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    • 专利摘要:
    Tire load estimation system using filtering adaptive to the road profile. The present invention relates to a dynamic load estimation system that is provided including: a tire carrying the load of a vehicle; at least one tire sensor mounted to the tire, the sensor being operable to measure a deformation of the tire and generate a signal indicating gross load carrying the measured deformation data; road roughness estimating device for determining a road roughness estimate; filtering device to filter deformation data measured by road roughness estimation; and load estimating device for estimating an estimated load on the tire from measured and filtered strain data. a road profile estimate is merged with the static load estimate to obtain an instantaneous load estimate for the tire.
    公开号:BR102014005211B1
    申请号:R102014005211-9
    申请日:2014-03-06
    公开日:2021-07-13
    发明作者:Marc Engel；Kanwar Bharat Singh；Anthony William Parsons；Peter Jung-Min Suh
    申请人:The Goodyear Tire & Rubber Company；
    IPC主号:
    专利说明:

    Field of Invention
    [001] The invention generally relates to tire monitoring systems to collect data from tire parameters measured during vehicle operation, and more particularly to systems and method for generating a tire load estimate based on data from measured tire parameters. Fundamentals of the Invention
    [002] Vehicle mounted tires can be monitored through tire pressure monitoring systems (TPMS) that measure tire parameters such as pressure and temperature during vehicle operation. Data from TPMS equipped tire systems is used to check the condition of a tire based on measured tire parameters and alert the driver of conditions such as low tire pressure or tire leakage, which may require repair maintenance . Sensors inside each tire are either installed at a pre-cure stage during tire manufacture or at a post-cure stage during tire assembly.
    [003] Other factors, such as tire load, are important considerations to vehicle operation and safety. It is therefore still desirable to estimate the tire load in real time and under driving conditions and communicate the load information to a vehicle operator and/or vehicle systems, such as the braking system in conjunction with the pressure and pressure parameters. temperature measured on the tire mentioned above. Invention Summary
    [004] In accordance with one aspect of the invention, a dynamic load estimation system is provided including: a tire carrying the load of a vehicle; at least one tire sensor mounted on the tire, the sensor being operable to measure the deformation of the tire and generate a signal indicating the gross load carrying the measured deformation data; road roughness estimating device for determining a road roughness estimate; unfiltered charge estimating device for generating a static charge estimate from the signal indicating gross charge; filtering device to filter static charge estimation through road roughness estimation; and load estimating device for estimating an estimated load on the tire based on the filtered load estimate.
    [005] In a further aspect, the tire sensor works in the configuration of an energy collection device that generates a signal responsive to tire deformation.
    [006] According to an additional aspect, the filtering device is configured as an adaptive filter, such as a Kalman adaptive filter, using a recursive-based procedure. Kalman's adaptive filter generates, from the tire footprint length estimate on unfiltered ground, an estimate of the tire footprint length on the ground that is adaptively filtered by a road roughness estimate. The tire's footprint length on the filtered ground, along with the measured tire inflation pressure and tire identification data, are then used to extract the estimated tire load from a tire-specific database. In a first embodiment, the road roughness estimating device operationally applies a surface roughness classification system to the sign indicating gross load to determine a relative road roughness estimate. The filtering device then operates to filter the measured strain data by estimating the roughness of the relative road. Data from vehicle mounted sensors are used in an adaptive filter and applied to a static load estimate from the tire to generate an estimate of the road profile height and a load variation estimate. The estimated road profile height is operably merged with the filtered load estimate to produce a calculated instantaneous tire load estimate. Definitions
    [007] "ANN" or "Artificial Neural Network" means an adaptive tool for modeling non-linear statistical data that changes its structure based on internal or external information that flows through a network during a learning phase. ANN neural networks are non-linear statistical data modeling tools used to model the complex relationships between inputs and outputs or to find patterns in data.
    [008] A tire's “Aspect Ratio” means the ratio of its section height (SH) to its section width (SW) multiplied by 100 percent expressed as a percentage.
    [009] “Asymmetric tread” means a tread that has an asymmetric pattern around the central plane or EP equatorial plane of the tyre.
    [010] “Axially” and “axially” mean lines or directions that are parallel to the axis of rotation of the tire.
    [011] “CAN Bus” is an abbreviation for Control Area Network.
    [012] "Anti-friction" is a narrow strip of material placed around the outside of a tire bead to protect the bead plies from wear and tear against the rim and to distribute the flex above the rim.
    [013] “Circumferential” means lines or directions that extend along the perimeter of the surface of the annular tread perpendicular to the axial direction.
    [014] “Central Equatorial Plane (CP)” means the plane perpendicular to the axis of rotation of the tire and passing through the center of the tread.
    [015] “Tyre Footprint on Ground” means the stretch or contact area created by the tire tread with a flat surface as the tire rotates or rotates.
    [016] "Groove" means an elongated hollow area in a tire wall that may extend circumferentially or laterally around said tire wall. The “slot width” is equal to its average width along its length. Grooves are sized to accommodate an air tube as described.
    [017] “Inner side” means the side of the tire closest to the vehicle when the tire is mounted on a wheel and the wheel is mounted on the vehicle.
    [018] “Kalman filter” is a set of mathematical equations that implement a corrector-predictor type estimator that is optimal in the sense that it minimizes the estimated error covariance when some assumed conditions are met.
    [019] “Lateral” means an axial direction.
    [020] "Side edges" means a line tangent to the tire footprint on the ground or axially outermost contact area measured under normal tire loading and inflation, the lines being parallel to the central equatorial plane.
    [021] "Luenberger observer" is a state observer or an estimation model. A “state observer” is a system that provides an estimate of the internal state of a given real system from measurements of the input and output of the real system. It is usually computer-implemented and provides the basis for many practical applications.
    [022] “MSE” is an abbreviation for Mean Square Error, the error between a measured signal and an estimated signal that the Kalman Filter minimizes.
    [023] "Net contact area" means the total area of tread elements in contact with the ground between the side edges around the entire circumference of the tread divided by the gross area of the entire tread between the side edges.
    [024] “Non-directional tread” means a tread that does not have a preferred direction of travel and is not required to be positioned on a vehicle in a specific wheel position or positions to ensure that the tread pattern is aligned with the preferred direction of travel. In contrast, a directional tread pattern has a preferred direction of travel requiring specific wheel placement.
    [025] “Outer side” means the side of the tire furthest away from the vehicle when the tire is mounted on a wheel and the wheel is mounted on the vehicle.
    [026] "Peristaltic" means operating through wavelike contractions that propel contained matter, such as air, along tubular paths.
    [027] "Piezoelectric film sensor" is a device in the form of a film body that uses the piezoelectric effect triggered by a bending of the film body to measure pressure, acceleration, resistance or force converting them into an electrical charge.
    [028] “PSD” is a spectral density of energy (a technical name synonymous with FFT (Fast Fourier Transform).
    [029] “Radial” and “radially” mean the directions radially to or away from the tire's rotation axis.
    [030] "Ribbon" means a rubber strip that extends circumferentially on the tread that is defined by at least one circumferential groove and/or a second such groove or a side edge, the strip being not laterally divided by grooves of full depth.
    [031] “Transverse groove” means small slits molded in the tread elements of the tire that subdivide the surface of said tread and improve traction. The transverse grooves are generally narrow in width and close to the tire footprint on the tire floor and opposite the grooves that remain open in the tire footprint on the tire floor.
    [032] “Tread element” or “traction element” means a spline or a block element defined as having a shape of adjacent grooves.
    [033] "Tread arc width" means the tread arc length measured between the side edges of said tread. Brief Description of Drawings
    [034] The invention will be described by way of example and with respect to the attached drawings in which:
    [035] FIG. 1 is a diagrammatic view of a vehicle showing the sensors mounted on the tire.
    [036] FIG. 2 is an exploded perspective view of a part of a tire to which the sensor package is mounted.
    [037] FIG. 3 is a graph showing a characteristic waveform for a tire revolution.
    [038] FIG. 4 is a graph showing raw signal amplitude versus surface roughness.
    [039] FIG. 5 is a diagram showing signal comparison methods for use in a signal comparison algorithm.
    [040] FIG. 6A is a graph showing reference signal data for a tire rotation under smooth asphalt conditions at 60 Kph.
    [041] FIG. 6B is a graph showing test data for one tire rotation under smooth asphalt conditions at 60 Kph.
    [042] FIG. 6C is a graph showing the cross-correlation coefficient (R) for a smooth versus smooth classification case, Rmax approximately equal to 0.85.
    [043] FIG. 7A is a graph showing reference signal data for a tire rotation under smooth asphalt conditions at 60 Kph.
    [044] FIG. 7B is a graph showing test data for a tire rotation under rough asphalt conditions at 60 Kph.
    [045] FIG. 7C is a graph showing the cross-correlation coefficient (R) for a smooth versus rough classification case, Rmax approximately equal to 0.6.
    [046] FIG. 8A is a graph showing reference signal data for a tire rotation under smooth asphalt conditions at 60 Kph.
    [047] FIG. 8B is a graph showing test data for a tire rotation under very rough asphalt conditions at 60 Kph.
    [048] FIG. 8C is a graph showing the cross-correlation coefficient (R) for a smooth versus very rough classification case, Rmax approximately equal to 0.3.
    [049] FIG. 9A is a graph showing reference signal data for a tire rotation under smooth asphalt conditions at 90 Kph.
    [050] FIG. 9B is a graph showing test data for a tire rotation under rough asphalt conditions at 90 Kph.
    [051] FIG. 9C is a graph showing the cross-correlation coefficient (R) for a smooth versus smooth classification case, Rmax approximately equal to 0.85.
    [052] FIG. 10A is a graph showing reference signal data for a tire rotation under smooth asphalt conditions at 90 Kph.
    [053] FIG. 10B is a graph showing test data for a tire rotation under smooth asphalt conditions at 9 Kph.
    [054] FIG. 10C is a graph showing the cross-correlation coefficient (R) for a smooth versus rough classification case, Rmax approximately equal to 0.6.
    [055] FIG. 11A is a graph showing reference signal data for a tire rotation under smooth asphalt conditions at 90 Kph.
    [056] FIG. 11B is a graph showing test data for a tire rotation under very rough asphalt conditions at 90 Kph.
    [057] FIG. 11C is a graph showing the cross-correlation coefficient (R) for a smooth versus very rough classification case, Rmax approximately equal to 0.3.
    [058] FIGs. 12A, 12B, 13A, 13B, 14A and 14B are velocity dependence study summary graphs showing Cross Correlation coefficient graphs at 60 and 90 Kph velocity and for the three conditions: smooth versus smooth, smooth versus rough and smooth versus very rough. The Rmax for each condition is indicated on each graph.
    [059] FIG. 15 is a diagram illustrating the Classification Rules for the Circular Cross Correlation Coefficient.
    [060] FIG. 16A shows the sensor raw signal graphs for smooth and rough surface situations.
    [061] FIG. 16B shows a wavelet Decomposition Energy Energy Distribution graph.
    [062] FIG. 17A shows a selection View 1 of the Selected Data plot for the Autocorrelation Coefficient.
    [063] FIG. 17B shows a statistical graph for the data in FIG. 17A.
    [064] FIG. 18A shows a selection View 2 index of the Selected Data.
    [065] FIG. 18B shows a statistical distribution plot for the data in FIG. 18A.
    [066] FIG. 19 is a diagram of a Road Surface Classification algorithm.
    [067] FIG. 20A is a graph showing the Observed Straight Line Noise Sensor Estimate of Load vs. Actual Average Load.
    [068] FIG. 20B shows a graph of the Noise Sensor Estimate of the observed rough surface of the load versus Actual Average Load.
    [069] FIG. 21 is a normal distribution curve showing the dependence of the standard deviation on the surface roughness level.
    [070] FIG. 22A is a graph of the performance of the Load Estimation Algorithm versus the Smooth Surface Condition for Tire Load Case 1.
    [071] FIG. 22B is a graph of Load Estimation Error over time showing the results of Moving Average Filter performance versus Kalman Filter Performance.
    [072] FIG. 23A is a graph of the performance of the Load Estimation Algorithm versus the Smooth Surface Condition for Tire Load Case 2.
    [073] FIG. 23B is a graph of Load Estimation Error as a percentage over time showing the results of Moving Average Filter performance versus Kalman Filter Performance in Case 2.
    [074] FIG. 24A is a plot of estimation error over time for a conventional Kalman filter.
    [075] FIG. 24B is a plot of estimation error over time for an adaptive Kalman filter.
    [076] FIG. 25 is a data flowchart of the load estimation algorithm in question.
    [077] FIG. 26 is a data flowchart of an alternative load estimation algorithm using a quarter car model.
    [078] FIG. 27 is a plot of actual versus estimated road profile elevation using the algorithm of FIG. 26, with a Correlation Coefficient (R) = 0.966.
    [079] FIG. 28 is a graph of load over time comparing actual to estimated (using the Kalman filter) for a Correlation Coefficient of 67.979. Detailed Description of the Invention
    [080] With respect to FIGs. 1 and 2, a vehicle 10 is shown supported by multiple tires 12, each tire equipped with a sensor package 14. While the vehicle 10 is in the general form of a passenger car, the system in question can be used in any system. vehicle. The sensor module 14 is of a commercially available type, suitable for mounting on an inner lining 16 of the vehicle 10. The sensor module or package 10 includes a pressure sensor and a temperature sensor to measure air pressure and temperature respectively. tire cavity temperature during tire operation. In addition, the sensor module 10 according to the invention includes a vibration sensor mounted to measure tire deformation during tire operation. The vibration sensor is preferably based on the piezoelectric effect and generates a signal indicative of tire deformation. From the signal, the tire footprint on the ground of tire 12 as it rotates against the ground surface can be roughly estimated (also called here an “unfiltered” or “raw” estimate) verified by means of the methodology taught in copending US Patent Application No. 13/534,043, filed June 27, 2012, entitled "Load Estimation System and Method for a Vehicle Tire" and incorporated herein by reference.
    [081] The piezoelectric sensor inside module 14 generates a signal indicative of tire deformation within the tire footprint on the rotating ground. The piezoelectric sensor transmits a raw signal to a signal processor (not shown). The peak-to-peak length of the signal is analyzed to verify the length of the tire's footprint on the tire's ground. The appropriate tables that are then consulted provide tire-specific load information for the tire based on the length of the tire's ground footprint measured by the piezoelectric sensor, tire air pressure, and tire cavity temperature data.
    [082] The tire load estimate according to the request identified above can use a filtering model, such as a Kalman filter. Determining the tire load from measuring its deflection, however, is problematic because of the presence of a “noise” contribution to the tire's deformation. As used here, “noise” refers to external influences on a tire, other than the tire load, that affect tire deformation and thus make load estimates based on the measurement of such tire deformation inaccurate. For example, road roughness affects tire deformation, the greater the roughness of the road the vehicle is running on, the greater the potential for noise distortion in any static tire load estimate based on tire deformation. The subject invention proposes to minimize the effect of noise contribution in the form of road roughness on the tire load estimation by implementing an adaptive filter that takes into account the road surface roughness in the load estimation procedure.
    [083] The use of an algorithm that is proposed uses a tire mounted sensor, preferably a piezoelectric sensor, to stimulate both the tire load and the road roughness. The algorithm adapts to the effect of road roughness variation for the purpose of load estimation, an important aspect in real driving conditions. The system and method described here use a piezoelectric energy capture signal for both tire load and road roughness estimation. The aforementioned objective of minimizing the “noise” effect on load estimation is achieved using a Kalman filter, as will be explained. The overall load and load distribution information can subsequently be usefully used by advanced brake control systems such as the electronic brake distribution system (EBD) to optimize system performance and reduce vehicle braking distance. In the case of a commercial vehicle application, the estimated weight on each wheel can be averaged to produce an estimate of the vehicle weight. The vehicle's weight can then be transmitted to a central location, thus eliminating the need for weighing stations.
    [084] The algorithm used in the system in question uses the piezoelectric signal to capture both the load estimate and the roughness estimate of the road, instead of relying on an accelerometer or a strain sensor. The algorithm described here takes into account road roughness during load estimation and, as such, more faithfully reflects actual driving conditions. And as the Kalman filter based on the approximation of the algorithm in question is a recursive-based procedure, there is no need to store historical information, unlike a moving average method of analysis.
    [085] A conventional Kalman filter is relatively sensitive to dynamic model noise level selection. As such, the conventional filter is vulnerable to inaccuracies due to varying road roughness. The approach in question, in contrast, using an adaptive Kalman filtering algorithm, is more robust and adaptive to sudden changes in the roughness condition of the road.
    [086] The load estimation algorithm in question is diagramed in FIG. 25. As shown, tire 12 is equipped with a sensor module coupled to tire 14, generally including pressure and temperature measurement sensors (TPMS) plus a piezoelectric sensor to generate a signal indicative of tire deformation within a footprint of the tire. tire on the ground of the rotating tire. Module 14 is coupled to the inner tire liner within the crown region of the tire by suitable devices such as an adhesive. From module 14, the raw energy collection signal that is produced is indicative of the tire deformation within a tire footprint on the rotating tire tread. Tire deformation is proportional to the load borne by the tire. The raw signal is used in a tire-to-ground footprint length estimation procedure 54 of the type described by copending U.S. application No. 13/534,043 incorporated herein.
    [087] The raw signal from the piezoelectric sensor in module 14 is processed in the tire footprint length estimate 54 to produce a raw tire footprint length on the ground. As the rough tire footprint length estimate on the ground is vulnerable to error from the roughness of the road, the raw tire footprint length on the ground is further filtered by an Adaptive Kalman Filter 56. The raw signal from the sensor piezoelectric in module 14, in addition to being used in an initial rough ground footprint estimate 54, is also used in a roughness estimation algorithm 52 shown. The roughness estimation algorithm 52 operates on a Surface Classification System, as will be explained. The Adaptive Kalman Filter 56 uses the filter parameters that are adjusted as a function of the road surface condition by the Roughness Estimation Algorithm 52. Consequently, the Tire Footprint Length on Filtered Ground is obtained from the Kalman Filter Adaptive. The Filtered Tire Footprint Length on the ground accurately estimates the length of the tire footprint on the ground after compensating for road roughness.
    [088] A Tire Specific Lookup Table 58 is created empirically, which provides the tire load based on the Tire Footprint Length, Tire Identification, and Tire Inflation Pressure entries. Tire pressure is obtained from the TPMS 14 module along with the Tire Identification. Combined with the Tire Footprint Length on the Ground Filtered from the Adaptive Kalman Filter 56, the load for a particular tire can be obtained as a function of the measured inflation pressure and the length of the tire footprint on the ground.
    [089] With respect to FIG. 3, a raw signal waveform is shown. The waveform of a tire rotation is displayed. The waveform shown is experimentally derived from a tire with a pressure of 210 KPa (2.1 bar); and traveling at a speed of 60 Kph. The graph in FIG. 3 is shows signal amplitude [V] versus Sample Number. In FIG. 4, a raw signal from a tire with a pressure of 290 kPa (2.9 bar); traveling at 60 Kph it is graphically shown under three road conditions: smooth surface, rough surface and very rough surface. It is appreciated that the signal amplitude (from which the tire load will be estimated) fluctuates significantly from surface condition to surface condition. The present invention acts to characterize the level of surface roughness in order to compensate for the effect that the surface roughness “noise” present in a raw tire deformation signal.
    [090] A signal comparison algorithm can be constructed, as seen in FIG. 5, using three distinct signal comparison methods: a maximum circular cross-correlation coefficient (see FIG. 6); a wavelet-based feature extraction (see FIG. 16); and an autocorrelation coefficient distribution (see FIGs. 17 and 18). The three methodologies extract different characteristics of the signal comparison based on the methodology employed. In FIGs. 6A to 6C, the maximum circular cross-correlation coefficient methodology is shown in graph form. FIG. 6A shows a test of Case 1, in which a smooth asphalt reference surface supports a tire with a rotation of 60 Kph. FIG. 6B shows a graph for Case 1 for a test surface, also configured as smooth asphalt. In this smooth versus smooth test, a Cross Correlation Coefficient is examined as a basis for ranking. The Cross Correlation Coefficient R is defined as a measurement of the similarity of two waveforms as a function of a time interval applied to one of them. For the smooth versus smooth Case 1 test (FIGs. 6A and 6B), an Rmax of approximately 0.95 was determined empirically and is shown graphically in FIG. 6C. Descriptively determined, a tire that undergoes a deformation on a smooth surface is expected to experience a radius deviation from the smooth surface of no greater than 0.05.
    [091] FIGs. 7A and 7B show the graphs for a Case 2 test, in which a smooth reference surface is compared to a rough asphalt test surface. A comparison of the two plots results in a Maximum Circular Cross Correlation Coefficient Rmax for the Case 2 test of approximately 0.6 is graphically indicated in FIG. 7C. Thus, a rough asphalt surface will cause a larger circular tire deformation than that caused by a smooth asphalt surface (FIGs. 6A and 6B).
    [092] In FIGs. 8A and 8B, a Case 3 test parameter is used comparing a graph of the smooth reference surface of FIG. 8A with a rough asphalt test surface shown graphically in FIG. 8B. The resulting Maximum Circular Cross Correlation Coefficient Rmax for the Case 3 test is determined to be approximately 0.3, shown graphically in FIG. 8C.
    [093] FIGs. 9A, 9B and 9C (Case 1), FIGs. 10A, 10B and 10C (Case 2) and FIGs. 11A, 11B and 11C (Case 3) are graphs similar to the test graphs of FIGs. 6A to 6C, FIGs. 7A to 7C and FIGs. 8A to 8C, but driven at a high speed of 90 Kph. The Cross Correlation Coefficient plots of FIGs. 9C, 10C and 11C generally correspond to the low speed graphics of FIGs. 6C, 7C and 8C, demonstrating that the Rmax values under each road surface condition are unchanged over a range of vehicle speeds. A comparative summary of the velocity dependence study is represented in the graphs of FIGs. 12A and 12B (smooth versus smooth); FIGs. 13A and 13B (smooth versus rough); and from FIGs. 14A and 14B (smooth versus very rough). Comparative Rmax values at the two speeds validate the use of Rmax as a basis for categorizing road surfaces for the purpose of filtering load estimate measurements. FIG. 15 shows the ranges of Rmax (Maximum Circular Cross Correlation Coefficient) for each of the three road surface levels, and represents the classification rule(s) that are followed in the algorithm.
    [094] FIGs. 16A and 16B show a second approach to constructing a filter that will adapt a load estimate to road roughness conditions. A wavelet-based Feature Extraction Subband Wavelet Entropy methodology is graphically demonstrated for smooth and rough road conditions.
    [095] The energy distribution of the wavelet decomposition energy is shown in FIG. 16B. Entropy represents the degree of disorder that the filter variable has. It is seen from FIG. 16B that there is a difference in subband power distribution. A subband wavelet entropy can be defined by the above mathematical formula in terms of the relative energy of the wavelet coefficients. The relative energy of the wavelet coefficients can then be used in adaptive filtering of the load estimate to compensate for surface conditions.
    [096] FIGs. 17A, 17B, 18A and 18B illustrate a third alternative feature for filtering the estimated load, the use of Autocorrelation Coefficient differentiation. In FIGs. 17A and 17B, the distribution of a first selection index 1 and its corresponding statistical graph, respectively, are shown. In FIGs. 18A and 18B, an index of selection of 2 is graphed, showing the distribution and the statistical plot, respectively. The distribution of the autocorrelation function indicates the surface characteristics of the road on which the tire is moving. By discerning the distribution function, an assessment of road roughness can be made. As used here, the “autocorrelation correlation” can be defined as the correlation of a series of times with its own past and future values.
    [097] With respect to FIG. 19, a summary of the three feature extraction approaches and their respective alternative uses in achieving a surface differentiation classification 18 is shown. The raw signal 20 from the piezoelectric sensor can be analyzed using the maximum cross-correlation coefficient 24 methodology which employs a Reference Signal in the memory of the processor 22. The maximum cross-correlation coefficient as a surface differentiation characteristic can be used in a 30 Surface Classifier (ANN) to determine the relative surface type (smooth, intermediate, or rough).
    [098] Alternatively, wavelet subband entropy differentiation based on entropy decomposition 26 can be used as a characteristic methodology. Wavelet entropy decomposition can be used to determine, through the use of classifier 30, which of the three surface classifications is found by a tire. As a further alternative, autocorrelation correlation 28 can be used as a third alternative feature approach. The distribution of the autocorrelation correlation function is evaluated and from the distribution data, a conclusion can be reached using the Surface Classifier 30 as to which of the three types of surfaces are encountered by the tire during deformation detection.
    [099] From the straight-line test results of load variation under constant speed of FIGs. 20A and 20B, the rationale for interest in the level of road roughness will be appreciated. FIG. 20A shows a load variation on a smooth surface, both the observed value (Noise Sensor Estimate) and the Actual Average Load. It is seen that a 15 to 20 percent variation in load estimates can occur even under straight-line and smooth surface driving conditions. From FIG. 20B, it is seen that the roughened surface can cause even greater variance. A 30 to 40 percent range is reflected in the rough surface test results of FIG. 20B.
    [0100] From above, it is useful to consider road roughness in any tire load estimate. A Kalman Filter is thus employed. A Kalman Filter, given a discrete-time noise LTI process (set) and a noise measurement, can determine an optimized state estimate that minimizes the quadratic cost function MSE (set) presented below:
    Where
    is a state estimation error vector. Here, the state of a discrete-time controlled process is governed by the linear stochastic difference equation:

    [0101] Matrix Z refers to the state of the time step prior to the state at the current step, in the absence of either a direction function or process noise. Matrix B refers to the operational control input into the state. The matrix C, in the measurement equation, relates state to measurement. Matrix D, in the measurement equation, relates the control input to the measurement. Real dynamical systems are subjected to a variety of “noise” signals that corrupt the response. Process noise (wk-1) corrupts states and sensor noise (K) corrupts the output. Modified governing equations that incorporate the effects of these exogenous inputs can be written as: and .

    [0102] The Kalman Filter operates under certain system assumptions. First, the state dynamics are linear, that is, the current state is a linear function of the previous state. Second, noise in state dynamics is normally distributed. Third, the observation process is linear, that is, observations are a linear function of state. Finally, it is hypothesized that the distribution noise is normally distributed.
    [0103] Although a prediction of process noise or measured noise values is problematic, it is possible to have some knowledge of their statistics. Below are statements presenting Process Noise and Measurement Noise. Since the average load does not change under stable sample-by-sample driveability conditions, the equation of state is given by:
    .
    [0104] And the output equation is given by:
    .
    [0105] The term θk represents the measurement noise responsible for load variations. It is this parameter that can be made adaptive with knowledge about the level of roughness. Following is a summary of the five Kalman Filter equations categorized as Prediction and Correction, and the reduction of the equation for the purpose of exemplified explanation. In FIG. 21, a normal distribution curve is plotted where Mean = 0 and Standard Deviation = X percent of the average tire load, depending on the level of surface roughness. So x is approximately equal to 15 percent for a smooth surface; 25 percent for a rough surface and 35 percent for a very rough surface. The estimation results obtained experimentally are shown in FIG. 22A for a smooth road condition of Case 1. The graph of FIG. 22A shows Load Estimation Algorithm Performance graphing Tire Load over time comparing: Observed Value (Noise Sensor Estimate), Moving Average Filter Estimates, Kalman Filter Estimates, and Actual Average Load . In FIG. 22B, the Estimation Error between Moving Average Filter Performance and Kalman Filter Performance is graphically displayed. The Kalman Filter provided a minor estimation error and an estimate within a 5% precision band for a static axle load.
    [0106] The Performance of the Load Estimation Algorithm on a Rough Surface of Case 2 is graphically shown in FIG. 23A, which compares Observed Value (Noise Sensor Estimate), Moving Average Filter Estimates, Kalman Filter Estimates, and Actual Load Average. FIG. 23B shows the Estimation Error between the Moving Average Filter Performance and the Kalman Filter Performance for the Rough Surface of Case 2. The Kalman Filter provided a lower estimation error than the Moving Average Filter and achieved an estimate within a 5% accuracy band for a static axle load on a rough surface.
    [0107] In FIG. 24A shows the Estimation Error for a conventional Kalman Filter using a constant noise variance, compared to the Moving Average Filter Performance. FIG. 24B shows a graph representing an Adaptive Kalman Filter performance using a noise variance that changes as a function of road roughness level. As seen in FIG. 24B, Adaptive Kalman Filter performance is superior to Moving Average Filter Performance. Comparing the performance of the Adaptive Kalman Filter in FIG. 24B with the performance of the Conventional Kalman Filter, it is appreciated that the performance of the Adaptive Kalman Filter results in a lower percentage error than the Conventional Kalman Filter. Consequently, the graphics support the conclusion that using an Adaptive Kalman Filter, which changes a noise variance to reflect the level of road roughness, achieves superior predictive performance than a filter that uses a constant noise level.
    [0108] Referring again to FIG. 25, the algorithm for tire load prediction uses one of the feature-based approaches explained earlier in the application of the Surface Classification block 52. The Filter parameters are thus adjusted as a function of the road surface condition. Once adjusted, the Adaptive Kalman Filter will apply the adaptive filter parameters to the Gross Tire Foot Foot Length Estimate 54 to create a Tire Foot Foot Length in filtered ground length. The Filtered Tire Footprint Length on Ground can be used with the tire identification and inflation pressure to derive a load estimate from a Lookup 58 table. As explained, Adaptive Kalman Filter 54 is used because, for a conventional Kalman Filter application, the static noise levels of the model are given before the filtering process, and will remain unchanged throughout the recursive process. Commonly, this statistical information a priori that is determined by test analysis and some knowledge about the type of observation in advance. If such a priori information is inadequate to represent real statistical noise levels, the conventional Kalman estimate is not optimal and may cause undesirable results. The system and method in question overcome this possibility through the use of adaptive filtering application.
    [0109] From the above, it is appreciated that the algorithm in question in FIG. 25 uses a piezoelectric energy harvesting signal for load estimation as well as road roughness estimation. Actual driving conditions are addressed by applying road roughness during load estimation. Furthermore, since the Kalman filter-based approach is a recursive-based procedure, historical information does not need to be stored, a decided advantage over other methods, such as a moving average method. Additionally, a conventional Kalman filter is relatively more sensitive to selecting the dynamic model noise level that remains constant. In the approach in question, an adaptive Kalman filtering algorithm is used, resulting in a more robust load estimate that is responsible for sudden changes in the roughness condition of the road.
    [0110] FIGs. 26, 27 and 28 show an extension of the above approach encompassing a road profile height estimation scheme in addition to an instantaneous tire load estimate. FIGs. 26 to 28 refer to an estimate of the road profile height based on a multiple sensor fusion approach via Kalman Filtration techniques and its application in an instantaneous tire load estimation algorithm. The road profile is seen as an essential input that affects the vehicle's dynamic data. An accurate estimate of the road profile is thus useful in vehicle dynamics and in the control system model, such as an active and semi-active suspension model. The subject adaptation of FIGs. 26-28 provides an estimate of the road profile that can be used for the purpose of vehicle control systems as well as in estimating a dynamic tire load.
    [0111] With respect to the road profile, a data flowchart is presented in FIG. 26 and uses a real-time estimation methodology based on the use of a Kalman Filter. The method uses measurements from available sensors, accelerometers, and suspension deflection sensors. With reference to FIG. 26, algorithm 32 uses a quarter car vehicle model. On model 34: ms = suspended mass mu = unsprung mass Ksuspension = suspension stiffness Csuspension = suspension damping coefficient Ktire = tire stiffness Ctire = tire damping coefficient Zs = vertical displacement of suspended mass Zu = vertical displacement of mass not suspended Zr = height of road profile. The standard notational convention for describing state-space representations is given by: x' =A x + B u }state equations y = C x + D u }output equations where: x(t) - State vector x '(t) - Derivative of state vector A - State matrix B - Input matrix u(t) - Input vector y(t) - Output vector C - Output matrix D - Direct transmission matrix.
    [0112] The equivalent state-space representation of the "quarter car model" used in the Kalman filter has been specified below as:


    [0113] An accelerometer 38 and suspension deflection sensor 40 of the commercially available type are attached to the vehicle and respectively measure the chassis acceleration (Zs’’) and the suspension deflection (Zs - Zu). The estimated states of the linear Kalman filter are Zs, Zs’, Zu, Zu’, Zr and Zr’. The Kalman Filter 42 generates an estimate of the Road Profile Height (Zr) which is further used to estimate the tire load variation caused by road swells. The load variation is given by the expression Fz, load variation = [Ktire*(Zu-Zr) + Ctire*(Zu’-Zr’)].
    [0114] The Kalman filtering approach allows to achieve a successful estimate overcoming vibrational disturbances that considerably affect accelerometers. Modeling a quarter of a vehicle enables the use of a real-time estimation methodology. A secondary use of road profile height estimation is in calculating an (instant) load on the tire. The axle load variation estimator 44 receives an estimate of the road profile height from the Kalman Filter 42 and directs an axle load variation estimate to a tire load estimator 48. The tire footprint length on the ground of tire 12 is obtained from the implementation of the methodology described above. Static load estimator 46 receives a piezoelectric sensor signal from tire 12 and estimates a static load Fz on the tire. The static load is used by the tire load estimator 48 along with the load variation estimate. From a fusion of static load estimation with load variation estimation, tire load estimator 48 calculates Fz, the instantaneous load on the tire.

    [0115] FIG. 27 shows a graph (road profile elevation over time) that reflects the experimental results by comparing the road estimate (via a profilometer) to the road profile estimate using the system in FIG. 26. A close correlation coefficient (R) = 0.966 verifies the validity of the methodology and system in question in generating a quantified estimate of the road profile. In FIG. 28, a graph (Load over time) that reflects the experimental results between the actual load (dynamic model of total vehicle suspension with 8 degrees of freedom) and the estimated load (Kalman filter) is presented. The graph indicates a correlation coefficient (R) = 67.979, validating the system and approach in question in FIG. 26.
    [0116] It is assumed that the normal load of the tire is directly related to the contact forces between the tire and the load. Tire load measurement can then be useful to implement targeted control strategies to maximize vehicle maintenance performance on the road. In addition, global load information and load distribution among all tires on a vehicle can be used by advanced brake control systems such as the electronic brake distribution (EBD) system to optimize system performance and reduce vehicle braking distance. In the case of a commercial vehicle, the estimated weight on each wheel can be averaged to produce an estimate of the vehicle's weight, which can then be transmitted to a central location, thus eliminating the need for weighing stations. The road roughness methodology of FIGs. 1 to 25, as discussed above, and the discussion of the profile pertaining to FIGs. 26 to 28 are related in such a way that the road roughness can be considered as micro road profile changes and the road profile change can be considered as a macro variation of road roughness. The use of both macro (road profile) and micro (road roughness) variable estimates in an adaptive Kalman Filter analysis will result in a more accurate and robust estimate and thus more faithfully reflect actual driving conditions.
    [0117] Variations in the present invention are possible given the description given here. While certain representative embodiments and details have been shown for the purpose of illustrating the invention in question, it is clear to those skilled in this art that various changes and modifications can be made without departing from the scope of the invention in question. It is understood, therefore, that changes may be made in the particular embodiments described that will be within the intended full scope of the invention, as defined by the following appended claims.
    权利要求:
    Claims (10)
    [0001]
    1. Dynamic load estimation system for estimating a vehicle load, CHARACTERIZED by the fact that it comprises: at least one tire supporting a vehicle; tire sensing device mounted to a tire, the sensing device operable to measure a tire strain of a tire and generate a raw load indicative signal carrying measured strain data; load estimating device for determining an unfiltered load estimate on the tire from the raw load indicative signal; road roughness estimating device for determining a road roughness estimate; filtering device to filter the unfiltered load estimate through the road roughness estimate; and load estimating device for determining an estimated load filtered on a tire from the filtering device.
    [0002]
    2. Load estimation system, according to claim 1, CHARACTERIZED by the fact that the tire sensor device comprises the energy capture device, and the raw load indicative signal represents the energy captured from the deformation of the a tire.
    [0003]
    3. Load estimation system according to claim 1, CHARACTERIZED by the fact that the adaptive filtering device comprises an adaptive Kalman filter, which uses a recursive-based procedure.
    [0004]
    4. Load estimating system according to claim 3, CHARACTERIZED by the fact that the load estimating device determines an estimated tire footprint length on the unfiltered ground of the tire from the raw signal indicative of load.
    [0005]
    5. Load estimation system according to claim 4, CHARACTERIZED by the fact that the adaptive Kalman filter determines an estimate of the tire footprint length on filtered ground from the estimated tire footprint length on unfiltered ground .
    [0006]
    6. Load estimation system according to claim 5, CHARACTERIZED in that it further comprises a tire specific database that determines an estimate of load on a tire from inputs comprising the footprint length estimate of the tire on filtered ground, the measured tire inflation pressure and the tire identification data.
    [0007]
    7. Load estimation system according to claim 6, CHARACTERIZED by the fact that the road roughness estimating device operatively applies a surface roughness classification system to the raw load indicative signal to determine a roughness estimate of relative road, the adaptive filtering device being operative to filter the tire footprint length on unfiltered ground estimated by the relative road roughness estimate.
    [0008]
    8. Load estimation system according to claim 4, CHARACTERIZED in that it further comprises a road profile height estimating device for determining an estimated quantified road profile height from at least one sensing device mounted on the vehicle.
    [0009]
    9. Load estimation system according to claim 8, CHARACTERIZED by the fact that the vehicle-mounted sensor device comprises the suspension deflection measuring device and the vehicle-mounted sensor device additionally comprises the chassis-mounted accelerometer device to measure vehicle chassis acceleration, and wherein an estimated road profile height is operatively merged with the filtered load estimate to produce a calculated instantaneous tire load estimate.
    [0010]
    10. Method for estimating a dynamic load on a tire supporting the vehicle, CHARACTERIZED in that it comprises: mounting the tire sensor device on a tire, the sensor device is operable to measure a tire deformation of a tire and to generate a raw load indicative signal carrying the measured strain data; determining a road roughness estimate from the road roughness estimating device using the raw load indicative signal; calculate an unfiltered load estimate from the raw load indicative signal; filter the unfiltered load estimate by the road roughness estimate using the adaptive filtering device; and estimating an estimate of the filtered load on a tire subsequent to filtering by the adaptive filtering device.
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    同族专利:
    公开号 | 公开日
    CN104029684A|2014-09-10|
    US20140257629A1|2014-09-11|
    CN104029684B|2017-10-13|
    BR102014005211A2|2014-11-11|
    EP2774784B1|2016-11-09|
    US8844346B1|2014-09-30|
    EP2774784A1|2014-09-10|
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    法律状态:
    2014-11-11| B03A| Publication of a patent application or of a certificate of addition of invention [chapter 3.1 patent gazette]|
    2018-11-13| B06F| Objections, documents and/or translations needed after an examination request according [chapter 6.6 patent gazette]|
    2020-03-31| B06U| Preliminary requirement: requests with searches performed by other patent offices: procedure suspended [chapter 6.21 patent gazette]|
    2021-06-22| B09A| Decision: intention to grant [chapter 9.1 patent gazette]|
    2021-07-13| B16A| Patent or certificate of addition of invention granted|Free format text: PRAZO DE VALIDADE: 20 (VINTE) ANOS CONTADOS A PARTIR DE 06/03/2014, OBSERVADAS AS CONDICOES LEGAIS. |
    优先权:
    申请号 | 申请日 | 专利标题
    US13/789,881|US8844346B1|2013-03-08|2013-03-08|Tire load estimation system using road profile adaptive filtering|
    US13/789.881|2013-03-08|
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