Vehicle control device and vehicle control system

Abstract

In one form of a vehicle control device and a vehicle control system according to the present invention, the slip ratio and slip angle of each wheel, the vertical load of each wheel, a physical quantity indicating the motion state of a vehicle, and the actual steering angle of a turnable wheel are acquired, a linear optimization algorithm is used to identify a reference tire coefficient, non-linear optimization is carried out using the deviation between the reference tire coefficient and a variable tire coefficient as an evaluation function, and a tire parameter included in the variable tire coefficient is identified. In this way, it is possible to reduce the processing load of nonlinear model prediction control.

Description

Vehicle control device and vehicle control system

[0001] The present invention relates to a vehicle control device and a vehicle control system.

[0002] The vehicle driving control device of Patent Document 1, when the vehicle requires emergency driving motion control, calculates the target control amount for each control means by calculating an evaluation function for all control means and distributing the target driving motion control amount for the entire vehicle to all control means, and when the vehicle does not require emergency driving motion control, calculates the target control amount for a specific control means based on the driving state of the vehicle, calculates the amount of change in the physical quantity of the vehicle due to control by the specific control means based on the target control amount of the specific control means, and calculates the target control amounts for other control means based on the target driving motion control amount for the entire vehicle and the amount of change in the physical quantity of the vehicle.

[0003] JP 2008-100579 A

[0004] In emergency driving control of a vehicle, the tire characteristics of each wheel reach a nonlinear range, so nonlinear model predictive control using a nonlinear tire model equation is an effective control method. However, there are issues with this, such as an increase in the size of the optimal control calculation equation due to the application of the nonlinear tire model equation to the control unit, and an increase in the size of the calculation equation for online identification of parameters included in the nonlinear tire model equation.

[0005] The present invention has been made in view of the conventional situation, and an object of the present invention is to provide a vehicle control device and a vehicle control system that can reduce the processing load of nonlinear model predictive control.

[0006] In one aspect, the vehicle control device according to the present invention includes a variable tire coefficient calculation unit that calculates a variable tire coefficient for each wheel using, as input values, a slip ratio and slip angle of each wheel, a ground contact load of each wheel, a physical quantity indicating the motion state of the host vehicle, and an actual steering angle of a steered wheel of the host vehicle, the variable tire coefficient calculation unit performing linear processing to determine a reference tire coefficient for each wheel using the input values ​​and a predetermined linear optimization algorithm, and performing nonlinear processing to identify tire parameters for calculating the variable tire coefficient using the reference tire coefficient, the input values, and a predetermined nonlinear optimization algorithm, and calculating the variable tire coefficient based on the tire parameters, the slip ratio, the slip angle, and the ground contact load; and an optimization control unit that inputs a preset reference state variable, the state variable, and the variable tire coefficient of the host vehicle, calculates a control variable that minimizes a preset evaluation function, and sets an output corresponding to the control variable as a traveling actuator of the host vehicle.

[0007] According to one aspect of the present invention, a vehicle control system includes a vehicle control device mounted on a host vehicle and a management server provided outside the host vehicle. The vehicle control device has a first information transmitting unit that transmits to the management server input values, such as a slip ratio and a slip angle of each wheel of the host vehicle, a ground contact load of each wheel, a physical quantity indicating a motion state of the host vehicle, and an actual steering angle of a steered wheel of the host vehicle, and a first information receiving unit that receives tire parameters transmitted from the management server, and calculates a variable tire coefficient based on the tire parameters, the slip ratio, the slip angle, and the ground contact load, and further inputs the variable tire coefficient and a state quantity estimated based on a preset reference state quantity of the host vehicle, a position of the host vehicle, and a physical quantity indicating a motion state of the host vehicle, and calculates a control variable that minimizes a preset evaluation function. On the other hand, the management server includes a second information receiving unit that receives the input values ​​transmitted from the first information transmitting unit, a tire parameter identifying unit that performs linear processing to determine a reference tire coefficient for each wheel using the input values ​​and a predetermined linear optimization algorithm, and performs nonlinear processing to identify the tire parameters for calculating the variable tire coefficient using the reference tire coefficient, the input values, and a predetermined nonlinear optimization algorithm, and a second information transmitting unit that transmits the tire parameters to the vehicle control device.

[0008] Furthermore, one aspect of the vehicle control device according to the present invention includes a vehicle state quantity estimating unit that estimates a state quantity of the host vehicle based on a position of the host vehicle and a physical quantity that indicates a motion state of the host vehicle; a ground contact load estimating unit that estimates a ground contact load of each wheel of the host vehicle based on the physical quantity that indicates the motion state of the host vehicle; a variable tire coefficient calculating unit that calculates the variable tire coefficient based on the slip ratio and slip angle of each wheel, the ground contact load of each wheel, and preset tire parameters among the state quantities; and an optimization control unit that inputs a preset reference state quantity of the host vehicle, the state quantity, and the variable tire coefficient, calculates a control quantity that minimizes a preset evaluation function, and sets an output corresponding to the control quantity to a traveling actuator of the host vehicle.

[0009] According to the present invention, the processing load of nonlinear model predictive control can be reduced.

[0010] 1 is a block diagram showing the configuration of a vehicle control system including a vehicle control device. FIG. 1 is a diagram showing an expression of nonlinearity in a pure longitudinal force characteristic. FIG. 2 is a diagram showing an expression of nonlinearity in a pure lateral force characteristic. FIG. 3 is a diagram showing an expression of nonlinearity in a composite characteristic. FIG. 4 is a block diagram showing details of a variable tire coefficient calculation unit. FIG. 5 is a block diagram showing details of a tire parameter identification unit. FIG. 6 is a flowchart showing the calculation process of the tire parameter identification unit. FIG. 7 is a flowchart showing the calculation process of the tire parameter identification unit. FIG. 8 is a flowchart showing the calculation process of the tire parameter identification unit. FIG. 9 is a flowchart showing the identification process of a pure longitudinal force characteristic tire parameter. FIG. 10 is a flowchart showing the identification process of a pure lateral force characteristic tire parameter. FIG. 11 is a flowchart showing the identification process of a composite characteristic tire parameter. FIG. 12 is a diagram showing an image of the process of acquiring a reference tire coefficient by linear optimization. FIG. 13 is a diagram showing an image of the process of acquiring tire parameters by nonlinear optimization. FIG. 14 is a block diagram showing the configuration of a variable tire coefficient calculation unit including a management server. FIG. 15 is a diagram showing a driving trajectory when steering avoidance assist is activated. FIG. 16 is a time chart showing the operation state of the steering wheel when steering avoidance assist is activated. FIG. 17 is a time chart showing the change in vehicle behavior when steering avoidance assist is activated. FIG. 18 is a time chart showing the change in actual steering angle when steering avoidance assist is activated. 10 is a time chart showing a change in braking torque due to steering avoidance assist.

[0011] Hereinafter, embodiments of a vehicle control device and a vehicle control system according to the present invention will be described with reference to the drawings. Fig. 1 is an overall configuration diagram showing one aspect of a vehicle control system mounted on a vehicle 10. The vehicle 10 is a four-wheeled automobile equipped with a pair of front wheels and a pair of rear wheels. The vehicle 10 includes a steering device 11, an ESC (Electronic Stability Control) 12, an IMU (Inertial Measurement Unit) 13, wheel speed sensors 14, a GPS (Global Positioning System) receiver 15, and an external sensor 16.

[0012] The steering device 11 is a steer-by-wire steering device in which the steering wheel, which is a steering operation input member, and the front wheels, which are steered wheels, are mechanically separated, and the actual steering angle of the steered wheels is varied by controlling a steering actuator based on a steering wheel angle signal, which is the steering wheel operation angle. The ESC 12 is a device that, when the vehicle 10 is skidding, controls the driving force of a drive device provided in the vehicle 10 and also controls the braking force applied to each wheel by a braking device provided in the vehicle 10, thereby suppressing skidding of the vehicle 10. The steering device 11, the braking device, and the drive device are included in the traveling actuators of the vehicle 10.

[0013] The IMU 13 detects three-axis angular velocities, i.e., yaw rate, roll rate, and pitch rate, as well as three-axis accelerations, i.e., longitudinal acceleration, lateral acceleration, and vertical acceleration. The wheel speed sensors 14 detect the rotational speeds of each of the four wheels of the vehicle 10. The GPS receiver 15 measures the latitude and longitude of the position of the vehicle 10 by receiving signals from GPS satellites. The external sensor 16 is a sensor that acquires external information about the vehicle 10, in other words, information about the driving environment of the road on which the vehicle 10 is traveling, and includes a camera, radar, LiDAR (Light Detection and Ranging, Laser Imaging Detection and Ranging), etc.

[0014] The vehicle control device 100 mounted on the vehicle 10 includes a microcomputer (in other words, an on-board computer) serving as a control unit that performs calculations based on acquired information and outputs the calculation results. The vehicle control device 100 executes evasive steering support (ESS) as driving assistance control. The evasive steering support is a driving assistance function that automatically controls the steering angle when it detects a risk of collision between the vehicle 10 and an obstacle ahead, so that the vehicle 10 moves in a direction free of the obstacle, in other words, in a direction that allows the vehicle 10 to avoid a collision.

[0015] The vehicle control device 100 acquires a signal of the steering wheel angle (in other words, the amount of operation of the steering operation input member) and a signal of the actual steering angle of the front wheels, which are the steered wheels, from the steering device 11. The vehicle control device 100 also acquires signals of the yaw rate, roll rate, pitch rate, longitudinal acceleration, lateral acceleration, and vertical acceleration from the IMU 13. The vehicle control device 100 also acquires signals of the wheel speed of each wheel from the wheel speed sensor 14.

[0016] The vehicle control device 100 also acquires a signal indicating the position of the vehicle 10, i.e., the vehicle itself, from the GPS receiver 15. Furthermore, the vehicle control device 100 acquires external environment recognition information of the vehicle 10 from the external environment sensor 16, specifically, external environment recognition information including a signal indicating the distance between the vehicle 10 and lane markers on the road, a signal indicating the distance between the vehicle 10 and an obstacle, and the like.

[0017] Lane markers are road markings that divide lanes into which vehicles are to travel, such as white lines painted on the road surface. The longitudinal acceleration, lateral acceleration, vertical acceleration, roll rate, pitch rate, and yaw rate of the vehicle 10, the wheel speeds of each wheel, and the actual steering angle of the steered wheels of the vehicle 10 are physical quantities that indicate the motion state of the vehicle 10, i.e., the host vehicle, in the vehicle control device 100.

[0018] The vehicle control device 100 then performs steering avoidance assist by performing calculations based on the various acquired signals, outputs an actual steering angle requirement value for collision avoidance (in other words, a target steering angle signal) to the steering device 11, and outputs a braking torque requirement value for collision avoidance (in other words, a target braking force signal) to the ESC 12. In other words, in steering avoidance assist, the vehicle control device 100 controls the steering angle of the steered wheels of the vehicle 10 and the braking force of each wheel of the vehicle 10 to avoid collision with an obstacle. Note that the vehicle control device 100 may be configured as a system in which it outputs the braking torque requirement value for steering avoidance assist directly to a controller of the braking device without going through the ESC 12.

[0019] In the steering avoidance assist, the vehicle control device 100 uses nonlinear model predictive control (NMPC) in which the control target is a nonlinear system. Model predictive control is a nonlinear optimal control that has a mathematical model of the system, predicts the output when a certain input is given to the system, and optimizes the input so that the predicted result approaches a target (in other words, an ideal value).

[0020] In an emergency avoidance scenario where steering avoidance assistance is performed, the tire characteristics of each wheel reach a nonlinear range. Therefore, the vehicle control device 100 performs nonlinear model predictive control using a nonlinear tire model equation in steering avoidance assistance. Furthermore, the vehicle control device 100 performs online identification of parameters included in the nonlinear tire model equation to perform appropriate control even when tire characteristics change due to tire replacement, changes in the road surface, etc.

[0021] The vehicle control device 100 has, as functional units for executing steering avoidance assist using nonlinear model predictive control, a ground contact load estimator 110, a vehicle state quantity estimator 120, a steering avoidance assist operation trigger unit 130, a variable tire coefficient calculator 140, an optimum controller 150, and an actuator required value converter 160. The ground contact load estimator 110 estimates the ground contact load of each wheel of the vehicle 10 based on physical quantities indicating the motion state of the vehicle 10, namely, longitudinal acceleration, lateral acceleration, vertical acceleration, roll rate, pitch rate, and yaw rate.

[0022] The vehicle state quantity estimating unit 120 estimates the state quantities of the vehicle 10 based on the position of the vehicle 10 and physical quantities indicating the motion state of the vehicle 10. In detail, the vehicle state quantity estimating unit 120 estimates the vehicle speed, vehicle body slip angle, yaw angle, and the like as state quantities of the vehicle 10 based on the physical quantities indicating the motion state of the vehicle 10, including the steering angle of the steered wheels, longitudinal acceleration, lateral acceleration, vertical acceleration, roll rate, pitch rate, yaw rate, and wheel speed, and the position of the vehicle 10, and also estimates the slip ratio and slip angle of each wheel.

[0023] The steering avoidance assist activation trigger unit 130 determines whether or not to activate steering avoidance assist for an obstacle, i.e., whether or not to implement steering avoidance assist, based on signals such as the steering wheel angle, the distance between the vehicle 10 and a lane marker, and the distance between the vehicle 10 and an obstacle, and outputs an activation flag indicating whether or not the steering avoidance assist needs to be activated.

[0024] The variable tire coefficient calculation unit 140 is a functional unit that performs online identification of parameters included in the nonlinear tire model equation. The variable tire coefficient calculation unit 140 acquires signals for the slip ratio and slip angle of each wheel, the ground contact load of each wheel, physical quantities indicating the motion state of the vehicle 10 (more specifically, the three-axial acceleration and three-axial angular velocities), and the actual steering angles of the steered wheels of the vehicle 10. The variable tire coefficient calculation unit 140 then calculates the variable tire coefficient of each wheel based on the acquired signals (in other words, the input values), more specifically, the variable tire coefficient for the longitudinal force of each wheel and the variable tire coefficient for the lateral force of each wheel.

[0025] The optimum control unit 150 is a functional unit that calculates the control amount for the steering avoidance assist using nonlinear model predictive control. The optimum control unit 150 acquires preset reference state quantities of the vehicle 10, signals of the vehicle speed, slip angle, yaw rate, yaw angle, and vehicle position that are states estimated by the vehicle state quantity estimation unit 120, signals of the variable tire coefficient calculated by the variable tire coefficient calculation unit 140, and further an operation flag signal from the steering avoidance assist operation trigger unit 130.

[0026] Then, in a state where steering avoidance assist operation is required, the optimization control unit 150 predicts and optimizes future vehicle behavior based on the tire model, and determines the control variables (in other words, control inputs) of the steering device 11 and braking device for collision avoidance. Here, the control variables calculated by the optimization control unit 150 are the actual steering angle of the steered wheels and the slip ratio of each wheel. Furthermore, the preset reference state variable of the vehicle 10 is, more specifically, a reference value of the slip angle, and for example, the reference slip angle can be set to 0 degrees.

[0027] The nonlinear model predictive control of the optimization control unit 150 requires a nonlinear tire model equation, but application of the nonlinear tire model equation to the optimization control unit 150 causes the scale of the optimal control calculation equation to increase, which may result in the processing not being able to be completed within the sampling period and making it impossible to execute steering avoidance assistance (in other words, emergency avoidance control). Therefore, by arranging the variable tire coefficient calculation unit 140, which is a block that calculates a variable tire coefficient that can express tire nonlinear characteristics, outside the optimization control unit 150, the scale of the optimal control calculation equation in the optimization control unit 150 can be reduced, processing can be completed within the sampling period, and steering avoidance assistance can be executed stably.

[0028] Furthermore, as will be described later, the variable tire coefficient calculation unit 140 reduces the overall processing load by performing a two-stage optimization process in online identification of tire parameters, in which a reference tire coefficient obtained by linear optimization, which is a low-load process, is used in nonlinear optimization. In other words, the processing load of the optimization control unit 150 is reduced by arranging the variable tire coefficient calculation unit 140 outside the optimization control unit 150, and the overall processing load is further reduced by the variable tire coefficient calculation unit 140 performing online identification of tire parameters through two-stage optimization process. Therefore, even if tire parameter update processing is not performed, the effect of reducing the scale of the optimal control formula can be obtained by arranging the variable tire coefficient calculation unit 140 outside the optimization control unit 150.

[0029] The actuator requirement value conversion unit 160 acquires a control amount signal from the optimum control unit 150 and converts the acquired control amount into an actuator requirement value. In detail, the actuator requirement value conversion unit 160 acquires signals of the actual steering angle of the steered wheels and the slip ratio of each wheel from the optimum control unit 150 and converts these into an actual steering angle requirement value of the steered wheels and a braking torque requirement value of each wheel.

[0030] Then, the actuator requirement value conversion unit 160 outputs the actual steering angle requirement value of the steered wheels to the steering device 11, and outputs the braking torque requirement value of each wheel to the ESC 12. In other words, the optimization control unit 150 outputs the calculated control amount command to the steering device 11 of the vehicle 10 and traveling actuators such as the braking device.

[0031] The variable tire coefficients calculated by the variable tire coefficient calculation unit 140 will be described in detail below. xXX_t , tire lateral force F yXX_t is calculated by the variable tire coefficient calculation unit 140. pXX , variable tire coefficient C for lateral force pXX is used and calculated according to Equation 1. In addition, in Equation 1, s XX is the slip ratio of each wheel, β XX is the slip angle of each wheel, F zXX_t is the ground load of each wheel. XX " are symbols that distinguish the four wheels of the vehicle 10, where "fl" indicates the left front wheel, "fr" indicates the right front wheel, "rl" indicates the left rear wheel, and "rr" indicates the right rear wheel.

[0032] As a tire model that can take into account the nonlinearity of a tire, there is a model called "Magic Formula," which is a method of adjusting parameters in the model based on actual data and expressing tire characteristics in an approximate formula that characterizes the tire characteristics. In the "Magic Formula," a longitudinal variable tire coefficient D related to the longitudinal force of the vehicle 10 is calculated by partially differentiating the composite characteristic formula of the tire longitudinal force and the composite characteristic formula of the tire lateral force with respect to the main slip factor (specifically, the slip ratio or the slip angle). pXX , a lateral variable tire coefficient C related to the lateral force of the vehicle 10 pXX can be obtained.

[0033] Equation 2 is a pure characteristic equation of the tire longitudinal force in the "Magic Formula" model, and represents, for example, the longitudinal force when the vehicle 10 is in a straight-line braking / driving state. Here, B in Equation 2xXX , C xXX , D xXX is expressed by Equation 3, and p Cx1 , p Dx1 , p kx1 is a parameter specific to the tire. z0nom is the steady load.

[0034] Further, for example, a composite characteristic equation of tire longitudinal forces, which represents longitudinal forces when the vehicle 10 is in a braking / driving and cornering state, is given by Equation 4. Here, G in Equation 4 xβXX is expressed by Equation 5, and r in Equation 5 Bx1 , r Bx2 , r Cx1 is a tire-specific parameter.

[0035] Then, the variable tire coefficient calculation unit 140 (first variable tire coefficient calculation unit 143 described later) calculates the composite characteristic equation (Equation 4) of the tire longitudinal force by calculating the slip ratio s of each wheel, which is the main slip factor for the longitudinal force. XX The longitudinal variable tire coefficient D is calculated by Equation 6, which is a partial differential equation obtained by pXX Calculate. In addition, the longitudinal variable tire coefficient D pXX The parameter p used to calculate Cx1 , p Dx1 , p kx1 , r Bx1 , r Bx2 , r Cx1 Since is a unique value depending on the type of tire and the coefficient of friction between the tire and the road surface, it must be identified using actual data.

[0036] Furthermore, Equation 7 is a pure characteristic equation of tire lateral force in the "Magic Formula" model, and represents, for example, the lateral force when the vehicle 10 is turning without braking or driving. Here, By in Equation 7 XX , Cy XX , Dy XX is expressed by Equation 8, and p Cy1 , p Dy1 , p ky1 , pky2 is a tire-specific parameter.

[0037] Further, for example, a composite characteristic equation of tire lateral force, which represents the lateral force when the vehicle 10 is in a braking / driving and cornering state, is given by Equation 9. Here, Gys in Equation 9 XX is expressed by Equation 10, and r in Equation 10 By1 , r By2 , r Cy1 is a tire-specific parameter.

[0038] The variable tire coefficient calculation unit 140 (second variable tire coefficient calculation unit 144 described later) calculates the composite characteristic equation (Equation 9) of the tire lateral force by calculating the slip angle β of each wheel, which is the main slip factor for the lateral force. XX By using Equation 11, which is a partial differential equation, the lateral variable tire coefficient C pXX Calculate. In addition, the lateral variable tire coefficient C pXX The parameter p used to calculate Cy1 , p Dy1 , p ky1 , p ky2 , r By1 , r By2 , r Cy1 Since is a unique value depending on the type of tire and the coefficient of friction between the tire and the road surface, it must be identified using actual data.

[0039] Figures 2 to 4 show the tire force calculation results using the "Magic Formula" model and the variable tire coefficient D pXX , C pXX 2 is a graph comparing the tire force calculation results using the slip ratio s XX and longitudinal force F xXX_t Correlation with the ground load F zXX_t 3 is a diagram showing the net longitudinal force characteristics as a function of slip angle β XX and lateral force F yXX_t Correlation with the ground load F zXX_t FIG. 1 is a diagram showing a line diagram for each of the four wheels, in other words, a diagram showing a pure lateral force characteristic.

[0040] Figure 4 shows the lateral force FyXX_t and longitudinal force F xXX_t Correlation with the ground load F zXX_t 2 to 4 show that the tire force calculation results using the "Magic Formula" model are similar to the tire force calculation results using the variable tire coefficient, and the variable tire coefficient D pXX , C pXX This shows that the nonlinear characteristics of a tire can be expressed using

[0041] FIG. 5 shows the variable tire coefficient D calculated by the variable tire coefficient calculation unit 140. pXX , C pXX 1 is a block diagram showing in more detail the calculation function of the variable tire coefficient calculation unit 140. The variable tire coefficient calculation unit 140 includes functional units, namely, a differentiation unit 141, a tire parameter identification unit 142, a first variable tire coefficient calculation unit 143, and a second variable tire coefficient calculation unit 144.

[0042] The differentiating unit 141 obtains a time differential value of the yaw rate. The tire parameter identifying unit 142 obtains signals of the longitudinal acceleration, the lateral acceleration, the differential value of the yaw rate, and the actual steering angle of the steered wheels. The tire parameter identifying unit 142 also obtains the ground load F of each wheel. zXX_t , slip angle β XX and slip ratio s XX Furthermore, the tire parameter identification unit 142 acquires an operation flag for the steering avoidance assistance output by the steering avoidance assistance operation trigger unit 130. Then, the tire parameter identification unit 142 calculates the longitudinal force tire parameters and the lateral force tire parameters based on the acquired signals.

[0043] The first variable tire coefficient calculation unit 143 calculates a variable tire coefficient for the longitudinal force of each wheel, and the second variable tire coefficient calculation unit 144 calculates a variable tire coefficient for the lateral force of each wheel. The first variable tire coefficient calculation unit 143 calculates a variable tire coefficient for the slip angle β of each wheel. XX and slip ratio s XX , the ground load of each wheel F zXX_tand further acquires a signal of the longitudinal force tire parameter determined by the tire parameter identification unit 142. Then, the first variable tire coefficient calculation unit 143 calculates a variable tire coefficient D for the longitudinal force of each wheel based on the acquired signals. pXX is calculated and output.

[0044] On the other hand, the second variable tire coefficient calculation unit 144 calculates the slip angle β of each wheel. XX and slip ratio s XX , the ground load of each wheel F zXX_t and further acquires a signal of the lateral force tire parameter determined by the tire parameter identification unit 142. Then, the second variable tire coefficient calculation unit 144 calculates a variable tire coefficient C for the lateral force of each wheel based on the acquired signals. pXX is calculated and output.

[0045] 6 is a block diagram showing the functions of the tire parameter identification unit 142 in more detail. The tire parameter identification unit 142 includes functional units: a linear optimization unit 142A, a nonlinear optimization unit 142B, and a tire parameter update determination unit 142C. The linear optimization unit 142A acquires signals for the longitudinal acceleration, the lateral acceleration, the differential value of the yaw rate, the actual steering angle of the steered wheels, the ground contact load of each wheel, the slip ratio of each wheel, and the slip angle of each wheel. The linear optimization unit 142A then performs linear processing to determine the reference tire coefficient of each wheel using the acquired signals and a predetermined linear optimization algorithm.

[0046] The nonlinear optimization unit 142B acquires the signal of the reference tire coefficient of each wheel calculated by the linear optimization unit 142A, and further calculates the ground load F zXX_t , slip ratio s of each wheel XX , slip angle β of each wheel XXThe nonlinear optimization unit 142B then uses the acquired signals and a predetermined nonlinear optimization algorithm to perform nonlinear processing to identify tire parameters (more specifically, longitudinal force tire parameters and lateral force tire parameters) for determining variable tire coefficients. In this way, the tire parameter identification unit 142 performs a two-stage optimization process in which the reference tire coefficients obtained by linear optimization, which is a low-load process, are used in nonlinear optimization, thereby identifying tire parameters online.

[0047] The tire parameter update determination unit 142C acquires preset tire parameter reference values, the tire parameters identified by the nonlinear optimization unit 142B, and the operation flag output by the steering avoidance assist operation trigger unit 130. Then, if the error index calculated from the preset tire parameter reference values ​​and the tire parameters identified by the nonlinear optimization unit 142B is greater than the threshold value and the steering avoidance assist is not being executed, the tire parameter update determination unit 142C updates and outputs the tire parameters (more specifically, the longitudinal force tire parameters and the lateral force tire parameters).

[0048] On the other hand, if the error index is equal to or less than the threshold value or if steering avoidance assistance is being performed, the tire parameter update determination unit 142C stops updating the tire parameters. Note that the tire parameter update determination unit 142C determines whether to update the tire parameters when the tires of the vehicle 10 are replaced, when a change in the road surface such as a change in the friction coefficient occurs, or the like.

[0049] In this way, the tire parameter identification unit 142 updates and outputs the tire parameters, the first variable tire coefficient calculation unit 143 acquires the longitudinal force tire parameters output by the tire parameter identification unit 142, and the second variable tire coefficient calculation unit 144 acquires the lateral force tire parameters output by the tire parameter identification unit 142. Then, the first variable tire coefficient calculation unit 143 and the second variable tire coefficient calculation unit 144 calculate the tire parameters and the ground contact loads F of each wheel. zXX_t , slip ratio s of each wheel XX , and the slip angle β of each wheel XXA variable tire coefficient is calculated based on the above.

[0050] 7 to 9 are flowcharts showing the flow of the calculation process of tire parameters (longitudinal force tire parameters and lateral force tire parameters) by the vehicle control device 100, more specifically, by the tire parameter identification unit 142. In step S201, the tire parameter identification unit 142 calculates the tire parameters (longitudinal force tire parameters and lateral force tire parameters) based on sensor information such as longitudinal acceleration, lateral acceleration, yaw rate, and actual steering angle of the steered wheels, and the slip ratio s XX , slip angle β of each wheel XX , the ground load of each wheel F zXX_t A certain number of samples of estimated information such as the above are acquired. The certain number of samples is a number that is adapted in consideration of identification accuracy and identification processing speed, and may be set to, for example, about 100.

[0051] Next, in step S202, the tire parameter identification unit 142 determines whether or not at least one of the following conditions is satisfied for all of the multiple data acquired in step S201: the absolute value of the longitudinal acceleration is smaller than a longitudinal acceleration threshold (longitudinal acceleration threshold > 0) and the absolute value of the lateral acceleration is smaller than a lateral acceleration threshold (lateral acceleration threshold > 0). The longitudinal acceleration threshold is a threshold for distinguishing whether the vehicle 10 is accelerating / decelerating or traveling at a constant speed, and the lateral acceleration threshold is a threshold for distinguishing whether the vehicle 10 is turning or traveling straight.

[0052] Here, a state in which the absolute value of the longitudinal acceleration is greater than the longitudinal acceleration threshold and the absolute value of the lateral acceleration is greater than the lateral acceleration threshold means that the vehicle 10 is turning with acceleration and deceleration. Therefore, in step S202, the tire parameter identification unit 142 determines whether the vehicle 10 is in a driving state other than turning with acceleration and deceleration (in other words, a constant speed turning state or a straight running state) based on the sampling data of the longitudinal acceleration and the lateral acceleration.

[0053] If the vehicle 10 is turning with acceleration or deceleration, the tire parameter identification unit 142 returns to step S201 and performs data sampling processing. On the other hand, if the tire parameter identification unit 142 determines that the vehicle 10 is in a driving state other than turning with acceleration or deceleration, that is, a constant speed turning state, that is, a turning state without acceleration or deceleration, or a straight driving state, the tire parameter identification unit 142 proceeds to step S203.

[0054] In step S203, the tire parameter identification unit 142 determines whether the absolute value of the longitudinal acceleration is greater than the longitudinal acceleration threshold value for all of the multiple data acquired in step S201. The state in which the tire parameter identification unit 142 determines in step S203 that the absolute value of the longitudinal acceleration is greater than the longitudinal acceleration threshold value means that the vehicle 10 is traveling straight while accelerating and decelerating.

[0055] If the tire parameter identification unit 142 determines in step S203 that the absolute value of the longitudinal acceleration is greater than the longitudinal acceleration threshold value, the process proceeds to step S204, where it identifies a longitudinal force pure characteristic tire parameter corresponding to the longitudinal force of the vehicle 10. The longitudinal force pure characteristic tire parameter identified in step S204 is the parameter p Cx1 , p Dx1 , p kx1 In addition, the longitudinal force pure characteristic tire parameter p Cx1 , p Dx1 , p kx1 In step S204, the tire parameter identification unit 142 identifies the pure longitudinal force characteristic tire parameter p Cx1 , p Dx1 , p kx1 Once the identification is completed, the process proceeds to step S205.

[0056] In step S205, the tire parameter identification unit 142 identifies the tire speed based on the sensor information such as the longitudinal acceleration and the slip ratio s of each wheel, similarly to step S201. XXNext, the tire parameter identification unit 142 proceeds to step S206, where, similarly to step S202, it determines whether or not at least one of the conditions that the absolute value of the longitudinal acceleration is smaller than the longitudinal acceleration threshold value and the absolute value of the lateral acceleration is smaller than the lateral acceleration threshold value is satisfied for all of the plurality of data acquired in step S205.

[0057] If the tire parameter identification unit 142 determines in step S206 that the vehicle 10 is in a driving state other than cornering accompanied by acceleration or deceleration, the process proceeds to step S207. On the other hand, if the tire parameter identification unit 142 determines in step S206 that the vehicle 10 is in a cornering state accompanied by acceleration or deceleration, the process returns to step S205 and performs data sampling processing.

[0058] In step S207, the tire parameter identification unit 142 determines whether the absolute value of the lateral acceleration is greater than the lateral acceleration threshold value for all of the multiple data sets acquired in step S205. The state in which the tire parameter identification unit 142 determines in step S207 that the absolute value of the lateral acceleration is greater than the lateral acceleration threshold value means that the vehicle 10 is turning without accelerating or decelerating.

[0059] If the tire parameter identification unit 142 determines in step S207 that the absolute value of the lateral acceleration is greater than the lateral acceleration threshold value, the process proceeds to step S208, where it identifies a pure lateral force characteristic tire parameter corresponding to the lateral force of the vehicle 10. The pure lateral force characteristic tire parameter identified in step S208 is the parameter p Cy1 , p Dy1 , p ky1 , p ky2 The pure lateral force characteristic tire parameter p Cy1 , p Dy1 , p ky1 , p ky2 The identification process will be described in detail later.

[0060] On the other hand, if the tire parameter identification unit 142 determines in step S207 that the absolute value of the lateral acceleration is equal to or less than the lateral acceleration threshold value, the process returns to step S205 and performs data sampling processing. Cx1 , p Dx1 , p kx1 Then, when the vehicle 10 is turning without accelerating or decelerating, the pure lateral force characteristic tire parameter p Cy1 , p Dy1 , p ky1 , p ky2 Identification is carried out.

[0061] If the tire parameter identifying unit 142 determines in step S203 that the absolute value of the longitudinal acceleration is equal to or less than the longitudinal acceleration threshold value, the process proceeds to step S209. In step S209, similar to step S207, the tire parameter identifying unit 142 determines whether the absolute value of the lateral acceleration is greater than the lateral acceleration threshold value for all of the acquired data.

[0062] If the tire parameter identification unit 142 determines in step S209 that the absolute value of the lateral acceleration is greater than the lateral acceleration threshold, the tire parameter identification unit 142 proceeds to step S210, where the tire parameter identification unit 142 determines the lateral force pure characteristic tire parameter p Cy1 , p Dy1 , p ky1 , p ky2 On the other hand, if the tire parameter identifying unit 142 determines in step S209 that the absolute value of the lateral acceleration is equal to or less than the lateral acceleration threshold value, the process returns to step S201 and performs data sampling processing.

[0063] In step S210, the tire parameter identification unit 142 calculates the pure lateral force characteristic tire parameter p Cy1 , p Dy1 , p ky1 , p ky2In step S211, similarly to steps S201 and S205, the tire parameter identification unit 142 identifies the slip ratio s of each wheel based on sensor information such as longitudinal acceleration. XX A certain number of samples of estimated information such as the above are obtained.

[0064] Next, the tire parameter identification unit 142 proceeds to step S212, where, similarly to steps S202 and S206, it determines whether at least one of the conditions that the absolute value of the longitudinal acceleration is smaller than the longitudinal acceleration threshold and the condition that the absolute value of the lateral acceleration is smaller than the lateral acceleration threshold is satisfied for all of the multiple data acquired in step S211. If the tire parameter identification unit 142 determines in step S212 that the vehicle 10 is in a driving state other than cornering accompanied by acceleration and deceleration, it proceeds to step S213. On the other hand, if the tire parameter identification unit 142 determines in step S212 that the vehicle 10 is in a cornering state accompanied by acceleration and deceleration, it returns to step S211 and performs data sampling processing.

[0065] In step S213, the tire parameter identification unit 142 determines whether the absolute value of the longitudinal acceleration is greater than the longitudinal acceleration threshold value for all of the multiple data acquired in step S211. If the tire parameter identification unit 142 determines in step S213 that the absolute value of the longitudinal acceleration is greater than the longitudinal acceleration threshold value, the process proceeds to step S214, where the tire parameter identification unit 142 calculates the longitudinal force pure characteristic tire parameter p Cx1 , p Dx1 , p kx1 Identification is carried out.

[0066] On the other hand, if the tire parameter identification unit 142 determines in step S213 that the absolute value of the longitudinal acceleration is equal to or less than the longitudinal acceleration threshold value, the process returns to step S211 and the tire parameter identification unit 142 performs data sampling processing. Cy1 , p Dy1 , p ky1 , p ky2Then, when the vehicle 10 is traveling straight with acceleration and deceleration, the longitudinal force pure characteristic tire parameter p kx1 , p Cx1 , p Dx1 Identification is carried out.

[0067] The tire parameter identification unit 142 determines the pure lateral force characteristic tire parameter p Cy1 , p Dy1 , p ky1 , p ky2 After the identification process of the pure longitudinal force characteristic tire parameter p Cx1 , p Dx1 , p kx1 In step S215, the tire parameter identification unit 142 performs the identification process using the sensor information such as the longitudinal acceleration and the slip ratio s of each wheel, similarly to steps S201, S205, and S211. XX A certain number of samples of estimated information such as the above are obtained.

[0068] Next, the tire parameter identification unit 142 proceeds to step S216 and determines whether or not the conditions that the absolute value of the longitudinal acceleration is greater than the longitudinal acceleration threshold and the absolute value of the lateral acceleration is greater than the lateral acceleration threshold are satisfied for all of the multiple data acquired in step S215. In other words, in step S216, the tire parameter identification unit 142 determines whether or not the vehicle 10 is in a cornering state involving acceleration and deceleration.

[0069] If the conditions that the absolute value of the longitudinal acceleration is greater than the longitudinal acceleration threshold and the absolute value of the lateral acceleration is greater than the lateral acceleration threshold are not satisfied, the tire parameter identifying unit 142 returns to step S215 and performs data sampling processing. On the other hand, if the conditions that the absolute value of the longitudinal acceleration is greater than the longitudinal acceleration threshold and the absolute value of the lateral acceleration is greater than the lateral acceleration threshold are satisfied, the tire parameter identifying unit 142 proceeds to step S217.

[0070] In step S217, the tire parameter identification unit 142 identifies a composite characteristic tire parameter corresponding to a composite force that is a combination of the longitudinal force and the lateral force of the vehicle 10. The composite characteristic tire parameter identified in step S217 is the parameter r Bx1 , r Bx2 , r Cx1 , and the parameter r included in the above-mentioned Equation 10 By1 , r By2 , r Cy1 In addition, the composite characteristic tire parameter r Bx1 , r Bx2 , r Cx1 , r By1 , r By2 , r Cy1 The identification of will be explained in detail later.

[0071] Next, the tire parameter identification unit 142 proceeds to step S218 and determines whether the deviation index between each identified tire parameter and a preset reference value of each tire parameter is greater than a predetermined deviation threshold value. The tire parameter identification value that is the target of the deviation index calculation in step S218 is the longitudinal force pure characteristic tire parameter p Cx1 , p Dx1 , p kx1 , lateral force pure characteristic tire parameter p Cy1 , p Dy1 , p ky1 , p ky2 , and composite characteristic tire parameter r Bx1 , r Bx2 , r Cx1 , r By1 , r By2 , r Cy1 is.

[0072] The tire parameter identification unit 142 uses the root mean squared percentage error (RMSPE) as the deviation index. If the tire parameters vary widely, the influence of tire parameters that are large in numerical value will be large in the evaluation of the deviation of the identified value from the reference value of the tire parameter. Therefore, by evaluating the deviation using an error rate such as the root mean squared percentage error, an appropriate deviation evaluation can be performed. The root mean squared percentage error (RMSPE) is calculated by multiplying the tire parameter reference value by x ref_i , tire parameter identification value x i , where N is the number of parameters, it can be calculated by Equation 12.

[0073] If the root mean square error RMSPE is equal to or less than the deviation threshold, the tire parameter identification unit 142 returns to step S215 and performs data sampling. Note that by returning from step S218 to step S215, the tire parameter identification unit 142 outputs reference values ​​as tire parameters if the tire parameters are not to be updated. On the other hand, if the root mean square error RMSPE is greater than the deviation threshold, the tire parameter identification unit 142 proceeds to step S219.

[0074] In step S219, the tire parameter identification unit 142 determines, based on an operation flag indicating whether operation of the steering avoidance assist is required, whether the steering avoidance assist is operating, in other words, whether the optimization control unit 150 is in an operating state in which it calculates a control amount using a variable tire coefficient and outputs the calculated control amount to a traveling actuator. Here, if the steering avoidance assist is operating (in other words, if optimization control using a variable tire coefficient is operating), the tire parameter identification unit 142 waits and suspends updating of the tire parameters, but if the steering avoidance assist is not operating, the process proceeds to step S220 and updates the tire parameters. Note that the processes from step S218 to step S220 are specifically performed by the tire parameter update determination unit 142C.

[0075] In the following, the longitudinal force pure characteristic tire parameter p Cx1, p Dx1 , p kx1 , lateral force pure characteristic tire parameter p Cy1 , p Dy1 , p ky1 , p ky2 , and composite characteristic tire parameter r Bx1 , r Bx2 , r Cx1 , r By1 , r By2 , r Cy1 Each identification process will be described in detail. "Identification of longitudinal force pure characteristic tire parameter" Fig. 10 shows the process of identifying the longitudinal force pure characteristic tire parameter p Cx1 , p Dx1 , p kx1 10 is a flowchart showing the process of the identification process.

[0076] In step S301, the tire parameter identification unit 142 (linear optimization unit 142A) calculates the reference tire coefficient D of each wheel by a linear optimization algorithm. pXX_ref Next, in step S302, the tire parameter identification unit 142 (nonlinear optimization unit 142B) calculates the longitudinal force pure characteristic tire parameter p Cx1 , p Dx1 , p kx1 Calculate.

[0077] Here, the calculation processes in steps S301 and S302 will be described in detail. In this embodiment, as one aspect, the tire parameter identification unit 142 uses the least squares method using normal equations as the linear optimization algorithm and the steepest descent method as the nonlinear optimization algorithm.

[0078] Reference tire coefficient D related to longitudinal force in step S301 pXX_ref The calculation process of (hat) is performed in a driving scene where the vehicle 10 is accelerating and decelerating in a straight-ahead state. The estimated longitudinal acceleration αx (hat) in such a driving scene is calculated by the estimated tire longitudinal force F of each wheel in the vehicle coordinate system. xXX (hat) and the vehicle weight m, using Equation 13.

[0079] The estimated tire longitudinal force F of each wheel in the vehicle coordinate system is xXX (Hat) represents the actual steering angle δxx of each wheel (where the rear wheel steering angles δrl and δrr = 0 (neutral position)) and the estimated tire longitudinal force F in the tire coordinate system. xXX_t (hat), according to Equation 14. Note that δrl = δrr = 0 (neutral position) indicates that the rear wheel steering angle is fixed at the neutral position because the vehicle 10 is not equipped with a rear wheel steering device.

[0080] In addition, the estimated tire longitudinal force F xXX_t (Hat) is the slip ratio s of each wheel XX and the reference tire coefficient D for each wheel pXX_ref (hat) according to Equation 15.

[0081] Here, based on the optimization problem of minimizing the difference between the sensor value and the estimated value for the longitudinal acceleration, the optimization parameter (reference tire coefficient D pXX_ref First, the estimator vector b (hat) is expressed by Equation 16, where A is the coefficient matrix and x is the optimization parameter.

[0082] Here, the coefficient matrix A in Equation 16 is the slip ratio s of each wheel. XX , front wheel steering angle δ fl , δ fr , based on the vehicle weight m, is given as Equation 17. The optimization parameter x is the reference tire coefficient D pXX_ref (hat), which is expressed as Equation 18.

[0083] On the other hand, the sensor quantity vector b is expressed by Equation 19. Then, the tire parameter identifying unit 142 uses the least squares method as a linear optimization algorithm, and sets the evaluation function (minimization problem) to Equation 20.

[0084] Here, the optimization parameter x (reference tire coefficient D pXX_refThe normal equation for finding (hat) is Equation 21.

[0085] As described above, in step S301, the tire parameter identification unit 142 determines the reference tire coefficient D by linear optimization, which does not require repeated calculations and has a small processing load. pXX_ref (hat) is calculated. Note that the reference tire coefficient D pXX_ref The formula for finding (hat) represents the case where one sampling of data is used, and in reality, it is an equation for the amount of data acquired from a fixed number of samples.

[0086] Next, in step S302, the longitudinal force pure characteristic tire parameter p Cx1 , p Dx1 , p kx1 The identification of the longitudinal variable tire coefficient D pXX In Equation 6, which is the calculation formula for the tire slip angle β, since the driving scene is straight-line acceleration and deceleration, XX = 0. Then, the tire slip angle β XX = 0, Equation 6 can be rewritten as Equation 22.

[0087] On the other hand, the reference tire coefficient D pXX_ref (hat) and variable tire coefficient D pXX Based on the minimization problem of minimizing the difference between Here, the error vector e in Equation 23 is the reference tire coefficient D pXX_ref (hat) and variable tire coefficient D pXX From the minimization problem of minimizing the difference between

[0088] Furthermore, the weighting matrix Q in Equation 23 is given by Equation 25. Here, the optimization parameter x is calculated using the longitudinal force pure characteristic tire parameter p as shown in Equation 26. Cx1 , p Dx1 , p kx1 Let's say.

[0089] Then, as shown in Equation 27, the longitudinal force pure characteristic tire parameter p Cx1 , p Dx1 , p kx1 Ask for. In addition, the aforementioned longitudinal force pure characteristic tire parameter p Cx1 , p Dx1 , p kx1 The formula for determining the above expression represents the case where one sampling of data is used, and in reality, the formula is for data acquired from a fixed number of samples.

[0090] "Identification of Pure Lateral Force Characteristics Tire Parameter" FIG. 11 shows the pure lateral force characteristics tire parameter p Cy1 , p Dy1 , p ky1 , p ky2 1 is a flowchart showing the process of the identification process of the tire parameter identification unit 142 (linear optimization unit 142A) in step S401, the tire parameter identification unit 142 calculates the reference tire coefficient C of each wheel by a linear optimization algorithm. pXX_ref Next, in step S402, the tire parameter identification unit 142 (nonlinear optimization unit 142B) calculates the pure lateral force characteristic tire parameter p Cy1 , p Dy1 , p ky1 , p ky2 Identify.

[0091] Here, the calculation processes in steps S401 and S402 will be described in detail. pXX_ref The calculation process of αy (hat) is performed when the vehicle 10 is turning at a constant speed. The estimated lateral acceleration αy (hat) when turning at a constant speed is calculated based on the estimated tire lateral force F of each wheel in the vehicle coordinate system. yXX (hat) and the vehicle weight m, is calculated using Equation 28.

[0092] The estimated tire lateral force F of each wheel in the vehicle coordinate system is yXX (Hat) is the actual steering angle δxx of each wheel and the estimated tire lateral force F in the tire coordinate system. yXX_t(hat) according to Equation 29.

[0093] In addition, the estimated yaw angular velocity (estimated yaw rate) during constant speed turning can be calculated using Equation 30, where Iz is the yaw moment of inertia, lf is the distance between the front wheel axle and the center of gravity, and lr is the distance between the rear wheel axle and the center of gravity.

[0094] The estimated tire lateral force F of each wheel in the vehicle coordinate system is yXX (Hat) is the actual steering angle δxx of each wheel and the estimated tire lateral force F in the tire coordinate system. yXX_t (hat) according to Equation 31.

[0095] In addition, the estimated tire lateral force F yXX_t (Hat) is the slip angle β of each wheel XX and the reference tire coefficient C for each wheel pXX_ref (hat) according to Equation 32.

[0096] Here, based on the optimization problem of minimizing the difference between the sensor value and the estimated value for the lateral acceleration and yaw rate, the optimization parameters (reference tire coefficient C pXX_ref First, the estimator vector b (hat) is expressed by Equation 33, where A is the coefficient matrix and x is the optimization parameter. The coefficient matrix A in Equation 33 is the slip angle β of each wheel. XX , front wheel steering angle δ fl , δ fr , vehicle weight m, yaw moment of inertia Iz, distance lf between the front wheel axle and the center of gravity, and distance lr between the rear wheel axle and the center of gravity, and is given by Equation 34.

[0097] The optimization parameter x is the reference tire coefficient C pXX_ref (hat), which is expressed as Equation 18.

[0098] On the other hand, the sensor quantity vector b is expressed by Equation 36. Then, the tire parameter identifying unit 142 uses the least squares method as a linear optimization algorithm, and sets the evaluation function (minimization problem) to Equation 37.

[0099] Here, the optimized parameters (reference tire coefficient C pXX_ref The normal equation for finding (hat) is Equation 38.

[0100] As described above, in step S401, the tire parameter identification unit 142 determines the reference tire coefficient C by linear optimization, which does not require repeated calculations and has a small processing load. pXX_ref (hat). Note that the reference tire coefficient C pXX_ref The formula for finding (hat) represents the case where one sampling of data is used, and in reality, it is an equation for the amount of data acquired from a fixed number of samples.

[0101] Next, in step S402, the pure lateral force characteristic tire parameter p is calculated by a nonlinear optimization algorithm. Cy1 , p Dy1 , p ky1 , p ky2 The identification of the laterally variable tire coefficient C pXX In Equation 11, since the driving scene is a constant speed turn, the tire slip ratio s XX = 0. Then, the tire slip ratio s XX = 0, Equation 11 can be rewritten as Equation 39.

[0102] On the other hand, the reference tire coefficient C pXX_ref (hat) and variable tire coefficient C pXX Based on the minimization problem of minimizing the difference between Here, the error vector e in Equation 40 is the reference tire coefficient C pXX_ref (hat) and variable tire coefficient C pXX From the minimization problem of minimizing the difference between

[0103] Furthermore, the weighting matrix Q in Equation 40 is given by Equation 42. Here, the optimization parameter x is calculated using the pure lateral force characteristic tire parameter p as shown in Equation 43. Cy1 , p Dy1 , p ky1 , p ky2 Let's say.

[0104] Then, as shown in Equation 44, the pure lateral force characteristic tire parameter p Cy1 , p Dy1 , p ky1 , p ky2 Ask for. In addition, the above-mentioned pure lateral force characteristic tire parameter p Cy1 , p Dy1 , p ky1 , p ky2 The formula for determining the above expression represents the case where one sampling of data is used, and in reality, the formula is for data acquired from a fixed number of samples.

[0105] "Identification of Composite Characteristic Tire Parameter" FIG. 12 shows the composite characteristic tire parameter r Bx1 , r Bx2 , r Cx1 , r By1 , r By2 , r Cy1 1 is a flowchart showing the process of the identification process of the tire parameter identification unit 142 (linear optimization unit 142A) by a linear optimization algorithm in step S501. pXX_ref (hat), and the reference tire coefficient C for each wheel pXX_ref Next, in step S502, the tire parameter identification unit 142 (nonlinear optimization unit 142B) calculates the composite characteristic tire parameter r Bx1 , r Bx2 , r Cx1 , r By1 , r By2 , r Cy1 Calculate.

[0106] The calculation processes in steps S501 and S502 will be described in detail below. pXX_ref (Hat), C pXX_ref The calculation process of (hat) is performed when the vehicle 10 is turning and accelerating or decelerating. Here, the estimated longitudinal acceleration αx (hat) in the above-mentioned traveling scene is obtained by Equation 45.

[0107] The estimated lateral acceleration αy (hat) in the above driving scene is calculated using Equation 46. Note that Equation 45 is the same as Equation 13 described above, and Equation 46 is the same as Equation 28 described above.

[0108] Furthermore, the estimated yaw angular velocity (estimated yaw rate) in the above driving scene is calculated using Equation 47. In addition, in Equation 47, "tr f " is half the value of the front wheel tread, "tr r " is half the rear wheel tread.

[0109] Here, the estimated tire longitudinal force F of each wheel in the vehicle coordinate system is xXX (hat) is calculated according to Equation 48. In addition, the estimated tire longitudinal force F xXX_t (hat) is calculated according to Equation 49.

[0110] Furthermore, the estimated tire lateral force F yXX (hat) is calculated according to Equation 50. In addition, the estimated tire lateral force F yXX_t (hat) is calculated according to Equation 51.

[0111] Next, based on the optimization problem of minimizing the difference between the sensor value and the estimated value for each of the longitudinal acceleration, lateral acceleration, and yaw rate, the optimization parameters (reference tire coefficient D pXX_ref (Hat), C pXX_refFirst, the estimator vector b (hat) is expressed by Equation 52, where A is the coefficient matrix and x is the optimization parameter.

[0112] Here, the coefficient matrix A in Equation 52 is given by Equation 53.

[0113] The optimization parameter x is the reference tire coefficient D pXX_ref (Hat), C pXX_ref (hat), which is expressed by Equation 54.

[0114] On the other hand, the sensor quantity vector b is expressed by Equation 55. Then, the tire parameter identifying unit 142 uses the least squares method as a linear optimization algorithm, and sets the evaluation function (minimization problem) to Equation 56.

[0115] Here, the optimization parameter D pXX_ref (Hat), C pXX_ref The normal equation for finding (hat) is Equation 57.

[0116] As described above, in step S501, the tire parameter identification unit 142 determines the reference tire coefficient D by linear optimization, which does not require repeated calculations and has a small processing load. pXX_ref (Hat), C pXX_ref (hat) is calculated. Note that the reference tire coefficient D pXX_ref (Hat), C pXX_ref The formula for finding (hat) represents the case where one sampling of data is used, and in reality, it is an equation for the amount of data acquired from a fixed number of samples.

[0117] Next, in step S502, the composite characteristic tire parameter r is calculated by a nonlinear optimization algorithm. Bx1 , r Bx2 , r Cx1 , r By1 , r By2 , r Cy1Here, since the vehicle 10 is in a turning state accompanied by acceleration and deceleration, the longitudinal variable tire coefficient D pXX is expressed by Equation 58. Similarly, the laterally variable tire coefficient C pXX is expressed by Equation 59.

[0118] On the other hand, the reference tire coefficient D pXX_ref (Hat), C pXX_ref (hat) and variable tire coefficient D pXX， C pXX Based on the minimization problem of minimizing the difference between

[0119] Here, the error vector e in Equation 60 is expressed as Equation 61 from the minimization problem.

[0120] Furthermore, the weighting matrix Q in Equation 60 is given by Equation 62. Here, the optimization parameter x is calculated using the composite characteristic tire parameter r as shown in Equation 63. Bx1 , r Bx2 , r Cx1 , r By1 , r By2 , r Cy1 Let's say.

[0121] Then, as shown in Equation 64, the composite characteristic tire parameter r Bx1 , r Bx2 , r Cx1 , r By1 , r By2 , r Cy1 Ask for.

[0122] The composite characteristic tire parameter r Bx1 , r Bx2 , r Cx1 , r By1 , r By2 , r Cy1The formula for determining the reference tire coefficient D is used when one sample of data is used, and in reality, it is an equation for data acquired over a fixed number of samples. As described above, the tire parameter identification unit 142 uses the reference tire coefficient D obtained by linear optimization, which is a low-load process. pXX_ref (Hat), C pXX_ref (hat) is used for nonlinear optimization to find the tire parameter r Bx1 , r Bx2 , r Cx1 , r By1 , r By2 , r Cy1 Get.

[0123] 13 and 14 are diagrams for explaining an outline of a two-stage optimization process in which tire parameters are obtained by using reference tire coefficients obtained by linear optimization, which is a low-load process, in nonlinear optimization. Note that, among the two-stage optimization processes, Fig. 13 and Fig. 14 show an example of tire parameter identification corresponding to the pure tire lateral force characteristic equation. In Fig. 13 and Fig. 14, the horizontal axis represents tire slip angle β. XX , the vertical axis is tire lateral force F yXX_t is.

[0124] FIG. 13 shows the reference tire coefficient C pXX_ref Here, the reference tire coefficient C for each arbitrary data N is pXX_ref The characteristic using (hat) is expressed by Equation 65.

[0125] Also, FIG. 14 shows the reference tire coefficient C pXX_ref (hat) and variable tire coefficient C pXX The lateral force pure characteristic tire parameter p Cy1 , p Dy1 , p ky1 , p ky2 Here, the variable tire coefficient C for each arbitrary data N is shown. pXX The characteristic using is expressed by Equation 66.

[0126] That is, the reference tire coefficient C pXX_ref (hat) and variable tire coefficient C pXX The optimization is performed by using the deviation from the reference tire coefficient C pXX_ref (hat) and the variable tire coefficient C pXX The lateral force pure characteristic tire parameter p included in Equation 67 is calculated so as to minimize the difference between the tire lateral force calculated using Cy1 , p Dy1 , p ky1 , p ky2 will be optimized.

[0127] 1 and 5 is a functional unit included in the vehicle control device 100 mounted on the vehicle 10, but a management server (in other words, a cloud server) provided outside the vehicle 10 can be configured as a system having the functions of the tire parameter identification unit of the variable tire coefficient calculation unit 140. In other words, the system is configured to enable wireless communication between the management server outside the vehicle 10 and the vehicle control device 100 mounted on the vehicle 10. The management server can then be configured as a system that identifies tire parameters based on various information received from the vehicle control device 100 and transmits information on the identified tire parameters to the vehicle control device 100.

[0128] 15 is a block diagram showing a vehicle control system in which a management server identifies tire parameters. In FIG. 15, the same blocks as those shown in FIG. 5 are designated by the same reference numerals, and detailed descriptions thereof will be omitted.

[0129] 5 does not include the tire parameter identification unit 142, but instead includes a first information transmission unit 145 and a first information receiving unit 146. On the other hand, the management server 700 provided outside the vehicle 10 includes a second information receiving unit 701 that receives various information (input values) transmitted from the first information transmission unit 145 of the vehicle control device 100, a tire parameter identification unit 702, and a second information transmitting unit 703 that transmits signals of the tire parameters identified by the tire parameter identification unit 702 to the first information receiving unit 146 of the vehicle control device 100.

[0130] The first information transmission unit 145 of the vehicle control device 100 receives the slip ratio s of each wheel. XX , slip angle β of each wheel XX , the ground load of each wheel F zXX_t The first information transmitting unit 145 transmits, as input values, physical quantities indicating the motion state of the vehicle 10, such as longitudinal acceleration, lateral acceleration, and yaw rate, as well as the actual steering angle of the steered wheels, to the management server 700. In other words, when the vehicle control device 100 has the function of the tire parameter identifying unit 142, the first information transmitting unit 145 transmits, to the management server 700, various pieces of information that the tire parameter identifying unit 142 acquires to identify the tire parameters.

[0131] The input values ​​transmitted by the first information transmission unit 145 are received by the second information reception unit 701 of the management server 700. The second information reception unit 701 outputs the received input values ​​to the tire parameter identification unit 702. Similar to the tire parameter identification unit 142, the tire parameter identification unit 702 performs linear processing to determine the reference tire coefficient of each wheel using the input values ​​acquired via the second information reception unit 701 and a predetermined linear optimization algorithm. Furthermore, the tire parameter identification unit 702 performs nonlinear processing to identify tire parameters for calculating the variable tire coefficient using the reference tire coefficient, the input values, and a predetermined nonlinear optimization algorithm.

[0132] The tire parameter identification unit 702 outputs a signal of the identified tire parameter to the second information transmission unit 703. Then, the second information transmission unit 703 transmits the information of the tire parameter to the vehicle control device 100, and the first information receiving unit 146 of the vehicle control device 100 receives the information of the tire parameter transmitted from the second information transmission unit 703.

[0133] The first information receiving unit 146 outputs the information on the longitudinal force tire parameters among the received information on the tire parameters to the first variable tire coefficient calculating unit 143, and outputs the information on the lateral force tire parameters to the second variable tire coefficient calculating unit 144. As described above, the variable tire coefficient calculating units 143 and 144 calculate the slip ratio s of each wheel. XX , slip angle β of each wheel XX , the ground load of each wheel F zXX_t , and further, a variable tire coefficient (longitudinal variable tire coefficient D) is calculated based on the tire parameters preset by the tire parameter identification unit 702. pXX , laterally variable tire coefficient C pXX ) is calculated.

[0134] As described above, in a system in which the vehicle control device 100 acquires tire parameters identified by the external management server 700 through cloud collaboration and performs optimal control using the acquired tire parameters, the vehicle control device 100 does not need to perform tire parameter identification processing, thereby reducing the processing load on the vehicle control device 100. Furthermore, according to the above-described tire parameter identification method, the load of the identification processing is reduced, making it easier to build a system in which the management server 700 performs the identification processing.

[0135] The following compares the above-described two-stage optimization process for identifying tire parameters with a typical identification method. For example, when identifying tire parameters by applying a variable tire coefficient model equation, the typical identification procedure is as follows:

[0136] - Step 1: Formulate estimation equations for tire longitudinal force and tire lateral force using a linear equation based on the variable tire coefficient and each major slip factor. - Step 2: Formulate estimation equations for longitudinal acceleration, lateral acceleration, and yaw angular acceleration (yaw rate) using the estimation equations for tire longitudinal force and tire lateral force. - Step 3: Perform nonlinear optimization for longitudinal acceleration, lateral acceleration, and yaw angular acceleration using the deviation between the sensor value and the estimated value as an evaluation function, and identify the tire parameters included in the variable tire coefficient model equation.

[0137] In the case of such a general identification method, the nested structure of the evaluation function becomes complicated, which increases the processing load. On the other hand, when identifying tire parameters by the above-mentioned two-stage optimization process, the identification procedure is as follows.

[0138] ・Step 1: Identify the reference tire coefficient through linear optimization, which requires a small processing load. Step 1-1: Formulate estimation equations for tire longitudinal force and tire lateral force using a linear expression based on the reference tire coefficient and each major slip factor. Step 1-2: Formulate estimation equations for longitudinal acceleration, lateral acceleration, and yaw angular acceleration (yaw rate) using the estimation equations for tire longitudinal force and tire lateral force. Step 1-3: For longitudinal acceleration, lateral acceleration, and yaw angular acceleration, perform linear optimization using the deviation between the sensor value and the estimated value as the evaluation function to identify the reference tire coefficient. ・Step 2: Perform nonlinear optimization using the deviation between the reference tire coefficient and variable tire coefficient as the evaluation function to identify the tire parameters included in the variable tire coefficient.

[0139] In the identification method using the above two-stage optimization process, the nested structure of the evaluation function is alleviated, and the processing load is smaller than in a general identification method. Furthermore, in the identification method of this embodiment, identification parameters are selected according to the driving scene of the vehicle 10, as described below.

[0140] ・Stage 1: "Straight-line braking and driving with parameter p kx1 , p Cx1 , p Dx1 " or "Parameter p ky1 , p ky2 , p Cy1 , p Dy1・Stage 2: "Identification in driving scenes not selected in stage 1" ・Stage 3: "Identification of parameter r in cornering driving with acceleration / deceleration" Bx1 , r Bx2 , r Cx1 , r By1 , r By2 , r Cy1 Identify

[0141] In other words, the pure tire characteristic equation is the input to the combined tire characteristic equation, so the parameter p Cx1 , p Dx1 , p kx1 and the parameter p Cy1 , p Dy1 , p ky1 , p ky2 After identifying the parameter r Bx1 , r Bx2 , r Cx1 , r By1 , r By2 , r Cy1 This reduces the number of parameters to be identified at once, thereby reducing the processing load of nonlinear optimization.

[0142] As described above, according to the tire parameter identification method of this embodiment, the load of identifying parameters included in the nonlinear tire model equation and the load of nonlinear optimization processing are reduced, so tire parameters can be updated quickly and the accuracy of tire parameter identification is improved. Furthermore, the improved accuracy of tire parameter identification makes it possible to calculate control variables that enable expected vehicle control. For example, even if emergency avoidance becomes necessary after a tire change, the nonlinear tire model equation can be updated quickly, so that it is possible to reliably avoid the object and further prevent secondary accidents, such as lane departure after avoidance, from occurring.

[0143] The influence of the identification accuracy of tire parameters on the steering avoidance assist will be described below. Fig. 16 shows how the travel trajectory of the vehicle 10 when the steering avoidance assist is activated differs depending on the level of identification accuracy of the tire parameters. Here, when the identification accuracy of the tire parameters is high, the vehicle 10 is prevented from going too far in the avoidance direction after avoiding an obstacle, and the travel position of the vehicle 10 also returns to the center of the lane more quickly.

[0144] Fig. 17 is a time chart showing the correlation between the steering wheel operation angle by the driver, the steering wheel operation angular velocity by the driver, and the operation of the steering avoidance assist in the collision avoidance scene shown in Fig. 16. The driver operates the steering wheel to the right to avoid a collision, and then operates the steering wheel to the left to return the vehicle 10 to its original traveling path. Here, the operation condition for the steering avoidance assist is satisfied in conjunction with the operation of the steering wheel to the right to avoid a collision, and the operation of the steering avoidance assist is started at time t1.

[0145] 18 shows the differences in vehicle behavior quantities, specifically, lateral acceleration, vehicle body slip angle, and yaw rate, when differences in tire parameter identification accuracy cause the differences in the traveling trajectories shown in FIG. 16. When the tire parameter identification accuracy is high, the vehicle 10 is prevented from going too far in the avoidance direction, so that an increase in the absolute value of lateral acceleration is suppressed and the lateral acceleration converges more quickly than when the tire parameter identification accuracy is low.

[0146] Furthermore, when the identification accuracy of the tire parameters is high, the vehicle 10 is prevented from going too far in the avoidance direction, and therefore, compared to when the identification accuracy of the tire parameters is low, the absolute value of the vehicle body slip angle becomes smaller and the posture of the vehicle 10 becomes more stable. Furthermore, when the identification accuracy of the tire parameters is high, the vehicle 10 is prevented from going too far in the avoidance direction, and therefore, the yaw rate of the vehicle 10 converges more quickly compared to when the identification accuracy of the tire parameters is low.

[0147] Fig. 19 shows the difference in the actual steering angle of the steered wheels due to differences in the identification accuracy of the tire parameters. The upper part of Fig. 19 shows the actual steering angle obtained by adding the control amount by the steering avoidance assist to the steering amount by the driver, and the lower part of Fig. 19 shows the actual steering angle by the control amount by the steering avoidance assist. Fig. 19 shows that when the identification accuracy of the tire parameters is high, the steering angle of the steered wheels converges more quickly than when the identification accuracy of the tire parameters is low.

[0148] 20 shows how the braking torque applied to each wheel as the control component of the steering avoidance assist differs depending on the identification accuracy of the tire parameters. Here, when the identification accuracy of the tire parameters is high, the braking torque applied by the steering avoidance assist converges more quickly than when the identification accuracy of the tire parameters is low.

[0149] The technical ideas described in the above embodiments can be used in appropriate combinations as long as no contradictions arise. Furthermore, although the contents of the present invention have been specifically described with reference to preferred embodiments, it is obvious that a person skilled in the art can adopt various modified embodiments based on the basic technical ideas and teachings of the present invention.

[0150] Driving assistance using nonlinear model predictive control is not limited to steering avoidance assistance, but can also be applied to automatic driving control (trajectory tracking control), vehicle attitude control on low-friction roads, etc. Therefore, the traveling actuators of the vehicle 10 controlled by the vehicle control device 100 are not limited to the steering device 11 and the braking device, but can include drive devices such as an internal combustion engine and a motor.

[0151] Furthermore, management server 700 may be configured with a plurality of servers that perform distributed processing. Furthermore, the steering device serving as a travel actuator of vehicle 10 may include a steering device that controls the steering angle of the front wheels and a steering device that controls the steering angle of the rear wheels.

[0152] The tire parameter update determination unit 142C can also obtain information indicating that a tire change has been performed and information on the friction coefficient of the road surface, and determine whether or not the tire parameters need to be updated. Here, the tire parameter update determination unit 142C can obtain the information indicating that a tire change has been performed and the information on the friction coefficient of the road surface from a management server (in other words, a cloud server) provided outside the vehicle 10.

[0153] As the linear optimization algorithm, in addition to the least squares method using normal equations, the recursive least squares method (RLS) can be used. In addition to the steepest descent method, the quasi-Newton method can also be used.

[0154] 10... Vehicle, 100... Vehicle control device, 110... Ground load estimation unit, 120... Vehicle state quantity estimation unit, 130... Steering avoidance support operation trigger unit, 140... Variable tire coefficient calculation unit, 142... Tire parameter identification unit, 142A... Linear optimization unit, 142B... Non-linear optimization unit, 143... First variable tire coefficient calculation unit, 144... Second variable tire coefficient calculation unit, 150... Optimization control unit

Claims

1. A vehicle state quantity estimating unit that estimates state quantities of the host vehicle based on a position of the host vehicle and physical quantities indicating the motion state of the host vehicle; a ground contact load estimating unit that estimates a ground contact load of each wheel of the host vehicle based on the physical quantities indicating the motion state of the host vehicle; and a variable tire coefficient calculation unit that calculates a variable tire coefficient of each wheel using as input values, among the state quantities, a slip ratio and slip angle of each wheel, the ground contact load of each wheel, the physical quantities indicating the motion state of the host vehicle, and an actual steering angle of a steered wheel of the host vehicle, the variable tire coefficient calculation unit performing linear processing to obtain a reference tire coefficient of each wheel using each of the input values ​​and a predetermined linear optimization algorithm; performing nonlinear processing to identify tire parameters for calculating the variable tire coefficient using the reference tire coefficient, each of the input values, and a predetermined nonlinear optimization algorithm; and calculating the variable tire coefficient based on the tire parameters, the slip ratio, the slip angle, and the ground contact load. an optimization control unit that inputs a preset reference state quantity, the state quantity, and the variable tire coefficient of the host vehicle, calculates a control quantity that minimizes a preset evaluation function, and sets an output corresponding to the control quantity to a traveling actuator of the host vehicle.

2. A vehicle control device as claimed in claim 1, wherein the variable tire coefficient calculation unit identifies, among the tire parameters, a longitudinal force tire parameter corresponding to the longitudinal force of the host vehicle, or a lateral force tire parameter corresponding to the lateral force of the host vehicle, and then identifies a composite characteristic tire parameter corresponding to a combined force of the longitudinal force and the lateral force of the host vehicle.

3. A vehicle control device as described in claim 2, wherein the variable tire coefficient calculation unit: identifies the longitudinal force tire parameter when, in a fixed number of samples of multiple data for each of the input values ​​acquired, the absolute value of the longitudinal acceleration of the vehicle is greater than a predetermined longitudinal acceleration threshold; identifies the lateral force tire parameter when, in a fixed number of samples of multiple data for each of the input values ​​acquired, the absolute value of the lateral acceleration of the vehicle is greater than a predetermined lateral acceleration threshold; and identifies the composite characteristic tire parameter when, in a fixed number of samples of multiple data for each of the input values ​​acquired, the absolute value of the longitudinal acceleration of the vehicle is greater than a predetermined longitudinal acceleration threshold and the absolute value of the lateral acceleration of the vehicle is greater than a predetermined lateral acceleration threshold.

4. A vehicle control device as claimed in claim 1, wherein the variable tire coefficient calculation unit updates the tire parameters when a difference between a preset tire parameter reference value and the identified tire parameter exceeds a predetermined threshold value, and calculates the variable tire coefficient based on the updated tire parameter.

5. A vehicle control device as described in claim 1, further comprising a steering avoidance assistance operation trigger unit that determines whether to operate steering avoidance assistance for the obstacle based on a steering wheel angle of the host vehicle, a distance between the host vehicle and a lane marker, and a distance between the host vehicle and the obstacle, and the optimization control unit calculates a control amount that minimizes the evaluation function in response to an operation flag from the steering avoidance assistance operation trigger unit.

6. A vehicle control device as claimed in claim 1, wherein the physical quantities indicating the motion state of the host vehicle include the longitudinal acceleration, lateral acceleration, vertical acceleration, roll rate, pitch rate and yaw rate of the host vehicle, the speed of the host vehicle and the actual steering angle of the steered wheels of the host vehicle.

7. A vehicle control device according to claim 6, wherein the optimum control unit inputs the state quantities including the speed, slip angle, yaw rate, yaw angle, and position of the host vehicle.

8. A vehicle control device according to claim 7, wherein the optimization control unit calculates the control amount by executing a nonlinear model predictive control in which the controlled object is a nonlinear system.

9. A vehicle control system comprising a vehicle control device mounted on a host vehicle and a management server provided outside the host vehicle, wherein the vehicle control device has: a vehicle state quantity estimation unit that estimates a state quantity of the host vehicle based on a position of the host vehicle and a physical quantity indicating a motion state of the host vehicle; a ground contact load estimation unit that estimates a ground contact load of each wheel of the host vehicle based on the physical quantity indicating the motion state of the host vehicle; a first information transmission unit that transmits to the management server, as input values, the slip ratio and slip angle of each wheel, the ground contact load of each wheel, the physical quantity indicating the motion state of the host vehicle, and the actual steering angle of the steered wheels of the host vehicle, among the state quantities; and a first information receiving unit that receives tire parameters transmitted from the management server, and a variable tire coefficient calculation unit that calculates a variable tire coefficient based on the tire parameters, the slip ratio, the slip angle, and the ground contact load. an optimization control unit that receives as input predetermined reference state quantities, the state quantities, and the variable tire coefficient of the host vehicle, calculates a control quantity that minimizes a predetermined evaluation function, and sets an output corresponding to the control quantity to a traveling actuator of the host vehicle; wherein the management server comprises: a second information receiving unit that receives the input values ​​transmitted from the first information transmitting unit; and a tire parameter identifying unit that performs linear processing to determine a reference tire coefficient of each of the wheels using the input values ​​and a predetermined linear optimization algorithm, and performs nonlinear processing to identify the tire parameters for calculating the variable tire coefficient using the reference tire coefficient, the input values, and a predetermined nonlinear optimization algorithm; and a second information transmitting unit that transmits the tire parameters to the vehicle control device.

10. A vehicle control device comprising: a vehicle state quantity estimation unit that estimates a state quantity of the host vehicle based on a physical quantity indicating a position of the host vehicle and a motion state of the host vehicle; a ground contact load estimation unit that estimates a ground contact load of each wheel of the host vehicle based on the physical quantity indicating the motion state of the host vehicle; a variable tire coefficient calculation unit that calculates a variable tire coefficient based on the slip ratio and slip angle of each wheel, the ground contact load of each wheel, and preset tire parameters among the state quantities; and an optimization control unit that inputs a preset normative state quantity of the host vehicle, the state quantity, and the variable tire coefficient, calculates a control quantity that minimizes a preset evaluation function, and sets an output corresponding to the control quantity to a driving actuator of the host vehicle.

11. A vehicle control device as claimed in claim 10, wherein the variable tire coefficient calculation unit calculates a longitudinal variable tire coefficient relating to the longitudinal force of the vehicle among the variable tire coefficients by an equation obtained by partially differentiating, with respect to the slip ratio, a characteristic equation for the longitudinal force of the vehicle, which is represented by an approximation equation that adjusts the tire parameters based on actual data, and calculates a lateral variable tire coefficient relating to the lateral force of the vehicle among the variable tire coefficients by an equation obtained by partially differentiating, with respect to the slip angle, a characteristic equation for the lateral force of the vehicle, which is represented by an approximation equation that adjusts the tire parameters based on actual data.

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