WO2018029914A1

WO2018029914A1 - Vehicle state quantity estimation device

Info

Publication number: WO2018029914A1
Application number: PCT/JP2017/015973
Authority: WO
Inventors: 奈須　真吾; 絢也高橋; 修之一丸; 隆介平尾
Original assignee: 日立オートモティブシステムズ株式会社
Priority date: 2016-08-08
Filing date: 2017-04-21
Publication date: 2018-02-15
Also published as: JP2018024265A; JP6628702B2

Abstract

The purpose of the present invention is to provide a vehicle state quantity estimation device capable of estimating with high accuracy a vehicle state quantity using the detection values of an external recognition means, such as a camera or GPS. This vehicle state quantity estimation device (1) comprises: inertial sensors (3), (4) which detect acceleration, angular velocity or the like; a state quantity estimation unit (23) which estimates the state quantity of the vehicle on the basis of the detection values of the inertial sensors and preset parameters, such as the mass or the position of the center of gravity; and an external recognition means (2) for detecting the relative displacement, angle or the like between structures around the vehicle and the vehicle. The parameters are updated while the detection values of the external recognition means are stable.

Description

Vehicle state quantity estimation device

The present invention relates to a vehicle state quantity estimating device for estimating a state quantity of a vehicle.

State quantities such as side slip angle, lateral speed, and roll angle, which are difficult to detect directly with a sensor, are estimated using detected values from an inertial sensor and a vehicle motion model. An estimation error occurs. In order to reduce this estimation error, conventionally, in addition to correction of an estimated value using an observer or a Kalman filter, for example, a relative yaw detected directly using an external recognition means such as a camera or GPS as described in Patent Document 1 is used. A vehicle state quantity estimation method that corrects an estimated side slip angle using a corner is known.

Japanese Patent No. 5402244

However, in the estimation method of the vehicle state quantity described in Patent Document 1, a high-precision estimated value can be obtained under good environmental conditions, but the estimated value is obtained under bad conditions that are difficult for the outside recognition means such as rain to be detected. There is a possibility that the correction cannot be made and the accuracy is greatly lowered.

The present invention is an invention for solving the above-mentioned problems, and accurately estimates state quantities such as a side slip angle, a lateral speed, and a roll angle of a vehicle body even under adverse conditions that are difficult to detect by an external recognition means. An object of the present invention is to provide a vehicle state quantity estimating device.

In order to achieve the above object, the vehicle state quantity estimating device according to the present invention receives the detection value of the inertial sensor and the detection value of the external recognition means, and based on the detection value of the inertial sensor and a preset vehicle parameter. In the vehicle state quantity estimation device that outputs the vehicle state quantity, the vehicle parameter is updated using a detection value of the external environment recognition means.

According to the present invention, the vehicle state quantity can be estimated with high accuracy.

The conceptual diagram of the vehicle state quantity estimation apparatus 1. FIG. The figure which shows a four-wheeled vehicle model. The figure which shows the equivalent two-wheeled vehicle model of a four-wheeled vehicle. The figure which shows the roll motion accompanying the lateral acceleration which acts on the gravity center point 11. FIG. The figure which shows the pitch motion accompanying the longitudinal acceleration which acts on the gravity center point. The figure which shows positional relationships, such as a gravity center point. The figure which shows the vehicle structure carrying the vehicle state quantity estimation apparatus 1 which concerns on Embodiment 1. FIG. 5 is a flowchart showing an outline of processing by the vehicle state quantity estimation device 1 according to the first embodiment. 5 is a flowchart showing stability determination by the vehicle state quantity estimation device 1 according to the first embodiment. 5 is a flowchart showing parameter update by the vehicle state quantity estimation device 1 according to the first embodiment. The figure which shows the time change of the process result by the vehicle state quantity estimation apparatus 1 which concerns on Embodiment 1. FIG. The figure which shows the positional relationship of the gravity center point 11 with respect to the coordinate system fixed to the ground concerning Embodiment 1. FIG. The figure which shows the locus | trajectory of the gravity center point 11 at the time of the steady circle turning which concerns on Embodiment 1. FIG. 7 is a flowchart showing stability determination by the vehicle state quantity estimation device 1 according to the second embodiment. The figure which shows the time change of the processing result by the vehicle state quantity estimation apparatus 1 which concerns on Embodiment 2. FIG. 10 is a flowchart showing parameter update by the vehicle state quantity estimation device 1 according to the third embodiment. The figure which shows the time change of the processing result by the vehicle state quantity estimation apparatus 1 which concerns on Embodiment 3. FIG. The conceptual diagram of the vehicle state quantity output device 30 which concerns on Embodiment 4. FIG. 10 is a flowchart showing output value determination by a vehicle state quantity output device 30 according to the fourth embodiment. The figure which shows the time change of the process result by the vehicle state quantity output device 30 which concerns on Embodiment 4. FIG. The figure which shows the vehicle structure carrying the vehicle state quantity estimation apparatus 1 or the vehicle state quantity output device 30 which concerns on Embodiment 5. FIG. FIG. 9 is a conceptual diagram of a suspension control unit 40 that performs riding comfort control, which is one function of a control suspension device 41 according to a fifth embodiment. FIG. 10 is a conceptual diagram of a suspension control unit 40 that performs anti-roll control, which is one function of a control suspension device 41 according to a fifth embodiment. FIG. 9 is a conceptual diagram of a suspension control unit 40 that performs anti-dive control and anti-squat control, which are one function of a control suspension device 41 according to a fifth embodiment. FIG. 10 is a diagram showing an acceleration PSD as a processing result of a suspension control unit according to a fifth embodiment. FIG. 10 is a view showing a change over time of a processing result of a suspension control unit according to a fifth embodiment. The figure which shows the vehicle structure carrying the vehicle state quantity estimation apparatus 1 or the vehicle state quantity output device 30 which concerns on Embodiment 6. FIG. It shows a vertical displacement of the vehicle body mass point of mass m _p according to the sixth embodiment is applied. The conceptual diagram of the vehicle state quantity estimation apparatus 1 'which concerns on Embodiment 7. FIG. The figure which shows the 1/4 vehicle model of 2 freedom degree which concerns on Embodiment 7. FIG. 18 is a flowchart showing parameter update by the vehicle state quantity estimation device 1 ′ according to the seventh embodiment. The figure which shows the load movement accompanying the acceleration which acts on the gravity center point 11 which concerns on Embodiment 7. FIG. FIG. 10 is a diagram illustrating acceleration acting on a gravity center point 11 of a vehicle traveling on a bank road having a cant angle σ according to the seventh embodiment.

Embodiments for carrying out the present invention will be described in detail with reference to the drawings as appropriate.

1 to 5, a method for determining the stability of the detected value of the external recognition means, updating the parameter based on the detected value, and estimating the vehicle state quantity using the parameter according to the present invention will be described.

FIG. 1 is a conceptual diagram of a vehicle state quantity estimation device 1 that performs stability determination of a detection value of the external environment recognition unit 2, parameter update based on the detection value, and vehicle state quantity estimation using the parameter.

The vehicle state quantity estimation device 1 includes, for example, a first vehicle motion state quantity detection value 100 detected by an inertia sensor or a gyro sensor, a driver input quantity detection value detected by a steering angle sensor, a stroke sensor, or the like, or external environment recognition. The second vehicle motion state detection value 200 detected by the means is input. And based on the input signal, the vehicle momentum state quantity estimated value 300 is output.

Here, the first vehicle motion state quantity detection value 100 is a value such as a wheel speed, a longitudinal acceleration of the vehicle body, a lateral acceleration, or a yaw rate. The driver input amount detection value is a value such as a steering angle, an accelerator opening degree, or a brake depression force. The second vehicle motion state quantity detection value 200 is a value such as a side slip angle, a lateral speed, a yaw angle, a roll angle, or a pitch angle of the vehicle body detected by the external environment recognition unit 2. Further, the vehicle motion state quantity estimated value 300 is a side slip angle, a lateral speed, a roll angle, a pitch angle, or the like of the vehicle body.

The vehicle state quantity estimation device 1 includes a stability determination unit 21, a parameter update unit 22, and a state quantity estimation unit 23. The vehicle state quantity estimation device 1 includes a stability determination unit 21, a parameter update unit 22, and a state quantity estimation unit 23.

The state quantity estimation unit 23 stores preset vehicle parameters, and calculates a vehicle state quantity estimation value 300 using the vehicle motion state quantity detection value 100 and the parameters.

The stability determination unit 21 determines whether the second vehicle motion state quantity detection value 200 that is an input value from the external environment recognition unit 2 is stable by using an input value from the external environment recognition unit 2 or the inertial sensor. The judgment result is output.

The parameter update unit 22 updates the parameters stored in the state quantity estimation unit 23 using the input values from the external environment recognition unit 2 and the inertial sensor and the determination result from the stability determination unit 21. Specifically, the vehicle parameter is updated so that the vehicle motion state quantity detection value 200 detected by the external environment recognition unit 2 is the same as the estimated value calculated by the state quantity estimation unit using an inertial sensor or parameter. . In other words, by updating the parameters, the estimated value obtained by the state quantity estimating unit 23 is identified by the actually measured value obtained by the external field recognition means.

Here, the parameter update in the present invention means that parameters such as vehicle mass, position of the center of gravity, moment of inertia, cornering power, etc. that change due to people getting on and off, changing tires, etc. are calculated based on input values, and stored parameters Is replaced with the calculated parameter.

Vehicle parameters change depending on people getting on and off, changing tires, and tire deterioration. Therefore, a difference occurs between the vehicle parameter stored in advance and the actual vehicle parameter, and this difference becomes an error of the estimated value 300 of the vehicle motion state. In the present invention, the parameter is updated so that the detected value of the external environment recognition unit 2 matches the estimated value in the state quantity estimating unit 23, and the vehicle motion state quantity estimated value 300 is based on the updated parameter and the value of the inertial sensor. Is calculated. Therefore, it becomes possible to cope with fluctuations in vehicle parameters, and state quantities that are difficult to detect directly with an inertial sensor such as a side slip angle, a lateral speed, and a roll angle can be estimated with high accuracy using the value of the inertial sensor.

Here, since the estimation accuracy in the state quantity estimation unit 23 depends on the accuracy of the update parameter, the vehicle motion state quantity detection value 200 used for the update is stable, that is, only a highly reliable value is used. Therefore, the stability determination of the vehicle motion state quantity detection value 200 in the stability determination unit 21 is performed. Further, FIG. 1 shows a case where only the vehicle motion state quantity estimated value 300 is output, but a stability determination result and an update parameter may be output as necessary. The output value is not limited.

Even in a situation where the external recognition means 2 is difficult to detect, the vehicle state quantity is estimated using the detected value of the inertial sensor and the updated parameter. The state quantity can be estimated.

A specific example of the vehicle motion state quantity estimation method in the state quantity estimation unit 23 and the parameter calculation method in the parameter update unit 22 will be described with reference to FIGS.

First, an example of a vehicle motion state quantity estimation method in the state quantity estimation unit 23 will be described.

FIG. 2 is a diagram showing a four-wheel vehicle model. In this embodiment, the center of gravity 11 of the vehicle is the origin, the longitudinal direction of the vehicle is x, the lateral direction of the vehicle is y, and the vertical direction of the vehicle is z. FIG. 2 shows the movement of a four-wheeled vehicle during a turn. The actual steering angle is δ, the vehicle traveling speed is V, the vehicle longitudinal speed is V _x , and the vehicle lateral speed is V _y , the slip angle β formed by the angle between the traveling direction and the longitudinal direction of the vehicle that occurs in a vehicle turning at a speed V, the front wheel right tire, the front wheel left tire, the rear wheel right tire, the rear wheel left tire, and the respective moving speed directions and tires Let β _fr , β _fl , β _rr , β _{rl be} the tire side slip angles that are the angles formed by the front-rear direction, and _let Y _fr , Y _fl , Y _rr , Y _rl be the cornering forces that act on these tires. Further, the yaw rate generated around the z-axis passing through the center of gravity 11 is r, the distance between the center of gravity 11 and the front wheel axis, and the distance between the rear wheel axis are l _f and l _r , respectively, and the distance between the front wheel axis and the rear wheel axis. wheelbase l, respectively _d f a tread width of the vehicle front and rear wheels, and _{d r.}

FIG. 3 is a diagram showing an equivalent two-wheeled vehicle model of a four-wheeled vehicle. FIG. 3 shows that the left and right left and right wheels ignore the tread of the vehicle and the left and right front and rear wheels are in the range where the side slip angle of the left and right tires is smaller than that of FIG. It is replaced with a model that concentrates on the intersection with. Here, the cornering

forces

2Y _f and 2Y _r are the resultant forces of the cornering forces acting on the left and right of the front and rear wheel tires shown in FIG.

FIG. 4 is a diagram illustrating a roll motion associated with the lateral acceleration acting on the barycentric point 11. Figure 4 is a lateral acceleration G _y is showing how vehicle roll angle φ of the sprung mass m _b occurs acting. Here, the roll stiffness, which is the magnitude of the moment generated by the expansion and contraction of the front and rear suspensions per unit roll angle, is represented by K _φf , K _φr , and the height from the ground of the roll center, which is the geometric instantaneous rotation center of the vehicle body, is represented by h _f. , H _r , the height of the center of gravity 11 is h, the distance between the roll shaft 12 connecting the front and rear roll centers and the center of gravity 11 is h _φ , and the spring constants of the front and rear suspensions including the tires are K _sf , K _sr , _Let C _sf and C _sr be the damping coefficients of the front and rear suspensions including the tire.

FIG. 5 is a diagram illustrating pitch motion associated with longitudinal acceleration acting on the barycentric point 11. Figure 5 is a graph showing how the pitch angle ψ occurs in the vehicle sprung mass m _b acting longitudinal acceleration G _x. Here, the pitch rigidity, which is the magnitude of the moment generated by the expansion and contraction of the front and rear suspensions including the tire per unit pitch angle, is represented by K _ψ , and the distance between the center of gravity 11 and the pitch axis 13 which is the geometrical instantaneous rotation center of the vehicle body. _{Let the} distance be h _ψ .

Here, as an example of state quantity estimation in the state quantity estimation unit 23 shown in FIG. 1, first, the side slip angle β and the lateral velocity V of the vehicle using the equivalent two-wheel vehicle model of the four-wheel vehicle shown in FIG. A method of calculating _y will be described. DV _y / dt, which is the time derivative of the lateral velocity V _y , and dr / dt, which is the time derivative of the yaw rate r generated around the z-axis, are the cornering force per unit side slip angle of the front and rear tires, m. the cornering power each _K f, _{K r,} the yaw inertia moment of the vehicle as _{I z,} the following equation (1), the formula (2).

Further, an observer that feeds back the output deviation of the yaw rate r is constructed, and the following equations (3) and (4) are obtained by expressing the equations (1) and (2) by the state equation and the output equation.

And (V _y ^, r ^) is an estimated value of (V _y , r). In the observer, the observer input is corrected so that the deviation e decreases, and the state quantity estimation error is reduced. An estimated value V _y ^ of the lateral speed is obtained from the equations (3) and (4), and an estimated value β ^ of the side slip angle of the vehicle body can be calculated using the following equation (5).

Next, a method for calculating the roll angle φ of the vehicle body using the roll motion model associated with the lateral acceleration G _y acting on the center of gravity 11 shown in FIG. 4 and the longitudinal acceleration G _x acting on the center of gravity 11 shown in FIG. An example of a method for calculating the pitch angle ψ of the vehicle body using the pitch motion model associated with will be described. Assuming that the vehicle has a constant acceleration and a constant inertial force is acting on the center of gravity 11, the roll angle φ and the pitch angle ψ are expressed by the following equations (6) and (7) It can calculate using the equation represented by.

In the above formulas (1) to (7), the vehicle mass m, the sprung mass m _b , the distance between the center of gravity 11 and the front wheel axis, and the distances l _f and l _r between the rear wheel axis and the yaw inertia of the vehicle Moment I _z , cornering power K _f , K _r , roll stiffness K _φf , K _φr , pitch stiffness K _ψ , distance h _φ between the roll axis 12 connecting the front and rear roll centers and the center of gravity point 11, body geometry The distance h _ψ between the pitch axis 13 and the center of gravity 11 which is the center of instantaneous rotation is a parameter, and the other longitudinal acceleration G _x and yaw rate r are the detection values described in FIG.

Conventionally, these parameters have been defined based on driving tests and numerical analysis results at the time of vehicle design, taking into account robustness to cope with aging and environmental changes, etc., and used as constants for estimating state quantities, etc. It has been. However, since it is a constant considering the robustness, there is a problem that an estimation error always occurs. On the other hand, in the present invention, this parameter is treated as a variable, and the estimation error is reduced by updating to a value according to the actual vehicle based on the detection value of the external recognition means 2 described in FIG. .

Next, an example of a parameter calculation method in the parameter update unit 22 will be described. 6, the center of gravity by mass 16 mass m _p is applied to the vehicle is a diagram showing a state that has moved to 14 from 15. In the present embodiment, for ease of understanding, this is an example assuming that the mass point 16 is acting at a position where the roll angle φ of the vehicle body does not occur. When calculating the load movement in more detail, It is desirable to consider the roll angle φ of the vehicle body. In this embodiment, a parameter based on a running test at the time of vehicle design, a result of numerical analysis, or the like is a design value, an update parameter is an update value, and a symbol with an 'in the expression described below is an update value. Here, l _f ′ and l _r ′ are the distance between the center of gravity (update value) 15 and the front wheel axis, and the distance between the rear wheel axis, and x _G is the center of gravity (design value) 14 and the center of gravity (update value) 15. the distance, _{x P} is the distance between the mass point 16 and the center of gravity point (updated value) 15.

First, a method of calculating the mass m and the sprung mass m _b of the vehicle based on the detected value.

When the suspension-related parameters such as the spring constant are not changed and the spring constants K _sf and K _sr of the suspension including the tire are assumed to be linear springs, the vehicle mass and the sprung mass update value m ′, m _b ′ can be calculated using an equation represented by the following equation (8).

Here m, m _b is the mass and the sprung mass of the design value of the vehicle of the formula (8), m _P is the mass of mass point 16, z _f, z _r is a vehicle body front side, the rear vertical displacement, respectively, It can calculate using the equation represented by the following formula | equation (9).

Here, the pitch angle ψ in the equation (9) is the vehicle motion state quantity detection value 200 detected by the external recognition means 2. In addition, l _f ′ and l _r ′ in Expression (9) are updated values of the distance between the center of gravity (updated value) 15 and the front wheel shaft and the distance from the rear wheel shaft shown in FIG. It can be calculated using the equation represented by 10).

Here, the equation (10) is established when the vehicle is turning at a low speed such that the vehicle speed V can be regarded as 0, and the side slip angle β of the vehicle body is the vehicle motion state amount detection value 200 detected by the external recognition means 2 and the actual steering angle δ. Is a conversion value of the driver input amount detection value. By using the above equations (8) to (10), the updated values m ′ and m _b ′ of the vehicle mass and sprung mass can be calculated.

However, this calculation method is based on the premise that the pitch angle ψ changes with a change in the mass of the vehicle body, and they do not change depending on the mass and position of the person in the vehicle or the load, and the equations (8) to (10) ) Alone cannot be used to calculate the correct mass. Therefore, it is desirable to use the method of calculating using the equation represented by the following formula (11). Here, T in the equation (11) is a tire output torque, and R is a tire radius. The tire output torque T can be obtained by, for example, a method of calculating from a torque map of the engine or motor, a transmission gear ratio, efficiency, or the like, or a method of directly detecting the torque of the drive shaft using a torque sensor. When Equation (11) is used in combination, two updated values m ′ and m _b ′ of the vehicle mass and sprung mass are calculated, but when the pitch angle ψ changes, these values are equal. Therefore, when the pitch angle ψ does not change, the updated value according to the equation (11) becomes larger than the updated values according to the equations (8) to (10). The updated values m ′ and m _b ′ of the sprung mass can be calculated.

Here, the calculation method of the updated values m ′ and m _b ′ of the vehicle mass and the sprung mass using the detection value of the external recognition means 2 is not limited to the method described above. Is stored in the vehicle state quantity estimating device 1 in advance, and the characteristic map representing the relationship between the detected value of the external recognition means 2 taking into account the updated value m ′ and m _b ′ of the vehicle mass and the sprung mass is stored in advance. The update values m ′ and m _b ′ of the vehicle mass and sprung mass may be calculated by inputting the detection value of the external recognition means 2 into Furthermore, by adding an acceleration axis to the characteristic map, it is possible to calculate updated values m ′ and m _b ′ of the vehicle mass and the sprung mass from which the influence of the gravitational acceleration due to the road surface inclination is removed.

Next, a method of calculating the center of gravity position such as the center of gravity height h and the distance h _φ between the roll shaft 12 and the center of gravity 11 based on the detected value will be described.

Assuming that the suspension-related parameters such as roll stiffness K _φf , K _φr , and pitch stiffness K _ψ have not changed, the distance between the roll shaft 12 and the center of gravity 11, the distance between the pitch shaft 13 and the center of gravity 11, The updated values h _φ ′, h _ψ ′, h ′ of the center of gravity height can be calculated using an equation represented by the following equation (12).

Next, based on the detected value, the distance x _G between the centroid point (design value) 14 and the centroid point (update value) 15 and the distance x _P between the mass point 16 and the centroid point (update value) 15 shown in FIG. 6 are calculated. A method will be described. The distance x _G is the absolute value of the difference between the design value lr and the update value lr of the distance between the center of gravity and the rear wheel axle, and the distance x _P is the design value m of the vehicle mass centered on the center of gravity (update value) 15. From the balance of moments due to the mass m _P of the mass point 16, it can be calculated using the equation represented by the following equation (13).

Here, the calculation method of the center of gravity position such as the center of gravity height h using the detection value of the external environment recognition unit 2 is not limited to the method described above, and for example, the external environment recognition unit 2 considering the suspension geometry. A characteristic map representing the relationship between the detected value and the center of gravity position may be stored in advance in the vehicle state quantity estimating device 1, and the center of gravity position may be calculated by inputting the detected value of the external field recognition means 2 to the characteristic map. Furthermore, by adding an acceleration axis to the characteristic map, it is possible to calculate the position of the center of gravity from which the influence of the gravitational acceleration due to the road surface inclination is removed.

Next, a method of calculating the moment of inertia of such a yaw inertia moment I _z on the basis of the detection value.

As an example of a method of calculating the moment of inertia, the updated value I _z ′ of the yaw moment of inertia can be calculated by an equation represented by the following formula (14) using the parallel axis theorem. Here, I _z in equation (14) is the design value of the yaw moment of inertia.

Next, a method for calculating tire characteristics such as cornering powers K _f and K _r based on the detected values will be described.

As an example of a tire characteristic calculation method, the cornering power update values K _f ′ and K _r ′ can be calculated using an equation represented by the following equation (15).

Here, the cornering forces Y _f and Y _r and the tire side slip angles β _f and β _r shown in the equation (15) can be calculated using equations represented by the following equations (16) and (17), respectively.

The above is an example of the vehicle motion state estimation method and parameter calculation method in the present invention.

Specific embodiments of the vehicle state quantity estimation device 1 of the present invention described above will be described with reference to FIGS.

FIG. 7 shows a configuration diagram of a vehicle 10 equipped with the vehicle state quantity estimating device 1 according to the embodiment of the present invention.

The vehicle state quantity estimation device 1 according to the present embodiment is mounted on a vehicle 10, and includes a state quantity related to vehicle motion and an operation angle sensor 5 from an external environment recognition means 2 such as a camera and GPS, an acceleration sensor 3, a gyro sensor 4, and a wheel speed sensor 6. The detection value of the state quantity related to the driver operation is acquired from the above. The vehicle state quantity estimation device 1 determines the stability of the external recognition means 2 using the detection value as described in FIG. 1, updates the parameter based on the determination result, and uses the updated parameter and detection value. The side slip angle and lateral speed of the vehicle body are estimated, and the results are output to the drive control unit 8 and the brake control unit 9 that control the braking / driving force of the vehicle.

FIG. 8 is a flowchart showing a processing outline of the vehicle state quantity estimation device 1. First, the vehicle state quantity estimation device 1 acquires the detected values of the vehicle motion state quantity and the driver operation quantity necessary for parameter update and state quantity estimation from the acceleration sensor 3 and the gyro sensor 4 (step S801). Next, the vehicle motion state quantity detection value 100 that is the detection value of the acceleration sensor 3 and the like acquired in step S801 is compared with the vehicle motion state quantity detection value 200 that is the detection value of the external recognition means 2, and based on the magnitude relationship. Then, it is determined whether or not the vehicle motion state quantity detection value 200 is stable, and the stability determination result is output (step S802). Then if the stability determination is made in step S802, the updated parameters such as the mass m and the yaw inertia moment I _z of the vehicle based on the detection value using the above equations (8) to (18), the update A parameter is output (step S803). Finally, based on the parameters updated in step S803, the vehicle motion state detection value 100, and the driver operation amount detection value, the side slip angle β and the lateral speed of the vehicle body are calculated using the above formulas (1) to (7). V _y , roll angle φ, and pitch angle ψ are estimated, and the vehicle motion state estimated value 300 is output to drive control unit 8 and brake control unit 9, and the process ends. In general, the output cycle of the detection value of the external recognition means 2 is slower than the output cycle of the detection value of an inertial sensor such as an acceleration sensor. Therefore, in order to prevent erroneous processing due to the difference in output cycle in stability determination and parameter update, which will be described later, the processing cycle is adjusted to the output cycle of the sensor with the slowest output cycle, or the detection value of the sensor with the slowest output cycle It is desirable to use a value obtained by performing time differentiation on the basis of the time derivative and predicting based on the time derivative value. Further, the process of evaluating the reliability of the update parameter based on the information acquired from the fuel gauge, the seating sensor, the seat belt sensor, etc., and determining whether to update the parameter based on the evaluation result is performed in steps S803 and S803. It may be added during S804.

FIG. 9 is a flowchart showing a processing outline of the stability determination unit 21 of the vehicle state quantity estimation device 1. In this embodiment, it is assumed that the yaw angle θ, which is the time integral value of the yaw rate r detected by the gyro sensor 4, is a true value, and the stability is based on the error of the yaw angle θ ^ detected by the external field recognition means 2 with respect to the true value. A method of determining the will be described. First, the stability determination unit 21 acquires the yaw rate r from the gyro sensor 4 and the yaw angle θ ^ from the external environment recognition unit 2 (step S901). Next, the yaw rate r acquired in step S901 is time-integrated to calculate the yaw angle θ (step S902). Next, with the yaw angle θ calculated in step S902 as a true value, a yaw angle error that is the difference between the true value and the yaw angle θ ^ acquired in step S901 is calculated (step S903). Next, it is determined whether or not the yaw angle error calculated in step S903 is small with respect to a predetermined threshold (step S904). If small (step S904, YES), the process proceeds to step S905 and the count value is set to a predetermined value. Is increased (NO in step S904), the process proceeds to step S906, and a count reset process for setting the count value to 0 is performed. Next, it is determined whether or not the count value obtained by the count-up process in step S905 or the count reset process in step S906 is larger than a predetermined threshold (step S907). If the count value is larger (YES in step S907). If the vehicle motion state quantity detection value 200 detected by the external environment recognition means 2 continues to be stable during a predetermined period and is determined to be reliable, the process proceeds to step S908 to output a stability determination, and if small (step S907, NO), the vehicle motion state quantity detection value 200 detected by the external environment recognition means 2 is determined to be unreliable and unreliable for a predetermined period, and the process proceeds to step S909 to output an instability determination and processing. Exit. Here, the method of determining the stability in steps S901 to S909 is not limited to the above-described processing by count-up, and for example, a determination method by count-down that subtracts a predetermined value from the count value, or external recognition A determination method based on information self-diagnosed by the means 2 may be used. The stability determination target is not limited to the yaw angle error described above. For example, the yaw rate r detected by the gyro sensor 4 is set to the true value, and the true value and the yaw angle detected by the external recognition unit 2 are determined. The yaw rate error, which is the difference from the yaw rate r ^ obtained by time differentiation of, may be determined.

FIG. 10 is a flowchart showing an outline of processing of the parameter update unit 22 of the vehicle state quantity estimation device 1. First, the parameter update unit 22 acquires the stability determination result that is the output of the stability determination unit 21 (step S1001). Next, it is determined whether the stability determination result acquired in step S1001 is a stability determination or an instability determination (step S1002). If it is a stability determination (step S1002, YES), detection of the external recognition means 2 is performed. If it is determined that the value has reliability for updating the parameter, the process proceeds to step S1003. If the value is unstable (NO in step S1002), the reliability for updating the parameter to the detected value of the external recognition unit 2 is determined. The process is terminated without determining that the parameter is not updated. If it is determined in step S1002 that the detected value of the external environment recognizing means 2 is reliable for updating the parameter, the detected values of the vehicle motion state amount and the driver operation amount are acquired from the acceleration sensor 3 or the gyro sensor 4 or the like. (Step S1003), the process proceeds to step S1004. In steps S1004 to S1007, based on the detection value acquired in step S1003, the mass, the position of the center of gravity, the moment of inertia, and the tire characteristics are calculated using the above formulas (8) to (17), and the parameters are updated. The updated parameter is output and the process ends.

Here, the parameter updating method is not limited to the above-described method. For example, the parameter may be updated from time-series data acquired by a sensor during traveling using an optimization method or a system identification method. In addition, the parameter value range to be updated in the parameter updating unit 22 is set such that, for example, in the case of mass, the mass when empty is the lower limit value and the mass when maximum loading is the upper limit in order to prevent divergence of the updated value due to erroneous sensor detection. It is desirable to predefine a range that may change depending on each parameter, such as a value, and update within that range. Further, the storage form of the update parameter is not limited to a numerical value. For example, the tire characteristics based on the cornering forces Y _f and Y _r calculated using the equations (16) and (17) and the tire side slip angles β _f and β _r are used. You may save it as a map.

FIG. 11 is a diagram showing a detected value of the vehicle state quantity estimating apparatus 1, an error of the detected value, a count value of a stable period, and a time change of the estimated value and the detected value. The detection value, the error of the detection value, and the count value of the stable period are examples of the processing result in the stability determination unit 21 described with reference to FIG. Further, the estimated value is an example of a result estimated by the state quantity estimating unit 23 using the update parameter output from the parameter updating unit 22 described in FIG. The stability determination period shown in FIG. 11 is a period in which the detected value error (Q) is smaller than the threshold value (a) and the count value (J) is larger than the threshold value (b). This is a period during which the stability determination unit 21 outputs a stability determination that the state quantity detection value 200 is continuously stable and reliable in a predetermined period. The parameter update unit 22 updates the parameters within the stability determination period, and stops the parameter update during other periods (unstable determination period). As a result, the state quantity estimation unit 23 can output a highly accurate estimated value substantially equal to the true value in the stability determination period as in the conventional method. Furthermore, since the state quantity estimation unit 23 of the present embodiment performs estimation using the latest parameters updated in the stability determination period, the estimated value cannot be corrected by the detected value of the external field recognition unit 2 in the instability determination period. An estimated value closer to the true value than the method can be output. In this way, a parameter that has been treated as a constant in the conventional method is treated as a variable, the parameter is updated to a value that matches the actual vehicle based on the detection value of the external recognition means 2, and estimation is performed using the updated parameter. Thus, the estimation error can be reduced compared to the conventional method.

The effect of applying the vehicle state quantity estimation device 1 as described above will be described below.

In the case of a skid prevention device, for example, when replacing a tire with a lower performance than at the time of design, or using a tire whose tire air pressure is reduced or worn, the skid force command of the skid prevention device is insufficient in the conventional method, and skidding increases. There was a risk that running stability would be reduced. On the other hand, when the vehicle state quantity estimation device 1 of the present embodiment is applied, the braking force command of the skid prevention device can be corrected based on the current tire characteristics, so the skid is reduced compared to the conventional method and the running stability is improved. Can be made.

Further, when the vehicle state quantity estimation device 1 of the present embodiment is applied, a highly accurate estimated value approximately equal to the true value is obtained, that is, a predicted value of the state quantity with respect to the driver's operation input is obtained. Accordingly, the braking force command of the skid prevention device can be corrected, and the side slip can be reduced as compared with the conventional method, and the running stability can be improved.

In the case of a power steering device, for example, when a tire with a lower performance than that at the time of design is used, or when a tire with a decreased or worn tire pressure is used, the assist of the power steering device does not change in the conventional method. Vehicle behavior was occurring. On the other hand, when the vehicle state quantity estimating device 1 of the present embodiment is applied, the assist force and the steering angle of the power steering device can be increased / decreased according to the current tire characteristics. The vehicle behavior equivalent to the time can be realized. In addition, when the vehicle state quantity estimation device 1 of the present embodiment is applied, a decrease in tire air pressure or the like can be detected by comparing the estimated values of the nominal model and the latest parameter estimation model, and can be transmitted to the driver.

Further, in the case of an electric booster device, for example, when a tire having a lower performance than that at the time of design or a tire pressure is reduced or worn is used, the braking force command of the electric booster device does not change in the conventional method. A braking force corresponding to the characteristics is generated. On the other hand, when the vehicle state quantity estimation device 1 of the present embodiment is applied, the braking force command of the electric booster device can be corrected according to the current tire characteristics. Equivalent braking force can be generated.

Also, in the case of an electric parking brake device, an appropriate braking force command can be generated based on the latest mass, so that power consumption can be reduced as compared with the conventional method.

FIG. 12 is a diagram showing the positional relationship of the center of gravity 11 of the vehicle with respect to the coordinate system fixed to the ground. The trajectory of the center of gravity 11 of the vehicle is expressed by the following equation (18), where (X, Y) is the position of the center of gravity 11 of the vehicle with respect to the coordinate system fixed to the ground, and the yaw angle with respect to the X axis of the vehicle is θ. Is done.

Here, X ₀ , Y ₀ , and θ ₀ are values of X, Y, and θ at t = 0, respectively, and t is an arbitrary time. This equation (18) uses the detected value of the external recognition means 2 such as the speed V in the traveling direction of the vehicle and the side slip angle β of the vehicle body, or an estimated value calculated by equation (1) using a vehicle model. Further, in order to calculate the trajectory of the center of gravity 11 of the vehicle without using the detected value of the external recognition means 2 or the estimated value calculated using the vehicle model, for example, there is a method using the following equation (19). .

FIG. 13 is a diagram showing the trajectory of the center of gravity 11 during a steady circle turn of a vehicle whose cornering power has decreased due to replacement with a low-performance tire or a decrease in tire air pressure. The true value (e) is the actual locus of the center of gravity point 11 of the vehicle calculated using the vehicle motion state quantity detection value 200 that is the detection value of the external environment recognition means 2 and the equation (18). No vehicle model (f) is an estimated trajectory of the center of gravity 11 of the vehicle calculated using Expression (19). “With vehicle model and without heel parameter update (g)” is an estimated value calculated using a vehicle model whose parameters are not updated and an estimated locus of the center of gravity 11 of the vehicle calculated using equation (18). The vehicle model present and the saddle parameter not updated (h) are an estimated value calculated using the vehicle model with updated parameters and an estimated locus of the center-of-gravity point 11 of the vehicle calculated using Equation (18).

As shown in FIG. 13, the estimated trajectory with the vehicle model and with the heel parameter updated (h) is approximately equal to the true value (e) that is the actual trajectory of the center of gravity point 11 of the vehicle, but with no vehicle model (f). The estimation trajectory with the vehicle model and without the saddle parameter update (g) has a large estimation error.

In the case of an automatic driving device, the conventional method performs self-position estimation based on the detection value of the external recognition means, and there is a risk that automatic detection cannot be continued due to poor detection accuracy due to bad conditions such as rain or lens contamination. It was. As a method of continuing automatic driving in response to a decrease in the detection accuracy of the external recognition means, a method of performing map matching based on information such as the vehicle trajectory estimated using Equation (18) or Equation (19) is considered. However, there is a possibility that safe automatic driving cannot be continued by a method with a large estimation error of the trajectory such as no vehicle model (f) or a vehicle model and no saddle parameter update (g). On the other hand, when the vehicle state quantity estimation apparatus 1 of the present embodiment is applied, the vehicle trajectory and the like can be estimated with high accuracy, so that automatic driving can be continued to at least a place where the vehicle can be safely stopped.

In the second embodiment, differences from the first embodiment will be described, and the same description as the first embodiment will be omitted.

The main difference between the second embodiment and the first embodiment is the stability determination method in the stability determination unit 21. The processing outline of the vehicle state quantity estimation device 1 in the second embodiment is described with reference to FIGS. Will be explained.

FIG. 14 is a flowchart showing a processing outline of the stability determination unit 21 of the vehicle state quantity estimation device 1 according to the second embodiment. The processing in steps S1401 to S1403 in FIG. 14 is the same as that in steps S901 to S903 in FIG. In step S1404, a value obtained by time-integrating the yaw angle error calculated in step S1403 over a predetermined period is calculated. Next, it is determined whether or not the integrated value of the yaw angle error calculated in step S1404 is smaller than a predetermined threshold (step S1405). If it is smaller (YES in step S1405), the vehicle detected by the external environment recognition unit 2 is determined. It is determined that the motion state quantity detection value 200 is continuously stable and reliable in a predetermined period, and the process proceeds to step S1406 to output a stability determination. If it is large (NO in step S1405), it is detected by the external recognition means 2. It is determined that the detected vehicle motion state quantity value 200 is not stable for a predetermined period and is not reliable, and the process proceeds to step S1407 to output an instability determination, and the process ends. Here, the stability determination method in steps S1404 and S1405 is not limited to the determination method based on the integral value described above, and may be a determination method based on an average value, for example.

FIG. 15 is a diagram illustrating a detected value of the vehicle state quantity estimation device 1 in the second embodiment, an error of the detected value, an integrated value of the detected value error, and a time change of the estimated value and the detected value. The stability determination period shown in FIG. 15 is a period in which the integrated value (N) obtained by integrating the error (Q) of the detected value in a predetermined period is smaller than the threshold value (c), and the vehicle motion state quantity detected by the external recognition means 2 This is a period in which the stability determination unit 21 outputs a stability determination that the detection value 200 is continuously stable and reliable in a predetermined period. The instantaneous noise exceeding the threshold value (a) indicated by the error between the detection value and the detection value in FIG. 15 may occur under the actual use environment of the vehicle. When such noise occurs, the count value is reset in the vehicle state quantity estimation device 1 in the first embodiment, and the parameter update is stopped. On the other hand, in the vehicle state quantity estimation device 1 in the second embodiment, the actual vehicle use is determined by setting the period during which the magnitude of noise is small and the integral value (N) is smaller than the threshold value (c) as the stability determination period. Under the environment, it is possible to increase the parameter update frequency while ensuring a certain accuracy in the parameter update.

In the third embodiment, differences from the first embodiment and the second embodiment will be described, and the same description as the first embodiment and the second embodiment will be omitted.

The main difference between the third embodiment, the first embodiment, and the second embodiment is a method for determining whether the parameter update unit 22 has updated or not updated parameters. The vehicle in the third embodiment is described with reference to FIGS. 16 and 17. A processing outline of the state quantity estimation device 1 will be described.

FIG. 16 is a flowchart showing an outline of processing of the parameter update unit 22 of the vehicle state quantity estimation device 1 in the third embodiment. First, the parameter update unit 22 according to the third embodiment acquires the vehicle motion state quantity detection value 200 detected by the external field recognition unit 2 before one processing cycle and the estimation value estimated by the state quantity estimation unit 23 (step S1601). . Here, in this embodiment, one process is defined from START to END in the flowchart showing the outline of the process of the vehicle state quantity estimating apparatus 1 described in FIG. Next, an estimation error that is a difference between the detected value and the estimated value acquired in step S1601 is calculated (step S1602). Next, it is determined whether or not the estimation error calculated in step S1602 is larger than a predetermined threshold (step S1603). If it is larger (step S1603, YES), it is determined that the accuracy of the estimated value is insufficient. In S1604, if small (NO in Step S1603), the accuracy of the estimated value is necessary and sufficient, and it is determined that the parameter update is unnecessary, and the process ends. In step S1604, the parameters are updated according to the flow described in FIG. 10, and the process ends.

FIG. 17 is a diagram illustrating a detected value of the vehicle state quantity estimating apparatus 1 in the third embodiment, an error in the detected value, a count value in a stable period, an estimated error, and a time change of the estimated value and the detected value. As shown in FIG. 17, the parameter update is stopped if the estimation error is smaller than the predetermined threshold (d) even during the stability determination period. Thus, by determining whether the parameter is updated / not updated based on the magnitude of the estimation error, the calculation load of the vehicle state quantity estimation device 1 can be reduced while ensuring a certain estimation accuracy, and the power consumption And heat generation can be reduced.

In the fourth embodiment, differences from the first to third embodiments will be described, and the same description as the first to third embodiments will be omitted.

The main difference between the fourth embodiment and the first to third embodiments is that the vehicle state quantity output device 30 is configured by adding an output value determining unit 24 to the vehicle state quantity estimating device 1 of the first to third embodiments. Therefore, an outline of the process of the vehicle state quantity output device 30 according to the fourth embodiment will be described with reference to FIGS.

FIG. 18 is a conceptual diagram of the vehicle state quantity output device 30 according to the fourth embodiment. As shown in FIG. 18, the vehicle state quantity output device 30 has a configuration in which an output value determination unit 24 is added to the vehicle state quantity estimation device 1 of the first to third embodiments. The output value determination unit 24 includes a vehicle motion state quantity estimated value 300 that is an output of the state amount estimation unit 23, a stability determination result that is an output of the stability determination unit 21, and a vehicle motion state detected by the external environment recognition unit 2. Based on the amount detection value 200, it is determined which of the vehicle motion state amount estimated value 300 and the vehicle motion state amount detection value 200 is output as the vehicle motion state amount output value 400. Here, FIG. 18 shows a case where only the vehicle motion state quantity output value 400 is output. However, if necessary, the stability determination result, the update parameter, and the vehicle motion state quantity estimated value 300 may be output. The output value of the vehicle state quantity output device 30 is not limited.

FIG. 19 is a flowchart showing a processing outline of the output value determination unit 24 of the vehicle state quantity output device 30 according to the fourth embodiment. First, the output value determination unit 24 acquires the stability determination result that is the output of the stability determination unit 21 (step S1901). Next, it is determined whether the stability determination result acquired in step S1901 is a stability determination or an instability determination (step S1902). If it is a stability determination (step S1902, YES), detection of the external recognition means 2 is performed. It is determined that the value has reliability for updating the parameter, and the process proceeds to step S1903. If the value is unstable (NO in step S1902), the reliability for updating the parameter to the detected value of the external recognition unit 2 is determined. If it is determined that there is no possibility, the process advances to step S1907 to output an estimated value, and the process ends. If it is determined in step S1902 that the detected value of the external environment recognizing means 2 is reliable for updating the parameter, the vehicle motion state quantity detected value 200 detected by the external environment recognizing means 2 one processing cycle before, and the state quantity The estimated value estimated by the estimating unit 23 is acquired (step S1903). Here, in this embodiment, one process is defined from START to END in the flowchart showing the outline of the process of the vehicle state quantity estimation device 1 described in FIG. 8 of the first embodiment. Next, an estimation error that is a difference between the detected value and the estimated value acquired in step S1903 is calculated (step S1904). Next, it is determined whether or not the estimation error calculated in step S1904 is larger than a predetermined threshold (step S1905). If it is larger (step S1905, YES), it is determined that the accuracy of the estimated value is insufficient. The process proceeds to S1906 to output the detected value. If the detected value is small (NO in step S1905), it is determined that the accuracy of the estimated value is necessary and sufficient, and the process proceeds to step S1907 to output the estimated value and the process is terminated.

FIG. 20 is a diagram illustrating a detected value of the vehicle state quantity output device 30 in the fourth embodiment, an error in the detected value, a count value in a stable period, an estimation error, and a time change in the estimated value, the detected value, and the output value. As shown in FIG. 20, the vehicle state quantity output device 30 outputs the detected value as an output value when the estimation error is greater than the threshold (d) during the stability determination period, and the estimated value is output as the output value otherwise. Output as. As described above, by determining the output value output from the vehicle state quantity output device 30 based on the stability determination result and the magnitude of the estimation error, the value closest to the true value in the vehicle state quantity output device 30 is obtained. Can be output.

In the fifth embodiment, differences from the first to fourth embodiments will be described, and the same description as in the first to fourth embodiments will be omitted.

The main difference between the fifth embodiment and the first to fourth embodiments is that a vehicle 10 ′ in which a suspension control unit 40 and a control suspension device 41 are added to the vehicle 10 of the first to fourth embodiments is configured. A processing outline of the suspension control unit 40 in the fifth embodiment will be mainly described with reference to FIGS. Note that the vehicle state quantity estimation device 1 according to the fifth embodiment may be the vehicle state quantity output device 30.

FIG. 21 shows a configuration diagram of a vehicle 10 ′ equipped with the vehicle state quantity estimation device 1 or the vehicle state quantity output device 30 in the fifth embodiment. FIG. 21 shows a configuration in which a suspension control unit 40 and a control suspension device 41 are added to FIG. The control suspension device 41 is a damping force adjustment type shock absorber capable of adjusting a damping characteristic or an active suspension capable of adjusting a vertical force between a vehicle body and a wheel. The suspension control unit 40 is a vehicle state required for riding comfort control, anti-roll control, and the like based on detection values of inertial sensors, gyro sensors, and the like, and updated parameters such as mass and center of gravity updated by the vehicle state quantity estimation device 1. The amount is estimated, and a control signal for controlling the damping characteristic or the vertical force of the control suspension device 41 is generated.

Next, an outline of processing of ride comfort control, anti-roll control, and anti-dive / anti-squat control in the suspension control unit 40 using the updated parameters updated by the vehicle state quantity estimating device 1 will be described with reference to FIGS.

FIG. 22 is a conceptual diagram of a suspension control unit 40 that performs riding comfort control, which is one function of the control suspension device 41 in the fifth embodiment. The suspension control unit 40 includes update parameters such as mass and center of gravity updated by the vehicle state quantity estimation device 1 and vehicle motion state quantity detection values 100 such as sprung vertical acceleration and unsprung vertical acceleration detected by an inertial sensor. Is entered.

The suspension control unit 40 includes a vertical speed estimation unit 43, a target damping force calculation unit 44, and a damping force map 45.

The vertical speed estimation unit 43 receives the update parameter of the vehicle state quantity estimation device 1 and the vehicle motion state quantity detected value 100 as input, and estimates the vertical speed of the spring and unsprung.

The target damping force calculation unit 44 calculates the target damping force of the control suspension device 41 based on the vertical speed estimated by the vertical speed estimation unit 42 and the vehicle motion state quantity detection value 100.

The damping force map 45 is map information of the characteristics of the control suspension device 41 stored in advance. The control suspension device 41 receives the target damping force calculated by the target damping force calculation unit 44 and the vehicle motion state quantity detection value 100 as inputs. The command current for controlling the is derived and output.

FIG. 23 is a conceptual diagram of a suspension control unit 40 that performs anti-roll control, which is one function of the control suspension device 41 in the fifth embodiment. The suspension control unit 40 includes update parameters such as mass and center of gravity updated by the vehicle state quantity estimation device 1 and vehicle motion state quantity detection values 100 such as sprung vertical acceleration and unsprung vertical acceleration detected by an inertial sensor. The driver input amount detection value such as the steering angle detected by the steering angle sensor is input.

The suspension control unit 40 mainly includes a vehicle motion model 46 and roll control gain and pitch control gain stored in advance.

The vehicle motion model 46 estimates the lateral acceleration of the vehicle using the update parameter of the vehicle state quantity estimation device 1, the vehicle motion state quantity detection value 100, and the driver input quantity detection value as inputs.

The suspension control unit 40 controls the control suspension device 41 based on the lateral jerk and roll control gain obtained by differentiating the lateral acceleration estimated by the vehicle motion model 46, and the absolute value and pitch control gain of the lateral acceleration estimated by the vehicle motion model 46. Calculate and output the command current to be controlled.

FIG. 24 is a conceptual diagram of a suspension control unit 40 that performs anti-dive control and anti-squat control, which are one function of the control suspension device 41 in the fifth embodiment. The suspension control unit 40 is input with updated parameters such as mass and center of gravity updated by the vehicle state quantity estimation device 1 and vehicle motion state quantity detection values 100 such as brake master cylinder pressure, engine torque, and gear position. .

The suspension control unit 40 mainly includes a vehicle motion model 46 and a pitch control gain stored in advance.

The vehicle motion model 46 estimates the longitudinal acceleration of the vehicle using the update parameter of the vehicle state quantity estimation device 1 and the vehicle motion state quantity detection value 100 as inputs.

The suspension control unit 40 calculates and outputs a command current for controlling the control suspension device 41 based on the longitudinal jerk obtained by differentiating the longitudinal acceleration estimated by the vehicle motion model 46 and the pitch control gain.

FIG. 25 and FIG. 26 are examples of simulation results showing the effect of using the update parameter in the fifth embodiment as an input. FIG. 25 and FIG. 26 both show the results of comparing the influence of mass update, which is an update parameter, on the riding comfort on a high-speed wavy road. FIG. 25 shows the vertical acceleration PSD of the floor, Fr tower, and Rr tower. It is a figure which shows the time change of a floor up-down displacement, a pitch angle, and a roll angle. As shown in FIG. 25 and FIG. 26, the parameter update (invention) is particularly effective in the vertical acceleration of the Rr tower, compared with the parameter update using the parameters stored in the design in consideration of the robustness stored in advance (conventional method). PSD and pitch angle are small, and higher performance riding comfort control can be realized.

With the above configuration, a command current for controlling the suspension can be generated based on vehicle state quantities such as the sprung vertical speed and lateral acceleration estimated with high accuracy using parameters such as the latest mass and center of gravity. Higher-performance suspension control can be realized than when design parameters are used in consideration of robustness stored in advance. In addition, it is desirable to update the damping force map, roll control gain, and pitch control gain stored in advance, which are affected by the mass, the center of gravity position, and the like, using parameters such as the latest mass and the center of gravity position.

In the sixth embodiment, differences from the fifth embodiment will be described, and the same description as in the fifth embodiment will be omitted.

The main difference between the sixth embodiment and the fifth embodiment is that a vehicle 10 ″ in which a vehicle height sensor 42 is added to the vehicle 10 ′ of the fifth embodiment is configured, and mainly using FIGS. 27 to 28. A processing outline of the suspension control unit 40 in the sixth embodiment will be described.

FIG. 27 shows a configuration diagram of a vehicle 10 ″ equipped with the vehicle state quantity estimating device 1 or the vehicle state quantity output device 30 according to the sixth embodiment. FIG. 27 shows a vehicle height sensor 42 with respect to FIG. Is added.

The vehicle height sensor 42 detects the relative distance between the road surface and the vehicle body in the z-axis direction or the displacement of the vehicle suspension. The suspension control unit 40 determines the damping characteristics or the vertical force of the control suspension device 41 based on the detection values of various sensors such as the vehicle height sensor 42 and the update parameters of the vehicle state quantity estimation device 1 or the vehicle state quantity output device 30. Control. The vehicle height sensor 42 can directly detect the vertical displacements z _fr , z _fl , z _rr , z _rl on the right front side, left front side, right rear side, and left rear side of the vehicle body. The mass update values m ′ and m _b ′ can be calculated using an equation represented by the following equation (20).

In general, the vehicle height sensor 42 can directly detect the vertical displacement compared to the vertical displacement calculated based on the pitch angle ψ detected by the external environment recognition means 2, and the vehicle state quantity estimating device 1 or the vehicle is highly accurate. The state quantity output device 30 can implement highly accurate parameter updating and high-performance suspension control using the parameter updating. Further, since the mass, the position of the center of gravity, and the moment of inertia can be calculated from the detected values of the external recognition means 2 and the vehicle height sensor 42, even if one of these parameters fails, the vehicle state quantity estimating device 1 or The parameter update by the vehicle state quantity output device 30 can be continued.

FIG. 28 is a diagram illustrating the vertical displacement generated in the vehicle on which the mass point of mass m _P acts. FIG. 28 shows a state in which the vertical displacements z _fr , z _fl , z _rr , and z _rl of the right front side, the left front side, the right rear side, and the left rear side of the vehicle body on which the mass point of mass m _P acts are generated. . Using the vertical displacement of the vehicle body, the roll angle φ and the pitch angle ψ of the vehicle body can be calculated using an equation expressed by the following equation (21). In this embodiment, in order to facilitate understanding, it is assumed that the tread widths of the vehicle front and rear wheels are equal d.

The presence / absence of a sensor failure and the reliability of the detected value can be determined by comparing the roll angle φ and the pitch angle ψ calculated using the equation (21) with the detected value of the external recognition means 2.

In Example 7, differences from Example 5 and Example 6 will be described, and the same description as Example 5 and Example 6 will be omitted. The main difference between the seventh embodiment, the fifth embodiment, and the sixth embodiment is that the vehicle state quantity estimating device 1 determines whether the parameter can be updated based on the tire ground contact load calculated by the suspension control unit 40. A processing outline of the suspension control unit 40 and the vehicle state quantity control device 1 or the vehicle state quantity output device 30 in the seventh embodiment will be mainly described with reference to FIGS. 29 to 33.

FIG. 29 is a conceptual diagram of the vehicle state quantity estimation device 1 ′ in the seventh embodiment. As shown in FIG. 29, the vehicle state quantity estimation device 1 ′ inputs the tire ground contact load calculation value calculated by the suspension control unit 40 to the parameter update unit 22 of the vehicle state quantity estimation device 1 of the first to third embodiments. It is configured to do. Note that the cornering powers K _f and K _r described in the equation (15) and the like change in magnitude depending on the ground load. Therefore, in order to improve the estimation accuracy of the vehicle motion state quantity estimated value 300, the ground load calculated value is the state. It is desirable that the input is input to the quantity estimation unit 23. The ground load calculation value may be input to the parameter updating unit 22 of the vehicle state quantity output device 30 of the fourth embodiment.

Next, a method of estimating the tire ground contact load calculation value in the suspension control unit 40 will be described.

FIG. 30 is a diagram showing a ¼ vehicle model with one degree of freedom. Figure 30 is one in which the sprung mass m _b showed how the vertical displacement by the unsprung vertical displacement z _t of the mass m _t. Here, it is assumed that the suspension spring constant is K _s , the suspension damping coefficient is C _s , the vertical displacement on the spring is z _b , the vertical displacement on the spring is z _t , and the tire ground load is W ₀ . The unsprung and unsprung movements are expressed by the following equations (22) and (23), respectively.

Here, in the suspension damping coefficient C _s of the equations (22) and (23), the command current of the suspension control unit 40 is used in the case of a damping force adjustment type shock absorber in which the control suspension device 41 can adjust the damping characteristics. Enter the damping factor based. Further, in the case of an active suspension capable of adjusting the vertical force between the vehicle body and the wheel, the right side of the equations (22) and (23) is replaced with the vertical force derived by the suspension control unit 40. Further, d ² z _b / dt ² and d ² z _t / dt ² are not limited to the detected values of the vertical acceleration sensor installed on the spring and under the spring and the estimated value based on them, for example, the acceleration sensor 3 or an estimated value based on a detected value of the vehicle height sensor 42 or the like.

In equation (23), ΔW ₀ is the amount of change in the contact load, and the contact load W ₀ is expressed by the following equation (24) in which this change is added to the contact load at rest.

The ground contact load of the tire can be calculated by the above method.

FIG. 31 is a flowchart showing a processing outline of the parameter update unit 22 of the vehicle state quantity estimation device 1 ′ in the seventh embodiment. In the flowchart shown in FIG. 31, the ground load variation frequency and the ground load variation difference are calculated in correcting the parameters. The ground load variation frequency and the difference in ground load variation are calculated in order to derive a correction gain for correcting parameters such as ground load vibration caused by a rough road surface and missing ground load caused by road surface depression. First, prior to the description of FIG. 31, a method for calculating the difference in ground load variation will be described with reference to FIGS. 32 and 33 so that the understanding of FIG. 31 is facilitated.

FIG. 32 is a diagram illustrating load movement accompanying acceleration acting on the barycentric point 11. Figure 32 shows how the variation amount [Delta] W _fl ground load on each tire of the vehicle sprung mass _{m b} acting longitudinal acceleration _{G x} and the lateral acceleration _{_{_{G y, ΔW fr, ΔW rl}}} , the [Delta] W _rr occur It is. Note that FIG. 32 is an example assuming that the roll angle φ and the pitch angle ψ of the vehicle body do not occur so as to facilitate understanding. When calculating the load movement in more detail, the roll angle φ and the pitch of the vehicle body are illustrated. It is desirable to consider the load movement associated with the angle ψ.

FIG. 33 is a diagram showing acceleration acting on the gravity center point 11 of the vehicle traveling on the bank road having the cant angle σ. Note that FIG. 33 is an example on the assumption that the roll angle φ of the vehicle body and the side slip angle β of the vehicle do not occur so that the understanding can be easily understood. It is desirable to calculate load transfer.

An example of a method for calculating the contact load variation amounts ΔW _fl , ΔW _fr , ΔW _rl , ΔW _rr of each tire will be described using the models shown in FIGS. 32 and 33. Assuming that the vehicle has a constant acceleration and a constant inertial force is acting on the center of gravity point 11, the estimated values ΔW _fl , ΔW _fr , ΔW _rl , ΔW _rr of the ground load variation are expressed by the following equation (25 ).

Here, the first term on the right side of Equation (25) is the amount of ground load variation associated with the longitudinal acceleration Gx, the second term on the right side is the amount of ground load variation associated with the lateral acceleration Gy, and the third term on the right side is the vertical acceleration caused by the cant angle σ. This is the amount of change in grounding load due to change. Further, the cant angle σ used in the equation (25) can be calculated by an equation represented by the following equation (26).

Next, the ground load fluctuation differences ΔW _FY , ΔW _RY , ΔW _LX , ΔW _RX are the latest ground loads inputted from the suspension control unit 40, W _fl , W _fr , W _rl , W _rr , the latest ground contact at rest. _Assuming that the loads are W _fl0 , W _fr0 , W _rl0 , W _rr0 , and using the estimated values ΔW _fl , ΔW _fr , ΔW _rl , ΔW _rr of ground contact load fluctuation amounts calculated from the equations (25), (26), (27)

Here, the first expression of Expression (27) is the difference between the calculated value and the estimated value of the contact load fluctuation amount on the left and right front wheels, the second expression is the difference between the calculated value and the estimated value of the contact load fluctuation amount on the left and right of the rear wheel, The formula is the difference between the calculated value and the estimated value of the ground load variation before and after the left wheel, and the fourth formula is the difference between the calculated value and the estimated value of the ground load variation before and after the right wheel.

Referring back to FIG. 31, a flowchart showing an outline of the process of the parameter update unit 22 of the vehicle state quantity estimation device 1 'in the seventh embodiment will be described.

First, the parameter updating unit 22 according to the seventh embodiment acquires the contact load calculation value calculated by the suspension control unit 40 (step S3101). Here, in this embodiment, one process is defined from START to END in the flowchart showing the outline of the process of the vehicle state quantity estimating apparatus 1 described in FIG.

Next, the variation frequency of the ground load is calculated by performing FFT processing on the ground load calculated value acquired in step S3001 (step S3102).

Next, a ground load variation estimation value is calculated based on detected values such as longitudinal acceleration G _x , lateral acceleration G _y , and yaw rate r, and equations (25) and (26) (step S3103).

Next, a ground load variation difference is calculated based on the estimated value of the ground load variation calculated in step S3103, the calculated ground load obtained in step S3101, and equation (27) (step S3104).

Next, a correction gain is calculated based on the ground load fluctuation frequency calculated in step S3102 and the ground load fluctuation difference calculated in step S3104. As a method of calculating the correction gain, a method of deriving based on a correction gain map stored in advance with the ground load fluctuation frequency and the ground load fluctuation difference as inputs can be considered. There is no limitation on the method to be performed.

In step S3105, the parameter is corrected using the correction gain calculated in step S3105. As a correction method, a method of multiplying a parameter by a correction gain is conceivable, but a method of division may be used, and the method of correcting the parameter by the correction gain is not limited.

With the above configuration, it is possible to use an appropriate parameter according to the ground contact state even when the detected value of the external environment recognition unit 2 is unstable and the vehicle is traveling on a rough road where the ground load varies greatly. Therefore, it is possible to realize more accurate estimation in the state quantity estimation unit 23 and higher-performance suspension control in the suspension control unit 40.

1: vehicle state quantity estimation device, 2: external recognition means, 3: acceleration sensor, 4: gyro sensor, 5: steering angle sensor, 6: wheel speed sensor, 7: tire, 8: drive control unit, 9: brake control Unit: 10, 10 ′: Vehicle, 11: Center of gravity, 12: Roll axis, 13: Pitch axis, 14: Center of gravity (design value), 15: Center of gravity (updated value), 16: Mass, 21: Stability Determination unit, 22: parameter update unit, 23: state quantity estimation unit, 24: output value determination unit, 30: vehicle state quantity output device, 40: suspension control unit, 41: control suspension device, 42: vehicle height sensor, 43 : Vertical velocity estimation unit, 44: target damping force calculation unit, 45: damping force map, 46: vehicle motion model, 100: vehicle state quantity detection value, 200: vehicle state quantity detection value

Claims

In a vehicle state quantity estimation device that receives a detection value of an inertial sensor and a detection value of an external recognition means, and outputs a state quantity of the vehicle based on the detection value of the inertial sensor and a preset vehicle parameter.
A vehicle state quantity estimation apparatus, wherein the vehicle parameter is updated using a detection value of the external world recognition means.
2. The vehicle state quantity estimating device according to claim 1, wherein the vehicle parameter is updated when a detected value of the outside world recognizing means is stable.
When the time integral value or average value of the difference between the detected value of the inertial sensor and the detected value of the external field recognizing unit in a predetermined period is smaller than a predetermined value, the detected value of the external field recognizing unit is stable The vehicle state quantity estimation device according to claim 2, wherein the determination is made.
When the count value from when the difference between the detected value of the inertial sensor and the detected value of the external environment recognizing means becomes smaller than a predetermined value, the detected value of the external environment recognizing means becomes stable. The vehicle state quantity estimation device according to claim 2, wherein the vehicle state quantity estimation device is determined to be.
3. The vehicle state quantity estimation according to claim 2, wherein self-diagnosis information is input from the external environment recognition means, and based on the self-diagnosis information, it is determined that a detection value of the external environment recognition means is stable. apparatus.
2. The vehicle state quantity estimating device according to claim 1, wherein the vehicle parameter is updated when a difference between a detected value of the external environment recognizing means and an estimated value of the state quantity estimating unit is larger than a predetermined value.
2. A characteristic map for outputting the vehicle parameter with the detection value of the inertial sensor and the detection value of the external field recognition means as inputs, and updating the vehicle parameter based on the characteristic map. The vehicle state quantity estimation apparatus according to any one of 1 to 5.
2. The vehicle state quantity estimating device according to claim 1, wherein the vehicle parameter is updated within a range of a predetermined upper limit value and a predetermined lower limit value.
A vehicle motion control device to which an updated vehicle parameter is input from the vehicle state quantity estimation device according to any one of claims 1 to 8, wherein the second vehicle state is based on the updated vehicle parameter. A vehicle motion control device characterized by estimating a quantity.
A characteristic map or gain for outputting a vehicle movement control command value based on a state quantity of the second vehicle is provided in advance, and the characteristic map or gain is updated based on the updated vehicle parameter. The vehicle motion control device according to claim 9.
The means for detecting a road surface unevenness level and a characteristic map or a gain with the road surface unevenness level as an input are provided, and the vehicle parameter is corrected using the characteristic map or the gain. Vehicle state quantity estimation device.
The vehicle parameter updated according to claim 1, wherein a map for outputting a correction gain is provided in advance by inputting a frequency of a calculated value of a ground contact load of each tire, and the vehicle parameter updated based on the correction gain is corrected. Vehicle state quantity estimation device.
2. The vehicle state quantity estimating device according to claim 1, wherein the vehicle parameter is updated when a frequency of a calculated value of a contact load of each tire is lower than a predetermined value.
2. The vehicle state quantity estimating device according to claim 1, wherein the vehicle parameter is updated when a difference between a fluctuation calculation value of a ground contact load between tires and a fluctuation estimation value is smaller than a predetermined value.