into a preset DC-side bus voltage prediction model to obtain the actual available bus voltage on the DC side of the inverter in the permanent magnet synchronous motor. The DC-side bus voltage prediction model is configured to generate the actual available bus voltage based on the expected bus current and the battery parameters. Specifically, the actual available bus voltage is negatively correlated with the expected bus current, the actual available bus voltage is negatively correlated with the battery internal resistance in the battery parameters, and the actual available bus voltage is positively correlated with the battery open-circuit voltage in the battery parameters.

[0016] The above scheme has the following technical effects: it enables accurate prediction of the actual available bus voltage.

[0017] In at least one embodiment of the method provided in this disclosure, adjusting the anti-integral saturation limit of the PI regulator in the current loop based on the actual available bus voltage to perform predictive anti-saturation limiting processing on the output of the PI regulator includes: The actual available bus voltage is input into a preset anti-integral saturation limit generation model to obtain the anti-integral saturation limit, wherein the anti-integral saturation limit generation model is configured to generate the anti-integral saturation limit based on the actual available bus voltage divided by a configurable modulation coefficient; and, Based on the anti-integral saturation limit, the voltage synthesis vector output by the PI regulator is subjected to predictive anti-saturation limiting processing so that the amplitude of the voltage synthesis vector does not exceed the dynamic range defined by the anti-integral saturation limit.

[0018] The above solution has the following technical effects: it achieves the effect of suppressing integral saturation. By dynamically adjusting the anti-integral saturation limit in advance, the PI regulator can detect possible integral saturation in advance, suppress the growth of the current loop PI integral term in advance, or directly limit the current command of the motor controller, thus significantly suppressing the integral saturation problem caused by voltage mismatch.

[0019] In at least one embodiment of the method provided in this disclosure, the modulation coefficient is configured as follows: When the motor controller allows its SVPWM module to overmodulate, the modulation coefficient is set to a first value; and, When the motor controller does not allow its SVPWM module to be overmodulated, the modulation coefficient is set to a second value that is different from the first value.

[0020] The above solution has the following technical effects: it realizes active adjustment of control limits, ensuring control stability and torque response accuracy under high dynamic conditions.

[0021] In at least one embodiment of the method provided in this disclosure, adjusting the modulation region switching threshold of the SVPWM module in the motor controller based on the actual available bus voltage to perform predictive overmodulation control on the SVPWM module includes: The actual available bus voltage is input into a preset switching threshold generation model to obtain the modulation region switching threshold, wherein the switching threshold generation model is configured to generate the modulation region switching threshold based on the predicted actual available bus voltage divided by a second value. Obtain the synthesized voltage vector after predictive anti-saturation limiting processing; In response to the absolute value of the synthesized voltage vector being greater than the modulation region switching threshold, the SVPWM module is controlled to operate in overmodulation mode; and, In response to the absolute value of the synthesized voltage vector being less than the modulation region switching threshold, the SVPWM module is controlled to operate in linear modulation processing; Furthermore, the method also includes: Determine whether the expected power consumption of the permanent magnet synchronous motor and the battery parameters meet the preset judgment conditions of the DC-side bus current prediction model. If so, generate first information available for characterizing the DC-side bus current prediction model; and, If not, generate second information to characterize that the expected power consumption of the permanent magnet synchronous motor exceeds the output capacity limit of the battery, and trigger the corresponding power limit.

[0022] The above solution offers the following technical advantages: it achieves smooth and controllable overmodulation, enabling the controller to actively and smoothly enter the overmodulation region. This not only avoids the dangers of passive saturation but also maximizes the utilization of the reduced bus voltage, extracting the maximum torque / power output capability under high load conditions.

[0023] At least one embodiment of this disclosure also provides a system for controlling a permanent magnet synchronous motor, applied to a motor controller connected to the permanent magnet synchronous motor, including: The first acquisition unit is configured to acquire battery parameters used to characterize the current transient characteristics of the battery, wherein the battery is powered by a permanent magnet synchronous motor. The second acquisition unit is configured to acquire the current control command value used by the current loop in the motor controller in the current control cycle. The preprocessing unit is configured to predict the expected bus current on the DC side of the inverter in the motor controller based on the current control command value, and to predict the actual available bus voltage of the motor controller in the next control cycle of the current control cycle based on the battery parameters and the expected bus current. The first-level processing unit is configured to adjust the anti-integral saturation limit of the PI regulator in the current loop based on the actual available bus voltage, so as to perform predictive anti-saturation limiting processing on the output of the PI regulator; and, The second-level processing unit is configured to adjust the modulation region switching threshold of the SVPWM module in the motor controller based on the actual available bus voltage, so as to perform predictive overmodulation control on the SVPWM module.

[0024] At least one embodiment of this disclosure also provides a storage medium storing a program or instructions, wherein the program or instructions, when executed by a processor, implement the steps of the method provided in any embodiment of this disclosure.

[0025] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and are not intended to limit this disclosure. Attached Figure Description

[0026] To more clearly illustrate the technical solutions in the embodiments of this disclosure, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of this disclosure. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0027] Figure 1 A flowchart illustrating a method for controlling a permanent magnet synchronous motor, provided for at least one embodiment of this disclosure; Figure 2 A flowchart illustrating a battery parameter acquisition scheme provided in at least one embodiment of this disclosure; Figure 3 Flowchart of a scheme for obtaining the expected DC bus current in at least one embodiment of this disclosure; Figure 4 Flowchart of a DC-side available bus voltage prediction scheme provided for at least one embodiment of this disclosure; Figure 5 Flowchart of a predictive anti-integral saturation limiting scheme provided for at least one embodiment of this disclosure; Figure 6 This is a control diagram of an SVPWM module provided in at least one embodiment of the present disclosure; Figure 7 A flowchart of an active overmodulation control scheme provided in at least one embodiment of this disclosure; Figure 8 Flowchart of another method for controlling a permanent magnet synchronous motor provided in at least one embodiment of this disclosure; Figure 9 A structural block diagram of a system for controlling a permanent magnet synchronous motor provided in at least one embodiment of the present disclosure; Figure 10 A schematic diagram illustrating the composition of a program product provided for at least one embodiment of this disclosure.

[0028] Figure label: 10- System for controlling a permanent magnet synchronous motor; 11- First acquisition unit; 12- Second acquisition unit; 13- Preprocessing unit; 14- First-level processing unit; 15- Second-level processing unit; 21- Processor; 22- Memory; 23- Input device; 24- Output device; - Composite voltage vector; α - α-axis; β - β-axis; 1- The first boundary of the sector-shaped region; 2- The second boundary of the sector-shaped region; U dc - DC voltage amplitude. Detailed Implementation

[0029] The present disclosure will now be described in further detail with reference to the accompanying drawings and embodiments. It should be particularly noted that the following embodiments are for illustrative purposes only and do not limit the scope of the disclosure. Similarly, the following embodiments are only some, not all, embodiments of the present disclosure, and all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this disclosure.

[0030] The terms "first," "second," and "third" used in the embodiments of this disclosure are for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Therefore, a feature defined as "first," "second," and "third" may explicitly or implicitly include at least one of that feature.

[0031] In the description of this disclosure, "multiple" means at least two, such as two or three, unless otherwise expressly and specifically limited.

[0032] In this disclosure, the terms "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., refer to a specific feature, structure, material, or characteristic described in connection with that embodiment or example, which is included in at least one embodiment or example of this disclosure. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0033] The terms “comprising” and “having”, and any variations thereof, used in this disclosure are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or device that includes a series of steps or units is not limited to the steps or units listed, but may optionally include steps or units not listed, or may optionally include other steps or components inherent to such processes, methods, products, or devices.

[0034] The term "dq coordinate system" used in this disclosure is also called a rotating coordinate system. In the dq coordinate system, the d-axis typically coincides with the direction of the magnetic flux linkage of the rotor permanent magnet, and the q-axis leads the d-axis by 90 electrical degrees.

[0035] The term "αβ coordinate system" used in this disclosure is also called a two-phase stationary coordinate system. In the αβ coordinate system, the α axis typically coincides with the axis of the U-phase winding in the three-phase coordinate system, and the β axis leads the α axis by 90 electrical degrees.

[0036] The term "permanent magnet synchronous motor" (PMSM) used in this disclosure refers to a motor that converts electrical energy from a battery into mechanical energy output.

[0037] The term "motor controller" used in this disclosure, abbreviated as MCU, is used to control a permanent magnet synchronous motor. For a three-phase permanent magnet synchronous motor, the MCU converts the DC bus power into the three-phase AC power required by the motor through a three-phase inverter full-bridge circuit. The MCU typically uses the FOC algorithm to control the motor torque output.

[0038] The term "battery management system" as used in this disclosure is abbreviated as BMS. Electric vehicles typically use lithium batteries as their power source. In an electric vehicle, the battery management system is an electronic control system used to manage the power battery.

[0039] The term "space vector modulation" in this disclosure is abbreviated as SVPWM.

[0040] The term "anti-integral saturation limit" in this disclosure, also known as anti-saturation integral limiting or saturation integral limiting, refers to the voltage limiting used to limit the synthesized dq-axis voltage vector within the bus voltage range. If the magnitude of the synthesized dq-axis voltage vector output by the current loop PI regulator exceeds the anti-integral saturation limit, the target dq-axis voltage cannot be modulated subsequently, potentially causing integral saturation. Therefore, voltage limiting processing is required on the dq-axis voltage output by the PI regulator to limit the magnitude of the synthesized voltage vector to within the range defined by the anti-integral saturation limit.

[0041] Figure 1 This is a flowchart illustrating a method for controlling a permanent magnet synchronous motor according to at least one embodiment of the present disclosure. This method can be applied to a motor controller connected to the permanent magnet synchronous motor. Figure 1 As shown, the method may include the following steps S10-S50.

[0042] Step S10: Obtain battery parameters to characterize the current transient characteristics of the battery, wherein the battery powers the permanent magnet synchronous motor.

[0043] Step S20: Obtain the current control command value (also known as reference current) used by the current loop in the motor controller in the current control cycle.

[0044] Step S30: Predict the expected bus current (also known as the expected DC bus current) on the DC side of the inverter in the motor controller based on the current control command value.

[0045] Step S40: Based on battery parameters and expected bus current, predict the actual available bus voltage of the motor controller in the next control cycle of the current control cycle.

[0046] Step S50: Adjust the anti-integral saturation limit of the PI regulator in the current loop based on the actual available bus voltage to perform predictive anti-saturation limiting processing on the output of the PI regulator.

[0047] Step S60: Adjust the modulation region switching threshold of the SVPWM module in the motor controller based on the actual available bus voltage to perform predictive overmodulation control on the SVPWM module.

[0048] It should be noted that steps S10-S60 can be executed within each control cycle of the FOC, or within a set control cycle of the FOC. Steps S10-S40 aim to integrate the battery transient characteristics to predict the actual available bus voltage of the motor controller's dynamic changes. This prediction uses current control command values ​​instead of measured current to achieve forward-looking prediction. Steps S50 and S60 aim to actively adjust control limits, using the actual available bus voltage as a dynamic voltage ceiling to adjust the output of the PI regulator in the current loop and the output of the SVPEM module in real time.

[0049] In the above scheme, this disclosure does not limit the battery parameters in step S10. The battery parameters are not a fixed combination, nor are they fixed values. In practical applications, in addition to the real-time battery internal resistance and real-time battery open-circuit voltage mentioned later, battery parameters can also include key indicators such as real-time battery remaining capacity (SOC), battery temperature, and battery health status (SOH). SOC directly affects its instantaneous output power limit, changes in battery temperature cause dynamic shifts in internal resistance and open-circuit voltage, and battery health status reflects the degree of performance degradation after long-term cycling. Incorporating these parameters into the analysis can further improve the accuracy of bus voltage prediction, thereby laying a more reliable foundation for subsequent adjustments to the anti-integral saturation limit of the current loop PI regulator and optimization of the modulation region switching threshold of the SVPWM module. All of the above battery parameters can be obtained through the Battery Management System (BMS).

[0050] When the system executes step S10, it may need to select appropriate battery parameters based on the specific actual working conditions.

[0051] In the above scheme, this disclosure does not limit the specific type of the current control command value in step S20. The current control command value corresponds to the current control axis. Real-time dq-axis voltages cannot be used here because the collected three-phase voltages are the result of the previous cycle. Since this scheme needs to calculate the actual available bus voltage for the next control cycle, the current control command value of the current cycle is required. In practical applications, the current control command value includes at least the d-axis reference current value and the q-axis reference current value, which are the outputs of the PI regulator and can be applied to the motor control circuit after modulation. The d-axis reference current value is mainly used for flux regulation. The q-axis reference current value is directly related to the motor's output torque. By comprehensively considering these current control command values, the stability, reliability, and dynamic adaptability of the current loop control can be further improved, providing a more comprehensive command basis for the efficient operation of permanent magnet synchronous motors under complex operating conditions.

[0052] When the system executes step S20, it may need to select the appropriate type of current control command value according to the specific application scenario and actual needs.

[0053] In the above scheme, this disclosure does not limit the scheme for obtaining the expected bus current in step S30. In practical application scenarios, in addition to the schemes described in the following embodiments, a generation scheme that selects more parameters and integrates multi-parameter calculations can also be adopted. For example, parameters such as the real-time speed of the motor, load rate, temperature compensation coefficient, bus voltage fluctuation, and flux saturation can be integrated. By constructing a weighted calculation model or a lightweight neural network model, nonlinear fitting is performed on multiple parameters, and the weight ratio of each parameter is dynamically adjusted—for example, increasing the weight of the load rate under high load conditions, and introducing a temperature compensation coefficient to correct deviations in low-temperature environments. This multi-parameter fusion scheme can more comprehensively reflect the actual operating state of the motor, effectively reduce the calculation error caused by dependence on a single parameter, and make the predicted result of the expected bus current closer to the actual operating conditions, providing a more reliable input basis for subsequent current loop optimization control.

[0054] When the system executes step S30, it can flexibly select a suitable expected bus current acquisition scheme according to the complexity of the actual working conditions and the control accuracy requirements.

[0055] In the above scheme, this disclosure does not limit the actual usable bus voltage prediction scheme in step S40. In practical application scenarios, in addition to the schemes described in the following embodiments, a prediction scheme that integrates multiple parameters such as the equivalent series resistance of the DC bus capacitor, the inverter switching frequency, and the real-time cooling efficiency of the heat dissipation system can also be adopted. By constructing an adaptive Kalman filter model or a micro-time series prediction model based on the attention mechanism, the parameters are fused and dynamically corrected in real time—for example, increasing the weight of the switching frequency under high-frequency switching conditions, and introducing the attenuation coefficient of the equivalent series resistance to compensate for prediction deviations in capacitor aging scenarios. This multi-dimensional parameter fusion prediction scheme can more accurately capture the transient change characteristics of the bus voltage, effectively avoid the prediction lag or distortion caused by fluctuations in a single parameter, and make the prediction result of the actual usable bus voltage more consistent with the real-time operating state of the system, providing more timely and reliable decision support for subsequent voltage loop closed-loop control and power device protection.

[0056] When the system executes step S40, it can flexibly select an appropriate actual available bus voltage prediction scheme based on the complexity of the system hardware configuration and the requirements of voltage control accuracy.

[0057] In the above scheme, this disclosure does not limit the predictive anti-integral saturation limiting scheme based on the predicted actual available bus voltage in step S50. In practical application scenarios, in addition to the schemes described in the following embodiments, a dynamic limit adjustment strategy combining the real-time load rate and speed range of the motor can also be adopted. For example, when the motor is under high load and low speed conditions, the upper limit of integral saturation can be appropriately reduced to accelerate the response speed of the voltage loop to bus voltage fluctuations; while under low load and high speed conditions, the limit range can be relaxed to reduce unnecessary control intervention. In addition, fuzzy control algorithms can also be introduced, and by defining input variables such as bus voltage prediction deviation, deviation change rate, and motor operating state, a multi-rule fuzzy inference model can be constructed to achieve adaptive adjustment of the anti-integral saturation limit.

[0058] When the system executes step S50, it can flexibly select an appropriate predictive anti-integral saturation limiting scheme based on the actual working conditions.

[0059] In the above scheme, this disclosure does not limit the active overmodulation control scheme based on the predicted actual available bus voltage in step S60. In practical application scenarios, in addition to the schemes described in the following embodiments, a dynamic threshold adjustment strategy that combines the real-time torque demand of the motor with the bus voltage fluctuation amplitude can also be adopted. For example, when the motor is operating under high load and large bus voltage fluctuation, the switching threshold of the field weakening modulation region can be appropriately reduced to enter the field weakening control mode in advance to avoid overmodulation; while under low load and stable bus voltage conditions, the threshold for switching from the constant torque modulation region to the field weakening modulation region can be increased to extend the operating range of the constant torque region, thereby improving the energy conversion efficiency of the system. In addition, a model predictive control algorithm can also be introduced. By constructing a predictive model that includes the predicted bus voltage value, motor current command, and modulation mode cost function, the control effect under different switching thresholds can be calculated in real time, and the optimal modulation region switching threshold can be adaptively selected to ensure that the motor can maintain an efficient and stable modulation state throughout the entire operating range.

[0060] When the system executes step S60, it can flexibly select an appropriate active overmodulation control scheme based on the actual operating conditions.

[0061] Steps S10-S60 provide a predictive motor control method based on battery transient characteristics. This method employs a predictive + active adjustment mechanism, enabling the motor controller to proactively and smoothly enter overmodulation control when a voltage drop is imminent. Alternatively, it can pre-adjust the anti-integral saturation limit of the PI regulator in the current loop to reasonably limit the voltage command output of the PI controller, allowing the PI regulator to detect potential integral saturation in advance and thus suppressing it, ensuring control stability under high dynamic operating conditions. The advantages of this method are mainly reflected in the following four aspects. First, compared with related technologies, this method does not directly use the actual collected bus voltage for limiting the output of the PI regulator. Instead, it uses the predicted actual available bus voltage for the next control cycle. Based on the advance prediction of battery parameters and the dynamic adjustment of the anti-integral saturation limit, the PI regulator can detect possible integral saturation in advance and suppress the growth of the current loop PI integral term or directly limit the current command of the motor controller. This significantly suppresses the integral saturation problem caused by voltage mismatch. By changing the control strategy from post-event anti-saturation compensation to advance prediction of battery parameters and dynamic adjustment of the anti-integral saturation limit, the PI regulator operates within the predicted actual available bus voltage limit, thereby suppressing integral saturation caused by voltage mismatch. Secondly, this method improves the transient response and stability of motor control. By utilizing battery parameters that characterize the current transient characteristics of the battery, the transient characteristics of the battery are introduced into the fast current loop of the motor controller in real time and in a feedforward manner, improving transient response and stability. Especially under high current impact conditions such as rapid acceleration, it avoids control delays and oscillations caused by passive saturation, resulting in more accurate current tracking, smoother torque output, and faster response, greatly improving the driving experience and system robustness. Thirdly, this method achieves smooth and controllable overmodulation. By using the predictive, dynamically changing actual available bus voltage as the control limit, instead of using the lagging parameter of the bus voltage collected at the current moment as the control limit in related technologies, the method synchronously updates the anti-integral saturation limit of the PI regulator and the modulation region switching threshold of the SVPWM module. This allows the controller to actively and smoothly enter the overmodulation region, which not only avoids the harm of passive saturation but also maximizes the utilization of the reduced bus voltage, achieving a large torque / power output capability under high load conditions. Fourth, this method improves the safety of motor control. By avoiding the huge current distortion and torque pulsation caused by passive overmodulation, it reduces the impact on the motor, inverter and transmission system, thereby improving the reliability and safety of the motor control system.

[0062] Some embodiments of this disclosure also provide systems, storage media, and program products corresponding to the methods described above.

[0063] The method provided by at least one embodiment of this disclosure is applicable to any existing scenario requiring predictive anti-saturation limiting and overmodulation control of permanent magnet synchronous motors. For example, in the traction permanent magnet synchronous motor system of new energy vehicles, when frequently starting and stopping or accelerating uphill in congested urban areas, the bus voltage is easily affected by fluctuations in the real-time discharge state of the power battery, and the motor is often under high load conditions; while during high-speed cruising, the load is relatively stable, and the bus voltage is more stable. Applying the method of the embodiments of this disclosure, the modulation region switching threshold can be dynamically adjusted for different driving conditions, and the control strategy can be optimized in real time by combining model predictive control algorithms. This not only effectively avoids overmodulation phenomena during high loads when climbing hills, but also extends the constant torque operating range to improve energy conversion efficiency during low loads while cruising, thereby ensuring the continuous stability and high energy efficiency of the vehicle drive system. Another example is the joint drive permanent magnet synchronous motor of an industrial robot. When performing high-precision heavy-load operations, the motor load increases sharply, and the bus voltage may deviate due to grid fluctuations; while during light-load positioning or standby phases, the load is smaller and the voltage is stable. The predictive control logic disclosed herein can adaptively adjust the switching threshold, preventing overmodulation and torque saturation during heavy-load operations and optimizing operating efficiency during light-load phases, thus ensuring the accuracy and reliability of robot joint movements.

[0064] In some embodiments, Figure 1 Based on the above, to further optimize motor control, the battery parameters obtained in step S10 include battery voltage and battery internal resistance. The battery voltage includes at least one of battery terminal voltage and battery open-circuit voltage. The battery terminal voltage is the actual voltage value across the positive and negative terminals of the battery during load operation; its magnitude dynamically adjusts with changes in load current and directly affects the input voltage stability of the motor drive inverter. The battery open-circuit voltage is the voltage of the battery when it is in a quiescent state with no external current output, and it typically has a clear correspondence with the battery's state of charge (SOC). These battery parameters provide reliable voltage constraints for the vector control of the permanent magnet synchronous motor, ensuring that the modulation coefficient is configured within a reasonable range, thereby optimizing the motor's output torque characteristics and operating efficiency.

[0065] Figure 2 A flowchart illustrating a battery parameter acquisition scheme provided in at least one embodiment of this disclosure. Figure 1 Based on this, in order to accurately obtain the transient characteristics of the battery, such as Figure 2 As shown, step S10 further includes the following sub-steps S101-S102.

[0066] Sub-step S101: Send an acquisition command to the battery management system connected to the battery to obtain battery parameters, so that the battery management system can estimate the current battery parameters.

[0067] Sub-step S102: Receive the battery parameters estimated by the battery management system in response to the acquisition command.

[0068] The above scheme utilizes the battery management system's ability to estimate battery internal resistance and other parameters, introducing the battery's transient characteristics into the motor controller's current loop in real time and proactively. Through sub-steps S101-S102, the motor controller can accurately acquire battery parameters fed back from the battery management system in real time, using these parameters as key inputs for vector control to dynamically adjust the configuration of the anti-integral saturation limit and modulation region switching threshold. This not only ensures that the anti-integral saturation limit and modulation region switching threshold are always within a reasonable range, avoiding modulation distortion problems caused by insufficient battery voltage or excessive internal resistance, but also allows the current loop control to better match the actual transient characteristics of the battery, further optimizing the torque output linearity and operating efficiency of the permanent magnet synchronous motor.

[0069] In the above scheme, the battery parameters include V ocv and R batt It is not a fixed value; it comes from a real-time estimate by the Battery Management System (BMS). Because... V ocv and R batt These parameters are related to battery temperature, battery state of charge (SOC), and state of health (SOH), and do not change abruptly. Therefore, it is feasible for the BMS to transmit these two parameters to the motor controller via CAN communication, and given the slow changes in these parameters, the inherent latency of CAN communication is acceptable.

[0070] Figure 3 A flowchart illustrating a scheme for obtaining the expected DC-side bus current according to at least one embodiment of this disclosure. Figure 1 or Figure 2 Based on this, in order to ensure the accuracy of the prediction results, such as Figure 3 As shown, step S30 further includes the following sub-steps S301-S302.

[0071] Sub-step S301: Input the current control command value into the preset motor power consumption prediction model to obtain the expected power consumption of the permanent magnet synchronous motor, wherein the motor power consumption prediction model is configured to generate the expected power consumption based on the current control command value.

[0072] Sub-step S302: Input the expected power consumption and battery parameters into the preset DC-side bus current prediction model to obtain the expected bus current on the DC side of the inverter in the permanent magnet synchronous motor. The DC-side bus current prediction model is configured to generate the expected bus current based on the expected power consumption and battery parameters.

[0073] To estimate the expected bus current, the first step is to estimate the power consumption of the motor, i.e., the expected power consumption. Sub-steps S301-S302 enable accurate acquisition of the expected DC bus current from the current control command. This scheme first predicts the expected power consumption of the motor based on the current control command value, and then combines battery parameters to perform the mapping calculation of the DC bus current. This ensures the accuracy of the prediction results while reducing the complexity of a single model, facilitating efficient real-time computation in embedded control systems. Compared to traditional schemes that rely on hardware sensors to directly collect bus current, this prediction scheme can obtain the expected bus current value in advance, providing forward-looking data support for subsequent inverter modulation strategy optimization and power distribution control, thereby helping to improve the dynamic response performance and energy efficiency of the permanent magnet synchronous motor drive system.

[0074] In some embodiments, Figure 3 Based on this, in order to accurately estimate the expected power consumption, the motor power consumption prediction model in sub-step S301 is configured as follows:

[0075] In the formula, P elec_predict This indicates the expected electrical power consumption (also known as motor power). I d_ref This indicates the d-axis current command value. I q_ref This indicates the q-axis current command value. V d_pi This represents the d-axis voltage command value output by the PI regulator in the current loop. V q_pi This indicates the q-axis voltage command value output by the PI regulator in the current loop.

[0076] Among them, use I d_ref and I q_ref Non-d-axis current measured value I d Measured values ​​of q-axis current I q This is for forward-looking prediction. If measured values ​​are used for prediction, the delays in signal acquisition, transmission, and processing will cause the prediction results to lag behind the dynamic changes of the system, failing to meet the requirements of forward-looking control. However... I d_ref and I q_ref As the target control value of the current loop, it directly represents the current state that the motor needs to achieve in the next moment, combined with the output of the current loop PI regulator. V d_pi andV q_pi This model can accurately predict the power consumption of the motor under the given control command, ensuring a high degree of match between the prediction results and the actual power requirements of the motor during subsequent operation. Furthermore, the model relies solely on the existing command signals and PI outputs within the current loop, eliminating the need for additional sensors or complex computing modules. This simplifies the system structure, reduces the computational load on the embedded platform, and further enhances the real-time performance and reliability of the prediction process. This design approach aligns with the forward-looking requirements of the DC-side bus current prediction model, jointly supporting a complete forward-looking control chain from current command to power prediction and then to bus current prediction, providing a coherent and accurate foundation of upfront data for the dynamic optimization of the drive system.

[0077] In some embodiments, Figure 3 Based on this, in order to accurately estimate the expected bus current, the DC-side bus current prediction model in sub-step S302 is configured as follows:

[0078] In the formula, I dc_predict Indicates the expected bus current. V ocv Indicates the battery open-circuit voltage. R batt Indicates the battery's internal resistance. η inv This indicates the inverter efficiency.

[0079] It should be noted that an inverter converts DC input power into AC output power, incurs switching losses in power devices, and other heat losses. Inverter efficiency... η inv The inverter efficiency dynamically changes with the switching frequency and current magnitude, and can be obtained under different speed and torque conditions through thermal simulation or motor bench testing. For simplicity of control strategy, an average efficiency value, such as 96%, can also be used instead.

[0080] The above calculations can accurately obtain the expected bus current generated by the electrical power that the motor will consume under the current control command, providing reliable input parameters for subsequent calculations.

[0081] The derivation process of the DC-side bus current prediction model is as follows.

[0082] Based on DC side input power P dc Equal to expected power consumption P elec_predict Divide by inverter efficiency η inv We can obtain the following formula:

[0083] Meanwhile, based on DC side input power P dc This is also equal to the actual usable bus voltage on the DC side. V dc_predict Multiply by the expected DC bus current I dc_predict We can obtain the following formula:

[0084] In addition, based on the actual available bus voltage on the DC side V dc_predict equal to the battery open circuit voltage V ocv Subtracting the voltage drop caused by the battery's internal resistance, we get the following formula:

[0085] Combining the above three formulas, we obtain a formula about I dc_predict The quadratic equation of :

[0086] The roots of the above quadratic equation can be obtained using the quadratic formula:

[0087] The quadratic equation has two roots. The smaller root represents the use of a smaller DC bus current. I dc_predict Can produce P elec_predict / η inv The larger root represents the DC bus power input; a larger root indicates that a larger DC bus current is needed to produce the same bus power input. In this case, most of the voltage is diverted by the battery's internal resistance, which is clearly not realistic. Therefore, we take the smaller root here, i.e.:

[0088] like V ocv 2 Less than 4× R batt × P elec_predict / η inv If the requested power is too high, it means the system has reached its limit.

[0089] Figure 4 A flowchart illustrating a scheme for predicting the actual available DC bus voltage according to at least one embodiment of this disclosure. Figure 1 or Figure 2 or Figure 3 Based on this, in order to accurately predict the actual available bus voltage on the DC side of the inverter, such as Figure 4 As shown, step S40 further includes the following sub-step S401.

[0090] Sub-step S401: Input the expected bus current and battery parameters into the preset DC-side bus voltage prediction model to obtain the actual available bus voltage on the DC side of the inverter in the permanent magnet synchronous motor. The DC-side bus voltage prediction model is configured to generate the actual available bus voltage based on the expected bus current and battery parameters.

[0091] Specifically, the actual available bus voltage is negatively correlated with the expected bus current, negatively correlated with the battery internal resistance (a parameter in the battery parameters), and positively correlated with the battery open-circuit voltage (a parameter in the battery parameters). To ensure the real-time performance and accuracy of the prediction model, the battery parameters need to be dynamically collected and updated through the Battery Management System (BMS).

[0092] In some embodiments, Figure 4 Based on this, in order to accurately predict the actual available DC bus voltage of the inverter, the DC bus voltage prediction model in sub-step S401 is configured as follows:

[0093] In the formula, V dc_predict This indicates the actual usable bus voltage on the DC side.

[0094] The higher the battery open-circuit voltage, the higher the actual usable bus voltage; when the expected bus current or battery internal resistance increases, the actual usable bus voltage will decrease, which is completely consistent with the aforementioned description of the correlation between the actual usable bus voltage and various parameters.

[0095] Figure 5 A flowchart illustrating a predictive anti-integral saturation limiting scheme provided for at least one embodiment of this disclosure. Figure 1 or Figure 2 or Figure 3 or Figure 4 Based on this, in order to accurately predictively adjust the anti-integral saturation limit of the PI controller, such as... Figure 5 As shown, step S50 further includes the following sub-steps S501-S502.

[0096] Sub-step S501: Input the actual available bus voltage into the preset anti-integral saturation limit generation model to obtain the anti-integral saturation limit, wherein the anti-integral saturation limit generation model is configured to generate the anti-integral saturation limit based on the actual available bus voltage divided by a configurable modulation coefficient.

[0097] Sub-step S502: Perform predictive anti-saturation limiting processing on the voltage synthesis vector output by the PI regulator based on the anti-integral saturation limit, so that the amplitude of the voltage synthesis vector does not exceed the dynamic range defined by the anti-integral saturation limit.

[0098] The above-mentioned scheme effectively suppresses integral saturation. By dynamically adjusting the anti-integral saturation limit in advance, the PI regulator can detect potential integral saturation early, suppressing the growth of the current loop PI integral term or directly limiting the current command of the motor controller, thus significantly suppressing the integral saturation problem caused by voltage mismatch.

[0099] The above scheme is to adjust the actual usable bus voltage on the DC side. V dc_predict This is one of the two key nodes applied to the FOC algorithm.

[0100] Traditional methods for preventing integral saturation limits, V d_pi , V q_pi The composite voltage vector is constrained based on the bus voltage measured at the current moment. V dc_measured The calculated resistance to integral saturation limit V limit ’ It can be obtained through the following formula:

[0101] in, A It is a coefficient. When the system does not allow SVPWM overmodulation, then... V limit ’ Must be limited to the linear modulation region, see [link / reference] Figure 6 The inscribed circle of the hexagon, at this time A = If the system allows SVPWM overmodulation, then V limit ’ It can be limited to the maximum overmodulation region, see [reference]. Figure 6 The vertex of the hexagon, at this time A =3 / 2.

[0102] In this disclosed scheme, the anti-saturation integral limiting is not calculated using the bus voltage measured at the current moment, but rather using the predicted available bus voltage. V dc_predict Calculate its resistance to integral saturation limit. V limit It can be obtained through the following formula:

[0103] PI regulator output V d_pi and V q_pi Before being sent to the SVPWM module, the voltage synthesis vector is subject to this predictive anti-integral saturation limit. V limit Clamping. When the controller predicts... V dc When a significant drop is imminent, it immediately lowers the PI regulator's anti-integral saturation limit (saturation upper limit), allowing the PI regulator to detect potential integral saturation in advance and suppress the growth of the current loop PI integral term or directly limit the current command of the motor controller, significantly suppressing the integral saturation problem caused by voltage mismatch.

[0104] A The value ranges from 3 / 2 to between.

[0105] Modulation ratio MI = peak phase voltage / ( V dc / 2), For the SVPWM module, A The value ranges from 1.15 to 1.33.

[0106] Pick At that time, the maximum peak value of the phase voltage V limit = V dc / The maximum modulation ratio MI_max is 1.15 (i.e., 2 / The system is confined to the linear modulation region.

[0107] When the value is 3 / 2, the maximum peak value of the phase voltage is V limit = V dc / (3 / 2), the maximum modulation ratio MI_max is 1.33 (i.e. 4 / 3), and the system can reach the maximum overmodulation point.

[0108] Based on the maximum modulation ratio MI_max allowed by the strategy, it can be set A The value of A =2 / MI_max.

[0109] In some embodiments, Figure 5 Based on this, in sub-step S501, the modulation coefficient is configured as follows: when the motor controller allows its SVPWM module to overmodulate, the modulation coefficient is set to a first value; and when the motor controller does not allow its SVPWM module to overmodulate, the modulation coefficient is set to a second value different from the first value.

[0110] The first value is 3 / 2, and the second value is... The modulation coefficients are configured to match the operating mode of the SVPWM module, ensuring a clamping upper limit for the voltage synthesis vector under different modulation modes. V limit Based on the predicted actual available bus voltage V dc_predict Accurate calculations provide fundamental parameter support for subsequent prediction-based anti-integral saturation limiting to suppress integral saturation in advance.

[0111] Figure 7 A flowchart illustrating an active overmodulation control scheme provided in at least one embodiment of this disclosure. Figure 1 or Figure 2 or Figure 3 or Figure 4 or Figure 5 Based on this, in order to enable the controller to actively and smoothly enter the overmodulation region, step S60 is refined to include the following sub-steps S601-S604.

[0112] Sub-step S601: Input the actual available bus voltage into the preset switching threshold generation model to obtain the modulation region switching threshold, wherein the switching threshold generation model is configured to generate the modulation region switching threshold based on the predicted actual available bus voltage divided by a second value.

[0113] Sub-step S602: Obtain the synthesized voltage vector after predictive anti-saturation limiting processing.

[0114] Sub-step S603: In response to the absolute value of the synthesized voltage vector being greater than the modulation region switching threshold, control the SVPWM module to operate in overmodulation mode.

[0115] Sub-step S604: In response to the absolute value of the synthesized voltage vector being less than the modulation region switching threshold, control the SVPWM module to operate in linear modulation processing.

[0116] The aforementioned scheme achieves smooth and controllable overmodulation, enabling the controller to actively and smoothly enter the overmodulation region. This not only avoids the harm of passive saturation but also maximizes the utilization of the reduced bus voltage, achieving maximum torque / power output capability under high load conditions.

[0117] In some embodiments, Figure 7 Based on this, in order to obtain an accurate modulation region switching threshold, the switching threshold generation model in sub-step S601 is configured as follows:

[0118] This model enables the switching threshold to be dynamically adjusted in real time to follow changes in the actual bus voltage, ensuring accurate determination of the modulation region even when the bus voltage fluctuates, thus providing a reliable basis for the mode switching of the subsequent SVPWM module.

[0119] The above scheme is to adjust the actual usable bus voltage on the DC side. V dc_predict Another key node applied to the FOC algorithm. The SVPWM module receives dynamically clamped signals... V d_pi and V q_pi Then, the magnitude of its synthesized voltage vector will be calculated. V ref The SVPWM module simultaneously receives... V dc_predict As its true bus voltage reference. The switching thresholds for linear modulation and overmodulation in the modulation strategy of the SVPWM module are no longer based on... V dc_measured , but based on V dc_predict .

[0120] Based on the current traditional SVPWM for measuring bus voltage, its linear modulation condition is | V ref |≤ V dc_measured / The condition for overmodulation is | V ref |> V dc_measured / However, based on the current bus voltage measurement value V dc_measured Using bus voltage to calculate duty cycle is inaccurate.

[0121] Based on the SVPWM disclosed herein, its linear modulation condition is | V ref |≤ V dc_predict / The condition for overmodulation is | V ref |> V dc_predict / .when V dc_predict Even when falling rapidly, V refIf the threshold for overmodulation remains unchanged, the threshold will decrease. The system will automatically and smoothly switch from linear modulation to controlled overmodulation. This is completely different from "passive" saturation (i.e., duty cycle overflow), because the latter can lead to loss of control after integral saturation.

[0122] Figure 8 A flowchart illustrating another method for controlling a permanent magnet synchronous motor, provided for at least one embodiment of this disclosure. Figure 4 In order to ensure the accuracy of the DC-side bus voltage prediction model, the method further includes the following steps S31-S33.

[0123] Step S31: Determine whether the expected power consumption of the permanent magnet synchronous motor and the battery parameters meet the preset judgment conditions for the DC side bus current prediction model.

[0124] Step S32: If so, generate first information available to characterize the DC-side bus current prediction model.

[0125] Step S33: If not, generate second information to characterize that the expected power consumption of the permanent magnet synchronous motor exceeds the output capacity limit of the battery, and trigger the corresponding power limit.

[0126] Steps S31-S33 are set before step S401. When the expected power consumption and battery parameters meet the preset conditions for the availability of the DC-side bus current prediction model, the system will use the output of the DC-side bus current prediction model for overmodulation judgment and subsequent control procedures. In step S33, when the expected power consumption and battery parameters meet the preset conditions for the availability of the DC-side bus current prediction model, triggering the corresponding power limit can specifically manifest as: dynamically reducing the actual power consumption of the motor by adjusting the reference torque command or target speed of the permanent magnet synchronous motor, so that it falls back to the limit range of the battery output capacity. In addition, during the power limit triggering period, the system can temporarily switch to the traditional overmodulation control logic based on real-time measurement of the bus voltage to ensure the continuity and stability of motor control and avoid control interruption or instability caused by the unavailability of the prediction model.

[0127] In some embodiments, Figure 8 Based on this, in order to quickly determine whether the DC-side bus current prediction model is usable, the condition for determining the usability of the DC-side bus current prediction model is configured as follows:

[0128] The above conditions indicate that the requested power is too high. In actual systems, power matching is performed when the battery, motor controller, and motor are matched. Generally, the output capability of the motor controller needs to cover the external characteristics of the motor, and the output capability of the battery also needs to cover the external characteristics of the motor. However, when the battery output is limited due to low battery charge or other factors, the BMS will derive a maximum power limit that can be output at different SOCs. This value is provided by the battery manufacturer or measured through cell testing. This maximum power limit is given to the VCU, which then limits the requested torque. After receiving the torque request from the VCU, the motor controller will generally also limit it again based on this limit. Therefore, the actual requested power will not exceed the maximum output power that the current battery can output.

[0129] If the aforementioned limits fail to take effect for some specific reason, the BMS will detect overcurrent and report an overcurrent fault, typically triggering further power limiting. If these measures fail, the BMS may autonomously reduce the voltage. The BMS processing logic mentioned here is general; different BMS implementations may employ different handling methods.

[0130] Below is an example of a method for controlling a permanent magnet synchronous motor.

[0131] A predictive bus voltage estimation module is introduced into the motor controller. Within each control cycle of the motor controller, this module predicts the actual usable bus voltage that the battery can output when the current control command is applied to the permanent magnet synchronous motor, based on information such as battery parameters and current control command values. Based on this predicted actual usable bus voltage, rather than the currently acquired bus voltage, it determines whether the target modulation voltage amplitude output by current control, such as PI control, is in the linear modulation region or the overmodulation region.

[0132] The inputs to the motor controller system include: 1) Real-time battery internal resistance from the Battery Management System (BMS) R batt Real-time battery open-circuit voltage V ocv ; 2) D-axis current command value from inside the motor controller I d_ref q-axis current command value I q_ref and the electric angular velocity of the motor ω e ; 3) Known parameters obtained through bench calibration, including motor parameters and d-axis inductance. L d q-axis inductance L qRotor flux φ f Stator resistance R s and inverter efficiency η inv .

[0133] Inverter efficiency η inv It can be a constant or a Map.

[0134] The main steps of this method example are as follows: 1) Integrating BMS data: This step involves obtaining the battery internal resistance estimated by the BMS in real time. R batt and battery open circuit voltage V ocv Among them, the battery open-circuit voltage V ocv High-precision terminal voltage can also be used as a substitute; 2) Predict the expected DC-side bus current. This step is performed by the MCU before executing current loop control and PWM modulation, based on the current current command. I d_ref , I q_ref Based on motor parameters, predict the expected bus current to be generated. I dc_predict ; 3) Calculate the predicted dynamic changes in the actual available bus voltage, this step incorporating the expected bus current. I dc_predict Battery internal resistance provided by BMS R batt and battery open circuit voltage V ocv The MCU calculates the actual available DC bus voltage for the next control cycle in real time. 4) Actively adjust the control limits. This step uses the predicted actual available bus voltage as a dynamic voltage ceiling to update the output limit of the anti-integral saturation module of the current loop PI regulator in real time, and as a reference for the space vector modulation (SVPWM) module to judge linear modulation and overmodulation.

[0135] The above-mentioned scheme, through this "predictive-active" mechanism, enables the controller to anticipate the upcoming voltage drop, thereby proactively and smoothly entering the overmodulation control state, or by dynamically adjusting the anti-integral saturation limit in advance, the PI regulator can sense the possible integral saturation in advance, suppress the growth of the current loop PI integral term in advance, or directly limit the current command of the motor controller, which significantly suppresses the integral saturation problem caused by voltage mismatch and ensures the control stability and torque response accuracy under high dynamic conditions.

[0136] Figure 9 This is a structural block diagram of a system for controlling a permanent magnet synchronous motor according to at least one embodiment of the present disclosure. The method can be applied to a motor controller connected to the permanent magnet synchronous motor. A rotary transformer is provided in the permanent magnet synchronous motor. For example... Figure 9 As shown, the system 10 for controlling the permanent magnet synchronous motor includes a first acquisition unit 11, a second acquisition unit 12, a preprocessing unit 13, a first-level processing unit 14, and a second-level processing unit 15.

[0137] The first acquisition unit 11 is configured to acquire battery parameters used to characterize the current transient characteristics of the battery, which powers the permanent magnet synchronous motor.

[0138] The second acquisition unit 12 is configured to acquire the current control command value used by the current loop in the motor controller in the current control cycle.

[0139] The preprocessing unit 13 is configured to predict the expected bus current on the DC side of the inverter in the motor controller based on the current control command value, and to predict the actual available bus voltage of the motor controller in the next control cycle based on battery parameters and the expected bus current.

[0140] The first-level processing unit 14 is configured to adjust the anti-integral saturation limit of the PI regulator in the current loop based on the actual available bus voltage, so as to perform predictive anti-saturation limiting processing on the output of the PI regulator.

[0141] The second-level processing unit 15 is configured to adjust the modulation region switching threshold of the SVPWM module in the motor controller based on the actual available bus voltage in order to perform predictive overmodulation control on the SVPWM module.

[0142] The specific execution methods of each unit in the above system embodiments have been described in detail in the embodiments related to the method, and will not be elaborated here.

[0143] In some embodiments, Figure 9 Based on this, the first acquisition unit 11 can be implemented by a corresponding receiving module, the second acquisition unit 12 can be implemented by a corresponding data recognition module, and the preprocessing unit 13, the first-level processing unit 14 and the second-level processing unit 15 can be implemented by a controller or control module with corresponding programs.

[0144] This disclosure also provides a storage medium storing a program or instructions that, when executed by a processor, implement the steps of the method embodiments described above.

[0145] This disclosure also provides a program product, such as... Figure 10 As shown, the program product includes one or more processors 21 and memory 22. Figure 10 Take a processor 21 as an example.

[0146] The controller may also include an input device 23 and an output device 24.

[0147] The processor 21, memory 22, input device 23, and output device 24 can be connected via a bus or other means. Figure 10 Taking the example of a connection between China and Israel via a bus.

[0148] The processor 21 can be a central processing unit (CPU), or it can be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, or combinations of the above types of chips. The general-purpose processor can be a microprocessor or any conventional processor.

[0149] The memory 22, as a non-transitory computer-readable storage medium, can be used to store non-transitory software programs, non-transitory computer-executable programs, and modules, such as the program instructions / modules corresponding to the methods in the embodiments of this disclosure. The processor 21 executes various functional applications and data processing of the server by running the non-transitory software programs, instructions, and modules stored in the memory 22, thereby implementing the steps of the above-described method embodiments.

[0150] The memory 22 may include a program storage area and a data storage area. The program storage area may store the operating system and applications required for at least one function; the data storage area may store data created by the use of the processing device operated by the server. Furthermore, the memory 22 may include high-speed random access memory and may also include non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid-state storage device. In some embodiments, the memory 22 may optionally include memory remotely located relative to the processor 21, and these remote memories may be connected to a network connection device via a network. Examples of such networks include, but are not limited to, the Internet, intranets, local area networks, mobile communication networks, and combinations thereof.

[0151] Input device 23 can receive input digital or character information, and generate key signal inputs related to driver settings and function control of the server's processing unit. Output device 24 may include display devices such as a display screen.

[0152] One or more modules are stored in memory 22, and when executed by one or more processors 21, they perform actions such as... Figure 1 The method shown.

[0153] Those skilled in the art will understand that all or part of the processes in the above method embodiments can be implemented by a computer program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, it can include the processes described in the above method embodiments. The storage medium can be a magnetic disk, optical disk, read-only memory (ROM), random access memory (RAM), flash memory (FM), hard disk drive (HDD), or solid-state drive (SSD), etc.; the storage medium can also include combinations of the above types of memory.

[0154] Although embodiments of the present disclosure have been described in conjunction with the accompanying drawings, those skilled in the art can make various modifications and variations without departing from the spirit and scope of the present disclosure, and such modifications and variations all fall within the scope defined by the appended claims.

[0155] Although embodiments of the present disclosure have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present disclosure. Those skilled in the art can make changes, modifications, substitutions and variations to the above embodiments within the scope of the present disclosure.

Claims

1. A method of controlling a permanent magnet synchronous motor, applied to a motor controller connected to a permanent magnet synchronous motor, characterized in that, include: Obtain battery parameters to characterize the current transient characteristics of the battery, wherein the battery powers a permanent magnet synchronous motor; Obtain the current control command value used by the current loop in the motor controller during the current control cycle; The expected bus current on the DC side of the inverter in the motor controller is predicted based on the current control command value. Based on the battery parameters and the expected bus current, the actual available bus voltage of the motor controller in the next control cycle of the current control cycle is predicted. Adjusting the anti-integral saturation limit of the PI regulator in the current loop based on the actual available bus voltage, to perform predictive anti-saturation limiting processing on the output of the PI regulator; and, Based on the actual available bus voltage, the modulation region switching threshold of the SVPWM module in the motor controller is adjusted to perform predictive overmodulation control on the SVPWM module.

2. The method of claim 1, wherein, The battery parameters include battery voltage and battery internal resistance, wherein the battery voltage includes at least one of battery terminal voltage and battery open-circuit voltage.

3. The method according to claim 1 or 2, characterized in that, The acquisition of battery parameters used to characterize the current transient characteristics of the battery includes: A command is sent to the battery management system connected to the battery to obtain battery parameters, so that the battery management system can estimate the current battery parameters; and, The battery management system receives the estimated battery parameters in response to the acquisition command.

4. The method according to claim 1 or 2, characterized in that, The prediction of the expected bus current on the DC side of the inverter in the motor controller based on the current control command value includes: The current control command value is input into a preset motor power consumption prediction model to obtain the expected power consumption of the permanent magnet synchronous motor, wherein the motor power consumption prediction model is configured to generate the expected power consumption based on the current control command value; and, The expected power consumption and the battery parameters are input into a preset DC-side bus current prediction model to obtain the expected bus current on the DC side of the inverter in the permanent magnet synchronous motor. The DC-side bus current prediction model is configured to generate the expected bus current based on the expected power consumption and the battery parameters.

5. The method according to claim 1 or 2, characterized in that, The prediction of the actual available bus voltage of the motor controller in the next control cycle based on the battery parameters and the expected bus current includes: The expected bus current and the battery parameters are input into a preset DC-side bus voltage prediction model to obtain the actual available bus voltage on the DC side of the inverter in the permanent magnet synchronous motor. The DC-side bus voltage prediction model is configured to generate the actual available bus voltage based on the expected bus current and the battery parameters. Specifically, the actual available bus voltage is negatively correlated with the expected bus current, the actual available bus voltage is negatively correlated with the battery internal resistance in the battery parameters, and the actual available bus voltage is positively correlated with the battery open-circuit voltage in the battery parameters.

6. The method of claim 1 or 2, wherein, The step of adjusting the anti-integral saturation limit of the PI regulator in the current loop based on the actual available bus voltage to perform predictive anti-saturation limiting processing on the output of the PI regulator includes: The actual available bus voltage is input into a preset anti-integral saturation limit generation model to obtain the anti-integral saturation limit, wherein the anti-integral saturation limit generation model is configured to generate the anti-integral saturation limit based on the actual available bus voltage divided by a configurable modulation coefficient; and, Based on the anti-integral saturation limit, the voltage synthesis vector output by the PI regulator is subjected to predictive anti-saturation limiting processing so that the amplitude of the voltage synthesis vector does not exceed the dynamic range defined by the anti-integral saturation limit.

7. The method according to claim 1 or 2, characterized in that, The modulation coefficient is configured as follows: When the motor controller allows its SVPWM module to overmodulate, the modulation coefficient is set to a first value; and, When the motor controller does not allow its SVPWM module to be overmodulated, the modulation coefficient is set to a second value that is different from the first value.

8. The method of claim 1 or 2, wherein, The step of adjusting the modulation region switching threshold of the SVPWM module in the motor controller based on the actual available bus voltage to perform predictive overmodulation control on the SVPWM module includes: The actual available bus voltage is input into a preset switching threshold generation model to obtain the modulation region switching threshold, wherein the switching threshold generation model is configured to generate the modulation region switching threshold based on the predicted actual available bus voltage divided by a second value. Obtain the synthesized voltage vector after predictive anti-saturation limiting processing; In response to the absolute value of the synthesized voltage vector being greater than the modulation region switching threshold, the SVPWM module is controlled to operate in overmodulation mode; and, In response to the absolute value of the synthesized voltage vector being less than the modulation region switching threshold, the SVPWM module is controlled to operate in linear modulation processing; Furthermore, the method also includes: Determine whether the expected power consumption of the permanent magnet synchronous motor and the battery parameters meet the preset judgment conditions of the DC-side bus current prediction model. If so, generate first information available for characterizing the DC-side bus current prediction model; and, If not, generate second information to characterize that the expected power consumption of the permanent magnet synchronous motor exceeds the output capacity limit of the battery, and trigger the corresponding power limit.

9. A system for controlling a permanent magnet synchronous motor, applied to a motor controller connected with a permanent magnet synchronous motor, characterized in that, include: The first acquisition unit is configured to acquire battery parameters used to characterize the current transient characteristics of the battery, wherein the battery is powered by a permanent magnet synchronous motor. The second acquisition unit is configured to acquire the current control command value used by the current loop in the motor controller in the current control cycle. The preprocessing unit is configured to predict the expected bus current on the DC side of the inverter in the motor controller based on the current control command value, and to predict the actual available bus voltage of the motor controller in the next control cycle of the current control cycle based on the battery parameters and the expected bus current. The first-level processing unit is configured to adjust the anti-integral saturation limit of the PI regulator in the current loop based on the actual available bus voltage, so as to perform predictive anti-saturation limiting processing on the output of the PI regulator; and, The second-level processing unit is configured to adjust the modulation region switching threshold of the SVPWM module in the motor controller based on the actual available bus voltage, so as to perform predictive overmodulation control on the SVPWM module.

10. A storage medium, characterized by The storage medium stores a program or instructions, wherein the program or instructions, when executed by a processor, implement the steps of the method as described in any one of claims 1 to 8.

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