A method and system for determining a zero angle of an electric machine

Abstract

The embodiment of the application discloses a motor zero angle determination method and system, the method comprises the following steps: after detecting a learning instruction of a unified diagnostic service (UDS), triggering the motor to enter the following learning mode: based on a vector control system, making d-axis current id 0 and q-axis current iq 0, converging d-axis voltage Ud close to 0 through a phase-locked loop closed-loop negative feedback control system; when d-axis voltage Ud is successfully converged close to 0 within a preset learning number of times, calculating the average value of the zero position angle of the stable period of the phase-locked loop closed-loop negative feedback control system as the initial position of the motor, and writing the learning information into a preset storage medium. Through the embodiment scheme, the motor self-learning of the zero angle is realized, and the torque accuracy of the permanent magnet synchronous motor in the full speed domain is effectively improved.

Description

Technical Field

[0001] This application relates to motor control technology, and more particularly to a method and system for determining the zero-position angle of a motor. Background Technology

[0002] For permanent magnet synchronous motors, calibrating the initial position angle is a key factor in controlling the normal operation of the motor. Currently, there are many test schemes for zero-position detection of motors, including measuring zero position at the back EMF zero-crossing point and measuring zero position by applying DC current to the motor. However, the self-learning method is more commonly used by OEMs. Self-learning has its advantages, but also its shortcomings in strategy and algorithm.

[0003] The closed-loop speed control method achieves zero-position learning by adjusting the initial position angle to control the target speed. However, the magnitude of the motor's drag torque during testing can easily affect the accuracy of zero-position learning. The high-frequency voltage or current injection method estimates the initial position of the motor by injecting a high-frequency voltage or current signal onto the direct axis of the rotating coordinate system and utilizing the motor's magnetic circuit saturation to obtain effective salient pole characteristics. However, this method has a complex software strategy, and differences in software parameter calibration can also affect the initial position estimation. Another method calculates the motor's zero position by calculating the ratio of Ud to Uq in the rotating coordinate system, but this method requires manual adjustment and calculation, which is not conducive to mass production learning of motors. Summary of the Invention

[0004] This application provides a method and system for determining the zero-position angle of a motor, which enables the motor to perform self-learning of the zero-position angle and effectively improves the torque accuracy of the permanent magnet synchronous motor across the entire speed range.

[0005] This application provides a method for determining the zero-position angle of a motor, the method including:

[0006] Upon detecting the learning instruction from UDS (Unified Diagnostic Services), the motor is triggered to enter the following learning mode: Based on the vector control system, the d-axis current id is 0 and the q-axis current iq is 0. The d-axis voltage Ud is brought close to 0 through the phase-locked loop closed-loop negative feedback control system.

[0007] When the d-axis voltage Ud is successfully converged to near 0 within a preset number of learning iterations, the average zero-position angle of the phase-locked loop closed-loop negative feedback control system during the stable period is calculated as the initial position of the motor, and the learning information is written into a preset storage medium. The learning information includes: a learning success flag, the number of learning iterations, and the initial position of the motor obtained after successful learning.

[0008] In an exemplary embodiment of this application, the method may further include:

[0009] In the learning mode, the root mean square error ΔU1 of the d-axis voltage Ud within a preset time period and the mean error ΔU2 of the d-axis voltage Ud within a preset time period are calculated.

[0010] Detect whether the root mean square error ΔU1 is within a preset first error range, and detect whether the mean error ΔU2 is within a preset second error range;

[0011] When the root mean square error ΔU1 is within a preset first error range and the mean error ΔU2 is within a preset second error range, it is determined that the d-axis voltage Ud has successfully converged to near 0.

[0012] When the root mean square error ΔU1 is not within a preset first error range, and / or the mean error ΔU2 is not within a preset second error range, it is determined that the d-axis voltage Ud has not successfully converged to near 0.

[0013] In an exemplary embodiment of this application, the method may further include:

[0014] If the d-axis voltage Ud fails to converge to near 0 within a preset number of learning iterations, the learning feedback fails.

[0015] In an exemplary embodiment of this application, the method may further include:

[0016] The fluctuation amplitude of the d-axis voltage Ud is determined based on the magnitude of the root mean square error ΔU1.

[0017] The signal fluctuation amplitude of the electrical angle θ′ of the position sensor on the motor is calculated based on the fluctuation amplitude of the d-axis voltage Ud.

[0018] The axial displacement and radial tilt of the position sensor during installation are determined based on the amplitude of the signal fluctuation.

[0019] In an exemplary embodiment of this application, the method may further include:

[0020] Whether the vector control system is operating normally is determined based on the magnitude of the mean error ΔU2.

[0021] In an exemplary embodiment of this application, the method may further include:

[0022] After obtaining the initial position of the motor, the motor is adjusted to different speeds. Based on the phase-locked loop closed-loop negative feedback control system, id is made to reach the maximum value at the different speeds. The zero-position angle of the motor is adjusted so that the actual torque of the device under test is equal to the output torque of the motor. The zero-position angle obtained after adjustment is used as the zero-position angle to be compensated for the motor at the corresponding speed.

[0023] Based on the zero-position angle to be compensated corresponding to different rotational speeds, a compensation fitting calculation formula is established between the rotational speed and the zero-position angle to be compensated.

[0024] In an exemplary embodiment of this application, the method may further include:

[0025] Under normal motor operation, the zero-position angle corresponding to the motor speed is obtained based on the motor speed and the compensation fitting formula, and the motor is compensated accordingly based on the obtained zero-position angle.

[0026] In an exemplary embodiment of this application, the compensation fitting calculation formula may include:

[0027] δ1′=δ1+k·n+b;

[0028] Where δ1′ is the zero-position angle to be compensated at any speed when the motor is at any speed, δ1 is the zero-position angle to be compensated when the motor speed is 1000 rpm, n is the motor speed, k is the speed coefficient, and b is the compensation constant.

[0029] This application embodiment also provides a motor zero-position angle determination system for implementing the motor zero-position angle determination method in a test bench scenario. The system may include: a battery simulator, a power supply box, a motor controller, a motor under test, a torque flange, a dynamometer load motor inverter, and a dynamometer load motor.

[0030] The battery simulator and the power supply box are both connected to the motor controller. The motor controller is connected to the motor under test. The motor under test is connected to the dynamometer load motor. The dynamometer load motor is connected to the dynamometer load motor inverter. The torque flange is located between the motor under test and the dynamometer load motor.

[0031] The dynamometer load motor is configured to drag the motor under test to a set speed.

[0032] The motor under test is configured to learn the motor zero-position angle based on a preset learning mode at the set speed, and perform corresponding zero-position angle compensation.

[0033] The battery simulator and the power supply box are configured to supply a first power supply and a second power supply to the motor controller, respectively; the voltage of the first power supply is higher than the voltage of the second power supply.

[0034] The torque flange is configured to monitor the actual torque during the motor's zero-position angle learning and zero-position angle compensation process.

[0035] This application embodiment also provides a motor zero-position angle determination system for implementing the motor zero-position angle determination method in a real vehicle test scenario. The system may include: a power supply battery, a power supply, a pulse code modulation (PCM) controller, the motor under test, a clutch, and an engine.

[0036] The power supply battery and the power supply are both connected to the PCM controller, the PCM controller is connected to the motor under test, and the motor under test is connected to the engine; the clutch is located between the motor under test and the engine.

[0037] The engine is configured to drive the tested motor to a set speed.

[0038] The motor under test is configured to learn the motor zero-position angle based on a preset learning mode at the set speed, and perform corresponding zero-position angle compensation.

[0039] The power supply battery is configured to provide a first power supply to the PCM controller;

[0040] The power supply is configured to provide a second power source to the PCM controller; the voltage of the first power source is higher than the voltage of the second power source.

[0041] The clutch is configured to rigidly connect the engine and the tested motor when fully engaged.

[0042] Compared with related technologies, the embodiments of this application include: after detecting the learning instruction of the Unified Diagnostic Service (UDS), triggering the motor to enter the following learning mode: based on the vector control system, making the d-axis current id 0 and the q-axis current iq 0, and converging the d-axis voltage Ud close to 0 through the phase-locked loop closed-loop negative feedback control system; when the d-axis voltage Ud is successfully converged to close to 0 within a preset number of learning times, the average zero-position angle of the phase-locked loop closed-loop negative feedback control system during the stable period is calculated as the initial position of the motor, and the learning information is written to a preset storage medium; the learning information may include: a learning success flag, the number of learning times, and the initial position of the motor obtained after successful learning. Through this embodiment, the motor achieves self-learning of the zero-position angle, effectively improving the torque accuracy of the permanent magnet synchronous motor across the entire speed range, and realizing a one-click self-learning mode, which is beneficial for mass production and full automation in factories; it also eliminates the influence of external environmental conditions, improving the stability and consistency of the permanent magnet synchronous motor's self-learning; making the motor self-learning test simple, reliable, safe, and efficient.

[0043] Other features and advantages of this application will be set forth in the following description, and will be apparent in part from the description, or may be learned by practicing the application. Other advantages of this application can be realized and obtained by means of the solutions described in the description and the accompanying drawings. Attached Figure Description

[0044] The accompanying drawings are used to provide an understanding of the technical solutions of this application and constitute a part of the specification. They are used together with the embodiments of this application to explain the technical solutions of this application and do not constitute a limitation on the technical solutions of this application.

[0045] Figure 1 This is a flowchart of the motor zero-position angle determination method according to an embodiment of this application;

[0046] Figure 2 This is a schematic diagram of a phase-locked loop closed-loop negative feedback control system according to an embodiment of this application;

[0047] Figure 3 This is a schematic diagram of three typical convergence results of Ud under the learning mode in an embodiment of this application.

[0048] Figure 4 This is a block diagram of vector control for permanent magnet synchronous motors in related technologies;

[0049] Figure 5 This is a schematic diagram of the overall self-learning process in an embodiment of this application;

[0050] Figure 6 This is a schematic diagram of a motor zero-position angle determination system based on a test bench scenario, according to an embodiment of this application.

[0051] Figure 7 This is a schematic diagram of a motor zero-position angle determination system based on a real vehicle test scenario, according to an embodiment of this application. Detailed Implementation

[0052] This application describes several embodiments, but these descriptions are exemplary and not restrictive, and it will be apparent to those skilled in the art that many more embodiments and implementations are possible within the scope of the embodiments described herein. Although many possible combinations of features are shown in the drawings and discussed in the detailed description, many other combinations of the disclosed features are also possible. Unless specifically limited, any feature or element of any embodiment may be used in combination with, or may replace, any feature or element of any other embodiment.

[0053] This application includes and contemplates combinations of features and elements known to those skilled in the art. The embodiments, features, and elements disclosed in this application may also be combined with any conventional features or elements to form a unique inventive scheme as defined by the claims. Any feature or element of any embodiment may also be combined with features or elements from other inventive schemes to form another unique inventive scheme as defined by the claims. Therefore, it should be understood that any feature shown and / or discussed in this application may be implemented individually or in any suitable combination. Therefore, the embodiments are not limited except by the limitations imposed by the appended claims and their equivalents. Furthermore, various modifications and changes may be made within the scope of the appended claims.

[0054] Furthermore, in describing representative embodiments, the specification may have presented methods and / or processes as a specific sequence of steps. However, the method or process should not be limited to the specific order of steps described herein, to the extent that it does not depend on such a specific order. As will be understood by those skilled in the art, other sequences of steps are also possible. Therefore, the specific order of steps set forth in the specification should not be construed as a limitation of the claims. Moreover, the claims concerning the method and / or process should not be limited to the steps performed in the written order, and those skilled in the art will readily understand that these orders can be varied and still remain within the spirit and scope of the embodiments of this application.

[0055] This application provides a method for determining the zero-position angle of a motor, such as... Figure 1 As shown, the method may include steps S101-S102:

[0056] S101. After detecting the learning instruction of UDS (Unified Diagnostic Services), the motor is triggered to enter the following learning mode: Based on the vector control system, the d-axis current id is 0 and the q-axis current iq is 0. The d-axis voltage Ud is brought close to 0 through the phase-locked loop closed-loop negative feedback control system.

[0057] S102. When the d-axis voltage Ud is successfully converged to near 0 within a preset number of learning iterations, the average zero-position angle of the phase-locked loop closed-loop negative feedback control system during the stable period is calculated as the initial position of the motor, and the learning information is written into a preset storage medium. The learning information may include, but is not limited to: a learning success flag, the number of learning iterations, and the initial position of the motor obtained after successful learning.

[0058] In an exemplary embodiment of this application, the self-learning program sets a special learning mode (or self-learning mode). When the UDS service issues a learning instruction, it automatically enters the special self-learning mode. The self-learning process can be completed in just a few seconds, and the learning results are directly written into the storage medium, which can realize batch automated testing in the factory.

[0059] In the exemplary embodiments of this application, compared with the method of calculating the zero-position angle by multiple measurements using the arctangent method of Ud and Uq in related technologies, the solution of this application embodiment can trigger a one-click self-learning special mode and directly write the learning information into the storage medium, realizing a fully automated method for determining the zero-position angle of motors in mass production in factories, which has an absolute advantage.

[0060] In the exemplary embodiment of this application, a phase-locked loop closed-loop negative feedback control system is adopted, and the average zero-position angle during the stable phase of the system is selected as the zero-position angle of the motor during the voltage convergence process, which can ensure the stability and consistency of the learning results each time.

[0061] In the exemplary embodiments of this application, a phase-locked loop closed-loop negative feedback control system is adopted to realize the continuity of self-learning zero-position angle calculation, which not only ensures the simple and reliable operation of the software system, but also makes the self-learning test of the motor bench safe and efficient.

[0062] In the exemplary embodiments of this application, compared with self-learning methods such as speed closed-loop in related technologies, the solution of the embodiments of this application is free from the influence of external environmental conditions and improves the stability and consistency of self-learning of permanent magnet synchronous motors.

[0063] In an exemplary embodiment of this application, the method may further include:

[0064] In the learning mode, the root mean square error ΔU1 of the d-axis voltage Ud within a preset time period and the mean error ΔU2 of the d-axis voltage Ud within a preset time period are calculated.

[0065] Detect whether the root mean square error ΔU1 is within a preset first error range, and detect whether the mean error ΔU2 is within a preset second error range;

[0066] When the root mean square error ΔU1 is within a preset first error range and the mean error ΔU2 is within a preset second error range, it is determined that the d-axis voltage Ud has successfully converged to near 0.

[0067] When the root mean square error ΔU1 is not within a preset first error range, and / or the mean error ΔU2 is not within a preset second error range, it is determined that the d-axis voltage Ud has not successfully converged to near 0.

[0068] In an exemplary embodiment of this application, for a permanent magnet synchronous motor, based on the synchronous rotating dq coordinate coefficient mathematical model, the stator voltage equation is expressed as follows when the zero-position angle is δ:

[0069]

[0070] Among them, L1=1 / 2(Lq+Ld), L2=1 / 2(Lq-Ld); u d u q These are the d-axis and q-axis components of the stator voltage, respectively; i d i q These are the d-axis and q-axis components of the stator current, respectively; R is the stator resistance; ω e It is the electric angular velocity; Ld and Lq are the d-axis and q-axis inductance components, respectively; ψ f δ represents the permanent magnet flux linkage; δ is the zero-position angle of the motor.

[0071] Given id = 0 and iq = 0, substituting into equation (1), we can obtain:

[0072]

[0073] When δ = 0, substituting into equation (1), we get:

[0074]

[0075] Given id = 0, iq = 0, and δ = 0, substituting into equation (2) or (3) above, we can obtain:

[0076]

[0077] In summary, when id = 0, iq = 0 and the zero-position angle is correct, the d-axis voltage Ud = 0 in the dq rotating coordinate system of the permanent magnet synchronous motor vector control system. That is, by continuously adjusting the zero-position angle, Ud gradually converges to Ud = 0. At this time, the initial position angle of the motor can be obtained (that is, the zero-position angle after adjustment that makes Ud gradually converge to Ud = 0).

[0078] In an exemplary embodiment of this application, a phase-locked loop closed-loop negative feedback control system is used for self-learning, such as Figure 2 As shown, the reference input target Ud*=0 of the system is a constant. The three-phase voltage Vabc is transformed by the phase estimate θ′. The negative feedback PI (proportional-integral) controller continuously corrects θ′ until Ud*=0. The final stable δ is the initial position angle of the motor.

[0079] In an exemplary embodiment of this application, since the stable output value of the PI controller is a constant, it cannot be directly substituted into the Park transform as the phase estimate θ′. Considering:

[0080]

[0081] Therefore, an integrator can be added after the PI controller. The output of the integrator is defined as phase θ′. The front-end signal of the integrator feeds back the output Ud to the input terminal. After the loop is adjusted, the phase difference between the input signal and the output signal is kept constant, that is, the loop reaches the "locked state". This realizes the function of the phase-locked loop.

[0082] The expression for the PI controller is:

[0083]

[0084] Where: k p k i These are the proportional gain and integral gain of the PI controller.

[0085] Based on the above analytical formulas (5) and (6), we obtain:

[0086]

[0087] in: is u d The reference input target.

[0088] In an exemplary embodiment of this application, the convergence effect of the d-axis voltage Ud = 0 is further verified based on the phase-locked loop closed-loop negative feedback control system. This system can not only more accurately identify the initial position of the motor, but also detect the hardware mounting gap of the motor's position sensor.

[0089] In exemplary embodiments of this application, as Figure 3 As shown, C1, C2, and C3 are examples of three typical convergence results in the learning mode. The long solid line represents the target voltage Ud*=0, the long broken line represents the real-time calculated d-axis voltage of Ud, and the long dashed line represents the mean value of Ud over a certain period. Additionally, ΔU1 is the root mean square error of Ud over a certain period (i.e., the preset duration), and ΔU2 is the mean error of Ud over a certain period.

[0090] In an exemplary embodiment of this application, the self-learning mode must satisfy the requirement that ΔU1 and ΔU2 are both within a certain error range to meet the requirement (i.e., Ud is close to 0), and the average zero-position angle of the motor over a certain period of time is obtained as the initial position angle of the motor.

[0091] In an exemplary embodiment of this application, the method may further include:

[0092] If the d-axis voltage Ud fails to converge to near 0 within a preset number of learning iterations, the learning feedback fails.

[0093] In the exemplary embodiments of this application, it can be seen from the above analysis that when ΔU1 and ΔU2 are smaller, C1 performs the best in comparison; on the other hand, ΔU1 and ΔU2 of C3 are larger, especially when ΔU2 is larger than the set mean error (i.e. the second error range), it is determined that the negative feedback of the PI controller does not converge, and the zero-position angle learning fails in the C3 learning mode.

[0094] In an exemplary embodiment of this application, the method may further include:

[0095] The fluctuation amplitude of the d-axis voltage Ud is determined based on the magnitude of the root mean square error ΔU1.

[0096] The signal fluctuation amplitude of the electrical angle θ of the position sensor on the motor is calculated based on the fluctuation amplitude of the d-axis voltage Ud.

[0097] The axial displacement and radial tilt of the position sensor during installation are determined based on the amplitude of the signal fluctuation.

[0098] In an exemplary embodiment of this application, analyzing the practical meaning of ΔU1, ΔU1 represents the root mean square error of Ud relative to the target value over a certain period of time. Figure 4 It can be seen that Ud is the output signal of PI control adjustment of id and iq. id and iq are derived from the three-phase currents iabc through Clark and Park transformations. Importantly, the electrical angle θ is used, which shows that the signal fluctuation amplitude of the position sensor's electrical angle θ determines the fluctuation amplitude of Ud, thus determining the magnitude of ΔU1. Furthermore, it is necessary to understand that the axial displacement and radial tilt of the position sensor installation determine the fluctuation amplitude of the position sensor's angle signal, thus proving that this zero-position angle learning program can be used to detect the hardware installation gap of the position sensor.

[0099] In an exemplary embodiment of this application, the method for determining the axial displacement and radial tilt of the position sensor during installation based on the signal fluctuation amplitude can be performed using any currently available feasible solution.

[0100] In an exemplary embodiment of this application, the method may further include:

[0101] Whether the vector control system is operating normally is determined based on the magnitude of the mean error ΔU2.

[0102] In an exemplary embodiment of this application, ΔU2 reflects the quality of the current loop PI controller or whether the motor vector control system is working properly, thus determining whether the learning process is normal. Specifically, when the mean error ΔU2 is greater than a set numerical threshold, it can be determined that the vector control system is not operating normally or the current loop PI controller is malfunctioning; conversely, when the mean error ΔU2 is less than the set numerical threshold, it can be determined that the vector control system is operating normally or the current loop PI controller is functioning correctly.

[0103] In an exemplary embodiment of this application, the method may further include:

[0104] After obtaining the initial position of the motor, the motor is adjusted to different speeds. Based on the phase-locked loop closed-loop negative feedback control system, id is made to reach the maximum value at the different speeds. The zero-position angle of the motor is adjusted so that the actual torque of the device under test is equal to the output torque of the motor. The zero-position angle obtained after adjustment is used as the zero-position angle to be compensated for the motor at the corresponding speed.

[0105] Based on the zero-position angle to be compensated corresponding to different rotational speeds, a compensation fitting calculation formula is established between the rotational speed and the zero-position angle to be compensated.

[0106] In an exemplary embodiment of this application, after the program reports successful learning, compensation can be performed on the motor zero position at various speeds. Theoretically, the speed signal is determined by the rate of change of the rotor position signal over a certain time interval, therefore the position signal and speed signal are correlated. As the motor's rotational speed increases, the position accuracy error becomes larger, and the motor zero-position angle also changes linearly with increasing speed. Extensive experiments have also demonstrated that due to errors caused by software operation and sensor accuracy, the initial position angle of the motor changes in a certain regular pattern with increasing speed, thus requiring compensation for the motor zero-position angle at different speeds.

[0107] In an exemplary embodiment of this application, for example, for a motor under test with a maximum speed of 7000 rpm, a dynamometer is selected to drive the motor under test at 1000 rpm to learn the zero position. Verifying the accuracy of the motor's zero-position angle generally involves applying the maximum d-axis current and monitoring whether the test bench has actual torque (the d-axis is aligned with the permanent magnet direction; theoretically, applying d-axis current when the motor's zero position is correct will not generate torque). Based on the zero-position learning at 1000 rpm, the dynamometer load motor can be set to speeds of 1000 rpm, 2000 rpm, 3000 rpm…7000 rpm. At each of these seven operating points, the maximum d-axis current is applied, and the zero-position angle is fine-tuned until the test bench torque equals the drag torque. This completes the zero-position verification at each speed, thereby obtaining the corresponding zero-position angle to be compensated (or corrected zero position) at different speeds, as shown in Table 1 below.

[0108] Table 1

[0109]

[0110]

[0111] In an exemplary embodiment of this application, the compensation fitting calculation formula may include:

[0112] δ1′=δ1+k·n+b;

[0113] Where δ1′ is the zero-position angle to be compensated at any speed when the motor is at any speed, δ1 is the zero-position angle to be compensated when the motor speed is 1000 rpm, n is the motor speed, k is the speed coefficient, and b is the compensation constant.

[0114] In an exemplary embodiment of this application, based on the data in Table 1 and the zero-position result δ (zero-position angle) learned at 1000 rpm, a linear compensation fitting calculation formula related to the zero-position angle δ′ at different speeds and the speed n can be established:

[0115] δ′=δ+k·n+b; (8)

[0116] Finally, the motor zero-position learning and zero-position angle compensation at each speed were completed. The correctness of the motor zero position at each speed can be verified by applying the maximum id current.

[0117] In an exemplary embodiment of this application, the method may further include:

[0118] Under normal motor operation, the zero-position angle corresponding to the motor speed is obtained based on the motor speed and the compensation fitting formula, and the motor is compensated accordingly based on the obtained zero-position angle.

[0119] In an exemplary embodiment of this application, based on the successful learning of the motor zero position, the zero position angle at each speed is compensated by injecting d-axis current, and the angle compensation parameters based on the motor speed are obtained by fitting, which makes up for the calculation deviation caused by the software operation, thereby improving the torque control accuracy of the motor in the full speed range.

[0120] In the exemplary embodiments of this application, it can be understood from the above content that, as Figure 5 As shown, the overall self-learning process in self-learning mode can include:

[0121] 1. Trigger the motor to enter a special learning mode via UDS command;

[0122] 2. Based on the vector control system, id=0 and iq=0, and the phase-locked loop closed-loop negative feedback control system converges the Ud voltage to close to 0. The average zero-position angle during the stable period of the system is calculated as the initial position of the motor.

[0123] 3. If the above learning process is repeated three times and the voltage Ud still fails to converge to close to 0, then the feedback learning has failed.

[0124] 4. Upon successful learning, the system identifies the successful learning flag and automatically writes the successful learning flag, the number of learning attempts, and the successful learning angle into the NVRAM (non-volatile random access memory) storage medium.

[0125] 5. Adjust different speeds to make id = the maximum value at different speeds. After the system stabilizes, correct the zero-position angle. By establishing the fitting relationship between the zero-position angle and different speeds, the zero-position accuracy in the full speed domain can be effectively improved.

[0126] 6. After completing the learning process, the system will automatically exit the special learning mode and enter the freewheel (free stop) state.

[0127] The exemplary embodiments of this application include at least the following advantages:

[0128] 1. It can achieve automated zero-point learning with one-click operation and can also be applied to batch testing in factories;

[0129] 2. The design of the self-learning software strategy can ensure the stability and consistency of each learning result;

[0130] 3. In self-learning mode, not only can the convergence degree of the motor vector control current loop PI be verified, but the position detection accuracy of the position sensor can also be verified.

[0131] 4. It can perform zero-position compensation for the injected d-axis current at different speeds, and obtain the zero-position angle compensation based on the motor speed, further improving the torque control accuracy of the motor in the full speed range.

[0132] This application also provides a motor zero-position angle determination system for implementing the aforementioned motor zero-position angle determination method in a test bench setting, such as... Figure 6 As shown, the system may include: a battery simulator 11, a power supply box 12, a motor controller 13, a motor under test 14, a torque flange 15, a dynamometer load motor frequency converter 16, and a dynamometer load motor 17.

[0133] The battery simulator 11 and the power supply box 12 are both connected to the motor controller 13. The motor controller 13 is connected to the motor under test 14. The motor under test 14 is connected to the dynamometer load motor 17. The dynamometer load motor 17 is connected to the dynamometer load motor inverter 16. The torque flange 15 is disposed between the motor under test 14 and the dynamometer load motor 17.

[0134] The load motor 17 of the dynamometer is configured to drag the motor 14 under test to a set speed.

[0135] The motor under test 14 is configured to learn the motor zero angle based on a preset learning mode at the set speed and perform corresponding zero angle compensation.

[0136] The battery simulator 11 and the power supply box 12 are configured to supply a first power supply and a second power supply to the motor controller 13, respectively; the voltage of the first power supply is higher than the voltage of the second power supply.

[0137] The torque flange 15 is configured to monitor the actual torque during the motor's zero-position angle learning and zero-position angle compensation process.

[0138] In an exemplary embodiment of this application, the test bench can be arranged as follows in a test bench scenario:

[0139] Under normal circumstances, the load motor 17 of the dynamometer drives the motor 14 under test to the set speed according to the preset speed target.

[0140] The tested motor 14 performs zero-position angle learning under the condition of being dragged to a set speed;

[0141] The battery simulator 11 and the power supply box 12 provide high-voltage power and low-voltage power to the motor controller 13, respectively.

[0142] The torque flange 15 can monitor the actual torque during zero-angle learning and zero-angle compensation.

[0143] This application also provides a motor zero-position angle determination system for implementing the motor zero-position angle determination method in a real vehicle testing scenario, such as... Figure 7 As shown, the system may include: a power supply battery 21, a power supply 22, a pulse code modulation (PCM) controller 23, a motor under test 24, a clutch 25, and an engine 26;

[0144] The power supply battery 21 and the power supply 22 are both connected to the PCM controller 23, the PCM controller 23 is connected to the motor under test 24, and the motor under test 24 is connected to the engine 26; the clutch 25 is disposed between the motor under test 24 and the engine 26.

[0145] The engine 26 is configured to drag the tested motor 24 to a set speed.

[0146] The motor under test 24 is configured to learn the motor zero-position angle based on a preset learning mode at the set speed, and perform corresponding zero-position angle compensation.

[0147] The power supply battery 21 is configured to provide a first power supply to the PCM controller 23;

[0148] The power supply 22 is configured to provide a second power supply to the PCM controller 23; the voltage of the first power supply is higher than the voltage of the second power supply.

[0149] The clutch 25 is configured to rigidly connect the engine 26 and the tested motor 24 when fully engaged.

[0150] In an exemplary embodiment of this application, the power supply battery 21 may be a high-voltage battery, and the power supply 22 may be a low-voltage power supply.

[0151] In an exemplary embodiment of this application, the test vehicle can be arranged as follows in a real-vehicle testing scenario:

[0152] The engine 26 drags the tested motor 24 to the set speed according to the preset speed target;

[0153] The tested motor 24 learns its zero-position angle while being driven to a set speed.

[0154] The high-voltage battery and the low-voltage power supply provide high-voltage and low-voltage power to the PCM controller 23 under test, respectively.

[0155] When fully engaged, clutch 25 rigidly connects engine 26 and the tested motor 24.

[0156] It will be understood by those skilled in the art that all or some of the steps, systems, or apparatuses disclosed above, and their functional modules / units, can be implemented as software, firmware, hardware, or suitable combinations thereof. In hardware implementations, the division between functional modules / units mentioned above does not necessarily correspond to the division of physical components; for example, a physical component may have multiple functions, or a function or step may be performed collaboratively by several physical components. Some or all components may be implemented as software executed by a processor, such as a digital signal processor or microprocessor, or as hardware, or as an integrated circuit, such as an application-specific integrated circuit (ASIC). Such software may be distributed on a computer-readable medium, which may include computer storage media (or non-transitory media) and communication media (or transient media). As is known to those skilled in the art, the term computer storage media includes volatile and non-volatile, removable and non-removable media implemented in any method or technology for storing information (such as computer-readable instructions, data structures, program modules, or other data). Computer storage media include, but are not limited to, RAM, ROM, EEPROM, flash memory or other memory technologies, CD-ROM, digital versatile disc (DVD) or other optical disc storage, magnetic cartridges, magnetic tape, disk storage or other magnetic storage devices, or any other medium that can be used to store desired information and can be accessed by a computer. Furthermore, it is well known to those skilled in the art that communication media typically contain computer-readable instructions, data structures, program modules, or other data in modulated data signals such as carrier waves or other transmission mechanisms, and may include any information delivery medium.

Claims

1. A method for determining the zero-position angle of a motor, characterized in that, The method includes: Upon detecting the learning instruction from the Unified Diagnostic Service (UDS), the motor is triggered to enter the following learning mode: Based on the vector control system, the d-axis current id is 0, the q-axis current iq is 0, and the d-axis voltage Ud is brought close to 0 through the phase-locked loop closed-loop negative feedback control system. In the learning mode, the root mean square error ΔU1 of the d-axis voltage Ud within a preset time period and the mean error ΔU2 of the d-axis voltage Ud within a preset time period are calculated. Detect whether the root mean square error ΔU1 is within a preset first error range, and detect whether the mean error ΔU2 is within a preset second error range; When the root mean square error ΔU1 is within a preset first error range and the mean error ΔU2 is within a preset second error range, it is determined that the d-axis voltage Ud has successfully converged to near 0. When the root mean square error ΔU1 is not within the preset first error range, and / or the mean error ΔU2 is not within the preset second error range, it is determined that the d-axis voltage Ud has not successfully converged to near 0. When the d-axis voltage Ud is successfully converged to near 0 within a preset number of learning iterations, the average zero-position angle of the phase-locked loop closed-loop negative feedback control system during the stable period is calculated as the initial position of the motor, and the learning information is written into a preset storage medium.

2. The method for determining the zero-position angle of a motor according to claim 1, characterized in that, The method further includes: If the d-axis voltage Ud fails to converge to near 0 within a preset number of learning iterations, the learning feedback fails.

3. The method for determining the zero-position angle of a motor according to claim 1, characterized in that, The method further includes: The fluctuation amplitude of the d-axis voltage Ud is determined based on the magnitude of the root mean square error ΔU1. The signal fluctuation amplitude of the electrical angle θ' of the position sensor on the motor is calculated based on the fluctuation amplitude of the d-axis voltage Ud. The axial displacement and radial tilt of the position sensor during installation are determined based on the amplitude of the signal fluctuation.

4. The method for determining the zero-position angle of a motor according to claim 1, characterized in that, The method further includes: Whether the vector control system is operating normally is determined based on the magnitude of the mean error ΔU2.

5. The method for determining the zero-position angle of a motor according to claim 1, characterized in that, The method further includes: After obtaining the initial position of the motor, the motor is adjusted to different speeds. Based on the phase-locked loop closed-loop negative feedback control system, id is made to reach the maximum value at the different speeds. The zero-position angle of the motor is adjusted so that the actual torque of the device under test is equal to the output torque of the motor. The zero-position angle obtained after adjustment is used as the zero-position angle to be compensated for the motor at the corresponding speed. Based on the zero-position angle to be compensated corresponding to different rotational speeds, a compensation fitting calculation formula is established between the rotational speed and the zero-position angle to be compensated.

6. The method for determining the zero-position angle of a motor according to claim 5, characterized in that, The method further includes: Under normal motor operation, the zero-position angle corresponding to the motor speed is obtained based on the motor speed and the compensation fitting formula, and the motor is compensated accordingly based on the obtained zero-position angle.

7. The method for determining the zero-position angle of a motor according to claim 5, characterized in that, The compensation fitting calculation formula includes: δ1'=δ1+k•n+b; Where δ1' is the zero-position angle to be compensated at any speed when the motor is at any speed, δ1 is the zero-position angle to be compensated when the motor speed is 1000 rpm, n is the motor speed, k is the speed coefficient, and b is the compensation constant.

8. A motor zero-position angle determination system, characterized in that, The system is used to implement the motor zero-position angle determination method as described in any one of claims 1-7 in a test bench scenario. The system includes: a battery simulator, a power supply box, a motor controller, a motor under test, a torque flange, a dynamometer load motor inverter, and a dynamometer load motor. The battery simulator and the power supply box are both connected to the motor controller. The motor controller is connected to the motor under test. The motor under test is connected to the dynamometer load motor. The dynamometer load motor is connected to the dynamometer load motor inverter. The torque flange is located between the motor under test and the dynamometer load motor. The dynamometer load motor is configured to drag the motor under test to a set speed. The motor under test is configured to learn the motor zero-position angle based on a preset learning mode at the set speed, and perform corresponding zero-position angle compensation. The battery simulator and the power supply box are configured to supply a first power supply and a second power supply to the motor controller, respectively; the voltage of the first power supply is higher than the voltage of the second power supply. The torque flange is configured to monitor the actual torque during the motor's zero-position angle learning and zero-position angle compensation process.

9. A system for determining the zero-position angle of a motor, characterized in that, The system is used to implement the motor zero-position angle determination method as described in any one of claims 1-7 in a real vehicle test scenario. The system includes: a power supply battery, a power supply, a pulse code modulation (PCM) controller, the motor under test, a clutch, and an engine. The power supply battery and the power supply are both connected to the PCM controller, the PCM controller is connected to the motor under test, and the motor under test is connected to the engine; the clutch is located between the motor under test and the engine. The engine is configured to drive the tested motor to a set speed. The motor under test is configured to learn the motor zero-position angle based on a preset learning mode at the set speed, and perform corresponding zero-position angle compensation. The power supply battery is configured to provide a first power supply to the PCM controller; The power supply is configured to provide a second power source to the PCM controller; the voltage of the first power source is higher than the voltage of the second power source. The clutch is configured to rigidly connect the engine and the tested motor when fully engaged.

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