Method of controlling a permanent magnent synchronous motor and a motor circuit

Abstract

A method of controlling a motor, for example of an electric power steering system, includes receiving a motor torque demand signal indicative of a torque required from the motor. A current demand signal indicative of the currents to be applied to each phase to meet the torque demand is generated from the motor torque demand signal. One or more operational values from the motor are determined. The current demand signal is set as a function of one or more parameters of the motor obtained by fitting a flux linkage model to the measured operational values. A motor circuit having the permanent magnet electric motor includes a control stage arranged to generate the current demand signal in response to a torque demanded of the motor. A modifying means modifies the magnitude and/or the phase of the current demanded for each phase of the motor by the controller.

Claims

1. A method of controlling a motor of an electric power steering system, comprising the steps of: receiving a motor torque demand signal indicative of a torque required from the motor; generating from the motor torque demand signal a current demand signal indicative of the currents to be applied to each phase to meet the torque required; determining one or more operational values from the motor; setting the current demand signal as a function of one or more parameters of the motor obtained by fitting a flux linkage model to the measured operational values; and maximizing an electromagnetic torque produced by the motor to modify a performance of the motor at a commanded operating speed subject to operating constraints, the operating constraints including a maximum phase current and an available supply voltage.

2. A method according to claim 1, further comprising determining from use of the flux linkage model a maximum torque per ampere below a based speed and a maximum torque per volt above the base speed.

3. A method according to claim 1, further comprising determining from the flux linkage model a winding temperature or rise or fall in temperature of windings of the motor.

4. A method according to claim 1, further comprising combining the flux linkage model with a thermal model of the motor.

5. A method according to claim 1, further comprising determining a winding resistance from use of the flux linkage model and comparing the winding resistance determined during use of the motor from the flux linkage model with a predefined nominal winding resistance to perform phase resistance diagnostics where any major or unexpected change in the winding resistance would be observed by monitoring an estimated value of a stator winding resistance or interconnects between the motor and control electronics.

6. A method according to claim 1, wherein a thermal model is used together with the flux linkage model.

7. A method according to claim 6, further comprising measuring a temperature using a temperature sensor and inputting the measured temperature to the thermal model.

8. A method according to claim 1, wherein the flux linkage model utilises a power series based upon Taylor's theorem for multivariable functions.

9. A method of controlling a motor of an electric power steering system, comprising the steps of: receiving a motor torque demand signal indicative of a torque required from the motor; generating from the motor torque demand signal a current demand signal indicative of the currents to be applied to each phase to meet the torque required; determining one or more operational values from the motor; setting the current demand signal as a function of one or more parameters of the motor obtained by fitting a flux linkage model to the measured operational values; and fitting the operational values to the flux linkage model that includes terms representing a variation in a permanent magnet flux linkage with a magnet temperature.

10. A method according to claim 9, further comprising calculating an electromagnetic torque given d- and q-axis flux linkages and d- and q-axis currents supplied to the motor during use of the motor utilizing the flux linkage model that includes a magnet flux variation, a magnetic saturation and cross-coupling effects between the d- and q-axes.

11. A method according to claim 10, further comprising: comparing an estimated torque with a demanded torque indicated by the motor torque demand signal to derive an error term indicative of a difference in the torques; and inputting the error term to a controller.

12. A method according to claim 10, further comprising using a computed torque value for monitoring and diagnostic functions to evaluate a health of the motor.

13. A method of controlling a motor of an electric power steering system, comprising the steps of: receiving a motor torque demand signal indicative of a torque required from the motor; generating from the motor torque demand signal a current demand signal indicative of the currents to be applied to each phase to meet the torque required; determining one or more operational values from the motor; setting the current demand signal as a function of one or more parameters of the motor obtained by fitting a flux linkage model to the measured operational values; and using the flux linkage model to identify an operating temperature of permanent magnets within the motor during use of the motor or when the motor is offline.

14. A method according to claim 13, further comprising: predetermining one or more parameters of the motor prior to use of the motor that are relatively insensitive to temperature and at a later time during use of the motor measuring the operational values of the motor and from those establish those same parameters of the motor from the flux linkage model; and comparing offline and online values of those parameters to identify how much a magnet flux has varied.

15. A method according to claim 13, further comprising actively de-rating the motor as a function of the identified operational temperature.

16. A motor circuit including a permanent magnet electric motor having a plurality of phase windings, particularly of an electric power steering system, the motor circuit comprising: a control stage arranged to generate a current demand signal in response to a torque demanded of the permanent magnetic electric motor; and a drive stage comprising an inverter having a plurality of drive stage switches and a driver which generates drive signals for the switches of the inverter in response to the current demand signal, the drive signals determining a magnitude and phase of a current in each phase of the permanent magnetic electric motor; means for determining one or more operational values from the permanent magnetic electric motor; processing means configured to fit a flux linkage model to the determined operational values to determine one or more parameters of the permanent magnetic electric motor defined by the flux linkage model; and modifying means that in use modifies one or more of the magnitude and the phase of the current demanded for each phase of the permanent magnetic electric motor by a controller to modify a performance of the motor as a function of the one or more parameters.

17. A motor circuit according to claim 16, wherein the means for determining the one or more operational values comprises one or more sensors that measure one or more of the operational values.

18. A motor circuit according to claim 16, wherein the determining means determines a phase voltage and phase currents and a rotor position of the permanent magnetic electric motor when the permanent magnetic electric motor is in use and comprises one or more sensors enabling the one or more operational values to be measured.

Description

BRIEF DESCRIPTION OF THE DRAWINGS

(1) There will now be described, by way of example only, one embodiment of the present invention with reference to and as illustrated in the accompanying drawings of which:

(2) FIG. 1 is a block diagram showing the main elements of a motor circuit which uses closed loop control;

(3) FIG. 2 is an overview of the key components of an electric power steering system that may include a motor which uses closed loop control;

(4) FIG. 3 is a simplified circuit diagram of a three phase motor and a drive stage inverter which applies terminal voltages to the phases of the motor;

(5) FIG. 4 is flow chart showing the top level steps of an exemplary method in accordance with the present invention;

(6) FIG. 5 is block diagram of the key steps that are carried out online to determine parameters of the motor from an flux linkage model of the motor;

(7) FIG. 6 is an alternative representation of the steps of FIG. 5 showing the operational values fed into the method and the output parameters obtained from the flux linkage model;

(8) FIG. 7 is a representation of a thermal model of the motor circuit which may be used alongside the flux linkage model; and

(9) FIG. 8 is an illustration of a motor control circuit within the scope of an aspect of the invention in which the feedback control generates a torque error signal based on a torque value obtained from fitting of a flux linkage model to operational values obtained during use of the motor.

DESCRIPTION

(10) Referring to FIG. 2, a steering system 10 comprises a steering shaft 11 connected at one end to a steering wheel (not shown) and at the other to a steering rack (also not shown) that is in turn connected to the road wheels (not shown) of the vehicle. As the driver turns the steering wheel the steering shaft 11 rotates which in turn moves the rack. Power assistance is provided by means of an electric motor 12 which is connected to either the steering shaft 11 or the rack through an output shaft 13 that engages a gearbox 14.

(11) It is within the scope of the present invention that the motor may form a part of a steering system where may be no mechanical connection from the steering wheel to the road wheels, a so called steer-by-wire electric steering system, the motor providing the sole means for turning the steering rack or working along with a secondary source of power for turning the steering rack such as a second motor or hydraulic circuit. A position sensor for measuring the angular position of the steering wheel would of course then be needed. It is also within the scope of this invention for the motor to be used in a system that is not a steering system and so this example should not be considered limiting to the scope of the invention.

(12) Referring to FIG. 3, the motor 12 is a three phase motor and more specifically, may comprise an electrically commutated sinusoidal AC brushless permanent magnet synchronous motor which comprises a rotor having, for example, six magnets mounted on it arranged so as to provide six poles which alternate between north and south around the rotor. The rotor therefore defines three direct or d axes evenly spaced around the rotor and three quadrature or q axes interspaced between the d axes. The d axes are aligned with the magnetic poles of the magnets where the lines of magnetic flux from the rotor are in the radial direction, and the q axes are spaced between the d axes where the lines of magnetic flux from the rotor are in the tangential direction. As the rotor rotates, the directions of the d and q axes clearly rotate with it.

(13) The motor stator may comprise, for example, a nine slot copper wound element having three groups of three teeth, each group of teeth having a common winding forming a respective one of the three phases. There are therefore three electrical cycles in each full rotation of the rotor, and the three teeth in any phase are always in the same electrical position as each other.

(14) The three motor windings, generally designated as phases A, B and C in FIG. 3, are connected in a star network. In other embodiments, other arrangements, such as delta networks, can be used. The phase windings are coiled around the stator teeth. One end of each coil is connected to a respective terminal of an inverter bridge. The other ends, of the coils are connected together to form the star centre.

(15) The motor 12 forms a part of a motor circuit 20 as shown in FIG. 8. The motor 12 is driven by an inverter 15 shown in FIG. 3 which defines a motor drive stage. The inverter 15 of the exemplary embodiment comprises a three-phase inverter bridge with one phase for each motor phase. A DC voltage is applied by a battery between a supply rail and a ground rail. The inverter comprises three parallel arms (A, B, C), each of which has a pair of transistors connected in series between a supply rail and a ground rail. The terminals connecting the motor phases A, B, C to the bridge are located between each pair of FETs.

(16) The gate of each FET is connected to a gate driver of a drive stage to switch each FET ON or OFF. Each gate driver receives control signals from a control stage. More specifically and referring to FIG. 8 of the drawings the motor is controlled by a closed loop motor current control system according to an embodiment of the invention, which comprises a current sensing system, a position sensing system, a drive stage and a current controller. It may optionally include a torque sensor 16 as shown in FIG. 2.

(17) The current sensing system may comprise a resistor R in the ground line between the motor and ground which enables measurements to be made of the total current flowing though all of the windings. In order to measure the current in each of the windings the total current has to be sampled at precise instants within the PWM period where the voltage applied to each terminal of the winding (and hence the conduction state of a particular phase) is known. As is well known, in order for the currents in each of the windings to be measured in any one PWM period, the drive circuit needs to be in each of at least two different active states for a predetermined minimum time.

(18) The current sensing system measures the currents i.sub.A, i.sub.B, i.sub.C in the three phases of the motor, which comprise stationary windings, and outputs a signal indicative of the current vector in the stationary coordinates having α and β components. These measurements are then processed within a current controller 16, or control stage, where a signal processing executes a coordinate transformation block arranged to convert the current vector from the α and β components in the stationary reference frame, to D and Q components i.sub.D and i.sub.Q defining the current vector in the rotor reference frame, which rotates relative to the fixed windings, with the Q axis current being the torque generating component and the D axis current being non-torque generating.

(19) A comparator (not shown) that forms part of the current controller in this example, receives the measured D and Q currents from the current sensing system and compares them with demanded D and Q current components to generate a current error. These demanded components form a current demand signal. Although this is expressed in the rotor reference frame the skilled person will understand that this may be transformed to the stationary reference frame and as such the current demand signal is indicative of the individual phase currents that are required at any given time.

(20) The current controller 17, or current control stage, therefore receives the current error and outputs a demanded voltage vector, in the form of a D and Q axis voltage demand V.sub.DQ calculated to reduce the current error so that the measured current vector I.sub.DQ approaches the demanded current vector i.sub.DQ*. A further transformation block receives the voltage demand from the current controller 17 and converts it to α and β components V.sub.αβ which are input to the drive stage inverter 15 which controls the switches to apply voltages to the phase windings of the motor in a PWM pattern which produces the net voltage in the windings having a magnitude and direction corresponding to the voltage demand vector.

(21) The current request received by the current controller is typically a function of a torque demand signal indicative of the level of assistance torque to be applied by the motor. A torque sensor 16 may be provided which measures the torque carried by the steering shaft. This torque demand signal may be generated by a torque controller which receives as an input the torque signal from the torque sensor measuring the torque applied at the steering wheel. The torque controller will typically use the torque signal to look up a torque demand signal using a look up table or boost curve stored in a memory of the system.

(22) In use, the drive stage inverter 15 applies voltage signals to the gate of each FET to switch them ON and OFF rapidly in a predefined sequence, thus controlling the voltage applied to each phase of the motor and current flowing through the windings. This in turn controls the strength and orientation of the magnetic field produced by the windings, and hence the torque and speed of the motor. By using a sufficiently rapid pulse width modulation (PWM) switching pattern, a phase drive waveform can be applied that approximates the ideal sinusoidal waveform required to rotate the motor smoothly.

(23) Using the motor circuit 20 described above, a number of alternative methods may be implemented during use of the motor for modifying the performance of the motor. Each method is built around fitting a novel flux linkage model to operational value of the motor obtained when it is in use, by measuring or estimating, thereby to determine various parameters of the motor. The performance of the motor is modified depending on the value of one or more of these parameters, for example by setting one or more of the magnitude and the phase of the current demanded for each phase of the motor by the controller to thereby modify the performance of the motor. An exemplary method of determining these parameters by fitting the model to the operational values at the end of this description.

(24) The key steps of each method are shown in the flow diagram of FIG. 4. In summary, a torque demand signal is received (step 100), and from this a current demand signal is generated (step 110) based on knowledge of the motor parameters. As the motor is running, and in use, various operational values of the motor including the rotor position, phase currents and phase voltages, are measured or otherwise obtained (step 120) and these are then fitted to a flux linkage model (step 130). The output of this fitting is a set of parameters of the motor defined by the model. Based on these parameters the controller modifies the current demand signal or otherwise modifies the output of the current controller that is fed to the drive stage (step 140) so as to modify the operation of the motor.

Exemplary Method 1—Adaptive Phase Advance

(25) In this method the electromagnetic torque produced by the motor is maximised at the commanded operating speed subject to operating constraints. These constraints may include the maximum phase current and available supply voltage.

(26) Typically the phase advance applicable at any speed is determined in a laboratory and programmed in to the current controller. This might be done at only one temperature. In the method of the invention, this phase advance may be varied in use of the motor as the current demands are modified dependent on the value of the parameters obtained using the model.

(27) Through computing the optimal phase advance online using the model, rather than using fixed predetermined phase advance values, the capability of the motor under the actual operating conditions can be maximised. Alternatively costly and lengthy procedures to identify the optimal phase advance at a range of operating temperatures can be avoided.

(28) The process of optimizing the dq-currents requested by the current controller 20 to meet a given torque demand may comprise the minimization of the sum of the squared errors for the given torque by using equation (10), with the consideration of the given constraints as maximum phase current and maximum phase voltage in the form of equations
i.sub.d.sup.2+i.sub.q.sup.2≤i.sub.phase_max.sup.2  (17)
u.sub.d.sup.2+u.sub.q.sup.2≤u.sub.o_max.sup.2  (18)

(29) As equations (10) and (16) are a function of the magnet permanent flux linkage ψ.sub.m, the magnet temperature will affect the optimal dq-currents. In a similar way, the winding resistance is part of equations (15) and (16) affecting the dq-voltages and contributing to the voltage constraint. For accurate dq-current optimization the winding temperature may be involved as well.

Exemplary Method 2=Torque Estimation

(30) In the example, the method fits the values to a flux linkage model that includes terms representing the variation in permanent magnet flux linkage with the magnet temperature. By calculating the electromagnetic torque to be given the d- and q-axis flux linkages and d- and q-axis currents supplied to the motor during use of the motor. Through utilising a flux linkage model that includes the magnet flux variation, magnetic saturation and cross-coupling effects between the d- and q-axes an accurate online estimate of the motor torque can be obtained.

(31) The method may comprise comparing the estimated torque with the demanded torque indicated by the torque demand signal to derive an error term indicative of the difference in the torques and input the error term to the controller. The controller may generate a current demand signal from the error term.

Exemplary Method 3 and Method 4=Magnet or Rotor Winding Temperature Estimation

(32) In these exemplary methods the parameters obtained using the flux linkage model are processed to determine the temperature of either the magnets of the motor or the windings of the motor.

Exemplary Method 5—Thermal Model Improvement

(33) In an alternative example the flux linkage model may be combined with a thermal model to improve accuracy. A suitable thermal model 30 is shown in FIG. 7.

Exemplary Method 6—Winding Resistance Estimation

(34) In this example the method determines the winding resistance from use of the model and comprises comparing the winding resistance determined during use of the motor with a predefined nominal winding resistance to perform phase resistance diagnostics where any major or unexpected change in the winding resistance would be observed by monitoring the estimated value of the stator winding resistance or interconnects between the motor and the control electronics.

(35) All of methods 1 to 6 may be combined.

(36) Each of the example methods described above use a flux linkage model that includes the effects of saturation for the fitting of the values. In particular, the exemplary methods use a flux linkage model that includes the effects of cross-coupling between d- and q-axes instead of, or in addition to, the effects of saturation. Prior art models are generally linear models that do not allow for these effects, making them unsuitable for applications were accurate determination of the parameters is required.

(37) In a particularly advantageous arrangement the flux linkage model utilises a power series based upon Taylor's theorem for multivariable functions. This enables the power series to be valid for all infinite differentiable functions.

(38) Exemplary Method for Online Motor Parameter Determination

(39) A method of obtaining the parameters online from the model is illustrated in FIGS. 5 and 6. In this online method, a set of base or nominal values for one or more of the parameters are first obtained using an offline method. In other arrangements all of the parameters may be determined from the model.

(40) Where nominal values for the parameters are obtained, those parameters which are thermally insensitive such as the cross coupling between d-axis flux and q-axis flux are retained and those which are temperature dependent are discarded. The method will then identify temperature dependent parameters during use of the motor.

(41) Parameter values identified offline may be used to constrain parameter values identified online to realistic values. For example if the phase resistance of a copper winding is known to be 10 mOhm at room temperature, then allowing for a spread of production tolerances and considering the environmental conditions the motor is deployed within and anticipating some self-heating within the winding, allows constraints to be placed upon the online optimization. Conversely, if no, or a very wide range of values are allowed, a value outside of the anticipated normal range could be used to detect an abnormality has occurred within the motor, controller or data acquisition.

(42) During the use of the motor, measurements of motor operational values are then obtained including the motor phase voltage and motor phase currents and the motor rotor position. The later allows the voltages and currents to be transformed into the rotor reference frame. The motor torque does not need to be measured in this online part of the method, although if available the accuracy of the motor parameters obtained can be improved. A further check of the validity of the motor parameter provided by the model is available by calculating the torque produced by the motor using the model parameters, and comparing it to the range of credible values the system is designed for.

(43) Through tracking the variation of model parameters with time during model operation, further checks can be made upon the validity of the identified model parameters. For example, if the magnet flux identified by the procedure suddenly doubled within a few milliseconds when the thermal time constant of the motor is several orders of magnitudes longer, it would call in to question the validity of the parameter values or the operating state of the motor and controller.

(44) Through comparing the temperature dependent parameter values identified online, with those identified offline for the same motor allows the temperature change to be identified. To do this required knowledge of the temperature coefficient relating the material property to temperature. For example the electrical resistivity of a winding.

(45) Within a volume production run of many nominally identical motors there will be mechanical and material tolerances. Identified model parameters found on online will therefore likely differ from model parameters obtained from a pre-production laboratory sample at the same temperature and motor operating point. Provided the parameter values identified online are within the anticipated range for the motor operating temperature their numerical absolute values may not be so useful as the parameter variation over time. For example if two sets of measurements are taken 10 seconds apart in an online system, and the model parameters are identified after each measurement, and the winding resistance is found to have increased by 20% and the magnet flux reduced by 4% from the following equations the temperature of the winding can be deduced to have risen by approximately 50 degrees Celcius, and the magnet temperature to have risen by approximately 36 degrees Celcius.

(46) $\begin{array}{cc}\frac{R_{s_{t=10s}}}{R_{s_{t=0s}}}=1+\left(T_{\mathrm{winding}}-20\right)\frac{\mathrm{0.393`}}{100}& \left(19\right)\\ \frac{{\psi }_{m_{t=10s}}}{{\psi }_{m_{t=0s}}}=1+\left(T_{\mathrm{magnet}}-20\right)\frac{0.110}{100}& \left(20\right)\end{array}$

(47) Where in the above equations T.sub.winding is the winding temperature in degrees Celcius and T.sub.magnet is the magnet temperature in Degrees Celcius and the winding is substantially made from copper and the magnet substantially from an alloy of Neodymium iron and boron.

(48) Batches of measurements of operational values are obtained containing at least the number of unique sample points corresponding to the number of unknown parameters of the model. For example if only Rs, magnet flux and temperature are unknown, then a minimum of 3 measurement samples are needed. More samples will help improve the accuracy of the model but will take longer to compute. The skilled person will appreciate the need to balance accuracy of an individual iteration of the model parameter calculation with the resources available to sample the necessary signals and the time available within a task scheduler in an online system to perform the computations. The retained parameters are used as constraints to the model so only the temperature dependent, and parameters need to be determined. Of course, in other arrangements all parameters may be determined online.

(49) Depending on how the measurements are collected, it may be some time before enough measurements are available for an update of the parameters to be made. In this time, the temperature of the motor parts may have varied, which will affect the accuracy of the modelling. To ameliorate this, the method may include use of a motor thermal model. Such a lumped parameter thermal model is illustrated in FIG. 7. Other models produced by statistical approaches such as through ‘design of experiments’ or by computer calculation using finite element analysis could be used. Within the thermal model of FIG. 7 the power loss within the motor windings is calculated based upon measured values used within the current controller of the ‘control’ block in FIG. 18. Many readers will be familiar with thermal equivalent circuits where thermal resistance and thermal capacitance are analogues of resistance and capacitance in an electrical circuit. Similarly power loss within a winding in a thermal equivalent circuit is the analogue of current in an electrical circuit, and temperature difference the analogue of potential difference in an electrical circuit. Thermal equivalent circuits are solved using the same methods and equations as electrical equivalent circuits. The thermal equivalent circuit of FIG. 7 is highly simplified and only contains a few components for the purposes of illustration. By studying the thermal circuit the reader will appreciate that heat generated within the windings through the resistive losses (calculated according to the square of the instantaneous current multiplied by the electrical resistance) is predominantly passing through the thermal resistance to the stator material, then through the housing material to either the ambient or mounting face of the motor. A parallel thermal path also exists from the housing through the bearing, and in to the rotor to the magnets. Those skilled in the art will appreciate that more complicated, higher fidelity thermal equivalent circuits including effects of convection, radiation, and forced cooling are well known and utilised.

(50) In use the thermal model calculates the electrical losses and then solves for the temperatures at points within the thermal equivalent circuit. Ideally at least one of the temperatures calculated within the equivalent circuit is known. For example by means of a thermistor monitoring the temperature of the air around the motor. The difference between the known measured temperatures and those estimated in the model can then be included in the least squares objective function i.e. equation?? alongside the temperature dependent motor parameters such as stator winding resistance and magnet flux.

(51) In cases where a direct measurement of temperature is unavailable, the thermal model is used to obtain a prediction of how much the temperatures should have changed by since there was enough measurement data available to compute the temperature dependent parameters. This allows additional constraints to be applied to the optimization of the temperature dependent motor parameters. It is likely that assumptions about the environmental temperature surrounding the motor will need to be made in these instances. Similarly some of the thermal resistances within a typical thermal equivalent circuit will likely vary non-linearly with temperature. Consequently a suitable tolerance for errors within the thermal model will need to be applied so the constraints applied to the motor parameter optimization. Such tolerance limits could be obtained through a series of offline tests upon the motor when validating the motor thermal model against measured temperatures in both stalled and rotating conditions at various ambient temperatures with the motor operating in an environmental chamber.

(52) By fixing a sub-set of motor parameters during the offline stage of the method, the temperature change can be inferred from the variation identified in the motor resistance and magnet flux. This will allow the temperature of the windings and magnet or other parts of the motor to be measured accurately without additional sensors.