Estimation of a road friction coefficient using signals from a steering system

Abstract

Procedure that includes: Calculating a value of a first rack force (322) and a value of a second rack force (332) by a steering system (40), wherein the value of the first rack force (322) is calculated on the basis of a vehicle speed (U), an engine angle (θ) and a value of a road friction coefficient (µ) and wherein the value of the second rack force (332) is calculated using the engine angle (θ) and a spring model; Calculating a value of a model rack force (212) based on the sum of the value of the first rack force (322) and the value of the second rack force (332); Determining a difference between the value of the model rack force (212) and a value of a load rack force (232) by the steering system (40); Updating the value of the road friction coefficient (µ) by the steering system (40) based on the determined difference; and Steering the vehicle (100) based on the updated value of the road friction coefficient (µ).

Description

[0001] The forces that cause a vehicle to accelerate, decelerate, or change direction originate at the interface between the tires and the road. Longitudinal and / or lateral slippage of the tires generates the tire forces that determine the vehicle's movement. The coefficient of road friction is the primary factor influencing the maximum tire force. Therefore, knowledge of the coefficient of road friction is crucial for vehicle control.

[0002] DE 10 2010 042 135 A1 discloses a method for determining a rack force for a steering device in a vehicle, in which a quotient is formed based on a modeled rack force and an actual rack force, which is used to ensure adaptation to a current coefficient of friction by multiplying the modeled rack force by the quotient. DE 10 2011 052 881 B4 teaches a method for determining a resulting rack force RF based on a first rack force RFD and a second rack force RFC, wherein the first rack force RFD is determined as a function of a force or moment occurring in the steering device, and the second rack force RFC is determined as a function of at least one vehicle parameter, for example, the vehicle speed or the steering angle.DE 10 2018 219 560 A1 describes a method for estimating a rack force in a steer-by-wire system (SbW system) in which the frictional force of the SbW system is estimated based on a frictional force model which uses a rack velocity.

[0003] One of the problems underlying the invention is to provide an improved method, steering system and computer program product for vehicle control.

[0004] The problem is solved by a method having the features of claim 1, by a steering system having the features of claim 10, and by a computer program product having the features of claim 17. Advantageous further developments are set forth in the dependent claims.

[0005] According to one or more embodiments, a method comprises calculating a value of a first rack force and a value of a second rack force by a steering system, wherein the value of the first rack force is calculated based on a vehicle speed, an engine angle, and a value of a road friction coefficient, and wherein the value of the second rack force is calculated using the engine angle and a spring model. The method further comprises calculating a value of a model rack force based on the sum of the values ​​of the first rack force and the second rack force. The method further comprises determining a difference between the value of the model rack force and a value of a load rack force by the steering system.The procedure further includes updating the value of the road friction coefficient by the steering system based on the determined difference and steering the vehicle based on the updated value of the road friction coefficient.

[0006] According to one or more embodiments, a steering system comprises a motor and a controller that performs a method for calculating a first rack force and a second rack force. The first rack force is calculated based on a vehicle speed, a motor angle, and a road friction coefficient. The second rack force is calculated using the motor angle and a spring model. The method further comprises calculating a model rack force based on the sum of the first rack force and the second rack force. The method also includes determining the difference between the model rack force and a load rack force value.The procedure further includes updating the value of the road friction coefficient by the steering system based on the determined difference and steering the vehicle based on the updated value of the road friction coefficient.

[0007] According to one or more embodiments, a computer program product includes a storage device on which one or more computer-executable instructions are stored, wherein the computer-executable instructions, when executed by a processor, cause the processor to perform a method for determining a value of the road friction coefficient. The method includes calculating a value of a first rack force and a value of a second rack force by a steering system, wherein the value of the first rack force is calculated based on a vehicle speed, an engine angle, and a value of a road friction coefficient, and wherein the value of the second rack force is calculated using the engine angle and a spring model.The procedure further includes calculating a model rack force value based on the sum of the first rack force and the second rack force values. The procedure further includes determining the difference between the model rack force value and a load rack force value by the steering system. The procedure further includes updating the road friction coefficient value by the steering system based on the determined difference and steering the vehicle based on the updated road friction coefficient value.

[0008] These and other advantages and features will become clearer from the following description when read in conjunction with the drawings.

[0009] The subject matter considered to be the invention is specifically disclosed and claimed separately in the claims at the end of the description. The foregoing and further features and advantages of the invention will become apparent from the following detailed description when read in conjunction with the accompanying drawings, which: Fig. 1 represents an EPS system according to one or more embodiments; Fig. 2 is an exemplary embodiment of an SbW system 40 for implementing the described embodiments; Fig. 3 represents an automated driver assistance system according to one or more embodiments; Fig. Figure 4 shows a block diagram of a system for the continuous and iterative updating of a value of the road friction coefficient according to one or more embodiments; Fig. 5 shows a block diagram of an exemplary vehicle model calculation according to one or more embodiments; and Fig. Figure 6 shows a flowchart of an example procedure for detecting a change in the value of the road friction coefficient and for updating the value of the road friction coefficient accordingly in a steering system according to one or more embodiments.

[0010] The terms module and submodule used here refer to one or more processing circuits, such as an application-specific integrated circuit (ASIC), an electronic circuit, a processor (shared, dedicated, or grouped) with memory that executes one or more software or firmware programs, a combinational logic circuit, and / or other suitable components that provide the described functionality. As can be seen, the submodules described below can be combined and / or further subdivided.

[0011] For vehicle control, knowledge of the road friction coefficient is crucial. Existing technical solutions determine the road friction coefficient during acceleration or deceleration. Typically, during acceleration / deceleration, the longitudinal slip of a tire is used by a braking module to determine the road friction coefficient.

[0012] The technical solutions described here facilitate the detection of changes in the road friction coefficient using signals from the steering system. When the vehicle changes direction, the steering load and other information can be used to determine the road friction coefficient. A change in the road friction coefficient affects not only the generation of tire lateral forces but also the generation of rack forces, which are closely related to steering loads. Therefore, rack force information can be used to detect a change in the road friction coefficient during steering-induced lateral vehicle movement. Detecting a change in the road friction coefficient using steering system signals according to the technical solutions described here can be performed more quickly than with existing techniques.

[0013] Furthermore, existing techniques that use steering system signals to detect the road friction coefficient and / or changes thereof can only perform the detection after the steering wheel has reached a steady state. Therefore, a specific subset of driving maneuvers where the steady state is not met cannot be used to determine road friction. Additionally, detection with existing techniques is typically binary or ternary (tri-state) and cannot be performed continuously.

[0014] The technical solutions described here address the technical challenges of continuously estimating the road friction coefficient using steering signals. A virtual sensor module calculates steering loads, while a vehicle model predicts reference steering loads. The comparison of predicted and calculated steering loads is used to iteratively predict the road friction coefficient using signal processing techniques. Furthermore, the output of the road friction coefficient is a coefficient of friction number between 0.1 and 0.9, which is continuously updated as the vehicle travels on different surfaces. The technical solutions described here are applicable to both electric power steering systems (EPS systems) and steer-by-wire systems (SbW systems).

[0015] Now, with reference to the figures in which the technical solutions are described with reference to specific embodiments, without limiting them, it is Fig. 1 an exemplary embodiment of an EPS 40 in a vehicle 100 suitable for implementing the disclosed embodiments, and Fig. Figure 2 is an exemplary embodiment of an SbW system 40 for implementing the described embodiments. Unless expressly stated otherwise, this document refers to a steering system 40, which may be either an EPS system, an SbW system, or any other type of steering system in which the technical solutions described herein can be used.

[0016] In Fig. 1 The steering mechanism 36 is a rack and pinion system and comprises a rack (not shown) in a housing 50 and a pinion (also not shown) located beneath a gearbox housing 52. When the driver's input, hereinafter referred to as the handwheel or steering wheel 26, is turned, the upper steering shaft 29 rotates, and the lower steering shaft 51, which is connected to the upper steering shaft 29 via a universal joint 34, rotates the pinion. The rotation of the pinion moves the rack, which moves tie rods 38 (only one is shown), which in turn move steering knuckles 39 (only one is shown), which rotate or turn one or more steerable wheels or tires 44 (only one is shown). Although a rack and pinion system is described here, in other embodiments the EPS system can be a column-support EPS (CEPS), a pinion-support EPS (PEPS), a double-pinion-support EPS, or any other type of EPS system.

[0017] Electric power steering assistance is provided by the control device generally designated by reference numeral 24, which comprises the controller 16 and an electric motor 46, which may be a permanent magnet synchronous motor (PMSM), a permanent magnet direct current motor (PMDC), or any other type of motor, and is hereinafter referred to as the motor 46. The controller 16 is powered by the vehicle power supply 10 via a line 12. The controller 16 receives a vehicle speed signal 14, representative of the vehicle speed, from a vehicle speed sensor 17. A steering angle is measured by a position sensor 32, which may be an optically coded sensor, a variable resistance sensor, or another suitable position sensor, and which provides a position signal 20 to the controller 16.The motor speed can be measured with a tachometer or other device and transmitted to the controller 16 as a motor speed signal 21. This motor speed is denoted by ω. m It can be measured, calculated, or determined by a combination of these methods. For example, the motor speed ω can be... m as the change in motor position θ, measured by a position sensor 32, over a prescribed time interval. For example, the motor speed ω can be calculated. m as a derivative of the motor position θ from the equation ω m The function can be determined as Δθ / Δt, where Δt is the sampling time and Δθ is the change in position during the sampling interval. Alternatively, the motor speed can be derived from the motor position as the rate of change of the position over time. It should be noted that there are numerous known methods for performing a derivation of this function.

[0018] When the handwheel 26 is turned, a torque sensor 28 detects the torque applied to the handwheel 26 by the vehicle's driver. The torque sensor 28 can include a torsion bar (not shown) and a variable resistance sensor (also not shown), which outputs a variable torque signal 18 to the controller 16 depending on the amount of rotation of the torsion bar. Although this is one type of torque sensor, any other suitable torque sensor used with known signal processing techniques will suffice. In response to the various inputs, the controller sends a command 22 to the electric motor 46, which, via a worm gear 47 and a worm wheel 48, provides torque assistance to the steering system, thus providing torque assistance for the vehicle's steering.

[0019] It should be noted that although the disclosed embodiments are described with reference to a motor control system for electric power steering applications, these references are for illustrative purposes only, and the disclosed embodiments can be applied to any motor control application with an electric motor, e.g., steering, valve control, and the like. Furthermore, the references and descriptions provided here can apply to many types of parameter sensors, including, but not limited to, torque, position, speed, and the like. It should also be noted that references are made here to electrical machines, including, but not limited to, motors. For the sake of brevity and simplicity, the following text will refer exclusively to motors without limitation.

[0020] In the illustrated control system 24, the controller 16 uses the torque, position, speed, and other parameters to calculate one or more commands to deliver the required output power. The controller 16 communicates with the various systems and sensors of the motor control system. The controller 16 receives signals from each of the system sensors, quantifies the received information, and, in response, outputs one or more command signals, in this case, for example, to the motor 46. The controller 16 is configured to develop the appropriate voltage(s) from an inverter (not shown), which can optionally be integrated into the controller 16 and is referred to here as the controller 16, so that when applied to the motor 46, the desired torque or position is generated.In one or more examples, the controller 24 operates in a feedback control mode as a current controller to generate the command 22. Alternatively, in one or more examples, the controller 24 operates in a feedforward control mode to generate the command 22. Since these voltages are related to the position and speed of the motor 46 and the desired torque, the position and / or speed of the rotor and the torque applied by a driver are determined. A position sensor is connected to the steering shaft 51 to detect the angular position θ. The sensor can detect the rotational position based on optical sensing, magnetic field fluctuations, or other methods. Typical position sensors include potentiometers, resolvers, synchros, encoders, and the like, as well as combinations comprising at least one of the aforementioned elements.The position sensor outputs a position signal 20, which indicates the angular position of the steering shaft 51 and thus that of the motor 46.

[0021] The desired torque can be determined by one or more torque sensors 28 that transmit torque signals 18 indicating an applied torque. One or more exemplary embodiments comprise such a torque sensor 28 and its torque signal(s) 18, which can respond to a compliant torsion bar, T-bar, spring, or similar device (not shown) configured to provide a response indicating the applied torque.

[0022] In one or more examples, one or more temperature sensors 23 can be located on the electric motor 46. Preferably, the temperature sensor 23 is configured to directly measure the temperature of the sensor section of the motor 46. The temperature sensor 23 transmits a temperature signal 25 to the controller 16 to enable the processing and compensation described herein. Typical temperature sensors are thermocouples, thermistors, thermostats, and the like, as well as combinations of at least one of the aforementioned sensors, which, when appropriately positioned, provide a calibratable signal proportional to the respective temperature.

[0023] Among other things, the position signal 20, the speed signal 21, and one or more torque signals 18 are applied to the controller 16. The controller 16 processes all input signals to generate values ​​corresponding to each signal, providing a rotor position value, a motor speed value, and a torque value for processing in the algorithms described here. Measurement signals, such as those mentioned above, are also frequently linearized, compensated, and filtered as desired to improve the characteristics of the acquired signal or eliminate undesirable characteristics. For example, the signals can be linearized to improve processing speed or to address a large dynamic range of the signal. Furthermore, frequency- or time-based compensation and filtering can be used to eliminate noise or avoid undesirable spectral characteristics.

[0024] To perform the prescribed functions and the desired processing, as well as the corresponding calculations (e.g., identification of motor parameters, control algorithms, and the like), the controller 16 may include, among other things, one or more processors, computers, DSPs, memory, mass storage, registers, timers, interrupts, communication interfaces, and input / output signal interfaces, as well as combinations of at least one of the aforementioned elements. For example, the controller 16 may include input signal processing and filtering to enable accurate sampling and conversion or the acquisition of such signals from communication interfaces. Additional features of the controller 16 and specific processes within it are discussed in detail later in this document.

[0025] Fig. Figure 2 shows an exemplary SbW system according to one or more embodiments. The SbW system 40 comprises a handwheel actuator (HWA) 70 and a road wheel actuator (RWA) 80. The control unit 16 is divided into two blocks, a control unit 16A and a control unit 16B, which are assigned to the HWA 70 and the RWA 80, respectively. In other examples, the control unit 16 may be divided in any other way.

[0026] The HWA 70 comprises one or more mechanical components, such as the handwheel 26 (steering wheel), a steering column, and a motor / inverter, which is attached to the steering column either via a gearbox or a direct drive system. The HWA 70 also includes the microcontroller 16A, which controls the operation of the mechanical components. The microcontroller 16A receives and / or generates torque using the one or more mechanical components. For example, the microcontroller 16A can send a torque command request to a motor / inverter, which then generates that torque.

[0027] The RWA 80 comprises one or more mechanical components, such as a steering rack coupled to a motor / inverter via a ball nut / ball screw (gearbox) or a pinion gear, and the rack is connected to the vehicle's road wheels / tires 44 via tie rods. The RWA 80 contains the microcontroller 16B, which controls the operation of the mechanical components. The microcontroller 16B receives and / or generates torque using the one or more mechanical components. For example, the microcontroller 16B can send a torque command request to a motor / inverter, which then generates that torque.

[0028] The 16A and 16B microcontrollers are coupled via electrical connections that enable the transmission and reception of signals. As mentioned here, a controller can contain a combination of the HWA controller 16A and the RWA controller 16B, or either of the specific microcontrollers.

[0029] In one or more examples, the controllers 12 and 16B of the SbW system 40 communicate with each other via a CAN interface (or other similar digital communication protocols). The steering of the vehicle 100 equipped with the SbW system 40 is accomplished by means of the steering gear. The RWA 80 receives an electronic communication signal of the steering wheel rotation by the driver. A driver steers the steering wheel to control the direction of the vehicle 100. The angle from the HWA 70 is sent to the RWA 80, which performs the position control to control the rack movement for guiding the road wheel. However, due to the lack of a mechanical connection between the steering wheel and the road wheels, the driver does not readily receive a feel for the road without torque feedback (unlike with an EPS, as described above).

[0030] In one or more examples, the HWA 70, which is coupled to the steering column and steering wheel, simulates the driver's driving sensation on the road. The HWA 70 can apply tactile feedback in the form of torque to the steering wheel. The HWA 70 receives a rack and pinion force signal from the RWA 80 to generate a corresponding torque sensation for the driver. Alternatively, the handwheel angle and vehicle speed can also be used to generate the desired torque sensation for the driver.

[0031] As already mentioned, the SbW and EPS systems described here are exemplary, and the technical solutions described here are applicable to any type of steering system, and therefore, unless expressly stated otherwise, a ‘steering system 40’ here refers to any type of steering system.

[0032] Fig. Figure 3 represents an automated driver assistance system according to one or more embodiments. It should be noted that the steering system 40 shown and described can be used in an autonomous or semi-autonomous vehicle or in a more conventional vehicle. An Advanced Drive Assistance System (ADAS) 110 can be coupled to the steering system 40, the road wheels 44 (via one or more control units), and other control units in the vehicle 100. The ADAS 110 can include one or more processors 112 and one or more memory devices 114. The ADAS 110 receives one or more input signals, comprising data and / or commands, from the control units, such as the control unit 16 of the steering system 40. The ADAS 40 can also send signals, comprising data and / or commands, to the control units, such as the control unit 16 of the steering system 40. Furthermore, the ADAS 110 can receive inputs from the human driver, such as...a destination, one or more preferences, and the like. The ADAS 110 can provide the driver with notifications, e.g., during an interaction with the driver and / or in response to one or more conditions in the vehicle 100.

[0033] In one or more examples, the ADAS 110 automatically determines a driving trajectory for the vehicle 100. The trajectory can be generated based on driver input and one or more input signals received by the control units, such as the road friction coefficient. Furthermore, in one or more examples, the ADAS 110 can communicate with external modules (not shown) such as traffic servers, road map servers, and the like to generate a route / path for the vehicle 100 from a starting point to a destination. The ADAS 110 sends one or more commands to the control units to maneuver the vehicle based on the generated route and / or trajectory. It should be noted that while a "route" is a high-level mapping that allows the vehicle to find a destination on a map (e.g.,While a "trajectory" is a specific set of maneuvers that the vehicle 100 must perform to get from its current position to the next position on the route (e.g., apartment, office, or restaurant), the ADAS 110 can perform maneuvers that may include, but are not limited to, changes in vehicle direction, speed, acceleration, and the like. To perform such maneuvers, the ADAS 110 sends one or more commands to the appropriate control unit(s).

[0034] Regardless of whether the vehicle is driven by an ADAS 110 or manually by a driver, maneuvering is generally based on the road friction coefficient, which determines the maximum tire grip in the lateral and longitudinal directions. The road friction coefficient is crucial information for vehicle handling (manual and autonomous). Even though a human driver cannot see the numerical value of the coefficient during maneuvers, they can feel the different road surfaces—dry, wet, snowy, and so on—due to their varying coefficients of friction and typically maneuver the vehicle accordingly. The ADAS 110 can maneuver the vehicle based on this road friction coefficient value.In this document, the term "driver" will henceforth be used to refer to both the human driver and the ADAS 110, both of which can maneuver the vehicle 100 based on the coefficient of road friction.

[0035] The faster a change in the coefficient of road friction can be detected and displayed to the driver, the faster the driver can react and maneuver the vehicle differently. For example, when the vehicle moves from a dry surface to a slippery surface (e.g., ice), it must be maneuvered differently to avoid skidding; for instance, the vehicle's stability control system may use smaller steering and / or braking inputs on a slippery surface than on a dry one.

[0036] Fig. Figure 4 shows a block diagram of a system for continuously and iteratively updating the value of the road friction coefficient according to one or more embodiments. The system 200 is a steering-signal-based system that can detect changes in the road friction coefficient faster than braking for active steering inputs. In one or more examples, the system 200 can be a separate electronic circuit within the steering system 40. Alternatively or additionally, at least some parts of the system 200 can be implemented by the steering system 40 using the controller 16. In one or more examples, the system 200 includes one or more computer-executable instructions stored on a memory device.

[0037] The system 200 includes, among other components, a vehicle model calculation 210, a calculation 220 of the road friction coefficient (µ), and a rack force measurement 230.

[0038] Fig. Figure 5 shows a block diagram of an exemplary vehicle model calculation according to one or more embodiments. The vehicle model calculation 210 uses a predefined vehicle model to calculate a model rack force 212. The vehicle model is a nonlinear vehicle model that calculates the model rack force 212 directly from slip angles.

[0039] The vehicle model calculation includes a module 310 for slip angle calculation. In one or more examples, the slip angle calculation 310 uses a bicycle model with nonlinear tire force curves and tire deceleration dynamics dependent on road friction (µ) to obtain a front axle slip angle (αf). As mentioned earlier, the road friction coefficient used as input is an estimated value (i.e., the result of the road friction coefficient calculation 220) µ̂ from a previous iteration, which is updated iteratively. The slip angle αf, together with the predicted road friction coefficient µ̂ and a vehicle speed (U), is used to obtain the model rack force 212.

[0040] The slip angle calculation 310 uses a steering angle (θ) (handwheel position or motor angle) measured by the steering system 40. The steering angle is converted into a tire angle using reference tables or a multiplier. Equations from a bicycle model are used to calculate yaw rate and lateral velocity states. The yaw dynamics equation that can be used includes: Izzr˙=aFcf−bFcr

[0041] Furthermore, it includes an equation for lateral dynamics that can be used: m(V˙+rU)=Fcf+Fcr

[0042] In the equations above, I zz : Rotational inertia, r: Yaw rate, a: Distance between center of gravity (CG) and front axle, b: Distance between CG and rear axle, V: Lateral velocity at CG, U: Longitudinal velocity at CG, F cf : Front axle force and F cr Rear axle force.

[0043] Furthermore, the vehicle model calculation 210 includes a module 320 for rack force calculation. The rack force calculation uses the slip angle αf and the road friction coefficient (µ) with a nonlinear (Fiala) tire model to calculate tire lateral forces: Fy={−Cα tan α+Cα23|tan α|tan α Iα,|α|≤αsl −1Ifsgn α, otherwise αsl=tanh−1(eCαIf) I f = µ × Vertical load on the axis where F y : Tire lateral force, C α : Parameters for stiffness during cornering, α tire slip angle, I f : Reciprocal of the maximum tire lateral force, which is a function of µ and α sl : sliding slip angle.

[0044] The slip angles for the front and rear axles can be determined using equations (see below). The calculated values ​​are filtered using a vehicle speed-dependent filter to represent the relaxation length dynamics of the tires. A delayed slip angle (i.e., the output of the low-pass filter) is also used as the front or rear tire slip angle in the equations mentioned above. αf=(V+ar)U−δ αr=(V−br)U where α f : Front axle slip angle, α r : Rear axle slip angle and δ: Tire angle. The tire angle is derived from the engine angle using kinematic tables or a gain factor.

[0045] The model rack force 212 is the sum of two forces – rack force 1 322 and rack force 2 332. Rack force 322 is determined using the front slip angle, vehicle speed, and engine angle, either via nonlinear tables, empirical models, or tire models. Rack force 332 is determined by a gain modulus 330 using the engine angle and a spring model, or any other model or lookup table. Rack force 2 332 represents a compressive torque and other additional torques / forces acting on the vehicle 100 due to the suspension geometry as a function of the engine angle. Rack force 1 322 and rack force 2 332 are added (340) to calculate the model rack force 212.

[0046] Looking back on Fig. 4. The model rack force 212 is entered into the calculation 220 of the road friction coefficient. Additionally, the road friction coefficient calculation 220 receives a load rack force 232 from the rack force measurement 230. In one or more examples, the load rack force 232 is a measurement from a force / torque sensor that measures the force / torque experienced by the rack during vehicle maneuvers. Alternatively or additionally, the load rack force 232 is calculated using a rack force observer that uses steering signals such as steering motor torque, steering speed, and steering handwheel torque (in the case of EPS) to calculate the load rack force 232. The load rack force 232 is a virtual rack force calculated from measured driver and engine force expenditures. Accordingly, the load rack force 232 is a reference against which the model rack force 212 is compared.

[0047] In one or more examples, the calculation of the road friction coefficient is performed based on LMS (Least Mean Square) filtering; however, other equivalent filtering techniques can be used to achieve essentially similar results. In the example in Fig. In the example shown, the calculation 220 of the road friction coefficient includes one or more modules for an update factor calculation 222, a road friction coefficient update 224 and a learning release 226.

[0048] The update factor calculation 222 calculates a difference (e1) between the two input rack forces, which is entered into the road friction coefficient calculation 220. The road friction coefficient calculation 220 receives the model rack force 212 and the load rack force 232. Accordingly: e1 = Load-rack force − Model-rack force

[0049] In one or more examples, the error e1 is processed to calculate an adjusted error value e2. The adjusted error e2 is calculated by applying a low-pass filter to the value e1, where the low-pass filter is a function of the vehicle speed (U), the steering angle (θ), and the steering speed.

[0050] The difference e2 is used to calculate an update factor (Δµ1) using the following calculations: u−θconstant+θ2 Δμ1=k.e2.u

[0051] Here, u is a system input, such as a steering angle or a function of the steering angle, as shown. In other examples, u may be calculated differently. Furthermore, k in the preceding equations is a predetermined value that may be configurable. The update factor calculation 222 outputs the update factor Δµ calculated in this way.

[0052] The road friction coefficient update 224 receives the update factor as input and sets a second update value (Δµ) for the road friction coefficient based on the input value and a learn enable flag. L ) fixed, which is issued by learning release 226.

[0053] The learning release 226 uses vehicle and steering signals to determine whether conditions are favorable for updating the road friction coefficient value. One or more of the following conditions can be used: whether the vehicle's acceleration / deceleration is below a threshold; whether a steering speed value is greater than a threshold; whether the steering angle value is greater than a threshold; whether the product of steering angle and steering speed is greater than a threshold; whether the vehicle is not in an oversteer state, etc. The determination of the oversteer state can be calculated either by the steering system 40 or by other modules of the vehicle 100, such as the brake control (not shown), using known techniques. Based on the evaluation of one or more of the aforementioned conditions, the learning release 226 outputs the flag. LThe result is a Boolean output - TRUE indicates favorable conditions for updating the value of the road friction coefficient, and FALSE indicates that the value of the road friction coefficient should not be updated at this time.

[0054] The road friction coefficient update 224 determines the second update factor to update the estimate of the road friction coefficient based on the marker flag. L to be performed: Δμ={Δμ1…if FlagLTRUE0………if FlagLFLASCH

[0055] The value of the road friction coefficient (µ̂ or µ) t+1 ) is updated using the second update factor and the current value of the road friction coefficient: μt+1=μt+Δμ

[0056] Fig.Figure 6 shows a flowchart of an example procedure for detecting a change in the value of the road friction coefficient and updating the corresponding value of the road friction coefficient in a steering system according to one or more embodiments. The procedure, as described in Figure 602, includes calculating the model rack force value 212 based on a vehicle speed, a steering angle, and a current value of the road friction coefficient. The model rack force value 212 is calculated using a front slip angle determined based on a vehicle model, e.g., a nonlinear vehicle model, as described herein.

[0057] The procedure in section 604 further includes determining the difference between the value of the model rack force 212 and the value of the load rack force 232. The value of the load rack force 232 is determined in one or more examples based on an estimate using a state observer. Alternatively or additionally, the load rack force 232 can be measured using a tie rod sensor. Any other technique can be used to estimate the load rack force 232. The calculated difference is further processed in section 606, for example, by scaling it using one or more factors to calculate an initial update factor. The scaling factors can be based on one or more steering signals such as steering angle, steering speed, and the like.

[0058] The procedure at 608 further includes the calculation of an update marker based on one or more steering and vehicle signals. The update marker indicates whether the value of the road friction coefficient should be updated. If the update marker is FALSE, i.e., indicating that the road friction coefficient should not be updated, the update factor at 610 is set to 0 (zero). Alternatively, if the update marker is TRUE, i.e., indicating that the road friction coefficient can be updated, the update factor at 612 is set to the first calculated update factor. Furthermore, at 614, the update factor is added to the current value of the road friction coefficient to calculate the updated value of the road friction coefficient.

[0059] In one or more examples, at 616, the updated value of the road friction coefficient is transmitted to one or more other modules in the vehicle 100. For example, the ADAS 110 receives the updated value of the road friction coefficient to adjust the trajectory for the vehicle 100. Alternatively or additionally, the updated value of the road friction coefficient is sent to a brake module, an electronic stability control module, and other such modules in the vehicle 100 that control one or more vehicle maneuvers based on driver input. For example, the brake module can, based on the updated value of the road friction coefficient, change how responsive the brakes are, i.e., the deceleration rate when pumping the brakes, or change how the brakes are applied to individual wheels. Furthermore, a user notification can be sent, e.g.,via tactile feedback, audiovisual feedback, and the like.

[0060] Furthermore, the updated value of the road friction coefficient can be transmitted to another vehicle via a vehicle-to-vehicle network (not shown) that enables communication between one or more vehicles, particularly to share driving conditions.

[0061] Furthermore, the updated road friction coefficient is used by the steering system 40 itself to modify the steering effort, e.g., in EPS, SbW, and / or MTO in closed-loop control. For example, the model rack force, calculated based on the updated road friction coefficient, is used to calculate a handwheel torque reference for the driver. For instance, the controller 16 generates an auxiliary torque command based on the handwheel torque reference for the motor 19 to produce the auxiliary torque. The auxiliary torque command is generated based on a difference between the model rack force 212 and an input torque applied by the driver to the handwheel 26.In the case of an SbW system, the model rack force is used by the HWA 70 to generate a feedback torque, whereby the feedback torque provides the driver with a surface feel in the absence of a mechanical connection.

[0062] In one or more examples, the steering system 40 at 618 applies a steering torque superposition to help the driver stay out of a high-slip-angle zone. For example, if the updated road friction coefficient is below a predetermined threshold, the controller 16 assumes that the vehicle 100 is currently traveling on a slippery surface, such as wet, icy, etc., and in this case, the controller 16 limits the value of the steering angle. In one or more examples, the controller 16 generates a superposition torque command that causes the motor 19 to produce a superposition torque that opposes the input torque applied by the driver. The superposition torque prevents or at least limits the driver's ability to maneuver the vehicle 100, which can improve the safety of the vehicle 100.The superimposed torque is generated based on the updated value of the road friction coefficient. In one or more examples, the superimposed torque is generated when the updated road friction coefficient falls below a predefined threshold, indicating that the road is slippery.

[0063] Accordingly, the technical solutions described here enable the detection of changes in the road friction coefficient value in a steering system. The calculations of the road friction coefficient described here have demonstrated significant improvements over existing techniques, particularly those using brake modules. These improvements include faster detection of changes in the road friction coefficient value.

[0064] Furthermore, the technical solutions described here enable the calculation of a continuous road friction coefficient (µ) using steering signals. A vehicle model is used to calculate a rack force as a function of µ, which is continuously estimated. The calculated rack force is compared to an estimated rack force from a steering observer or a tie rod sensor. The road friction coefficient is calculated using both rack forces. In one or more examples, the road friction coefficient is updated only when learning enable conditions are met.

[0065] The technical solutions described here enable the use of steering system signals to detect changes in the coefficient of road friction. The detected change can be used to modify auxiliary torque or other torque generated by the steering system and / or other vehicle modules. For example, if the change in the coefficient of road friction indicates that the vehicle is now traveling on a slippery surface (such as snow, aquaplaning, etc.), steering maneuvers can be prevented by generating a torque that prevents the driver from moving the steering wheel. Alternatively or additionally, in an ADAS system, the detected change in road friction can cause the ADAS system to modify one or more vehicle operating states, such as vehicle speed, steering angle, and the like.

[0066] The technical solutions presented here can be a system, a process, and / or a computer program product at any possible level of technical integration. The computer program product can include one or more computer-readable storage media containing computer-readable program instructions to instruct a processor to execute aspects of the technical solutions presented here.

[0067] Aspects of the present technical solutions are described here with reference to flowchart diagrams and / or block diagrams of processes, devices (systems), and computer program products according to embodiments of the technical solutions. It is understood that each block of the flowchart diagrams and / or block diagrams, and combinations of blocks in the flowchart diagrams and / or block diagrams, can be implemented by computer-readable program instructions.

[0068] The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, procedures, and computer program products according to various embodiments of the present technical solutions. In this respect, each block in the flowchart or block diagrams can represent a module, segment, or part of instructions comprising one or more executable instructions for implementing the specified logical function(s). In some alternative implementations, the functions specified in the blocks may occur out of the order shown in the figures. For example, two blocks shown consecutively may be executed essentially simultaneously, or the blocks may sometimes be executed in reverse order, depending on the functionality involved.It is also noted that each block of the block diagrams and / or flowchart representation, as well as combinations of blocks in the block diagrams and / or flowchart representation, can be implemented by hardware-based systems for special purposes that perform the specified functions or actions, or execute combinations of special hardware and computer instructions.

[0069] It should also be noted that every module, unit, component, server, computer, terminal, or device illustrated herein as an example and executing instructions may contain or otherwise have access to computer-readable media such as storage media, computer storage media, or data storage devices (removable and / or non-removable), such as magnetic disks, optical disks, or tapes. Computer storage media may include volatile and non-volatile, removable and non-removable media implemented in any method or technology for storing information, such as computer-readable instructions, data structures, program modules, or other data. Such computer storage media may be part of the device, access it, or be connected to it.Each application or module described herein can be implemented using computer-readable / executable instructions that can be stored on such computer-readable media or otherwise maintained.

[0070] Although the technical solutions are described in detail in connection with only a limited number of embodiments, it should be readily apparent that the technical solutions are not limited to these disclosed embodiments. Rather, the technical solutions can be modified to include any number of variations, changes, substitutions, or equivalent arrangements not yet described, but which are consistent with the spirit and scope of the technical solutions. Furthermore, although various embodiments of the technical solutions have been described, it is understood that aspects of the technical solutions may encompass only some of the described embodiments. Accordingly, the technical solutions should not be considered limited by the foregoing description.

Claims

[1] Procedure that includes: Calculating a value of a first rack force (322) and a value of a second rack force (332) by a steering system (40), wherein the value of the first rack force (322) is calculated on the basis of a vehicle speed (U), an engine angle (θ) and a value of a road friction coefficient (µ) and wherein the value of the second rack force (332) is calculated using the engine angle (θ) and a spring model; Calculating a value of a model rack force (212) based on the sum of the value of the first rack force (322) and the value of the second rack force (332); Determining a difference between the value of the model rack force (212) and a value of a load rack force (232) by the steering system (40); Updating the value of the road friction coefficient (µ) by the steering system (40) based on the determined difference; and Steering the vehicle (100) based on the updated value of the road friction coefficient (µ). [2] Method according to claim 1, characterized by , that the value of the load-rack force (232) is an estimated value of the tire load on a rack, wherein the value of the load-rack force is estimated using a state observer model based on steering signals. [3] Method according to claim 1, characterized by , that the value of the model rack force (212) is calculated using a nonlinear vehicle model. [4] Method according to claim 1, characterized by , that the value of the road friction coefficient (µ) is updated based on an update marker that is set based on one or more steering system signals and vehicle signals. [5] Method according to claim 1, characterized by , that the value of the road friction coefficient (µ) is a first value of the road friction coefficient (µ) and includes updating the value of the road friction coefficient (µ): Calculating an update factor based on the difference between the value of the model rack force (212) and the value of the load rack force (232); and Calculating a second value of the road friction coefficient (µ) by adding the update factor to the first value of the road friction coefficient (µ). [6] Method according to claim 1, further characterized by : Calculating a handwheel torque reference value based on the updated value of the road friction coefficient (µ), wherein the handwheel torque reference value is summed with an input torque from a driver into the steering system (40). [7] Method according to claim 6, characterized by, that a superimposed torque acting in the opposite direction to the input torque is generated based on the updated value of the road friction coefficient (µ). [8] Method according to claim 1, furthermore characterized by the transmission of the updated value of the road friction coefficient (µ) to an advanced driving assistance system (110). [9] Method according to claim 1, furthermore characterized by the modification of a steering system maneuver (40) based on the fact that there is a difference between the value of the road friction coefficient (µ) and the updated value of the road friction coefficient (µ) above a predetermined threshold. [10] Steering system (40) comprising: an engine (19); and a controller (16) that executes a procedure comprising: Calculating a value of a first rack force (322) and a value of a second rack force (332), wherein the value of the first rack force (322) is calculated on the basis of a vehicle speed (U), an engine angle (θ) and a value of a road friction coefficient (µ), and wherein the value of the second rack force (332) is calculated using the engine angle (θ) and a spring model; Calculating a value of a model rack force (212) based on the sum of the value of the first rack force (322) and the value of the second rack force (332); Determining a difference between the value of the model rack force (212) and a value of a load rack force (232); Updating the value of the road friction coefficient (µ) based on the determined difference; and Steering the vehicle (100) based on the updated value of the road friction coefficient (µ). [11] System according to claim 10, characterized by , that the value of the load-rack force (232) is an estimated value of the tire load on a rack, wherein the value of the load-rack force is estimated using a state observer model based on steering signals. [12] System according to claim 10, characterized by , that the value of the model rack force (212) is calculated using a nonlinear vehicle model. [13] System according to claim 10, characterized by , that the value of the road friction coefficient (µ) is updated based on an update marker that is set based on one or more steering system signals and vehicle signals. [14] System of claim 10, characterized by, that the value of the road friction coefficient (µ) is a first value of the road friction coefficient (µ) and includes updating the value of the road friction coefficient (µ): Calculating an update factor based on the difference between the value of the model rack force (212) and the value of the load rack force (232); and Calculating a second value of the road friction coefficient (µ) by adding the update factor to the first value of the road friction coefficient (µ). [15] System according to claim 10, characterized by : Calculating a handwheel torque reference value based on the updated value of the road friction coefficient (µ), summing the handwheel torque with an input torque from a driver into the steering system (40). [16] System according to claim 15, characterized by, that a superimposed torque is generated based on the updated value of the road friction coefficient (µ). [17] Computer program product comprising a storage device (114) on which one or more computer-executable instructions are stored, wherein the computer-executable instructions, when executed by a processor (112), cause the processor (112) to perform a procedure, the procedure comprising: Calculating a value of a first rack force (322) and a value of a second rack force (332), wherein the value of the first rack force (322) is calculated on the basis of a vehicle speed (U), an engine angle (θ) and a value of a road friction coefficient (µ), and wherein the value of the second rack force (332) is calculated using an engine angle (θ) and a spring model; Calculating a value of a model rack force (212) based on the sum of the value of the first rack force (322) and the value of the second rack force (332); Determining a difference between the value of the model rack force (212) and a value of a load rack force (232); Updating the value of the road friction coefficient (µ) based on the determined difference; and Steering the vehicle (100) based on the updated value of the road friction coefficient (µ). [18] Computer program product according to claim 17, characterized by , that the value of the load-rack force (232) is an estimated value of the tire load on a rack, wherein the value of the load-rack force is estimated using a state observer model based on steering signals. [19] Computer program product according to claim 17, characterized by, that the value of the road friction coefficient (µ) is updated based on an update marker that is set based on one or more steering system signals and vehicle signals. [20] Computer program product according to claim 17, characterized by , that the value of the road friction coefficient (µ) is a first value of the road friction coefficient (µ) and includes updating the value of the road friction coefficient (µ): Calculating an update factor based on the difference between the value of the model rack force (212) and the value of the load rack force (232); and Calculation of a second value of the road friction coefficient (µ) by adding the update factor to the first value of the road friction coefficient (µ). [21] Computer program product according to claim 17, characterized by : Calculating a handwheel torque reference value based on the updated value of the road friction coefficient (µ), summing the handwheel torque with an input torque from a driver into the steering system (40). [22] Computer program product according to claim 17, characterized by , that a superposition moment is generated based on the updated value of the road friction coefficient (µ).

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