Method for estimating a tire lateral grip indicator and system for a vehicle

Abstract

Methods and systems for estimating the lateral grip of vehicle tires are provided. The methods include receiving sensor signals, processing the signals to determine a restoring torque rate (RTR).Self-Aligning Torque (SAT) to estimate a lateral force rate and a slip angle rate, performing a state synchronization process to reduce a timing mismatch between the lateral force rate and the slip angle rate, performing a filtering process to provide a body roll estimate and a SAT slope estimate, performing a normalization process on the body roll estimate and the SAT slope estimate, classifying the normalized SAT slope estimate, and performing an arbitration and fusion process to adjust the normalized body roll estimate based on the classification of the normalized SAT estimate to estimate a final indicator of lateral grip level.

Description

[0001] The technical field relates generally to the lateral grip of tires of a vehicle and, more specifically, to systems and methods for estimating the lateral grip of a vehicle taking into account the estimation of the slope of the self-aligning torque (SAT).

[0002] A tire's lateral grip refers to the tire's ability to maintain grip and traction when a vehicle performs lateral movements, such as cornering or changing lanes. Several factors can contribute to tire lateral grip, including specific tire parameters (e.g., material, size / shape, tread depth / design, internal pressure, internal structure, etc.), vehicle parameters (e.g., suspension system, current tire speed and camber angle, vertical loading, etc.), and driving conditions (e.g., road surface, weather conditions, temperature, etc.).

[0003] The tire lateral grip limit indicates the maximum lateral force acting on the tires before skidding occurs. Certain modern vehicle systems can use this limit when implementing various vehicle control functions, such as steering assist.

[0004] DE 603 05 232 T2 describes a device for estimating a grip factor which determines a degree of grip of a tire in the lateral direction of a vehicle wheel.

[0005] DE 10 2018 108 111 B4 describes a computer-implemented method for controlling a vehicle with a vehicle control module, wherein the vehicle control module comprises an electronic power steering system.

[0006] It can be considered a task to provide systems and methods that enable accurate and efficient estimation of the tire grip limit during vehicle operation.

[0007] The object is achieved by a method according to the invention according to claim 1 and a system according to the invention according to claim 6. Furthermore, a vehicle is described which is operated with the method and in which the system is integrated.

[0008] A method is provided for estimating a final indicator of tire lateral grip for a vehicle traveling on tires. The method according to the invention comprises, with one or more processors of a controller onboard the vehicle: receiving signals from an onboard sensor system of the vehicle indicative of vehicle operating parameters; processing the signals to estimate a aligning torque rate, a lateral force rate, and a slip angle rate; performing a state synchronization process to reduce a timing mismatch between the lateral force rate and the slip angle rate, thereby providing a synchronized slip angle rate; performing a filtering process to provide an estimate of the roll angle and an estimate of the slope of the aligning torque, each based on the aligning torque rate;the lateral force rate and the synchronized slip angle rate, performing a normalization process to reduce the noise associated with the body roll estimate and the aligning moment slope estimate, thereby generating a normalized body roll estimate and a normalized aligning moment slope estimate, classifying the normalized aligning moment slope estimate, and performing an arbitration and fusion process to adjust the normalized body roll estimate based on the classification of the normalized aligning moment slope estimate to estimate the final lateral grip level indicator.

[0009] In various embodiments, the operating parameters used in the method include a lateral force, a steering torque, a longitudinal speed, a lateral acceleration, a yaw rate, steering angles, and various vehicle parameters.

[0010] In various embodiments, processing the signals to estimate the aligning torque rate is based on a total torque received from a controller area network of the vehicle, an aligning torque of the tires, a position and a velocity of the tires, a flat mass of a steering system of the vehicle, and a flat damping of the vehicle.

[0011] In various embodiments, processing the signals to estimate the lateral force rate is based on the lateral forces of the tires, the vertical forces of the tires, and a steering road wheel angle.

[0012] In various embodiments, processing the signals to estimate the slip angle rate is based on a longitudinal velocity, a lateral acceleration, a yaw rate, one or more vehicle parameters, and a steering wheel angle.

[0013] In various embodiments, performing the filtering process to provide the lateral pitch estimate and the aligning moment slope estimate comprises using a recursive least squares estimator or a Kalman filter.

[0014] In various embodiments, performing the normalization process to reduce the noise associated with the body roll estimation and the aligning torque slope estimation includes taking road conditions into account.

[0015] A system for a vehicle is provided. The system according to the invention comprises a sensor system configured to detect observable conditions of an environment outside the vehicle, an environment inside the vehicle, and / or a condition of one or more components of the vehicle, and a controller configured to receive signals from the sensor system with one or more processors, display the operating parameters of the vehicle while traveling on tires, process the signals to estimate a aligning torque rate, a lateral force rate, and a slip angle rate, perform a state synchronization process to reduce a timing mismatch between the lateral force rate and the slip angle rate and thereby provide a synchronized slip angle rate, perform a filtering process,to provide a body roll estimate and a aligning moment slope estimate based respectively on the aligning moment rate, the lateral force rate, and the synchronized slip angle rate, perform a normalization process to reduce the noise associated with the body roll estimate and the aligning moment slope estimate, thereby generating a normalized body roll estimate and a normalized aligning moment slope estimate, classify the normalized aligning moment slope estimate, and perform an arbitration and fusion process to adjust the normalized body roll estimate based on the classification of the normalized aligning moment slope estimate to estimate a final indicator of the lateral grip level.

[0016] In various embodiments, the operating parameters used by the system controller include lateral force, steering torque, longitudinal velocity, lateral acceleration, yaw rate, steering angle, and various vehicle parameters.

[0017] In various embodiments, the controller of the system is configured to process, through the one or more processors, the signals to estimate the aligning torque rate based on a total torque received from a controller area network of the vehicle, an aligning torque of the tires, a position and a velocity of the tires, a blanket mass of a steering system of the vehicle, and a blanket damping of the vehicle.

[0018] In various embodiments, the controller of the system is configured to process, through the one or more processors, the signals to estimate the lateral force rate based on the lateral forces of the tires, the vertical forces of the tires, and a steering road wheel angle.

[0019] In various embodiments, the controller of the system is configured to process, through the one or more processors, the signals to estimate the slip angle rate based on a longitudinal velocity, a lateral acceleration, a yaw rate, one or more vehicle parameters, and a steering wheel angle.

[0020] In various embodiments, the controller of the system is configured to perform, through the one or more processors, the filtering process to provide the body roll estimate and the aligning moment slope estimate using a recursive least squares estimator or a Kalman filter.

[0021] In various embodiments, the controller of the system is configured to perform, through the one or more processors, the normalization process to reduce the noise associated with the estimation of the body roll and the estimation of the slope of the aligning torque, taking into account the road conditions.

[0022] A vehicle is provided that, in one example, includes a sensor system configured to sense observable conditions of an environment external to the vehicle, an environment internal to the vehicle, and / or a state of one or more components of the vehicle, and a controller configured to communicate with one or more processors to receive signals from the sensor system indicative of operating parameters of the vehicle while traveling on tires, process the signals to estimate a aligning torque rate, a lateral force rate, and a slip angle rate, perform a state synchronization process to reduce a timing mismatch between the lateral force rate and the slip angle rate, thereby providing a synchronized slip angle rate, perform a filtering process to provide an estimate of body roll and an estimate of the slope of the aligning torque,which are based respectively on the aligning moment rate, the lateral force rate and the synchronized slip angle rate, to perform a normalization process to reduce the noise associated with the body roll estimate and the aligning moment slope estimate, thereby generating a normalized body roll estimate and a normalized aligning moment slope estimate, to classify the normalized aligning moment slope estimate, and to perform an arbitration and fusion process to adjust the normalized body roll estimate based on the classification of the normalized aligning moment slope estimate to estimate a final indicator of the lateral grip level.

[0023] In various examples, the operating parameters used by the vehicle's controller include lateral force, steering torque, longitudinal velocity, lateral acceleration, yaw rate, steering angle, and various vehicle parameters.

[0024] In various examples, the controller of the vehicle is configured to process, through the one or more processors, the signals to estimate the aligning torque rate based on a total torque received from a controller area network of the vehicle, an aligning torque of the tires, a position and a velocity of the tires, a blanket mass of a steering system of the vehicle, and a blanket damping of the vehicle.

[0025] In various examples, the controller of the vehicle is configured to process, through the one or more processors, the signals to estimate the lateral force rate based on the lateral forces of the tires, the vertical forces of the tires, and a steering road wheel angle.

[0026] In various examples, the controller of the vehicle is configured to process, through the one or more processors, the signals to estimate the slip angle rate based on a longitudinal velocity, a lateral acceleration, a yaw rate, one or more vehicle parameters, and a steering wheel angle.

[0027] In various examples, the controller of the vehicle is configured to perform, through the one or more processors, the normalization process to reduce noise associated with the body roll estimation and the aligning torque slope estimation taking into account road conditions.

[0028] The embodiments are described below in conjunction with the following drawings, wherein like reference numerals denote like elements, and wherein: Fig. 1 is a functional block diagram of an exemplary vehicle with an estimation system; Fig. 2 a data flow diagram of the estimation system of Fig. 1 is; Fig. 3 an algorithm for estimating a restoring moment using the estimation system of Fig. 1 is; Fig. 4 is a data flow diagram of a submodule for estimating the lateral force rate; Fig. 5 is a data flow diagram of a slip angle rate estimation submodule; Fig. 6 is a line graph illustrating an estimated lateral force rate and an estimated slip angle rate for an exemplary vehicle during a slalom maneuver; Fig. 7 is a flowchart illustrating a method for synchronizing the lateral force rate and the slip angle rate of an exemplary vehicle, thereby providing a synchronized slip angle rate; Fig. Figure 8 is a data flow diagram of a lateral inclination estimation submodule; Fig. 9 is a flowchart showing a method for normalizing the estimated body roll of a vehicle; Fig. 10 is a flowchart illustrating a method for classifying the aligning torque rate of a vehicle; Fig. 11 is a flowchart illustrating a method for estimating final lateral grip levels for a vehicle; and Fig. Figure 12 is a flowchart illustrating a method for estimating vehicle tire lateral grip indicators.

[0029] As used herein, the term module refers to any hardware, software, firmware, electronic control component, processing logic, and / or processor device, individually or in any combination, including, without limitation, an application-specific integrated circuit (ASIC), an electronic circuit, a processor (common, dedicated, or group) and memory executing one or more software or firmware programs, a combined logic circuit, and / or other suitable components that provide the described functionality.

[0030] Examples of the present description may be described herein in terms of functional and / or logical block components and various processing steps. Such block components may be implemented by any number of hardware, software, and / or firmware components configured to perform the specified functions. For example, an embodiment of the present description may utilize various integrated circuit components, such as memory elements, digital signal processing elements, logic elements, lookup tables, or the like, capable of performing a variety of functions under the control of one or more microprocessors or other control units.Furthermore, those skilled in the art will recognize that the examples of the present description may be used in connection with any number of systems and that the systems described herein are merely examples of the present description.

[0031] For the sake of brevity, conventional techniques related to signal processing, data transmission, signaling, control, and other functional aspects of the systems (and the individual operating components of the systems) are not described in detail here. Furthermore, the connecting lines depicted in the various figures are intended to represent exemplary functional relationships and / or physical couplings between the various elements. It should be noted that many alternative or additional functional relationships or physical connections may be present in an example of the present description.

[0032] Fig. 1 shows a vehicle 10, according to one example. The vehicle 10 includes an estimation system 100 for estimating the lateral grip of tires of a vehicle considering an estimate of the slope of the self-aligning torque (SAT). In certain examples, the vehicle 10 comprises an automobile. In various examples, the vehicle 10 may be any of a number of different types of automobiles, such as a sedan, station wagon, truck, or sport utility vehicle (SUV), and may be two-wheel drive (2WD) (i.e., rear-wheel drive or front-wheel drive), four-wheel drive (4WD), or all-wheel drive (AWD), and / or various other types of vehicles or mobile platforms in certain examples.

[0033] As in Fig. 1, the exemplary vehicle 10 generally includes a chassis 12, a body 14, front wheels 16, and rear wheels 18. The body 14 is disposed on the chassis 12 and substantially encloses components of the vehicle 10. The body 14 and the chassis 12 may collectively form a frame. Wheels 16-18 are each rotatably connected to the chassis 12 near a corner of the body 14.

[0034] The vehicle 10 further includes a drive system 20, a transmission system 22, a steering system 24, a braking system 26, a sensor system 28, an actuator system 30, at least one data storage device 32, at least one controller 34, and a steering assist system 35. The drive system 20 includes an internal combustion engine and / or motor 21, such as an internal combustion engine (e.g., a gasoline or diesel engine), an electric motor (e.g., a 3-phase AC motor), or a hybrid system including more than one type of internal combustion engine and / or motor. The transmission system 22 is configured to transfer the power of the drive system 20 to the wheels 16-18 according to selectable gear ratios. According to various examples, the transmission system 22 may include a continuously variable automatic transmission, a continuously variable transmission, or other suitable transmission. The steering system 24 influences the positions of the wheels 16-18.Although a steering wheel 24a is shown for illustrative purposes, the steering system 24 may not include a steering wheel in some examples contemplated by the present description. The wheels 16-18 include tires configured to contact a roadway or other surface.

[0035] The sensor system 28 includes one or more sensing devices 40a-40n that sense observable conditions of the external environment, the internal environment, and / or a status or condition of a corresponding component of the vehicle 10 and communicate this condition and / or status to other systems of the vehicle 10, such as the controller 34. It should be understood that the vehicle 10 may include any number of sensing devices 40a-40n. The sensing devices 40a-40n may include, but are not limited to, current sensors, voltage sensors, temperature sensors, engine speed sensors, position sensors, velocity sensors, acceleration sensors, etc.

[0036] The actuator system 30 includes one or more actuator devices 42a-42n that control one or more vehicle functions, such as, but not limited to, the drive system 20, the transmission system 22, and / or the steering system 24.

[0037] The data storage device 32 stores data for controlling the vehicle 10 and / or its systems and components. As can be appreciated, the data storage device 32 may be part of the controller 34, separate from the controller 34, or part of the controller 34 and part of a separate system. The data storage device 32 may be any suitable type of storage device, including various types of random access memory and / or other storage devices. In one example, the data storage device 32 includes a program product from which a computer-readable storage device can receive a program that performs one or more examples of one or more processes of the present description.In another example, the program product may be stored and / or otherwise accessed directly on the storage device and / or one or more other disks and / or other storage devices.

[0038] The controller 34 includes at least one processor 44, a communications bus 45, and a computer-readable storage device or medium 46. The processor 44 performs the computation and control functions of the controller 34. The processor 44 may be any custom or off-the-shelf processor, a central processing unit (CPU), a graphics processing unit (GPU), an auxiliary processor among multiple processors associated with the controller 34, a semiconductor-based microprocessor (in the form of a microchip or chipset), a macroprocessor, any combination thereof, or generally any device for executing instructions. The computer-readable storage device or media 46 may include volatile and non-volatile memory, such as read-only memory (ROM), random access memory (RAM), and keep-alive memory (KAM).KAM is a persistent or non-volatile memory that can be used to store various operating variables while the processor 44 is powered off. The computer-readable storage device or media 46 can be implemented using any number of known storage devices such as PROMs (programmable read-only memories), EPROMs (erasable PROMs), EEPROMs (electrically erasable PROMs), flash memory, or other electrical, magnetic, optical, or combination storage devices capable of storing data, some of which may be executable instructions used by the control unit 34 in controlling the vehicle 10. The communication bus 45 is used to transfer programs, data, status, and other information or signals between the various components of the vehicle 10.The communication bus 45 may be any suitable physical or logical means for interconnecting computer systems and components. These include, but are not limited to, direct, hard-wired connections, fiber optics, infrared, and wireless bus technologies.

[0039] The instructions may comprise one or more separate programs, each comprising an ordered collection of executable instructions for implementing logical functions. When executed by the processor 44, the instructions receive and process signals from the sensor system 28, perform logic, calculations, methods, and / or algorithms, and generate data based on the logic, calculations, methods, and / or algorithms. Although in Fig. 1 only one controller 34 is shown, examples of the vehicle 10 may include any number of controllers 34 that communicate via any suitable communication medium or combination of communication media and that cooperate to process the sensor signals, perform logic, calculations, methods and / or algorithms, and generate data.

[0040] As can be seen, the control 34 can also be different from the Fig. 1. For example, the controller 34 may be coupled to or otherwise utilize one or more remote computer systems and / or other control systems, for example, as part of one or more of the vehicle devices and systems mentioned above. While this example is described in the context of a fully functional computer system, those skilled in the art will recognize that the mechanisms of the present description may be distributed as a program product having one or more types of non-transitory, computer-readable, signal-bearing media used to store the program and its instructions and to facilitate its distribution, such as a non-transitory, computer-readable medium carrying the program and having computer instructions stored therein for causing a computer processor (such as processor 44) to execute and execute the program.Such a program product may take a variety of forms, and the present description applies equally regardless of the particular type of computer-readable signal-bearing medium used to effect distribution. Examples of signal-bearing media include writable media such as floppy disks, hard disks, memory cards, and optical discs, as well as transmission media such as digital and analog communication links. In certain examples, cloud-based storage and / or other technologies may also be used. It will also be appreciated that the computer system of the controller 34 may otherwise differ from that shown in FIG. Fig. 1, for example, in that the computer system of the controller 34 may be coupled to or otherwise utilize one or more remote computer systems and / or other control systems.

[0041] The braking system 26 is configured to apply braking torque, pressure, or force to the wheels 16-18. The braking system 26 may, in various examples, include friction brakes, brake-by-wire systems, a regenerative braking system such as an electric machine, and / or other suitable braking systems. In one example, the vehicle 10 includes the brake pedal 31, which can be moved by the operator from a released position to a depressed position to activate the braking system 26 and apply the braking torque (i.e., pressure or force).

[0042] The driver assistance system 36 may include various software and / or hardware components configured to provide driving assistance, for example, by automatically controlling one or more of the controllers 42a-42n, thereby controlling the acceleration, steering, and / or braking of the vehicle without user intervention, or otherwise adjusting the acceleration, steering, and / or braking input by the user. In some examples, the driver assistance system 36 may include an all-wheel drive (AWD) system, a torque vectoring system, an electric power steering (EPS), and / or an active rear-axle steering (ARS).

[0043] With reference to Fig. 2 and with continued reference to Fig. 1 illustrates a data flow diagram of elements of the estimation system 100 of Fig. 1 in accordance with various examples. As can be appreciated, various examples of the estimation system 100 according to the present description may include any number of modules embedded within the controller 34, which may be combined and / or further subdivided to implement the systems and methods described herein in a similar manner. Furthermore, inputs to the estimation system 100 may be received from other control modules (not shown) connected to the vehicle 10 and / or determined / modeled by other sub-modules (not shown) within the controller 34. Furthermore, the inputs may also be subjected to preprocessing, such as subsampling, noise reduction, normalization, feature extraction, missing data reduction, and the like.In various examples, the estimation system 100 includes a signal conditioning module 210, a state synchronization module 212, a slope estimation module 214, and an arbitration and fusion module 216.

[0044] In various examples, the signal conditioning module 210 receives as input various data indicative of operating parameters and vehicle parameters of the vehicle 10. In the example of Fig. 2, the signal conditioning module 210 receives sensor data 230 generated by the sensor system 28 and vehicle parameter data 242 retrieved from a data storage device (e.g., the data storage device 32). The sensor data 230 includes various data indicative of, for example, the lateral force on the tires sensed by a lateral force sensor, the steering torque of the vehicle 10 sensed by a steering torque sensor, the longitudinal velocity of the vehicle 10 sensed by a speed sensor, the lateral acceleration of the vehicle 10 sensed by an acceleration sensor, the yaw rate of the vehicle 10 sensed by a yaw rate sensor, and the steering angles of the vehicle 10 sensed by steering angle sensors. The vehicle parameter data 242 includes various data indicative of certain aspects of the vehicle 10, such as the vehicle mass, the distances between the vehicle's center of gravity and the centers of the front and rear axles, etc.

[0045] The signal conditioning module 210 processes the sensor data 230 and the vehicle parameter data 242 to estimate a restoring torque (SAT) rate, a lateral force rate, and a slip angle rate of the vehicle 10. In some examples, these processes may be performed by a SAT rate estimation submodule 211, a lateral force rate estimation submodule 213, and a slip angle rate estimation submodule 215.

[0046] Fig. Figure 3 illustrates an exemplary algorithm with respect to Equations 1-3 below for use in operating the SAT rate estimation submodule 211. In this example, reference numeral 310 represents u, reference numeral 312 represents Equation 2 below, reference numeral 314 represents y, reference numeral 316 represents e, reference numeral 318 represents an observer gain (L), reference numeral 320 represents Equation 3 below, reference numeral 322 represents the estimated SAT of the tires, the position and speed of the steering system (x̂; less friction torque, if any), reference numeral 324 represents C, and reference numeral 326 represents ŷ. In Equations 1-3 below, T total for a total torque received from a controller area network (CAN) of the vehicle 10, T r for a SAT of the tires (optionally with the friction torque, if available (e.g., T r = SAT + friction torque), x rand r represent a position or a speed of the steering system, m r represents a flat-rate mass of a steering system 24 of the vehicle 10, b r represents a flat-rate damping of the steering system 24 of the vehicle 10. The observer gain (L) of the observer can be adjusted using pole placement, optimal and / or robust filtering techniques. With the Fig. 3, SAT and SAT rate can be accurately estimated by comparing the estimated and measured signals and adjusting the state estimate using the observer gain (L). {[x˙rx¨rT˙r]=[0100−brmr−1mr000][xrx˙rTr]︸x˙+[01mr0]TTotal︸u︸x˙y=[1 0 0]︸c[xrx˙rTr]︸x x˙=Ax+Bu y=Cx x^˙=Ax^+Bu+L(y−y^)

[0047] Fig. 4 illustrates an exemplary operation of submodule 213 for estimating the lateral force rate of the rear tires (Fy Rrate). In this example, a first Fy submodule 424 receives lateral force data 418 indicating the lateral force in the y-direction (Fy) of the left rear tire (LR) and the right rear tire (RR), and vertical force data 420 indicating the vertical force in the z-direction (Fz) of the left rear tire (LR) and the right rear tire (RR). The first Fy submodule 424 differentiates the lateral forces of the rear tires (Fy(LR,RR)) with respect to the vertical forces of the rear tires (Fz(LR,RR)) to form a first derivative (d / dFz). The first Fy submodule 424 outputs first result data 425 indicating the first derivative (d / dFz). The first result data 425 is received by a second Fy submodule 426, which then differentiates the first derivative (d / dFz) with respect to time to produce a second derivative (d / dt). The second Fy submodule 426 outputs second result data 427 indicating the second derivative (d / dt).The second result data 427 is received by a third Fy submodule 428, which may apply a low-pass filter to the second derivative to estimate the y-direction lateral force rate for the rear tires. The third Fy submodule 428 may output estimated lateral force rates 429 indicating the estimated lateral force rate for the rear tires (Fy. R Rate). This process can be repeated for the front tires using the lateral force (Fy) and vertical force (Fz) on the left front tire (LF) and the right front tire (RF) to determine the lateral force rate for the front tires (Fy F rate).

[0048] Fig. 5 illustrates an exemplary operation of the sub-module 215 for estimating the slip angle rate for the rear tires (α˙R). In this example, a first α submodule 530 receives rear angle data 522 indicative of a rear road wheel angle (RWA). The first α submodule 530 differentiates the rear road wheel angle (RWA) with respect to time (t) to generate a first derivative (d / dt). The first α submodule 530 outputs first result data 531 indicative of the first derivative (d / dt). A second α submodule 532 receives the first result data 531 and may apply a low-pass filter to the first derivative (d / dt) to determine the rear road wheel angle rate. (δ˙r) The second α submodule 532 may output second result data 533 that includes the estimated rear steering road wheel angle rate (δ˙r) A third α-submodule 534 receives the second result data 533 from the second α-submodule 532 and receives the sensor data 230 indicating the longitudinal velocity (V x ), the lateral acceleration (A y), the yaw rate (r) and the yaw acceleration (ṙ), as well as the vehicle parameter data 242 to determine the slip angle rate of the rear tires (α˙R) In some examples, the third α submodule 534 may use Equation 4 below. (α˙R)=(αy−Lrr˙)Vx−r−δ˙r

[0049] The third α submodule 534 may output estimated slip angle data 535 indicating the estimated slip angle rate of the rear tires. This process may be repeated for the front tires using the front steering road wheel angle (RWA) to determine the slip angle rate of the front tires. (α˙F). to appreciate.

[0050] The signal conditioning module 210 generates conditioned input data 244 indicative of the estimated aligning torque rate, the estimated lateral force rate, and the estimated slip angle rate of the vehicle 10.

[0051] In various examples, the state synchronization module 212 receives as input the conditioned input data 244 generated by the signal conditioning module 210. The state synchronization module 212 performs a state synchronization process to coordinate various estimation inputs and reduce delays and / or missynchronization issues. In some examples, the state synchronization process may be performed to reduce a timing mismatch between the lateral force rate and the slip angle rate, thereby providing a synchronized slip angle rate. Fig. For example, Figure 6 is a line graph depicting an estimated lateral force rate (line 630) and an estimated slip angle rate (line 640) for an example vehicle during a slalom maneuver. The line graph includes time (620) on the x-axis and the original variables (i.e., the derivative of the lateral force and the derivative of the slip angle) on the y-axis (610). As illustrated, the rates were offset, corresponding to a time lag of approximately 0.18 seconds.

[0052] In various examples, the state synchronization module 212 may adjust one or more variables to reduce timing variances. Fig. For example, Figure 7 illustrates a method 700 for synchronizing the lateral force rate and the slip angle rate of an example vehicle and thereby providing a synchronized slip angle rate. The method 700 may begin at 710. At 712, the method 700 may include obtaining the lateral force rate and the slip angle rate, for example, from the conditioned input data 244. At 714, the method 700 may include determining a time delay between the lateral force rate and the slip angle rate. At 716, the method 700 may include comparing the determined time delay to a time delay threshold (e.g., zero). If the determined time delay is greater than the time delay threshold at 716, the slip angle rate may be modified at 718 to synchronize it with the lateral force rate, e.g., the determined time delay (at 720) may be added to the slip angle rate.At 722, method 700 may include outputting the modified slip angle rate as the synchronized slip angle rate. Method 700 may end at 724. State synchronization module 212 generates state synchronization data 246 including various data indicative of the estimated SAT rate, the estimated lateral force rate, and the synchronized slip angle rate.

[0053] In various examples, the slope estimation module 214 receives as input the state synchronization data 246 generated by the state synchronization module 212. The slope estimation module 214 processes the state synchronization data 246 with a roll submodule 218 to estimate the roll. Fig. For example, Figure 8 illustrates an exemplary operation of the roll submodule 218 for estimating the roll of a vehicle. A reset submodule 822 may receive the sensor data 230 representing the lateral acceleration (Ay ), the longitudinal speed (V x ) and the normalized lateral force (e.g., Fy / SAT) to determine whether to estimate body roll. In some examples, the reset submodule 822 determines that body roll should be estimated in response to the absolute values of lateral acceleration (A y ), the longitudinal velocity (Vx), and the normalized lateral force are below a calibrated threshold. The reset submodule 822 may generate reset data 830 indicating a decision as to whether to estimate the roll.

[0054] An estimator submodule 824 may receive the state synchronization data 246 and process the state synchronization data 246 to estimate the roll based on the synchronized slip angle rate, the lateral force rate, and the SAT rate. In some examples, the estimator submodule 824 may use an estimator or filter to estimate the roll and thereby reduce noise. Non-constrained estimators / filters may include a recursive least squares estimator or a Kalman filter. In such examples, the estimator submodule 824 may consider any necessary calibration variables while using the estimators / filters (e.g., a recursive least squares estimator that estimates noise covariance), as indicated, for example, by estimator / filter data 818 retrieved from a data storage device (e.g., the data storage device 32).The estimator submodule 824 may generate estimator data 832 indicative of the estimated body roll (θ). A unit delay submodule 826 receives the reset data 830, the estimator data 832, and the stiffness data 820 indicative of a maximum cornering stiffness. If the reset data 830 indicates that the body roll should be estimated / updated, the delay submodule 826 uses the estimated body roll and the maximum cornering stiffness to generate delayed body roll data 834. The delayed body roll data 834 is received by an arithmetic submodule 828 configured to normalize the delayed body roll with respect to the maximum cornering stiffness. The arithmetic submodule 828 may generate estimated body roll data 248 indicative of a final normalized estimated body roll.

[0055] Again with reference to Fig. 2, once the roll has been estimated by the estimator submodule 824, a normalization submodule 220 may receive the estimated roll data 248 and normalize the final estimated roll. Fig. For example, FIG. 9 shows an example method 900 for normalizing the estimated body roll of a vehicle. The method 900 may begin at 910. At 912, the method 900 may include obtaining the estimated body roll, for example, from the estimated body roll data 248. At 914, the method 900 may include determining if the road condition (mu) is available. If so, at 914, the method 900 may include normalizing the estimated body roll at 916, for example, based on the road condition (mu) and the normalized stiffness at the origin at 918 (i.e., the estimated roll at a body roll angle of zero). In some examples, the road condition (mu) and the normalized stiffness at the origin may be retrieved from a lookup table.If the road condition (mu) is not available at 916, the method 900 may include normalizing the estimated side slope without considering the road condition (mu) at 920. At 922, the method 900 may include outputting an indicator of the slope adhesion degree of the lateral force (e.g., between zero and one). The method 900 may end at 924. The normalization submodule 220 may generate normalized data 250 indicative of the slope adhesion degree of the lateral force indicator.

[0056] The slope estimation module 214 processes the state synchronization data 246 with a SAT slope submodule 222 to estimate the SAT slope. For example, the state synchronization data 246 may be processed by the estimator submodule 824, the unit delay submodule 826, and the arithmetic submodule 828 to calculate the normalized sideslip slope.

[0057] As in Fig. As shown in Figure 2, after the SAT rate is estimated by the SAT slope submodule 222, a classification submodule 224 may receive the estimated SAT slope data 249 and classify the estimated SAT rate. Various classifications and criteria for such classifications may be used. Fig. For example, Figure 10 shows a method 1000 for classifying a vehicle's SAT rate. In this example, the SAT rate is classified into one of four classes, including SAT neutral, linear, pre-saturation, and saturation.

[0058] The method 1000 may begin at 1010. At 1012, the method 1000 may include determining whether the conditions for classifying SAT neutral are met. For example, the steering wheel angle (SWA) may be compared to a steering wheel angle calibration threshold (e.g., | SWA | >A1), or the steering wheel angle rate may be compared to a steering wheel angle rate calibration threshold (e.g., | SWA rate | >A2). If the conditions are met at 1012, the method 1000 may include classifying the SAT rate as SAT neutral at 1020. If the conditions are not met at 1012, the method 1000 may continue to 1014.

[0059] At 1014, the method 1000 may include determining whether the conditions for the linear class are met. For example, the SAT slope may be compared to a first SAT slope calibration threshold (e.g., SAT rate > T1 > 0). If the conditions at 1014 are met, the method 1000 may include classifying the SAT rate as linear at 1022. If the conditions at 1014 are not met, the method 1000 may continue to 1016.

[0060] At 1016, the method 1000 may include determining whether the conditions for the pre-saturation class are met. For example, the SAT slope may be compared to a second SAT slope calibration threshold (e.g., | SAT rate | < P1), or the SAT may be compared to a first SAT calibration threshold (e.g., SAT > P2). If the conditions at 1016 are met, the method 1000 may include classifying the SAT rate as pre-saturation at 1024. If the conditions at 1016 are not met, the method 1000 may continue to 1018.

[0061] At 1018, the method 1000 may include determining whether the conditions for the saturation class are met. For example, the SAT slope may be compared to a third SAT slope calibration threshold (e.g., SAT rate < S1 < 0), or the SAT may be compared to a second SAT calibration threshold (e.g., SAT < S2). If the conditions at 1018 are met, the method 1000 may include classifying the SAT rate as saturation at 1026. If the conditions at 1018 are not met, the method 1000 may include classifying the SAT rate as SAT neutral at 1028. The method 1000 may end at 1030.

[0062] The classification submodule 224 may generate classified SAT slope data 252 containing various data indicating the classification of the SAT slope.

[0063] Referring again to Fig. 2, the arbitration and fusion module 216 may receive the normalized data 250 and the classified SAT gradient data 252 output from the gradient estimation module 214 and process this data to accurately estimate the final lateral grip levels of the front and rear tires of the vehicle 10. Fig. For example, FIG. 11 illustrates a method 1100 for estimating final lateral grip levels for a vehicle. The method 1100 may begin at 1110. At 1112, the method 1100 may include determining the lateral grip level indicator (LFSALI) and the SAT slope classification status, for example, from the normalized data 250 and the classified SAT slope data 252.

[0064] At 1114, method 1100 may include determining whether the SAT slope classification status is saturation. If the classification is saturation at 1114, method 1100 may include determining at 1116 whether the lateral force lateral adhesion indicator is less than a first threshold (e.g., 0.2). If the lateral force lateral adhesion indicator is less than the first threshold, method 1100 may include setting a final lateral adhesion level indicator (FLALI) equal to the lateral adhesion level indicator at 1124. If the lateral force lateral adhesion indicator is greater than the first threshold at 1116, the method 1100 may include, at 1126, setting the final lateral adhesion level indicator to the lateral force lateral adhesion indicator multiplied by a first predefined value (e.g., 0.9).

[0065] If the classification at 1114 is not saturated, the method 1100 may include determining at 1118 whether the SAT slope classification is pre-saturated. If the classification at 1118 is pre-saturated, the method 1100 may include determining at 1120 whether the lateral force lateral adhesion level indicator is less than a second threshold (e.g., 0.4). If the lateral adhesion level indicator is less than the second threshold, the method 1100 may include setting the final lateral adhesion level indicator equal to the lateral adhesion level indicator at 1128.If the lateral force tilt level indicator is greater than the second threshold at 1120, the method 1100 may include at 1130 setting the final lateral force tilt level indicator equal to the lateral force tilt level indicator multiplied by a second predefined value (e.g., 0.95).

[0066] If the classification at 1118 is not pre-saturation, the method 1100 may include, at 1122, determining whether the lateral force slope adhesion level indicator is less than a third threshold (e.g., 0.4). If the lateral force slope adhesion level indicator is less than the third threshold, the method 1100 may include, at 1132, setting the final lateral adhesion level indicator equal to the lateral force lateral adhesion level indicator. If the lateral force slope level indicator is greater than the third threshold at 1122, the method 1100 may include, at 1134, setting the final lateral adhesion level indicator equal to the lateral force lateral adhesion level indicator multiplied by a third predefined value (e.g., 1.1). The process 1100 may end at 1136.

[0067] The arbitration and fusion module 216 generates arbitration and fusion data 254 including various data indicative of the final indicator of the level of lateral grip of the front tires and / or a final indicator of the level of lateral grip of the rear tires. In some examples, the arbitration and fusion data 254 includes various data indicative of pre-saturation warnings for the front tires and / or rear tires. In various examples, the arbitration and fusion module 216 may store the arbitration and fusion data 254 in a database, for example, on the data storage device 32. In various examples, the arbitration and fusion module 216 may transmit the arbitration and fusion data 254 to one or more other systems of the vehicle 10, e.g., the steering assistance system 35.

[0068] With reference to Fig. 12 and with continued reference to the Fig. 1-11 shows a flowchart of a method 1200 for estimating indicators of lateral grip of tires of a vehicle, such as performed by the estimation system 100 in accordance with various examples. As will be apparent from the description, the order of the method 1200 is not limited to the Fig. 12, but may be performed in one or more varying orders as applicable in accordance with the present description. In various examples, method 1200 may be scheduled to run based on one or more predetermined events and / or may run continuously during operation of vehicle 10.

[0069] In one example, method 1200 may begin at 1210. At 1212, method 1200 may include receiving signals from an onboard vehicle sensor system indicative of vehicle operating parameters. At 1214, method 1200 may include processing the signals to estimate a aligning torque rate, a lateral force rate, and a slip angle rate. At 1216, method 1200 may include performing a state synchronization process to reduce a timing mismatch between the lateral force rate and the slip angle rate, thereby providing a synchronized slip angle rate. At 1218, method 1200 may include performing a filtering process to provide a roll estimate and a slope estimate of the aligning torque based, respectively, on the aligning torque rate, the lateral force rate, and the synchronized slip angle rate.At 1220, method 1200 may include performing a normalization process to reduce the noise associated with the body roll estimate and the aligning moment slope estimate, thereby generating a normalized body roll estimate and a normalized aligning moment slope estimate. At 1222, method 1200 may include classifying the normalized aligning moment slope estimate. At 1224, method 1200 may include performing an arbitration and fusion process to adjust the normalized body roll estimate based on the classification of the normalized aligning moment slope estimate to estimate a final indicator of the degree of lateral grip. Method 1200 may end at 1226.

[0070] The systems and methods described here offer several advantages over certain existing systems and methods. For example, the systems and methods described here are capable of estimating the degree of lateral grip based on information from on-board sensors, using multiple fusion algorithms, and taking the aligning torque into account. This enables reliable estimates for both the front and rear axles.

Claims

[1] A method for estimating an indicator of the degree of lateral grip of tires for a vehicle (10) running on tires, the method comprising: Receiving, with a controller (34) on board the vehicle (10), signals from an on-board sensor system (28) of the vehicle (10) indicative of operating parameters of the vehicle (10); Processing, with one or more processors (44) of the controller (34), the signals to estimate a aligning torque rate, a lateral force rate, and a slip angle rate; Performing, with the one or more processors (44) of the controller (34), a state synchronization process to reduce a timing mismatch between the lateral force rate and the slip angle rate and thereby provide a synchronized slip angle rate; Performing, with the one or more processors (44) of the controller (34), a filtering process to provide a roll estimate and a aligning moment slope estimate based on the aligning moment rate, the lateral force rate, and the synchronized slip angle rate, respectively; Performing, with the one or more processors (44) of the controller (34), a normalization process to reduce the noise associated with the roll estimate and the aligning moment slope estimate, thereby generating a normalized roll estimate and a normalized aligning moment slope estimate; Classifying, with the one or more processors (44) of the controller (34), the normalized estimate of the slope of the restoring torque to obtain a classification; and Performing, with the one or more processors (44) of the controller (34), an arbitration and fusion process to adjust the normalized estimate of body roll based on the classification of the normalized slope of the restoring moment to estimate the final indicator of the degree of lateral grip. [2] The method of claim 1, wherein the operating parameters include a lateral force, a steering torque, a longitudinal speed, a lateral acceleration, a yaw rate, steering angle, and various vehicle parameters. [3] The method of claim 1, wherein processing the signals to estimate the aligning torque rate is based on a total torque received from a controller area network of the vehicle (10), an aligning torque of the tires, a position and a speed of the tires, a flat mass of a steering system (24) of the vehicle (10), and a flat damping of the vehicle (10). [4] The method of claim 1, wherein processing the signals to estimate the lateral force rate is based on the lateral forces of the tires, the vertical forces of the tires and a steering road wheel angle. [5] The method of claim 1, wherein processing the signals to estimate the slip angle rate is based on a longitudinal velocity, a lateral acceleration, a yaw rate, one or more vehicle parameters, and a steering wheel angle. [6] System for a vehicle (10), comprising: a sensor system (28) configured to detect observable conditions of an environment outside the vehicle (10), an environment inside the vehicle (10), and / or a state of one or more components of the vehicle (10); and a controller (34) configured with one or more processors (44): to receive signals from the sensor system (28) indicating the operating parameters of the vehicle (10) while driving on tires; process the signals to estimate a restoring torque rate, a lateral force rate and a slip angle rate; perform a state synchronization process to reduce a timing mismatch between the lateral force rate and the slip angle rate and thereby provide a synchronized slip angle rate; perform a filtering process to provide a body roll estimate and a aligning moment slope estimate based on the aligning moment rate, the lateral force rate, and the synchronized slip angle rate, respectively; perform a normalization process to reduce the noise associated with the lateral inclination estimate and the aligning moment slope estimate, thereby producing a normalized lateral inclination estimate and a normalized aligning moment slope estimate; classify the normalized estimate of the slope of the restoring moment to obtain a classification; and to perform an arbitration and fusion process to adjust the normalized estimate of the body roll based on the classification of the normalized estimate of the slope of the restoring moment to estimate a final indicator of the degree of lateral grip. [7] The system of claim 6, wherein the operating parameters include a lateral force, a steering torque, a longitudinal speed, a lateral acceleration, a yaw rate, steering angles, and various vehicle parameters. [8] The system of claim 6, wherein the controller (34) is configured to process, through the one or more processors (44), the signals to estimate the aligning torque rate based on a total torque received from a controller area network of the vehicle (10), an aligning torque of the tires, a position and a speed of the tires, a flat mass of a steering system (24) of the vehicle (10), and a flat damping of the vehicle (10). [9] The system of claim 6, wherein the controller (34) is configured to process the signals through the one or more processors (44) to estimate the lateral force rate based on the lateral forces of the tires, the vertical forces of the tires, and a steering road wheel angle. [10] The system of claim 6, wherein the controller (34) is configured to process the signals through the one or more processors (44) to estimate the slip angle rate based on a longitudinal speed, a lateral acceleration, a yaw rate, one or more vehicle parameters, and a steering wheel angle.

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