CN114741806A - Suspension optimization method, system, device, equipment and medium - Google Patents

Suspension optimization method, system, device, equipment and medium Download PDF

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CN114741806A
CN114741806A CN202210378610.XA CN202210378610A CN114741806A CN 114741806 A CN114741806 A CN 114741806A CN 202210378610 A CN202210378610 A CN 202210378610A CN 114741806 A CN114741806 A CN 114741806A
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road surface
vehicle
target vehicle
current position
suspension
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韩亚凝
张建
王御
赵凤凯
王珊
姜洪伟
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FAW Group Corp
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Abstract

The invention discloses a suspension optimization method, a system, a device, equipment and a medium. The method comprises the following steps: before the target vehicle runs to the current position point, acquiring the road surface unevenness grade of the current position point in a setting list according to the position coordinate of the target vehicle; obtaining the sprung mass grading of the target vehicle according to the sprung mass of the target vehicle and the sprung mass grading standard; combining the spring load quality grades of the target vehicles and the road surface unevenness grades of the current position points to obtain target groups; and acquiring suspension optimization parameters corresponding to the target group from a preset optimization template, and controlling the target vehicle to optimize the suspension parameters by using the suspension optimization parameters. By the technical scheme, the suspension of the vehicle can be efficiently and accurately optimized, and the comfort of the vehicle and the possibility of passing through different road conditions are improved.

Description

Suspension optimization method, system, device, equipment and medium
Technical Field
The invention relates to the technical field of automobile suspension optimization, in particular to a suspension optimization method, a system, a device, equipment and a medium.
Background
With the intelligent development of the automobile field, the chassis system of the vehicle gradually tends to be intelligent and networked. Moreover, the requirements for safety, economy and comfort of automobiles are also increasing. As an important system affecting the comfort of a vehicle, a suspension system has gradually transited from a conventional passive suspension to an active suspension, an intelligent suspension and an internet suspension, so that the characteristic parameters of the suspension can be changed by an intelligent control means under different working conditions to improve the comfort of the vehicle and the possibility of passing through different road conditions.
In the prior art, a camera is generally used for collecting road surface information to control a suspension. However, the cost of the whole vehicle is increased by using the camera to collect road surface information, the estimation difficulty of the suspension is improved by the algorithm complexity in the camera, the lag time is longer, and the limited data collection amount of the camera can reduce the estimation precision of the suspension. Therefore, how to efficiently and accurately optimize the suspension of the vehicle, and improve the comfort of the vehicle and the possibility of passing through different road conditions is a problem to be solved urgently at present.
Disclosure of Invention
The invention provides a suspension optimization method, a system, a device, equipment and a medium, which can solve the problem of low optimization precision of a vehicle suspension.
According to an aspect of the present invention, there is provided a suspension optimization method, the method comprising:
before the target vehicle runs to the current position point, acquiring the road surface unevenness grade of the current position point in a setting list according to the position coordinate of the target vehicle;
obtaining the classification of the sprung mass of the target vehicle according to the sprung mass of the target vehicle and the classification standard of the sprung mass;
combining the spring load mass grades of the target vehicle and the road surface unevenness grades of the current position points to obtain a target group;
and acquiring suspension optimization parameters corresponding to the target group from a preset optimization template, and controlling the target vehicle to optimize the suspension parameters by using the suspension optimization parameters.
According to another aspect of the present invention, there is provided a suspension optimization system, comprising: the system comprises a cloud end and a vehicle end, wherein the vehicle end comprises at least one vehicle;
the cloud end is used for acquiring the road surface unevenness grade of the current position point in the setting list according to the position coordinate of the target vehicle before the target vehicle runs to the current position point; obtaining the classification of the sprung mass of the target vehicle according to the sprung mass of the target vehicle and the classification standard of the sprung mass; combining the spring load mass grades of the target vehicle and the road surface unevenness grades of the current position points to obtain a target group; acquiring suspension optimization parameters corresponding to the target group from a preset optimization template, and controlling the target vehicle to optimize the suspension parameters by using the suspension optimization parameters;
the vehicle end is used for acquiring the position coordinates of the target vehicle and transmitting the position coordinates to the cloud end; acquiring the sprung mass of a target vehicle and transmitting the sprung mass to a cloud end; and receiving suspension optimization parameters transmitted by the cloud and optimizing the suspension parameters.
According to another aspect of the present invention, there is provided a suspension optimizing apparatus comprising:
the grade acquisition module is used for acquiring the grade of the road surface unevenness of the current position point in the setting list according to the position coordinate of the target vehicle before the target vehicle runs to the current position point;
the mass grading module is used for obtaining the spring load mass grading of the target vehicle according to the spring load mass of the target vehicle and the spring load mass grading standard;
the grouping generation module is used for combining the sprung mass grade of the target vehicle and the road surface unevenness grade of the current position point to obtain a target grouping;
and the parameter acquisition module is used for acquiring suspension optimization parameters corresponding to the target group from a preset optimization template and controlling the target vehicle to optimize the suspension parameters by using the suspension optimization parameters.
According to another aspect of the present invention, there is provided an electronic apparatus including:
at least one processor; and
a memory communicatively coupled to the at least one processor; wherein the content of the first and second substances,
the memory stores a computer program executable by the at least one processor, the computer program being executable by the at least one processor to enable the at least one processor to perform a suspension optimization method according to any embodiment of the invention.
According to another aspect of the present invention, there is provided a computer-readable storage medium storing computer instructions for causing a processor to perform a suspension optimization method according to any one of the embodiments of the present invention when the computer instructions are executed.
According to the technical scheme of the embodiment of the invention, the road surface unevenness grade of the current position point of the target vehicle obtained by inquiring in the set list and the calculated sprung mass grade of the target vehicle are combined to obtain the target grouping, and the corresponding suspension optimization parameters are obtained from the preset optimization template according to the target grouping so as to optimize the suspension parameters, so that the problem of efficiently and accurately optimizing the suspension of the vehicle is solved, and the comfort of the vehicle and the possibility of passing through different road conditions are improved.
It should be understood that the statements in this section do not necessarily identify key or critical features of the embodiments of the present invention, nor do they necessarily limit the scope of the invention. Other features of the present invention will become apparent from the following description.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings needed to be used in the description of the embodiments will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts.
Fig. 1 is a flowchart of a suspension optimization method according to an embodiment of the present invention;
FIG. 2a is a flow chart of a suspension optimization method according to a second embodiment of the present invention;
FIG. 2b is a schematic diagram of a quarter model of a vehicle to which the second embodiment of the present invention is applied;
FIG. 2c is a schematic flow chart of a suspension optimization method according to a second embodiment of the present invention;
FIG. 3 is a schematic structural diagram of a suspension optimization system according to a third embodiment of the present invention;
fig. 4 is a schematic structural diagram of a suspension optimization device according to a fourth embodiment of the present invention;
fig. 5 is a schematic structural diagram of an electronic device implementing the suspension optimization method according to the embodiment of the present invention.
Detailed Description
In order to make the technical solutions of the present invention better understood, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
It should be noted that the terms "target," "original," and the like in the description and claims of the present invention and in the drawings described above are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used is interchangeable under appropriate circumstances such that the embodiments of the invention described herein are capable of operation in sequences other than those illustrated or described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
Example one
Fig. 1 is a flowchart of a suspension optimization method according to an embodiment of the present invention, where the present embodiment is applicable to a case of optimizing a vehicle suspension, and the method may be performed by a suspension optimization apparatus, which may be implemented in a form of hardware and/or software, and the suspension optimization apparatus may be configured in an electronic device. As shown in fig. 1, the method includes:
and S110, before the target vehicle runs to the current position point, acquiring the road surface unevenness grade of the current position point in a setting list according to the position coordinates of the target vehicle.
The Position point may refer to a preset Position identification point of the current road surface, and for example, a Position point may be set at a distance of five meters in the current road surface by using a Global Positioning System (GPS). The target vehicle may refer to a vehicle to which a suspension to be optimized belongs. The current position point may refer to a position point through which the target vehicle is about to pass, and for example, whether the target vehicle travels to the current position point may be determined according to a difference between the coordinates of the absolute position of the target vehicle and the coordinates of the current position point. The road surface irregularity level may refer to a level obtained by evaluating the road surface irregularity at the position point. The setting list may refer to a list including information such as coordinates of the position points and road surface irregularity levels of the position points.
Specifically, in the running process of the target vehicle, the position coordinates of the target vehicle are acquired in real time and compared with the coordinates of each position point, and when the distance between the target vehicle and the current position point is less than a five-meter travel range, the road unevenness grade of the current position point is acquired in the set list according to the coordinates of the current position point, so that the road unevenness grade of the current position point is acquired by using the position coordinates of the target vehicle acquired by the vehicle end, the credibility of information can be greatly improved, and an effective basis is provided for subsequent operations.
And S120, obtaining the classification of the sprung mass of the target vehicle according to the sprung mass of the target vehicle and the classification standard of the sprung mass.
The sprung mass may refer to the mass carried by the target vehicle chassis frame and all other resilient components. The sprung mass grading criterion of the target vehicle may refer to a criterion for grading the sprung mass of the target vehicle according to the sprung mass of the target vehicle at different loads. The sprung mass classification of the target vehicle may refer to a grade of the sprung mass according to a sprung mass classification standard, and for example, if the sprung mass classification standard of the target vehicle divides the sprung mass in the range of twenty kilograms to forty kilograms into two grades, the sprung mass of the target vehicle is classified into two grades when the sprung mass of the target vehicle is thirty kilograms.
Optionally, the obtaining the classification of the sprung mass of the target vehicle according to the sprung mass of the target vehicle and the classification standard of the sprung mass includes: acquiring a standard load of a target vehicle and a full load of the target vehicle, and averagely dividing the spring load mass between the standard load of the target vehicle and the full load of the target vehicle into set levels to obtain a spring load mass grading standard corresponding to the target vehicle; and comparing the sprung mass of the target vehicle with the sprung mass grading standard corresponding to the target vehicle to obtain the sprung mass grading of the target vehicle. The standard load can refer to a standard load value marked when the vehicle is out of the field. Full load may refer to the load value when the vehicle is full. The set level may refer to the number of spring load mass division levels between the standard load of the target vehicle and the full load of the target vehicle, and is preferable in the embodiment of the present inventionThe mean division of the spring load mass between the standard load and the full load of the target vehicle into 5 grades is selected, and then the grading standards of the spring load mass corresponding to the target vehicle can be respectively marked as m1a、m1b、m1c、m1d、m1e. Therefore, the spring load mass between the standard load and the full load of the target vehicle is averagely divided into levels with set number to obtain the spring load mass grading standard corresponding to the target vehicle, and the spring load mass of the target vehicle is compared with the spring load mass grading standard corresponding to the target vehicle to obtain the spring load mass grading corresponding to the target vehicle at present.
And S130, combining the sprung mass grade of the target vehicle and the road surface unevenness grade of the current position point to obtain a target group.
Wherein the target group may refer to a set consisting of a sprung mass classification of the target vehicle and a road surface irregularity classification of the current location point.
And S140, obtaining suspension optimization parameters corresponding to the target group from a preset optimization template, and controlling the target vehicle to optimize the suspension parameters by using the suspension optimization parameters.
The optimization template may refer to a preset template for optimizing suspension parameters, and may include, for example, a set consisting of sprung mass grading and road surface unevenness grade of a current position point, and suspension optimization parameters corresponding to the set. The suspension optimization parameters can refer to values expected to be reached after the suspension parameters are optimized and adjusted. In an embodiment of the present invention, the suspension parameters may include a suspension damping coefficient (C) and a suspension stiffness coefficient (K). Therefore, a set corresponding to the target grouping can be obtained by calculation in a preset optimization template according to the target grouping, suspension optimization parameters corresponding to the set are further used as suspension optimization parameters corresponding to the target grouping, and the suspension parameters of the target vehicle are optimized according to the suspension optimization parameters.
According to the technical scheme of the embodiment of the invention, the road surface unevenness grade of the current position point of the target vehicle obtained by inquiring in the set list and the calculated sprung mass grade of the target vehicle are combined to obtain the target grouping, and the corresponding suspension optimization parameters are obtained from the preset optimization template according to the target grouping so as to optimize the suspension parameters, so that the problem of efficiently and accurately optimizing the suspension of the vehicle is solved, and the comfort of the vehicle and the possibility of passing through different road conditions are improved.
Example two
Fig. 2a is a flowchart of a suspension optimization method according to a second embodiment of the present invention, which is added based on the second embodiment, and in this embodiment, specifically, how to construct a preset optimization template is added, which may specifically include: grading the road surface to be tested according to the road surface bumping degree to obtain a road surface grade; acquiring target vehicle information and target suspension parameters of each test vehicle when the test vehicle runs in a test field of each road surface grade, and inputting the road surface grade, the target vehicle information and the target suspension parameters into a vehicle quarter model to obtain a corresponding road surface unevenness grade; dividing groups according to the road surface unevenness grade and the spring load mass grade of the test vehicle, and clustering the unsprung mass acceleration and the sprung mass acceleration in the target vehicle information corresponding to each group to obtain a clustering center point of each group; the clustering center point comprises spring load mass grades and road surface unevenness grades corresponding to all groups; obtaining corresponding suspension optimization parameters according to the characteristics of the suspensions in each group, and storing the corresponding suspension optimization parameters and the clustering center points of each group to obtain a preset optimization template; correspondingly, acquiring suspension optimization parameters corresponding to the target group from a preset optimization template, wherein the suspension optimization parameters comprise: and positioning a clustering central point corresponding to the target group in a preset optimization template, and acquiring corresponding suspension optimization parameters according to the clustering central point.
As shown in fig. 2a, the method comprises:
and S210, grading the road surface to be tested according to the road surface bumping degree to obtain the road surface grade.
The road surface grade may refer to a grade of dividing the unevenness of the road surface to be tested according to a road grade division standard, and the unevenness of the road surface to be tested may be divided into A, B, C, D, E, F, G, H grades in total, for example. Specifically, the road surface with relatively flat and few continuous curves, including high speed, national road, provincial road and the like, can be divided into A level; dividing continuous winding mountain road surfaces, road surfaces with more bends and no visible front angle when passing the bends, flat soil roads and gravel road surfaces into B grades; by analogy, the higher the grade, the higher the road surface bumping degree.
S220, obtaining test vehicle information and test vehicle suspension parameters of each test vehicle when the test vehicle runs in a test field of each road surface grade, and inputting the road surface grade, the test vehicle information and the test vehicle suspension parameters into a vehicle quarter model to obtain a corresponding road surface unevenness grade.
The test vehicle may refer to a vehicle used when a test is performed in advance. The test vehicle information may refer to vehicle information during the running of the test vehicle at each road surface level, and may include, for example, sprung mass, unsprung mass acceleration, sprung mass acceleration, vehicle speed, and the like.
The vehicle quarter model may refer to a model for analyzing the most basic frequency and mode shape characteristics of the vehicle, and is a model schematic diagram of the vehicle quarter model as shown in fig. 2 b. Specifically, m1 is the sprung mass, m2 is the unsprung mass, z1 is the vertical displacement of the sprung mass, z2 is the vertical displacement of the unsprung mass, K is the suspension stiffness, and C is the suspension damping. The method can be used for modeling a suspension system dynamic model by adopting a quarter vehicle model, and analyzing the influence on the running smoothness of the vehicle, namely the road surface unevenness grade under the conditions of different suspension stiffness and suspension damping, different road surface grades, different spring load mass and the like by combining a state space analysis method. It is to be noted that since the road surface irregularity levels correspond to the road surface levels one to one, the road surface irregularity levels for the eight road surface levels may be sequentially labeled as qA、qB、qC、qD、qE、qF、qGAnd q isH。
S230, dividing groups according to the road surface unevenness grade and the sprung mass grade of the test vehicle, and clustering the unsprung mass acceleration and the sprung mass acceleration in the test vehicle information corresponding to each group to obtain a clustering center point of each group; and the clustering center point comprises the spring load mass grades and the road surface unevenness grades corresponding to all groups.
The clustering may refer to a technique of comparing similarity of the unsprung mass acceleration and the sprung mass acceleration, and grouping the unsprung mass acceleration and the sprung mass acceleration that are compared to be similar, such as a Kmeans clustering algorithm. It is noted that the unsprung mass acceleration and the sprung mass acceleration need to be filtered by a filtering algorithm, such as moving average filtering, and then clustered.
In an optional embodiment, the classifying the groups according to the road unevenness grade and the sprung mass grade of the test vehicle, and clustering the unsprung mass acceleration and the sprung mass acceleration in the test vehicle information corresponding to each group to obtain the clustering center point of each group includes: the road surface unevenness grade and the spring load quality grade of the test vehicle are in one-to-one correspondence, and the whole group is divided; in each group, clustering is carried out on the unsprung mass acceleration and the sprung mass acceleration in the tested vehicle information by utilizing a clustering algorithm respectively to obtain a clustering central point of each group. Specifically, the collected sprung mass acceleration (a)z1) And unsprung mass acceleration (a)z2) According to the road surface unevenness grade (q)A-qH) And sprung mass grading (m)1a-m1e) The grouping is performed into 8-by-5-40 groups, and the grade a road is divided into 5 groups according to 5 grades of sprung mass. Then, a in each group is clustered by using a Kmeans clustering algorithmz1And az2Clustering to obtain 40 groups of cluster central points, respectively marking as NqAm1a、NqAm1b、……、NqHm1e。
S240, obtaining corresponding suspension optimization parameters according to the characteristics of the suspensions in each group, and storing the corresponding suspension optimization parameters and the clustering center points of each group to obtain a preset optimization template.
The suspension characteristics can refer to the hardness of the suspension, if the suspension is too soft, the vehicle body can shake seriously, so that the suspension parameters can be optimized according to the characteristic pair of the suspension in 40 groups of data to obtain 40 groups of suspension optimization parameters comprising damping optimization values and rigidity optimization values, and each suspension optimization parameter can be marked as [ K ] for exampleqAm1a,CqAm1a]、[KqAm1b,CqAm1b]、……、[KqHm1e,CqHm1e]。
And S250, before the target vehicle runs to the current position point, acquiring the road surface unevenness grade of the current position point in a setting list according to the position coordinate of the target vehicle.
Specifically, the method further comprises: acquiring unsprung mass acceleration and sprung mass acceleration of an original vehicle at a current position point, and clustering the unsprung mass acceleration and the sprung mass acceleration according to the sprung mass grading of the original vehicle to obtain a clustering result; grouping corresponding to the clustering center point with the minimum distance with the clustering result as the group of the original vehicle at the current position point; acquiring the original road surface unevenness grade of the current position point according to the spring load quality grade of the original vehicle and the group of the original vehicle at the current position point; and storing the original road surface unevenness grade and the position coordinate of the current position point to obtain a set list. The original vehicle may refer to a vehicle previously traveling in the current road surface. The original road unevenness grade may refer to a road unevenness grade calculated when the original vehicle travels to the position point. Illustratively, when an original vehicle runs to a preset position point, acquiring unsprung mass acceleration and sprung mass acceleration of the original vehicle, clustering the unsprung mass acceleration and the sprung mass acceleration to obtain a clustering result, taking a group corresponding to a clustering center point which is closest to the clustering result and is obtained in advance from 8 clustering center points which are matched with the sprung mass of the original vehicle in a grading manner as a group of the original vehicle at the current position point, and then carrying out classification according to the groupAnd the grade of the original road surface unevenness of the current position point can be obtained by grading the spring load mass of the original vehicle. Specifically, if the sprung mass of the original vehicle is classified as m1bThen is in contact with m1bThe clustering center point of 8 road surface unevenness corresponding to the grade is NqAm1b、NqBm1b、NqCm1b、NqDm1b、NqEm1b、NqFm1b、NqGm1b、NqHm1bThe method comprises the steps of calculating the distances from the unsprung mass acceleration and the sprung mass acceleration of an original vehicle at a current position point to the eight clustering center points, finding out the clustering center point with the minimum distance, determining the group of the current position point, and grading according to the sprung mass of the original vehicle to obtain the original road surface unevenness grade of the current position point.
It should be noted that, in this embodiment, the vehicle information may be acquired once every 50ms and clustered, and all the center point results calculated within the 5m travel range corresponding to the current position point are recorded, and the result with the highest occurrence frequency is selected as the clustering result.
S260, acquiring the standard load of the target vehicle and the full load of the target vehicle, and averagely dividing the sprung mass between the standard load of the target vehicle and the full load of the target vehicle into set levels to obtain the sprung mass grading standard corresponding to the target vehicle.
And S270, comparing the sprung mass of the target vehicle with the sprung mass grading standard corresponding to the target vehicle to obtain the sprung mass grading of the target vehicle.
And S280, combining the sprung mass grades of the target vehicles and the road surface unevenness grades of the current position points to obtain target groups.
S290, locating a clustering center point corresponding to the target group in a preset optimization template, and acquiring corresponding suspension optimization parameters according to the clustering center point.
And S2100, controlling the target vehicle to optimize the suspension parameters by using the suspension optimization parameters.
In an optional implementation, the above embodiment further includes: acquiring unsprung mass acceleration and sprung mass acceleration of a target vehicle at a current position point, and clustering the unsprung mass acceleration and the sprung mass acceleration according to the sprung mass grading of the target vehicle to obtain a clustering result; taking the group corresponding to the clustering center point with the minimum distance with the clustering result as the group of the target vehicle at the current position point; acquiring the road surface unevenness grade of the current position point according to the spring load quality grade of the target vehicle and the group of the target vehicle at the current position point, and storing the road surface unevenness grade; if the road surface unevenness grade of the current position point is different from the original road surface unevenness grade of the current position point, marking the road surface unevenness grade of the current position point and the original road surface unevenness grade of the current position point; if the road unevenness grades of the current position points with the set number are different from the original road unevenness grades of the current position points, the original road unevenness grades of the current position points are updated to the road unevenness grades with the highest occurrence frequency in the set number. Specifically, when the target vehicle runs to the current position point, besides optimizing the suspension parameters of the target vehicle according to a preset optimization template, the road surface unevenness grade of the current position point is calculated according to the vehicle information of the target vehicle, if the road surface unevenness grade of the current position point is different from the original road surface unevenness grade of the current position point, the road surface unevenness grade of the current position point and the original road surface unevenness grade are marked, and when the road surface unevenness grade calculation results of 10 continuous groups of current position points are different from the original road surface unevenness grade, the original road surface unevenness grade is updated to the road surface unevenness grade with the highest proportion in 10 groups of data, so that the suspension optimization method is perfected, and the accuracy of the suspension optimization is further improved.
According to the technical scheme of the embodiment of the invention, the corresponding road surface unevenness grade is obtained by inputting the test vehicle information and the test vehicle suspension parameters which are obtained in a test field with the set road surface grade into the vehicle quarter model; clustering unsprung mass acceleration and sprung mass acceleration in the test vehicle information by using the road surface unevenness grade and the sprung mass grade of the test vehicle to obtain a clustering center point of each group; then storing the corresponding suspension optimization parameters and the clustering center points of each group to obtain a preset optimization template; before the target vehicle runs to the current position point, the road surface unevenness grade of the current position point can be obtained in a set list according to the position coordinate of the target vehicle, and the road surface unevenness grade and the calculated spring load quality grade of the target vehicle are combined to obtain a target group; finally, a cluster center point corresponding to the target group is positioned in a preset optimization template, corresponding suspension optimization parameters are obtained according to the cluster center point, and the suspension parameters of the target vehicle are optimized, so that the problem of efficiently and accurately optimizing the suspension of the vehicle is solved, and the comfort of the vehicle and the possibility of passing through different road conditions are improved.
Fig. 2c is a schematic flow chart of a suspension optimization method according to an embodiment of the present invention. Specifically, dividing the sprung mass of a test vehicle from standard load to full load into 5 grades, driving the test vehicles corresponding to the 5 sprung mass grades in the road surface of 8 road surface grades, and collecting the test vehicle information and the test vehicle suspension parameters of the test vehicle; then, inputting the road surface grade, the test vehicle information and the test vehicle suspension parameters into a vehicle quarter model to obtain a corresponding road surface unevenness grade; grouping according to the road surface unevenness grade and the sprung mass grade of the test vehicle, clustering the unsprung mass acceleration and the sprung mass acceleration in the test vehicle information corresponding to each group to obtain a clustering center point of each group; further, according to the characteristics of the suspensions in each group, corresponding suspension optimization parameters are obtained. When the original vehicle runs to the current position point, the original road surface unevenness grade of the current position point is calculated according to the original vehicle information, and the current position point and the corresponding original road surface unevenness grade are stored for subsequent vehicles to use. And before the target vehicle drives to the current position point, acquiring the road surface unevenness grade of the current position point in a set list according to the position coordinate of the target vehicle 100ms in advance, optimizing suspension parameters according to the stored suspension optimization parameters, meanwhile, calculating the road surface unevenness grade of the current position point according to the vehicle information of the target vehicle at the current position point, and verifying the original road surface unevenness grade of the current position point to perfect the suspension optimization method.
EXAMPLE III
Fig. 3 is a schematic structural diagram of a suspension optimization system according to a third embodiment of the present invention. As shown in fig. 3, the system includes: the cloud end 310 and the vehicle end 320, wherein the vehicle end 310 comprises at least one vehicle;
the cloud end 310 is used for acquiring the road surface unevenness grade of the current position point in the setting list according to the position coordinate of the target vehicle before the target vehicle runs to the current position point; obtaining the classification of the sprung mass of the target vehicle according to the sprung mass of the target vehicle and the classification standard of the sprung mass; combining the spring load mass grades of the target vehicle and the road surface unevenness grades of the current position points to obtain a target group; acquiring suspension optimization parameters corresponding to the target group from a preset optimization template, and controlling the target vehicle to optimize the suspension parameters by using the suspension optimization parameters;
the vehicle end 320 is used for acquiring the position coordinates of the target vehicle and transmitting the position coordinates to the cloud end; acquiring the sprung mass of a target vehicle and transmitting the sprung mass to a cloud end; and receiving suspension optimization parameters transmitted by the cloud and optimizing the suspension parameters.
Optionally, the cloud 310 may be specifically configured to grade the road surface to be tested according to the road surface bumping degree to obtain a road surface grade; obtaining test vehicle information and test vehicle suspension parameters of each test vehicle when the test vehicle runs in a test field of each road surface grade, and inputting the road surface grade, the test vehicle information and the test vehicle suspension parameters into a vehicle quarter model to obtain a corresponding road surface unevenness grade; dividing groups according to the road surface unevenness grade and the sprung mass grade of the test vehicle, and clustering the unsprung mass acceleration and the sprung mass acceleration in the test vehicle information corresponding to each group to obtain a clustering center point of each group; the clustering center point comprises spring load mass grades and road surface unevenness grades corresponding to all groups; obtaining corresponding suspension optimization parameters according to the characteristics of the suspensions in each group, and storing the corresponding suspension optimization parameters and the clustering center points of each group to obtain a preset optimization template; positioning a clustering center point corresponding to the target group in a preset optimization template, and acquiring corresponding suspension optimization parameters according to the clustering center point;
correspondingly, the vehicle end 320 can be specifically used for acquiring test vehicle information and test vehicle suspension parameters and transmitting the test vehicle information and the test vehicle suspension parameters to the cloud.
Optionally, the cloud 310 may be specifically configured to obtain the unsprung mass acceleration and the sprung mass acceleration of the original vehicle at the current position point, and cluster the unsprung mass acceleration and the sprung mass acceleration according to the sprung mass classification of the original vehicle to obtain a clustering result; grouping corresponding to the clustering center point with the minimum distance with the clustering result as the group of the original vehicle at the current position point; acquiring the original road surface unevenness grade of the current position point according to the spring load quality grade of the original vehicle and the group of the original vehicle at the current position point; and storing the original road surface unevenness grade and the position coordinate of the current position point to obtain a set list.
Optionally, the cloud 310 may be specifically configured to obtain a standard load of a target vehicle and a full load of the target vehicle, and averagely divide the sprung mass between the standard load of the target vehicle and the full load of the target vehicle into set levels to obtain a sprung mass classification standard corresponding to the target vehicle; and comparing the sprung mass of the target vehicle with the sprung mass grading standard corresponding to the target vehicle to obtain the sprung mass grading of the target vehicle.
Optionally, the cloud 310 may be specifically configured to perform one-to-one correspondence between the road surface unevenness grade and the sprung mass grade of the test vehicle, and perform full-group classification; in each group, clustering is carried out on the unsprung mass acceleration and the sprung mass acceleration in the tested vehicle information by utilizing a clustering algorithm respectively to obtain a clustering central point of each group.
Optionally, the cloud 310 may be specifically configured to obtain the unsprung mass acceleration and the sprung mass acceleration of the target vehicle at the current position point, and cluster the unsprung mass acceleration and the sprung mass acceleration according to the sprung mass classification of the target vehicle to obtain a clustering result; taking the group corresponding to the clustering center point with the minimum distance with the clustering result as the group of the target vehicle at the current position point; acquiring the road surface unevenness grade of the current position point according to the spring load quality grade of the target vehicle and the group of the target vehicle at the current position point, and storing the road surface unevenness grade; if the road surface unevenness grade of the current position point is different from the original road surface unevenness grade of the current position point, marking the road surface unevenness grade of the current position point and the original road surface unevenness grade of the current position point; and if the road surface unevenness grades of the current position points with the set number are different from the original road surface unevenness grades of the current position points, updating the original road surface unevenness grades of the current position points to the road surface unevenness grades with the highest occurrence frequency in the set number.
Example four
Fig. 4 is a schematic structural diagram of a suspension optimization device according to a fourth embodiment of the present invention. As shown in fig. 4, the apparatus includes: a grade acquisition module 410, a quality grading module 420, a grouping generation module 430 and a parameter acquisition module 440;
the grade acquiring module 410 is configured to acquire a road unevenness grade of a current position point in a setting list according to a position coordinate of a target vehicle before the target vehicle travels to the current position point;
the mass grading module 420 is used for obtaining the grade of the sprung mass of the target vehicle according to the sprung mass of the target vehicle and the grading standard of the sprung mass;
the grouping generation module 430 is used for combining the sprung mass grade of the target vehicle and the road surface unevenness grade of the current position point to obtain a target grouping;
and a parameter obtaining module 440, configured to obtain suspension optimization parameters corresponding to the target group from a preset optimization template, and control the target vehicle to optimize the suspension parameters by using the suspension optimization parameters.
According to the technical scheme of the embodiment of the invention, the road surface unevenness grade of the current position point of the target vehicle obtained by inquiring in the set list and the calculated sprung mass grade of the target vehicle are combined to obtain the target grouping, and the corresponding suspension optimization parameters are obtained from the preset optimization template according to the target grouping so as to optimize the suspension parameters, so that the problem of efficiently and accurately optimizing the suspension of the vehicle is solved, and the comfort of the vehicle and the possibility of passing through different road conditions are improved.
Optionally, the suspension optimization device may further include a template generation module, configured to grade the road surface to be tested according to the degree of road surface jolt, so as to obtain a road surface grade; obtaining test vehicle information and test vehicle suspension parameters of each test vehicle when the test vehicle runs in a test field of each road surface grade, and inputting the road surface grade, the test vehicle information and the test vehicle suspension parameters into a vehicle quarter model to obtain a corresponding road surface unevenness grade; dividing groups according to the road surface unevenness grade and the sprung mass grade of the test vehicle, and clustering the unsprung mass acceleration and the sprung mass acceleration in the test vehicle information corresponding to each group to obtain a clustering center point of each group; the clustering center point comprises spring load mass grades and road surface unevenness grades corresponding to all groups; obtaining corresponding suspension optimization parameters according to the characteristics of the suspensions in each group, and storing the corresponding suspension optimization parameters and the clustering center points of each group to obtain a preset optimization template;
correspondingly, the parameter obtaining module 440 may be specifically configured to locate a cluster center point corresponding to the target group in a preset optimization template, and obtain a corresponding suspension optimization parameter according to the cluster center point.
Optionally, the suspension optimization device may further include a list generation module, configured to obtain unsprung mass acceleration and sprung mass acceleration of the original vehicle at the current position point, and cluster the unsprung mass acceleration and the sprung mass acceleration according to the sprung mass classification of the original vehicle to obtain a clustering result; grouping corresponding to the clustering center point with the minimum distance with the clustering result as the group of the original vehicle at the current position point; acquiring the original road surface unevenness grade of the current position point according to the spring load quality grade of the original vehicle and the group of the original vehicle at the current position point; and storing the original road surface unevenness grade and the position coordinate of the current position point to obtain a set list.
Optionally, the mass grading module 420 may be specifically configured to obtain a standard load of the target vehicle and a full load of the target vehicle, and averagely divide the sprung mass between the standard load of the target vehicle and the full load of the target vehicle into set levels to obtain a sprung mass grading standard corresponding to the target vehicle; and comparing the sprung mass of the target vehicle with the sprung mass grading standard corresponding to the target vehicle to obtain the sprung mass grading of the target vehicle.
Optionally, the template generation module may be specifically configured to perform one-to-one correspondence between the road surface unevenness grade and the spring load quality grade of the test vehicle, and perform full-group classification; in each group, clustering is carried out on the unsprung mass acceleration and the sprung mass acceleration in the tested vehicle information by utilizing a clustering algorithm respectively to obtain a clustering central point of each group.
Optionally, the suspension optimization device may further include a post-processing module, which is specifically configured to obtain the unsprung mass acceleration and the sprung mass acceleration of the target vehicle at the current position point, and cluster the unsprung mass acceleration and the sprung mass acceleration according to the sprung mass classification of the target vehicle to obtain a clustering result; taking the group corresponding to the clustering center point with the minimum distance with the clustering result as the group of the target vehicle at the current position point; acquiring the road surface unevenness grade of the current position point according to the spring load quality grade of the target vehicle and the group of the target vehicle at the current position point, and storing the road surface unevenness grade; if the road surface unevenness grade of the current position point is different from the original road surface unevenness grade of the current position point, marking the road surface unevenness grade of the current position point and the original road surface unevenness grade of the current position point; and if the road surface unevenness grades of the current position points with the set number are different from the original road surface unevenness grades of the current position points, updating the original road surface unevenness grades of the current position points to the road surface unevenness grades with the highest occurrence frequency in the set number.
The suspension optimization device provided by the embodiment of the invention can execute the suspension optimization method provided by any embodiment of the invention, and has corresponding functional modules and beneficial effects of the execution method.
EXAMPLE five
FIG. 5 illustrates a schematic diagram of an electronic device 510 that may be used to implement an embodiment of the invention. Electronic devices are intended to represent various forms of digital computers, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. The electronic device may also represent various forms of mobile devices, such as personal digital assistants, cellular phones, smart phones, wearable devices (e.g., helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions, are meant to be exemplary only, and are not meant to limit implementations of the inventions described and/or claimed herein.
As shown in fig. 5, the electronic device 510 includes at least one processor 520, and a memory communicatively coupled to the at least one processor 520, such as a Read Only Memory (ROM)530, a Random Access Memory (RAM)540, etc., where the memory stores computer programs executable by the at least one processor, and the processor 520 may perform various suitable actions and processes according to the computer programs stored in the Read Only Memory (ROM)530 or loaded from a storage unit 590 into the Random Access Memory (RAM) 540. In the RAM540, various programs and data required for the operation of the electronic device 510 can also be stored. The processor 520, the ROM530, and the RAM540 are connected to each other through a bus 550. An input/output (I/O) interface 560 is also connected to bus 550.
A number of components in the electronic device 510 are connected to the I/O interface 560, including: an input unit 570 such as a keyboard, a mouse, and the like; an output unit 580 such as various types of displays, speakers, and the like; a storage unit 590 such as a magnetic disk, optical disk, or the like; and a communication unit 5100 such as a network card, modem, wireless communication transceiver, or the like. The communication unit 5100 allows the electronic device 510 to exchange information/data with other devices through a computer network such as the internet and/or various telecommunication networks.
Processor 520 may be a variety of general and/or special purpose processing components with processing and computing capabilities. Some examples of processor 520 include, but are not limited to, a Central Processing Unit (CPU), a Graphics Processing Unit (GPU), various specialized Artificial Intelligence (AI) computing chips, various processors running machine learning model algorithms, a Digital Signal Processor (DSP), and any suitable processor, controller, microcontroller, or the like. Processor 520 performs the various methods and processes described above, such as a suspension optimization method.
The method comprises the following steps:
before the target vehicle runs to the current position point, acquiring the road surface unevenness grade of the current position point in a setting list according to the position coordinate of the target vehicle;
obtaining the classification of the sprung mass of the target vehicle according to the sprung mass of the target vehicle and the classification standard of the sprung mass;
combining the spring load mass grades of the target vehicle and the road surface unevenness grades of the current position points to obtain a target group;
and acquiring suspension optimization parameters corresponding to the target group from a preset optimization template, and controlling the target vehicle to optimize the suspension parameters by using the suspension optimization parameters.
In some embodiments, the suspension optimization method may be implemented as a computer program tangibly embodied in a computer-readable storage medium, such as storage unit 590. In some embodiments, part or all of the computer program can be loaded and/or installed onto the electronic device 510 via the ROM530 and/or the communication unit 5100. When loaded into RAM540 and executed by processor 520, may perform one or more of the steps of the suspension optimization method described above. Alternatively, in other embodiments, the processor 520 may be configured to perform the suspension optimization method by any other suitable means (e.g., by way of firmware).
Various implementations of the systems and techniques described here above may be implemented in digital electronic circuitry, integrated circuitry, Field Programmable Gate Arrays (FPGAs), Application Specific Integrated Circuits (ASICs), Application Specific Standard Products (ASSPs), system on a chip (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and/or combinations thereof. These various embodiments may include: implemented in one or more computer programs that are executable and/or interpretable on a programmable system including at least one programmable processor, which may be special or general purpose, receiving data and instructions from, and transmitting data and instructions to, a storage system, at least one input device, and at least one output device.
A computer program for implementing the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus, such that the computer programs, when executed by the processor, cause the functions/acts specified in the flowchart and/or block diagram block or blocks to be performed. A computer program can execute entirely on a machine, partly on a machine, as a stand-alone software package partly on a machine and partly on a remote machine or entirely on a remote machine or server.
In the context of the present invention, a computer-readable storage medium may be a tangible medium that can contain, or store a computer program for use by or in connection with an instruction execution system, apparatus, or device. A computer readable storage medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. Alternatively, the computer readable storage medium may be a machine readable signal medium. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
To provide for interaction with a user, the systems and techniques described here can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to a user; and a keyboard and a pointing device (e.g., a mouse or a trackball) by which a user can provide input to the electronic device. Other kinds of devices may also be used to provide for interaction with a user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form, including acoustic, speech, or tactile input.
The systems and techniques described here can be implemented in a computing system that includes a back-end component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front-end component (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such back-end, middleware, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: local Area Networks (LANs), Wide Area Networks (WANs), blockchain networks, and the internet.
The computing system may include clients and servers. A client and server are generally remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. The server can be a cloud server, also called a cloud computing server or a cloud host, and is a host product in a cloud computing service system, so that the defects of high management difficulty and weak service expansibility in the traditional physical host and VPS service are overcome.
It should be understood that various forms of the flows shown above, reordering, adding or deleting steps, may be used. For example, the steps described in the present invention may be executed in parallel, sequentially, or in different orders, and are not limited herein as long as the desired result of the technical solution of the present invention can be achieved.
The above-described embodiments should not be construed as limiting the scope of the invention. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions may be made in accordance with design requirements and other factors. Any modification, equivalent replacement, and improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims (10)

1. A method of suspension optimization, comprising:
before the target vehicle runs to the current position point, acquiring the road surface unevenness grade of the current position point in a setting list according to the position coordinate of the target vehicle;
obtaining the classification of the sprung mass of the target vehicle according to the sprung mass of the target vehicle and the classification standard of the sprung mass;
combining the spring load quality grades of the target vehicles and the road surface unevenness grades of the current position points to obtain target groups;
and acquiring suspension optimization parameters corresponding to the target group from a preset optimization template, and controlling the target vehicle to optimize the suspension parameters by using the suspension optimization parameters.
2. The method of claim 1, further comprising:
grading the road surface to be tested according to the road surface bumping degree to obtain a road surface grade;
obtaining test vehicle information and test vehicle suspension parameters of each test vehicle when the test vehicle runs in a test field of each road surface grade, and inputting the road surface grade, the test vehicle information and the test vehicle suspension parameters into a vehicle quarter model to obtain a corresponding road surface unevenness grade;
dividing groups according to the road surface unevenness grade and the spring load mass grade of the test vehicle, and clustering the unsprung mass acceleration and the spring load mass acceleration in the test vehicle information corresponding to each group to obtain a clustering center point of each group; the clustering center point comprises spring load mass grades and road surface unevenness grades corresponding to all groups;
obtaining corresponding suspension optimization parameters according to the characteristics of the suspensions in each group, and storing the corresponding suspension optimization parameters and the clustering center points of each group to obtain a preset optimization template;
correspondingly, acquiring suspension optimization parameters corresponding to the target group from a preset optimization template, wherein the suspension optimization parameters comprise:
and positioning a clustering central point corresponding to the target group in a preset optimization template, and acquiring corresponding suspension optimization parameters according to the clustering central point.
3. The method of claim 1, further comprising:
acquiring unsprung mass acceleration and sprung mass acceleration of an original vehicle at a current position point, and clustering the unsprung mass acceleration and the sprung mass acceleration according to the sprung mass grading of the original vehicle to obtain a clustering result;
taking the group corresponding to the clustering center point with the minimum distance with the clustering result as the group of the original vehicle at the current position point;
acquiring the original road surface unevenness grade of the current position point according to the spring load quality grade of the original vehicle and the group of the original vehicle at the current position point;
and storing the original road surface unevenness grade and the position coordinate of the current position point to obtain a set list.
4. The method of claim 1, wherein deriving the sprung mass rating of the target vehicle from the sprung mass of the target vehicle and the sprung mass rating criteria comprises:
acquiring a standard load of a target vehicle and a full load of the target vehicle, and averagely dividing the spring load mass between the standard load of the target vehicle and the full load of the target vehicle into set levels to obtain a spring load mass grading standard corresponding to the target vehicle;
and comparing the sprung mass of the target vehicle with the sprung mass grading standard corresponding to the target vehicle to obtain the sprung mass grading of the target vehicle.
5. The method according to claim 2, wherein the dividing groups according to the road surface unevenness grade and the sprung mass grade of the test vehicle, and clustering unsprung mass acceleration and sprung mass acceleration in the test vehicle information corresponding to each group to obtain a clustering center point of each group comprises:
the road surface unevenness grade and the spring load quality grade of the test vehicle are in one-to-one correspondence, and the whole group is divided;
in each group, clustering is carried out on the unsprung mass acceleration and the sprung mass acceleration in the tested vehicle information by utilizing a clustering algorithm respectively to obtain a clustering central point of each group.
6. The method of claim 3, further comprising:
acquiring unsprung mass acceleration and sprung mass acceleration of a target vehicle at a current position point, and clustering the unsprung mass acceleration and the sprung mass acceleration according to the sprung mass grading of the target vehicle to obtain a clustering result;
taking the group corresponding to the clustering center point with the minimum distance with the clustering result as the group of the target vehicle at the current position point;
acquiring the road surface unevenness grade of the current position point according to the spring load quality grade of the target vehicle and the group of the target vehicle at the current position point, and storing the road surface unevenness grade;
if the road surface unevenness grade of the current position point is different from the original road surface unevenness grade of the current position point, marking the road surface unevenness grade of the current position point and the original road surface unevenness grade of the current position point;
and if the road surface unevenness grades of the current position points with the set number are different from the original road surface unevenness grades of the current position points, updating the original road surface unevenness grades of the current position points to the road surface unevenness grades with the highest occurrence frequency in the set number.
7. A suspension optimization system, comprising: the system comprises a cloud end and a vehicle end, wherein the vehicle end comprises at least one vehicle;
the cloud end is used for acquiring the road surface unevenness grade of the current position point in the setting list according to the position coordinate of the target vehicle before the target vehicle runs to the current position point; obtaining the sprung mass grading of the target vehicle according to the sprung mass of the target vehicle and the sprung mass grading standard; combining the spring load quality grades of the target vehicles and the road surface unevenness grades of the current position points to obtain target groups; acquiring suspension optimization parameters corresponding to the target group from a preset optimization template, and controlling the target vehicle to optimize the suspension parameters by using the suspension optimization parameters;
the vehicle end is used for acquiring the position coordinates of the target vehicle and transmitting the position coordinates to the cloud end; acquiring the sprung mass of a target vehicle and transmitting the sprung mass to a cloud end; and receiving suspension optimization parameters transmitted by the cloud and optimizing the suspension parameters.
8. A suspension optimization device, comprising:
the grade acquisition module is used for acquiring the grade of the road surface unevenness of the current position point in the setting list according to the position coordinate of the target vehicle before the target vehicle runs to the current position point;
the mass grading module is used for obtaining the spring load mass grading of the target vehicle according to the spring load mass of the target vehicle and the spring load mass grading standard;
the grouping generation module is used for combining the sprung mass grade of the target vehicle and the road surface unevenness grade of the current position point to obtain a target grouping;
and the parameter acquisition module is used for acquiring suspension optimization parameters corresponding to the target group from a preset optimization template and controlling the target vehicle to optimize the suspension parameters by using the suspension optimization parameters.
9. An electronic device, characterized in that the electronic device comprises:
at least one processor; and
a memory communicatively coupled to the at least one processor; wherein, the first and the second end of the pipe are connected with each other,
the memory stores a computer program executable by the at least one processor to enable the at least one processor to perform the suspension optimization method of any one of claims 1-6.
10. A computer-readable storage medium storing computer instructions for causing a processor to perform the suspension optimization method of any one of claims 1-6 when executed.
CN202210378610.XA 2022-04-12 2022-04-12 Suspension optimization method, system, device, equipment and medium Pending CN114741806A (en)

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Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN115320307A (en) * 2022-10-17 2022-11-11 浙江孔辉汽车科技有限公司 Vibration damping method and device for vehicle, electronic equipment and storage medium

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN115320307A (en) * 2022-10-17 2022-11-11 浙江孔辉汽车科技有限公司 Vibration damping method and device for vehicle, electronic equipment and storage medium
CN115320307B (en) * 2022-10-17 2023-01-20 浙江孔辉汽车科技有限公司 Vibration damping method and device for vehicle, electronic equipment and storage medium

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