Rotor flux strength estimation device for permanent magnet synchronous motors

Abstract

The present invention relates to a rotor magnetic flux intensity estimation device for a permanent magnet synchronous motor, used in connection with a drive control device for a permanent magnet synchronous motor, and to a rotor magnet temperature estimation device utilizing the output thereof. In particular, the present invention provides a rotor magnetic flux intensity estimation device that outputs an estimated rotor magnetic flux intensity value stably, with high-speed response, over a wide speed range, and with high accuracy using the voltage, current, etc. of the stator on the dq synchronous coordinate system. [Solution] The problem was solved by equipping the rotor flux strength estimation device with a basic rotor flux strength estimation means that generates and outputs an estimated rotor flux strength using a D-factor filter, an adaptive identification means that adaptively identifies at least the d-axis and q-axis inductances among the stator parameters used in the D-factor filter, and an identification command value generation and superposition means that superimposes a signal having a non-zero frequency component, which is essential for adaptive identification, onto the d-axis current.

Description

Technical Field

[0001] The present invention relates to a rotor magnetic flux intensity estimation device for a permanent magnet synchronous motor. More specifically, the present invention provides an estimation device for the rotor magnetic flux intensity itself of a permanent magnet synchronous motor, and further provides a rotor magnet temperature estimation device for a permanent magnet synchronous motor that uses an estimated value of the rotor magnetic flux intensity, which is an output signal of the estimation device. Hereinafter, for simplicity, the "permanent magnet synchronous motor" is abbreviated as "PMSM" and used. The two terms are completely synonymous. It is widely known that a 2×1 vector has magnitude and phase. In the present invention, the phase and angle of a vector are used synonymously. These units are "rad". In addition, an estimated value of a parameter is indicated by attaching the symbol "^" to the parameter. In addition, a command value of a signal is indicated by attaching the prefix symbol "*" to the signal.

Background Art

[0002] The total torque generated by a PMSM is, in principle, divided into a magnet torque and a reluctance torque. There are various contribution rates of both torques to the total torque, but basically, the magnet torque forms the center of the total torque. The magnet torque is proportional to the magnetic flux linkage of the rotor magnet (hereinafter referred to as "rotor magnetic flux intensity") and the stator current (q-axis current). Therefore, if the rotor magnetic flux intensity is constant, the magnet torque can be easily controlled through the stator current. However, depending on the material of the rotor magnet, the coercive force of the rotor magnet decreases as the temperature rises during motor drive, which in turn causes an unexpected decrease in the magnet torque and, in the worst case, irreversible demagnetization. To avoid this type of torque reduction phenomenon, it is necessary to grasp the rotor magnet temperature in real time. If it is the temperature on the stator side, it can be grasped in real time with a temperature sensor such as a thermocouple. However, when it is desired to avoid mechanical contact with the rotor, the temperature on the rotor side must be estimated in real time from the stator side by some method.

[0003] Recent research indicates that representative rotor magnet temperature estimation methods can be broadly classified into three types: the heat transfer model method, the high-frequency impedance estimation method, and the rotor magnetic flux strength estimation method (see Non-Patent Literature (2)). This invention relates to the rotor magnetic flux strength estimation method. The rotor magnetic flux strength estimation method focuses on the linear characteristics between rotor magnet temperature and rotor magnetic flux strength. This linear characteristic is commonly observed in many PMSMs, and together with its independence from mechanical shape, it is a highly universal estimation method. Considering that "the original purpose of rotor magnet temperature estimation is to estimate the decrease in rotor magnet coercivity in real time," the rotor magnetic flux strength estimation method can be said to be the most direct estimation method.

[0004] The linear relationship between rotor magnet temperature and rotor magnetic flux intensity is expressed by the following equation (see Non-Patent Document (2)).

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[0005] Rotor flux strength estimation methods basically estimate the rotor flux strength via some kind of flux observer that can track changes in rotor flux strength. For example, in Patent Document (1) and Non-Patent Document (1), a rotor flux estimation method based on a current model is prepared for low-speed ranges, and a rotor flux estimation method based on a voltage model is prepared for high-speed ranges, and a Gopinath-type flux observer is used which combines these two estimation methods via a filter.

[0006] In rotor flux strength estimation using a magnetic flux observer, at least a good value of the d-axis inductance, which changes with the stator current, is required. Due to this characteristic, Non-Patent Document (2) suggests the effectiveness of a large LUT (look-up table) that takes the d-axis and q-axis currents as arguments to overcome the dependence on the stator current. As an alternative to the large LUT that needs to be created each time the PMSM or power converter is changed, Patent Document (1) and Non-Patent Document (1) have very recently proposed a method to identify the d-axis inductance in real time by forcing a rectangular d-axis current through it.

[0007] The conventional rotor magnetic flux strength estimation method exemplified above had the following problems. The Gopinath-type flux observer merely combines a rotor flux estimation method based on a current model and a rotor flux estimation method based on a voltage model via a filter, and fails to solve any of the flux estimation problems inherent in each estimation method. Therefore, it is not possible to obtain reasonable flux estimates outside of the high-speed range where the voltage model is effective. The conventional method of forcing a rectangular d-axis current through to identify the d-axis inductance in real time utilizes a step transient response accompanied by sharp current changes. As a result, it is not possible to obtain an accurate estimate of the fundamentally changing d-axis inductance necessary for normal operation. [Prior art documents] [Patent Documents]

[0008] [Patent Document 1] Kensuke Sasaki and Takashi Kato: "Method for estimating the magnet temperature of a motor, and apparatus for estimating the magnet temperature," Published Patent Application, JP 2021-16226 (2019-7-10) [Non-patent literature]

[0009] [Non-Patent Document 1] Takashi Kato, Kensuke Sasaki, DFLaborda, DFAlonso, DDReigosa: "A Method for Estimating Magnet Temperature in a Variable Leakage Flux IPMSM Using a Magnet Flux Linkage Observer," Transactions of the Institute of Electrical Engineers of Japan, Vol. 140, No. 4, pp. 265-271 (2020-4). [Non-Patent Document 2] D. Reigosa, D Fermandez, T, Tanimoto, T. Kato, and F. Briz: “Comparative Analysis of BEMF and Pulsating High-Frequency Current Injection Methods for PM Temperature Estimation in PMSMs”, IEEE Trans. Power Electronics, Vol. 32, No 5, pp 3691-3699 (2017-5) [Overview of the project] [Problems that the invention aims to solve]

[0010] This invention was made against the background described above, and its objectives are as follows. (1) Modify the rotor flux estimation method developed for rotor flux phase estimation, replacing rotor flux phase estimation with rotor flux intensity estimation, to construct a high-performance rotor flux intensity estimation device. (2) When estimating rotor flux strength, which is a rotor-side characteristic, from the stator side, the estimated value of rotor flux strength is affected by stator parameters (winding resistance, d-axis inductance, q-axis inductance), regardless of the method used. On the other hand, both d-axis and q-axis inductances generally fluctuate greatly depending on the stator current, as seen in magnetic saturation. In this invention, in order to grasp the d-axis and q-axis inductances that fluctuate depending on the stator current etc. in real time, an adaptive identification algorithm applicable to these fluctuations is developed, and a rotor flux strength estimation device equipped with this algorithm is constructed. (3) Construct a rotor flux strength estimation device that includes a rotor flux strength estimation method that takes into account the effect of the current detection offset originating from the power converter (inverter) on the rotor flux strength estimation value, and further includes an adaptive identification algorithm that takes into account the effect of the same current detection offset on the inductance identification value. (4) Construct a rotor magnetic flux strength estimation device that can stably perform real-time estimation of rotor magnetic flux strength while dealing with fluctuations in d-axis and q-axis inductances that depend on stator current, etc. (5) Construct a rotor magnetic flux strength estimation device that can be applied over a wide driving range from 10% rated speed to 100% rated speed. [Means for solving the problem]

[0011] To achieve the above objective, the invention of claim 1 provides a rotor magnetic flux intensity estimation device for a permanent magnet synchronous motor, which is connected to and used in a drive control device for a permanent magnet synchronous motor, and which includes at least a current control means for capturing the stator current as a vector signal on a dq synchronous coordinate system of two orthogonal axes with the N pole phase of the rotor permanent magnet as the d-axis phase and controlling it to follow a stator current command value, wherein information on the stator voltage, stator current, and rotor speed that change according to the drive state of the permanent magnet synchronous motor is obtained as input signals from outside the rotor magnetic flux intensity estimation device, and the input signals are processed by a D-factor filter, The system is characterized by comprising: a basic rotor flux strength estimation means that outputs an estimated rotor flux strength value as a final signal after processing to the outside of the rotor flux strength estimation device; an adaptive identification means that adaptively identifies at least the d-axis inductance and q-axis inductance among the stator parameters used in the D-factor filter within the basic rotor flux strength estimation means, using information on the d-axis voltage, d-axis current, q-axis current, and rotor speed, which change according to the driving state of the permanent magnet synchronous motor; and an identification command value generation superposition means for superimposing a signal having a non-zero frequency component, which is essential for adaptive identification, onto the d-axis current.

[0012] The invention of claim 2 is a rotor magnetic flux intensity estimation device for a permanent magnet synchronous motor as described in claim 1, characterized in that the D factor filter in the basic rotor magnetic flux intensity estimation means is configured on a dq synchronous coordinate system.

[0013] The invention of claim 3 is a rotor magnetic flux intensity estimation device for a permanent magnet synchronous motor as described in claim 1, characterized in that the adaptive identification in the adaptive identification means is performed using a recurrent adaptive identification algorithm.

[0014] The invention of claim 4 is a rotor magnetic flux intensity estimation device for a permanent magnet synchronous motor as described in claim 1, characterized in that the signal having a non-zero frequency component in the identification command value generation superposition means is a signal equivalent to a sine signal having a single non-zero frequency component or a plurality of non-zero frequency components. [Effects of the Invention]

[0015] The effects of the invention of claim 1 are explained below. The rotor magnetic flux of a three-phase PMSM is described as a 2×1 vector quantity obtained by transforming it. The 2×1 vector quantity consists of the intensity (magnitude) of a scalar quantity and the phase of a scalar quantity. The rotor magnetic flux intensity estimation device for PMSM of the present invention estimates the rotor magnetic flux intensity, which is a scalar quantity, among the rotor magnetic flux. If a reasonable estimate of the rotor magnetic flux itself is obtained, a reasonable rotor magnetic flux intensity estimate can be obtained immediately therefrom. According to the invention of claim 1, the rotor magnetic flux estimate is obtained by utilizing a D-factor filter, which is recognized as one of the high-performance methods among rotor magnetic flux estimation methods. Consequently, according to the invention of claim 1, it is possible to obtain a highly reasonable rotor magnetic flux estimate, and consequently, a highly reasonable rotor magnet temperature estimate according to equations (1) and (2).

[0016] The stator parameter that has the greatest impact on the rotor flux intensity estimation is the stator inductance. The inductance changes moment by moment depending on the stator current. The best way to grasp in real time the inductance that changes according to the stator current is the adaptive identification of the inductance using the stator current on the premise of this change. Generally, for rotor flux estimation and rotor flux intensity estimation, an accurate inductance value is essential. Instead, generally, for inductance identification, an accurate rotor flux intensity value is required. Due to this interdependence between the estimation and the identification, it is not easy to reasonably estimate and identify both, and the simultaneous execution of the estimation and the identification often destabilizes the estimation device and the identification device. The only solution to resolve this interdependence is the identification based on the d-axis side circuit equation in the dq synchronous coordinate system. The invention of claim 1 adaptively identifies at least the d-axis inductance and the q-axis inductance using information on the d-axis voltage, d-axis current, q-axis current, and rotor speed that change according to the driving state of the PMSM. In other words, according to the invention of claim 1, the effect is obtained that at least the d-axis inductance and the q-axis inductance can be adaptively identified stably and in real time based on the d-axis side circuit equation in the dq synchronous coordinate system.

[0017] The stator voltage and current on the dq synchronous coordinate system are, in a steady state, DC signals with a frequency of zero. In other words, the frequency components of the voltage and current in the steady state are only components with a zero frequency. From the stator voltage and current having only such components with a zero frequency, it is not possible to simultaneously identify two or more parameters. According to the invention of claim 1, it is provided with an identification command value generation superposition means for superposing and adding a signal having a non-zero frequency component to the d-axis current. As a result, the effect is obtained that a plurality of stator parameters can be appropriately simultaneously identified.

[0018] Next, the effects of the invention of claim 2 will be described. The invention of claim 2 constructs a factor filter in the dq synchronous coordinate system. In this case, the d-axis component of the estimated rotor flux becomes the estimated rotor flux intensity itself. Also, the inductances required for the processing of the stator current input to the factor filter are the d-axis inductance and the q-axis inductance. As a result, the effect of being able to obtain the effect of claim 1 most simply is obtained, and furthermore, the effect of enhancing the effect of claim 1 is obtained.

[0019] Next, the effects of the invention of claim 3 will be described. There are recursive and non-recursive adaptive identification algorithms that can be used in the invention of claim 1. When performing adaptive identification of two or more parameters, the former recursive type can reduce the computational amount. Since the invention of claim 2 uses a recursive adaptive identification algorithm, the effect of being able to generate identification values while keeping the computational amount low is obtained. Also, in the recursive adaptive identification algorithm, the effect of being able to easily insert a limiter for suppressing unnecessary jumps in the identification values is obtained. Consequently, the invention of claim 3 obtains the effect of enhancing the effect of claim 1.

[0020] Next, the effects of the invention of claim 4 will be described. As the shape of the current or voltage having a non-zero frequency component in the identification command value generation superposition means, a sine shape, a rectangular shape, a triangular shape, a trapezoidal shape, etc. can be considered. When looking at the shape based on the time axis, the shape that generally contains identification information evenly is the sine shape. The invention of claim 4 uses, as the signal having a non-zero frequency component in the identification command value generation superposition means, a signal conforming to a sine signal having a single non-zero frequency component or a plurality of non-zero frequency components. Consequently, according to the invention of claim 4, when based on the time axis, the effect of being able to obtain adaptive identification values at the same speed at any time can be obtained. Furthermore, the invention of claim 4 obtains the effect of enhancing the effect of claim 1.

Brief Description of the Drawings

[0021] [Figure 1] "Figure showing the relationship between three coordinate systems and rotor phase" [Figure 2] "Block diagram showing the basic configuration of a drive control device equipped with a rotor magnetic flux intensity estimation device in one embodiment." [Figure 3] "Block diagram showing the basic configuration of a rotor magnetic flux intensity estimation device in one embodiment." [Figure 4] "Block diagram showing the configuration of a D-factor filter according to one embodiment" [Figure 5] "Block diagram showing the configuration of a stator parameter adaptive identifier according to one embodiment." [Figure 6] "A diagram showing the performance of the rotor magnetic flux intensity estimation device in one embodiment." [Figure 7] "A diagram showing the performance of the rotor magnetic flux intensity estimation device in one embodiment." [Figure 8] "Block diagram showing the basic configuration of a rotor magnetic flux intensity estimation device in one embodiment." [Figure 9] "Block diagram showing the configuration of a D-factor filter according to one embodiment" [Figure 10] "Block diagram showing the configuration of a stator parameter adaptive identifier according to one embodiment." [Figure 11] "A diagram showing the performance of the rotor magnetic flux intensity estimation device in one embodiment." [Figure 12] "A diagram showing the performance of the rotor magnetic flux intensity estimation device in one embodiment." [Modes for carrying out the invention]

[0022] The embodiments of the present invention will be described in detail below with reference to the drawings. For the purposes of the following explanation, the relationship between the three coordinate systems and the rotor phase and velocity will be explained first. Consider Figure 1. This figure shows three coordinate systems as two-axis Cartesian coordinate systems for a PMSM: a dq synchronous coordinate system synchronized without phase difference with the rotor's magnetic pole (N pole) rotating at a rotor (electric) velocity ω²n; an αβ fixed coordinate system where the phase of the α axis is the same as the center phase of the u-phase winding (u-axis phase of the uvw coordinate system); and a γδ general coordinate system rotating at an arbitrary velocity ωγ. The direction from the base axis to the sub-axis is considered the positive direction. θα and θγ are the rotor phase (phase of the rotor's magnetic pole) evaluated from the α axis and γ axis, respectively. [Examples]

[0023] The rotor magnetic flux intensity estimation device for PMSM of the present invention is based on a mathematical model of PMSM, particularly the circuit equations (first fundamental equation) that constitute the mathematical model. As part of the preparation for explaining the present invention, the mathematical model (circuit equations) on this γδ general coordinate system is shown below for a PMSM in which interaxial magnetic flux interference can be ignored.

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[0024] In the mathematical model above, "s" represents the time differential operator d / dt. The vectors shown in bold are 2x1 spatial vectors defined on the γδ general coordinate system, where v1, i1, and φ1 represent the stator voltage, current, and magnetic flux, respectively. φi and φm represent the components constituting the stator flux (stator flux linkage) φ1, where φi represents the reaction flux generated by the stator current i1, and φm represents the rotor flux originating from the rotor permanent magnets. As explicitly stated in equation (3d), Φ, which corresponds to the amplitude (i.e., magnitude) of the rotor flux φm, represents the estimated "rotor flux strength" (more precisely, the rotor flux linkage number). em, proportional to the rotor velocity ω2n, is the speed electromotive force generated by the rotation of the rotor flux. Np and R1 are the number of pole pairs and winding resistance, respectively. Ld and Lq are the d-axis and q-axis inductances, respectively. I is a 2x2 identity matrix, and other 2x2 matrices and 2x1 vectors are defined as follows.

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[0025] Figure 2 shows an example of applying the drive control device of the present invention to a PMSM. 1 is a PMSM, 2 is a power converter, 3 is a current detector, 4a and 4b are a 3-phase 2-phase converter and a 2-phase 3-phase converter, respectively, 5a and 5b are both vector rotors, 6 is a current controller, 7 is a phase detector, 8 is a speed detector, 9 is a cosine-sine signal generator, 10 is a rotor flux strength estimation device utilizing the present invention, and the dashed block 11 represents a flux strength / magnet temperature converter that converts the rotor flux strength estimate to a rotor magnet temperature estimate. In Figure 2, the devices from 2 to 9 constitute the drive control device, excluding the PMSM (1), the rotor flux strength estimation device (10), and the flux strength / magnet temperature converter (11). In this figure, to ensure simplicity, a 2x1 vector signal is represented by a single thick signal line. The following block diagrams will also follow this convention. In this figure, to clearly indicate the defined coordinate systems for the vector signals stator current and stator voltage, additional footnotes r (dq synchronous coordinate system), s (αβ fixed coordinate system), and t (uvw coordinate system) are added to these vector signals. The definitions of the coordinate systems are as shown in Figure 1.

[0026] The three-phase stator current detected by the current detector 3 is converted to a two-phase current on the αβ fixed coordinate system by the three-phase to two-phase converter 4a, and then converted to a two-phase current on the dq synchronous coordinate system by the vector rotator 5a. The converted current is sent to the current controller 6. The current controller 6 generates two-phase voltage command values ​​on the dq synchronous coordinate system so that the two-phase current on the dq synchronous coordinate system follows the current command values ​​of each phase. The two-phase voltage command values ​​on the dq synchronous coordinate system are sent to the vector rotator 5b. In 5b, the voltage command values ​​on the dq synchronous coordinate system are converted to two-phase voltage command values ​​on the αβ fixed coordinate system and sent to the two-phase to three-phase converter 4b. In 4b, the two-phase voltage command values ​​are converted to three-phase voltage command values ​​and output as the final command value to the power converter 2. The power converter 2 generates a voltage corresponding to the voltage command value and applies it to the PMSM1 to drive it.

[0027] In the embodiment shown in Figure 2, 10 represents the rotor magnetic flux strength estimation device, which is the core component of the present invention. This device receives information on the stator voltage (stator voltage command value in this embodiment), stator current (detected value in this embodiment), rotor speed, and winding resistance as input signals from outside the device, on a dq synchronous coordinate system. The temperature of the stator winding can be easily obtained externally using a temperature sensor such as a thermocouple. Furthermore, a good proportional relationship exists between the winding temperature and the winding resistance. As a result, the winding resistance can be easily obtained in real time via winding temperature measurement (see Non-Patent Documents (1) and (2)). In addition, this device outputs an estimated value of the rotor magnetic flux strength Φ, Φ^d, to the outside of the device. Furthermore, in this embodiment, in order to obtain a reasonable adaptive identification value, a low-frequency current command value i*1l is generated within the rotor magnetic flux strength estimation device as shown in the following equation and superimposed on the original drive current command value i*1f.

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[0028] Figure 3 shows the internal structure of this embodiment of the rotor flux strength estimation device. As is clear from the figure, the rotor flux strength estimation device is broadly composed of a basic rotor flux strength estimator 10a that implements a basic rotor flux strength estimation means, a stator parameter adaptive identifyr 10b that implements an adaptive identification means, and an identification command value generator 10c that implements an identification command value generation superposition means. The basic rotor flux strength estimator 10a, the stator parameter adaptive identifyr 10b, and the identification command value generator 10c will be described in detail below.

[0029] The basic rotor flux strength estimator 10a has a D-factor filter F(D) implemented, which processes the input signal and outputs the result. The D-factor filter defined on the γδ general coordinate system uses the D-factor defined by equation (6f) and is described as follows.

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[0030] The signal to be processed by the D-factor filter in equation (6) is the rate electromotive force em defined in equation (3e). The rate electromotive force can be expressed again from equation (3) as follows:

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[0031] The following four conditions are added to equations (6) to (8). (1) Perform filtering on the dq-synchronous coordinate system encompassed by the γδ general coordinate system. In other words, add the dq-synchronous coordinate system conditions "θγ=0, ωγ=ω2n" to the signals of the γδ general coordinate system. (2) The minimum selectable filter order n is set to the first order. That is, "n=1" is added. (3) The D factor filter is implemented according to equation (6b). (4) The zero-order filter coefficient a0, which is a design parameter, is determined exponentially (according to the speed) as shown in the following equation.

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[0032] It should be noted that there are various configurations for the basic rotor flux strength estimator 10a other than those shown in Figures 4(a) and (b). Figures 4(a) and (b) are the two simplest implementations obtained by adding the four conditions mentioned above.

[0033] An embodiment of the stator parameter adaptive identifyr 10b, which implements an adaptive identification means, is described below. In principle, this adaptive identifyr is constructed based on the circuit equations of (3) and (4). These circuit equations indicate that "stator parameters including inductance are necessary for estimating rotor flux strength." Conversely, they indicate that "rotor flux strength is necessary for identifying stator parameters including inductance." The interdependence between estimation and identification is the main cause of instability in the implemented basic rotor flux strength estimator and stator parameter adaptive identifyr.

[0034] To resolve this instability issue, the dq synchronous coordinate system condition "θγ=0, ωγ=ω2n" is added to the circuit equations in the γδ general coordinate system (3) and (4), thereby reducing them to circuit equations in the dq synchronous coordinate system. The circuit equations in the dq synchronous coordinate system are written as follows.

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[0035] The internal configuration of the stator parameter adaptive identifyr 10b was configured according to equation (11), part 1. One embodiment of this configuration is shown in Figure 5. This adaptive identifyr is broadly composed of three subsystems: a shaping and separation filter section 10b-1, an adaptive identification algorithm section 10b-2, and an output filter section 10b-3.

[0036] The shaping and separation filter unit 10b-1 receives the d-axis voltage vd, d-axis current id (the command value in this embodiment), q-axis current iq, rotor speed ω2n, and winding resistance R1 as input signals, which change according to the drive state of the PMSM, and outputs three scalar identification signals ζ0, ζ1, and ζ2. These three identification signals in this embodiment are as follows.

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[0037] The adaptive identification algorithm unit 10b-2 is responsible for simultaneously and in real time generating adaptive identification values ​​L^d and L^q for the d-axis inductance and q-axis inductance using three types of scalar identification signals ζ0, ζ1, and ζ2. In this case, the 2×1 vector signal related to the adaptive identification algorithm unit is defined as follows.

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[0038] For adaptive identification of d-axis and q-axis inductances using identification signals, for example, the following recurrent adaptive identification algorithm proposed in this invention can be used.

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[0039] Due to current detection offsets originating from the power converter, the inductance identification value may generate ripple depending on the rotor speed. This type of ripple can be eliminated by selecting the forgetting adaptation coefficients λ1,k, but this may unnecessarily slow down the adaptability. To suppress the ripple in the identification value while maintaining the required adaptability, a low-pass filter can be added to the final stage. The output filter section 10b-3 of the final stage in Figure 5 was added for this purpose. Considering the addability, this block is shown with a dashed line. The bandwidth of this low-pass filter can be about the same as the bandwidth ωss of the shaping and separation filter in equation (12). For example, if this filter Fl(s) is second order, the following simple equation is sufficient.

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[0040] Next, we will describe the identification command value generator 10c, which implements the superposition means for generating identification command values. The configuration of the identification command value generator 10c is as described using equation (5). For the generation of the low-frequency current command value i*1l, a particularly low frequency ωl A selection must be made. The guideline for this selection is approximately 1 / 10 to 1 / 20 of the rated electrical velocity ω2n of the PMSM being driven. The guideline for selecting the amplitude of the low-frequency current command value i*1l is approximately 5% of the d-axis current command value when converted to RMS. These values ​​are merely guidelines and do not necessarily have to be followed. Taking this into consideration, in the block diagram of Figure 3, the signal line for electrical velocity ω2n, which is one of the inputs to the identification command value generator 10c, is shown as a dashed line. [Examples]

[0041] Numerical experiments were conducted to verify and confirm the effects of utilizing all the inventions in claims 1 to 4, and these results are presented below. Table 1 shows the characteristics of the PMSM used in the test for a small electric vehicle. [Table 1]

[0042] The drive control device was configured precisely according to Figure 2. Similarly, the rotor magnetic flux strength estimation device connected to the drive control device was also configured precisely according to Figures 2-5. The current controller was designed to obtain a current control system bandwidth of 2000 [rad / s]. Only standard PI controllers were used for the current controller. The control period Ts was set to Ts = 0.0001 [s]. The rated value on the minimum copper loss trajectory (MTPA trajectory) was adopted as the current command value. That is,

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[0043] As the basic rotor magnetic flux strength estimator 10a, which is the first component of the rotor magnetic flux strength estimation device 10, Figure 4(b) was used. The matrix gain, which is a design parameter of Figure 4(b), was given by the following equation in order to eliminate the influence of the current detection offset originating from the power converter.

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[0044] The stator parameter adaptive identifyr 10b, which is the second component of the rotor magnetic flux strength estimation device 10, had its sampling period the same as the control period. The bandwidth of equation (12b), which is the design parameter of the shaping separation filter unit 10b-1, was selected to ωss = 25. The following simple design parameters and initial values ​​were selected for the adaptive identification algorithm unit 10b-2.

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[0045] The identification command value generator 10c, which is the third component of the rotor magnetic flux strength estimation device 10, was configured according to equation (5). In this configuration, the frequency was set to ω1 = 50 [rad / s], and the amplitude was set to 10 [A] without fractions, which corresponds to approximately 5.8 [%] [RMS] of the d-axis fundamental wave current command value shown in equation (16).

[0046] Figure 6 shows the response of the rotor magnetic flux strength estimation device immediately after startup at 100% rated speed. These normalization errors are defined as follows: TIFF2026137009000022.tif12167

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[0047] The same numerical experiment as in Figure 6 was performed, but with the PMSM speed changed to 10% of its rated speed. The experimental results are shown in Figure 7. Figure 7 shows that, under conditions where all initial values ​​of the identified and estimated values ​​were set to zero, approximately 0.9 seconds after startup, the influence of the current detection offset originating from the power converter was eliminated, and the inductance identified value and rotor magnetic flux strength estimated value stably converged to their respective true values. Figures 6 and 7 support the conclusion that "the objectives of the present invention have been appropriately achieved." [Examples]

[0048] Examples 1 and 2 were both based on the premise that "PMSM does not have interaxial magnetic flux interference." The present invention has the characteristic that "it can be applied without modification even when PMSM does have interaxial magnetic flux interference." Next, an example in which the present invention is applied to a PMSM that does have interaxial magnetic flux interference will be described.

[0049] If the PMSM has interaxial flux interference, the circuit equation for the dq synchronous coordinate system corresponding to equation (11) is described by the following equation.

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[0050] Even when the PMSM has inter-axis magnetic flux interference, the coordinate system relationship shown in Figure 1 and the basic configuration of the drive control device equipped with the rotor magnetic flux intensity estimation device shown in Figure 2 remain essentially unchanged. The basic configuration of the rotor magnetic flux intensity estimation device shown in Figure 3 is changed as shown in Figure 8. The basic configuration of the rotor magnetic flux intensity estimation device in Figure 8 appears to be largely the same as the basic configuration in Figure 3. The crucial difference is that the inductance identification values ​​passed from the fixed parameter adaptive identifyr 10b to the basic rotor magnetic flux intensity estimator 10a are of three types: d-axis, q-axis, and interference inductance identification values. In response to this change, the configurations of the basic rotor magnetic flux intensity estimator 10a and the fixed parameter adaptive identifyr 10b are modified.

[0051] Figure 9 shows the basic configuration of the basic rotor flux strength estimator 10a when the PMSM has inter-axis flux interference. Figures 9(a) and 9(b) correspond to Figures 4(a) and 4(b), respectively. The difference between Figure 4 and Figure 9 is that the inductance matrix responsible for generating the reaction flux is composed of three types of inductance estimates: d-axis, q-axis, and interference inductance, according to equation (21c). These three types of inductance estimates are obtained from the stator parameter adaptive identifyr 10b.

[0052] Figure 10 shows the configuration of the stator parameter adaptive identifyr 10b when the PMSM has interaxial magnetic flux interference. The stator parameter adaptive identifyr 10b in Figure 10 is composed of three subsystems, a shaping and separation filter section 10b-1, an adaptive identification algorithm section 10b-2, and an output filter section 10b-3, which is no different from the embodiment in Figure 5.

[0053] The practical difference lies in the shaping and separation filter section 10b-1. The shaping and separation filter section 10b-1 receives the d-axis voltage vd, d-axis current id (the command value in this embodiment), q-axis current iq, rotor speed ω2n, and winding resistance R1 as input signals, which change according to the driving state of the PMSM, and outputs four types of scalar identification signals ζ0, ζ1, ζ2, and ζ3. These four identification signals in this embodiment are defined as follows.

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[0054] The adaptive identification algorithm unit 10b-2 is responsible for simultaneously and in real time generating adaptive identification values ​​θ^1, θ^2, and θ^3 for the d-axis inductance, q-axis inductance, and interference inductance using four types of scalar identification signals ζ0, ζ1, ζ2, and ζ3. In this case, the 3×1 vector signal related to the adaptive identification algorithm unit is defined as follows.

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[0055] The three low-pass filters in the final stage output filter section 10b-3 of the stator parameter adaptive identifier 10b are unchanged from those in Example 1. In other words, basically, the low-pass filters of equation (15) can be used. Furthermore, the configuration of the identification command value generator 10c, which implements the identification command value generation superposition means, is unchanged from that in Example 1. [Examples]

[0056] Numerical experiments were conducted to verify the effectiveness of utilizing all the inventions of claims 1 to 4 concerning the rotor magnetic flux strength estimation device for a PMSM with inter-axis magnetic flux interference, and these results are presented here. The characteristics of the PMSM under test are basically the same as those in Table 1. However, since Table 1 does not have interference inductance, an interference inductance equivalent to approximately 10% of the d-axis inductance was newly added. Based on this, the same numerical experiment as in Figure 6, which assumes there is no inter-axis magnetic flux interference, was performed. The experimental conditions were basically the same as those in Figure 6, and the speed was set to 100% of the rated speed.

[0057] Figure 11 shows the response of the rotor magnetic flux strength estimation device immediately after startup at 100% rated speed. This shows TIFF2026137009000027.tif12167. The normalization error of the inductance identification value is defined as follows, based on equation (20a):

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[0058] Figure 12 shows the response of the rotor flux strength estimation device immediately after startup at 10% rated speed. Figure 12 shows that, under conditions where all initial values ​​of the identified and estimated values ​​are set to zero, approximately 0.9 s after startup, the influence of the current detection offset originating from the power converter is eliminated, and the inductance identified value and rotor flux strength estimated value stably converge to the same true value. Figures 11 and 12 support the fact that "the objectives of the present invention are appropriately achieved" even for PMSMs with interaxial flux interference. [Examples]

[0059] In Examples 1-4 based on the system configuration in Figure 2, the winding resistance was obtained from outside the rotor magnetic flux strength estimation device. It should be noted that by including winding resistance as part of the identification parameter targets for adaptive identification of d-axis inductance and q-axis inductance, adaptive identification of winding resistance is possible simultaneously with these inductances. The configuration of the stator parameter adaptive identifyr 10b when adaptively identifying the three stator parameters of d-axis inductance, q-axis inductance, and winding resistance is similar to the configuration in Figure 10 which shows the configuration of the three stator parameters of d-axis, q-axis, and interference inductance. A similar extension is possible when adaptively identifying winding resistance in addition to d-axis, q-axis, and interference inductance, i.e., when simultaneously identifying four stator parameters. When simultaneously identifying four stator parameters, it is necessary to generate and superimpose signals with multiple non-zero frequency components in the identification command value generation superposition means. [Examples]

[0060] In the drive control system shown in Figure 2, the d-axis current command value for identification is superimposed on the original stator current command value as a means for generating and superimposing identification command values. Alternatively, the d-axis voltage command value for identification may be superimposed on the original stator voltage command value, and as a result, non-zero frequency components included in the d-axis voltage command value may be superimposed on the d-axis current. [Examples]

[0061] In the examples, the invention was explained using a three-phase PMSM as the subject. However, it should be noted that the present invention is not limited to three-phase PMSMs, but is also applicable to multi-phase PMSMs such as a six-phase PMSM using two three-phase windings. [Industrial applicability]

[0062] The present invention is particularly suitable for traction motors in electric vehicles such as electric cars and hybrid vehicles, among other applications using PMSMs. [Explanation of Symbols]

[0063] 1. Permanent magnet synchronous motor 2 Power Converters 3 Current detector 4a 3-phase 2-phase converter 4b 2-phase 3-phase converter 5a Vector Rotator 5b Vector Rotator 6. Current Controller 7 Phase detector 8. Speed ​​detector 9 cosine sine signal generator 10 Rotor magnetic flux intensity estimation device 10a Basic rotor magnetic flux strength estimator 10b Stator parameter adaptive identifyr 10b-1 Shaping and Separation Filter Section 10b-2 Adaptive Identification Algorithm Section 10b-3 Output filter section 10c Identification Command Value Generator 11 Magnetic flux strength / magnetic temperature converter

Claims

1. A rotor magnetic flux intensity estimation device for a permanent magnet synchronous motor, which is connected to and used with a drive control device for a permanent magnet synchronous motor, comprising at least a current control means for capturing the stator current as a vector signal on a two-axis orthogonal dq synchronous coordinate system with the N pole phase of the rotor permanent magnet as the d-axis phase, and controlling it to follow the stator current command value, A basic rotor flux strength estimation means obtains information on stator voltage, stator current, and rotor speed, which change according to the drive state of a permanent magnet synchronous motor, as input signals from outside the rotor flux strength estimation device, processes the input signals using a D-factor filter, and outputs the rotor flux strength estimate value as the final signal after processing to the outside of the rotor flux strength estimation device. An adaptive identification means that adaptively identifies at least the d-axis inductance and q-axis inductance among the stator parameters used in the D-factor filter within the basic rotor magnetic flux strength estimation means, using information on the d-axis voltage, d-axis current, q-axis current, and rotor speed, which change according to the driving state of the permanent magnet synchronous motor. A means for generating and superimposing an identification command value to superimpose a signal having non-zero frequency components essential for adaptive identification onto the d-axis current, A rotor magnetic flux intensity estimation device for permanent magnet synchronous motors, characterized by comprising the following features:

2. The rotor magnetic flux intensity estimation device for a permanent magnet synchronous motor according to claim 1, characterized in that the D factor filter in the basic rotor magnetic flux intensity estimation means is configured on a dq synchronous coordinate system.

3. The rotor magnetic flux strength estimation device for a permanent magnet synchronous motor according to claim 1, characterized in that the adaptive identification in the adaptive identification means is performed using a recurrent adaptive identification algorithm.

4. The rotor magnetic flux intensity estimation device for a permanent magnet synchronous motor according to claim 1, characterized in that the signal having a non-zero frequency component in the identification command value generation superposition means is a signal equivalent to a sine signal having a single non-zero frequency component or a plurality of non-zero frequency components.

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