System and method for vibration detection and control of vehicle

Abstract

A system (102) and a method 200 for detecting and controlling vibrations of vehicle component based on Fast Fourier Transform (FFT). Comprising receiving 202 input data pertaining to one or more opera

Description

TECHNICAL FIELD

[0001] The present disclosure relates to a system and a method for vibration control of a vehicle such as an automobile. BACKGROUND

[0002] During scenarios like off-road and snowy road conditions, Electronic Stability Program (ESP) focuses on stabilizing a vehicle with intervention which leads to torque oscillations at the axles of the wheel of the vehicle. The torque oscillations result in Noise, Vibration, and Harshness (NVH), which are experienced by the driver, and in some extreme cases, the torque oscillations may also lead to wear, tear, and damage to components including, but not limited to, axle shafts, differential parts, and the like.

[0003] In existing techniques, when the vehicle encounters a snowy or an off-road terrain, extra torque may be delivered to the wheels of the vehicle to move forward. When the ESP detects wheel slip continuously, in some cases, the torque value can be high. During such scenarios, the reaction from ESP would be too harsh and too late causing NVH, and wear and tear of components. This impacts customer satisfaction and product performance significantly and may impact autonomous driving acceptance.

[0004] Further, other existing systems adapt complex mechanisms which require additional electronic components to control occurrence of vibration during torque oscillations of the vehicle. The complexity leads to higher costs for manufacturing, maintenance, and repair.

[0005] Patent document US2022237259A1 discloses a method of operating a microcontroller to perform Fast Fourier Transform (FFT). The method includes receiving, by the microcontroller, N samples from a signal, and performing, by the microcontroller, a first butterfly operation of the FFT before all the N samples have been received from the signal, based on performing the first butterfly operation. The microcontroller performs the FFT at an improved trade-off between power, speed, and resolution.

[0006] Patent document US2022334024A1 discloses a vibration detection device that includes an A / D conversion unit for receiving a sine wave signal of an acoustic emission (AE) wave corresponding to vibration generated in a target machine from an AE sensor that detects the AE wave, and converting the received sine wave signal into digital data. The device further includes an extraction unit for extracting, from the digital data, a data point of a local maximum value for each cycle of the sine wave signal, and an output processing unit for outputting the data point and cycle data including data points with the number of points which can be recognized as a sine wave and including the data point of a local maximum value so that an output unit visibly outputs the data point and the cycle data.

[0007] Various systems and methods are provided in the art for detecting the torque fluctuations in real-time causing NVH, and in some scenarios, leading to component damage; however, there is scope to provide an improved and more efficient solution to overcome the above-mentioned issues. OBJECTS OF THE INVENTION

[0008] An object of the present disclosure is to overcome the above-mentioned issues, and provide a system and a method for detecting and controlling vibrations of a vehicle based on the occurrence of vibrations in advance.

[0009] An object of the present disclosure is to enhance wheel speed stability by proactively detecting slip conditions. [00101 Another object of the present disclosure is to eliminate the requirement of special hardware like sensors, or Digital Signal Processors (DSPs) to compute complex operations for predicting the occurrence of vibrations in advance.

[0011] Another object of the present disclosure is to eliminate delayed reaction from the system, by predicting the scenario in advance to conduct necessary actions for adapting torque in a vehicle component.

[0012] Another object of the present disclosure for vibration detection and control is to implement Fast Fourier Transformation (FFT) as a standalone function which can be scaled to different frequency detections.

[0013] Yet another object of the present disclosure is to provide a simple, efficient, and cost-effective technique for controlling noise, vibration, and harshness of the vehicle. SUMMARY

[0014] Aspects of the present disclosure relates to techniques for controlling vibrations of vehicle by detecting the occurrence of vibrations in advance.

[0015] According to an aspect, the present disclosure relates to a method for detecting and controlling vibrations of a vehicle component. The method includes the step of receiving, by a system, input data pertaining to one or more operational parameters of the vehicle component. The one or more operational parameters include at least one of: a speed of a vehicle, a radius of a wheel of the vehicle, an accelerator pedal position, a brake pedal, a historical torque demand, and a rotational speed of the vehicle. The method includes the step of predicting, by the system, a torque signal of the vehicle component based on the received input data. The method includes the step of capturing, by the system, torque signal samples of the predicted torque signal at pre-defined time intervals. The method includes the step of estimating, by the system, a frequency of the torque signal using a Fast Fourier Transform (FFT) technique. The method includes the step of comparing, by the system, the estimated frequency with an expected frequency of the vehicle component for detecting and controlling the vibrations of the vehicle component. Therefore, the method helps in maintaining vehicle stability and control by reducing the risk of accidents caused by failure of vehicle component or instability. Early detection of abnormal vibrations can prevent catastrophic failures that might compromise safety.

[0016] In an aspect, estimating the frequency may include determining a fundamental frequency of the vehicle component, fetching one or more gain coefficient values from a gain lookup table for the determined fundamental frequency, and computing power spectral density of the torque signal by employing the FFT technique to the captured torque signal samples using the one or more gain coefficient values, wherein the power spectral density may provide power values of whole frequency spectrum. Therefore, the method enables adapting to dynamic changes corresponding to the vehicle components, and aids in maintaining consistent performance

[0017] hi an aspect, the method may further include identifying the frequency component which has a power value in the power spectral density, and estimating the frequency of the torque signal based on the identified frequency component.

[0018] In an aspect, determining the fundamental frequency may include determining the expected frequency based on a reference frequency model and an actual frequency model, wherein the reference frequency model may include instrumented vehicle data including problematic frequencies for different vehicle components, and the actual frequency model may include actual frequency values of the different vehicle components, and determining the fundamental frequency by taking the expected frequency to be controlled as a reference.

[0019] hi an aspect, the expected frequency may be a weighted average of a reference frequency value identified from the reference frequency model and an actual frequency value identified from the actual frequency model.

[0020] In an aspect, weights assigned to the reference frequency value and the actual frequency value may be substantially same.

[0021] hi an aspect, the method may further include gradually increasing the weights to the actual frequency value from the reference frequency value as the number of learnings for the estimated frequency increases with time.

[0022] In an aspect, the method may further include computing a delta value between the estimated frequency and the expected frequency, and enabling a control command comprising one of: an action command or an ignore command to control the vibrations of the vehicle component based on the computed delta value.

[0023] In an aspect, the FFT technique may correspond to N point FFT, and wherein N may be based on a fundamental frequency of the vehicle component and may be in power of 2 which is 2, 4, 8, 16.

[0024] In an aspect, the N-point FFT may be 16-point FFT for detecting and controlling the vibrations of the vehicle component, where the vehicle component may be a side shaft.

[0025] According to another aspect, the present disclosure relates to a system for detecting and controlling vibrations of a vehicle component. The system includes one or more processors and a memory. The memory is coupled to the one or more processors, where said memory stores instructions which when executed by the one or more processors cause the system to receive input data pertaining to one or more operational parameters of the vehicle component. The system is configured to predict a torque signal of the vehicle component based on the received input data. The system is configured to capture torque signal samples of the predicted torque signal at pre-defmed time intervals. The system is configured to estimate a frequency of the torque signal using Fast Fourier Transform (FFT) technique. The system is configured to compare the estimated frequency with an expected frequency of the vehicle component to detect and control the vibrations of the vehicle component. Therefore, the system helps in maintaining vehicle stability and control by reducing the risk of accidents caused by failure of vehicle component or instability. Early detection of abnormal vibrations can prevent catastrophic failures that might compromise safety.

[0026] Various objects, features, aspects and advantages of the inventive subject matter will become more apparent from the following detailed description of preferred embodiments, along with the accompanying drawing figures in which like numerals represent like components. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] The accompanying drawings are included to provide a further understanding of the present disclosure and are incorporated in and constitute a part of this specification. The drawings illustrate exemplary embodiments of the present disclosure and, together with the description, serve to explain the principles of the present disclosure.

[0028] FIG. 1 illustrates an exemplary block diagram of a system for detecting and controlling vibrations of a vehicle component, in accordance with an embodiment of the present disclosure.

[0029] FIG. 2 illustrates an exemplary flow chart of a method for detecting and controlling vibrations of the vehicle component, in accordance with an embodiment of the present disclosure.

[0030] FIGs. 3A-3C illustrates a detailed flow chart of the method steps shown in FIG. 2, in accordance with an embodiment of the present disclosure.

[0031] FIGs. 4A-4E illustrate exemplary graphs depicting predicted torque signal and stack positions, in accordance with embodiments of the present disclosure.

[0032] FIG. 4F illustrates an exemplary graphical representation depicting power of frequency component, in accordance with an embodiment of the present disclosure.

[0033] FIGs. 5A-5E illustrate exemplary graphs for explaining the method of vibration control of the vehicle component, in accordance with embodiments of the present disclosure.

[0034] FIGs. 6A-6E illustrate graphs for explaining the method of vibration control of the vehicle component, in accordance with the prior art(s).

[0035] FIG. 7 illustrates an exemplary computer system in which or with which embodiments of the present disclosure may be implemented. DETAILED DESCRIPTION

[0036] The following is a detailed description of embodiments of the disclosure depicted in the accompanying drawings. The embodiments are in such details as to clearly communicate the disclosure. However, the amount of detail offered is not intended to limit the anticipated variations of embodiments; on the contrary, the intention is to cover all modifications, equivalents, and alternatives falling within the spirit and scope of the present disclosure as defined by the appended claims.

[0037] Embodiments explained herein relate to techniques for detecting and controlling vibrations of vehicle component(s) by detecting the occurrence of vibrations in advance based on Fast Fourier Transform (FFT), to avoid damage of vehicle components.

[0038] The embodiments of the present disclosure are applicable to different components of the vehicle. For example, the embodiments of the present disclosure are related to axles of wheels of vehicle or a differential shaft of the vehicle, but not limited to the like.

[0039] FIG. 1 illustrates an exemplary block diagram 100 of a system 102 for detecting and controlling vibrations of a vehicle component, in accordance with an embodiment of the present disclosure.

[0040] The system 102, as shown in FIG. 1. relates to detecting and controlling vibrations of the vehicle component based on FFT technique. The system 102 may include a torque determination module 104, a stack control module 106, an FFT module 108, a frequency detection module 110, a vibration control module 112, and a power computation module 114.

[0041] The torque determination module 104 is configured to receive input data pertaining to one or more operational parameters of the vehicle component. The one or more operational parameters may include, but not limited to, a speed of the vehicle, a wheel slip, a radius of the wheel, a rotational speed of the vehicle, an accelerator pedal position, a brake pedal, and the like. The vehicle component may include, but not limited to, a side shaft, a steering rack, a control arm, an accelerator pedal position, a brake pedal, a wheel, and the like. Upon receiving the input data, the torque determination module 104 predicts a torque signal of the vehicle component based on the received input data, and captures torque signal samples of the predicted torque signal at pre-defined time intervals, for example, at 5 milliseconds (ms) intervals. For example, torque values at the vehicle components, i.e., at the axles of the wheels, may be predicted using any known techniques. In one example, torque values may be predicted by employing machine learning techniques such as Long Short Term Memory (LSTM) models or any other model. The machine learning technique may use different data for training the model. The data may include, but not limited to, operational data related to accelerator pedal, brake pedal, historical torque demand, vehicle speed, and the like.

[0042] The stack control module 106 is configured to manage the captured torque signal samples. The stack control module 106 may include a control unit (not shown in the Figures) which controls the torque signal samples in the stack. For example, until a first set of torque signal samples are collected in the stack, a waiting state is enabled by the stack control module 106. Once the first set of torque signal samples are collected for every new incoming torque signal sample, the oldest torque signal sample present in the stack is removed and the new sample may be added from the bottom of the stack.

[0043] The FFT module 108 is configured to estimate the frequency of the vehicle component by receiving a fundamental frequency of the vehicle component from the frequency detection module 110, and one or more gain coefficient values from a gain lookup table for the corresponding fundamental frequency. Further, the power computation module 114 is configured to compute power spectral density of the torque signal by employing the FFT technique to the captured torque signal samples using the one or more gain coefficient values, where the power spectral density provides power values of whole frequency spectrum. According to an embodiment of the present disclosure, an N-point FFT technique may be employed for power spectral density computation, where N may be based on the fundamental frequency of the vehicle component. In one example, an 8-point point FFT technique may be employed for power spectral density computation of the torque signal. In another example, a 16-point point FFT technique may be employed for power spectral density computation. Further, the FFT module 108 in conjunction with the power computation module 114 is configured to identify the frequency component which has a power value in the power spectral density and estimates the frequency of the torque signal based on the identified frequency component.

[0044] The frequency detection module 110 is configured to determine the expected frequency based on a reference frequency model and an actual frequency model. The reference frequency model comprises instrumented vehicle data. The test vehicles may be instrumented and sufficient number of data readings may be measured and stored to obtain the reference frequency model which will indicate what the problematic frequency is for a particular vehicle component. The actual frequency model comprises actual frequency values of the different vehicle components. In particular, the actual frequency model comes from the released vehicle which is running on road. As the vehicle runs, the frequency detection module 110 may detect the frequency which has the highest power value and store the same. Now as the vehicle runs, the frequency detection module 110 may store these frequency values for the vehicle components.

[0045] In an embodiment, the expected frequency is a weighted average of a reference frequency value identified from the reference frequency model and an actual frequency value identified from the actual frequency model.

[0046] Further, the frequency detection module 110 is configured to determine the fundamental frequency by taking the expected frequency to be controlled as a reference. For example, from the reference frequency model and the actual frequency model, if the expected frequency of 7 Hz is obtained, then the fundamental frequency may be 8 Hz, which is the closest power of 2 from 7 Hz, as the fundamental frequency can always be in power of 2. Similarly, if the expected frequency is coming to be 14 Hz or 15 Hz, then the frequency detection module 110 selects the fundamental frequency as 16 Hz.

[0047] In an embodiment, the frequency detection module 110 assigns same or similar weights to the reference frequency value and the actual frequency value to calculate the expected frequency. In another embodiment, the frequency detection module 110 is configured to gradually increase the weights assigned to the actual frequency value compared to the weights assigned to the reference frequency value as the number of learnings for the estimated frequency increases with time.

[0048] Upon determining the expected frequency, the vibration control module 112 is configured to compute a delta value between the estimated frequency and the expected frequency, and enable a control command comprising one of: an action command or an ignore command to control the vibrations of the vehicle component based on the computed delta value. Thus, the proposed system 102 predicts the occurrence of vibrations in advance to avoid N VH related concerns and component damages, without employing any special hardware like sensors, Digital Signal Processors (DSPs) to do complex operations.

[0049] Although FIG. 1 shows example components of the proposed disclosure, in other embodiments, fewer components, different components, differently arranged components, or additional functional components may be implemented than those depicted in FIG. 1.

[0050] FIG. 2 illustrates an exemplary flow chart of a method 200 for detecting and controlling vibrations of the vehicle component, in accordance with an embodiment of the present disclosure.

[0051] As illustrated in the FIG. 2, at block 202, the method 200 includes receiving, by a system (e.g., 102), input data pertaining to one or more operational parameters of the vehicle component. In an embodiment, the one or more operational parameters may include, but not limited to, a speed of the vehicle, a wheel slip, a radius of the wheel, a rotational speed of the vehicle, an accelerator pedal position, a brake pedal, and the like. The vehicle component may include, but not limited to, a side shaft, a steering rack, a control arm, an accelerator pedal position, a brake pedal, a wheel, and the like.

[0052] At block 204, the method 200 includes predicting, by the system 102, torque values or torque signal at the vehicle component based on the received input data. Upon predicting torque signals at the vehicle component, the method 200 includes capturing, by the system 102, torque signal samples of the predicted torque signal at pre-defined time intervals, as indicated at block 206.

[0053] At block 208, the method 200 includes estimating, by the system 102, a frequency of the torque signal using FFT technique. According to an embodiment of the present disclosure, an N-point FFT technique may be employed for estimating the frequency of the vehicle component. In one example, an 8-point point FFT technique may be employed for estimating the frequency of the vehicle component. In another example, a 16-point FFT technique may be employed for estimating the frequency of the vehicle component

[0054] At block 210, the method 200 includes comparing, by the system 102, the frequency component with an expected frequency of the vehicle component for detecting and controlling the vibrations of the vehicle component. It may be appreciated that method blocks 202 to 210 will be explained in detail with respect to FIG. 3.

[0055] FIGs. 3A-3C illustrate a detailed flow diagram of a method 300 (also shown in FIG. 2), in accordance with an embodiment of the present disclosure. As indicated in block 202 of FIG. 2, the method includes receiving input data pertaining to one or more operational parameters of the vehicle component. As indicated previously, the one or more operational parameters may include, but not limited to, a speed of the vehicle, a radius of the wheel, a rotational speed of the vehicle, an accelerator pedal position, a brake pedal, and the like.

[0056] Referring to FIG. 3A, as indicated at block 302, the method 300 includes calculating a wheel slip factor. The wheel slip factor may be calculated using equation (1), as shown below: vr—rPI2 . ... s =-------------equation (1) vx where vx is the vehicle speed, re is the effective wheel radius, and 12 is the longitudinal component of the rotational speed of the wheel. The vehicle speed may be obtained from a wheel speed sensor. Upon calculating the wheel slip factor, the method 300 includes detecting and controlling the occurrence of vibrations at the vehicle component by employing FFT technique, when the wheel slip factor is a non-zero value. Upon identifying that calculated slip factor is a non-zero value, the method 300 includes predicting a torque signal at the vehicle component based on the received input data, as indicated at block 304. For example, torque values at the vehicle components, i.e., at the axles of the wheels, may be predicted using any known techniques. In one example, torque values may be predicted by employing machine learning techniques such as LSTM models or any other model. Machine learning techniques may be used for different data for training the model. The data may include, but not limited to, operational data related to accelerator pedal, brake pedal, historical torque demand, vehicle speed, and the like.

[0057] As indicated in at block 204, the method 200 includes predicting torque values or torque signals at the vehicle component based on the received input data. An exemplary predicted torque signal is depicted in graph 400 of FIG. 4A, which has frequency components of 8 Hz, 16 Hz and combination of both 8 Hz and 16 Hz.

[0058] In an embodiment, the predicted wheel torque results in calculating a corresponding predicted torque at the vehicle component, for example, at least one axle, using equation (2), as shown below: „ , (Torque at wheel + Differential loss) _ . Torque at axle =------------------------------ --------Equation (2) ~ Differential Ratio “

[0059] Upon predicting or determining torque values or the torque signals at the vehicle component, as indicated at block 304, the method 300 includes capturing torque values samples, i.e. torque signal samples of the predicted torque signal at pre-defined time intervals, for example, at every 5 ms, as indicated at block 306. Further, at block 308, the method 300 includes storing the resulting torque signal samples in a stack (e.g., by the stack control module 108). At block 310, the method 300 includes performing a stack check, to detect whether the stack is full or empty. At block 314, when the stack status level indicates the full state, an old torque signal is discarded from a top position of the stack. At step 312, when the stack status level indicates the empty state, the stack is filled with corresponding size with zero value(s). Further, the method 300 includes stacking the torque signals, until a first set of the one or more torque signals is collected in the stack. At block 316 of FIG. 3B, the method 300 includes shifting the whole stack up by one place and filling the stack with new torque samples from the bottom. FIG. 4B to FIG. 4E show exemplary predicted torque signals at different indexes of the stack. FIG. 4B depicts a graph 402 of an exemplary predicted torque signal at 0th index of the stack. FIG. 4C depicts a graph 404 of an exemplary predicted torque signal at 5th index of the stack. FIG. 4D depicts a graph 406 of an exemplary predicted torque signal at 10th index of the stack. FIG. 4E depicts a graph 408 of an exemplary predicted torque signal at 15th index of the stack.

[0060] Referring to FIG. 3B, at block 318, the method 300 includes checking if all the torque samples are valid or not. The method 300 checks whether the stack is filled with set of torque samples. For example, the method 300 checks whether the stack has 8 torque samples when the technique employed is a 8-point FFT technique. Similarly, the method 300 checks whether the stack has 16 torque samples when the technique employed is a 16-point FFT.

[0061] Upon having the valid torque samples in the stack, the method 300 includes detecting and controlling vibrations of the vehicle component, for example, a side shaft. For this, the method 300 computes power spectral density of the torque signal by employing the FFT technique to the captured torque signal samples using the one or more gain coefficient values from a gain look-up table based on a fundamental frequency, as indicated at block 320. The gain lookup table includes predefined set of gain coefficient values for different fundamental frequencies.

[0062] According to an embodiment of the present disclosure, an N-point FFT technique may be employed for power spectral density computation of torque signal, where N may be based on the fundamental frequency of the vehicle component. In one example, an 8-point FFT technique may be employed for power spectral density computation of the torque signal. In another example, a 16-point FFT technique may be employed for power spectral density computation of the torque signal. For example, to identify the vibration of the side shaft which is generally around 8 to 10 Hz, i.e. to detect the frequency component, the method 300 considers 16-point FFT technique, so that the fundamental frequency becomes at 200 / 16, which is equal to 12.5 Hz.

[0063] In an aspect, the fundamental frequency is determined based on an expected frequency. So, in order to compute the power spectral density, the method 300 determines the expected frequency. At block 322, the method 300 includes determining the expected frequency of the vehicle component based on a reference frequency model and an actual frequency model.

[0064] The reference frequency model comprises instrumented vehicle data. The test vehicles may be instrumented and sufficient number of data readings may be measured and stored to obtain the reference frequency model which indicates the problematic frequency for a particular vehicle component.

[0065] The actual frequency model comprises actual frequency values of the different vehicle components. In particular, the actual frequency model comes from the released vehicle which is running on road. As the vehicle runs, it will store these frequency values for the vehicle components.

[0066] In an embodiment, the expected frequency is a weighted average of a reference frequency value identified from the reference frequency model and an actual frequency value identified from the actual frequency model.

[0067] Further, at block 324, the method 300 includes determining the fundamental frequency by taking the expected frequency to be controlled as a reference. For example, from the reference frequency model and the actual frequency model, the expected frequency is obtained as 7 Hz, then the fundamental frequency will be 8 Hz, which is the closest power of 2 from 7 Hz, as the fundamental frequency can always be in power of 2. Similarly, if the expected frequency is coming to be 14 Hz or 15 Hz, then the frequency detection module (e.g., 110 of FIG. 1) selects the fundamental frequency as 16 Hz.

[0068] In an embodiment, the method 300 includes assigning same or similar weights to the reference frequency value and the actual frequency value to calculate the expected frequency. In another embodiment, the method 300 includes gradually increasing the weights assigned to the actual frequency value compared to the weights assigned to the reference frequency value as the number of learnings for the estimated frequency increases with time.

[0069] Based on the determined fundamental frequency, the method 300 computes the power spectral density by applying the FFT technique by fetching one or more gain coefficient values from the gain look table for the determined fundamental frequency and the predefined sampling rate. The FFT output may include a real part and an imaginary part calculated by using a simulation approach, for example, a Model Based Development (MBD) toolkit. For instance, the power spectral density computation may be performed by a fixed-point arithmetic in embedded systems. Further, a realizable solution may be provided for performing efficient and precise computations without the complexity and overhead of floatingpoint arithmetic or complex numbers.

[0070] Next, at block 326, the method 300 includes identifying the frequency component which has a power value in the power spectral density, and estimating the frequency of the torque signal based on the identified frequency component. FIG. 4F illustrates an exemplary graph 410 depicting power of frequency component, in accordance with embodiments of the present disclosure. The graph 410 represents power of frequency component along y-axis and time along x-axis. The graph 410 shows two signals. The first signal (continuous line) gives the power when the simulated input torque signal has a frequency of 8 Hz. The second torque signal (dashed line) gives the power when the simulated input torque signal has a frequency of 16 Hz. After about 0.6 seconds, it can be seen that both the signals have a power value since both the frequency components are present in the simulated input torque signal.

[0071] In an embodiment, the fundamental frequency and the estimated frequency can be different from the expected frequency. The fundamental frequency will always be in powers of 2. For example, based on the reference frequency model and the actual frequency model, the fundamental frequency may be determined by taking the target frequency to control as a reference. The reference model, for example, 7 Hz frequency needs to be controlled. By comparing the data with the actual frequency model and considering the detection of the target frequency, the fundamental frequency may be calculated. If the target frequency to detect is 7 Hz, by considering the appropriate component from the actual frequency model, the decided fundamental frequency may be 8 Hz. In such a case, the FFT computation may detect the 7 Hz frequency from the power of the first harmonic. Similarly, 14 / 15Hz target frequency component can be detected from the power of the second harmonic.

[0072] At block 328, the method 300 includes comparing the estimated frequency with the expected frequency to compute a delta value. Based on the delta value between the estimated frequency and the expected frequency, the method 300 includes triggering a command to control the vibrations in advance. For example, at block 330, the method 300 includes comparing the delta value with a threshold value. If the delta value is greater than the threshold value, at block 332, no action is taken. If the delta value is less than or equal to the threshold value, at block 334, the torque fluctuations of the vehicle component are stabilized to avoid NVH related concerns and component damages.

[0073] According to the present disclosure, the FFT is implemented as a standalone function which can be scaled to different frequency detection. The present system 102 does not need any special hardware like sensors, DSPs, and the like for performing complex operations, and since it is being done in MBD fixed point arithmetic, speed of the execution is high. Further, unlike the conventional techniques, delayed reaction from ESP can be avoided by predicting the scenario early and taking the necessary action(s).

[0074] FIGs. 5A-5E illustrate exemplary graphs for explaining the method of vibration control of the vehicle component, in accordance with an embodiment of the present disclosure. The vehicle speed is depicted in graph 502 of FIG. 5A. The wheel speeds in rotations per minute (RPM) at the individual wheels (Rear Right, Rear Left) are depicted in graph 504 of FIG. 5B. The engine speed is depicted in graph 506 of FIG. 5C. From FIG. 5C, it is understood that the oscillations are not seen in the engine speed graph 506. FIG. 5D depicts a graph 508 of variation of the slip. It may be seen from graphs 502 and 506, that when slip is detected, there are no oscillations or minimal oscillations in the wheel speeds and the engine speed. FIG. 5E depicts a graph 510 of an exemplary torque signal at the vehicle component. Hence, it is understood that with the proposed system 102, the torque fluctuations of the vehicle component are stabilized to avoid NVH related concerns and component damages.

[0075] FIGs. 6A-6E illustrate graphs for explaining the method of vibration control of the vehicle component, in accordance with the prior art(s). The vehicle speed is depicted in graph 602 of FIG. 6A. The wheel speeds in RPM at the individual wheels (Rear Right, Rear Left) are depicted in graph 604 of FIG. 6B. The engine speed is depicted in graph 606 of FIG. 6C. From FIG. 6B and FIG. 6C, it is understood that the oscillations are seen in the engine speed graph 606. FIG. 6D depicts a graph 608 of variation of the slip. It may be seen from graphs 602 and 606, that when slip is detected, there are oscillations in the wheel speeds and the engine speed. According to the prior art, the ESP takes control to mitigate the oscillations. FIG. 6E depicts a graph 610 of an exemplary torque signal at the vehicle component. As can be observed from FIG. 6B to FIG. 6E, when ESP takes control, the intervention is too late and clear oscillations are observed. [00761 Thus, in accordance with the present disclosure, the torque fluctuations of the vehicle component are stabilized to avoid NVH related concerns and component damages.

[0077] With the proposed disclosure, the control technique starts to intervene beforehand, and there is no occurrence of oscillations at the wheel speeds, thereby smoothening the engine speed. The improved oscillations in wheel speed can provide stability to the vehicle component which is easier to control, enhancing overall driving safety. This is especially important during acceleration, braking, and cornering.

[0078] FIG. 7 illustrates an exemplary computer system 700 to implement the proposed system, in accordance with embodiments of the present disclosure.

[0079] As illustrated in FIG. 7, the computer system 700 may include an external storage device 710, a bus 720, a main memory 730, a read only memory 740, a mass storage device 750, communication port 760, and a processor 770. A person skilled in the art will appreciate that the computer system 700 may include more than one processor 770 and communication ports 760. Processor 770 may include various modules associated with embodiments of the present disclosure. Communication port 760 may be any of an RS-232 port for use with a modem based dialup connection, a 10 / 100 Ethernet port, a Gigabit or 10 Gigabit port using copper or fiber, a serial port, a parallel port, or other existing or future ports. Communication port 760 may be chosen depending on a network, such a Local Area Network (LAN), Wide Area Network (WAN), or any network to which computer system 700 connects.

[0080] Memory 730 may be Random Access Memory (RAM), or any other dynamic storage device commonly known in the art. Read-only memory 740 may be any static storage device(s) e.g., but not limited to, a Programmable Read Only Memory (PROM) chips for storing static information e.g., start-up or BIOS instructions for processor 770. Mass storage 750 may be any current or future mass storage solution, which can be used to store information and / or instructions. Exemplary mass storage solutions include, but are not limited to, Parallel Advanced Technology Attachment (PATA) or Serial Advanced Technology Attachment (SATA) hard disk drives or solid-state drives (internal or external, e.g., having Universal Serial Bus (USB) and / or Firewire interfaces), one or more optical discs, Redundant Array of Independent Disks (RAID) storage, e.g. an array of disks (e.g., SATA arrays).

[0081] Bus 720 communicatively couples processor(s) 770 with the other memory, storage and communication blocks. Bus 720 may be, e.g. a Peripheral Component Interconnect (PCI) / PCI Extended (PCI-X) bus, Small Computer System Interface (SCSI), USB, or the like, for connecting expansion cards, drives and other subsystems as well as other buses, such a front side bus (FSB), which connects processor 770 to software system.

[0082] Optionally, operator and administrative interfaces, e.g. a display, keyboard, and a cursor control device, may also be coupled to bus 720 to support direct operator interaction with the computer system 700. Other operator and administrative interfaces can be provided through network connections connected through communication port 760. The external storage device 710 may be any kind of external hard-drives, floppy drives, Compact Disc - Read Only Memory (CD-ROM), Compact Disc-Re-Writable (CD-RW), Digital Video Disk-Read Only Memory (DVD-ROM). Components described above are meant only to exemplify various possibilities. In no way should the aforementioned exemplary computer system 700 limit the scope of the present disclosure.

[0083] While the foregoing describes various embodiments of the proposed disclosure, other and further embodiments of the proposed disclosure may be devised without departing from the basic scope thereof. The scope of the proposed disclosure is determined by the claims that follow. The proposed disclosure is not limited to the described embodiments, versions or examples, which are included to enable a person having ordinary skill in the art to make and use the invention when combined with information and knowledge available to the person having ordinary skill in the art. ADVANTAGES OF THE INVENTION [00841 The present disclosure facilitates in detecting and controlling vibrations of vehicle component(s) based on Fast Fourier Transform (FFT) technique.

[0085] The present disclosure enhances wheel speed stability, by proactively detecting slip conditions.

[0086] The present disclosure eliminates the requirement of special hardware like sensors, or Digital Signal Processors (DSPs) to compute complex operations for predicting the occurrence of vibrations in advance.

[0087] The present disclosure eliminates delayed reaction from the vehicle’s system, by predicting the scenario to conduct necessary actions for adapting torque in a vehicle component.

[0088] The present disclosure implements FFT as a standalone function which can be scaled to different frequency detections.

[0089] The present disclosure provides a simple, efficient, and cost-effective technique for controlling noise, vibration, and harshness of the vehicle component(s).

Claims

1. A method (200) for detecting and controlling vibrations of a vehicle component, the method (200) comprising:receiving (202) input data pertaining to one or more operational parameters of the vehicle component, the one or more operational parameters comprise at least one of: a speed of a vehicle, a radius of a wheel of the vehicle, an accelerator pedal position, a brake pedal, a historical torque demand, and a rotational speed of the vehicle;predicting (204) a torque signal of the vehicle component based on the received input data;capturing (206) torque signal samples of the predicted torque signal at pre-defined time intervals;estimating (208) a frequency of the torque signal using Fast Fourier Transform (FFT) technique; andcomparing (210) the estimated frequency with an expected frequency of the vehicle component to detect and control the vibrations of the vehicle component.

2. The method (200) as claimed in claim 1, wherein estimating (208) the frequency comprises:determining a fundamental frequency of the vehicle component;fetching one or more gain coefficient values from a gain lookup table for the determined fundamental frequency; andcomputing power spectral density of the torque signal by employing the FFT technique to the captured torque signal samples using the one or more gain coefficient values, wherein the power spectral density provides power values of whole frequency spectrum.

3. The method (200) as claimed in claim 2, wherein the method (200) further comprises:identifying the frequency component which has a power value in the power spectral density; andestimating the frequency of the torque signal based on the identified frequency component.

4. The method (200) as claimed in claim 2, wherein determining the fundamental frequency comprises:determining the expected frequency based on a reference frequency model and an actual frequency model, wherein the reference frequency model comprises instrumented vehicle data comprising problematic frequencies for different vehicle components, and the actual frequency model comprises actual frequency values of the different vehicle components; anddetermining the fundamental frequency by taking the expected frequency to be controlled as a reference.

5. The method (200) as claimed in claim 4, wherein the expected frequency is a weighted average of a reference frequency value identified from the reference frequency model and an actual frequency value identified from the actual frequency model.

6. The method (200) as claimed in claim 5, wherein weights assigned to the reference frequency value and the actual frequency value are substantially same.

7. The method (200) as claimed in claim 6, wherein the method (200) further comprises gradually increasing the weights to the actual frequency value from the reference frequency value as the number of learnings for the estimated frequency increases with time.

8. The method (200) as claimed in claim 1, wherein the method (200) further comprises:computing a delta value between the estimated frequency and the expected frequency; andenabling a control command comprising one of: an action command or an ignore command to control the vibrations of the vehicle component based on the computed delta value.

9. The method (200) as claimed in claim 1, wherein the FFT technique corresponds to N point FFT, and wherein N is based on a fundamental frequency of the vehicle component and is in power of 2 which is 2, 4, 8, 16.

10. The method (200) as claimed in claim 9, wherein the N-point FFT is 16-point FFT, and wherein the vehicle component is a side shaft.

11. A system (102) for detecting and controlling vibrations of a vehicle component, the system (102) comprising:one or more processors (770); anda memory (730) coupled to the one or more processors (770), wherein said memory (730) stores instructions which, when executed by the one or more processors (770), cause the system (102) to:receive input data pertaining to one or more operational parameters of the vehicle component, the one or more operational parameters comprise at least one of: a speed of a vehicle, a radius of a wheel of the vehicle, an accelerator pedal position, a brake pedal, a historical torque demand, and a rotational speed of the vehicle;predict a torque signal of the vehicle component based on the received input data;capture torque signal samples of the predicted torque signal at pre-defined time intervals;estimate a frequency of the torque signal using Fast Fourier Transform (FFT) technique; andcompare the estimated frequency with an expected frequency of the vehicle component to detect and control the vibrations of the vehicle component.

12. The system (102) as claimed in claim 11, wherein the system (102) comprises a FFT module (108), which is configured to:determine a fundamental frequency of the vehicle component;fetch one or more gain coefficient values from a gain lookup table for the determined fundamental frequency; andcompute power spectral density of the torque signal by employing the FFT technique to the captured torque signal samples using the one or more gain coefficient values, wherein the power spectral density provides power values of whole frequency spectrum.

13. The system (102) as claimed in claim 12, wherein the FFT module (108) is further configured to:identify the frequency component which has a power value in the power spectral density; andestimate the frequency of the torque signal based on the identified frequency component.

14. The system (102) as claimed in claim 12, wherein the system (102) comprises a frequency detection module (110) configured to:determine the expected frequency based on a reference frequency model and an actual frequency model, wherein the reference frequency model comprises instrumented vehicle data comprising problematic frequencies for different vehicle components, and the actual frequency model comprises actual frequency values of the different vehicle components; anddetermine the fundamental frequency by taking the expected frequency to be controlled as a reference.

15. The system (102) as claimed in claim 14, wherein the expected frequency is a weighted average of a reference frequency value identified from the reference frequency model and an actual frequency value identified from the actual frequency model.

16. The system (102) as claimed in claim 15, wherein weights assigned to the reference frequency value and the actual frequency value are substantially same.

17. The system (102) as claimed in claim 16, wherein the frequency detection module (110) is further configured to gradually increasing the weights to the actual frequency value from the reference frequency value as the number of learnings for the estimated frequency increases with time.

18. The system (102) as claimed in claim 11, wherein the system (102) comprises a vibration control module (112) configured to:compute a delta value between the estimated frequency and the expected frequency; and5 enable a control command comprising one of: an action commandor an ignore command to control the vibrations of the vehicle component based on the computed delta value.

19. The system (102) as claimed in claim 18, wherein the FFT technique corresponds to N point FFT, and wherein N is based on the fundamental 10 frequency of the vehicle component and is in power of 2 which is 2, 4, 8,16.

20. The system (102) as claimed in claim 19, wherein the N-point FFT is 16-point FFT and the vehicle component is a side shaft.

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