Method and system for selecting sensor locations on a vehicle for active road noise control

Abstract

The present disclosure provides a method for determining an arrangement of reference sensors for active road noise control (ARNC) in a vehicle with an automatic calibration system. The method includes mounting a plurality of vibrational sensors on a plurality of structure elements of the vehicle to generate a plurality of vibrational input signals and mounting at least one microphone inside a cabin of the vehicle to capture at least one acoustic input signal. The method further includes determining an arrangement of reference sensors from the plurality of vibrational sensors by determining a subset of vibrational sensors which sense the main mechanical inputs of road noise contributing to the at least one acoustic input signal.

Claims

1. A method for determining an arrangement of at least one reference sensor for active road noise control (ARNC) in a vehicle with an automatic calibration system, the method comprising: mounting a plurality of vibrational sensors of the automatic calibration system on a plurality of structure elements of the vehicle, the structure elements representing strongest contributions to a transfer of road noise into a cabin of the vehicle, and the vibrational sensors being configured to generate a plurality of vibrational input signals based on vibrations of the respective structure elements and to input the plurality of vibrational input signals to a processing unit of the automatic calibration system; mounting at least one microphone of the automatic calibration system inside the cabin of the vehicle, the at least one microphone being configured to capture at least one acoustic input signal and to input the captured at least one acoustic input signal to the processing unit; and determining the arrangement of the at least one reference sensor from the plurality of vibrational sensors with the processing unit by determining a subset of vibrational sensors which sense main mechanical inputs of road noise contributing to the at least one acoustic input signal; and wherein determining the arrangement of the at least one reference sensor includes: forming a plurality of proper subsets of vibrational input signals from the plurality of vibrational input signals; calculating a multiple-coherence function for each of the proper subsets of the vibrational input signals and for each of the at least one acoustic input signal using the processing unit to determine a coherence between the respective acoustic input signal and the vibrational input signals of the respective subset; and for each of the at least one acoustic input signal, automatically selecting with the processing unit, the proper subset based on the calculated multiple-coherence function as the arrangement of the at least one reference sensor for the ARNC of the at least one acoustic input signal.

2. The method of claim 1, wherein the plurality of vibrational sensors is accelerometers configured to generate the plurality of vibrational input signals.

3. The method of claim 1 further comprising: determining a road noise spectrum from the at least one acoustic input signal with the processing unit; determining at least one resonance frequency from the road noise spectrum with the processing unit; and automatically selecting, with the processing unit, a first subset for which the multiple-coherence function evaluated at a first determined resonance frequency is maximum as the arrangement of the at least one reference sensor.

4. The method of claim 3 further comprising: automatically selecting, with the processing unit, a second subset for which the multiple-coherence function evaluated at a second determined resonance frequency is maximum; and combining the first and second subsets to determine the arrangement of the at least one reference sensor.

5. The method of claim 1, wherein calculating the multiple-coherence function comprises: processing a time series of the plurality of vibrational input signals with the processing unit to compute an auto- and cross-power spectra matrix of the respective vibrational input signals for each of the subsets; performing singular value decomposition of the computed auto- and cross-power spectra matrices by the processing unit to determine diagonal power spectrum matrices with respect to virtual vibration signals; and calculating the multiple-coherence functions for the subsets based on cross-power spectra between the virtual vibration signals and the at least one acoustic input signal.

6. The method of claim 5 further comprising: for at least one of the subsets, determining with the processing unit, a pair of vibrational input signals having a largest cross-power spectrum of the computed auto- and cross-power spectra matrix; automatically eliminating one vibrational input signal of the pair of vibrational input signals and a corresponding vibrational sensor from the subset; and calculating the multiple-coherence function for a reduced subset.

7. The method of claim 6, wherein the one vibrational input signal is only eliminated if a corresponding cross-power spectrum is larger or equal than a predetermined threshold.

8. The method of claim 1, wherein the plurality of vibrational sensors comprises at least a first group of vibrational sensors and a second group of vibrational sensors, the first group of vibrational sensors being mounted on structure elements associated with a front axle of the vehicle, and the second group of vibrational sensors being mounted on structure elements associated with a rear axle of the vehicle; and wherein the subsets of the plurality of vibrational input signals are formed so as to avoid combining the plurality of vibrational input signals from different groups.

9. An automatic calibration system for determining an arrangement of at least one reference sensor for active road noise control (ARNC) in a vehicle, the system comprising: a processing unit; a plurality of vibrational sensors mountable on a plurality of structure elements of the vehicle and configured to generate a plurality of vibrational input signals based on vibrations of the plurality of structure elements and to input the plurality of vibrational input signals to the processing unit; wherein the plurality of structure elements represent strongest contributions to a transfer of road noise into a cabin of the vehicle; and at least one microphone mountable inside the cabin of the vehicle and configured to capture at least one acoustic input signal and to input the captured at least one acoustic input signal to the processing unit; wherein the processing unit is configured to determine the arrangement of the at least one reference sensor from the plurality of vibrational sensors by determining a subset of vibrational sensors which sense main mechanical inputs of road noise contributing to the at least one acoustic input signal; and wherein the processing unit comprises: a multiple-coherence calculation unit configured to calculate a multiple-coherence function for each of a plurality of proper subsets of vibrational input signals formed from the plurality of vibrational input signals and for each of the at least one acoustic input signal to determine a coherence between the respective acoustic input signal and the vibrational input signals of the respective subset.

10. The system of claim 9, wherein the processing unit further comprises: a selection unit configured to automatically select, for each of the at least one acoustic input signal, a proper subset based on the calculated multiple-coherence function as the arrangement of the at least reference sensor for the ARNC of the at least one acoustic input signal.

11. The system of claim 10, wherein the plurality of vibrational sensors is accelerometers configured to generate the plurality of vibrational input signals.

12. The system of claim 10, wherein the multiple-coherence calculation unit comprises: a Fourier transform unit configured to process a time series of the plurality of vibrational input signals to compute an auto- and cross-power spectra matrix of the respective vibrational input signals for each of the subsets; and an eigenvalue calculation unit to perform singular value decomposition of the computed auto- and cross-power spectra matrices to determine diagonal power spectrum matrices with respect to virtual vibration signals; wherein the multiple-coherence calculation unit is configured to calculate the multiple-coherence functions for the subsets based on cross-power spectra between the virtual vibration signals and the at least one acoustic input signal.

13. The system of claim 12, wherein the multiple-coherence calculation unit comprises a subset size reduction unit configured to determine a pair of vibrational input signals having a largest cross-power spectrum of the computed auto- and cross-power spectra matrix for at least one of the subsets; and to eliminate one vibrational input signal of the pair of vibrational input signals and a corresponding vibrational sensor from the subset; and wherein the multiple-coherence calculation unit is further configured to calculate the multiple-coherence function for a reduced subset.

14. An automatic calibration system for determining an arrangement of at least reference sensor for active road noise control (ARNC) in a vehicle, the system comprising: a processing unit configured to receive a plurality of vibrational input signals; a plurality of vibrational sensors mountable on a plurality of structure elements of the vehicle and configured to generate the plurality of vibrational input signals based on vibrations of the plurality of structure elements; wherein the plurality of structure elements is indicative of contributions to a transfer of road noise into a cabin of the vehicle; and at least one microphone positioned within the cabin of the vehicle and configured to capture at least one acoustic input signal and to provide the captured at least one acoustic input signal to the processing unit; wherein the processing unit is configured to determine the arrangement of at least one reference sensor from the plurality of vibrational sensors by determining a subset of vibrational sensors which sense main mechanical inputs of road noise contributing to the at least one acoustic input signal; and wherein the processing unit comprises: a multiple-coherence calculation unit configured to calculate a multiple-coherence function for each of a plurality of proper subsets of vibrational input signals formed from the plurality of vibrational input signals and for each of the at least one acoustic input signal to determine a coherence between the respective acoustic input signal and the vibrational input signals of the respective subset.

15. The system of claim 14, wherein the processing unit further comprises: a selection unit configured to automatically select, for each of the at least one acoustic input signal, a proper subset based on the calculated multiple-coherence function as the arrangement of the at least reference sensor for the ARNC of the at least one acoustic input signal.

16. The system of claim 15, wherein the plurality of vibrational sensors is accelerometers configured to generate the plurality of vibrational input signals.

17. The system of claim 15, wherein the multiple-coherence calculation unit comprises: a Fourier transform unit configured to process a time series of the plurality of vibrational input signals to compute an auto- and cross-power spectra matrix of the respective vibrational input signals for each of the subsets; and an eigenvalue calculation unit to perform singular value decomposition of the computed auto- and cross-power spectra matrices to determine diagonal power spectrum matrices with respect to virtual vibration signals; wherein the multiple-coherence calculation unit is configured to calculate the multiple-coherence functions for the subsets based on cross-power spectra between the virtual vibration signals and the at least one acoustic input signal.

Description

BRIEF DESCRIPTION OF THE DRAWINGS

(1) Further features and exemplary embodiments as well as advantages of the present disclosure will be explained in detail with respect to the drawings. It shall be understood that the present disclosure should not be construed as being limited by the description of the following embodiments. It shall furthermore be understood that some or all of the features described in the following may also be combined in alternative ways.

(2) FIG. 1 shows a schematic diagram of the transfer paths of tire/road noise into a vehicle cabin.

(3) FIG. 2 shows a schematic side view of a vehicle.

(4) FIG. 3 shows a plan view from below of a front axle and suspension of the vehicle according to FIG. 2.

(5) FIG. 4 is a corresponding illustration of the front wheel suspension system and illustrates placement of the vibrational sensors according to an embodiment of the disclosure.

(6) FIG. 5 shows a schematic representation of a vehicle test stand with the automatic calibration system according to the present disclosure connected to the test vehicle.

(7) FIG. 6 shows a schematic representation of a vehicle with an active noise control system according to the present disclosure installed therein.

DETAILED DESCRIPTION

(8) FIG. 1 shows the transfer paths of tire/road noise into a vehicle cabin schematically. One contribution comes directly from tire radiation noise and is called air borne noise or directly transmitted noise. Air borne noise is influenced by two factors: the level of radiation noise generated during tire/road interaction and the acoustic performance of the vehicle body sealing. The other contribution is from so-called structure borne noise where vibration transfers through the chassis to the body and radiates noise into the vehicle cabin. Structure borne noise is influenced by the transfer function of tire/road force, tire/wheel exciting force attenuation and the transfer characteristics of the suspension. The last depends on dynamic stiffness of the chassis and the sensitivity of the body. Determination of the exact transfer paths for structure borne road noise has proven quite a challenging task, with results which strongly vary depending on the vehicle structure. As a result, active road noise control remained incomplete in terms of effective cancellation of all road noise resonances in the vehicle cabin.

(9) The present disclosure deals with the cancellation of structure borne noise and a method and system for the optimal arrangement of a plurality of vibrational sensors for a feedforward active road noise control inside a vehicle cabin.

(10) FIG. 2 shows a schematic side view of a vehicle 10. A typical vehicle 10, i.e., a car, comprises a pair of front wheels 12 and a pair of rear wheels 19, a cabin 11 and a vehicle body 8. In this disclosure, structure elements are associated with the front of the vehicle if they are related to the front wheels and/or their suspension. Similarly, structure elements are associated with the rear of the vehicle if they are related to the rear wheels and/or their suspension. The front and rear wheels 12 and 19 are coupled to the vehicle body 8 by a vehicle chassis. Vehicle chassis as used herein relates to any structure component which couples the front and/or rear wheels 12, 19 to the vehicle body 8 and can articulate or move relative to the vehicle body 8. The structure elements associated with the front of the vehicle are thus part of the vehicle chassis or part of the tire/wheel system. The same holds for the structure elements associated with the rear of the vehicle.

(11) The vehicle chassis and thus the structure elements mentioned herein may comprise, but are not limited to control arms, wishbones, subframes, dampers, springs, struts, wheel hubs, knuckles, anti-roll bars or anti-sway bars and/or steering components such as a steering rack.

(12) FIG. 3 is a plan view from below of a front portion of the underside of the vehicle according to FIG. 2. FIG. 4 is a corresponding illustration of the front wheel suspension system and illustrates placement of the vibrational sensors according to an embodiment of the disclosure.

(13) Each front wheel 12a, 12b is mounted on a wheel hub (not shown), each wheel hub is coupled to the subframe 18 by a first lower control arm 14a, 14b and by a second lower control arm 16a, 16b. The first lower control arm 14a, 14b and the second lower control arm 16a, 16b are also pivotally coupled to the subframe 18. The vehicle 10 also comprises one or more upper control arms 17a to form a double wishbone suspension configuration as shown in FIG. 4. The upper control arm 17a is pivotally coupled to the subframe 18. A coilover damper 13a comprising a coil spring and a damper is coupled to the lower control arms 14a/16a and 14b/16b or to the wheel hub, at its base and to the subframe 18, or body 8, at the top. A steering mechanism or rack 20 is coupled between each of the front wheels 12a, 12b by link arms and is mounted by bushes or supports to the subframe. It is understood that the wheel suspension shown in FIGS. 3 and 4 represents an illustrative example only to demonstrate the present disclosure but that the described calibration system and method are not limited to the particular choice of suspension. In fact, the present disclosure may be applied to any kind of suspension as well as any road-based vehicle.

(14) A plurality of vibrational sensors 30a-x is shown mounted on structure elements in FIGS. 3 and 4. As shown in FIG. 3, a rather large number of 16 vibrational sensors 30a-p may be mounted on structure elements associated with the front of the vehicle. When using two-dimensional accelerometers for the sensors, a total of 32 vibrational input signals will be generated by these sensors in operation of the automatic calibration system. FIG. 3 shows a symmetric arrangement of the sensors with respect to a longitudinal axis of the vehicle. Such a symmetric arrangement is, however, not essential. In fact, a non-symmetric arrangement can be used to virtually increase the number of mounting points as results from one side of the vehicle can generally be applied to the other side of the vehicle.

(15) Based on axle and suspension design or information from a vehicle design database, the vibrational sensors, respectively the vibrational input signals, may be divided into proper subsets which may partially overlap. By way of example, sensors 30a, 30b, 30g-i, 30k and 30m-n may form a first subset, based on their association with the left wheel 12a in FIG. 3 while sensors 30c-f, 30j, 301 and 30o-p may form a second subset, based on their association with the right wheel 12b. Vibrational input signals from corresponding sensors of these two subsets will likely be largely correlated due to their symmetric mounting positions. Consequently, combining sensors from these two subsets will unnecessarily increase the size of the numerical problem.

(16) Depending on their mounting positions, further and smaller subsets may be formed. By way of example, sensors 30a, 30h and 30i may form a third subset with at least one sensor mounted on every possible transfer path. Likewise, sensors 30b, 30g and 30i may form a fourth subset. The multiple-coherence functions for at least one acoustic input signal captured by a microphone inside the vehicle cabin 11 and the vibrational input signals generally differ for the third and fourth subsets due to the different mounting points of the vibrational sensors, reflecting a different coherence between the vibrations of the structure elements where the respective sensors are mounted and the acoustic input signal. As the first subset comprises all the sensors of the third and fourth subsets, the multiple-coherence for the first subset is naturally larger than for the third and fourth subsets. However, the difference may be small, especially for a particular road noise resonance if some of the sensors are either strongly correlated with the other sensors or mounted on a structure element which does not contribute to the transfer path of this particular road noise resonance. In that case, a smaller subset such as the third or fourth subset may suffice to effectively carry out active road noise control in the production vehicle.

(17) FIG. 3 shows sensors 30b and 30k as dashed circles, indicating that these sensors are not required for ARNC because they are strongly correlated with the other sensors. The above described method and system provide an efficient way to eliminate unnecessary vibrational sensors from the plurality of sensors by comparing the multiple-coherence functions calculated for the various subsets. This elimination may be performed in two phases: In a first phase, strongly correlated vibrational input signals may be eliminated from the subsets by analyzing the auto- and cross-power spectra matrices as described above. In a second phase, the remaining subset with the largest value of the respective multiple-coherence function for the specific road noise resonance frequency may be selected to determine the optimal arrangement of reference sensors for ARNC of this resonance. Although only a small number of subsets and vibrational input signals were discussed herein, it shall be understood that the described method is particularly powerful for large ensembles of vibrational input signals and large numbers of small-sized subsets. The number of subsets should be at least as large as the number of structural resonances coherent with the road noise in the cabin, preferably at least twice as large.

(18) Vibrational sensors which are mounted in close proximity to each other such as the pairs 30q and 30r, 30s and 30t, 30u and 30v, and 30w and 30x in FIG. 4 are generally strongly correlated such that one of each of the pairs of corresponding vibrational input signals will generally be eliminated during the calibration process, as indicated by the dashed lines. The remaining sensors are good candidates for the reference sensors but only the sensors of the determined optimal arrangement will ultimately be mounted on the production vehicle to reduce production cost and enable real-time ARNC.

(19) FIG. 5 shows a schematic representation of a vehicle test stand with the automatic calibration system according to the present disclosure connected to the test vehicle. For simplicity, only three vibrational sensors are shown per wheel/suspension, i.e., sensors 530a-c for wheel 512b, sensors 530d-f for wheel 512d, sensors 530g-i for wheel 512a and sensors 530j-l for wheel 512c. It is clear that a significantly larger number of sensors may be used and that the mounting points shown in the Figure only serve to illustrate the system. In the depicted embodiment, all sensors 530a-l are connected with the processing unit 550 of the automatic calibration system via cables. Equally, all microphones 540a-e provided in the head room of the driver and the four potential passengers, e.g., integrated in the head rests, are connected via cables with the processing unit 550. The microphones 540a-e are shown in this illustrative example to be provided near or inside the headrests. They may, however, also be provided in the headliner above the head rests, and may in particular be provided as part of an engine order cancellation (EOC) system of the vehicle. Sensors and/or microphones may alternatively be connected wirelessly with a transceiver 575 of the processing unit 550 or with an audio system (not shown) of the vehicle which connects with the processing unit 550 via cable or wirelessly. The measurements for the calibration may be performed on a roller rig with a stationary vehicle. This has the advantage that undesired wind friction noise is eliminated for the analysis of the structure borne road noise. The roller rig may be provided in an anechoic chamber to avoid the detrimental influence of noise reflections. The vehicle is then operated at a constant rotation speed of the wheels to produce stationary vibrational input signals in the vibrational sensors 530a-l and stationary acoustic input signals in the microphones 540a-e. These signals are transmitted to the processing unit 550 where they are processed by the multiple-coherence calculation unit 560.

(20) As shown in FIG. 5, the multiple-coherence calculation unit 560 may comprise a Fourier transform unit 562 and an eigenvalue calculation unit 564 to process the sampled time series of input signals into auto- and cross-power spectra matrices which are then diagonalized to compute the multiple-coherence functions for each subset and each acoustic input signal as described above. To reduce the size of the subsets, a subset size reduction unit 566 may detect pairs of vibrational input signals with high correlation and eliminate one of the signals as described above. A selection unit 570 of the processing unit 550 then selects a subset for each acoustic input signal as the optimal arrangement of reference sensors for ARNC of the acoustic input signal based on the calculated multiple-coherence function. The result may be displayed in a display device 580, such as an LCD display or a touch screen, of the calibration system.

(21) The calibration system may further include an input device 585 such as a keyboard, touch panel, touch screen, mouse or the like for user input. A user may in particular influence the definition of the subsets and the selection of detected road noise resonances for calibration via the input device 585. Also, a frequency range for the multiple-coherence functions or other parameters such as sampling rate, frequency resolution, maximum and minimum subset size, etc. may be set via the input device.

(22) The calibration system may include a transceiver 575 for communication with the vehicle and/or a wireless network, for instance for accessing a vendor's vehicle data base. Further components may be provided as needed for interaction with vehicle components, a user and/or the test stand.

(23) FIG. 6 shows a schematic representation of a vehicle with an active noise control system according to the present disclosure installed therein. As a result of the above described calibration method and system, a subset including two reference sensors was identified for each wheel. The Figure shows reference sensors 630a and 630c for wheel 612b, reference sensors 630d and 630f for wheel 612d, reference sensors 630g and 630i for wheel 612a, and reference sensors 630j and 630k for wheel 612c. It shall be understood that the number and locations of the reference sensors shown in the Figure are selected for illustrative purposes only and do not limit the scope of the present disclosure.

(24) The reference sensors are connected with the adaptive filter system 690 of the ARNC system via cables or wirelessly as indicated by the dashed lines. Furthermore, a total of five error microphones 640a-e provided inside or near the head rests of the driver and the four possible passengers are connected with the adaptive filter system 690. Again, headliner microphones may be provided instead or in addition, in particular as part of an EOC system. Finally, a speaker arrangement with five speakers 695a-e is connected with the adaptive filter system 690. The number and arrangement of the microphones and speakers are chosen for illustrative purposes only. Also, the adaptive filter system 690 may be part of the audio system of the vehicle which also includes the speaker arrangement and the error microphones. Consequently, an existing audio system of a vehicle may be extended by the depicted reference sensors and connections as well as the described adaptive filter unit or module to implement ARNC according to the present disclosure.

(25) As described above, the adaptive filter system 690 receives a plurality of reference signals from the reference sensors and processes them on the basis of a plurality of transfer functions for the reference signals with respect to one or several predetermined quiet zones in the cabin of the vehicle to generate a cancellation signal. The cancellation signal is then output by the speakers 695a-e to cancel out the road noise transmitted from the tires/wheels into the quiet zone 655 of the driver. Respective cancellation signals may be generated for the quiet zones of the passengers (not shown). Beamforming of the sound waves output by the speakers 695a-e may be used to cancel the road noise inside multiple quiet zones.

(26) A remnant noise signal is then captured by the error microphones 640a-e and input to the adaptive filter system 690 which may subtract an audio signal output by the vehicle's audio system, background noise for engine or other NVH sources and/or a speech signal to isolate the remaining road noise. Based on the remnant road noise signal, one or several filter coefficients of the adaptive filter system 690 may be updated in a feedback loop as known in the art.

(27) Due to the small number of reference sensors per subset (here two), calculation of the virtual vibration signals for ARNC is fast and can be performed in real time such that the described ARNC system can easily account for variations in the road noise, for instance due to varying speed or road conditions. Consequently, dominant road noise resonances can be effectively cancelled out, thereby significantly increasing the comfort of the driver and the passengers without complex adaptations of the vehicle design or appreciable increase of vehicle mass.