Apparatus and method for controlling vehicle suspension

Abstract

An apparatus and method for controlling a vehicle suspension may include: a variable damper disposed between a vehicle body and a wheel; a sensor that measures vehicle body vertical acceleration and wheel vertical acceleration; and a controller that estimates road surface roughness based on the vehicle body vertical acceleration and wheel vertical acceleration, predicts a road surface grade based on the estimated road surface roughness, and adjusts the damping force of the variable damper corresponding to the predicted road surface grade.

Description

[0001] CROSS-REFERENCE TO RELATED APPLICATIONS

[0002] This application claims priority from Korean Patent Application No. 10-2020-0095339, filed on Jul. 30, 2020, which is hereby incorporated by reference in its entirety for all purposes. Technical Field

[0003] The present invention relates to an apparatus and a method for controlling a vehicle suspension. Background Art

[0004] Vehicles are equipped with a suspension that absorbs impacts between the vehicle body and wheels (axles) and maintains tire-road contact. The suspension (system) includes arms or links that control wheel movement, springs that absorb and adjust impacts, and shock absorbers, also known as dampers. Because springs and shock absorbers operate only within a specific, physically defined range and move passively between the vehicle body and wheels, they affect the vehicle's ride comfort, handling, and / or steering response. Consequently, technologies for adjusting suspension characteristics (e.g., spring characteristics, shock absorber characteristics, etc.) have been developed to provide the vehicle driver with optimal ride comfort, handling, and / or steering response.

[0005] The information disclosed in this Background of the invention section is only for enhancement of understanding of the general background of the invention and should not be taken as an admission or any form of suggestion that this information constitutes the prior art already known to a person skilled in the art. Summary of the Invention

[0006] Various aspects of the present invention are directed to providing an apparatus and method for controlling a vehicle suspension, which classify the roughness level of a road surface by using road surface roughness estimated in real time through measurement values ​​of vertical acceleration sensors attached to a vehicle body and wheels and by using a prediction model pre-learned based on machine learning, and control the damping force of a variable damper based on the classified roughness level of the road surface.

[0007] The technical problems to be solved by the present invention are not limited to the above-mentioned problems, and any other technical problems not mentioned herein and any other technical problems not mentioned herein will be clearly understood through the following description by those skilled in the art related to various exemplary embodiments of the present invention.

[0008] According to various aspects of the present invention, an apparatus for controlling a vehicle suspension includes: a variable damper disposed between a vehicle body and a wheel; a sensor that measures vehicle body vertical acceleration and wheel vertical acceleration; and a controller that estimates road surface roughness based on the vehicle body vertical acceleration and wheel vertical acceleration, predicts a road surface grade based on the estimated road surface roughness, and adjusts the damping force of the variable damper corresponding to the predicted road surface grade.

[0009] The sensor may include: a first acceleration sensor mounted on each corner of the vehicle body to measure the vertical acceleration of each corner of the vehicle body; and a second acceleration sensor mounted on each wheel of the vehicle to measure the vertical acceleration of each wheel.

[0010] After filtering out noise from the vehicle body vertical acceleration and wheel vertical acceleration measured by the sensor, the controller can obtain the vertical velocity of the vehicle body and the wheel by integration.

[0011] The controller may estimate and cumulatively store the road surface roughness by using a Kalman filter during the first storage period.

[0012] The controller can convert the estimated road surface roughness into frequency domain data through frequency analysis, extract feature information from the estimated road surface roughness and frequency domain data, classify the road surface grade by using the feature information and a prediction model based on machine learning, accumulate and store the classified road surface grades within a second storage time period, and determine the final road surface grade based on the accumulated road surface grades.

[0013] The controller is configured to: determine a skyhook control gain and a passive damping control gain based on a road surface grade, determine a required damping force for the skyhook based on center of gravity motion information related to the vehicle body and the skyhook control gain, determine a passive damping force by using a vehicle body vertical velocity, a wheel vertical velocity, and the passive damping control gain, and determine a final damping force required for each corner based on the required damping force for the skyhook and the passive damping force.

[0014] The controller can be configured to: determine the vertical velocity, pitch angular velocity and roll angular velocity of the vehicle center of gravity by using the vehicle body vertical velocity and vehicle specification information, determine the damping force required for the skyhook proportional to the vertical velocity, pitch angular velocity and roll angular velocity of the vehicle center of gravity and the skyhook control gain, and distribute the damping force required for each corner based on the damping force required for the skyhook to weaken the vertical velocity, pitch angular velocity and roll angular velocity of the vehicle center of gravity.

[0015] The controller may be configured to generate a current signal based on a final damping force required for each corner and apply the current signal to the variable damper.

[0016] The controller may be configured to determine an amount of current to apply to the variable damper based on a final damping force required for each corner and characteristics of the variable damper.

[0017] The controller may limit the maximum current and the minimum current applied to the variable damper in consideration of the actuator load and the minimum current of the variable damper.

[0018] According to various aspects of the present invention, a method for controlling a vehicle suspension includes the following steps: measuring a vehicle body vertical acceleration and a wheel vertical acceleration; estimating a road surface roughness based on the vehicle body vertical acceleration and the wheel vertical acceleration; predicting a road surface grade based on the road surface roughness; and adjusting a damping force of a variable damper corresponding to the road surface grade.

[0019] The method may further include: filtering noise from the vehicle body vertical acceleration and the wheel vertical acceleration; and obtaining the vehicle body vertical velocity and the wheel vertical velocity by integrating the filtered vehicle body vertical acceleration and the wheel vertical acceleration.

[0020] Estimating the road surface roughness may further include estimating the road surface roughness by using a Kalman filter.

[0021] Estimating the road surface roughness may further include accumulating and storing the road surface roughness within a first storage period.

[0022] Predicting a pavement grade may include: converting an estimated pavement roughness into frequency domain data through frequency analysis; extracting feature information from the estimated pavement roughness and frequency domain data; classifying the pavement grade by using the feature information and a prediction model based on machine learning, accumulating and storing the classified pavement grades within a second storage time period, and determining a final pavement grade based on the accumulated pavement grades.

[0023] Adjusting the damping force of the variable damper may include: determining a skyhook control gain and a passive damping control gain based on a road surface grade; determining a required damping force for the skyhook based on center of gravity motion information related to the vehicle body and the skyhook control gain; determining a passive damping force by using a vehicle body vertical velocity, a wheel vertical velocity, and a passive damping control gain; and determining a final damping force required for each corner based on the required damping force for the skyhook and the passive damping force.

[0024] Determining the damping force required for the skyhook may include: determining the vertical velocity, pitch angular velocity, and roll angular velocity of the vehicle's center of gravity by using the vehicle body vertical velocity and vehicle specification information; determining the damping force required for the skyhook that is proportional to the vertical velocity, pitch angular velocity, and roll angular velocity of the vehicle's center of gravity and a skyhook control gain; and allocating the damping force required for each corner based on the damping force required for the skyhook to weaken the vertical velocity, pitch angular velocity, and roll angular velocity of the vehicle's center of gravity.

[0025] The method may further include generating a current signal based on a final damping force required for each corner and applying the current signal to the variable damper.

[0026] Generating the current signal and applying it to the variable damper may include determining an amount of current based on a final damping force required for each corner and characteristics of the variable damper.

[0027] Generating the current signal and applying it to the variable damper may include limiting a maximum current and a minimum current applied to the variable damper in consideration of an actuator load and a minimum current of the variable damper.

[0028] The method and apparatus of the present invention have other features and advantages which will be set forth in more detail from or in the accompanying drawings, which are incorporated herein and in the following detailed description, which together serve to explain certain inventive principles. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] Figure 1 is a block diagram illustrating an apparatus for controlling a vehicle suspension according to an exemplary embodiment of the present invention;

[0030] Figure 2 It shows Figure 1 A block diagram of the functional configuration of the controller shown;

[0031] Figure 3 is an exemplary diagram illustrating data conversion through spatial frequency analysis according to an exemplary embodiment of the present invention;

[0032] Figure 4 is a flowchart illustrating a process of generating a prediction model according to an exemplary embodiment of the present invention;

[0033] Figure 5 is a diagram showing a result of verifying prediction model matching according to an exemplary embodiment of the present invention;

[0034] Figure 6 is a flowchart illustrating a method of controlling a vehicle suspension according to an exemplary embodiment of the present invention;

[0035] Figure 7 is a flowchart illustrating a method of determining a road surface grade according to an exemplary embodiment of the present invention; and

[0036] Figure 8 is a block diagram illustrating a determination system for performing a method of controlling a suspension of a vehicle according to various exemplary embodiments of the present invention.

[0037] It should be understood that the drawings are not necessarily drawn to scale and present a somewhat simplified representation of various features illustrative of the basic principles of the invention. The specific design features of the present invention as incorporated herein, including, for example, specific dimensions, orientations, locations, and shapes will be determined in part by the particular intended application and use environment.

[0038] In the drawings, reference numbers refer to the same or equivalent parts of the present invention throughout the several figures of the drawing. DETAILED DESCRIPTION

[0039] Reference will now be made in detail to various embodiments of the present invention, examples of which are shown in the accompanying drawings and described below. Although the present invention will be described in conjunction with exemplary embodiments of the present invention, it should be understood that this description is not intended to limit the present invention to those exemplary embodiments. On the other hand, the present invention is intended to cover not only the exemplary embodiments of the present invention, but also various alternatives, modifications, equivalents and other embodiments that may be included within the spirit and scope of the present invention as defined by the appended claims.

[0040] Hereinafter, various exemplary embodiments of the present invention will be described in detail with reference to the exemplary drawings. When reference numerals are assigned to components in each drawing, it should be noted that even if the same or equivalent components are shown in other drawings, they are denoted by the same reference numerals. In addition, when describing exemplary embodiments of the present invention, detailed descriptions of well-known features or functions will be omitted to avoid unnecessarily obscuring the main purpose of the present invention.

[0041] When describing the components of the exemplary embodiments according to various exemplary embodiments of the present invention, terms such as first, second, "A", "B", (a), (b) and the like may be used. These terms are intended only to distinguish one component from another, and these terms do not limit the nature, order or sequence of the constituent components. Unless otherwise defined, all terms used in the current casein, including technical or scientific terms, have the same meaning as understood by those skilled in the art to which the various exemplary embodiments of the present invention belong. Such terms defined in the dictionary used should be interpreted as having the same meaning as the contextual meaning associated with the relevant field, and should not be interpreted as having an ideal or overly formal meaning unless expressly defined in this application as having an ideal or overly formal meaning.

[0042] Various aspects of the present invention are directed to providing a technique for controlling damping force by varying a control gain corresponding to road surface roughness while maintaining existing control logic (eg, sky-hook control) in a four-wheel vehicle having a continuous damping control (CDC) damper.

[0043] Figure 1is a block diagram illustrating an apparatus for controlling a suspension of a vehicle according to an exemplary embodiment of the present invention. Figure 2 It shows Figure 1 A block diagram of the functional configuration of the controller is shown. Figure 3 is an exemplary diagram illustrating data conversion through spatial frequency analysis according to an exemplary embodiment of the present invention.

[0044] Reference Figure 1 , an apparatus for controlling a vehicle suspension includes a sensor 100 , a variable damper 200 , and a controller 300 .

[0045] Sensors 100 are mounted on the vehicle body and wheels to measure vehicle body vertical acceleration and wheel vertical acceleration. Sensors 100 may include at least three or more first acceleration sensors 110 and at least two or more second acceleration sensors 120. The first acceleration sensors 110 may be attached to the top mounting portions of the four corners of the vehicle body to measure the vertical acceleration of each corner. For example, the first acceleration sensors 110 may be mounted at the left and right front corners and the left and right rear corners of the vehicle body, or may be located at the left and right front corners and a point at the rear. Second acceleration sensors 120 may be attached to the steering knuckles of the wheels to measure the vertical acceleration of each wheel.

[0046] The variable damper 200 can be installed between the vehicle body and the wheels (axles) to reduce the impact or vibration transmitted from the road surface to the tires when the vehicle is running. The variable damper 200 can be respectively arranged between the vehicle body and the left and right front wheels and the left and right rear wheels. The variable damper 200 may include a solenoid valve for adjusting the damping force. The variable damper 200 can adjust the actual damping force by operating the solenoid valve. In the present case, the variable damper 200 using the solenoid valve as the actuator is referred to as an example thereof, but a stepper motor or the like can be used as the actuator. As the variable damper 200, not only the CDC damper but also various variable dampers can be applied.

[0047] The controller 300 is an electronic control unit (ECU) that controls the damping force of the variable damper 200 in the suspension. The controller 300 can obtain the vehicle body vertical acceleration and the wheel vertical acceleration by using the sensor 100. In the present case, the vehicle body vertical acceleration may include: the vertical acceleration at each corner of the vehicle body, the vertical acceleration at the left front corner of the vehicle body, the vertical acceleration at the right front corner of the vehicle body, the vertical acceleration at the left rear corner of the vehicle body, and the vertical acceleration at the right rear corner of the vehicle body. The wheel vertical acceleration may include: the vertical acceleration of the left front wheel, the vertical acceleration of the right front wheel, the vertical acceleration of the left rear wheel, and the vertical acceleration of the right rear wheel.

[0048] The controller 300 can estimate the road surface roughness (disturbance velocity) in real time based on the vehicle body vertical acceleration and the wheel vertical acceleration. The controller 300 can extract feature information from the estimated road roughness and classify the road surface roughness level in real time using the extracted feature information and a learned prediction model. In the present case, the prediction model can be generated through machine learning using a road surface profile database. Based on the classified road surface roughness level (i.e., road surface grade), the controller 300 can control the damping force of the variable damper 200 by adjusting its control gain.

[0049] The controller 300 may include a processor 301 and a memory 302. The processor 301 controls the overall operation of the controller 300. The processor 301 may be implemented using at least one of an application-specific integrated circuit (ASIC), a digital signal processor (DSP), a programmable logic device (PLD), a field-programmable gate array (FPGA), a central processing unit (CPU), a microcontroller, and / or a microprocessor. The memory 302 may be a non-transitory storage medium that stores instructions executed by the processor 301. The memory 302 may be implemented using at least one of a storage medium (recording medium) such as a flash memory, a hard disk, a secure digital (SD) card, a random access memory (RAM), a static random access memory (SRAM), a read-only memory (ROM), a programmable read-only memory (PROM), an electrically erasable programmable ROM (EEPROM), an erasable programmable ROM (EPROM), a register, and the like.

[0050] In the following, reference will be made to Figure 2 The functional configuration of the controller 300 is described.

[0051] The controller 300 may include a signal measuring device 310, a signal processing device 320, a road surface roughness estimation device 330, an estimated signal analysis device 340, a feature extraction device 350, a road surface grade classification device 360, a control gain determination device 370, a damping force control device 380, and a damper current application device 390. In the present case, each configuration may represent logic for performing a specific function, and each logic may be executed by the processor 301.

[0052] The signal measuring device 310 can obtain (receive) signals measured by the first acceleration sensor 110 and the second acceleration sensor 120. The signal measuring device 310 can measure the vertical acceleration of the vehicle body at each corner by using the first acceleration sensor 110. The signal measuring device 310 can measure the vertical acceleration of the wheel at each wheel by using the second acceleration sensor 120.

[0053] The signal processing device 320 can perform filtering to remove noise included in the vehicle body vertical acceleration signal for each corner and the wheel vertical acceleration signal for each wheel measured by the first acceleration sensor 110 and the second acceleration sensor 120. In other words, the signal processing device 320 can filter out noise from the measurement signals (sensing signals) received from the first acceleration sensor 110 and the second acceleration sensor 120. In addition, the signal processing device 320 can determine the vehicle body vertical velocity for each corner and the wheel vertical velocity for each wheel by integrating the vehicle body vertical acceleration for each corner and the wheel vertical acceleration for each wheel. The signal processing device 320 can determine the vehicle body displacement information related to each corner and the wheel displacement information related to each wheel by integrating the vehicle body vertical velocity for each corner and the wheel vertical velocity for each wheel.

[0054] The road surface roughness estimation device 330 can estimate the road surface roughness in real time based on the vehicle body vertical acceleration at each corner and the wheel vertical acceleration at each wheel measured by the signal measurement device 310, and the vehicle body vertical velocity at each corner and the wheel vertical velocity at each wheel processed by the signal processing device 320. The road surface roughness estimation device 330 can estimate the road surface roughness by using a Kalman filter and / or a vehicle model.

[0055] The estimated signal analysis device 340 can analyze the estimated road roughness (road disturbance speed) signal output from the road roughness estimation device 330 in real time through frequency analysis (e.g., spatial frequency analysis) to output a frequency analysis result. The estimated signal analysis device 340 can accumulate and store the road roughness information estimated by the road roughness estimation device 330 within a first storage time period (t1) preset by the developer, and perform frequency analysis (spatial frequency analysis) on the accumulated and stored estimated road roughness information. For example, Figure 3 As shown, the estimated signal analysis device 340 can convert the road surface roughness as time domain data into frequency domain data (spatial frequency domain data) through frequency analysis (spatial frequency analysis).

[0056] Feature extraction device 350 can extract feature information from the estimated road roughness information (time domain data) and frequency analysis results (spatial frequency domain data). Feature information can include the root mean square (RMS) of each state variable in the time domain (e.g., the RMS of road roughness) and / or the slope and intercept of a linear feedback model of road roughness in the frequency domain. The feature information can be further defined by the developer.

[0057] The road surface grade classification device 360 ​​can determine the road surface roughness grade (road surface grade) in real time by using a road surface grade prediction (classification) model generated through machine learning and extracted feature information. The road surface grade classification device 360 ​​can be activated every first storage period t1 to classify the road surface grade (e.g., A, B, C, D, and E), and the classified road surface grade can be stored for a second storage period t2. In the present case, the first storage period t1 is shorter than the second storage period t2.

[0058] The control gain determination device 370 can determine the control gain of the variable damper 200 based on the road surface grade and vehicle state information classified in real time. In other words, the control gain determination device 370 can determine the skyhook control gain and the passive damping control gain by using the road surface grade and vehicle state information predicted by the prediction model. The vehicle state information may include the vehicle's speed (vehicle velocity), vertical acceleration, lateral acceleration, steering angle, and the like. The vehicle state information can be obtained by the vehicle speed sensor, vertical acceleration sensor, lateral acceleration sensor, steering angle sensor, and the like of the sensor 100. In the present case, the skyhook control logic and the passive damping control logic for controlling the damping force of the variable damper 200 have been described as an example, but the exemplary embodiment is not limited thereto, and other control logic may be added. When other control logic is added, the control gain determination device 370 can determine the control gain for the added control logic.

[0059] The damping force control device 380 can determine the vertical velocity, pitch angular velocity, and roll angular velocity of the vehicle center of gravity (vehicle center of gravity) based on the vertical velocity of the vehicle at each corner and the vehicle specification information. The vehicle control information can be stored in the memory 302 and can include the wheelbase, track width, the distance between the front wheels and the center of gravity of the vehicle, and / or the distance between the rear wheels and the center of gravity of the vehicle. The damping force control device 380 can determine the vertical velocity, pitch angular velocity, and roll angular velocity of the vehicle center of gravity by using a vehicle kinematic relationship formula. In the present case, the vehicle kinematic relationship formula can vary depending on the number of acceleration sensors installed on the vehicle body and wheels.

[0060] The damping force control device 380 can determine the required damping force for the skyhook in each direction based on the three-directional velocities of the vehicle center of gravity (e.g., vertical velocity, pitch angular velocity, and roll angular velocity) and the skyhook control gain. Specifically, the damping force control device 380 can determine the force and torque required for skyhook control based on the three-directional velocities of the vehicle center of gravity (vehicle center of gravity motion information) and the skyhook control gain determined by the control gain determination device 370. The damping force control device 380 can assign the required damping force to each corner of the vehicle body based on the determined required force and torque. The damping force control device 380 can determine the damping force required for virtual tire damping based on the vehicle body vertical velocity at each corner, the wheel vertical velocity of each wheel, and the passive damping control gain. In other words, the damping force control device 380 can determine the required damping force for the passive damper based on the relative velocity between the vehicle body and the wheel.

[0061] The damping force control device 380 can determine the final required damping force (i.e., damper control force) for each corner by using the determined damping force required for each corner of the vehicle body (the required skylight damping force for each wheel) and the damping force required for each wheel. The damping force control device 380 can determine the current applied by the damper based on the determined final required damping force for each corner.

[0062] The damper current application device 390 can apply an actual driving current to the variable damper 200 based on the damper application current determined by the damping force control device 380. The damping force of the variable damper 200 can be changed in accordance with the driving current applied by the damper current application device 390. The damper current application device 390 can control the operation of the solenoid valve of the variable damper 200 by adjusting the amount of current applied to the solenoid valve. When the solenoid valve of the variable damper 200 operates, the actual damping force at each corner of the vehicle body can be changed. The damper current application device 390 can prevent damage to the variable damper 200 by limiting the maximum current applied to the solenoid valve of the variable damper 200.

[0063] Figure 4 is a flowchart illustrating a process of generating a prediction model according to an exemplary embodiment of the present invention. Figure 5 4 is a diagram showing the result of verifying the prediction model matching according to an exemplary embodiment of the present invention. The generation of the prediction model can be performed in advance in an external device (eg, a computer, a server, etc.). The external device 400 may include a memory and a processor.

[0064] External device 400 can generate a road surface database as a preliminary task for generating a road surface grade prediction model. External device 400 can randomly generate road surface data (road surface profiles) of various grades based on a standard (ISO 8606) and specific rules for road surface roughness. In this case, the road surface data is road surface roughness data in the time domain.

[0065] The external device 400 may perform frequency analysis (eg, spatial frequency analysis) based on the generated road surface data to convert it into frequency domain data. In other words, the external device 400 may convert the time domain road surface roughness data into frequency domain road surface roughness data through spatial frequency analysis.

[0066] The external device 400 can extract feature information from the time-domain road roughness data and the frequency-domain road roughness data. For example, the external device 400 can extract the root mean square (RMS) derived from the time-domain data and the slope and intercept of the linear feedback equation derived from the frequency-domain data as feature information. The external device 400 can generate a road surface grade classification model (i.e., a road surface grade prediction model) by performing supervised learning using the extracted features and the road surface roughness grade.

[0067] The external device 400 may upload the generated prediction model to the memory 302 of the controller 300. In other words, the external device 400 may upload the machine-learned prediction model to the memory 302 of the controller 300 by using wired and / or wireless communication. Thereafter, the road surface grade classification device 360 ​​of the controller 300 may classify the road surface grade in real time by using the prediction model uploaded to the memory 302. Figure 5 ,The real-time classified pavement grade ,has been verified by using a prediction model to match the real-time classified pavement grade ,corresponding to the road speed and road displacement.

[0068] Figure 6 is a flowchart illustrating a method of controlling a vehicle suspension according to an exemplary embodiment of the present invention. Figure 7 is a flowchart illustrating a method of determining a road surface grade according to an exemplary embodiment of the present invention.

[0069] In S100, the controller 300 may obtain the vehicle body vertical acceleration and the wheel vertical acceleration. The controller 300 may obtain the vehicle body vertical acceleration at each corner via the first acceleration sensor 110 and the wheel vertical acceleration at each wheel via the second acceleration sensor 120. The vehicle body vertical acceleration at each corner (the vehicle body vertical acceleration) and the wheel vertical acceleration at each wheel (the wheel vertical acceleration) may be directly used for damping force control and road roughness estimation.

[0070] In S110, the controller 300 may perform post-processing on the vehicle body vertical acceleration and the wheel vertical acceleration. The controller 300 may perform filtering to remove noise included in the signals received from the first acceleration sensor 110 and the second acceleration sensor 120. The controller 300 may determine the vehicle body vertical velocity at each corner, the wheel vertical velocity at each wheel, the vehicle body displacement information related to each corner, and the wheel displacement information related to each wheel through integration.

[0071] In S120, the controller 300 can determine the vehicle center of gravity motion (the three-directional velocity of the vehicle center of gravity) by using the vehicle vertical velocity (the vehicle vertical velocity at each corner) and the vehicle specification information. The vehicle specification information may include the wheelbase, track width, the distance between the front wheels and the vehicle center of gravity (the vehicle center of gravity), the distance between the rear wheels and the vehicle center of gravity, etc. The controller 300 can determine the vertical velocity, pitch angular velocity, and roll angular velocity of the vehicle center of gravity based on the vehicle vertical velocity at each corner and the vehicle specification information. The controller 300 can use the vehicle kinematics relationship formula when determining the vehicle center of gravity motion, and the vehicle kinematics relationship formula can vary corresponding to the number of acceleration sensors installed on the vehicle body and wheels.

[0072] In S130 , the controller 300 may determine the road surface grade based on the vehicle body vertical acceleration and the wheel vertical acceleration. The controller 300 may determine the road surface roughness grade by using a road surface grade prediction model based on machine learning.

[0073] refer to Figure 7 , the pavement grade prediction (classification) process will be described in more detail.

[0074] In S131, the controller 300 can estimate the road surface roughness in real time by using the vehicle body vertical acceleration at each corner, the vertical acceleration at each wheel, and the post-processed vehicle body vertical velocity at each corner and the wheel vertical velocity at each wheel. The controller 300 can estimate the road surface roughness by using a Kalman filter. Compared to conventional estimators that estimate state quantities, the Kalman filter can estimate road surface roughness that cannot be measured.

[0075] At S132, controller 300 may accumulate and store the estimated road surface roughness in memory 302. The estimated road surface roughness is time-domain data. Because data cannot be continuously accumulated and stored due to storage space limitations of memory 302, controller 300 may transmit the accumulated data to the next process at a specified time and then delete the corresponding accumulated data from memory 302 to initialize and store new data.

[0076] In S133 , the controller 300 may determine whether the data storage period satisfies a first storage period (eg, 1 second or 2 seconds). The controller 300 may cumulatively store the estimated road surface roughness in a designated area of ​​the memory 302 during the first storage period t1 .

[0077] If the data storage period meets the first storage period, the controller 300 may perform a frequency analysis (e.g., spatial frequency analysis) based on the accumulated road roughness data in S134. If the data storage period meets the first storage period, the controller 300 may activate the next operation logic and transfer the accumulated road roughness data to the activated logic. The controller 300 may convert the time-domain road roughness data into frequency-domain data in real time through frequency analysis.

[0078] In S135, the controller 300 may extract feature information from the road roughness in the time domain and the road roughness in the frequency domain. The controller 300 may extract the road roughness RMS in the time domain and / or the slope and intercept of the frequency domain linear feedback function as features.

[0079] In S136 , the controller 300 may classify (predict) the roughness level of the road surface (ie, road surface grade) by using the extracted feature information and a road surface grade prediction model based on machine learning.

[0080] In S137, the controller 300 may cumulatively store the classified road surface grade. The controller 300 may store the real-time classified road surface grade for a second storage time.

[0081] In S138, the controller 300 may check whether the road surface grade storage period satisfies the second storage period. In the present case, the second storage period may be set to be greater than the first storage period.

[0082] In S139, when the road surface grade storage period meets the second storage time, the controller 300 may determine the final road surface grade. The controller 300 may store the predicted road surface roughness grades in the memory 302 during the second storage period and may use a predetermined algorithm to determine the final road surface grade (e.g., grade A, B, C, D, or E). For example, the controller 300 may determine the average of the road surface roughness grades stored in the memory 302 as the final road surface grade. Referring to Table 1 below, when the road surface grades are classified as A, B, and C, and the road surface grade indexes matching each road surface grade are 1, 2, and 3, the controller 300 may determine the average of the cumulative grades determined as the final road surface grade.

[0083]

Table 1

[0084]

[0085] The above-described final road surface grade determination method can prevent a shock sensation, such as chatter, that occurs when the control gain is changed within a short time interval due to repeated sudden changes and returns to the road surface grade. At S140, the controller 300 may determine the control gain of the variable damper 200 based on the predicted road surface grade and vehicle state information. Based on the vehicle driving information, the controller 300 may determine driving conditions such as cornering, acceleration, and / or deceleration to determine appropriate skyhook control gains and passive damping control gains.

[0086] In S150, the controller 300 may determine the force and torque required for skyhook control based on the three-directional velocities of the vehicle's center of gravity determined in S120 and the skyhook control gain determined in S 140. The controller 300 may determine the required skyhook damping force in proportion to the vertical velocity, pitch angular velocity, and roll angular velocity of the vehicle's center of gravity determined in S120 and the skyhook control gain determined in S140.

[0087] In S160, the controller 300 may allocate a required damping force to each corner by using the conversion formula used in S120, where the damping force can satisfy the force and torque required to reduce the three-directional velocity of the vehicle's center of gravity. The controller 300 may allocate the required damping force to each corner of the vehicle body based on the determined force and torque required for skyhook control.

[0088] In S170, the controller 300 may determine the final required damping force at each corner by integrating the required skylight damping force at each corner of the vehicle body and the passive damping force at each wheel. The controller 300 may determine the passive damping force at each wheel based on the passive damping control gain determined in S140, the vertical velocity of the vehicle body at each corner, and the vertical velocity of the wheel.

[0089] In S180, the controller 300 may generate a current signal to be actually applied to the variable damper 200 based on the final required damping force at each angle and the damper characteristics. The controller 300 may determine the amount of current to be applied to the solenoid valve of the variable damper 200, taking into account the final required damping force at each angle and the characteristics of the variable damper 200. The controller 300 may generate a current signal based on the determined amount of current and apply the current signal to the solenoid valve. In addition, the controller 300 may limit the upper and lower limits of the current signal by taking into account the actuator (e.g., solenoid valve) load and the minimum drive current.

[0090] Figure 8 is a block diagram illustrating a computing system for executing a method of controlling a vehicle suspension according to various exemplary embodiments of the present invention.

[0091] Reference Figure 8, the computing system 1000 may include at least one processor 1100 , a memory 1300 , a user interface input device 1400 , a user interface output device 1500 , a storage 1600 , and a network interface 1700 connected via a system bus 1200 .

[0092] The processor 1100 may be a central processing unit (CPU) or a semiconductor device that processes instructions stored in the memory 1300 and / or the memory 1600. The memory 1300 and the memory 1600 may include various types of volatile or non-volatile storage media. For example, the memory 1300 may include a read-only memory (ROM) 1310 and a random access memory (RAM) 1320.

[0093] Therefore, the processing of the methods or algorithms described with respect to the exemplary embodiments of the present invention can be implemented directly by hardware, software modules, or a combination thereof executed by the processor 1100. The software module can reside in a storage medium (i.e., memory 1300 and / or memory 1600) such as RAM, flash memory, ROM, EPROM, EEPROM, registers, a hard disk, a solid-state drive (SSD), a detachable disk, or a CD-ROM. An exemplary storage medium is coupled to the processor 1100, and the processor 1100 can read information from the storage medium and can write information to the storage medium. In another approach, the storage medium can be integrated with the processor 1100. The processor and the storage medium can reside in an application-specific integrated circuit (ASIC). The ASIC can reside in a user terminal. In another approach, the processor 1100 and the storage medium can reside in a user terminal as separate components.

[0094] According to various exemplary embodiments of the present invention, by using the road surface roughness estimated in real time through the measurement values ​​of vertical acceleration sensors attached to the vehicle body and wheels and a prediction model pre-learned based on machine learning, the roughness level of the road surface can be classified, and the damping force of the variable damper can be controlled based on the classified roughness level of the road surface, thereby providing differentiated ride comfort.

[0095] According to various exemplary embodiments of the present invention, since the road surface roughness level is determined in real time, the characteristics of the variable damper can be changed in real time to adapt to the vehicle's external environment, such as the road surface. For example, it is possible to increase ride comfort on soft roads and increase passive damping on rough roads to further ensure tire grip.

[0096] To facilitate explanation and accurately define the appended claims, the terms "above," "below," "inside," "outside," "up," "down," "upward," "downward," "front," "back," "backside," "inside," "outside," "inward," "outward," "inside," "outward," "forward," and "rearward" are used to refer to features of the exemplary embodiments as they are positioned as shown in the accompanying drawings. It will also be understood that the term "connect" or its derivatives refers to both direct and indirect connections.

[0097] The foregoing descriptions of specific exemplary embodiments of the present invention have been provided for the purposes of illustration and description. They are not intended to be exhaustive or to limit the invention to the precise forms disclosed, and it is apparent that many modifications and variations are possible in light of the foregoing teachings. The exemplary embodiments are selected and described to explain certain principles of the present invention and their practical applications so that others skilled in the art can make and utilize the various exemplary embodiments of the present invention and their various alternatives and modifications. The scope of the present invention is intended to be defined by the appended claims and their equivalents.

Claims

1. A device for controlling a vehicle suspension, the device comprising: variable dampers, set between the body and wheels; a sensor configured to measure a vehicle body vertical acceleration and a wheel vertical acceleration; as well as a controller including a processor configured to estimate road surface roughness based on the vehicle body vertical acceleration and the wheel vertical acceleration, predict a road surface grade based on the estimated road surface roughness, and adjust a damping force of the variable damper based on the predicted road surface grade, In which, the controller is configured to convert the estimated road surface roughness into frequency domain data through frequency analysis, extract feature information from the estimated road surface roughness and the frequency domain data, classify the road surface grade by using the feature information and a prediction model based on machine learning, accumulate and store the classified road surface grades within a second storage time period, and determine the final road surface grade based on the accumulated road surface grades.

2. The device according to claim 1, wherein The sensor comprises: a first acceleration sensor mounted at each corner of the vehicle body to measure the vertical acceleration of each corner of the vehicle body; and A second acceleration sensor is mounted on each wheel of the vehicle to measure the vertical acceleration of each wheel.

3. The device according to claim 1, wherein The controller is configured to obtain vertical velocity and displacement information related to the vehicle body and the wheels through integration after filtering out noise from the vehicle body vertical acceleration and the wheel vertical acceleration measured by the sensor.

4. The apparatus according to claim 1, wherein The controller is configured to estimate and cumulatively store the road surface roughness by using a Kalman filter during a first storage period.

5. The apparatus according to claim 1, wherein The controller is configured to determine a skyhook control gain and a passive damping control gain based on the road surface grade, determine a damping force required for the skyhook based on center of gravity motion information related to the vehicle body and the skyhook control gain, determine a passive damping force by using a vehicle body vertical velocity, a wheel vertical velocity, and the passive damping control gain, and determine a final damping force required for each corner based on the damping force required for the skyhook and the passive damping force.

6. The device according to claim 5, wherein The controller is configured to: determine the vertical velocity, pitch angular velocity and roll angular velocity of the vehicle center of gravity by using the vehicle body vertical velocity and vehicle specification information, determine the damping force required for the skyhook proportional to the vertical velocity, pitch angular velocity and roll angular velocity of the vehicle center of gravity and the skyhook control gain, and distribute the damping force required for each corner according to the damping force required for the skyhook to weaken the vertical velocity, pitch angular velocity and roll angular velocity of the vehicle center of gravity.

7. The apparatus according to claim 5, wherein The controller is configured to generate a current signal according to the final damping force required for each corner and apply the current signal to the variable damper.

8. The apparatus according to claim 7, wherein The controller is configured to determine an amount of current applied to the variable damper according to the final damping force required for each corner and characteristics of the variable damper.

9. The apparatus according to claim 7, wherein The controller is configured to limit a maximum current and a minimum current applied to the variable damper in consideration of an actuator load and a minimum current of the variable damper.

10. A method of controlling a vehicle suspension, the method comprising the steps of: Measure the vertical acceleration of the vehicle body and the vertical acceleration of the wheels; estimating, by a controller, road surface roughness based on the vehicle body vertical acceleration and the wheel vertical acceleration; predicting, by the controller, a road surface grade based on the road surface roughness; as well as The controller adjusts the damping force of the variable damper according to the road surface grade, Among them, the predicted road surface grade includes: converting the estimated road surface roughness into frequency domain data through frequency analysis; and extracting feature information from the estimated road surface roughness and the frequency domain data; Classifying the road surface grade by using the feature information and a prediction model based on machine learning, accumulating and storing the classified road surface grades within a second storage period, and A final road surface grade is determined based on the accumulated road surface grades.

11. The method according to claim 10, further comprising: filtering, by the controller, noise from the vehicle body vertical acceleration and the wheel vertical acceleration; as well as The controller obtains the vehicle body vertical velocity and the wheel vertical velocity by integrating the filtered vehicle body vertical acceleration and wheel vertical acceleration.

12. The method according to claim 10, wherein: Estimating the road surface roughness includes: The road surface roughness is estimated by using a Kalman filter.

13. The method according to claim 12, wherein: Estimating the road surface roughness further includes: The road surface roughness is accumulated and stored within a first storage period.

14. The method according to claim 10, wherein: Adjusting the damping force of the variable damper includes: determining a skyhook control gain and a passive damping control gain according to the road surface grade; determining a damping force required for the skyhook based on center-of-gravity motion information related to the vehicle body and the skyhook control gain; determining a passive damping force by using a vehicle body vertical velocity, a wheel vertical velocity, and the passive damping control gain; and The final damping force required for each corner is determined based on the damping force required for the skyhook and the passive damping force.

15. The method according to claim 14, wherein Determining the damping force required for the ceiling includes: determining a vertical velocity, a pitch angular velocity, and a roll angular velocity of the vehicle's center of gravity by using the vehicle body vertical velocity and vehicle specification information; determining a required damping force for the skyhook proportional to the vertical velocity, pitch velocity, and roll velocity of the vehicle's center of gravity and the skyhook control gain; and The damping force required for each corner is distributed according to the damping force required for the skyhook to reduce the vertical velocity, pitch angular velocity and roll angular velocity of the center of gravity of the vehicle.

16. The method according to claim 14, further comprising: A current signal is generated according to the final damping force required for each corner and applied to the variable damper.

17. The method according to claim 16, wherein Generating the current signal and applying it to the variable damper includes: The amount of current applied to the variable damper is determined according to the final damping force required for each corner and characteristics of the variable damper.

18. The method according to claim 16, wherein Generating the current signal and applying it to the variable damper includes: A maximum current and a minimum current applied to the variable damper are limited in consideration of an actuator load and a minimum current of the variable damper.

Images