Patent Application
20250038686
Electrically powered vehicle drive trains have a significantly quieter electromotive drive, or electric motor, compared to vehicles with an internal combustion engine. This makes noises noticeable that were not perceived in the combustion engine drive train. This applies in particular at low vehicle speeds, as the tire rolling noise and wind noise dominate at high speeds and mask any additional noise.
DE 10 2015 207 632 A1 relates to a device for reducing gearwheel noise of a drive gearwheel meshing with an output gearwheel, wherein forces can be introduced from the gearwheels into a housing via a support, wherein the device has a sensor device, a control device and an actuator and wherein a dynamic vibration signal can be detected by means of the sensor device and the vibration signal can be fed to the control device, wherein a vibration reduction signal can be generated by means of the control device, which signal can be fed to the actuator, wherein the actuator is arranged on or in the support in such a way that forces can be transmitted from the gearwheels via the actuator into the housing, wherein a relative displacement and/or a force application to a component carried by the support can be actively effected by means of the actuator. The device is also set up to use the actuator as a sensor device.
Furthermore, in the German patent application DE 10 2020 206 669.8, a method for operating a drive train with an electromotive drive has been applied for a patent, wherein a speed and a drive torque of the drive can be converted via a toothed transmission stage for an output, and the drive is controlled with a control signal.
DE 10 2020 206 669.8 recommends superimposing a periodic torque change signal on the control signal, which alternately reduces and increases the drive torque and is in phase with a change in tooth stiffness of the toothed transmission stage, wherein a signal strength of the torque change signal is lower with decreasing tooth stiffness than with increasing tooth stiffness.
In the method described in DE 10 2020 206 669.8, the vibrations of the transmission stage are actively damped with the meshing frequencies. The amplitude of the additionally applied periodic torque depends on the absolute values of the stiffness of the teeth involved in the toothed transmission stage. The additional torque course required for successful noise damping can, for example, be determined during the development phase and stored in the control of the electromotive drive. One advantage of this method is that there is little or no disturbing noise due to the variable tooth stiffness when two transmission gearwheels roll. Such a noise would be tonal and would increase with increasing torque transmitted via the transmission stage, as the tooth stiffness changes periodically over each individual pair of teeth currently in mesh. For simple spur gear stages, the frequency of this change is therefore the speed of the gearwheel multiplied by its number of teeth. The method described can be implemented as a software solution, wherein the software can also be implemented very late in the development process or even retrospectively, e.g. as a software update for vehicles in the field. The rotor position and the position of the gearwheels and an estimated value of the torque can be used as signals. The torque of the electric machine can be estimated by the field-oriented control and the rotor position is also measured or estimated for this. In principle, therefore, no additional sensors are required.
However, tribologically stressed machine elements, such as meshing gearwheels with their teeth, change over the service life caused by wear and ageing or fatigue. The material removal on the flanks of the gearwheels changes the tooth width over the service life and therefore also the stiffness of the teeth. This change in stiffness over a long period of time is progressive and can be linear, progressive or degressive, depending on the stress variables. The change in the tooth stiffness of a transmission stage over time caused by wear and ageing is not known at the time the transmission is designed and therefore cannot be mapped initially in the torque control.
The aim of the present invention is to detect a change in the state of the drive train, which is attributable to a long-term extended change in the tooth stiffness of the transmission stage over the service life, and to adjust a torque control based on these changes so that noise developments caused by ageing or wear can be better damped.
For this purpose, a method for operating a drive train with an electromotive drive is proposed, in which a speed and a drive torque of the electromotive drive can be converted via a toothed transmission stage for an output. The electromotive drive is controlled with a control signal, wherein a periodic torque change signal is superimposed on the control signal for damping transmission noise, which alternately reduces and increases the drive torque, wherein the periodic torque change signal is in phase with a periodic tooth stiffness change of the toothed transmission stage. The method according to the invention comprises at least the following steps:
The method according to the invention can be used in purely electrically powered drive trains of vehicles, but also in other drive trains with electric machines that have a transmission but no combustion engine. Such a drive train can be a drive train from industrial technology or white goods, for example. White goods include refrigerators, freezers, etc. whose refrigerant compressor is driven by an electromotive drive. However, the white goods also include washing machines and dishwashers whose pump or drum is driven by an electromotive drive. In addition, the drive train according to the invention can also be used for power tools with a transmission. Such power tools with transmission include drills in particular. However, electric saws and grinders, for example, can also have such drive trains, each with a transmission.
By means of the present invention, operating state signals of the drive train, such as a rotor position signal of the electromotive drive, a torque or motor current signal of the electromotive drive and/or a structure-borne sound sensor signal, which detects the structure-borne sound of at least one component of the drive train, can be used to detect a temporal change in the tooth stiffness of a transmission stage resulting from ageing or wear, and measures can be initiated which counteract the noise development caused by this. Since the control signal is superimposed with a periodic torque change signal in the method, which is generated as a function of a difference between a state variable derived from at least one operating state signal and a target state class, the state variable dependent on the operating state signal changes as a result. This makes a control process possible.
Advantageous embodiments and further embodiments of the invention are made possible by the features indicated in the dependent claims.
It is particularly advantageous that, if it is established in step d) of testing that a current state variable assigned to the target state class was determined in the preceding step a) of determining, the method does not perform steps e) of generating and f) of superimposing and instead forms the periodic torque change signal in one step independently of the current state variable and superimposes it on the control variable, and repeats steps a) through d) until it is established in step d) of testing that the determined current state class is not the specified target state class. When the target state class is reached, the torque control is in the desired target state; a further change in the state variable could therefore, in an unfavorable case, cause the control algorithm to run out of the target corridor. Therefore, when the target state class is reached, it is advantageous to suspend the step of generating the periodic torque change signal as a function of a difference between the current state variable determined in step a) and the specified target state class and the subsequent superimposing the control signal with a periodic torque change signal generated as a function of the difference. Instead, the periodic torque change signal, which can be generated analogously to the representation in DE 10 2020 206 669.8, is superimposed unchanged on the control signal. As soon as the method detects in subsequent runs that the currently determined state variable has left the target state class, this is recognized in step d) of the test, and steps e) and f) are carried out again until the state variable corresponds to the target state class again.
Advantageously, the current state variable determined in step a) is dependent on a change in the tooth stiffness of the toothed transmission stage caused by wear and/or ageing. Torque fluctuations on the output side of the electromotive drive, for example, cause inductive feedback on the motor current of the electromotive drive. Therefore, for example, the motor current contains information about the torque changes and therefore also about the changes in the transmission caused by wear and/or ageing. Therefore, depending on an operating state signal such as the motor current signal, for example, a state variable can also be formed which reflects the changes caused by wear and/or ageing.
The target state class in the classification space can be advantageously specified on the basis of reference state variables. The reference state variables can be determined on the basis of machine-learning systems in a manner analogous to step a) from the same operating state signals of reference drive trains, wherein the reference drive trains are assigned to different damage classes and each reference drive train of each damage class has a largely similar structure except for an individually specified damage or wear state.
The classification space with the state classes as the basis for evaluating the system state can, for example, be trained using a classification algorithm based on reference drive trains, wherein each individual reference drive train is assigned a known state class. An ensemble of drive trains can, for example, comprise at least ten or one hundred individual reference drive trains. The reference drive trains differ in that at least one transmission stage is known to have changed caused by wear or ageing, otherwise they are identical in design. The classification algorithm can be trained by operating the reference machines and receiving the same operating state signal or the same group of operating state signals for each reference drive train, for example a reference AC signal associated with a stator coil of the electromotive drive of the drive train. The state variables evaluated in this way can be summarized in state classes. The state classes can represent damage classes. The classification space data obtained in this way can, for example, be stored in a memory in a control unit of a drive train.
It is particularly advantageous if the method is carried out using a classification algorithm, wherein in step a) of the determination, values of a plurality of features are derived from the at least one operating state signal of the drive train, and so-called principal component variables are formed from the values of the plurality of features by coordinate transformation, wherein the number of features form the dimensions of a principal component space, wherein the principal component space forms the classification space and wherein each position in the principal component space represents a state variable which is associated with the values of the plurality of features or the features derived therefrom. The course of the change in the current state variable caused by wear or ageing describes a space curve in the principal component space. This makes it possible to control the periodic torque change signal, which returns the currently determined state variable to the spatial range of a target state class.
In a simple way, neighboring positions in a limited spatial range of the principal component space can be specified as a single target state class. Positions outside this spatial range are then recognized as an anomaly, which causes the state variable to be returned to the target state class. Positions outside the spatial range of the target state class can, for example, be defined as a second state class “abnormal”.
However, it is not absolutely necessary to carry out a principal component analysis and classify the states in a principal component space. In principle, it is also possible to describe the classification space directly with the specific states. However, the principal component analysis improves the performance of the classification algorithms and is therefore particularly advantageous.
However, the items outside the spatial range assigned to the target state class can also be subdivided into at least two further state classes, in particular state classes defining damage classes.
The at least one operating state signal or several operating state signals can advantageously be selected from the following group of operating state signals:
The at least one operating state signal can be detected in particular with a corresponding sensor element. For example, a motor current signal fed back into the motor current in the electromotive drive due to torque fluctuations on the output side can be detected as an operating state signal with a motor current sensor.
Advantageously, the size of an application parameter can be formed in the generation step as a function of a difference between the state class determined in step c) and a predefined target state class. This application parameter is used to change the periodic torque change signal.
The method according to the invention can be implemented on a computer, wherein a computer in the broadest sense can also be a data processing system or a microprocessor. Accordingly, the invention also comprises a computer program comprising instructions which, when executed by a computer, cause the computer to carry out the method according to the invention, and a computer-readable data carrier on which this computer program is stored.
Finally, the invention also comprises a computer comprising such a computer-readable data carrier and an evaluation and control unit comprising such a computer-readable data carrier and further comprising means for carrying out the method steps according to the invention.
In the following, possible embodiment examples of the invention are explained with reference to the accompanying drawing. The following is shown in the drawings:
An output shaft 8 of the electromotive drive 4, which is rotatably mounted by means of a roller bearing 7, is non-rotatably connected to a first gearwheel 1 of a toothed transmission stage 12, which is arranged inside a transmission housing 13. The first gearwheel 1 meshes with a second gearwheel 14 of the transmission stage 12. The second gearwheel 14 can be coupled via a differential transmission to two drive shafts 16, which are mounted in roller bearings 17 and connected to vehicle wheels 18 so that they cannot rotate.
The first gearwheel 1 is smaller in diameter than the second gearwheel 14 and thus forms a pinion. A speed and a drive torque of the drive 4 are converted via the toothed transmission stage 12 for an output 19, which has the drive shafts 16. By means of the transmission stage 12, the speed of the drive 4 is converted to a lower transmission output speed and the drive torque is converted to a higher transmission output torque. The second gearwheel 14 can include a differential transmission which distributes the transmission output torque evenly between the two vehicle wheels 18. Alternatively, the transmission stage 12 can also be designed as a planetary transmission and/or as a shiftable transmission with several stages, in particular two stages, which have different ratios from one another.
The gearwheels 1, 14 can be straight-toothed or helical-toothed. The teeth 20, 22 of the gearwheels 1, 14 mesh with each other. The system consisting of the two meshing gearwheels 1, 14 with a variable tooth stiffness represents a dual-mass oscillator with a variable spring constant. The first gearwheel 1 has a first mass inertia and the second gearwheel 14 has a second mass inertia. The two gearwheels 1, 14 therefore form the dual-mass oscillator, which oscillates at a variable frequency dependent on the rotational path, a so-called tooth meshing frequency. Due to the variable tooth stiffness of the teeth, vibrations are excited on the meshing gearwheels 1, 14 during rotation, which are transmitted via the gearwheels 1, 14, the shafts 8, 16 and the roller bearings 7, 17 to the transmission housing 13, where they are emitted as noise by a vibrating surface. In addition to the rotational vibrations, the gearwheels 1, 14 also vibrate translationally with the bearings 7, 17 against the transmission housing 13, which causes the noise. This causes the transmission housing 13 to vibrate, so that sound waves propagate in the air in the form of pressure and density fluctuations.
The control unit 6 can have a control which is based on the imprint, i.e., superimposing a periodic additive torque oscillation via the electric drive 4 for damping unwanted noise with the gear mesh frequency during operation of the electric motor vehicle. For this purpose, a periodic torque change signal 5 is superimposed on a control signal 40 of the drive 4. The periodic torque change signal 5 alternately reduces and increases the drive torque. The periodic torque change signal 5 is in phase with the tooth stiffness of the transmission stage 12 connected to the power flow. The control signal 40 can in particular be a torque control signal or an output voltage signal of a torque control system. In particular, this torque control can be field-oriented, i.e., vector control. Field-oriented control ensures that the speed and positioning accuracy is improved with a frequency converter provided in the control unit 6.
This periodic torque change signal 5 ideally has no DC component or a DC component of zero. The torque change signal 5 increases or reduces a transmitted total torque that is set at the drive 4 on the basis of the torque control signal and the drive control signal. The tooth stiffness of the teeth 20, 22 currently in mesh determines whether the total torque is increased or reduced. On average, therefore, the output torque requested by the driver, which is set by the torque control operating in parallel, is not changed. The periodic torque change signal 5 can simulate the exact course of the torque fluctuation or, for example, be approximated by a sinusoidal signal of the same phase and frequency.
Since the influence of the change in tooth stiffness changes with a transmission output torque requested by the driver, the amplitude, i.e., a signal strength, of the torque control signal must be adjusted accordingly with the requested transmission output torque.
By adding a periodic stationary torque setpoint or voltage setpoint signal to an output signal of the, for example, field-oriented torque or current controller, a transmission noise can be damped with the gear mesh frequency.
Such a method is described in detail in the German patent application DE 10 2020 206 669.8 filed on May 28, 2020. In this respect, explicit reference is made at this point to the disclosure content of DE 10 2020 206 669.8.
Values of several features, for example features T1-T11; F1-F13, in particular features as the basis of a principal component analysis, can be derived from the at least one operating state signal S or the several operating state signals of the drive train. If, for example, the motor current signal is used as the operating state signal S, it can be broken down into components x(n); s(k) and fk, wherein x(n) is the time-sampled alternating current signal, s(k) is the associated discrete frequency spectrum and fk is the frequency associated with s(k).
From the components x(n); s(k) and fk, for example, features T1 through T11 and F1 through F13 can be formed in the time domain or frequency domain, for example the following features proposed in (“A new approach to intelligent fault diagnosis of rotating machinery”, Yaguo Lei et. al., Expert Systems with Applications 35, (2008) 1593-1600):
Characteristics derived from the features T1-T11; F1-F13 can be formed by a coordinate transformation, in particular a principal axis transformation or a principal component analysis (PCA). The derived characteristics can, for example, be principal component sizes or “principal components” for short. The number of characteristics corresponds to the number of principal component sizes. The number of characteristics T1-T11; F1-F13 forms the dimensions of a principal component space. The principal component space may be viewed as a classification space 100, wherein each position in the principal component space represents a state variable Z associated with the values of the plurality of features T1-T11; F1-F13 or the features derived therefrom. The assignment of the state variables Z can therefore be carried out on the basis of derived characteristics, which are derived from the characteristics by means of a transformation, in particular a linear transformation, preferably a principal axis transformation or a principal component analysis.
The assignment of the state variable Z is explained using the example in
Insights into the state variables Z of drive trains can be gained from evaluations of reference drive trains. The reference drive trains can have the same structure as in
The knowledge gained in this way about the positions of the state classes in the spatial ranges of the classification space 100 can be stored in the control unit 6 of a drive train 2. A classification space 100 is preferably stored, in which at least the target state class 120 is defined as a delimited spatial range.
As the drive train 2 ages, the current state variable Z′, which is determined over a longer period of time according to the same criteria, follows a course during ageing as shown by the curve in
With the method described below with reference to
In a first step 300, a current state variable Z′ of the drive train 4, which is dependent on the tooth stiffness of the toothed transmission stage 12, is determined from the operating state signal S. Then, in a subsequent step 301, this current state variable Z′ is assigned to the classification space 100 of state variables. In a further step 302 following step 301, the current state class assigned to the current state variable Z′ in the classification space 100 can be determined. In the subsequent step 303, the system checks whether the current state class determined in step 302 is the target state class 120. If it is established that the current state class determined in step 302 is not the target state class 120, the periodic torque change signal 5 is generated in a subsequent step 304 as a function of a difference between the current state class determined in step 300 and the predetermined target state class 120.
This can be done in a simple way, for example, by forming an application parameter A if a difference is detected between the current state variable Z′ determined in step a) and the target state class. For example, the application parameter A is preferably formed as a function of the size of the distance of the current state variable Z′ from the target state class 120 in the classification space 100. Depending on the application parameter formed in this way, the periodic torque change signal 5 formed as described above is changed in the control unit 6, wherein the application parameter modifies, for example, the amplitude of the torque change signal 5. The modified periodic torque change signal 5 is superimposed on the control signal 40 in the subsequent step 305. The method then returns to step 300 and determines a new current state variable Z′ of the drive train. If it is established in step 303 for the newly determined current state variable Z′ that the current state variable Z′ is still not in the target state class 120, steps 304 and 305 are carried out again and the application parameter is changed again until the currently determined state variable Z′ corresponds to the target state class 120. The application parameter can therefore be changed step by step. The method runs through a control loop.
However, if it is established in step 303 that a current state variable Z′ assigned to the target state class 120 was determined in the previous step of determining 300, steps 304 and 305 are not carried out and instead the periodic torque change signal 5 is formed in a step 306 independently of the current state variable and superimposed on the control variable. Finally, after performing step 306, steps a) through d) are repeated until step d) of testing 303 establishes at a point in time that the determined current state class no longer corresponds to the predetermined target state class 120. Then, as explained above, the method is continued with steps 304 and 305.
It goes without saying that the target state class 120 can be freely specified. Depending on the desired quality of noise reduction, a wider or narrower spatial range of the classification space 100 can therefore be assumed as target state class 120.
| Number | Date | Country | Kind |
|---|---|---|---|
| 10 2021 213 252.9 | Nov 2021 | DE | national |
| Filing Document | Filing Date | Country | Kind |
|---|---|---|---|
| PCT/EP2022/080928 | 11/7/2022 | WO |
