Tire radius monitoring system for determining the tire effective radius

Abstract

A tire radius monitoring system (200) for dynamically determining a tire effective radius for each of the wheels (20) on a vehicle (100), comprising: a GPS (Global Positioning System) sensor (36); a plurality of wheel speed sensors (22) configured to monitor the rotational speeds of a plurality of vehicle wheels (20); a controller (15); and an ascending speed filter (230); wherein the controller (15) communicates with the GPS sensor (36) and the multiple wheel speed sensors (22); wherein the controller (15) contains a set of commands configured to: determine a longitudinal velocity vector (Vx) (210) for the vehicle (100) via the GPS sensor (36); determine the wheel rotational speeds for the multiple vehicle wheels (20) via the multiple wheel speed sensors (22); detect a wheel slip-free condition for the multiple vehicle wheels (20);to determine effective tire radii for the multiple vehicle wheels (20) based on the longitudinal velocity vector (210) for the vehicle (100) and the wheel rotational speeds for the multiple vehicle wheels (20) during the wheel-slip-free state; to execute the rate-of-rise filter (230) to determine the effective tire radii, wherein the rate-of-rise filter (230) comprises a state vector (235) based on the effective tire radii and a measurement vector based on the wheel rotational speeds for the plurality of vehicle wheels (20); wherein the rate-of-rise filter (230) comprises a plurality of calibrated gain factors corresponding to a plurality of vehicle speed ranges associated with the longitudinal velocity vector (210) for the vehicle (100); and to control the operation of the vehicle (100) based on the effective tire radii.

Description

Vehicle control systems can benefit from information relating to vehicle parameters, including the effective tire radius. This information can be used as input for controlling one or more vehicle systems that manage braking, cornering, and / or acceleration. Common vehicle systems treat the tire effective radius as a constant calibration value. However, dynamically changing factors such as tire pressure, ambient temperature, surface temperature, tire temperature, vehicle load, etc., can lead to fluctuations in the tire effective radius, which in turn can affect the performance of vehicle systems with regard to braking, cornering, and acceleration. Furthermore, inaccuracies or errors in determining the tire effective radius can impair the performance of driver assistance systems such as an advanced driver assistance system (ADAS) or other autonomous vehicle systems. DE 10 2017 113 172 A1 describes a method and a device for evaluating the functionality of tires by monitoring an effective rolling radius. The method comprises obtaining speed data for a vehicle from a global positioning system; obtaining angular velocity data for a wheel of the vehicle; processing the speed data and the angular velocity data using a digital filter; and determining an effective rolling radius of a tire coupled to the wheel based on the processed speed and angular velocity data. DE 10 2004 044 788 A1 describes a method for determining the coefficient of friction between a vehicle tire and a road surface. The method comprises measuring the wheel speed with a first measuring system, evaluating at least one wheel rotation rate and one wheel circumference; measuring the absolute speed of the vehicle with a second measuring system; determining the slip from the wheel speed and the absolute speed; measuring the braking or driving torque acting on the vehicle; and determining the friction using an algorithm that takes into account the slip and the braking or driving torque. It can be considered an object of the invention to provide a method for determining the effective tire radius of a vehicle. The invention relates to a tire radius monitoring system for dynamically determining the effective radius of each tire on a vehicle. The tire radius monitoring system comprises a GPS (Global Positioning System) sensor, a plurality of wheel speed sensors configured to monitor the rotational speeds of multiple vehicle wheels, and a controller. The controller communicates with the GPS sensor and the plurality of wheel speed sensors and contains a command set. The command set is configured to determine the vehicle's longitudinal speed via the GPS sensor. The command set also determines the wheel speeds for multiple vehicle wheels via the multiple wheel speed sensors and detects a wheel slip-free state for multiple vehicle wheels and the longitudinal speed vector from the GPS sensor.The instruction set determines the effective tire radii for the multiple vehicle wheels based on the longitudinal velocity vector for the vehicle and the wheel speeds for the multiple vehicle wheels during the wheel-slip-free state, and controls the vehicle operation based on the effective tire radii. According to the invention, the tire radius monitoring system comprises a gradient filter, which includes a state vector based on the effective tire radii and a measurement vector based on the wheel rotational speeds for the plurality of vehicle wheels. The command set is configured to execute the gradient filter to determine the effective tire radii. In one embodiment, the rate-of-rise filter includes a Kalman filter, and wherein the instruction set is configured to execute the Kalman filter to determine the effective tire radii using the state vector and the measurement vector based on the effective tire radii, and the measurement vector based on the wheel rotation speeds for the plurality of vehicle wheels. According to the invention, the rate-of-rise filter comprises a plurality of calibrated gain factors corresponding to a plurality of vehicle speed ranges linked to the longitudinal speed vector for the vehicle. In one embodiment, the plurality of calibrated gain factors specific to the vehicle speed range comprises a first calibrated gain factor corresponding to a first of the plurality of vehicle speed ranges corresponding to a low speed range, a second calibrated gain factor corresponding to a second of the plurality of vehicle speed ranges corresponding to a medium speed range, and a third calibrated gain factor corresponding to a third of the plurality of vehicle speed ranges corresponding to a high speed range. In one embodiment, the tire radius monitoring system includes the command set which is configured to determine tire pressures for the multitude of vehicle wheels based on the effective tire radii. In one embodiment, the tire radius monitoring system comprises the command set configured to detect the non-wheel slip state for the multiple vehicle wheels based on the longitudinal velocity vector for the vehicle and the wheel rotation speeds for the multiple vehicle wheels. In one embodiment, the tire radius monitoring system includes the command set which is configured to correlate vehicle mass and tire pressure with the effective tire radii. According to the invention, a tire radius monitoring system for a vehicle comprises a GPS (Global Positioning System) sensor, a plurality of wheel speed sensors configured to monitor the rotational speeds of a plurality of vehicle wheels, and a controller. The controller communicates with the GPS sensor and the multiple wheel speed sensors and includes a command set configured to determine a longitudinal velocity vector for the vehicle via the GPS sensor; to determine the wheel speeds for the plurality of vehicle wheels via the multiple wheel speed sensors; to determine a measurement vector containing the wheel speeds for the plurality of vehicle wheels and the longitudinal velocity vector for the vehicle; and to execute a rate-of-rise filter to determine a state vector containing the effective tire radii for the multiple vehicle wheels.The state vector is determined based on the measurement vector, which contains the wheel speeds for the multitude of vehicle wheels and the longitudinal velocity vector for the vehicle. Vehicle operation is controlled based on the state vector, including the effective tire radii. In one embodiment, the rate-of-rise filter includes a Kalman filter. The instruction set is configured to execute the Kalman filter to determine the state vector, including the effective tire radii, based on the measurement vector, including the wheel speeds for the plurality of vehicle wheels, and the longitudinal velocity vector for the vehicle. One or more embodiments are now described by way of example with reference to the accompanying drawings, in which: Fig. 1 schematically shows a top view of a vehicle. Figs. 2 and 3 schematically show a routine for estimating the effective tire radii in real time using information from wheel speed sensors and a GPS sensor. Fig. 4 graphically shows a relationship between an estimated effective tire radius and the tire pressure. Fig. 1 schematically shows, in accordance with the embodiments disclosed herein, a vehicle 100 arranged on a driving surface, the vehicle 100 comprising operating systems, for example, a drive system 10, a steering system 16, and a wheel brake system 26. The steering system 16 comprises a steering wheel 12 and a steering actuator 14. The vehicle 100 also comprises a plurality of wheels 20, wheel speed sensors 22, and wheel brakes 24, as well as a navigation system 32. The vehicle 100 also comprises a tire radius monitoring system 200 for dynamically determining a tire effective radius for each of the plurality of vehicle wheels 20. Details of the tire radius monitoring system 200 are described with reference to Fig. 2 and Fig. 3. In some embodiments, the vehicle 100 may include a tire pressure monitoring system (TPMS) 25.The vehicle 100 may, in some embodiments, include a space monitoring system 30 and an advanced driver assistance system (ADAS) 40. The operation of the vehicle 100, including the aforementioned operating systems, is controlled by a plurality of controllers that execute control routines, hereinafter referred to as controllers 15. Each of the multiple wheels 20 can be arranged with a rigid rim section on which an inflatable tire is mounted. Each of the multiple wheels 20 is characterized by various parameters, including an effective tire radius. The effective tire radius, as used here, represents a linear distance that can be measured between the center of the wheel 20 and the ground surface when the wheel 20 is carrying the vehicle 100 under load. The effective tire radius is influenced by the internal air pressure, vehicle speed, tire and ambient temperature, vehicle acceleration, braking, tilt, yaw, etc. A top view of the vehicle 100 is shown. The vehicle 100 and the driving surface define a spatial area in the form of a three-dimensional coordinate system 50, which includes a longitudinal axis (X) 51, a transverse axis (Y) 52, and a vertical axis (Z) 53. The longitudinal axis 51 is defined by a longitudinal axis of the vehicle 100, the transverse axis 52 is defined by a transverse axis of the vehicle 100, and the vertical axis 53 is defined such that it is orthogonal to a plane defined by the longitudinal axis 51 and the transverse axis 52. The vehicle 100 can be, but is not limited to, a mobile platform in the form of a commercial vehicle, an industrial vehicle, an agricultural vehicle, a passenger car, an aircraft, a watercraft, a train, an all-terrain vehicle, a personal mobility device, a robot, and the like, in order to fulfill the purposes of this disclosure. The navigation system 32 uses information from a GPS sensor 36 (Global Positioning System) and, in one embodiment, an IMU (Inertial Measurement Unit) 34. In another embodiment, a GNSS sensor (Global Navigation Satellite System) can be used instead of the GPS sensor 36. The IMU 34 is an electronic device that uses one or more combinations of accelerometers, gyroscopes, and magnetometers to measure and report the specific force, angular velocity, yaw, and orientation of the vehicle 100. In certain embodiments of the vehicle 100, the ADAS 40 is arranged to provide operator assistance functions by controlling one of the operating systems, i.e., one or more of the propulsion system 10, the steering system 16, or the wheel braking system 26, with or without direct interaction from the vehicle operator. The ADAS 40 comprises a controller and one or more subsystems that provide driver assistance functions, including one or more systems such as an adaptive cruise control (ACC), a lane keeping assist (LKY), a lane change assist (LCC), an autonomous braking / collision avoidance system, and / or other systems configured to command and control the autonomous operation of the vehicle independently of, or in conjunction with, driver requests.The ADAS 40 can interact with and access an onboard map database to plan the route and control the operation of the vehicle via the lane keeping system, lane centering system, and / or other systems configured to command and control autonomous vehicle operation. Autonomous operating commands can be generated to control the ACC system, LKY system, LCC system, autonomous braking / collision avoidance system, and / or other systems. Vehicle operation can occur in response to operator requests and / or autonomous vehicle requests. Vehicle operation includes accelerating, braking, steering, stationary driving, coasting, and idling. Operator requests can be generated based on operator input for an accelerator pedal, brake pedal, steering wheel, gear selector, ACC system, etc. The onboard navigation system 32 may include a computer-readable storage device or medium (memory) containing a digitized road map and communicating with the ADAS 40. The term "controller" and related terms such as microcontroller, control unit, control device, processor, etc., refer to one or more combinations of application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), electronic circuits, central processing units, e.g., microprocessors, and associated non-transient memory components in the form of storage and memory devices (read-only, programmable read-only, direct access, hard disk devices, etc.).The non-transitory memory component is capable of storing machine-readable instructions in the form of one or more software or firmware programs or routines, combinational logic circuits, input / output circuits and devices, signal conditioning, buffer circuits, and other components that can be accessed and executed by one or more processors to provide a described functionality. Input / output circuits and devices include analog-to-digital converters and related devices that monitor sensor inputs, either at a preset sampling rate or in response to a triggering event. Software, firmware, programs, instructions, control routines, code, algorithms, and similar terms refer to sets of instructions, including calibrations and lookup tables, that can be executed by controllers.Each controller executes control routine(s) to provide desired functions. These routines can be executed at regular intervals, such as every 100 microseconds during operation. Alternatively, they can be executed in response to a triggering event. Communication between controllers, actuators, and / or sensors can occur via a directly wired point-to-point connection, a networked communication bus, a wireless connection, or another communication link. This communication involves the exchange of data signals, such as electrical signals over a conductive medium, electromagnetic signals over air, optical signals over fiber optics, and so on. The data signals can include discrete analog and / or digitized analog signals representing sensor inputs, actuator commands, and communication between controllers.The term "signal" refers to a physically perceptible indicator that transmits information and can be a suitable waveform (e.g., electrical, optical, magnetic, mechanical, or electromagnetic), such as direct current, alternating current, sine wave, triangle wave, square wave, vibration, and the like, which can propagate through a medium. A parameter is defined as a measurable quantity that represents a physical property of a device or other element, detectable by one or more sensors and / or a physical model. A parameter can have a discrete value, e.g., either "1" or "0," or it can have a continuously variable value. With reference to Figures 2 and 3, and with further reference to the vehicle 100 described in Figure 1, the tire radius monitoring system 200 for dynamically determining an effective tire radius for each of the multiple vehicle wheels 20 is described, using information from the GPS sensor 36, the IMU 34, and the wheel speed sensors 22. The details of the tire radius monitoring system 200 are described in the context of a vehicle using four wheels. However, it is understood that the concepts described here can be applied to two-wheeled vehicles, three-wheeled vehicles, five-wheeled vehicles, six-wheeled vehicles, etc. The tire radius monitoring system 200 includes regular monitoring data generated by the GPS sensor 36, the IMU 34 and the wheel speed sensors 22. The GPS sensor 36 generates a first set of parameters associated with a vehicle speed vector 202, along with the geographic position and heading of the vehicle 100. In one embodiment, the vehicle speed vector 202 is described with reference to an ENU (East-North-Up) reference frame and a vehicle reference frame, wherein the positive x-axis of the vehicle points toward the front of the vehicle, the positive y-axis or pitch axis of the vehicle points to the left, and the positive z-axis or yaw axis points upward. The vehicle speed vector 202 includes vehicle speed parameters such as VE, VN, and VU, which relate to the speeds along the respective axes East (E), North (N), and Up (U). The IMU 34 generates a second set of parameters related to the vehicle's yaw angle (204). The term heading or yaw refers to the direction in which a vehicle is pointing. This second set of parameters can include the acceleration along each of the x, y, and z axes (a_xm, a_ym, a_zm) and the angular velocity along each of the x, y, and z axes (ω_x, ω_y, ω_z). The angular accelerations (A_x, A_y, A_z) can be obtained by numerically differentiating the angular velocities. The vehicle velocity vector 202 and the vehicle yaw angle 204 are input into an estimator 205, from which a longitudinal velocity vector Vx 210 is estimated. The longitudinal velocity vector Vx 210 represents the forward motion of the vehicle 100 along the longitudinal axis. The longitudinal velocity vector Vx 210 is denoted here as follows: where: υxlr represents the vehicle's speed in the x-direction for the left rear wheel, υxrr represents the vehicle's speed in the x-direction for the right rear wheel, υxlf represents the vehicle's speed in the x-direction for the left front wheel; and υxlr represents the vehicle's speed in the x-direction for the right front wheel. The wheel speed sensors 22 generate angular velocities 206 for the vehicle wheels 20. The angular velocities 206 are input into an averaging routine 207, from which an angular velocity vector 220 is determined. The angular velocity vector 220 contains moving averages for the angular velocities 206, which are designated here as follows: where: ωxrf represents the rotational velocity in the x-direction for the left rear wheel, ωxrr represents the rotational velocity in the x-direction for the right rear wheel, ωxlf represents the rotational velocity in the x-direction for the left front wheel; and ωxlr represents the rotational velocity in the x-direction for the right front wheel. The vehicle velocity vector Vx 210 and the angular velocity vector 220 are combined to form a measurement vector as follows: The measurement vector is provided as input for a rotational speed filter 230, which is executed to determine an effective tire radius for each of the wheels 20 of the vehicle 100. In one embodiment, and as described herein, the rotational speed filter 230 takes the form of a Kalman filter, specifically a linear Kalman filter in one embodiment. Kalman filtering, also known as linear quadratic estimation (LQE), is an analytical construct that can be reduced in practice to an algorithm that uses a series of measurements observed over time, which contain statistical noise and other inaccuracies, and generates estimates of unknown variables based on the measurements observed over a period of time. The rate-of-rise filter 230 contains the measurement vector based on the wheel rotational speeds of the multiple vehicle wheels and the vehicle speed. The rate-of-rise filter 230 contains a state vector representing the effective tire radii of the wheels 20 of the vehicle 100. The state vector can be expressed as follows: If the rate-of-rise filter 230 is a Kalman filter, the state and measurement equations can be represented as follows: The state vector xk for the rate-of-rise filter 230 is represented as follows: The measurement vector yk for the rate of change filter 230 is represented as follows: Process noise wk and measurement noise vk are zero-mean white noise processes with covariance matrices Qw and Rv, respectively. The single-stage prediction performed by the gradient rate filter 230 to determine the effective radius of the tire is as follows: The term x̂ represents an estimate of the tire effective radius for one of the vehicle wheels 20, where A ∈ Rn× n, B ∈ Rn× m, G ∈ Rx× gund C ∈ Rp × n, where n is the number of states, m is the number of outputs, p is the number of inputs and x and g are assumed to be equal to the number of states. The term kp represents an amplification factor, which can be determined as follows: An initial suboptimal filter is based on initial values ​​for the covariance matrices Qw and Rv. The goal of speed-dependent gain calibration is to estimate the covariance matrices Qw and Rv and calculate the gain factor kp as a function of vehicle speed. The gain factor kp as a function of vehicle speed can be determined for several vehicle speed ranges, one example of which is given in Table 1, as follows. Table 1 Speed ​​range: 0-5 m / s, 5-15 m / s, 15-30 m / s, >30 m / s Gain factor kp(0) kp(1) kp(2) kp(3) These gain factors kp(0), kp(1), kp(2), and kp(3) are used to improve the accuracy of the effective tire radii estimated in the following sections. The gains are calibrated to achieve faster convergence at low speeds and slower convergence at high speeds. Changes in the effective tire radius can occur over a relatively long period. In one embodiment, the calibrated gain factors are specific to a vehicle speed range. This may include a first calibrated gain factor kp(0), corresponding to a first of the plurality of vehicle speed ranges, which corresponds to a low speed range, where the low speed range in one embodiment is 0–5 m / s. This may include second calibrated gain factors kp(1) and kp(2), corresponding to intermediate speed ranges.In one embodiment, the medium speed ranges and calibrated gain factors include a medium speed range of 5-15 m / s and the associated gain factor kp(1), and a medium speed range of 15-30 m / s and the associated gain factor kp(2). This may include a third calibrated gain factor kp(3), corresponding to a third of the plurality of vehicle speed ranges, which corresponds to a high-speed range, wherein the high-speed range in one embodiment is >30 m / s. The number of speed ranges, the gain factors, and the associated speed range values ​​may be application-specific and are determined based on factors relating to vehicle capabilities, etc. The rate-of-rise filter 230 generates a state vector 235, which represents the effective radii of the tires of the wheels 20 of the vehicle 100. The state vector 235 is fed into a post-processing step 240, which is described in detail with reference to Fig. 3. The state vector 235 and other vehicle operating parameters are evaluated to determine whether the vehicle 100 experiences a change in the driving cycle, e.g., a change in speed (237). If there is no change in the driving cycle (237)(0), the post-processing step 240 is executed (240). If there has been a change in the driving cycle (237)(1), the execution of the post-processing step 240 is postponed, and the effective tire radii of the wheels 20 are restored from a non-volatile memory device (250) for use by the vehicle control unit 15. Referring to Fig. 3, post-processing step 240 involves evaluating the effective tire radii of the wheels 20 in the context of vehicle operation to eliminate singular or exceptional data points output by the rotational speed filter 230 (241). Singular or exceptional data points output by the rotational speed filter 230 may be associated with predefined non-stationary events, such as a braking event, a throttling event that can induce wheel slip, and non-linear dynamic maneuvers, including, for example, road surfaces with snow, ice, and road conditions, including uphill or downhill maneuvers. The occurrence of slip-free states can be determined according to the following relationships: where: σ represents the wheel slip; ω represents the wheel speed; and Vx represents the vehicle's velocity vector, i.e., the longitudinal velocity vector. The wheel slip σ is calculated for each of the vehicle wheels 20 using the EQS. 5 and 6. The detection of slip-free conditions can further include a comparative analysis of the wheel speeds of the multiple vehicle wheels 20. Post-processing step 240 involves evaluating the vehicle speed in the context of the speed ranges, e.g. as described with reference to Table 1 above, in order to identify the corresponding speed range (242). Post-processing step 240 involves determining and, if necessary, modifying the respective gain, i.e., one of the gains kp(0), kp(1), kp(2), and kp(3), to improve convergence based on the velocity and uncertainty of the input variables. The gains are determined based on factors such as the velocity threshold, the uncertainty of the received velocity input to the algorithm, and a tunable calibration parameter for adjustment. (244) The post-processing step 240 comprises capturing successive estimates of the effective tire radii of the wheels 20 in a data buffer over a period of time during a driving cycle and determining mean values ​​for the effective tire radii of the wheels 20 (245). Post-processing step 240 comprises correlating the effective tire radii of the wheels 20 with the tire pressures of the wheels 20 using a lookup table or another form of predefined calibrations (243). An example of a correlation between the effective tire radii of the wheels 20 and the tire pressures of the wheels 20 is shown graphically in Fig. 4. Data can be developed to provide a correlation between the tire radius, tire pressure, tire temperature, and vehicle mass, as shown in Table 2 below. This can be reduced to a practical lookup table implemented in a memory device in the controller 15 and accessible to the tire radius monitoring system 200. Radius (mm) Pressure (kPa) Temperature (°C) Mass (kg) When the elements shown with reference to Table 2 are developed and implemented on the vehicle, the effective radii of the wheels 20 determined with the help of the tire radius monitoring system can be used to estimate the vehicle mass, determine the tire pressure and determine other parameters associated with the tire pressure of the wheels 20. Post-processing step 240 includes compiling and storing the mean values ​​for the effective tire radii of the wheels 20, the correlated tire pressures from step 243 and the updated gains kp(0), kp(1), kp(2), and kp(3) from step 244 (246). The mean values ​​for the effective tire radii of the wheels 20, the correlated tire pressures from step 243 and the updated reinforcements kp(0), kp(1), kp(2) and kp(3) are provided as output 247 from post-processing step 240. Referring again to Fig. 2, the output 247 from the post-processing step 240, i.e. the mean values ​​for the effective tire radii of the wheels 20, the correlated tire pressures from step 243 and the updated gains kp(0), kp(1), kp(2) and kp(3), is provided to a non-volatile memory device for storage and subsequent use by the vehicle control unit 15 (250). Furthermore, output 247 from post-processing step 240, i.e., the mean values ​​for the effective tire radii of the wheels 20, is evaluated to determine whether a change has occurred in one or more of the effective tire radii of the wheels 20 that may necessitate notification of the vehicle operator (260). As a non-restrictive example, this could be the need to inform the driver about the occurrence of a low tire pressure event. The output 247 from the post-processing step 240, i.e. the mean values ​​for the effective tire radii of the wheels 20, can be transmitted to the controller 15 and used in the operation of vehicle control systems, including, for example, the drive system 10, the steering system 16, the wheel brake system 26 and / or the ADAS 40, which can control routines that take into account or mitigate tire pressure fluctuations, low tire pressure events, etc. Fig. 4 graphically shows data associated with the operation of an embodiment of the vehicle 100, described with reference to Fig. 1, which implements an embodiment of the tire radius monitoring system 200 to dynamically determine an effective tire radius for each of the multiple vehicle wheels 20. The data includes data points of the effective tire radius for a right front tire (data indicated by 411) and the effective tire radius for a left front tire (data indicated by 413), accumulated during operation of the vehicle 100 over a range of vehicle speeds from 5 mph to 35 mph and at a tire pressure varying between 24 psi and 45 psi. The graph shows the tire radius 410 on the vertical axis in relation to the tire pressure 420 on the horizontal axis. The data also include a static ground truth measurement at 33 psi (data indicated by 415).The results show that the embodiment of the tire radius monitoring system 200 was able to dynamically monitor the effective tire radius with a maximum error of less than 0.5 mm. The concepts described here make it possible to determine robust tire effect radius estimates by eliminating dynamic, high slip conditions through event-based data buffering and calibrating the tire effect radii over several driving cycles, taking into account the vehicle mass. In one embodiment, the tire radius monitoring system 200 described herein has the capability to detect tire pressure fluctuations which can be used to control vehicle operation based on the effective tire radii, including supplementing or verifying information from a tire pressure monitoring system (TPMS) on an embodiment of the vehicle 100. In one embodiment, the tire radius monitoring system 200 described here has the ability to detect tire pressure fluctuations, which can be used to replace a tire pressure monitoring system (TPMS), thereby making the TPMS unnecessary in one embodiment of the vehicle 100.

Claims

A tire radius monitoring system (200) for dynamically determining a tire effective radius for each of the wheels (20) on a vehicle (100), comprising: a GPS (Global Positioning System) sensor (36); a plurality of wheel speed sensors (22) configured to monitor the rotational speeds of a plurality of vehicle wheels (20); a controller (15); and an ascending speed filter (230); wherein the controller (15) communicates with the GPS sensor (36) and the multiple wheel speed sensors (22); wherein the controller (15) contains a set of commands configured to: determine a longitudinal velocity vector (Vx) (210) for the vehicle (100) via the GPS sensor (36); determine the wheel rotational speeds for the multiple vehicle wheels (20) via the multiple wheel speed sensors (22); detect a wheel slip-free condition for the multiple vehicle wheels (20);to determine effective tire radii for the multiple vehicle wheels (20) based on the longitudinal velocity vector (210) for the vehicle (100) and the wheel rotational speeds for the multiple vehicle wheels (20) during the wheel-slip-free state; to execute the rate-of-rise filter (230) to determine the effective tire radii, wherein the rate-of-rise filter (230) comprises a state vector (235) based on the effective tire radii and a measurement vector based on the wheel rotational speeds for the plurality of vehicle wheels (20); wherein the rate-of-rise filter (230) comprises a plurality of calibrated gain factors corresponding to a plurality of vehicle speed ranges associated with the longitudinal velocity vector (210) for the vehicle (100); and to control the operation of the vehicle (100) based on the effective tire radii. Tire radius monitoring system (200) according to claim 1, wherein the rate of change filter (230) comprises a Kalman filter, and wherein the instruction set is configured to execute the Kalman filter to determine the effective tire radii using the state vector (235) based on the effective tire radii and the measurement vector based on the wheel rotation speeds for the plurality of vehicle wheels (20). Tire radius monitoring system (200) according to claim 1, wherein the plurality of calibrated gain factors specific to the vehicle speed range comprises a first calibrated gain factor corresponding to a first of the plurality of vehicle speed ranges corresponding to a low speed range, a second calibrated gain factor corresponding to a second of the plurality of vehicle speed ranges corresponding to a medium speed range, and a third calibrated gain factor corresponding to a third of the plurality of vehicle speed ranges corresponding to a high speed range. Tire radius monitoring system (200) according to claim 1, further comprising the command set which is configured to determine the tire pressures for the plurality of vehicle wheels (20) on the basis of the effective tire radii. Tire radius monitoring system (200) according to claim 1, wherein the command set is configured to detect the non-wheel slip state for the multiple vehicle wheels (20) based on the longitudinal velocity vector (210) for the vehicle (100) and the wheel rotation speeds for the multiple vehicle wheels (20). Tire radius monitoring system (200) according to claim 1, further comprising the command set which is configured to correlate vehicle mass and tire pressure with the effective tire radii. A tire radius monitoring system (200) for a vehicle (100), comprising: a GPS (Global Positioning System) sensor (36); a plurality of wheel speed sensors (22) configured to monitor the rotational speeds of a plurality of vehicle wheels (20); a controller (15); and an ascending speed filter (230); wherein the controller (15) communicates with the GPS sensor (36) and the multiple wheel speed sensors (22); wherein the controller (15) contains a set of commands configured to: determine a longitudinal velocity vector (210) for the vehicle (100) via the GPS sensor (36); determine the wheel rotational speeds for the multiple vehicle wheels (20) via the multiple wheel speed sensors (22); determine a measurement vector containing the wheel rotational speeds for the multiple vehicle wheels (20) and the longitudinal velocity vector (210) for the vehicle (100);to execute the rate-of-rise filter (230) to determine a state vector (235) containing the effective tire radii for the multiple vehicle wheels (20), wherein the state vector (235) is determined based on the measurement vector containing the wheel rotation speeds for the multiple vehicle wheels (20) and the longitudinal speed vector (210) for the vehicle (100); to execute the rate-of-rise filter (230) to determine the effective tire radii, wherein the rate-of-rise filter (230) contains a state vector (235) based on the effective tire radii and a measurement vector based on the wheel rotation speeds for the plurality of vehicle wheels (20); wherein the rate-of-rise filter (230) contains a plurality of calibrated gain factors corresponding to a plurality of vehicle speed ranges associated with the longitudinal speed vector (210) for the vehicle (100);and to control vehicle operation based on the state vector (235) including the effective tire radii.; Tire radius monitoring system (200) according to claim 7, wherein the rate of change filter (230) comprises a Kalman filter; and wherein the instruction set is configured to execute the Kalman filter to determine the state vector (235) including the effective tire radii on the basis of the measurement vector including the wheel rotation speeds for the plurality of vehicle wheels (20) and the longitudinal velocity vector (210) for the vehicle (100).

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