NVH (Noise Vibration and Harshness) optimization method and device of hub motor, electronic equipment and vehicle

Abstract

The invention provides an NVH optimization method and device of a hub motor, electronic equipment and a vehicle. The method comprises the following steps: in response to an obtained real-time end physical quantity and a real-time NVH characteristic parameter of the hub motor, correcting a pre-constructed mapping relationship between the end physical quantity and the NVH characteristic parameter by using the real-time end physical quantity and the real-time NVH characteristic parameter to obtain a target mapping relationship; according to the target mapping relation, target NVH characteristic parameters generated by the end effect of the hub motor are determined from the NVH characteristic parameters; and according to the target NVH characteristic parameter, determining a control parameter of the hub motor, and controlling the hub motor by using the control parameter so as to optimize the NVH of the hub motor. According to the method, the NVH characteristic parameters generated by the end effect of the hub motor can be accurately identified and extracted, and the hub motor is optimized based on the NVH characteristic parameters, so that the NVH generated by the end effect of the hub motor is reduced.

Description

Technical Field

[0001] This invention belongs to the field of new energy vehicle technology, and in particular relates to an NVH optimization method, device, electronic equipment and vehicle for a hub motor. Background Technology

[0002] In-wheel motors, as a type of direct-drive motor, are increasingly widely used in the field of new energy vehicles due to their high transmission efficiency and ease of achieving independent four-wheel drive. Furthermore, as a key component directly driving the wheels in new energy vehicles, the NVH (Noise, Vibration, and Harshness) performance of the in-wheel motor has a crucial impact on the overall NVH level of the vehicle. However, due to the end effect of in-wheel motors, problems such as electromagnetic noise and vibration can occur. Additionally, noise and vibration are also generated due to gear meshing and road surface excitation. Current technologies struggle to specifically optimize the NVH characteristics caused by the end effect of in-wheel motors, thus affecting vehicle ride comfort and driving experience. Summary of the Invention

[0003] In view of the shortcomings of the prior art, the purpose of this invention is to provide an NVH optimization method, device, electronic device and vehicle for hub motors to solve the above problems.

[0004] The present invention provides an NVH optimization method for a hub motor, comprising: responding to the acquired real-time end physical quantities and real-time NVH characteristic parameters of the hub motor, modifying the pre-constructed mapping relationship between the end physical quantities and NVH characteristic parameters using the real-time end physical quantities and real-time NVH characteristic parameters to obtain a target mapping relationship; determining the target NVH characteristic parameters generated by the end effect of the hub motor from the NVH characteristic parameters according to the target mapping relationship; determining the control parameters of the hub motor according to the target NVH characteristic parameters, and controlling the hub motor using the control parameters to optimize the NVH of the hub motor.

[0005] In one embodiment of the present invention, before correcting the pre-constructed mapping relationship between the end physical quantities and NVH characteristic parameters using real-time end physical quantities and real-time NVH characteristic parameters, the method further includes: acquiring the end magnetic field strength of the hub motor at a preset frequency, and acquiring initial NVH characteristic parameters and the motor operating parameters of the hub motor; determining the mapping relationship between the end physical quantities and NVH characteristic parameters based on the end magnetic field strength, initial NVH characteristic parameters, and motor operating parameters. In this way, the constructed mapping relationship can more accurately reflect the inherent connection between the end physical quantities and NVH characteristic parameters during the actual operation of the hub motor, providing a basis for subsequent correction of the mapping relationship using real-time end physical quantities, real-time NVH characteristic parameters, and a target correction algorithm.

[0006] In one embodiment of the present invention, the end physical quantities include the end leakage magnetic flux coefficient and the axial force fluctuation amplitude, and the NVH characteristic parameters include vibration acceleration and noise sound pressure level. The mapping relationship between the end physical quantities and the NVH characteristic parameters is determined based on the end magnetic field strength, initial NVH characteristic parameters, and motor operating parameters. This includes determining the mapping relationship between the end leakage magnetic flux coefficient, axial force fluctuation amplitude, vibration acceleration, and noise sound pressure level using an electromagnetic simulation model and a structural vibration simulation model, based on the end magnetic field strength, initial NVH characteristic parameters, and motor operating parameters. This allows for more accurate identification of the NVH characteristic parameters generated by the end effect of the hub motor, distinguishing the NVH characteristic parameters caused by the end effect of the hub motor from those caused by other factors such as gear meshing and road surface excitation.

[0007] In one embodiment of the present invention, the mapping relationship between pre-built end-body physical quantities and NVH characteristic parameters is corrected using real-time end-body physical quantities and real-time NVH characteristic parameters. This includes: determining the target vehicle model of the vehicle to which the hub motor belongs; determining a target correction algorithm corresponding to the target vehicle model based on the target vehicle model; and correcting the pre-built mapping relationship between the end-body physical quantities and NVH characteristic parameters based on the real-time end-body physical quantities, the real-time NVH characteristic parameters, and the correction algorithm corresponding to the target vehicle model. This method of using a correction algorithm corresponding to the target vehicle model allows for adaptation to the computing power requirements of different vehicle models while ensuring appropriate production costs.

[0008] In one embodiment of the present invention, the mapping relationship between pre-constructed end-body physical quantities and NVH characteristic parameters is corrected based on real-time end-body physical quantities, real-time NVH characteristic parameters, and a correction algorithm corresponding to the target vehicle model. This includes: if the target vehicle model is a first target vehicle model, the mapping relationship between the pre-constructed end-body physical quantities and NVH characteristic parameters is corrected using the real-time end-body physical quantities, real-time NVH characteristic parameters, and a first correction algorithm corresponding to the first target vehicle model at a first correction cycle; if the target vehicle model is a second target vehicle model, the mapping relationship is corrected using the real-time end-body physical quantities, real-time NVH characteristic parameters, and a second correction algorithm corresponding to the second target vehicle model at a second correction cycle, wherein the computing power of the first target vehicle model is lower than that of the second target vehicle model. This allows for differentiated correction strategies to be adopted for vehicles with different computing power levels, ensuring that the correction process can operate stably on the corresponding vehicle type and meeting the different NVH correction needs of different vehicle models.

[0009] In one embodiment of the present invention, the control parameters of the in-wheel motor are determined based on the target NVH characteristic parameters, including: acquiring the real-time vehicle speed; determining a first optimized weight for vibration acceleration and a second optimized weight for noise sound pressure level based on the real-time vehicle speed and the target NVH characteristic parameters; determining a third optimized weight for energy consumption compensation based on the real-time vehicle speed; and determining the control parameters of the in-wheel motor based on the first, second, and third optimized weights. This allows the in-wheel motor to achieve optimal NVH performance at the current vehicle speed, ensuring that the in-wheel motor maintains good quietness and stability under different driving conditions, thereby improving the overall driving comfort of the vehicle.

[0010] In one embodiment of the present invention, the hub motor includes an end compensation winding and a magnetorheological damper. The control parameters of the hub motor are determined according to a first optimization weight, a second optimization weight, and a third optimization weight. This includes: determining the first control parameters of the end compensation winding and the second control parameters of the magnetorheological damper using a target optimization algorithm based on the first, second, and third optimization weights; and controlling the hub motor using these control parameters, including: controlling the end compensation winding according to the first control parameters and controlling the magnetorheological damper according to the second control parameters. This fully utilizes the functions of the end compensation winding and the magnetorheological damper, optimizing the NVH performance of the hub motor from multiple aspects, reducing the overall NVH level of the vehicle, and providing passengers with a quieter and more comfortable driving environment.

[0011] The present invention also provides an NVH optimization device for a hub motor, comprising: a correction module configured to, in response to the acquired real-time end physical quantities and real-time NVH characteristic parameters of the hub motor, correct a pre-constructed mapping relationship between the end physical quantities and NVH characteristic parameters using the real-time end physical quantities and real-time NVH characteristic parameters to obtain a target mapping relationship; an extraction module configured to, based on the target mapping relationship, determine the target NVH characteristic parameters generated by the end effect of the hub motor from the NVH characteristic parameters; and an optimization module configured to, based on the target NVH characteristic parameters, determine the control parameters of the hub motor, and use the control parameters to control the hub motor to optimize the NVH of the hub motor.

[0012] The present invention also provides an electronic device, comprising: one or more processors; and a storage device for storing one or more programs, which, when executed by one or more processors, cause the electronic device to perform the steps of the above-described method.

[0013] The present invention also provides a vehicle comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, when the processor executes the computer program, the vehicle performs the steps of the above-described method.

[0014] The beneficial effects of this technical solution are as follows: In this technical solution, in response to the real-time end physical quantities and real-time NVH characteristic parameters of the hub motor, the mapping relationship between the pre-constructed end physical quantities and NVH characteristic parameters is corrected using the real-time end physical quantities and real-time NVH characteristic parameters to obtain the target mapping relationship. Based on the target mapping relationship, the target NVH characteristic parameters generated by the end effect of the hub motor can be determined more accurately from the NVH characteristic parameters, eliminating the interference of gear meshing and road excitation. Furthermore, based on the target NVH characteristic parameters, the control parameters of the hub motor are determined, and the hub motor is controlled using the control parameters to optimize the NVH caused by the end effect of the hub motor, thereby improving the vehicle's ride comfort and driving experience.

[0015] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and are not intended to limit the invention. Attached Figure Description

[0016] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with the invention and, together with the description, serve to explain the principles of the invention. It is obvious that the drawings described below are merely some embodiments of the invention, and those skilled in the art can obtain other drawings based on these drawings without any inventive effort. In the drawings: Figure 1 This is a schematic flowchart illustrating an exemplary embodiment of the present invention for a hub motor NVH optimization method; Figure 2 This is a flowchart illustrating another NVH optimization method for a hub motor, as shown in an exemplary embodiment of the present invention. Figure 3 This is a schematic diagram of the structure of an NVH optimization device for a hub motor, as shown in an exemplary embodiment of the present invention. Figure 4 A schematic diagram of a computer system suitable for implementing embodiments of the present invention is shown. Detailed Implementation

[0017] The embodiments of the present invention will be described below with reference to the accompanying drawings and preferred embodiments. Those skilled in the art can easily understand other advantages and effects of the present invention from the content disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments, and various details in this specification can also be modified or changed based on different viewpoints and applications without departing from the spirit of the present invention. It should be understood that the preferred embodiments are only for illustrating the present invention and not for limiting the scope of protection of the present invention.

[0018] It should be noted that the illustrations provided in the following embodiments are only schematic representations of the basic concept of the present invention. Therefore, the drawings only show the components related to the present invention and are not drawn according to the actual number, shape and size of the components. In actual implementation, the form, quantity and proportion of each component can be arbitrarily changed, and the layout of the components may also be more complex.

[0019] In the following description, numerous details are explored to provide a more thorough explanation of embodiments of the invention. However, it will be apparent to those skilled in the art that embodiments of the invention may be practiced without these specific details. In other embodiments, well-known structures and devices are shown in block diagram form rather than in detail to avoid obscuring embodiments of the invention.

[0020] It is understood that the NVH optimization method for the hub motor provided in this example is applied to the vehicle controller. The vehicles mentioned above include new energy vehicles (including passenger vehicles (e.g., cars, buses, coaches, minibuses, etc.), cargo vehicles (e.g., ordinary trucks, box trucks, trailer trucks, enclosed trucks, tank trucks, flatbed trucks, container trucks, dump trucks, special structure trucks), special vehicles (e.g., logistics delivery vehicles, automated guided vehicles (AGVs), patrol vehicles, cranes, excavators, bulldozers, loaders, road rollers, off-road engineering vehicles, armored engineering vehicles, sewage treatment vehicles, sanitation vehicles, vacuum trucks, floor scrubbers, water sprinkler trucks, sweeping robots, food delivery robots, shopping guide robots, lawnmowers, golf carts, etc.), recreational vehicles (e.g., amusement vehicles, amusement park autonomous driving devices, balance bikes, etc.), and rescue vehicles (e.g., fire trucks, ambulances, power repair vehicles, engineering emergency vehicles, etc.)).

[0021] Please see Figure 1 , Figure 1 This is a flowchart illustrating an exemplary embodiment of the present invention for an NVH optimization method of a hub motor. Figure 1 As shown, in an exemplary embodiment, the NVH optimization method for hub motors includes steps S101 to S103, and each step is described in detail below.

[0022] Step S101: In response to the acquired real-time end physical quantities and real-time NVH characteristic parameters of the hub motor, the mapping relationship between the pre-built end physical quantities and NVH characteristic parameters is corrected using the real-time end physical quantities and real-time NVH characteristic parameters to obtain the target mapping relationship. Specifically, end physical quantities are parameters that characterize the magnetic coupling characteristics and mechanical load fluctuation characteristics of the end region of the hub motor (including end gaps, end windings / cores, end seals, etc.), such as the end leakage flux coefficient and axial force fluctuation amplitude. NVH characteristic parameters characterize the noise and vibration generated by the hub motor during operation.

[0023] In some examples, to acquire real-time end physical quantities and real-time NVH characteristic parameters of the hub motor, Hall sensors can be evenly distributed at both ends of the hub motor stator to collect end physical quantities. The output of the Hall sensors is connected to the data synchronization and integration unit via an SPI interface. The data synchronization and integration unit processes and integrates the collected end physical quantities before transmitting them to the core controller. Simultaneously, accelerometers can be installed on the motor housing (Z-axis fit), the inner side of the wheel rim (radial fit), and the lower control arm of the suspension (longitudinal fit). A microphone array can be installed circumferentially around the hub motor (50cm from the housing, equidistantly distributed along the circumference at 90° intervals) to collect NVH characteristic parameters. Similarly, the data synchronization and integration unit processes and integrates the collected NVH characteristic parameters before transmitting them to the core controller.

[0024] It should be noted that the pre-built mapping relationship between end-body physical quantities and NVH characteristic parameters is a quantitative correspondence rule established through simulation experimental data, specifically how NVH characteristic parameters change synchronously when end-body physical quantities change. Furthermore, NVH characteristic parameters characterize all NVH characteristic parameters generated by the hub motor during operation, including NVH characteristic parameters generated by the hub motor end effect, NVH characteristic parameters generated by gear meshing, and NVH characteristic parameters generated by road surface excitation.

[0025] It should be noted that the purpose of correcting the mapping relationship between the pre-built end physical quantities and NVH characteristic parameters using real-time end physical quantities and real-time NVH characteristic parameters is to adjust and optimize the mapping relationship through feedback from real-time data, so as to more accurately reflect the NVH characteristics of the hub motor under different operating conditions, and ensure that more accurate NVH characteristic parameters are obtained under the current real-time end physical quantities, thereby providing an optimization basis for subsequent NVH optimization and improving the accuracy of subsequently determining the target NVH characteristic parameters generated by the end effect of the hub motor.

[0026] Step S102: Based on the target mapping relationship, determine the target NVH characteristic parameters generated by the end effect of the hub motor from the NVH characteristic parameters; Specifically, although the corrected target mapping relationship is more accurate, it represents the mapping relationship between the end physical quantity of the hub motor and all NVH characteristic parameters (NVH characteristic parameters generated by the end effect of the hub motor, NVH characteristic parameters generated by gear meshing, and NVH characteristic parameters generated by road excitation). At this time, it is necessary to determine the target NVH characteristic parameters generated by the end effect of the hub motor from the NVH characteristic parameters based on the target mapping relationship.

[0027] In some examples, the target NVH characteristic parameters (2f, 6f, and 12f characteristic frequency components) generated by the end effect of the hub motor can be separated from the NVH characteristic parameters by 5-layer wavelet packet decomposition (with db4 as the decomposition basis function). This eliminates the NVH characteristic parameters generated by gear meshing and road excitation NVH characteristic parameters, ensuring that the subsequent optimization of the hub motor's NVH is not affected by road excitation and gear meshing.

[0028] In some examples, the specific process of 5-level wavelet packet decomposition is as follows: First, the NVH characteristic parameters are preprocessed simply (including DC removal, detrending, and normalization) to avoid signal drift affecting subsequent analysis. Then, the db4 wavelet packet is used for 5-level decomposition, which can finely split the signal according to frequency, and finally obtain multiple non-overlapping frequency bands (sub-bands) and corresponding decomposition coefficients. Each sub-band corresponds to a fixed frequency range.

[0029] Then, sub-bands are selected based on frequency matching: first, the target NVH characteristic parameters (2f, 6f, 12f, i.e. NVH characteristic parameters generated by the end effect of the hub motor) and interference frequencies (road excitation <200Hz, gear meshing noise 10f-15f) corresponding to the fundamental frequency f are determined, then the sub-bands containing the target NVH characteristic parameters are found and retained, and the sub-bands containing interference frequencies are directly removed (their signal coefficients are set to zero).

[0030] Finally, the selected target subband is reconstructed to obtain a clean end-effect signal that retains only 2f, 6f, and 12f features and completely eliminates road surface and gear interference. Furthermore, the separation effect can be verified using Fast Fourier Transform to check whether only 2f, 6f, and 12f frequencies are retained and whether interference frequencies have been effectively eliminated.

[0031] Step S103: Determine the control parameters of the hub motor based on the target NVH characteristic parameters, and use the control parameters to control the hub motor in order to optimize the NVH of the hub motor.

[0032] Specifically, after determining the target NVH characteristic parameters generated by the end effect of the hub motor, the control parameters of the hub motor can be further determined based on these target NVH characteristic parameters, and the hub motor can be controlled using these control parameters to optimize the NVH caused by the end effect of the hub motor.

[0033] According to the technical solution provided in the embodiments of this application, in response to the real-time end physical quantities and real-time NVH characteristic parameters of the hub motor, the mapping relationship between the pre-constructed end physical quantities and NVH characteristic parameters is corrected using the real-time end physical quantities, real-time NVH characteristic parameters, and target correction algorithm to obtain the target mapping relationship. Based on the target mapping relationship, the target NVH characteristic parameters generated by the end effect of the hub motor can be more accurately determined from the NVH characteristic parameters, eliminating the interference of gear meshing and road excitation. Furthermore, based on the target NVH characteristic parameters, the control parameters of the hub motor are determined, and the hub motor is controlled using the control parameters to optimize the NVH caused by the end effect of the hub motor, thereby improving the vehicle's ride comfort and driving experience.

[0034] In some embodiments, before correcting the pre-built mapping relationship between end physical quantities and NVH characteristic parameters using real-time end physical quantities and real-time NVH characteristic parameters, the method further includes: The end magnetic field strength of the hub motor is collected at a preset frequency, and the initial NVH characteristic parameters and the motor operating parameters of the hub motor are also collected. Based on the end magnetic field strength, the initial NVH characteristic parameters and the motor operating parameters, the mapping relationship between the end physical quantities and the NVH characteristic parameters is determined.

[0035] Specifically, the end magnetic field strength of the hub motor can be obtained by the Hall sensor configured in the above example, and the initial NVH characteristic parameters can be obtained by the configured acceleration sensor and microphone array. This embodiment will not elaborate further here. The motor operating parameters include speed, torque, current and temperature, which can be obtained by the speed sensor, torque sensor, current sensor and temperature sensor respectively.

[0036] In some examples, the end magnetic field strength, vibration acceleration in the 10-5000Hz frequency band (measurement range ±500g), sound pressure level in the 10-5000Hz frequency band (frequency response 20-20000Hz), and motor operating parameters (speed 0-10000rpm, torque 0-500N·m, current 0-300A, temperature -40℃-150℃) can be collected at a sampling frequency of 25kHz, providing a data foundation for subsequent modeling and optimization.

[0037] Furthermore, based on the acquired end magnetic field strength, initial NVH characteristic parameters, and motor operating parameters, the mapping relationship between end physical quantities and NVH characteristic parameters is determined. The specific process will be described in detail in subsequent embodiments, and will not be elaborated here.

[0038] In this way, the constructed mapping relationship can more accurately reflect the intrinsic connection between the end physical quantities and all NVH characteristic parameters of the hub motor during actual operation. This provides a basis for subsequent correction of the mapping relationship using real-time end physical quantities, real-time NVH characteristic parameters, and target correction algorithms, thereby effectively improving the accuracy and effect of hub motor NVH optimization.

[0039] In some embodiments, the end physical quantities include the end leakage magnetic coefficient and the axial force fluctuation amplitude, and the NVH characteristic parameters include vibration acceleration and noise sound pressure level. Based on the end magnetic field strength, the initial NVH characteristic parameters, and the motor operating parameters, the mapping relationship between the end physical quantity and the NVH characteristic parameters is determined, including: based on the end magnetic field strength, the initial NVH characteristic parameters, and the motor operating parameters, constructing the mapping relationship between the end leakage magnetic coefficient, axial force fluctuation amplitude, vibration acceleration, and noise sound pressure level through an electromagnetic simulation model and a structural vibration simulation model.

[0040] Specifically, in this embodiment, the end physical quantities include the end magnetic leakage coefficient and the axial force fluctuation amplitude. The end magnetic leakage coefficient is used to characterize the degree of distortion of the magnetic field at the end of the hub motor, and the axial force fluctuation amplitude is used to characterize the change of axial force during the operation of the hub motor. NVH characteristic parameters include vibration acceleration and noise sound pressure level. Vibration acceleration is a quantitative parameter characterizing the vibration intensity of the hub motor housing surface, and noise sound pressure level is an acoustic energy index characterizing the radiated noise of the hub motor during operation.

[0041] It is understandable that the end magnetic field strength, initial NVH characteristic parameters, and motor operating parameters are used as initial parameters and input into the electromagnetic simulation model and the structural vibration simulation model. The electromagnetic simulation model is a numerical analysis tool for determining the end magnetic field distribution of the hub motor, while the structural vibration simulation model is a dynamic analysis tool for simulating the mechanical vibration transmission process. In the electromagnetic simulation model, the end leakage flux coefficient is obtained by calculating the leakage flux density between the stator end winding and the rotor end ring, and the axial force fluctuation amplitude is obtained by integrating the axial electromagnetic force component generated by the magnetic field in the end region. In the structural vibration simulation model, the axial force fluctuation amplitude is used as the excitation source input. The vibration acceleration response of the motor housing is solved using the modal superposition method, and then the vibration acceleration is converted into radiated noise sound pressure level using the acoustic boundary element method, thus obtaining the mapping relationship between the end leakage flux coefficient, axial force fluctuation amplitude, vibration acceleration, and noise sound pressure level.

[0042] It should be noted that, compared to traditional methods that only use a single NVH characteristic parameter to correlate with motor operating parameters, this approach fails to establish a complete transmission relationship between end leakage flux coefficient, axial force fluctuation amplitude, vibration acceleration, and noise sound pressure level. This results in the inability to distinguish the specific NVH characteristic parameters of the hub motor end effect from those of gear meshing and road surface excitation. In this embodiment, the mapping relationship between end leakage flux coefficient, axial force fluctuation amplitude, vibration acceleration, and noise sound pressure level provides support for the subsequent extraction of specific NVH characteristic parameters of the hub motor end effect. Through this mapping relationship, the NVH characteristic parameters generated by the hub motor end effect can be identified more accurately, distinguishing them from those caused by other factors such as gear meshing and road surface excitation.

[0043] In some embodiments, the mapping relationship between pre-built end physical quantities and NVH characteristic parameters is corrected using real-time end physical quantities and real-time NVH characteristic parameters, including: determining the target vehicle model of the vehicle to which the hub motor belongs; determining a correction algorithm corresponding to the target vehicle model based on the target vehicle model; and correcting the mapping relationship between pre-built end physical quantities and NVH characteristic parameters based on the real-time end physical quantities, the real-time NVH characteristic parameters, and the correction algorithm corresponding to the target vehicle model.

[0044] Specifically, in practical applications, different target vehicle models have different requirements for correction computing power. For example, low-end vehicles may not require much computing power to complete the correction target due to the limited computing power of their controllers; while mid-to-high-end vehicles may require more powerful computing power to ensure the accuracy and real-time performance of the correction due to their complex system structure and higher performance requirements.

[0045] Therefore, before using real-time end physical quantities, real-time NVH characteristic parameters, and correction algorithms to correct the mapping relationship between the pre-built end physical quantities and NVH characteristic parameters, the target vehicle model to which the hub motor belongs can be determined first. Then, based on the target vehicle model to which the hub motor belongs, the correction algorithm corresponding to the target vehicle model can be determined, and the target vehicle model can be corrected using the corresponding correction algorithm to adapt to the computing power requirements of different vehicle models while ensuring appropriate production costs.

[0046] In some embodiments, the mapping relationship between the pre-built end physical quantities and NVH feature parameters is corrected according to the real-time end physical quantities, the real-time NVH feature parameters, and a correction algorithm corresponding to the target vehicle model. This includes: if the target vehicle model is a first target vehicle model, the mapping relationship between the pre-built end physical quantities and NVH feature parameters is corrected using the real-time end physical quantities, the real-time NVH feature parameters, and a first correction algorithm corresponding to the first target vehicle model at a first correction period; if the target vehicle model is a second target vehicle model, the mapping relationship between the pre-built end physical quantities and NVH feature parameters is corrected using the real-time end physical quantities, the real-time NVH feature parameters, and a second correction algorithm corresponding to the first target vehicle model at a second correction period, wherein the computing power of the first target vehicle model is lower than that of the second target vehicle model.

[0047] Specifically, in this embodiment, the target vehicle models include a first target vehicle model and a second target vehicle model. The first target vehicle model can be a low-end vehicle (such as a small car), and the second target vehicle model can be a mid-to-high-end vehicle (such as a mid-to-large car). It is understood that due to production costs and product positioning, low-end vehicles (such as small cars) are equipped with lower computing power devices than mid-to-high-end vehicles (such as mid-to-large cars). Therefore, the overall computing power of low-end vehicles (such as small cars) is lower than that of high-end vehicles (such as mid-to-large cars).

[0048] It is understandable that if the target vehicle is the first target vehicle (e.g., a low-end vehicle), then the mapping relationship between the pre-built end physical quantities and NVH characteristic parameters is corrected using real-time end physical quantities, real-time NVH characteristic parameters and the first correction algorithm corresponding to the first target vehicle at the first correction cycle. The first correction algorithm can be the Gradient Boosting Decision Tree (GBDT) algorithm. This algorithm does not require high computing power from the vehicle. The first correction cycle can be 15ms. This allows for efficient and accurate correction of the mapping relationship between the pre-built end physical quantities and NVH characteristic parameters, even when the computing power of the computing device is limited, thus meeting the basic correction needs of low-end models.

[0049] Furthermore, if the target vehicle is a second target vehicle (a mid-to-high-end vehicle), the mapping relationship between the pre-built end physical quantities and NVH characteristic parameters is corrected using real-time end physical quantities, real-time NVH characteristic parameters, and a second correction algorithm corresponding to the second target vehicle at a second correction cycle.

[0050] The second target correction algorithm can be a Least Squares Support Vector Machine (LS-SVM) algorithm with a radial basis function (RBF) kernel and a penalty factor C=100. This algorithm has higher computational power than the GBDT algorithm, making it suitable for the second target vehicle type (mid-to-high-end vehicles). The second correction cycle can be 8ms. This allows for faster and more accurate correction of the pre-built mapping relationship between end-point physical quantities and NVH characteristic parameters, given the controller's strong computational power, thus meeting the high requirements of mid-to-high-end vehicles for correction effectiveness and real-time performance.

[0051] In some embodiments, determining the control parameters of the hub motor based on the target NVH characteristic parameters includes: The vehicle's real-time speed is obtained; based on the real-time speed and target NVH characteristic parameters, a first optimized weight for vibration acceleration and a second optimized weight for noise sound pressure level are determined; based on the real-time speed, a third optimized weight for energy consumption compensation is determined; and based on the first, second, and third optimized weights, the control parameters of the hub motor are determined.

[0052] Specifically, real-time vehicle speed refers to the instantaneous speed value collected by the vehicle speed sensor during vehicle operation. Since the suppression priorities for vibration acceleration, noise sound pressure level, and energy consumption compensation differ at different vehicle speeds, it is necessary to determine the first optimization weight for vibration acceleration and the second optimization weight for noise sound pressure level based on the real-time vehicle speed and the target NVH characteristic parameters, as well as the third optimization weight for energy consumption compensation based on the real-time vehicle speed.

[0053] For example, if the real-time vehicle speed is greater than or equal to a preset speed threshold (e.g., 60 km / h), then it is necessary to prioritize suppressing vibration acceleration to avoid high-speed resonance. Based on the real-time vehicle speed and target NVH characteristic parameters, the first optimization weight for vibration acceleration is determined to be 0.6, the second optimization weight for noise sound pressure level is 0.3, and the third optimization weight for energy consumption compensation is 0.1.

[0054] For example, if the real-time vehicle speed is less than a preset speed threshold (e.g., 60 km / h), noise suppression should be prioritized to improve low-speed comfort. Based on the real-time vehicle speed and target NVH characteristic parameters, the first optimization weight for vibration acceleration is determined to be 0.3, the second optimization weight for noise sound pressure level is 0.6, and the third optimization weight for energy consumption compensation is 0.1.

[0055] It should be noted that the actual value of the preset speed threshold can be set according to the actual situation, and this embodiment does not impose specific limitations on it.

[0056] Then, the control parameters of the hub motor are determined according to the first optimization weight, the second optimization weight, and the third optimization weight. By assigning different weights to vibration acceleration, noise sound pressure level, and compensation energy consumption at different vehicle speeds, and comprehensively considering the impact of vibration acceleration, noise sound pressure level, and compensation energy consumption on the operation of the hub motor, the control parameters that enable the hub motor to achieve the best NVH performance at the current vehicle speed can be determined. This ensures that the hub motor can maintain good quietness and stability under different driving conditions, thereby improving the driving comfort of the entire vehicle.

[0057] In some embodiments, the hub motor includes an end compensation winding and a magnetorheological damper. Determining the control parameters of the hub motor according to a first optimization weight, a second optimization weight, and a third optimization weight includes: determining the first control parameters of the end compensation winding and the second control parameters of the magnetorheological damper by a target optimization algorithm according to the first optimization weight, the second optimization weight, and the third optimization weight. Specifically, the end compensation winding can be implemented using an electromagnetic actuator that adjusts the current phase to change the end leakage flux coefficient. This can be achieved using a multi-phase winding structure in conjunction with a current phase controller, and is used to counteract axial force fluctuations caused by the end magnetic field. The magnetorheological damper can be implemented using a mechanical actuator that adjusts the damping stiffness by changing the viscosity of the magnetorheological fluid. This can be achieved using a magnetic field-controllable piston-type damping structure, and is used to absorb mechanical vibration energy.

[0058] In some examples, the end compensation winding is installed at the stator end (using 0.5mm enameled wire, 200 turns), and the core controller outputs a PWM signal (frequency 20kHz) to adjust the winding current; the magnetorheological damper is installed between the motor housing and the rim, and the core controller adjusts the damping coefficient through the 485 bus; the FPGA (Field-Programmable Gate Array) timing coordination unit is connected to the two core controllers through GPIO (General Purpose Input Output) interfaces.

[0059] The target optimization algorithm can use the particle swarm optimization (PSO) algorithm, which optimizes the first optimization weight of vibration acceleration, the second optimization weight of noise sound pressure level, and the third optimization weight of energy compensation to determine the first control parameters of the end compensation winding and the second control parameters of the magnetorheological damper.

[0060] Furthermore, it should be noted that if the target vehicle is a low-end model (such as a small car), the target optimization algorithm can use a genetic algorithm (GA), while the adjustment range of the magnetorheological damper damping coefficient is narrowed to 1000-3000N. To reduce costs and computational requirements, the algorithm is s / m, which is suitable for low-end models. For mid-to-high-end models (such as mid-to-large-sized cars), the target optimization algorithm can adopt a hybrid algorithm of improved particle swarm optimization (PSO) and simulated annealing to improve optimization accuracy.

[0061] Furthermore, the hub motor is controlled using control parameters, including: controlling the end compensation winding according to the first control parameter, and controlling the magnetorheological damper according to the second control parameter.

[0062] Specifically, after determining the first control parameters of the end compensation winding and the second control parameters of the magnetorheological damper, control commands can be sent to the two core controllers through the FPGA timing coordination unit, so that the core controllers can precisely control the end compensation winding and the magnetorheological damper according to the received commands.

[0063] In addition, it should be noted that the hub motor also includes a temperature compensation device. When the end temperature of the hub motor is greater than the preset temperature (e.g., 120°C), the upper limit of the current amplitude of the end compensation winding is reduced to 3A to prevent the end compensation winding from overheating and burning out.

[0064] Figure 2 This is a flowchart illustrating another NVH optimization method for a hub motor, as shown in an exemplary embodiment of the present invention. Figure 2 As shown, the method includes: First, the multi-physics sensing module 201 is equipped with a Hall sensor, an accelerometer, and a microphone array, which are used to collect real-time end physical quantities and real-time NVH characteristic parameters of the hub motor, respectively. The data synchronization and integration unit 202 integrates the collected real-time end physical quantities and real-time NVH characteristic parameters and transmits them to the corresponding core controller as the parameter basis for subsequent correction steps.

[0065] Secondly, before correction, the multi-physics sensing module 201 can also collect the end magnetic field strength of the hub motor at a preset frequency, and collect the initial NVH characteristic parameters and the motor operating parameters of the hub motor; then transmit them to the mapping module 203 and the simulation module 204. The mapping module 203 and the simulation module 204 construct the mapping relationship between the end physical quantities and the NVH characteristic parameters through the target simulation model based on the end magnetic field strength, the initial NVH characteristic parameters and the motor operating parameters.

[0066] Then, the results of the mapping module 203 are corrected by using the real-time end physical quantities and real-time NVH characteristic parameters collected by the multi-physics sensing module 201, and the target NVH characteristic parameters caused by the end effect of the hub motor are determined based on the corrected mapping results.

[0067] Finally, the target optimization module 205 determines the optimal control parameters of the hub motor based on the target NVH characteristic parameters. The execution module 206 uses the FPGA timing coordination unit 207 to control the end compensation winding 208 and the magnetorheological damper 209 based on the optimal control parameters, so as to optimize the NVH caused by the end effect of the hub motor and ultimately improve the ride comfort and driving experience of the vehicle.

[0068] It should be understood that the sequence number of each step in the above embodiments does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the process of the embodiments of this application.

[0069] All of the above-mentioned optional technical solutions can be combined in any way to form the optional embodiments of this application, and will not be described in detail here.

[0070] The following are embodiments of the apparatus described in this application, which can be used to execute the embodiments of the method described in this application. For details not disclosed in the apparatus embodiments of this application, please refer to the embodiments of the method described in this application.

[0071] Figure 3 This is a schematic diagram illustrating the structure of an NVH optimization device for a hub motor, as shown in an exemplary embodiment of the present invention. Figure 3 As shown, the exemplary in-wheel motor NVH optimization method apparatus includes: The correction module 301 is configured to, in response to the acquired real-time end physical quantities and real-time NVH characteristic parameters of the hub motor, perform correction using the pre-constructed mapping relationship between the end physical quantities and NVH characteristic parameters to obtain the target mapping relationship. The extraction module 302 is configured to determine the target NVH feature parameters generated by the end effect of the hub motor from the NVH feature parameters according to the target mapping relationship; The optimization module 303 is configured to determine the control parameters of the hub motor based on the target NVH characteristic parameters, and use the control parameters to control the hub motor in order to optimize the NVH of the hub motor.

[0072] In some embodiments, the correction module 301 is further configured to acquire the end magnetic field strength of the hub motor at a preset frequency, and acquire the initial NVH characteristic parameters and the motor operating parameters of the hub motor; and determine the mapping relationship between the end physical quantity and the NVH characteristic parameters based on the end magnetic field strength, the initial NVH characteristic parameters and the motor operating parameters.

[0073] In some embodiments, the end physical quantities include the end leakage magnetic coefficient and the axial force fluctuation amplitude, the NVH characteristic parameters include vibration acceleration and noise sound pressure level, and the correction module 301 is further configured to determine the mapping relationship between the end leakage magnetic coefficient, the axial force fluctuation amplitude, the vibration acceleration and the noise sound pressure level based on the end magnetic field strength, the initial NVH characteristic parameters and the motor operating parameters, through the electromagnetic simulation model and the structural vibration simulation model.

[0074] In some embodiments, the correction module 301 is further configured to: determine the target vehicle model of the vehicle to which the hub motor belongs; determine a correction algorithm corresponding to the target vehicle model based on the target vehicle model of the vehicle to which the hub motor belongs; and correct the pre-built mapping relationship between the end physical quantity and the NVH characteristic parameter based on the real-time end physical quantity, the real-time NVH characteristic parameter and the correction algorithm corresponding to the target vehicle model.

[0075] In some embodiments, the correction module 301 is further configured to, if the target vehicle is a first target vehicle, use the real-time end physical quantity, the real-time NVH characteristic parameters, and a first correction algorithm corresponding to the first target vehicle to correct the pre-built mapping relationship between the end physical quantity and the NVH characteristic parameters at a first correction period; if the target vehicle is a second target vehicle, use the real-time end physical quantity, the real-time NVH characteristic parameters, and a second correction algorithm corresponding to the second target vehicle to correct the pre-built mapping relationship between the end physical quantity and the NVH characteristic parameters at a second correction period, wherein the computing power of the first target vehicle is lower than that of the second target vehicle.

[0076] In some embodiments, the optimization module 303 is further configured to acquire the real-time vehicle speed; determine a first optimization weight for the vibration acceleration and a second optimization weight for the noise sound pressure level based on the real-time vehicle speed and the target NVH characteristic parameters; determine a third optimization weight for compensating for energy consumption based on the real-time vehicle speed; and determine the control parameters of the hub motor based on the first optimization weight, the second optimization weight, and the third optimization weight.

[0077] In some embodiments, the hub motor includes an end compensation winding and a magnetorheological damper. The optimization module 303 is further configured to determine a first control parameter of the end compensation winding and a second control parameter of the magnetorheological damper by a target optimization algorithm based on a first optimization weight, a second optimization weight and a third optimization weight; control the end compensation winding according to the first control parameter and control the magnetorheological damper according to the second control parameter.

[0078] According to the apparatus provided in the embodiments of this application, in response to the acquired real-time end physical quantities and real-time NVH characteristic parameters of the hub motor, the mapping relationship between the pre-constructed end physical quantities and NVH characteristic parameters is corrected using the real-time end physical quantities and real-time NVH characteristic parameters to obtain a target mapping relationship; based on the target mapping relationship; in this way, using the corrected target mapping relationship, the target NVH characteristic parameters generated by the end effect of the hub motor can be determined more accurately from the NVH characteristic parameters, eliminating the interference of gear meshing and road excitation; furthermore, based on the target NVH characteristic parameters, the control parameters of the hub motor are determined, and the hub motor is controlled using the control parameters to optimize the NVH caused by the end effect of the hub motor, thereby improving the vehicle's ride comfort and driving experience.

[0079] Embodiments of the present invention also provide an electronic device, including: one or more processors; and a storage device for storing one or more programs, which, when executed by one or more processors, cause the electronic device to implement the methods provided in the above embodiments.

[0080] Figure 4 A schematic diagram of a computer system suitable for implementing embodiments of the present invention is shown. It should be noted that... Figure 4 The computer system 400 of the electronic device shown is merely an example and should not impose any limitation on the functionality and scope of use of the embodiments of the present invention.

[0081] like Figure 4 As shown, the computer system 400 includes a Central Processing Unit (CPU) 401, which can perform various appropriate actions and processes, such as executing the methods described in the above embodiments, based on programs stored in Read-Only Memory (ROM) 402 or programs loaded from storage portion 408 into Random Access Memory (RAM) 403. The RAM 403 also stores various programs and data required for system operation. The CPU 401, ROM 402, and RAM 403 are interconnected via a bus 404. An Input / Output (I / O) interface 405 is also connected to the bus 405.

[0082] The following components are connected to I / O interface 405: an input section 406 including a keyboard, mouse, etc.; an output section 407 including a cathode ray tube (CRT), liquid crystal display (LCD), etc., and speakers, etc.; a storage section 408 including a hard disk, etc.; and a communication section 409 including a network interface card such as a LAN (Local Area Network) card, modem, etc. The communication section 409 performs communication processing via a network such as the Internet. Drive 410 is also connected to I / O interface 404 as needed. Removable media 411, such as a disk, optical disk, magneto-optical disk, semiconductor memory, etc., are installed on drive 410 as needed so that computer programs read from them can be installed into storage section 408 as needed.

[0083] In particular, according to embodiments of the present invention, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of the present invention include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing a computer program for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via communication section 409, and / or installed from removable medium 411. When the computer program is executed by central processing unit (CPU) 401, it performs various functions defined in the system of the present invention.

[0084] It should be noted that the computer-readable medium shown in the embodiments of the present invention can be a computer-readable signal medium or a computer-readable storage medium, or any combination thereof. A computer-readable storage medium can be, for example, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of a computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM), flash memory, optical fiber, portable compact disc read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In the present invention, a computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, carrying a computer-readable computer program. Such propagated data signals may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. Computer-readable signal media can also be any computer-readable medium other than computer-readable storage media, which can send, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device. The computer program contained on the computer-readable medium can be transmitted using any suitable medium, including but not limited to wireless, wired, etc., or any suitable combination thereof.

[0085] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of the present invention. Each block in a flowchart or block diagram may represent a module, segment, or portion of code, which contains one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated 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 or flowchart, and combinations of blocks in a block diagram or flowchart, may be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0086] The units described in the embodiments of the present invention can be implemented in software or hardware, and the described units can also be located in a processor. The names of these units do not necessarily limit the specific unit itself.

[0087] Another aspect of the present invention provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a computer's processor, causes the computer to perform the method as described above. This computer-readable storage medium may be included in the electronic device described in the above embodiments, or it may exist independently and not assembled into the electronic device.

[0088] Another aspect of the present invention provides a computer program product or computer program including computer instructions stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium and executes the computer instructions, causing the computer device to perform the methods described in the various embodiments above.

[0089] The above embodiments are merely illustrative of the principles and effects of the present invention and are not intended to limit the present invention. Any person skilled in the art can modify or alter the above embodiments without departing from the spirit and scope of the present invention. Therefore, all equivalent modifications or alterations made by those skilled in the art without departing from the spirit and technical concept disclosed in the present invention should still be covered by the steps of the present invention.

Claims

1. A method for optimizing the NVH (Noise, Vibration, and Harshness) of a hub motor, characterized in that, include: In response to the acquired real-time end physical quantities and real-time NVH characteristic parameters of the hub motor, the pre-built mapping relationship between the end physical quantities and NVH characteristic parameters is corrected using the real-time end physical quantities and real-time NVH characteristic parameters to obtain the target mapping relationship; Based on the target mapping relationship, the target NVH characteristic parameters generated by the end effect of the hub motor are determined from the NVH characteristic parameters; Based on the target NVH characteristic parameters, the control parameters of the hub motor are determined, and the hub motor is controlled using the control parameters to optimize the NVH of the hub motor.

2. The method according to claim 1, characterized in that, Before correcting the pre-built mapping relationship between the end physical quantities and NVH characteristic parameters using the real-time end physical quantities and the real-time NVH characteristic parameters, the method further includes: The end magnetic field strength of the hub motor is collected at a preset frequency, and the initial NVH characteristic parameters and the motor operating parameters of the hub motor are also collected. The mapping relationship between the end physical quantity and the NVH characteristic parameter is determined based on the end magnetic field strength, the initial NVH characteristic parameter, and the motor operating parameter.

3. The method according to claim 2, characterized in that, The physical quantities at the end include the end leakage magnetic flux coefficient and the axial force fluctuation amplitude; the NVH characteristic parameters include vibration acceleration and noise sound pressure level. The step of determining the mapping relationship between the end physical quantity and the NVH characteristic parameters based on the end magnetic field strength, the initial NVH characteristic parameters, and the motor operating parameters includes: Based on the end magnetic field strength, the initial NVH characteristic parameters, and the motor operating parameters, the mapping relationship between the end leakage magnetic coefficient, axial force fluctuation amplitude, vibration acceleration, and noise sound pressure level is determined through electromagnetic simulation model and structural vibration simulation model.

4. The method according to claim 1, characterized in that, The step of correcting the mapping relationship between the pre-built end-physical quantities and NVH characteristic parameters using the real-time end-physical quantities and the real-time NVH characteristic parameters includes: Determine the target vehicle model to which the hub motor belongs; Based on the target vehicle model of the vehicle to which the hub motor belongs, determine the correction algorithm corresponding to the target vehicle model; The mapping relationship between the pre-built end physical quantities and NVH characteristic parameters is corrected based on the real-time end physical quantities, the real-time NVH characteristic parameters, and the correction algorithm corresponding to the target vehicle model.

5. The method according to claim 4, characterized in that, The step of correcting the pre-built mapping relationship between the end-body physical quantities and NVH characteristic parameters based on the real-time end-body physical quantities, the real-time NVH characteristic parameters, and the correction algorithm corresponding to the target vehicle model includes: If the target vehicle is the first target vehicle, then the mapping relationship between the pre-built end physical quantity and the NVH characteristic parameter is corrected using the real-time end physical quantity, the real-time NVH characteristic parameter and the first correction algorithm corresponding to the first target vehicle at the first correction cycle; If the target vehicle is the second target vehicle, the mapping relationship between the pre-built end physical quantity and the NVH characteristic parameter is corrected using the real-time end physical quantity, the real-time NVH characteristic parameter and the second correction algorithm corresponding to the second target vehicle at the second correction cycle, wherein the computing power of the first target vehicle is lower than that of the second target vehicle.

6. The method according to claim 3, characterized in that, The step of determining the control parameters of the hub motor based on the target NVH characteristic parameters includes: Obtain the vehicle's real-time speed; Based on the real-time vehicle speed and the target NVH characteristic parameters, determine the first optimized weight of the vibration acceleration and the second optimized weight of the noise sound pressure level; Based on the real-time vehicle speed, a third optimization weight for compensating energy consumption is determined; The control parameters of the hub motor are determined based on the first optimization weight, the second optimization weight, and the third optimization weight.

7. The method according to claim 6, characterized in that, The hub motor includes an end compensation winding and a magnetorheological damper. Determining the control parameters of the hub motor based on the first optimization weight, the second optimization weight, and the third optimization weight includes: Based on the first optimization weight, the second optimization weight, and the third optimization weight, the first control parameters of the end compensation winding and the second control parameters of the magnetorheological damper are determined by the target optimization algorithm. The control of the hub motor using the control parameters includes: The end compensation winding is controlled according to the first control parameter, and the magnetorheological damper is controlled according to the second control parameter.

8. An NVH optimization device for a hub motor, characterized in that, include: The correction module is configured to, in response to the acquired real-time end physical quantities and real-time NVH characteristic parameters of the hub motor, correct the pre-built mapping relationship between the end physical quantities and NVH characteristic parameters using the real-time end physical quantities and the real-time NVH characteristic parameters to obtain the target mapping relationship. The extraction module is configured to determine the target NVH feature parameters generated by the hub motor end effect from the NVH feature parameters according to the target mapping relationship; The optimization module is configured to determine the control parameters of the hub motor based on the target NVH characteristic parameters, and to control the hub motor using the control parameters in order to optimize the NVH of the hub motor.

9. An electronic device, characterized in that, include: One or more processors and a memory, wherein a computer program is stored in the memory, and when the one or more processors execute the computer program, the device performs the steps of the method as described in any one of claims 1 to 7.

10. A vehicle comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the method as described in any one of claims 1 to 7.

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