DESC:IN A MOTOR
CROSS-REFERENCE TO RELATED APPLICATIONS
The present application claims priority from Indian Provisional Patent Application No. 202321012852 filed on 25/02/2023, the entirety of which is incorporated herein by a reference.
TECHNICAL FIELD
The present disclosure generally relates to demagnetization control in motor. Particularly, the present disclosure relates to system for controlling demagnetization conditions in a motor. Furthermore, the present disclosure relates to a method of controlling demagnetization conditions in a motor.
BACKGROUND
The electric motors have been gaining traction in various applications including mobility. The electric motors convert the electric energy into mechanical energy to drive a load. The electric motors are versatile and are suitable for various applications as the electric motors are capable of utilizing energy from clean sources to deliver mechanical power output.
Generally, it is well-known that the electric motor consists of a stator and a rotor. The stator is a fixed portion of the motor, including an iron core that supports a current-flowing coil and a frame to which the iron core is attached. The rotor is a rotatable portion of the motor, including a permanent magnet and an iron core. The motor has different output and control performances as the characteristics of the permanent magnets embedded within the rotor are varied. However, the electric motors produce heat during operation due to various losses. The heat produced during the operation of the motor affects the performance of the motor.
It is well known that the magnetic flux of the permanent magnets decreases as temperature increases, thereby resulting in a decreased output of the motor. Thus, it is important to maintain an optimum temperature inside the motor for optimal performance of the motor.
Presently, a temperature sensor is attached to a stator coil to detect the temperature of a stator and the detected temperature of the stator is used to monitor the temperature of a motor. However, the temperature of the rotor plays a critical role in the performance of the rotor rather than the temperature of the stator. The existing methodologies of detecting temperature at the stator rather than the temperature are inadequate in determining accurate temperatures inside the rotor i.e. temperature of the permanent magnets and the rotor core. Presently, it is difficult to measure accurate rotor temperature as it is difficult to attach a temperature sensor to the rotor assembly due to the rotation of the rotor assembly. Furthermore, due to unknown rotor temperature or inaccurate rotor temperature estimation, either the derating of the motor is done too late causing permanent damage to the magnets (demagnetization of the permanent magnets) of the rotor or the derating is done too early causing sub-optimal and inefficient operation of the motor. Such inaccurate derating of the motor prevents motor from sustaining the peak power output and maximum power density.
Therefore, there exists a need for an improved mechanism for derating control in the motor that overcomes one or more problems associated as set forth above.
SUMMARY
An object of the present disclosure is to provide a system for controlling demagnetization conditions in a motor.
Another object of the present disclosure is to provide a method of controlling demagnetization conditions in a motor.
In accordance with an aspect of the present disclosure, there is provided a system for controlling demagnetization conditions in a motor. The system comprises at least one sensor configured to detect at least one motor parameter and a data processing arrangement. The data processing arrangement is configured to receive the at least one motor parameter from the at least one sensor arrangement, employ a pre-trained digital twin model to determine at least one rotor parameter based on the received at least one motor parameter, determine a real time field distribution inside the motor, and control at least one operation parameter of the motor based on the determined real time field distribution.
The present disclosure provides a system for controlling demagnetization conditions in a motor. Beneficially, the system as disclosed by the present disclosure is advantageous in terms of accurately determining (predicting) the rotor temperature. Beneficially, the system of the present disclosure is advantageous in terms of predicting the temperature of the rotor stack and the permanent magnets in real time during the operation of the motor. Beneficially, the system of the present disclosure is advantageous in terms of enabling the motor to sustain the maximum power output. Beneficially, the system of the present disclosure is advantageous in terms of maximum utilization of the flux generated by the permanent magnets of the rotor assembly. Beneficially, the system of the present disclosure is advantageous in terms of determining real time field distribution inside the motor. Beneficially, the system of the present disclosure is advantageous in terms of accurately controlling derating in the motor resulting in optimum power output without damaging the permanent magnets of the rotor.
In accordance with second aspect of the present disclosure, there is provided method of controlling demagnetization conditions in a motor. The method comprises receiving at least one motor parameter from at least one sensor arrangement, employing a pre-trained digital twin model to determine at least one rotor parameter based on the received at least one motor parameter, determining a real time field distribution inside the motor, controlling at least one operation parameter of the motor, based on the determined real time field distribution.
Additional aspects, advantages, features, and objects of the present disclosure would be made apparent from the drawings and the detailed description of the illustrative embodiments constructed in conjunction with the appended claims that follow.
It will be appreciated that features of the present disclosure are susceptible to being combined in various combinations without departing from the scope of the present disclosure as defined by the appended claims.
BRIEF DESCRIPTION OF DRAWINGS
The summary above, as well as the following detailed description of illustrative embodiments, is better understood when read in conjunction with the appended drawings. For the purpose of illustrating the present disclosure, exemplary constructions of the disclosure are shown in the drawings. However, the present disclosure is not limited to specific methods and instrumentalities disclosed herein. Moreover, those in the art will understand that the drawings are not to scale. Wherever possible, like elements have been indicated by identical numbers.
Embodiments of the present disclosure will now be described, by way of example only, with reference to the following diagrams wherein:
Figure 1 illustrates a block diagram of a system for controlling demagnetization conditions in a motor, in accordance with an aspect of the present disclosure.
Figure 2 illustrates a flow chart of a method of controlling demagnetization conditions in a motor, in accordance with another aspect of the present disclosure.
In the accompanying drawings, an underlined number is employed to represent an item over which the underlined number is positioned or an item to which the underlined number is adjacent. A non-underlined number relates to an item identified by a line linking the non-underlined number to the item. When a number is non-underlined and accompanied by an associated arrow, the non-underlined number is used to identify a general item at which the arrow is pointing.
DETAILED DESCRIPTION
The following detailed description illustrates embodiments of the present disclosure and ways in which they can be implemented. Although some modes of carrying out the present disclosure have been disclosed, those skilled in the art would recognize that other embodiments for carrying out or practicing the present disclosure are also possible.
The description set forth below in connection with the appended drawings is intended as a description of certain embodiments of a system for controlling demagnetization in a motor and is not intended to represent the only forms that may be developed or utilized. The description sets forth the various structures and/or functions in connection with the illustrated embodiments; however, it is to be understood that the disclosed embodiments are merely exemplary of the disclosure that may be embodied in various and alternative forms. The figures are not necessarily to scale; some features may be exaggerated or minimized to show details of particular components. Therefore, specific structural and functional details disclosed herein are not to be interpreted as limiting, but merely as a representative basis for teaching one skilled in the art to variously employ the present invention.
While the disclosure is susceptible to various modifications and alternative forms, specific embodiment thereof has been shown by way of example in the drawings and will be described in detail below. It should be understood, however, that it is not intended to limit the disclosure to the particular forms disclosed, but on the contrary, the disclosure is to cover all modifications, equivalents, and alternatives falling within the scope of the disclosure.
The terms “comprise”, “comprises”, “comprising”, “include(s)”, or any other variations thereof, are intended to cover a non-exclusive inclusion, such that a setup, or system that comprises a list of components or steps does not include only those components or steps but may include other components or steps not expressly listed or inherent to such setup or system. In other words, one or more elements in a system or apparatus preceded by “comprises... a” does not, without more constraints, preclude the existence of other elements or additional elements in the system or apparatus.
In the following detailed description of the embodiments of the disclosure, reference is made to the accompanying drawings which are shown by way of illustration-specific embodiments in which the disclosure may be practiced. These embodiments are described in sufficient detail to enable those skilled in the art to practice the disclosure, and it is to be understood that other embodiments may be utilized and that changes may be made without departing from the scope of the present disclosure. The following description is, therefore, not to be taken in a limiting sense.
The present disclosure will be described herein below with reference to the accompanying drawings. In the following description, well-known functions or constructions are not described in detail since they would obscure the description with unnecessary detail.
As used herein, the terms “electric motor”, “motor”, “surface permanent magnet motor”, “SPM motor”, “permanent magnet motor”, “permanent magnet synchronous reluctance motor” and “PMSRM” are used interchangeably and refer to type of electric motor in which the permanent magnets are attached to the surface of the rotor, in a radial or axial arrangement. The magnets generate a magnetic field that interacts with the magnetic flux generated by the stator windings to produce the rotational motion of the rotor. The permanent magnet motor has high efficiency, and compact design and is suitable for applications such as electric vehicles (EVs) and robotics.
As used herein, “rotor” and “rotor assembly” are used interchangeably and refer to the rotating part of the electric motor that generates a magnetic field through permanent magnets, for interacting with the stator’s magnetic field for the generation of the torque on the rotor. The rotor serves as the structural support for the permanent magnets and provides a path for the magnetic flux to circulate within the rotor. The rotor core is often made of ferromagnetic materials like laminated iron or steel sheets. These materials have high magnetic permeability, which helps concentrate and direct the magnetic field. The physical dimensions of the rotor, including diameter and length, physical size determine the power output of the motor.
As used herein, the term “rotor stack” refers to the central component of the rotor that supports and houses the permanent magnets. The rotor core is typically made from a magnetic material, such as laminated iron or steel to concentrate and direct the magnetic flux generated by the magnets.
As used herein, the terms “permanent magnet” and “magnet” are used interchangeably and refer to pieces of permanent magnets held in the curved surface of the rotor’s core to generate constant magnetic flux in the rotor for interaction with the magnetic field of the stator. The magnets are typically made from materials with strong magnetic properties, such as neodymium-iron-boron (NdFeB) or samarium-cobalt (SmCo).
As used herein, the term “at least one sensor” refers to a device that detects and responds to a particular parameter or change in the particular parameter. The sensor may include a physical and/or a chemical sensor. The physical sensor may be a mechanical, an electronic and/or an electromechanical sensor.
As used herein, the term “demagnetization condition” refers to weakening or loss of magnetic strength in the permanent magnets located on the rotor. The weakening may severely impact the motor's performance. It is to be understood that the permanent magnets may lose magnetic strength due multiple factors such as high operating temperature, overcurrent in the stator windings, mechanical stress, external magnetic fields and aging.
As used herein, the term “data processing arrangement” refers to a computational element that is operable to respond to and process instructions that operationalize the system for controlling demagnetization conditions in a motor. Optionally, the data processing arrangement may be a micro-controller, a micro-processor, a complex instruction set computing (CISC) microprocessor, a reduced instruction set (RISC) microprocessor, a very long instruction word (VLIW) microprocessor, or any other type of processing hardware. Furthermore, the term “processor” may refer to one or more individual processors, processing devices, and various elements associated with a processing device that may be shared by other processing devices. Furthermore, the data processing arrangement may comprise a software module residing in the data processing arrangement and executed by the microcontroller to control the operation of the system for controlling demagnetization conditions in a motor. It is to be understood that the software module may comprise algorithms and control instructions to control the operation of the system for controlling demagnetization conditions in a motor.
As used herein, the term “communicably coupled” refers to a bi-directional connection between the various components of the system. The bi-directional connection between the various components of the system enables exchange of data between two or more components of the system.
As used herein, the term “digital twin model” refer to virtual representation of a system or a process. The digital twin model acts as a mirror image in the digital world, continuously updated with real-time data from sensors and other sources to reflect the state of its physical counterpart. The digital twin model may utilize machine learning, artificial intelligence and advance modelling techniques to create a dynamic virtual representation of the real system. The digital twin model may be utilized for real time monitoring, performance analysis, prediction, simulation and optimization of the real system based on the output of the digital twin.
As used herein, the term “database arrangement” refers to computing unit with organization of one or more CPUs, memory, databases, communication interfaces etc. to store and provide required information. The database arrangement may store the information or data in an organized or inorganized manner. The database arrangement may create an index of the data stored in the database arrangement.
Figure 1, in accordance with first aspect of the disclosure, describes a system 100 for controlling demagnetization conditions in a motor. The system 100 comprises at least one sensor 102 configured to detect at least one motor parameter and a data processing arrangement 104. The data processing arrangement 104 is configured to receive the at least one motor parameter from the at least one sensor arrangement 102, employ a pre-trained digital twin model to determine at least one rotor parameter based on the received at least one motor parameter, determine a real time field distribution inside the motor, and control at least one operation parameter of the motor based on the determined real time field distribution.
Beneficially, the system 100 as disclosed by the present disclosure is advantageous in terms of accurately determining (predicting) the rotor temperature. Beneficially, the system 100 of the present disclosure is advantageous in terms of predicting the temperature of the rotor stack and the permanent magnets in real time during the operation of the motor. Beneficially, the system 100 of the present disclosure is advantageous in terms of enabling the motor to sustain the maximum power output. Beneficially, the system 100 of the present disclosure is advantageous in terms of maximum utilization of the flux generated by the permanent magnets of the rotor assembly. Beneficially, the system 100 of the present disclosure is advantageous in terms of determining real time field distribution inside the motor. Beneficially, the system 100 of the present disclosure is advantageous in terms of accurately controlling derating in the motor resulting in optimum power output without damaging the permanent magnets of the rotor. Beneficially, the system 100 utilizes the pre-trained digital twin model to accurately predict rotor temperature and thus accurately determine the real time field distribution during the operation of the motor to control the operation of the motor (derating of the motor).
In an embodiment, the at least one motor parameter comprises a motor speed, a stator current, an ambient temperature, a stator coil temperature, and/or a stator stack temperature. Beneficially, the system 100 comprises at least one sensor 102 for detecting each of the motor speed, the stator current, the ambient temperature, the stator coil temperature, and the stator stack temperature. It is to be understood that system 100 may comprise a speed sensor for detecting the motor speed. Furthermore, the system 100 may comprise a current sensor for detecting the stator current. Furthermore, the system 100 may comprise an ambient temperature sensor for detecting the ambient temperature. Furthermore, the system 100 may comprise at least one surface temperature sensors for detecting the stator coil temperature and/or the stator stack temperature. It is to be understood that the person skilled in the art may choose a suitable type of sensor for detecting each of the motor parameter. Beneficially, the at least one sensor 102 detect the at least one motor parameter with high accuracy.
In an embodiment, the system 100 comprises a database arrangement 106 configured to store at least one of: a pre-validated training dataset, a simulated dataset, and the at least one motor parameter. Beneficially, the pre-validated training dataset comprises actual correlation between the at least one motor parameter and the at least one rotor parameter. Furthermore, the simulated dataset comprises established correlation between the at least one motor parameter and the at least one rotor parameter. Beneficially, the at least one rotor parameter stored in the database arrangement 106 is determined by the pre-trained digital twin model during the operation of the motor. In an embodiment, the pre-trained digital twin model may be stored in the database arrangement 106. Beneficially, the data processing arrangement 104 and the database arrangement 106 are communicably coupled with each other. In an embodiment, the data processing arrangement 104 may access the pre-trained digital twin model stored in the database arrangement 106 to determine at least one rotor parameter. In a further embodiment, the data processing arrangement 104 may send the determined at least one rotor parameter to the database arrangement 106 for storing the at least one rotor parameter in the database arrangement 106.
In an embodiment, the data processing arrangement 104 is configured to pre-train the digital twin model using the pre-validated training dataset and the simulated dataset via supervised learning. Beneficially, the digital twin model is pre-trained using combination of the pre-validated training dataset and the simulated dataset in the supervised learning manner to make the digital twin model capable of determining the at least one rotor parameter. Beneficially, the pre-training of the digital twin model using the pre-validated training dataset and the simulated dataset enable high predictive accuracy of the digital twin model.
In an embodiment, the data processing arrangement 104 is configured to further train the pre-trained digital twin model using the at least one motor parameter and the predicted at least one rotor parameter via unsupervised learning. Beneficially, the pre-trained digital twin model continuously learns from its own output of the at least one rotor parameter. Beneficially, the continuous unsupervised learning of the pre-trained digital twin model further improves the predictive accuracy of the pre-trained digital twin model in real time.
In an embodiment, the data processing arrangement 104 is configured to determine the real time field distribution inside the motor based on the at least one rotor parameter, a magnetization curve and a permeance coefficient. Beneficially, the determination of the real time field distribution inside the motor enables the operation of the motor with optimized torque and enhanced efficiency. In an embodiment, the magnetization curve may be a temperature dependent magnetization curve. Beneficially, the permeance coefficient expresses the operating point of the permanent magnets of the rotor on the magnetization curve. It is to be understood that the temperature dependent magnetization curve is dynamic and changes according to temperature of the permanent magnets of the rotor. Beneficially, the determined real time field distribution inside the motor enables accurate control of the operation of motor. In other words, the determined real time field distribution inside the motor enables accurate derating control i.e. sustained operation of the motor on the maximum power without demagnetizing the permanent magnets of the rotor.
In an embodiment, the at least one rotor parameter comprises a temperature of permanent magnets and/or a temperature of rotor stack. Beneficially, combination of the temperature of permanent magnets and the temperature of rotor stack enables accurate determinization of the real time field distribution inside the motor as the field distribution may vary with difference in temperatures of permanent magnets and the rotor stack. In an embodiment, the rotor may comprise a combination of permanent magnets and coils for generating magnetic flux.
In an embodiment, the at least one operation parameter of the motor comprises a current demand, a torque demand and/or the stator current. Beneficially, the data processing arrangement 104 may control at least one of the current demand, the torque demand and/or the stator current to control the operation of the motor.
In an embodiment, the data processing arrangement 104 is configured to control the at least one operation parameter of the motor in response to the determined real time field distribution inside the motor. Beneficially, the data processing arrangement 104 may control at least one of the current demand, the torque demand and/or the stator current to control the operation of the motor. It is to be understood that the data processing arrangement 104 may increase or decrease the at least one of the current demand, the torque demand and/or the stator current in response to the determined real time field distribution inside the motor to control the operation of the motor.
In an embodiment, the system 100 comprises the at least one sensor 102 configured to detect the at least one motor parameter and the data processing arrangement 104. The data processing arrangement 104 is configured to receive the at least one motor parameter from the at least one sensor arrangement 102, employ the pre-trained digital twin model to determine the at least one rotor parameter based on the received at least one motor parameter, determine the real time field distribution inside the motor, and control the at least one operation parameter of the motor based on the determined real time field distribution. Furthermore, the at least one motor parameter comprises the motor speed, the stator current, the ambient temperature, the stator coil temperature, and/or the stator stack temperature. Furthermore, the system 100 comprises the database arrangement 106 configured to store at least one of: the pre-validated training dataset, the simulated dataset, and the at least one motor parameter. Furthermore, the data processing arrangement 104 is configured to pre-train the digital twin model using the pre-validated training dataset and the simulated dataset via supervised learning. Furthermore, the data processing arrangement 104 is configured to further train the pre-trained digital twin model using the at least one motor parameter and the predicted at least one rotor parameter via unsupervised learning. Furthermore, the data processing arrangement 104 is configured to determine the real time field distribution inside the motor based on the at least one rotor parameter, the magnetization curve and the permeance coefficient. Furthermore, the at least one rotor parameter comprises the temperature of permanent magnets and/or the temperature of rotor stack. Furthermore, the at least one operation parameter of the motor comprises the current demand, the torque demand and/or the stator current. Furthermore, the data processing arrangement 104 is configured to control the at least one operation parameter of the motor in response to the determined real time field distribution inside the motor.
Figure 2, in accordance with second aspect of the disclosure, describes method 200 of controlling demagnetization conditions in a motor. The method 200 starts at step 202 and finishes at step 208. At step 202, the method 200 comprises receiving at least one motor parameter from at least one sensor arrangement 102. At step 204, the method 200 comprises employing a pre-trained digital twin model to determine at least one rotor parameter, based on the received at least one motor parameter. At step 206, the method 200 comprises determining a real time field distribution inside the motor. At step 208, the method 200 comprises controlling at least one operation parameter of the motor, based on the determined real time field distribution.
In an embodiment, the method 200 comprises storing at least one of: a pre-validated training dataset, a simulated dataset, and the at least one motor parameter.
In an embodiment, the method 200 comprises pre-training the digital twin model using the pre-validated training dataset and the simulated dataset via supervised learning.
In an embodiment, the method 200 comprises further training the pre-trained digital twin model using the at least one motor parameter and the predicted at least one rotor parameter via unsupervised learning.
In an embodiment, the method 200 comprises determining the real time field distribution inside the motor based on the at least one rotor parameter, a magnetization curve and a permeance coefficient.
In an embodiment, the method 200 comprises controlling the at least one operation parameter of the motor in response to the determined real time field distribution inside the motor.
It would be appreciated that all the explanations and embodiments of the system 100 also applies mutatis-mutandis to the method 200.
In the description of the present invention, it is also to be noted that, unless otherwise explicitly specified or limited, the terms “disposed,” “mounted,” and “connected” are to be construed broadly, and may for example be fixedly connected, detachably connected, or integrally connected, either mechanically or electrically. They may be connected directly or indirectly through intervening media, or they may be interconnected between two elements. The specific meaning of the above terms in the present invention can be understood in specific cases to those skilled in the art.
Modifications to embodiments and combinations of different embodiments of the present disclosure described in the foregoing are possible without departing from the scope of the present disclosure as defined by the accompanying claims. Expressions such as “including”, “comprising”, “incorporating”, “have”, and “is” used to describe and claim the present disclosure are intended to be construed in a non-exclusive manner, namely allowing for items, components or elements not explicitly described also to be present. Reference to the singular is also to be construed to relate to the plural where appropriate.
Although embodiments have been described with reference to a number of illustrative embodiments thereof, it should be understood that numerous other modifications and embodiments can be devised by those skilled in the art that will fall within the spirit and scope of the principles of this disclosure. More particularly, various variations and modifications are possible in the component parts and/or arrangements of the subject combination arrangement within the scope of the present disclosure, the drawings, and the appended claims. In addition to variations and modifications in the component parts and/or arrangements, alternative uses will also be apparent to those skilled in the art.
,CLAIMS:We Claim:
1. A system (100) for controlling demagnetization conditions in a motor, wherein the system (100) comprises:
- at least one sensor (102) configured to detect at least one motor parameter;
- a data processing arrangement (104) configured to:
- receive the at least one motor parameter from the at least one sensor arrangement (102);
- employ a pre-trained digital twin model to determine at least one rotor parameter, based on the received at least one motor parameter;
- determine a real time field distribution inside the motor; and
- control at least one operation parameter of the motor, based on the determined real time field distribution.
2. The system (100) as claimed in claim 1, wherein the at least one motor parameter comprises a motor speed, a stator current, an ambient temperature, a stator coil temperature, and/or a stator stack temperature.
3. The system (100) as claimed in claim 1, wherein the system (100) comprises a database arrangement (106) configured to store at least one of: a pre-validated training dataset, a simulated dataset, and the at least one motor parameter.
4. The system (100) as claimed in claim 1, wherein the data processing arrangement (104) is configured to pre-train the digital twin model using the pre-validated training dataset and the simulated dataset via supervised learning.
5. The system (100) as claimed in claim 4, wherein the data processing arrangement (104) is configured to further train the pre-trained digital twin model using the at least one motor parameter and the predicted at least one rotor parameter via unsupervised learning.
6. The system (100) as claimed in claim 1, wherein the data processing arrangement (104) is configured to determine the real time field distribution inside the motor based on the at least one rotor parameter, a magnetization curve and a permeance coefficient.
7. The system (100) as claimed in claim 1, wherein the at least one rotor parameter comprises a temperature of permanent magnets and/or a temperature of rotor stack.
8. The system (100) as claimed in claim 1, wherein the at least one operation parameter of the motor comprises a current demand, a torque demand and/or the stator current.
9. The system (100) as claimed in claim 1, wherein the data processing arrangement (104) is configured to control the at least one operation parameter of the motor in response to the determined real time field distribution inside the motor.
10. A method (200) of controlling demagnetization conditions in a motor, wherein the method (200) comprises:
- receiving at least one motor parameter from at least one sensor arrangement (102);
- employing a pre-trained digital twin model to determine at least one rotor parameter, based on the received at least one motor parameter;
- determining a real time field distribution inside the motor; and
- controlling at least one operation parameter of the motor, based on the determined real time field distribution.
11. The method (200) as claimed in claim 10, wherein the method (200) comprises storing at least one of: a pre-validated training dataset, a simulated dataset, and the at least one motor parameter.
12. The method (200) as claimed in claim 10, wherein the method (200) comprises pre-training the digital twin model using the pre-validated training dataset and the simulated dataset via supervised learning.
13. The method (200) as claimed in claim 12, wherein the method (200) comprises further training the pre-trained digital twin model using the at least one motor parameter and the predicted at least one rotor parameter via unsupervised learning.
14. The method (200) as claimed in claim 10, wherein the method (200) comprises determining the real time field distribution inside the motor based on the at least one rotor parameter, a magnetization curve and a permeance coefficient.
15. The method (200) as claimed in claim 10, wherein the method (200) comprises controlling the at least one operation parameter of the motor in response to the determined real time field distribution inside the motor.