Self-learning updating method and device for resolver correction coefficient
By self-learning and updating the rotational correction coefficient when the vehicle is powered on, the problem of mismatch of correction coefficients in the prior art over time is solved, and more stable rotational signal correction is achieved, reducing the risk of rotational speed estimation fluctuations and vehicle NVH problems.
Patent Information
- Application Number
- CN202411713358.9
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-11-27
- Publication Date
- 2025-03-07
AI Technical Summary
In the existing rotary signal correction method, the preset correction coefficient will no longer match over time due to vibration, aging and circuit interference, resulting in large fluctuations in speed estimation and the occurrence of NVH problems in the whole vehicle.
A self-learning and updating method for rotary change correction coefficients is provided. By obtaining the motor speed when the vehicle is powered on, mechanical angle data within a preset time period is obtained, rotary change zero-drift correction coefficients and rotary change gain correction coefficients are calculated, and these coefficients are written to nonvolatile memory when they are within the preset effective range.
This method can obtain a rotational change correction coefficient that is more matched with the system, ensure the quality of the rotational change signal, and thus reduce the risks brought by speed estimation fluctuations and the NVH problems of the whole vehicle.
Smart Images
Figure CN119577339A_ABST
Abstract
Description
Technical Field
[0001] This application relates to the field of signal processing technology, and more specifically, to a self-learning update method and apparatus for resolver correction coefficients. Background Technology
[0002] Current resolver signal correction methods typically use pre-set correction coefficients to correct the acquired resolver signal. However, in practice, it has been found that as time goes on, the pre-set resolver correction coefficients may no longer match the system due to vibration, aging, and occasional circuit interference, leading to large fluctuations in speed estimation and vehicle NVH problems. Summary of the Invention
[0003] The purpose of this application is to provide a self-learning update method and apparatus for resolver correction coefficients, which can obtain resolver correction coefficients that are more compatible with the system, thereby helping to ensure the quality of resolver signals and reducing the risks caused by speed estimation fluctuations and vehicle NVH problems.
[0004] The first aspect of this application provides a self-learning update method for resolver correction coefficients, including:
[0005] When the vehicle is powered on, obtain the motor speed of the vehicle:
[0006] When the motor speed is greater than a preset speed threshold, mechanical angle data within a preset time period is acquired;
[0007] Based on the aforementioned mechanical angle data, calculations are performed to obtain the resolver correction coefficient, which includes the resolver zero drift correction coefficient and the resolver gain correction coefficient.
[0008] When the resolver correction coefficient is within a preset effective range, the resolver correction coefficient is written into the vehicle's non-volatile memory.
[0009] In the above implementation process, the method can obtain a resolver correction coefficient that is more compatible with the system, thereby helping to ensure the quality of the resolver signal and reducing the risks caused by speed estimation fluctuations and vehicle NVH issues.
[0010] Furthermore, when the vehicle is powered on, the motor speed of the vehicle is obtained, including:
[0011] Detect whether a self-learning request for the resolver correction coefficient initiated when the vehicle is powered on has been received;
[0012] When the self-learning request is received, the motor speed of the vehicle is obtained.
[0013] Furthermore, the method also includes:
[0014] When the resolver correction coefficient is not within the preset effective range, the original correction coefficient is not updated; the original correction coefficient includes the original zero drift correction coefficient and the original gain correction coefficient.
[0015] Furthermore, the method also includes:
[0016] The acquired resolver signal is corrected based on the resolver correction coefficient to obtain the corrected resolver signal.
[0017] A second aspect of this application provides a self-learning update device for resolver correction coefficients, the self-learning update device for resolver correction coefficients comprising:
[0018] The first acquisition unit is used to acquire the motor speed of the vehicle when the vehicle is powered on.
[0019] The second acquisition unit is used to acquire mechanical angle data within a preset time period when the motor speed is greater than a preset speed threshold.
[0020] The calculation unit is used to calculate based on the mechanical angle data to obtain the resolver correction coefficient, which includes the resolver zero drift correction coefficient and the resolver gain correction coefficient.
[0021] The update unit is used to write the resolver correction coefficient into the vehicle's non-volatile memory when the resolver correction coefficient is within a preset effective range.
[0022] Furthermore, the first acquisition unit includes:
[0023] The detection subunit is used to detect whether a self-learning request for the resolver correction coefficient initiated when the vehicle is powered on has been received.
[0024] The acquisition subunit is used to acquire the motor speed of the vehicle when the self-learning request is received.
[0025] Furthermore, the self-learning update device for the resolver correction coefficients also includes:
[0026] A holding unit is used to keep the original correction coefficients from being updated when the resolver correction coefficients are not within a preset effective range; the original correction coefficients include the original zero-drift correction coefficients and the original gain correction coefficients.
[0027] Furthermore, the self-learning update device for the resolver correction coefficients also includes:
[0028] The correction unit is used to correct the acquired resolver signal based on the resolver correction coefficient to obtain the corrected resolver signal.
[0029] A third aspect of this application provides an electronic device including a memory and a processor, the memory storing a computer program, and the processor running the computer program to cause the electronic device to perform the self-learning update method for the resolver correction coefficients as described in any one of the first aspects of this application.
[0030] The fourth aspect of this application provides a computer-readable storage medium storing computer program instructions, which, when read and executed by a processor, perform a self-learning update method for the resolver correction coefficients as described in any one of the first aspects of this application.
[0031] The beneficial effects of this application are: the method and apparatus can obtain a resolver correction coefficient that is more compatible with the system, thereby helping to ensure the quality of the resolver signal and reducing the risks caused by speed estimation fluctuations and vehicle NVH problems. Attached Figure Description
[0032] To more clearly illustrate the technical solutions of the embodiments of this application, the accompanying drawings used in the embodiments of this application will be briefly introduced below. It should be understood that the following drawings only show some embodiments of this application and should not be regarded as a limitation of the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.
[0033] Figure 1 A flowchart illustrating a self-learning update method for resolver correction coefficients provided in an embodiment of this application;
[0034] Figure 2 A flowchart illustrating another self-learning update method for the resolver correction coefficients provided in this application embodiment;
[0035] Figure 3 A schematic flowchart illustrating an example of a self-learning update method for resolver correction coefficients provided in this application embodiment;
[0036] Figure 4 A schematic diagram of a self-learning update device for a resolver correction coefficient provided in an embodiment of this application;
[0037] Figure 5 This is a schematic diagram of another self-learning update device for the resolver correction coefficient provided in an embodiment of this application. Detailed Implementation
[0038] The technical solutions in the embodiments of this application will now be described with reference to the accompanying drawings.
[0039] It should be noted that similar reference numerals and letters in the following figures indicate similar items; therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures. Furthermore, in the description of this application, terms such as "first," "second," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.
[0040] Example 1
[0041] Please refer to Figure 1 , Figure 1 This embodiment provides a flowchart illustrating a self-learning update method for resolver correction coefficients. The self-learning update method for resolver correction coefficients includes:
[0042] S101. When the vehicle is powered on, obtain the vehicle's motor speed.
[0043] S102. When the motor speed is greater than the preset speed threshold, acquire the mechanical angle data within the preset time period.
[0044] S103. Calculate based on mechanical angle data to obtain the resolver correction coefficient, which includes the resolver zero drift correction coefficient and the resolver gain correction coefficient.
[0045] S104. When the resolver correction coefficient is within the preset effective range, write the resolver correction coefficient into the vehicle's non-volatile memory.
[0046] In this embodiment, the subject executing the method can be a computing device such as a computer or server, and no limitation is made in this embodiment.
[0047] In this embodiment, the subject executing the method can also be a smart device such as a smartphone or tablet, and no limitation is made in this embodiment.
[0048] As can be seen, implementing the self-learning update method for the resolver correction coefficients described in this embodiment can obtain resolver correction coefficients that are more compatible with the system, thereby helping to ensure the quality of the resolver signal and reducing the risks caused by speed estimation fluctuations and vehicle NVH issues.
[0049] Example 2
[0050] Please refer to Figure 2 , Figure 2 This embodiment provides a flowchart illustrating a self-learning update method for resolver correction coefficients. The self-learning update method for resolver correction coefficients includes:
[0051] S201. Detect whether a self-learning request for resolver correction coefficients initiated when the vehicle is powered on has been received.
[0052] S202. When a self-learning request is received, obtain the vehicle's motor speed.
[0053] In this embodiment, the method proposes a self-learning mechanism for the resolver zero-drift correction coefficient and the resolver gain correction coefficient. This mechanism allows the vehicle to complete the self-learning of the resolver correction coefficient in 1 second each time it is powered on and driven, thereby ensuring that the resolver correction coefficient used in each power-on cycle is matched with the system.
[0054] In this embodiment, the self-learning process is requested once per power-on cycle.
[0055] S203. When the motor speed is greater than the preset speed threshold, acquire the mechanical angle data within the preset time period.
[0056] In this embodiment, the preset speed threshold can be 200 rpm.
[0057] In this embodiment, the preset time period can be 1 second.
[0058] In this embodiment, the mechanical angle data may include the number of revolutions.
[0059] S204. Calculate the resolver correction coefficient based on the mechanical angle data, including the resolver zero drift correction coefficient and the resolver gain correction coefficient, and then execute step S205 or S206.
[0060] In this embodiment, the resolver signal is called the rotary transformer signal, which includes a sine signal (sin) and a cosine signal (cos).
[0061] In this embodiment, the method can calculate the actual resolver signal based on the collected voltage value and mechanical angle data. Based on this, the method can further determine the zero-drift correction coefficient and the resolver correction coefficient based on the actual resolver signal, so that these coefficients can be used in subsequent resolver signal correction stages.
[0062] S205. When the resolver correction coefficient is within the preset effective range, write the resolver correction coefficient into the vehicle's non-volatile memory and execute step S207.
[0063] In this embodiment, the effective range can be a fixed numerical range, such as an error range of ±5%.
[0064] In this embodiment, the vehicle's non-volatile memory is NVM.
[0065] S206. When the resolver correction coefficient is not within the preset effective range, keep the original correction coefficient unchanged and execute step S207. The original correction coefficient includes the original zero drift correction coefficient and the original gain correction coefficient.
[0066] S207. Correct the acquired resolver signal based on the resolver correction coefficient / original correction coefficient to obtain the corrected resolver signal.
[0067] Please refer to Figure 3 , Figure 3 A schematic diagram illustrating an example of a self-learning update method for resolver correction coefficients is shown.
[0068] In this embodiment, the subject executing the method can be a computing device such as a computer or server, and no limitation is made in this embodiment.
[0069] In this embodiment, the subject executing the method can also be a smart device such as a smartphone or tablet, and no limitation is made in this embodiment.
[0070] As can be seen, implementing the self-learning update method for the resolver correction coefficients described in this embodiment can obtain resolver correction coefficients that are more compatible with the system, thereby helping to ensure the quality of the resolver signal and reducing the risks caused by speed estimation fluctuations and vehicle NVH issues.
[0071] Example 3
[0072] Please refer to Figure 4 , Figure 4 This is a schematic diagram of a self-learning update device for resolver correction coefficients provided in this embodiment. Figure 4 As shown, the self-learning update device for the resolver correction coefficient includes:
[0073] The first acquisition unit 310 is used to acquire the motor speed of the vehicle when the vehicle is powered on.
[0074] The second acquisition unit 320 is used to acquire mechanical angle data within a preset time period when the motor speed is greater than a preset speed threshold.
[0075] The calculation unit 330 is used to calculate based on mechanical angle data to obtain the resolver correction coefficient, which includes the resolver zero drift correction coefficient and the resolver gain correction coefficient.
[0076] The update unit 340 is used to write the resolver correction coefficient into the vehicle's non-volatile memory when the resolver correction coefficient is within a preset effective range.
[0077] In this embodiment, the explanation of the self-learning update device for the resolver correction coefficient can be referred to the description in Embodiment 1 or Embodiment 2, and will not be repeated here.
[0078] As can be seen, the self-learning update device for the resolver correction coefficient described in this embodiment can obtain a resolver correction coefficient that is more compatible with the system, thereby helping to ensure the quality of the resolver signal and reducing the risks caused by speed estimation fluctuations and vehicle NVH issues.
[0079] Example 4
[0080] Please refer to Figure 5 , Figure 5 This is a schematic diagram of a self-learning update device for resolver correction coefficients provided in this embodiment. Figure 5 As shown, the self-learning update device for the resolver correction coefficient includes:
[0081] The first acquisition unit 310 is used to acquire the motor speed of the vehicle when the vehicle is powered on.
[0082] The second acquisition unit 320 is used to acquire mechanical angle data within a preset time period when the motor speed is greater than a preset speed threshold.
[0083] The calculation unit 330 is used to calculate based on mechanical angle data to obtain the resolver correction coefficient, which includes the resolver zero drift correction coefficient and the resolver gain correction coefficient.
[0084] The update unit 340 is used to write the resolver correction coefficient into the vehicle's non-volatile memory when the resolver correction coefficient is within a preset effective range.
[0085] As an optional implementation, the first acquisition unit 310 includes:
[0086] The detection subunit 311 is used to detect whether a self-learning request for the resolver correction coefficient initiated when the vehicle is powered on has been received.
[0087] The acquisition subunit 312 is used to acquire the motor speed of the vehicle when a self-learning request is received.
[0088] As an optional implementation, the self-learning update device for the resolver correction coefficients further includes:
[0089] The holding unit 350 is used to keep the original correction coefficients from being updated when the resolver correction coefficients are not within the preset effective range; the original correction coefficients include the original zero drift correction coefficients and the original gain correction coefficients.
[0090] As an optional implementation, the self-learning update device for the resolver correction coefficients further includes:
[0091] The correction unit 360 is used to correct the acquired resolver signal based on the resolver correction coefficient to obtain the corrected resolver signal.
[0092] In this embodiment, the explanation of the self-learning update device for the resolver correction coefficient can be referred to the description in Embodiment 1 or Embodiment 2, and will not be repeated here.
[0093] As can be seen, the self-learning update device for the resolver correction coefficient described in this embodiment can obtain a resolver correction coefficient that is more compatible with the system, thereby helping to ensure the quality of the resolver signal and reducing the risks caused by speed estimation fluctuations and vehicle NVH issues.
[0094] This application provides an electronic device, including a memory and a processor. The memory stores a computer program, and the processor runs the computer program to enable the electronic device to perform the self-learning update method for the resolver correction coefficient in Embodiment 1 or Embodiment 2 of this application.
[0095] This application provides a computer-readable storage medium storing computer program instructions. When these computer program instructions are read and executed by a processor, they perform the self-learning update method for the resolver correction coefficients in Embodiment 1 or Embodiment 2 of this application.
[0096] In the several embodiments provided in this application, it should be understood that the disclosed apparatus and methods can also be implemented in other ways. The apparatus embodiments described above are merely illustrative. For example, the flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of apparatus, methods, and computer program products according to various embodiments of this application. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions marked in the blocks may occur in a different order than those marked in the drawings. For example, two consecutive blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in a block diagram and / or flowchart, and combinations of blocks in block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or action, or using a combination of dedicated hardware and computer instructions.
[0097] In addition, the functional modules in the various embodiments of this application can be integrated together to form an independent part, or each module can exist independently, or two or more modules can be integrated to form an independent part.
[0098] If the aforementioned functions are implemented as software functional modules and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.
[0099] The above description is merely an embodiment of this application and is not intended to limit the scope of protection of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of protection of this application. It should be noted that similar reference numerals and letters in the following figures indicate similar items; therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures.
[0100] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.
[0101] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.
Claims
1. A self-learning updating method for a resolver correction coefficient, characterized in that: include: When the vehicle is powered on, get the motor speed of the vehicle: When the motor speed is greater than a preset speed threshold, obtaining mechanical angle data within a preset time period; Calculating based on the mechanical angle data to obtain a resolver correction coefficient including a resolver zero drift correction coefficient and a resolver gain correction coefficient; When the resolver correction coefficient is within a preset effective range, the resolver correction coefficient is written into a vehicle non-volatile memory.
2. The self-learning updating method of the resolver correction coefficient according to claim 1, characterized in that: When the vehicle is powered on, the motor speed of the vehicle is obtained, including: Detect whether a self-learning request for the resolver correction coefficient initiated when the vehicle is powered on is received; When the self-learning request is received, the motor speed of the vehicle is acquired.
3. The self-learning updating method of the resolver correction coefficient according to claim 1, characterized in that: The method further comprises: When the resolver correction coefficient is not within a preset valid range, the original correction coefficient is kept not updated; the original correction coefficient includes an original zero drift correction coefficient and an original gain correction coefficient.
4. The self-learning updating method of the resolver correction coefficient according to claim 1, characterized in that: The method further comprises: The collected resolver signal is corrected based on the resolver correction coefficient to obtain a corrected resolver signal.
5. A self-learning updating device for a resolver correction coefficient, characterized in that: The self-learning updating device of the resolver correction coefficient comprises: The first acquisition unit is used to acquire the motor speed of the vehicle when the vehicle is powered on: A second acquisition unit, configured to acquire mechanical angle data within a preset time period when the motor speed is greater than a preset speed threshold; A calculation unit, configured to calculate based on the mechanical angle data to obtain a resolver correction coefficient including a resolver zero drift correction coefficient and a resolver gain correction coefficient; An updating unit is used to write the resolver correction coefficient into a vehicle non-volatile memory when the resolver correction coefficient is within a preset effective range.
6. The self-learning updating device of the resolver correction coefficient according to claim 5, characterized in that: The first acquisition unit includes: A detection subunit, used to detect whether a self-learning request for a resolver correction coefficient initiated when the vehicle is powered on is received; The acquisition subunit is used to acquire the motor speed of the vehicle when receiving the self-learning request.
7. The self-learning updating device of the resolver correction coefficient according to claim 5, characterized in that: The self-learning updating device of the resolver correction coefficient also includes: A holding unit is used to keep the original correction coefficient from being updated when the resolver correction coefficient is not within a preset effective range; the original correction coefficient includes an original zero drift correction coefficient and an original gain correction coefficient.
8. The self-learning updating device of the resolver correction coefficient according to claim 5, characterized in that: The self-learning updating device of the resolver correction coefficient also includes: The correction unit is used to correct the collected resolver signal based on the resolver correction coefficient to obtain a corrected resolver signal.
9. An electronic device, characterized in that: The electronic device comprises a memory and a processor, the memory is used to store a computer program, and the processor runs the computer program to enable the electronic device to perform the self-learning update method of the resolver correction coefficient according to any one of claims 1 to 4.
10. A readable storage medium, characterized in that: The readable storage medium stores computer program instructions, and when the computer program instructions are read and executed by a processor, the self-learning update method of the resolver correction coefficient according to any one of claims 1 to 4 is executed.