Motor torque control method and device, electronic device and vehicle
By acquiring state information under motor zero-crossing conditions and performing self-learning value correction, the problem of insufficient applicability and flexibility of torque gradient setting is solved, realizing flexible adjustment of motor torque gradient, reducing NVH problems and improving vehicle power response and calibration efficiency.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2026-03-03
- Publication Date
- 2026-03-31
AI Technical Summary
The existing technology has a low range of applicability and flexibility in setting the torque gradient under the zero-crossing condition of the motor, which makes it difficult to effectively solve the problems of noise, vibration and acoustic roughness (NVH).
By acquiring vehicle status information, the self-learning value of the motor under zero-crossing conditions is determined, and the initial torque gradient is corrected based on the status information, thereby achieving flexible adjustment of the torque gradient to adapt to different transmission system states.
It improves the applicability and flexibility of torque gradient adjustment, reduces NVH issues, and enhances vehicle power response and calibration development efficiency.
Smart Images
Figure CN121756933A_ABST
Abstract
Description
Technical Field
[0001] This application relates to the field of new energy electric vehicles, and in particular to motor torque control methods, devices, electronic devices, and vehicles. Background Technology
[0002] New energy electric vehicles can effectively improve their driving range through energy recovery technology. During energy recovery, the motor acts as a generator, outputting negative torque to brake or decelerate the vehicle, while converting kinetic energy into electrical energy. In driving mode, the motor functions as a motor, outputting positive torque to propel the vehicle forward or accelerate. The zero-crossing condition of the motor refers to the process where the motor's output torque changes from positive to negative, or from negative to positive, passing through the zero-torque point. During the zero-crossing condition, due to backlash in the transmission system, noise, vibration, and harshness (NVH) problems can easily occur.
[0003] Currently, in related technologies, a pre-calibrated torque gradient is often obtained by looking up a table under the zero-crossing condition of the motor, and the motor torque is controlled to change according to this calibrated torque gradient to reduce abnormal noise. This method of reading the pre-calibrated torque gradient has limitations in the range of applicable scenarios and has low flexibility.
[0004] There is currently no effective solution to the problem of limited applicability and flexibility of torque gradient settings in related technologies. Summary of the Invention
[0005] This embodiment provides a motor torque control method, device, electronic device, and vehicle to address the problem of limited applicability and flexibility of torque gradient settings in related technologies.
[0006] Firstly, this embodiment provides a motor torque control method for a vehicle, the method comprising:
[0007] When the vehicle's motor is in a zero-crossing condition, the vehicle's status information is acquired; the status information represents the state of the vehicle's transmission system under the current zero-crossing condition.
[0008] When the status information indicates that the torque gradient of the motor needs to be adjusted, the current self-learning value of the torque gradient under the current zero-crossing condition is determined according to the status information; the self-learning value is a dynamic value that is linearly inversely proportional to the degree to which the status information deviates from the reference state range; the reference state range represents that the transmission system is in a normal state under the current zero-crossing condition.
[0009] Based on the current self-learning value, the initial torque gradient of the motor under the current zero-crossing condition is corrected to obtain the target torque gradient of the motor under the current zero-crossing condition.
[0010] In some embodiments, the method further includes:
[0011] The vehicle speed, gear, actual torque of the motor, and wheel-end torque requirements at two adjacent detection times within the current detection cycle are obtained.
[0012] Based on the vehicle speed, the gear position, the actual torque of the motor, and the wheel end torque required at two adjacent detection times, it is determined whether the motor is in a zero-crossing condition.
[0013] In some embodiments, the vehicle status information includes the speed fluctuation of the motor; after acquiring the vehicle status information, the method further includes:
[0014] Under the current zero-crossing condition, it is determined whether the speed fluctuation exceeds the preset fluctuation range. If so, the number of times the speed fluctuation exceeds the limit is incremented by one; otherwise, the number of times the limit is exceeded is reset to zero.
[0015] Determine whether the number of times the limit is exceeded is greater than a preset threshold; if so, determine that the torque gradient of the motor needs to be adjusted; the preset threshold indicates that the transmission system has a reproducible abnormal state under the zero-crossing condition of the motor.
[0016] In some embodiments, when the state information indicates that the torque gradient of the motor needs adjustment, determining the current self-learning value of the torque gradient under the current zero-crossing condition based on the state information includes:
[0017] If the speed fluctuation is greater than the upper limit of the fluctuation range, then the relative change of the speed fluctuation with respect to the upper limit is determined as a correction coefficient.
[0018] If the speed fluctuation is less than or equal to the lower limit of the fluctuation range, then the relative change of the speed fluctuation with respect to the lower limit is determined as a correction coefficient.
[0019] Based on the correction coefficient, the self-learning value determined under the historical zero-crossing conditions is corrected to obtain the current self-learning value.
[0020] In some embodiments, the self-learning value determined under historical zero-crossing conditions is corrected according to the correction coefficient to obtain the current self-learning value, including:
[0021] Based on the correction coefficient, the self-learning value determined under the historical zero-crossing conditions and corresponding to the current driving mode of the vehicle is corrected to obtain the current self-learning value corresponding to the current driving mode.
[0022] In some embodiments, the self-learning value determined under historical zero-crossing conditions is corrected according to the correction coefficient to obtain the current self-learning value, including:
[0023] If the current zero-crossing condition is a positive zero-crossing condition, then the self-learning value determined under the historical positive zero-crossing conditions is corrected according to the correction coefficient to obtain the current self-learning value of the current zero-crossing condition.
[0024] If the current zero-crossing condition is a negative zero-crossing condition, then the self-learning value determined under the historical negative zero-crossing conditions is corrected according to the correction coefficient to obtain the current self-learning value of the current zero-crossing condition.
[0025] In some embodiments, the initial torque gradient of the motor under the current zero-crossing condition is corrected based on the current self-learning value to obtain the target torque gradient of the motor under the current zero-crossing condition, including:
[0026] Read the initial torque gradient of the motor under the current zero-crossing condition, corresponding to the current driving mode of the vehicle;
[0027] Based on the current self-learning value, the initial torque gradient is corrected to obtain the target torque gradient of the motor under the current zero-crossing condition.
[0028] Secondly, this embodiment provides a motor torque control device for a vehicle, the motor torque control device comprising: an acquisition module, a self-learning module, and a torque gradient correction module; wherein:
[0029] The acquisition module is used to acquire the vehicle's status information when the vehicle's motor is in a zero-crossing condition; the status information represents the status of the vehicle's transmission system under the current zero-crossing condition.
[0030] The self-learning module is used to determine the current self-learning value of the torque gradient under the current zero-crossing condition based on the state information when the state information indicates that the torque gradient of the motor needs to be adjusted.
[0031] The torque gradient correction module is used to correct the initial torque gradient of the motor under the current zero-crossing condition based on the current self-learning value, so as to obtain the target torque gradient of the motor under the current zero-crossing condition.
[0032] Thirdly, this embodiment provides an electronic device including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the motor torque control method described in the first aspect above.
[0033] Fourthly, this embodiment provides a vehicle having a computer program stored thereon, which, when executed by a processor, implements the motor torque control method described in the first aspect above.
[0034] Compared with related technologies, this embodiment provides a motor torque control method, apparatus, electronic device, and vehicle. The motor torque control method acquires the vehicle's state information when the vehicle's motor is in a zero-crossing condition. This state information characterizes the state of the vehicle's transmission system under the current zero-crossing condition. When the state information indicates that the motor's torque gradient needs adjustment, a current self-learning value for the torque gradient under the current zero-crossing condition is determined based on the state information. This self-learning value is a dynamic value that is linearly inversely proportional to the degree to which the state information deviates from a reference state range. The reference state range characterizes the transmission system in a normal state under the current zero-crossing condition. Based on the current self-learning value, the initial torque gradient of the motor under the current zero-crossing condition is corrected to obtain the target torque gradient of the motor under the current zero-crossing condition. This method enables flexible adjustment of the motor torque gradient under the zero-crossing condition according to different transmission system states, thereby improving the applicability and flexibility of torque gradient adjustment.
[0035] Details of one or more embodiments of this application are set forth in the following drawings and description to make other features, objects and advantages of this application more readily apparent. Attached Figure Description
[0036] The accompanying drawings, which are included to provide a further understanding of this application and form part of this application, illustrate exemplary embodiments of this application and are used to explain this application, but do not constitute an undue limitation of this application. In the drawings:
[0037] Figure 1 This is a hardware structure block diagram of the terminal of the motor torque control method according to an embodiment of this application;
[0038] Figure 2 This is a flowchart of a motor torque control method according to an embodiment of this application;
[0039] Figure 3 This is a schematic diagram of torque gradient adjustment for different driving modes in some embodiments of this application;
[0040] Figure 4 This is a flowchart of a motor torque control method according to some embodiments of this application;
[0041] Figure 5 This is a schematic diagram of the vehicle structure used to implement the motor torque control method of this embodiment;
[0042] Figure 6 This is a structural block diagram of the motor torque control device according to an embodiment of this application. Detailed Implementation
[0043] To better understand the purpose, technical solution, and advantages of this application, the application is described and illustrated below in conjunction with the accompanying drawings and embodiments.
[0044] Unless otherwise defined, the technical or scientific terms used in this application shall have the general meaning understood by one of ordinary skill in the art to which this application pertains. Words such as “a,” “an,” “an,” “the,” “the,” and “these” used in this application do not indicate quantitative limitation and may be singular or plural. The terms “comprising,” “including,” “having,” and any variations thereof used in this application are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or device that comprises a series of steps or modules (units) is not limited to the listed steps or modules (units) but may include steps or modules (units) not listed, or may include other steps or modules (units) inherent to these processes, methods, products, or devices. Words such as “connected,” “linked,” and “coupled” used in this application are not limited to physical or mechanical connections but may include electrical connections, whether direct or indirect. “Multiple” used in this application refers to two or more. “And / or” describes the relationship between related objects, indicating that three relationships may exist; for example, “A and / or B” can represent: A alone, A and B simultaneously, and B alone. Normally, the character " / " indicates that the objects before and after it are in an "or" relationship. The terms "first," "second," "third," etc., used in this application are merely to distinguish similar objects and do not represent a specific order of objects.
[0045] The method embodiments provided in this example can be executed on a terminal, computer, or similar computing device. For example, it can run on a terminal. Figure 1 This is a hardware structure block diagram of the terminal of the motor torque control method in this embodiment. For example... Figure 1 As shown, a terminal may include one or more ( Figure 1 Only one is shown in the diagram. A processor 102 and a memory 104 for storing data are also included. The processor 102 may be, but is not limited to, a microprocessor (MCU) or a programmable logic device (FPGA). The terminal may also include a transmission device 106 for communication functions and an input / output device 108. Those skilled in the art will understand that… Figure 1The structure shown is for illustrative purposes only and does not limit the structure of the terminal described above. For example, the terminal may also include components that are larger than... Figure 1 The more or fewer components shown, or having the same Figure 1 The different configurations shown are illustrated.
[0046] The memory 104 can be used to store computer programs, such as application software programs and modules, like the computer program corresponding to the motor torque control method in this embodiment. The processor 102 executes various functional applications and data processing by running the computer program stored in the memory 104, thereby implementing the above-described method. The memory 104 may include high-speed random access memory and may also include non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some instances, the memory 104 may further include memory remotely located relative to the processor 102, and these remote memories can be connected to the terminal via a network. Examples of such networks include, but are not limited to, the Internet, corporate intranets, local area networks, mobile communication networks, and combinations thereof.
[0047] The transmission device 106 is used to receive or send data via a network. This network includes a wireless network provided by the terminal's communication provider. In one example, the transmission device 106 includes a Network Interface Controller (NIC), which can connect to other network devices via a base station to communicate with the Internet. In another example, the transmission device 106 can be a Radio Frequency (RF) module used for wireless communication with the Internet.
[0048] This embodiment provides a motor torque control method. Figure 2 This is a flowchart of the motor torque control method in this embodiment, as shown below. Figure 2 As shown, the process includes the following steps:
[0049] Step S210: When the vehicle's motor is in a zero-crossing condition, acquire the vehicle's status information; the status information represents the state of the vehicle's transmission system under the current zero-crossing condition.
[0050] During energy recovery, the vehicle's electric motor functions as a generator, outputting negative torque to brake or decelerate the vehicle and convert its kinetic energy into electrical energy. When the vehicle is in motion, it functions as a motor, outputting positive torque to drive the vehicle forward and accelerate. Therefore, a zero-crossing condition is the process by which the motor's output torque changes from positive to negative, or vice versa, passing through the zero-torque point. When a zero-crossing condition is detected, the state of the vehicle's transmission system under this condition can be detected. Specifically, state information reflecting the transmission system's clearance state under the current zero-crossing condition can be acquired. For example, this state information could be the motor's speed fluctuation; or it could be the sound and vibration signals generated by the transmission system under the zero-crossing condition.
[0051] Step S220: When the status information indicates that the torque gradient of the motor needs to be adjusted, determine the current self-learning value of the torque gradient under the current zero-crossing condition based on the status information; the self-learning value is a dynamic value that is linearly inversely proportional to the degree to which the status information deviates from the reference state range; the reference state range represents that the transmission system is in a normal state under the current zero-crossing condition.
[0052] The baseline state range is the range of values for the state information of the transmission system when it is in a normal state under zero-crossing conditions. After obtaining the above state information, it is necessary to determine whether the torque gradient of the motor under the current zero-crossing condition can adapt to the state of the transmission system under the current zero-crossing condition. If so, the torque gradient of the motor does not need to be adjusted; otherwise, the torque gradient of the motor needs to be adjusted. Specifically, it can be determined whether the transmission system has an abnormal state under zero-crossing conditions based on whether the state information deviates from the baseline state range. For example, excessive clearance may cause NVH problems to persist under zero-crossing conditions. In particular, it can also be determined whether there are reproducible abnormalities under zero-crossing conditions by continuously observing the state of the transmission system under multiple consecutive zero-crossing conditions. If it is determined from the state information that the transmission system still has an abnormal state under zero-crossing conditions, it indicates that the torque gradient set for the motor is not suitable for the current state of the transmission system, and therefore the torque gradient of the motor still needs to be adjusted.
[0053] Therefore, when the status information indicates that the motor's torque gradient needs adjustment, a self-learning value for adjusting the torque gradient needs to be calculated based on the status information. This self-learning value can be linearly related to the degree to which the status information deviates from the reference state range; specifically, it can be a dynamic value that is linearly inversely proportional. That is, the greater the deviation of the transmission system from its normal state, the smaller the current self-learning value.
[0054] Optionally, each time the self-learning value is initiated, the learned self-learning value can be stored in real time. The current self-learning value is stored before the vehicle goes into sleep mode after power-off, and can be retrieved after the vehicle is powered on. Furthermore, upper and lower limits can be set for the self-learning value. These limits can be determined based on the throttle response requirements for driving performance. For example, the upper limit can be set to 1.2, and the lower limit to 0.8. When the learned current self-learning value exceeds the upper limit, it is set to the upper limit; when it falls below the lower limit, it is set to the lower limit.
[0055] Step S230: Based on the current self-learning value, correct the initial torque gradient of the motor under the current zero-crossing condition to obtain the target torque gradient of the motor under the current zero-crossing condition.
[0056] Specifically, the initial torque gradient of the motor under the current zero-crossing condition can be the torque gradient corrected by the most recent self-learning value. If the motor's torque gradient has not been corrected by the self-learning value, then the initial torque gradient of the motor under the current zero-crossing condition can be a torque gradient pre-calibrated for the motor. Correcting the initial torque gradient of the motor under the current zero-crossing condition using the current self-learning value can specifically involve using the current self-learning value as a correction factor to adjust the magnitude of the initial torque gradient. For example, the target torque gradient can be obtained by directly multiplying the current self-learning value by the initial torque gradient of the motor under the current zero-crossing condition.
[0057] Understandably, the current self-learning value obtained in step S220 reflects the degree to which the current state of the transmission system deviates from the normal state. Therefore, correcting the initial torque gradient under the current zero-crossing condition based on the current self-learning value is to correct the initial torque gradient based on the degree to which the current state of the transmission system deviates from the normal state, so that the corrected torque gradient can restore the transmission system to the normal state, thereby improving the adaptability of the torque gradient and the transmission system state under the current zero-crossing condition, and further mitigating NVH problems.
[0058] In related technologies, a pre-calibrated torque gradient is often obtained by looking up a table under the zero-crossing condition of the motor. The motor torque is then controlled to change at zero according to this calibrated torque gradient to reduce abnormal noise. However, this method of reading a pre-calibrated torque gradient has limitations in the range of applicable scenarios and lacks flexibility.
[0059] To address this, this embodiment identifies the zero-crossing conditions requiring torque gradient adjustment through steps S210 to S230, and determines a self-learning value based on a self-learning strategy that dynamically corrects the torque gradient based on the state of the transmission system. Compared to related technologies where poor transmission system consistency leads to significant deviations in the transmission systems of different vehicles, preventing the pre-calibrated torque gradient from covering different vehicles, this embodiment can perform different torque gradient corrections for different transmission system clearances corresponding to different vehicles, thus ensuring that the torque gradient under zero-crossing conditions covers different vehicles.
[0060] Compared to related technologies, where the transmission system clearance increases with vehicle mileage, making it impossible for the pre-calibrated torque gradient to cover the current transmission system clearance, this embodiment can automatically identify zero-crossing conditions. Based on a self-learning strategy, it can adjust the torque gradient differently according to the different clearances of the same vehicle's transmission system, adapting to transmission systems with increased clearance due to aging. This achieves flexible adaptation of the torque gradient to changes in transmission system status.
[0061] In related technologies, when the transmission system clearance of individual vehicles is small, a torque gradient adapted to a transmission system with a larger clearance is needed to maintain consistency, thus sacrificing the vehicle's power response speed. The self-learning strategy of this embodiment has bidirectional learning capabilities. When the transmission system clearance is small and the zero-crossing state is excellent, the torque gradient can be accelerated, thereby improving the vehicle's power response performance and achieving a balance between vehicle power response and NVH issues.
[0062] Furthermore, the fixed calibration of torque gradients in related technologies requires multiple rounds to complete, resulting in a long calibration cycle. This embodiment, however, eliminates the need for multiple rounds of fixed torque gradient calibration, thus shortening the calibration development cycle and reducing calibration costs.
[0063] Therefore, through the above steps S210 to S230, when the vehicle's motor is in a zero-crossing condition, the vehicle's state information is acquired. This state information represents the state of the vehicle's transmission system under the current zero-crossing condition. When the state information indicates that the motor's torque gradient needs adjustment, the current self-learning value of the torque gradient under the current zero-crossing condition is determined based on the state information. The self-learning value is a dynamic value that is linearly inversely proportional to the degree to which the state information deviates from the reference state range. The reference state range represents the transmission system being in a normal state under the current zero-crossing condition. Based on the current self-learning value, the initial torque gradient of the motor under the current zero-crossing condition is corrected to obtain the target torque gradient of the motor under the current zero-crossing condition. This allows for flexible adjustment of the motor torque gradient under the zero-crossing condition according to different transmission system states, thereby improving the applicability and flexibility of torque gradient adjustment.
[0064] In one embodiment, the above-described motor torque control method may further include:
[0065] The system acquires the vehicle's speed, gear, actual motor torque, and wheel-end torque requirements at two adjacent detection times within the current detection cycle. Based on the vehicle speed, gear, actual motor torque, and wheel-end torque requirements at two adjacent detection times, it identifies whether the motor is in a zero-crossing condition.
[0066] The testing cycle can be a pre-set time period used to periodically identify the vehicle's zero-crossing conditions. Specifically, the vehicle speed in the current testing cycle can be determined based on signals collected by the chassis controller; the gear and wheel-end torque requirements in the current testing cycle can be determined based on signals collected by the vehicle controller; and the actual motor torque in the current testing cycle can be determined based on signals collected by the motor controller.
[0067] Wheel-end torque requirement refers to the torque expected to be transmitted to the wheels for driving or braking. The actual torque of the motor, on the other hand, is the physical torque actually output by the motor rotor to its shaft after layers of constraints, conversions, and closed-loop control. More specifically, when the vehicle is determined to be manually driven, the wheel-end torque required by the driver at different times within the detection cycle is determined based on the throttle opening signal collected by the vehicle controller, thus obtaining the wheel-end torque requirement for two adjacent detection times. When the vehicle is determined to be autonomously driven, the wheel-end torque required for autonomous driving at different times within the detection cycle, collected by the vehicle controller, can be used to obtain the wheel-end torque requirement for two adjacent detection times.
[0068] Then, by combining the vehicle speed, gear, actual motor torque, and wheel end torque demand at two adjacent test times within the current test cycle, it can be determined whether the motor is in a zero-crossing condition.
[0069] The identification of zero-crossing conditions can be divided into positive and negative zero-crossing conditions. A positive zero-crossing condition specifically refers to the zero-crossing when the vehicle's driving intention switches from energy recovery mode to drive mode; a negative zero-crossing condition refers to the zero-crossing when the vehicle's driving intention switches from drive mode to energy recovery mode. If, in two adjacent detection moments, the wheel-end torque demand at the first detection moment is greater than a calibrable positive wheel-end torque threshold (e.g., 80 Nm), and the wheel-end torque demand at the second detection moment is less than a calibrable negative wheel-end torque threshold (e.g., -80 Nm), and the actual torque of the motor is detected to be within a calibrable torque range (e.g., [-5, 5]), and the vehicle speed is greater than a calibrable speed threshold (e.g., 8 kph), and the gear is D or R, then the motor is determined to be in a positive zero-crossing condition.
[0070] Similarly, if, in two adjacent detection moments, the wheel-end torque demand at the first detection moment is less than a calibrable negative wheel-end torque threshold (e.g., -80 Nm), and the wheel-end torque demand at the second detection moment is greater than a calibrable positive wheel-end torque threshold (e.g., 80 Nm), and the actual torque of the motor is detected to be within a calibrable torque range (e.g., [-5, 5]), and the vehicle speed is detected to be greater than a calibrable speed threshold (e.g., 8 km / h kph), and the gear is D or R, then the motor is determined to be in a negative zero-crossing condition. That is, the current zero-crossing condition can be either a positive or negative zero-crossing condition.
[0071] This embodiment combines vehicle speed, gear position, wheel end torque requirement, and actual motor torque to identify the motor's zero-crossing condition, thereby enabling automatic identification of the zero-crossing condition and timely initiation of torque gradient correction.
[0072] Additionally, in one embodiment, the vehicle's state information includes the motor's rotational speed fluctuation; after acquiring the vehicle's state information, the aforementioned motor torque control method may further include:
[0073] Under the current zero-crossing condition, determine whether the speed fluctuation exceeds the preset fluctuation range. If so, increment the number of speed fluctuations exceeding the limit by one; otherwise, reset the number of exceeding the limit to zero. Determine whether the number of exceeding the limit is greater than the preset number threshold. If so, determine that the torque gradient of the motor needs to be adjusted. The preset number threshold indicates that there is a reproducible abnormal state in the transmission system under the zero-crossing condition of the motor.
[0074] In this embodiment, several conditions are set for the initiation of the self-learning value. The self-learning value is only initiated when a continuous and reproducible abnormal state exists in the transmission system under zero-crossing conditions, and then the new self-learning value is used to correct the torque gradient. The speed fluctuation represents the fluctuation of the motor speed under the current zero-crossing condition; specifically, it can be the difference between the maximum and minimum motor speed within the time period corresponding to the current zero-crossing condition. The maximum and minimum speed values can be obtained by differentiating the motor speed over time within the time period corresponding to the current zero-crossing condition.
[0075] The fluctuation range can be a pre-calibrated range characterizing the stable fluctuation of the motor speed. Different fluctuation ranges can be set for different vehicle speeds. For example, when the vehicle speed is in the range of 10 kph to 20 kph, the impact of the transmission system under zero-crossing conditions is easily perceived, so the fluctuation range can be set within 30 revolutions per minute (rpm); when the vehicle speed is in the range of 20 kph to 30 kph, the corresponding fluctuation range can be within 40 rpm; when the vehicle speed is in the range of 30 kph to 50 kph, the corresponding fluctuation range can be within 50 rpm; and when the vehicle speed is above 50 kph, the corresponding fluctuation range can be within 60 rpm. In addition, a lower limit value can be set for the fluctuation range, for example, 10 rpm.
[0076] When the motor is detected to be in a zero-crossing condition, it is determined whether the speed fluctuation exceeds the aforementioned fluctuation range under the current zero-crossing condition. If so, the number of times the speed fluctuation exceeds the limit is incremented by one; this number is a cumulative value shared across different zero-crossing conditions. If the speed fluctuation does not exceed the fluctuation range under the current zero-crossing condition, the number of times it exceeds the limit is reset to zero. Then, it is determined whether the number of times it exceeds the calibrable threshold, for example, a threshold of 10. If so, it indicates that the motor has experienced speed fluctuations exceeding the fluctuation range in several consecutive zero-crossing conditions (e.g., 10 times), further indicating a persistent and reproducible abnormal state in the traditional system under zero-crossing conditions. Therefore, the motor's torque gradient needs adjustment. To adjust the motor's torque gradient, self-learning values need to be initiated.
[0077] In this embodiment, the motor speed fluctuation is first selected to measure the state of the transmission system under zero-crossing conditions. Compared to vehicle acceleration, motor speed fluctuation can more accurately characterize the transmission system state under zero-crossing conditions. Furthermore, unlike the acquisition of sound or vibration signals, which requires additional sensors and increases hardware costs, the motor speed signal is a signal inherent to the vehicle itself. This allows for direct reuse of the vehicle's existing hardware structure without additional hardware costs; only the existing control software needs to be upgraded, resulting in lower costs. Additionally, the speed fluctuation is used to assess whether self-learning values should be performed, thereby correcting the torque gradient. This allows for adjustment of the torque gradient only when abnormalities occur in the transmission system under zero-crossing conditions, reducing unnecessary torque gradient adjustments, enhancing the adaptability of the torque gradient to the transmission system, and reducing power consumption.
[0078] In one embodiment, when the state information indicates that the motor's torque gradient needs adjustment, the current self-learning value of the torque gradient under the current zero-crossing condition is determined based on the state information. Specifically, this may include:
[0079] If the speed fluctuation is greater than the upper limit of the fluctuation range, the relative change of the speed fluctuation with respect to the upper limit is determined as the correction coefficient; if the speed fluctuation is less than or equal to the lower limit of the fluctuation range, the relative change of the speed fluctuation with respect to the lower limit is determined as the correction coefficient; based on the correction coefficient, the self-learning value determined under the historical zero-crossing conditions is corrected to obtain the current self-learning value.
[0080] In this embodiment, the degree to which the rotational speed fluctuation deviates from the normal fluctuation range is quantified, and the self-learning value is corrected based on the quantization result. Specifically, let the rotational speed fluctuation be Δn. When Δn is greater than the upper limit N of the fluctuation range... up When the correction factor f is:
[0081] f=(△nN up ) / N up ;
[0082] That is, at this time, the correction coefficient f is the relative change of the speed fluctuation with respect to the upper limit of the fluctuation range.
[0083] When Δn is less than or equal to the lower limit N of the fluctuation range down When the correction factor f is:
[0084] f=(△nN down ) / N down ;
[0085] That is, at this time, the correction coefficient f is the relative change of the speed fluctuation with respect to the lower limit of the fluctuation range.
[0086] Then, the self-learning value determined under historical zero-crossing conditions is read. This historical zero-crossing condition can be the previous zero-crossing condition before the current zero-crossing condition. More specifically, if the current zero-crossing condition is a positive zero-crossing condition, then the historical zero-crossing condition can be the previous positive zero-crossing condition before the current zero-crossing condition; more specifically, if the current zero-crossing condition is a positive zero-crossing condition, then the historical zero-crossing condition can be the previous positive zero-crossing condition before the current driving mode. The vehicle's driving mode is a set of switchable, preset vehicle performance profiles provided by the vehicle's electronic control system. Through a set of integrated software instructions, the collaborative working logic and parameters of various vehicle subsystems are dynamically adjusted, allowing the same physical hardware to adapt to different driving needs, road conditions, or personal preferences. For example, the vehicle's current driving mode can be one of several driving modes such as Eco, Comfort, and Sport. Additionally, if there is no historical zero-crossing condition for the current zero-crossing condition, the initial self-learning value before correction is set to 1.
[0087] After reading the self-learning value 'a' determined under historical zero-crossing conditions, the self-learning value 'a' determined under historical zero-crossing conditions is corrected according to the correction coefficient 'f' to obtain the current self-learning value 'a'.
[0088] a' = a × (1 - f);
[0089] Therefore, the current self-learning value is a dynamic value that is linearly inversely proportional to the degree to which the speed fluctuation deviates from the normal fluctuation range. The greater the degree to which the speed fluctuation deviates from the normal fluctuation range, the smaller the current self-learning value.
[0090] Therefore, by correcting the historically calculated self-learning value based on the speed fluctuation and using it as the latest self-learning value, a continuous gradient correction can be achieved as the state of the transmission system changes. When the zero-crossing state is excellent, the torque slope can be accelerated, resulting in better power response performance.
[0091] Specifically, in one embodiment, the self-learning value determined under historical zero-crossing conditions is corrected according to a correction coefficient to obtain the current self-learning value, which may specifically include:
[0092] Based on the correction coefficient, the self-learning value determined under historical zero-crossing conditions and corresponding to the vehicle's current driving mode is corrected to obtain the current self-learning value corresponding to the current driving mode.
[0093] Different driving modes can correspond to independent self-learning values, but the correction logic for these self-learning values is consistent across different driving modes. For example, if the current driving mode is Eco mode, the self-learning value under historical zero-crossing conditions corresponding to Eco mode is read, and the read self-learning value is corrected according to the correction coefficient to obtain the new current self-learning value corresponding to Eco mode. Therefore, this embodiment can provide targeted torque gradient adjustment for different driving modes, thereby further improving the flexibility of torque gradient adjustment.
[0094] Specifically, in one embodiment, the self-learning value determined under historical zero-crossing conditions is corrected according to a correction coefficient to obtain the current self-learning value, which may specifically include:
[0095] If the current zero-crossing condition is a positive zero-crossing condition, the self-learning value determined under the historical positive zero-crossing conditions is corrected according to the correction coefficient to obtain the current self-learning value of the current zero-crossing condition; if the current zero-crossing condition is a negative zero-crossing condition, the self-learning value determined under the historical negative zero-crossing conditions is corrected according to the correction coefficient to obtain the current self-learning value of the current zero-crossing condition.
[0096] In other words, independent self-learning values are set for both positive and negative zero-crossing conditions. Combined with the independent self-learning values for different driving modes in the above embodiments, each driving mode has independent self-learning values for both positive and negative zero-crossing conditions. The self-learning value for the positive zero-crossing condition is the positive zero-crossing self-learning value, and the self-learning value for the negative zero-crossing condition is the negative zero-crossing self-learning value, as shown in Table 1.
[0097] Table 1
[0098]
[0099] Therefore, when correcting the self-learning value under the current zero-crossing condition, it is necessary to select the self-learning value under the historical zero-crossing condition that is consistent with the current zero-crossing condition (whether it is a positive or negative zero-crossing condition) and corresponds to the current driving mode, so as to obtain the current self-learning value under the current zero-crossing condition.
[0100] It is also understandable that the self-learning value only applies to the torque gradient corresponding to the zero-crossing condition; it has no effect on non-zero-crossing conditions. Furthermore, the updated self-learning value will adjust the torque gradient at the next zero-crossing condition. This embodiment can adjust the torque gradient differently for different zero-crossing conditions.
[0101] Furthermore, in one embodiment, the initial torque gradient of the motor under the current zero-crossing condition is corrected based on the current self-learning value to obtain the target torque gradient of the motor under the current zero-crossing condition. Specifically, this may include:
[0102] Read the initial torque gradient of the motor under the current zero-crossing condition, corresponding to the current driving mode of the vehicle; correct the initial torque gradient based on the current self-learning value to obtain the target torque gradient of the motor under the current zero-crossing condition.
[0103] The corrected target torque gradient can be obtained by multiplying the current self-learned value by the initial torque gradient under the current zero-crossing condition.
[0104] Target torque gradient = current self-learned value × initial torque gradient under the current zero-crossing condition;
[0105] Understandably, the torque gradients corresponding to different driving modes can also be independent. The initial torque gradient of the current driving mode under the current zero-crossing condition can be the torque gradient of the current driving mode after the last self-learning correction, or it can be the torque gradient pre-calibrated for the current driving mode (for example, this zero-crossing condition is the first time the torque gradient of the current driving mode has been adjusted). Therefore, the torque gradient can be adjusted independently for different driving modes.
[0106] Figure 3These are schematic diagrams illustrating torque gradient adjustments for different driving modes in some embodiments, such as... Figure 3 As shown, the system first identifies zero-crossing conditions based on vehicle speed, wheel-end torque demand (driver's wheel-end torque demand or autonomous driving wheel-end torque demand), gear position, and actual motor torque. If the motor is detected to be in a zero-crossing condition, a zero-crossing condition flag is activated. Then, based on the zero-crossing condition flag, and considering the motor's speed fluctuation, a self-learning value is developed for the current driving mode. Specifically, if the current driving mode is Eco mode, zero-crossing condition self-learning is performed to obtain the current self-learned value for Eco mode. Then, the torque gradient for Eco mode is corrected based on this current self-learned value to obtain the target torque gradient. If the current driving mode is Comfort mode, zero-crossing condition self-learning is performed to obtain the current self-learned value for Comfort mode. Then, the torque gradient for Comfort mode is corrected based on this current self-learned value to obtain the target torque gradient. If the current driving mode is Sport mode, zero-crossing condition self-learning is performed for Sport mode to obtain the current self-learned value. Then, the torque gradient of Sport mode is corrected based on the current self-learned value to obtain the target torque gradient. If the current driving mode is another mode, zero-crossing condition self-learning is performed for that other mode to obtain the current self-learned value. Then, the torque gradient of that other mode is corrected based on the current self-learned value to obtain the target torque gradient. Here, "other modes" refers to driving modes other than Eco, Sport, and Comfort modes mentioned above. Finally, the obtained target torque gradient is combined with the wheel-end torque demand, and signal difference or rate of change calculations related to differential operations are performed to finally obtain the target torque under zero-crossing conditions.
[0107] Figure 4 These are flowcharts of some embodiments of the motor torque control method, such as... Figure 4 As shown, the motor torque control method includes the following steps:
[0108] Step S401: Obtain the vehicle speed, gear, actual motor torque, and wheel end torque requirements at two adjacent detection times within the current detection cycle.
[0109] Step S402: Based on the vehicle speed, gear, actual motor torque, and wheel end torque requirements at two adjacent detection times, identify whether the motor is in a zero-crossing condition.
[0110] Step S403: When the vehicle's motor is in a zero-crossing condition, obtain the speed fluctuation of the vehicle's motor.
[0111] Step S404: Under the current zero-crossing condition, determine whether the speed fluctuation exceeds the preset fluctuation range. If so, increment the number of times the speed fluctuation exceeds the limit by one; otherwise, clear the number of times the limit is exceeded to zero.
[0112] Step S405: Determine whether the number of times the limit is exceeded is greater than the preset number threshold; if so, determine that the torque gradient of the motor needs to be adjusted; the preset number threshold indicates that there is a reproducible abnormal state in the transmission system under the zero-crossing condition of the motor.
[0113] Step S406: When the status information indicates that the torque gradient of the motor needs to be adjusted, if the speed fluctuation is greater than the upper limit of the fluctuation range, the relative change of the speed fluctuation with respect to the upper limit is determined as the correction coefficient; if the speed fluctuation is less than or equal to the lower limit of the fluctuation range, the relative change of the speed fluctuation with respect to the lower limit is determined as the correction coefficient.
[0114] Step S407: Based on the correction coefficient, correct the self-learning value determined under the historical zero-crossing conditions that corresponds to the current driving mode of the vehicle to obtain the current self-learning value.
[0115] Step S408: Read the initial torque gradient of the motor under the current zero-crossing condition, corresponding to the current driving mode of the vehicle;
[0116] Step S409: Based on the current self-learning value, correct the initial torque gradient to obtain the target torque gradient of the motor under the current zero-crossing condition.
[0117] The steps S401 to S409 described above can flexibly adjust the motor torque gradient under zero-crossing conditions according to different transmission system states, thereby improving the applicability and flexibility of torque gradient adjustment.
[0118] Figure 5 This is a schematic diagram of the vehicle structure used to implement the motor torque control method of this embodiment, as shown below. Figure 5 As shown, the vehicle speed can be acquired through the chassis controller (ESC), the throttle opening can be acquired through the accelerator pedal, and the motor speed and torque can be acquired through the motor controller (IPU). In addition, the vehicle controller (VCU) can determine the vehicle's driving mode, wheel-end torque requirements, and gear. The vehicle controller can execute the above-mentioned motor torque control method and output the obtained target torque gradient to the motor controller.
[0119] This embodiment also provides a motor torque control device for implementing the above embodiments and preferred embodiments; details already described will not be repeated. The terms "module," "unit," "subunit," etc., used below refer to combinations of software and / or hardware that implement a predetermined function. Although the device described in the following embodiments is preferably implemented in software, hardware implementation, or a combination of software and hardware, is also possible and contemplated.
[0120] Figure 6 This is a structural block diagram of the motor torque control device 60 in this embodiment. The motor torque control device 60 is used in vehicles, such as... Figure 6 As shown, the motor torque control device 60 includes: an acquisition module 62, a self-learning module 64, and a torque gradient correction module 66; wherein:
[0121] The acquisition module 62 is used to acquire the vehicle's state information when the vehicle's motor is in a zero-crossing condition; the state information represents the state of the vehicle's transmission system under the current zero-crossing condition; the self-learning module 64 is used to determine the current self-learning value of the torque gradient under the current zero-crossing condition based on the state information when the state information indicates that the motor's torque gradient needs to be adjusted; the torque gradient correction module 66 is used to correct the initial torque gradient of the motor under the current zero-crossing condition based on the current self-learning value to obtain the target torque gradient of the motor under the current zero-crossing condition.
[0122] This embodiment also provides an electronic device including a memory and a processor, the memory storing a computer program and the processor being configured to run the computer program to perform the steps in any of the above method embodiments.
[0123] Optionally, the electronic device may further include a transmission device and an input / output device, wherein the transmission device is connected to the processor and the input / output device is connected to the processor.
[0124] Optionally, in this embodiment, the processor can be configured to perform the following steps via a computer program:
[0125] S1: When the vehicle's motor is in a zero-crossing condition, acquire the vehicle's status information; the status information represents the state of the vehicle's transmission system under the current zero-crossing condition.
[0126] S2, when the status information indicates that the torque gradient of the motor needs to be adjusted, determine the current self-learning value of the torque gradient under the current zero-crossing condition based on the status information; the self-learning value is a dynamic value that is linearly inversely proportional to the degree to which the status information deviates from the reference state range; the reference state range characterizes the transmission system as being in a normal state under the current zero-crossing condition.
[0127] S3, based on the current self-learning value, corrects the initial torque gradient of the motor under the current zero-crossing condition, and obtains the target torque gradient of the motor under the current zero-crossing condition.
[0128] It should be noted that the specific examples in this embodiment can refer to the examples described in the above embodiments and optional implementations, and will not be repeated in this embodiment.
[0129] In addition, this embodiment may also provide a vehicle that stores a computer program; when the computer program is executed by a processor, it implements any of the motor torque control methods in the above embodiments.
[0130] It should be understood that the specific embodiments described herein are merely illustrative of the application and not intended to limit it. All other embodiments derived by those skilled in the art based on the embodiments provided in this application without inventive effort are within the scope of protection of this application.
[0131] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties.
[0132] Obviously, the accompanying drawings are merely some examples or embodiments of this application. Those skilled in the art can apply this application to other similar situations based on these drawings without any creative effort. Furthermore, it is understood that although the work done in this development process may be complex and lengthy, for those skilled in the art, certain design, manufacturing, or production modifications made based on the technical content disclosed in this application are merely conventional technical means and should not be considered as insufficient disclosure of this application.
[0133] The term "embodiment" in this application refers to a specific feature, structure, or characteristic described in connection with an embodiment that may be included in at least one embodiment of this application. The appearance of this phrase in various places in the specification does not necessarily imply the same embodiment, nor does it imply that it is mutually exclusive with or independent of other embodiments. It will be clearly or implicitly understood by those skilled in the art that the embodiments described in this application may be combined with other embodiments without conflict.
[0134] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of patent protection. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the appended claims.
Claims
1. A method for controlling motor torque, characterized in that, For use in a vehicle, the method includes: When the vehicle's motor is in a zero-crossing condition, the vehicle's status information is acquired; the status information represents the state of the vehicle's transmission system under the current zero-crossing condition. When the status information indicates that the torque gradient of the motor needs to be adjusted, the current self-learning value of the torque gradient under the current zero-crossing condition is determined according to the status information; the self-learning value is a dynamic value that is linearly inversely proportional to the degree to which the status information deviates from the reference state range; the reference state range represents that the transmission system is in a normal state under the current zero-crossing condition. Based on the current self-learning value, the initial torque gradient of the motor under the current zero-crossing condition is corrected to obtain the target torque gradient of the motor under the current zero-crossing condition.
2. The motor torque control method according to claim 1, characterized in that, The method further includes: The vehicle speed, gear, actual torque of the motor, and wheel-end torque requirements at two adjacent detection times within the current detection cycle are obtained. Based on the vehicle speed, the gear position, the actual torque of the motor, and the wheel end torque required at two adjacent detection times, it is determined whether the motor is in a zero-crossing condition.
3. The motor torque control method according to claim 1, characterized in that, The vehicle's status information includes the motor's rotational speed fluctuation; after acquiring the vehicle's status information, the method further includes: Under the current zero-crossing condition, it is determined whether the speed fluctuation exceeds the preset fluctuation range. If so, the number of times the speed fluctuation exceeds the limit is incremented by one; otherwise, the number of times the limit is exceeded is reset to zero. Determine whether the number of times the limit is exceeded is greater than a preset threshold; if so, determine that the torque gradient of the motor needs to be adjusted; the preset threshold indicates that the transmission system has a reproducible abnormal state under the zero-crossing condition of the motor.
4. The motor torque control method according to claim 3, characterized in that, When the status information indicates that the torque gradient of the motor needs adjustment, the current self-learning value of the torque gradient under the current zero-crossing condition is determined based on the status information, including: If the speed fluctuation is greater than the upper limit of the fluctuation range, then the relative change of the speed fluctuation with respect to the upper limit is determined as a correction coefficient. If the speed fluctuation is less than or equal to the lower limit of the fluctuation range, then the relative change of the speed fluctuation with respect to the lower limit is determined as a correction coefficient. Based on the correction coefficient, the self-learning value determined under the historical zero-crossing conditions is corrected to obtain the current self-learning value.
5. The motor torque control method according to claim 4, characterized in that, Based on the aforementioned correction coefficient, the self-learning value determined under historical zero-crossing conditions is corrected to obtain the current self-learning value, including: Based on the correction coefficient, the self-learning value determined under the historical zero-crossing conditions and corresponding to the current driving mode of the vehicle is corrected to obtain the current self-learning value corresponding to the current driving mode.
6. The motor torque control method according to claim 4, characterized in that, Based on the aforementioned correction coefficient, the self-learning value determined under historical zero-crossing conditions is corrected to obtain the current self-learning value, including: If the current zero-crossing condition is a positive zero-crossing condition, then the self-learning value determined under the historical positive zero-crossing conditions is corrected according to the correction coefficient to obtain the current self-learning value of the current zero-crossing condition. If the current zero-crossing condition is a negative zero-crossing condition, then the self-learning value determined under the historical negative zero-crossing conditions is corrected according to the correction coefficient to obtain the current self-learning value of the current zero-crossing condition.
7. The motor torque control method according to claim 1, characterized in that, Based on the current self-learning value, the initial torque gradient of the motor under the current zero-crossing condition is corrected to obtain the target torque gradient of the motor under the current zero-crossing condition, including: Read the initial torque gradient of the motor under the current zero-crossing condition, corresponding to the current driving mode of the vehicle; Based on the current self-learning value, the initial torque gradient is corrected to obtain the target torque gradient of the motor under the current zero-crossing condition.
8. A motor torque control device, characterized in that, For use in vehicles, the motor torque control device includes: an acquisition module, a self-learning module, and a torque gradient correction module; wherein: The acquisition module is used to acquire the vehicle's status information when the vehicle's motor is in a zero-crossing condition; the status information represents the status of the vehicle's transmission system under the current zero-crossing condition. The self-learning module is used to determine the current self-learning value of the torque gradient under the current zero-crossing condition based on the state information when the state information indicates that the torque gradient of the motor needs to be adjusted. The torque gradient correction module is used to correct the initial torque gradient of the motor under the current zero-crossing condition based on the current self-learning value, so as to obtain the target torque gradient of the motor under the current zero-crossing condition.
9. An electronic device comprising a memory and a processor, characterized in that, The memory stores a computer program, and the processor is configured to run the computer program to perform the motor torque control method according to any one of claims 1 to 7.
10. A vehicle having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps of the motor torque control method according to any one of claims 1 to 7.