Failure identification method of permanent magnet synchronous motor angle observer and related device

Abstract

The invention provides a failure identification method of a permanent magnet synchronous motor angle observer and a related device. The method belongs to the technical field of automobiles and comprises the following steps: acquiring real-time electrical data of a permanent magnet synchronous motor; inputting the real-time electrical data into an angle observer of the permanent magnet synchronous motor to obtain an estimated rotor angular frequency and an estimated rotor angle; the angle observer comprises a sliding mode observer; estimating a permanent magnet flux linkage of the permanent magnet synchronous motor based on the real-time electrical data, the estimated rotor angular frequency and the estimated rotor angle to obtain a permanent magnet flux linkage estimated value; and if the permanent magnet flux linkage estimation value is smaller than a flux linkage failure threshold value, determining that the angle observer fails. According to the invention, real-time on-line identification is carried out on the flux linkage of the permanent magnet, and a threshold determination mechanism is combined, so that divergence detection and rapid protection of the angle observer are realized.

Description

Technical Field

[0001] The present application relates to the technical field of automobiles, and particularly relates to a method for identifying the failure of an angle observer of a permanent magnet synchronous motor and related devices. Background Art

[0002] When a permanent magnet synchronous motor adopts vector control, the rotor angle of the motor is required, and generally angle sensing devices such as an optical encoder, a resolver, or a magnetic encoder are used. After the angle sensing device fails, an angle observer can be used to estimate the real-time position of the motor rotor for redundant control. Common observers include a sliding mode observer, a Romberg observer, and an extended Kalman filter, etc. Among them, the sliding mode observer has the advantages of strong robustness to motor parameters and simple design.

[0003] When designing a sliding mode observer, in order to ensure the stability of the observer, the sliding mode gain k needs to be designed to be large enough. However, an excessive k will exacerbate the jitter of the observed angle and speed, resulting in torque fluctuations. Therefore, the selection of the general sliding mode gain needs to be a compromise. The sliding mode gain needs to be designed in combination with the permanent magnet flux linkage parameter and the real-time speed of the motor.

[0004] However, in actual operation, when the motor parameters change dynamically or the speed suddenly increases, if the set sliding mode gain K is not large enough, the stability of the observer cannot be guaranteed, and the observer divergence will occur. When the angle observer diverges, there will be a large deviation in the estimated angle, and the motor will experience serious consequences such as a decrease in output torque, or even reverse rotation and overcurrent. Summary of the Invention

[0005] The present application provides a method for identifying the failure of an angle observer of a permanent magnet synchronous motor and related devices to solve the problem of motor failure caused by the divergence of the angle observer.

[0006] In a first aspect, the present application provides a method for identifying the failure of an angle observer of a permanent magnet synchronous motor, including:

[0007] Obtaining the real-time electrical data of the permanent magnet synchronous motor;

[0008] Inputting the real-time electrical data into the angle observer of the permanent magnet synchronous motor to obtain an estimated rotor angular frequency and an estimated rotor angle; the angle observer includes a sliding mode observer;

[0009] Estimating the permanent magnet flux linkage of the permanent magnet synchronous motor based on the real-time electrical data, the estimated rotor angular frequency, and the estimated rotor angle to obtain an estimated value of the permanent magnet flux linkage;

[0010] If the estimated value of the permanent magnet flux linkage is less than the magnetic flux failure threshold, it is determined that the angle observer fails.

[0011] As can be seen from the above embodiments, the method for identifying the failure of the permanent magnet synchronous motor angle observer provided in this embodiment aims to perform real-time online identification of the permanent magnet flux linkage by obtaining the real-time electrical parameters of the permanent magnet synchronous motor and the observation data of the angle observer, and combining with the electromagnetic model of the permanent magnet synchronous motor, and to achieve precise detection and rapid protection of the divergence of the angle observer by combining a threshold determination mechanism. Among them, based on the characteristics that the permanent magnet flux linkage is little affected by the motor load and has a limited change range, this embodiment uses it as a criterion for the stability of the angle observer to improve the accuracy of the failure identification of the angle observer.

[0012] In a possible implementation manner, before determining that the angle observer fails if the estimated value of the permanent magnet flux linkage is less than the flux linkage failure threshold, the method further includes:

[0013] Determine the flux linkage failure threshold according to the angle difference threshold of the angle observer and the rated value of the permanent magnet flux linkage.

[0014] As can be seen from the above embodiments, this embodiment determines the flux linkage failure threshold based on the angle difference threshold and the rated value of the permanent magnet flux linkage. The selection of this angle difference threshold not only considers the theoretical limit of the model but also takes into account the influence of actual noise and identification error, so as to achieve a balance between sensitivity and anti-misjudgment. By using the flux linkage failure threshold to determine whether the angle observer diverges, once it is detected that the estimated value of the permanent magnet flux linkage is lower than the threshold, the system can immediately trigger a protection action, which can effectively avoid torque fluctuations, motor reverse or overcurrent faults caused by the divergence of the observer.

[0015] In a possible implementation manner, the determining the flux linkage failure threshold according to the angle difference threshold of the angle observer and the rated value of the permanent magnet flux linkage includes:

[0016] According to the formula ψ rd = ψ f_e cosΔθ; determine the flux linkage failure threshold;

[0017] Wherein, ψ rd represents the flux linkage failure threshold, ψ f_e represents the rated value of the permanent magnet flux linkage, and Δθ represents the angle difference threshold.

[0018] As can be seen from the above embodiments, based on the flux linkage relationship of the angle observer, when the deviation angle between the observed motor rotor angle and the real angle is θ, the identified flux linkage is only cosθ of the real value. Therefore, this embodiment can adopt the formula ψ rd = ψ f_ eThe magnetic flux failure threshold is determined by cosΔθ. By comparing the magnetic flux failure threshold with the estimated value of the permanent magnet flux linkage, it is possible to determine whether the deviation between the observed angle of the angle observer and the true angle is too large, and thus determine whether the angle observer diverges.

[0019] In a possible implementation, the estimating of the permanent magnet flux linkage of the permanent magnet synchronous motor based on the real-time electrical data, the estimated rotor angular frequency, and the estimated rotor angle to obtain an estimated value of the permanent magnet flux linkage includes:

[0020] Based on the real-time electrical data, the estimated rotor angular frequency, and the estimated rotor angle, the permanent magnet flux linkage is estimated by using the recursive least squares method with a forgetting factor to obtain an estimated value of the permanent magnet flux linkage.

[0021] As can be seen from the above embodiments, the purpose of this embodiment is to use the recursive least squares method with a forgetting factor to identify the permanent magnet flux linkage in real time online, and combine the threshold determination mechanism to achieve accurate detection and rapid protection of the divergence of the angle observer. Among them, compared with the traditional least squares method, the recursive least squares method used in this embodiment can update the parameters in real time to adapt to the dynamically changing permanent magnet synchronous motor. The introduction of the forgetting factor can balance the influence of new and old data and avoid the interference of outdated electrical data on the current estimation.

[0022] In a possible implementation, the real-time electrical data includes the real-time value of the phase current of the permanent magnet synchronous motor and the real-time value of the bus voltage;

[0023] The estimating of the permanent magnet flux linkage of the permanent magnet synchronous motor based on the real-time electrical data, the estimated rotor angular frequency, and the estimated rotor angle to obtain an estimated value of the permanent magnet flux linkage includes:

[0024] Based on the estimated rotor angle, the real-time value of the phase current and the real-time value of the bus voltage are respectively transformed from the abc coordinate system to the dq rotating coordinate system to obtain the real-time current component and the real-time voltage component in the dq rotating coordinate system;

[0025] Obtain the motor factory parameters of the permanent magnet synchronous motor;

[0026] Substitute the motor factory parameters, the estimated rotor angular frequency, the real-time voltage component, and the real-time current component in the dq rotating coordinate system into the recursive least squares algorithm formula with a forgetting factor to iteratively update the permanent magnet flux linkage to obtain the estimated value of the permanent magnet flux linkage;

[0027] Among them, the formula of the observed value in the recursive least squares algorithm formula with a forgetting factor is determined based on the q-axis voltage equation of the motor electromagnetic model of the permanent magnet synchronous motor.

[0028] As can be seen from the above embodiments, in the data acquisition and preprocessing stage of this embodiment, the bus voltage and three-phase current of the permanent magnet synchronous motor are directly obtained through high-precision sensors, and based on the estimated rotor angular frequency and estimated rotor angle output by the sliding mode observer, the original real-time electrical data is converted into current components and voltage components in the dq rotating coordinate system. This process ensures the authenticity and applicability of the input data, and avoids the model mismatch problem caused by the inverter non-linearity or line loss when using the command value. In addition, by constructing a permanent magnet flux linkage identification formula through the q-axis voltage equation of the motor electromagnetic model, the relationship between voltage, current and permanent magnet flux linkage can be established through the q-axis voltage equation, and the q-axis voltage equation can provide sufficient accuracy and real-time performance under most medium and high-speed operating conditions without additional hardware or complex algorithms, thereby improving the estimation accuracy of the permanent magnet flux linkage.

[0029] In a possible implementation manner, based on the estimated rotor angle, converting the real-time value of the phase current and the real-time value of the bus voltage from the abc coordinate system to the dq rotating coordinate system respectively to obtain the real-time current component and the real-time voltage component in the dq rotating coordinate system includes:

[0030] Performing Clark transformation on the real-time value of the phase current to obtain the real-time value of the current in the two-phase stationary coordinate system;

[0031] Based on the estimated rotor angle, performing Park transformation on the real-time value of the current in the two-phase stationary coordinate system to obtain the real-time current component in the dq rotating coordinate system;

[0032] According to the duty ratio command value of the three-phase PWM signal, converting the real-time value of the bus voltage into the real-time voltage component of the q axis.

[0033] As can be seen from the above embodiments, in this embodiment, the three-phase current and voltage are converted into two-phase orthogonal components through Clark transformation, eliminating the redundant information between the three phases, which can reduce the dimension of the motor electromagnetic model from 3 to 2, making the model easier to analyze and reducing the computational complexity of the control algorithm. Then, through Park transformation, the real-time electrical data is converted into the dq rotating coordinate system, further linearizing the motor electromagnetic model, further reducing the control complexity, and being able to improve the signal-to-noise ratio of the signal and enhance the estimation accuracy of the angle observer.

[0034] In a possible implementation manner, the factory parameters of the motor include stator resistance, q-axis inductance and d-axis inductance;

[0035] The formula of the recursive least squares algorithm with forgetting factor is:

[0036]

[0037] Where denotes the estimated value of the permanent magnet flux linkage at the k-th iteration, K(k) denotes the gain matrix at the k-th iteration, y(k) denotes the observed value at the k-th iteration; P(k) denotes the covariance matrix at the k-th iteration; λ denotes the forgetting factor, denotes the estimated rotor angular frequency at the k-th iteration; denotes the estimated rotor angular frequency; u q denotes the real-time q-axis voltage component, R s denotes the stator resistance, L q denotes the q-axis inductance; L d denotes the d-axis inductance; i q denotes the real-time q-axis current component, i d denotes the real-time d-axis current component.

[0038] As can be seen from the above embodiments, in this embodiment, the q-axis voltage equation is constructed through the motor electromagnetic model of the permanent magnet synchronous motor and transformed into a flux linkage identification formula, and then the measured dq-axis voltage components and current components are substituted into the calculation to generate the estimated value of the permanent magnet flux linkage. This process makes full use of the physical characteristics of the motor body parameters and combines the dynamic electrical angular frequency provided by the sliding mode observer, so that the flux linkage estimation process not only depends on an accurate mathematical model but also can reflect the actual working conditions in real time. In the parameter identification link, the recursive least squares algorithm with a forgetting factor can gradually correct the estimated value of the permanent magnet flux linkage by dynamically updating the covariance matrix P(k) and the gain matrix K(k). The introduction of the forgetting factor λ effectively balances the weights of historical data and new data, retains both the trend information of long-term operation and enhances the response speed to mutations, avoids estimation biases caused by lags, and enables the flux linkage identification to maintain high precision under complex working conditions, laying a reliable foundation for subsequent divergence determination.

[0039] In a second aspect, the present application provides a failure identification device for an angle observer of a permanent magnet synchronous motor, including:

[0040] An electrical data acquisition module, configured to acquire real-time electrical data of the permanent magnet synchronous motor;

[0041] An angle estimation module, configured to input the real-time electrical data into the angle observer of the permanent magnet synchronous motor to obtain an estimated rotor angular frequency and an estimated rotor angle; the angle observer includes a sliding mode observer;

[0042] A permanent magnet flux linkage estimation module, configured to estimate the permanent magnet flux linkage of the permanent magnet synchronous motor based on the real-time electrical data, the estimated rotor angular frequency, and the estimated rotor angle to obtain an estimated value of the permanent magnet flux linkage;

[0043] An angle observer failure detection module, configured to determine that the angle observer fails if the estimated value of the permanent magnet flux linkage is less than a flux linkage failure threshold.

[0044] In a possible implementation, the failure identification device for the permanent magnet synchronous motor angle observer further includes:

[0045] A magnetic flux failure threshold determination module, configured to determine the magnetic flux failure threshold according to the angle difference threshold of the angle observer and the rated value of the permanent magnet magnetic flux.

[0046] In a possible implementation, the magnetic flux failure threshold determination module is specifically configured to:

[0047] According to the formula ψ rd = ψ f_e cosΔθ; determine the magnetic flux failure threshold;

[0048] where ψ rd represents the magnetic flux failure threshold, ψ f_e represents the rated value of the permanent magnet magnetic flux, and Δθ represents the angle difference threshold.

[0049] In a possible implementation, the permanent magnet magnetic flux estimation module includes:

[0050] Based on the real-time electrical data, the estimated rotor angular frequency, and the estimated rotor angle, the permanent magnet magnetic flux is estimated by using the recursive least squares method with a forgetting factor to obtain the estimated value of the permanent magnet magnetic flux.

[0051] In a possible implementation, the real-time electrical data includes the real-time value of the phase current of the permanent magnet synchronous motor and the real-time value of the bus voltage;

[0052] The permanent magnet magnetic flux estimation module includes:

[0053] A coordinate transformation unit, configured to respectively transform the real-time value of the phase current and the real-time value of the bus voltage from the abc coordinate system to the dq rotating coordinate system based on the estimated rotor angle, to obtain the real-time current component and the real-time voltage component in the dq rotating coordinate system;

[0054] A motor factory parameter acquisition unit, configured to acquire the motor factory parameters of the permanent magnet synchronous motor;

[0055] A permanent magnet magnetic flux estimated value acquisition unit, configured to substitute the motor factory parameters, the estimated rotor angular frequency, the real-time voltage component, and the real-time current component in the dq rotating coordinate system into the recursive least squares algorithm formula with a forgetting factor, and iteratively update the permanent magnet magnetic flux to obtain the estimated value of the permanent magnet magnetic flux;

[0056] where the formula of the observed value in the recursive least squares algorithm formula with a forgetting factor is determined based on the q-axis voltage equation of the motor electromagnetic model of the permanent magnet synchronous motor.

[0057] In a possible implementation, the coordinate conversion unit includes:

[0058] Perform a Clark transformation on the real-time value of the phase current to obtain the real-time value of the current in the two-phase stationary coordinate system;

[0059] Based on the estimated rotor angle, perform a Park transformation on the real-time value of the current in the two-phase stationary coordinate system to obtain the real-time current components in the dq rotating coordinate system;

[0060] According to the duty ratio command value of the three-phase PWM signal, convert the real-time value of the bus voltage into the real-time voltage component of the q-axis.

[0061] In a possible implementation, the factory parameters of the motor include the stator resistance, q-axis inductance, and d-axis inductance;

[0062] The formula of the recursive least squares algorithm with a forgetting factor is:

[0063]

[0064] Wherein, represents the estimated value of the permanent magnet flux linkage at the k-th iteration, K(k) represents the gain matrix at the k-th iteration, y(k) represents the observed value at the k-th iteration; P(k) represents the covariance matrix at the k-th iteration; λ represents the forgetting factor, represents the estimated rotor angular frequency at the k-th iteration; represents the estimated rotor angular frequency; u q represents the real-time voltage component of the q-axis, R s represents the stator resistance, L q represents the q-axis inductance; L d represents the d-axis inductance; i q represents the real-time current component of the q-axis, i d represents the real-time current component of the d-axis.

[0065] In a third aspect, an embodiment of the present application provides a computer-readable storage medium, where the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the method described in any possible implementation manner of the first aspect above are implemented.

[0066] In a fourth aspect, an embodiment of the present application provides a vehicle, including a controller, the controller includes a memory, a processor, and a computer program stored in the memory and executable on the processor, and when the processor executes the computer program, the steps of the method described in the possible implementation manner of the first aspect above are implemented. Description of the Drawings

[0067] To more clearly illustrate the technical solutions in the embodiments of the present application, the following will briefly introduce the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings in the following description are only some embodiments of the present application. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0068] Figure 1 is the implementation flowchart of the failure identification method for the angle observer of the permanent magnet synchronous motor provided by the embodiment of the present application;

[0069] Figure 2 is the structural schematic diagram of the failure identification device for the angle observer of the permanent magnet synchronous motor provided by the embodiment of the present application;

[0070] Figure 3 is the schematic diagram of the controller provided by the embodiment of the present application. Specific Embodiments

[0071] In the following description, specific details such as specific system structures and technologies are presented for the purpose of illustration rather than limitation, so as to thoroughly understand the embodiments of the present application. However, those skilled in the art should clearly understand that the present application can also be implemented in other embodiments without these specific details. In other cases, the detailed descriptions of well-known systems, devices, circuits, and methods are omitted to avoid unnecessary details from interfering with the description of the present application.

[0072] To make the objectives, technical solutions, and advantages of the present application clearer, the following will be described through specific embodiments in conjunction with the drawings.

[0073] In order to perform divergence identification on the sliding mode observer, first, the stability analysis of the sliding mode observer can be carried out.

[0074] Specifically, the mathematical model of the permanent magnet synchronous motor (PMSM) in the two-phase stationary coordinate system (αβ coordinate system) is expressed as a state equation with current as the state variable:

[0075]

[0076] In Equation (1), R s is the stator resistance, L q is the q-axis inductance, u α , u β are the voltage components in the αβ coordinate system respectively, i α , i β are the current components in the αβ coordinate system respectively, e ext,α , e ext,βare the back electromotive force components in the αβ coordinate system, respectively, and

[0077]

[0078] where ω e is the electrical angular frequency of the motor rotor, ψ f is the permanent magnet flux linkage, i d is the d-axis current component, L d is the d-axis inductance component, ψ ext is the active flux linkage.

[0079] Based on the state equation (1), a sliding mode observer for the permanent magnet synchronous motor is constructed as follows:

[0080]

[0081] In Equation (3), are the estimated current components of the sliding mode observer in the αβ coordinate system, z α , z β are the sliding mode control quantities in the αβ coordinate system, respectively, are the estimated values of the back electromotive force in the αβ coordinate system, respectively. Among them:

[0082]

[0083] In Equation (4), K is the sliding mode gain coefficient, sgn is the sign function, ω c is the low-pass cut-off frequency, s is the differential operator, s = jω e .

[0084] To ensure the stability of the sliding mode observer, according to the Lyapunov stability criterion, the convergence condition of the sliding mode observer can be derived as:

[0085]

[0086] When the cut-off frequency ω c is much greater than the motor operating frequency, the above formula can be further simplified as:

[0087]

[0088] Therefore, the value of the sliding mode gain generally needs to be dynamically adjusted in combination with the actual speed. The higher the speed, the larger the value. If the value is not large enough, convergence cannot be guaranteed and divergence will occur. However, it is usually not advisable to uniformly take a too large value, because it will cause large fluctuations in the observed speed and angle, affecting the steady-state performance of the motor. Therefore, the sliding mode observer may diverge due to improper selection of the sliding mode gain. In this embodiment, in order to timely identify the divergence of the sliding mode observer, a method for identifying the failure of the angle observer of the permanent magnet synchronous motor is provided. See Figure 1, which shows the implementation flowchart of the failure identification method for the permanent magnet synchronous motor angle observer provided by the embodiments of the present application, is described in detail as follows:

[0089] S101: Obtain the real-time electrical data of the permanent magnet synchronous motor.

[0090] In this embodiment, the real-time electrical data of the permanent magnet synchronous motor may include the bus voltage and the three-phase current. Among them, the bus voltage is the input voltage of the DC side of the inverter, that is, the voltage of the DC power supply at the front end of the inverter driving the permanent magnet synchronous motor. The bus voltage can be measured by a voltage sensor installed at the DC input terminals (between the positive and negative buses) of the inverter. The three-phase current refers to the real-time current in the three-phase windings (U, V, W) of the permanent magnet synchronous motor, reflecting the motor load and electromagnetic state. The three-phase current can be measured by current sensors installed in the three-phase lines of the motor.

[0091] Specifically, the above current sensors and voltage sensors can be Hall effect sensors, magnetoresistive sensors, and optical encoders; alternatively, the current and voltage signals can be collected collaboratively by Hall effect sensors, magnetoresistive sensors, and optical encoders, and the redundancy design is used to improve the reliability of the real-time electrical data.

[0092] In this embodiment, the sampling frequencies of the bus voltage and the three-phase current are dynamically adjusted according to the motor dynamic characteristics of the permanent magnet synchronous motor. The motor dynamic characteristics include the rotational speed or the rate of change of rotational speed. Taking the rotational speed as an example, the rotational speed can be positively correlated with the sampling rate, that is, the lower the rotational speed, the lower the sampling rate, so as to save computing resources, and the sampling rate is increased under high-speed or sudden change conditions to capture high-frequency components and ensure signal integrity.

[0093] S102: Input the real-time electrical data into the angle observer of the permanent magnet synchronous motor to obtain the estimated rotor angular frequency and the estimated rotor angle; the angle observer includes a sliding mode observer.

[0094] Specifically, the angle observer of the permanent magnet synchronous motor is a sliding mode observer. The basic principle of the sliding mode observer is to design an observer to estimate the state variables of the system. It is based on the concept of sliding mode control, uses the input and output information of the system, and constructs a sliding mode surface so that the state of the observer slides on the sliding mode surface, thereby achieving an accurate estimate of the system state.

[0095] In this embodiment, by inputting the real-time electrical data of the permanent magnet synchronous motor into the above formula (1), the back electromotive force can be estimated, and then the estimated rotor angular frequency and the estimated rotor angle are extracted from the estimated value of the back electromotive force. Specifically, based on the geometric relationship between the back electromotive force and the rotor angle, the formula is used to determine the estimated rotor angle. Based on the estimated rotor angular frequency is determined. Among them, Represents the estimated rotor angle, Represents the estimated rotor angular frequency.

[0096] S103: Based on the real-time electrical data, the estimated rotor angular frequency, and the estimated rotor angle, estimate the permanent magnet flux linkage of the permanent magnet synchronous motor to obtain an estimated value of the permanent magnet flux linkage.

[0097] Specifically, when using a sliding mode observer for angle estimation of a permanent magnet synchronous motor, since the angle sensor has failed, the only reliable state variables in the system are the bus voltage, phase current, and motor body parameters. According to the observability theory of control theory, when the angle sensor fails and the motor operates stably without a sensor, at most only one parameter can be identified online. Taking the surface-mounted permanent magnet synchronous motor (SPMSM) as an example, this motor includes three motor parameters: stator resistance, dq-axis inductance, and permanent magnet flux linkage ψ f . Since the permanent magnet flux linkage ψ f The identification process is not affected by the motor load and has a limited range of variation. Therefore, in this embodiment, ψ f is selected as the online identification parameter, and compared with the rated value of ψ f . If the deviation is too large, it is considered that the sliding mode observer has diverged at this time, and protection processing needs to be carried out in a timely manner.

[0098] In this embodiment, this embodiment can determine the identification formula of the permanent magnet flux linkage through the electromagnetic model of the permanent magnet synchronous motor. This identification formula is used to reflect the relationship between real-time electrical data, estimated rotor angular frequency, estimated rotor angle, and permanent magnet flux linkage. Then, substitute the real-time electrical data, estimated rotor angular frequency, and the estimated rotor angle into the identification formula of the permanent magnet flux linkage to estimate the permanent magnet flux linkage and determine the estimated value of the permanent magnet flux linkage.

[0099] Specifically, the identification of the permanent magnet flux linkage can be achieved by methods such as an extended Kalman filter, neural network algorithm, least squares support vector machine, etc.

[0100] S104: If the estimated value of the permanent magnet flux linkage is less than the flux linkage failure threshold, it is determined that the angle observer has failed.

[0101] In this embodiment, when it is monitored that the angle observer has failed, the protection action of the permanent magnet synchronous motor is triggered to avoid torque fluctuations, motor reverse, or overcurrent faults caused by the divergence of the observer. Among them, the protection action can include stopping the drive of the inverter of the permanent magnet synchronous motor or switching to open-loop control.

[0102] As can be seen from the above embodiments, the method for identifying the failure of the permanent magnet synchronous motor angle observer provided in this embodiment aims to obtain the real-time electrical parameters of the permanent magnet synchronous motor and the observation data of the angle observer, combine the electromagnetic model of the permanent magnet synchronous motor, perform real-time online identification of the permanent magnet flux linkage, and combine the threshold determination mechanism to achieve accurate detection and rapid protection of the divergence of the angle observer. Among them, based on the characteristics that the permanent magnet flux linkage is little affected by the motor load and has a limited change range, this embodiment uses it as the criterion for the stability of the angle observer to improve the accuracy of the failure identification of the angle observer.

[0103] In a possible implementation manner, before S104, the method provided in this embodiment further includes:

[0104] S201: Determine the magnetic flux failure threshold according to the angle difference threshold of the angle observer and the rated value of the permanent magnet flux linkage.

[0105] In this embodiment, taking the SPMSM as an example, the recursive least squares method RLS with a forgetting factor is used to identify the permanent magnet flux linkage ψ in real time f , and the identification process is based on the q-axis voltage equation. When there is a deviation in the observed rotor angle, for example, the observed rotor angle lags behind the true angle θ. At this time, the magnetic flux relationship, under steady state, the dq-axis voltage equations in the coordinate system are:

[0106]

[0107] In Equation (7), u d , u q , i d , i q , ω e are the estimated voltages, currents, and rotor angular frequencies in the d and q axis directions respectively. Among them, for the sliding mode observer, L = L d = L q . Since the magnetic flux identification used in this embodiment is based on the q-axis voltage equation in Equation (7), the theoretical value after identification is ψ rd = ψ f cosΔθ. For example, when the observed rotor angle deviates from the true rotor angle by 30° electrical angle, the identified magnetic flux is only cos30° of the true value. It can be seen that the rotor angle estimation error of the sliding mode observer will directly affect the accuracy of the permanent magnet flux linkage identification value.

[0108] According to the above theory, for the SPMSM motor, when the angle error reaches 60° electrical angle, the magnetic flux identification value is only 0.5 times the true value. Considering factors such as identification error, this embodiment selects cosΔθ = 0.5. ​

[0109] As can be seen from the above embodiments, in this embodiment, the magnetic flux failure threshold is determined based on the angle difference threshold and the rated value of the permanent magnet magnetic flux. The selection of the angle difference threshold not only considers the theoretical limit of the model but also takes into account the influence of actual noise and identification error, so as to achieve a balance between sensitivity and anti-misjudgment. Whether the angle observer diverges is determined by the magnetic flux failure threshold. Once it is detected that the estimated value of the permanent magnet magnetic flux is lower than the threshold, the system immediately triggers a protection action, which can effectively avoid torque fluctuations, motor reverse rotation, or overcurrent faults caused by the divergence of the observer.

[0110] In a possible implementation manner, the specific implementation process of S201 includes:

[0111] According to the formula ψ rd = ψ f_e cosΔθ; determine the magnetic flux failure threshold;

[0112] where ψ rd represents the magnetic flux failure threshold, ψ f_e represents the rated value of the permanent magnet magnetic flux, and Δθ represents the angle difference threshold.

[0113] Specifically, θ can be 60 degrees.

[0114] In this embodiment, this embodiment can adjust the angle difference threshold based on the motor temperature and humidity adaptability.

[0115] As can be seen from the above embodiments, based on the magnetic flux relationship of the angle observer, when the deviation angle between the observed motor rotor angle and the true angle is θ, the identified magnetic flux is only cosθ of the true value. Therefore, in this embodiment, the formula ψ rd = ψ f_ e cosΔθ can be used to determine the magnetic flux failure threshold. By comparing the size between the magnetic flux failure threshold and the estimated value of the permanent magnet magnetic flux, it can be determined whether the deviation value between the observed angle of the angle observer and the true angle is too large, so as to determine whether the angle observer diverges.

[0116] In a possible implementation manner, after S103, this embodiment can also determine whether the sliding mode observer fails in the following way, specifically including: Step S105:

[0117] Based on the formula calculate the comprehensive entropy value;

[0118] Determine the mean and standard deviation of the comprehensive entropy value, and based on the formula μ H +rσ H determine the entropy value threshold;

[0119] If the comprehensive entropy value is greater than the entropy threshold value, it is determined that the sliding mode observer fails; otherwise, it is determined that the sliding mode observer is normal;

[0120] wherein, H total represents the comprehensive entropy value, represents the information entropy of the estimated value of the permanent magnet flux linkage, represents the residual entropy, and the residual is the difference between the estimated value of the permanent magnet flux linkage and the rated value of the permanent magnet flux linkage; is the spectral entropy of the current component in the two-phase rotating coordinate system (αβ coordinate system); μ H represents the mean value of the comprehensive entropy value, σ H represents the standard deviation of the comprehensive entropy value, and r is a preset coefficient.

[0121] After determining the information entropy, the residual entropy, and the spectral entropy, it is necessary to perform normalization processing on the information entropy, the residual entropy, and the spectral entropy, and then perform weighted summation on the normalized information entropy, the residual entropy, and the spectral entropy to obtain the comprehensive entropy value.

[0122] As can be seen from the above embodiments, the comprehensive entropy value adopted in this embodiment integrates multi-source information such as current, residual, and spectrum. Compared with a single index (such as flux linkage deviation), it can capture system anomalies more comprehensively. At the same time, when the flux linkage estimation is inaccurate, the residual suddenly increases and the spectrum is chaotic, and the entropy value will increase significantly. Therefore, it is possible to determine whether the sliding mode observer fails according to the magnitude of the comprehensive entropy value.

[0123] This embodiment can comprehensively determine whether the sliding mode observer fails by combining the determination method of the comprehensive entropy value and the determination method of the estimated value of the permanent magnet flux linkage. If the step of S104 and / or the step of S105 determines that the sliding mode observer fails, it is determined that the sliding mode observer fails, thereby improving the sensitivity of the failure identification of the sliding mode observer.

[0124] In a possible implementation manner, the implementation process of S103 includes:

[0125] Based on the real-time electrical data, the estimated rotor angular frequency, and the estimated rotor angle, the permanent magnet flux linkage is estimated by using the recursive least squares method with a forgetting factor to obtain the estimated value of the permanent magnet flux linkage.

[0126] Specifically, the mathematical model of the permanent magnet synchronous motor is: This mathematical model determines the electromagnetic energy balance relationship of the permanent magnet synchronous motor. In order to determine the relationship between voltage, current, and permanent magnet flux linkage, this embodiment selects the q-axis voltage equation of the permanent magnet synchronous motor:

[0127]

[0128] By transforming the above formula (7), the permanent magnet flux linkage identification formula can be obtained:

[0129]

[0130] Then, according to the permanent magnet flux linkage identification formula, the observed value y(k) of the recursive least squares method with a forgetting factor is constructed, and the input parameters are set. The formula is described in detail as follows:

[0131]

[0132] In this embodiment, the permanent magnet flux linkage identification formula is constructed through the q-axis voltage equation of the motor electromagnetic model, which can establish the relationship between voltage, current and permanent magnet flux linkage through the q-axis voltage equation. Moreover, the q-axis voltage equation can provide sufficient accuracy and real-time performance under most medium and high-speed operating conditions, and no additional hardware or complex algorithms are required, thereby improving the estimation accuracy of the permanent magnet flux linkage.

[0133] As can be seen from the above embodiments, the purpose of this embodiment is to use the recursive least squares method with a forgetting factor to identify the permanent magnet flux linkage in real time online, and combine the threshold determination mechanism to achieve accurate detection and rapid protection of the divergence of the angle observer. Among them, compared with the traditional least squares method, the recursive least squares method is used in this embodiment to update the parameters in real time to adapt to the dynamically changing permanent magnet synchronous motor. The introduction of the forgetting factor can balance the influence of new and old data and avoid the interference of outdated electrical data on the current estimation.

[0134] In a possible implementation manner, the real-time electrical data includes the real-time value of the phase current and the real-time value of the bus voltage of the permanent magnet synchronous motor; the further implementation process of S103 includes:

[0135] S301: Based on the estimated rotor angle, the real-time value of the phase current and the real-time value of the bus voltage are respectively transformed from the abc coordinate system to the dq rotating coordinate system to obtain the real-time current component and the real-time voltage component in the dq rotating coordinate system.

[0136] Specifically, in order to optimize the parameter values, after obtaining the real-time current signal in this embodiment, the current signal can be filtered. The filtering method includes software filtering of RC low-pass filtering and moving average method.

[0137] S302: Obtain the motor factory parameters of the permanent magnet synchronous motor.

[0138] Specifically, the motor factory parameters may include the d-axis inductance, q-axis inductance, and rotor resistance involved in the above q-axis voltage equation. The above parameters are the motor factory calibration parameters, usually obtained through no-load experiments, locked-rotor experiments or parameters.

[0139] S303: Substitute the factory parameters of the motor, the estimated rotor angular frequency, the real-time voltage components and real-time current components in the dq rotating coordinate system into the recursive least squares algorithm formula with a forgetting factor to iteratively update the permanent magnet flux linkage and obtain the estimated value of the permanent magnet flux linkage;

[0140] Among them, the formula of the observed value in the recursive least squares algorithm formula with a forgetting factor is determined based on the q-axis voltage equation of the motor electromagnetic model of the permanent magnet synchronous motor.

[0141] As can be seen from the above embodiments, in the data acquisition and preprocessing stage of this embodiment, the bus voltage and three-phase current of the permanent magnet synchronous motor are directly obtained through high-precision sensors, and based on the estimated rotor angular frequency and estimated rotor angle output by the sliding mode observer, the original real-time electrical data is converted into current components and voltage components in the dq rotating coordinate system. This process ensures the authenticity and applicability of the input data, and avoids the model mismatch problem caused by the non-linearity of the inverter or line loss of the command value. In addition, by constructing the permanent magnet flux linkage identification formula through the q-axis voltage equation of the motor electromagnetic model, the relationship between voltage, current and permanent magnet flux linkage can be constructed through the q-axis voltage equation, and the q-axis voltage equation can provide sufficient accuracy and real-time performance under most medium and high-speed working conditions, and no additional hardware or complex algorithms are required, thereby improving the estimation accuracy of the permanent magnet flux linkage.

[0142] In a possible implementation manner, the specific implementation process of S301 includes:

[0143] Perform Clark transformation on the real-time value of the phase current to obtain the real-time value of the current in the two-phase stationary coordinate system;

[0144] Based on the estimated rotor angle, perform Park transformation on the real-time value of the current in the two-phase stationary coordinate system to obtain the real-time current components in the dq rotating coordinate system;

[0145] According to the duty cycle command value of the three-phase PWM signal, convert the real-time value of the bus voltage into the real-time q-axis voltage component.

[0146] In this embodiment, the Clark transformation formula is:

[0147]

[0148] where, i u , i v , i w are three-phase currents; i α represents the α-axis current component; i β represents the β-axis current component;.

[0149] In this embodiment, the Park transformation formula is:

[0150]

[0151] where, i d represents the d-axis current component, iq represents the q-axis current component; θ represents the rotor angle.

[0152] In this embodiment, the calculation formula for the real-time q-axis voltage component is:

[0153] u q = U dc ·D q (13)

[0154] In formula (13), u q represents the real-time q-axis voltage component; U dc represents the real-time value of the bus voltage; D q represents the PWM duty cycle command value corresponding to the q-axis.

[0155] As can be seen from the above embodiments, in this embodiment, the three-phase current and voltage are converted into two-phase orthogonal components through Clark transformation, eliminating the redundant information between the three phases, which can reduce the dimension of the motor electromagnetic model from 3 to 2, making the model easier to analyze and reducing the computational complexity of the control algorithm. Then, the real-time electrical data is converted into the dq rotating coordinate system through park transformation, further linearizing the motor electromagnetic model, further reducing the control complexity, and being able to improve the signal-to-noise ratio of the signal and enhance the accuracy of the angle observer estimation.

[0156] In a possible implementation manner, the factory parameters of the motor include stator resistance, q-axis inductance, and d-axis inductance;

[0157] The formula for the recursive least squares algorithm with a forgetting factor is:

[0158]

[0159] where, represents the estimated value of the permanent magnet flux linkage at the k-th iteration, K(k) represents the gain matrix at the k-th iteration, y(k) represents the observed value at the k-th iteration; P(k) represents the covariance matrix at the k-th iteration; λ represents the forgetting factor, represents the estimated rotor angular frequency at the k-th iteration; represents the estimated rotor angular frequency; u q represents the real-time q-axis voltage component, R s represents the stator resistance, L q represents the q-axis inductance; L d represents the d-axis inductance; i q represents the real-time q-axis current component, i d represents the real-time d-axis current component.

[0160] Specifically, represents the transpose of the estimated rotor angular frequency at the k-th iteration; k is the number of iterations, and the forgetting factor λ can take a value in the range of 0.9 < λ < 1.

[0161] Exemplarily, λ = 0.95, initial value P(0) = 1000,

[0162] In this embodiment, in order to improve the accuracy of the permanent magnet flux linkage estimation, this embodiment can also adopt a dynamically changing forgetting factor λ, and the process is described in detail as follows:

[0163] Calculate the estimated value of the permanent magnet flux linkage, and subtract the rated value of the permanent magnet flux linkage from the estimated value of the permanent magnet flux linkage to obtain the residual.

[0164] Determine the forgetting factor based on the residual, and the forgetting factor and the residual can be negatively correlated.

[0165] Specifically, the forgetting factor can change dynamically following the residual. When the residual is large, the forgetting factor is decreased to quickly track the mutation; when the residual is small, the forgetting factor is increased to suppress noise, balancing the convergence speed and stability.

[0166] As can be seen from the above embodiments, in this embodiment, the q-axis voltage equation is constructed through the motor electromagnetic model of the permanent magnet synchronous motor and transformed into a flux linkage identification formula, and then the measured dq-axis voltage components and current components are substituted into the calculation to generate an estimated value of the permanent magnet flux linkage value. This process makes full use of the physical characteristics of the motor body parameters and combines the dynamic electrical angular frequency provided by the sliding mode observer, making the flux linkage estimation process rely on both an accurate mathematical model and be able to reflect the actual working conditions in real time. In the parameter identification link, the recursive least squares algorithm with a forgetting factor can gradually correct the estimated value of the permanent magnet flux linkage by dynamically updating the covariance matrix P(k) and the gain matrix K(k). The introduction of the forgetting factor λ effectively balances the weights of historical data and new data, retains the trend information of long-term operation, enhances the response speed to mutations, avoids the estimation deviation caused by lag, enables the flux linkage identification to maintain high accuracy under complex working conditions, and lays a reliable foundation for subsequent divergence determination.

[0167] It should be understood that the magnitudes of the sequence numbers of the steps in the above embodiments do not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation to the implementation process of the embodiments of the present application.

[0168] The following is the device embodiment of the present application. For the details not described in detail therein, reference can be made to the corresponding method embodiment above.

[0169] Figure 2The structural schematic diagram of the failure identification device for the angle observer of the permanent magnet synchronous motor provided by the embodiment of the present application is shown. For the convenience of description, only the parts related to the embodiment of the present application are shown and are described in detail as follows:

[0170] As Figure 2 shown, the failure identification device 100 for the angle observer of the permanent magnet synchronous motor includes:

[0171] An electrical data acquisition module 110, configured to acquire real-time electrical data of the permanent magnet synchronous motor;

[0172] An angle estimation module 120, configured to input the real-time electrical data into the angle observer of the permanent magnet synchronous motor to obtain an estimated rotor angular frequency and an estimated rotor angle; the angle observer includes a sliding mode observer;

[0173] A permanent magnet flux linkage estimation module 130, configured to estimate the permanent magnet flux linkage of the permanent magnet synchronous motor based on the real-time electrical data, the estimated rotor angular frequency, and the estimated rotor angle, to obtain an estimated value of the permanent magnet flux linkage;

[0174] An angle observer failure detection module 140, configured to determine that the angle observer fails if the estimated value of the permanent magnet flux linkage is less than a flux linkage failure threshold.

[0175] As can be seen from the above embodiments, the failure identification device for the angle observer of the permanent magnet synchronous motor provided in this embodiment aims to perform real-time online identification of the permanent magnet flux linkage by acquiring the real-time electrical parameters of the permanent magnet synchronous motor and the observation data of the angle observer, and combining with the electromagnetic model of the permanent magnet synchronous motor, and combining with a threshold determination mechanism to achieve precise detection and rapid protection of the divergence of the angle observer. Among them, in this embodiment, based on the characteristics that the permanent magnet flux linkage is less affected by the motor load and has a limited change range, it is used as a criterion for the stability of the angle observer to improve the failure identification accuracy of the angle observer.

[0176] In a possible implementation manner, the failure identification device 100 for the angle observer of the permanent magnet synchronous motor further includes:

[0177] A flux linkage failure threshold determination module, configured to determine the flux linkage failure threshold according to the angle difference threshold of the angle observer and the rated value of the permanent magnet flux linkage.

[0178] In a possible implementation manner, the flux linkage failure threshold determination module is specifically configured to:

[0179] According to the formula ψ rd = ψ f_e cosΔθ; determine the flux linkage failure threshold;

[0180] Wherein, ψ rddenotes the magnetic flux failure threshold, ψ f_e denotes the rated value of the permanent magnet magnetic flux, and Δθ denotes the angle difference threshold.

[0181] In a possible implementation, the permanent magnet magnetic flux estimation module 130 includes:

[0182] Based on the real-time electrical data, the estimated rotor angular frequency, and the estimated rotor angle, the permanent magnet magnetic flux is estimated by using the recursive least squares method with a forgetting factor to obtain the estimated value of the permanent magnet magnetic flux.

[0183] In a possible implementation, the real-time electrical data includes the real-time value of the phase current and the real-time value of the bus voltage of the permanent magnet synchronous motor;

[0184] The permanent magnet magnetic flux estimation module 130 includes:

[0185] A coordinate transformation unit, configured to respectively transform the real-time value of the phase current and the real-time value of the bus voltage from the abc coordinate system to the dq rotating coordinate system based on the estimated rotor angle, to obtain the real-time current component and the real-time voltage component in the dq rotating coordinate system;

[0186] A motor factory parameter acquisition unit, configured to acquire the motor factory parameters of the permanent magnet synchronous motor;

[0187] A permanent magnet magnetic flux estimated value acquisition unit, configured to substitute the motor factory parameters, the estimated rotor angular frequency, the real-time voltage component, and the real-time current component in the dq rotating coordinate system into the recursive least squares algorithm formula with a forgetting factor, and iteratively update the permanent magnet magnetic flux to obtain the estimated value of the permanent magnet magnetic flux;

[0188] Wherein, the formula of the observed value in the recursive least squares algorithm formula with a forgetting factor is determined based on the q-axis voltage equation of the motor electromagnetic model of the permanent magnet synchronous motor.

[0189] In a possible implementation, the coordinate transformation unit includes:

[0190] Perform a Clark transformation on the real-time value of the phase current to obtain the real-time value of the current in the two-phase stationary coordinate system;

[0191] Based on the estimated rotor angle, perform a Park transformation on the real-time value of the current in the two-phase stationary coordinate system to obtain the real-time current component in the dq rotating coordinate system;

[0192] According to the duty ratio command value of the three-phase PWM signal, convert the real-time value of the bus voltage into the real-time q-axis voltage component.

[0193] In a possible implementation, the factory parameters of the motor include stator resistance, q-axis inductance, and d-axis inductance;

[0194] The formula of the recursive least squares algorithm with forgetting factor is:

[0195]

[0196] Wherein, represents the estimated value of the permanent magnet flux linkage at the k-th iteration, K(k) represents the gain matrix at the k-th iteration, y(k) represents the observed value at the k-th iteration; P(k) represents the covariance matrix at the k-th iteration; λ represents the forgetting factor, represents the estimated rotor angular frequency at the k-th iteration; represents the estimated rotor angular frequency; u q represents the q-axis real-time voltage component, R s represents the stator resistance, L q represents the q-axis inductance; L d represents the d-axis inductance; i q represents the q-axis real-time current component, i d represents the d-axis real-time current component.

[0197] As can be seen from the above embodiments, in this embodiment, the q-axis voltage equation is constructed through the motor electromagnetic model of the permanent magnet synchronous motor and transformed into a flux linkage identification formula, and then the measured dq-axis voltage components and current components are substituted into the calculation to generate the estimated value of the permanent magnet flux linkage. This process makes full use of the physical characteristics of the motor body parameters, combined with the dynamic electrical angular frequency provided by the sliding mode observer, so that the flux linkage estimation process not only depends on an accurate mathematical model but also can reflect the actual working conditions in real time. In the parameter identification link, the recursive least squares algorithm with forgetting factor can gradually correct the estimated value of the permanent magnet flux linkage by dynamically updating the covariance matrix P(k) and the gain matrix K(k) The introduction of the forgetting factor λ effectively balances the weights of historical data and new data, not only retains the trend information of long-term operation but also enhances the response speed to mutations, avoids estimation errors caused by lag, and enables the flux linkage identification to maintain high accuracy under complex working conditions, laying a reliable foundation for subsequent divergence determination.

[0198] The embodiment of the present application also provides a computer program product, which has program code that, when running in a corresponding processor, controller, computing device, or terminal, executes the steps in any one of the above embodiments of the failure identification method of the permanent magnet synchronous motor angle observer, for example Figure 1Steps S101 to S104 shown. Those skilled in the art should understand that the methods and the devices belonging thereto proposed in the embodiments of the present application can be implemented in various forms of hardware, software, firmware, a dedicated processor, or a combination thereof. The dedicated processor may include an application specific integrated circuit (ASIC), a reduced instruction set computer (RISC), and / or a field programmable gate array (FPGA). The proposed methods and devices are preferably implemented as a combination of hardware and software. The software is preferably installed as an application program on a program storage device. It is typically a machine based on a computer platform with hardware, such as one or more central processing units (CPUs), a random access memory (RAM), and one or more input / output (I / O) interfaces. An operating system is typically also installed on the computer platform. The various processes and functions described herein may be part of an application program, or a part of it may be executed by the operating system.

[0199] Figure 3 is a schematic diagram of a terminal provided by an embodiment of the present application. As Figure 3 shown, the controller 3 of this embodiment includes: a processor 30, a memory 31, and a computer program 32 stored in the memory 31 and executable on the processor 30. When the processor 30 executes the computer program 32, it implements the steps in the embodiments of the above-mentioned failure identification method for each permanent magnet synchronous motor angle observer, such as Figure 1 the steps S101 to S104 shown. Alternatively, when the processor 30 executes the computer program 32, it implements the functions of each module / unit in the above-mentioned device embodiments, such as Figure 2 the functions of the modules 110 to 140 shown.

[0200] Exemplarily, the computer program 32 may be divided into one or more modules / units, and the one or more modules / units are stored in the memory 31 and executed by the processor 30 to complete / implement the solution provided by the present application. The one or more modules / units may be a series of computer program instruction segments capable of performing specific functions, and these instruction segments are used to describe the execution process of the computer program 32 in the controller 3. For example, the computer program 32 may be divided into Figure 2 the modules 110 to 140 shown.

[0201] The controller 3 may be a computing device such as a desktop computer, a notebook, a palm computer, and a cloud server. The controller 3 may include, but is not limited to, a processor 30 and a memory 31. Those skilled in the art can understand, Figure 3It is only an example of the controller 3 and does not constitute a limitation on the controller 3. It may include more or fewer components than those shown in the figure, or combine certain components, or different components. For example, the terminal may further include input / output devices, network access devices, buses, etc.

[0202] The so-called processor 30 may be a central processing unit (CPU), or may also 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, etc. The general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc.

[0203] The memory 31 may be an internal storage unit of the controller 3, such as the hard disk or memory of the controller 3. The memory 31 may also be an external storage device of the controller 3, such as a plug-in hard disk equipped on the controller 3, a smart media card (SMC), a secure digital (SD) card, a flash card, etc. Further, the memory 31 may also include both an internal storage unit and an external storage device of the controller 3. The memory 31 is used to store the computer program and other programs and data required by the terminal. The memory 31 may also be used to temporarily store data that has been output or will be output.

[0204] In a possible implementation manner, this embodiment provides a vehicle including the controller 3 as described above.

[0205] Those skilled in the art can clearly understand that, for the convenience and conciseness of description, only the division of the above functional units and modules is used as an example. In actual applications, the above functions can be assigned to different functional units and modules according to needs, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. Each functional unit and module in the embodiments can be integrated into a processing unit, or each unit can exist physically alone, or two or more units can be integrated into one unit. The above integrated unit can be implemented in the form of hardware or in the form of a software functional unit. In addition, the specific names of the functional units and modules are only for the convenience of mutual distinction and do not limit the protection scope of this application. The specific working processes of the units and modules in the above system can refer to the corresponding processes in the foregoing method embodiments and will not be elaborated here.

[0206] In the above embodiments, the descriptions of the respective embodiments have their own emphases. For the parts not detailed or recorded in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0207] Those of ordinary skill in the art can realize that the units and algorithm steps of the examples described in combination with the embodiments disclosed herein can be implemented by electronic hardware, or by a combination of computer software and electronic hardware. Whether these functions are executed in the form of hardware or software depends on the specific application and design constraints of the technical solution. Professional technicians can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of this application.

[0208] In the embodiments provided in this application, it should be understood that the disclosed device / terminal and method can be implemented in other ways. For example, the device / terminal embodiments described above are only illustrative. For example, the division of the modules or units is only a logical function division. In actual implementation, there can be other division methods. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the displayed or discussed couplings or direct couplings or communication connections to each other can be through some interfaces, and the indirect couplings or communication connections of the devices or units can be in electrical, mechanical or other forms.

[0209] The units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they can be located in one place or distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0210] In addition, in each embodiment of the present application, each functional unit can be integrated into a processing unit, or each unit can exist physically alone, or two or more units can be integrated into one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of a software functional unit.

[0211] If the integrated module / unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on such an understanding, to implement all or part of the processes in the above-mentioned embodiment methods of the present application, it can also be completed by instructing relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium. When the computer program is executed by a processor, the steps of the above-mentioned embodiments of the failure identification method for each permanent magnet synchronous motor angle observer can be implemented. Among them, the computer program includes computer program code, and the computer program code can be in the form of source code, object code, executable file or some intermediate form, etc. The computer-readable medium can include: any entity or device that can carry the computer program code, recording medium, USB flash drive, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signal, telecommunication signal, and software distribution medium, etc.

[0212] In addition, the features of the embodiments shown in the drawings of the present application or various embodiments mentioned in this specification do not have to be understood as independent embodiments from each other. Instead, each feature described in one example of an embodiment can be combined with one or more other desired features from other embodiments, thereby generating other embodiments not described in words or with reference to the drawings.

[0213] The above-mentioned embodiments are only used to illustrate the technical solutions of the present application, rather than to limit it; although the present application has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that: they can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements on some of the technical features; and these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present application, and should all be included in the protection scope of the present application.

Claims

1. A method for identifying the failure of an angle observer of a permanent magnet synchronous motor, characterized in that, Including: Obtaining real-time electrical data of a permanent magnet synchronous motor; Inputting the real-time electrical data into an angle observer of the permanent magnet synchronous motor to obtain an estimated rotor angular frequency and an estimated rotor angle; The angle observer includes a sliding mode observer; Estimating the permanent magnet flux linkage of the permanent magnet synchronous motor based on the real-time electrical data, the estimated rotor angular frequency, and the estimated rotor angle to obtain an estimated value of the permanent magnet flux linkage; If the estimated value of the permanent magnet flux linkage is less than a flux linkage failure threshold, it is determined that the angle observer fails.

2. The method for identifying the failure of the permanent magnet synchronous motor angle observer according to claim 1, wherein Before the step of if the estimated value of the permanent magnet flux linkage is less than a flux linkage failure threshold, it is determined that the angle observer fails, the method further includes: Determining the flux linkage failure threshold according to an angle difference threshold of the angle observer and a rated value of the permanent magnet flux linkage.

3. The method for identifying the failure of the angle observer of the permanent magnet synchronous motor according to claim 2, characterized in that, The determining the flux linkage failure threshold according to an angle difference threshold of the angle observer and a rated value of the permanent magnet flux linkage includes: According to the formula ψ rd = ψ f_e cosΔθ; determine the magnetic flux failure threshold; Among them, ψ rd represents the magnetic flux failure threshold, ψ f_e represents the rated value of the permanent magnet magnetic flux, and Δθ represents the angle difference threshold.

4. The failure identification method of the permanent magnet synchronous motor angle observer according to claim 1, characterized in that The estimating the permanent magnet flux linkage of the permanent magnet synchronous motor based on the real-time electrical data, the estimated rotor angular frequency, and the estimated rotor angle to obtain an estimated value of the permanent magnet flux linkage includes: Estimating the permanent magnet flux linkage by using a recursive least squares method with a forgetting factor based on the real-time electrical data, the estimated rotor angular frequency, and the estimated rotor angle to obtain an estimated value of the permanent magnet flux linkage.

5. The method for identifying the failure of the angle observer of the permanent magnet synchronous motor according to claim 4, characterized in that, The real-time electrical data includes real-time values of phase currents and a real-time value of the bus voltage of the permanent magnet synchronous motor; The estimating the permanent magnet flux linkage by using a recursive least squares method with a forgetting factor based on the real-time electrical data, the estimated rotor angular frequency, and the estimated rotor angle to obtain an estimated value of the permanent magnet flux linkage includes: Based on the estimated rotor angle, respectively converting the real-time values of the phase currents and the real-time value of the bus voltage from the abc coordinate system to the dq rotating coordinate system to obtain real-time current components and real-time voltage components in the dq rotating coordinate system; Obtaining the motor factory parameters of the permanent magnet synchronous motor; Substituting the motor factory parameters, the estimated rotor angular frequency, the real-time voltage component, and the real-time current component in the dq rotating coordinate system into a recursive least squares algorithm formula with a forgetting factor to iteratively update the permanent magnet flux linkage to obtain the estimated value of the permanent magnet flux linkage; Wherein, the formula of the observed value in the recursive least squares algorithm formula with a forgetting factor is determined based on the q-axis voltage equation of the motor electromagnetic model of the permanent magnet synchronous motor.

6. The method for identifying the failure of the permanent magnet synchronous motor angle observer according to claim 5, characterized in that, The respectively converting the real-time values of the phase currents and the real-time value of the bus voltage from the abc coordinate system to the dq rotating coordinate system based on the estimated rotor angle to obtain real-time current components and real-time voltage components in the dq rotating coordinate system includes: Performing a Clark transformation on the real-time values of the phase currents to obtain real-time current values in a two-phase stationary coordinate system; Based on the estimated rotor angle, performing a Park transformation on the real-time current values in the two-phase stationary coordinate system to obtain real-time current components in the dq rotating coordinate system; Converting the real-time value of the bus voltage into a real-time q-axis voltage component according to the duty ratio command value of the three-phase PWM signal.

7. The method for identifying the failure of the angle observer of the permanent magnet synchronous motor according to claim 5, characterized in that The factory parameters of the motor include stator resistance, q-axis inductance, and d-axis inductance; The formula of the recursive least squares algorithm with forgetting factor is: Among them, represents the estimated value of the permanent magnet flux linkage at the k-th iteration, K(k) represents the gain matrix at the k-th iteration, and y(k) represents the observed value at the k-th iteration; P(k) represents the covariance matrix at the k-th iteration; λ represents the forgetting factor, represents the estimated rotor angular frequency at the k-th iteration; represents the estimated rotor angular frequency; u q represents the q-axis real-time voltage component, R s represents the stator resistance, L q represents the q-axis inductance; L d represents the d-axis inductance; i q represents the q-axis real-time current component, i d represents the d-axis real-time current component.

8. A failure identification device for an angle observer of a permanent magnet synchronous motor, characterized in that, Including: An electrical data acquisition module, configured to acquire real-time electrical data of the permanent magnet synchronous motor; An angle estimation module, configured to input the real-time electrical data into an angle observer of the permanent magnet synchronous motor to obtain an estimated rotor angular frequency and an estimated rotor angle; The angle observer includes a sliding mode observer; A permanent magnet flux linkage estimation module, configured to estimate the permanent magnet flux linkage of the permanent magnet synchronous motor based on the real-time electrical data, the estimated rotor angular frequency, and the estimated rotor angle, to obtain an estimated value of the permanent magnet flux linkage; An angle observer failure detection module, configured to determine that the angle observer fails if the estimated value of the permanent magnet flux linkage is less than a flux linkage failure threshold.

9. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the failure identification method of the angle observer of the permanent magnet synchronous motor according to any one of claims 1 to 7 above.

10. A vehicle, characterized in that, Including: A controller, the controller includes a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that when the processor executes the computer program, it implements the steps of the failure identification method of the angle observer of the permanent magnet synchronous motor according to any one of claims 1 to 7 above.

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