Active adaptive vehicle shock absorption control method and system

Abstract

The embodiments of this specification relate to the field of vehicle shock absorption technology, and more specifically, to an active adaptive vehicle shock absorption control method and system. In this vehicle shock absorption control method, a fuzzy control algorithm is optimized in stages using a genetic algorithm and stage-by-stage driving parameter information that can reflect the stage-by-stage driving stability of the tested vehicle. The global search capability of the genetic algorithm is utilized in conjunction with the stage-by-stage driving parameter information to achieve stage-by-stage optimization of the fuzzy control algorithm. This allows the fuzzy control algorithm ultimately applied to vehicle suspension control to achieve vehicle shock absorption to better integrate the real-time values ​​corresponding to multiple vehicle driving parameters to output the real-time values ​​corresponding to multiple suspension control parameters, thereby achieving better real-time vehicle shock absorption control and improving vehicle driving stability.

Description

Technical Field

[0001] Multiple embodiments of this specification relate to the technical field of vehicle shock absorption, and more particularly to an active adaptive vehicle shock absorption control method and system. Background Art

[0002] A vehicle's suspension is the device that connects the vehicle's body to the wheels. Its primary function is to absorb road shock, reduce vehicle vibration, and improve ride comfort and safety. Through the collaboration of springs and shock absorbers, the suspension effectively filters road bumps for a smoother ride.

[0003] Currently, existing solutions use fuzzy control algorithms to output suspension control parameters. Fuzzy control algorithms are based on rule-based reasoning, eliminating the need for complex mathematical models. Therefore, they can rapidly process input information and generate outputs. This enables them to respond quickly in dynamic environments, making them suitable for scenarios requiring high real-time performance, such as suspension control during vehicle operation. Furthermore, fuzzy control algorithms are highly adaptable to system uncertainties and nonlinearities, enabling rapid adjustment of control parameters in complex driving scenarios, thereby improving system stability.

[0004] However, in practical applications, the performance of fuzzy control algorithms is significantly affected by the settings of membership functions, fuzzy inference rules, and defuzzification functions. If these functions, fuzzy inference rules, and defuzzification functions are not properly configured, the output control parameters may not meet actual requirements. This is especially true for vehicle suspension control scenarios, where the numerous driving and suspension control parameters place even higher demands on the configuration of membership functions, fuzzy inference rules, and defuzzification functions. Summary of the Invention

[0005] The embodiments of this specification provide an active adaptive vehicle shock absorption control method and system, which can optimize the fuzzy control algorithm applied to vehicle suspension control to achieve vehicle shock absorption, thereby achieving better vehicle shock absorption control effect and improving vehicle driving stability.

[0006] The technical solution is as follows:

[0007] The embodiments of this specification provide an active adaptive vehicle shock absorption control method, including:

[0008] Obtain the initial fuzzy control algorithm;

[0009] Constructing a test road section for testing vehicles to conduct driving tests;

[0010] The test vehicle is driven on a test road section. During the driving test, the real-time values ​​of the multiple suspension control parameters of the test vehicle are respectively obtained based on the real-time values ​​of the multiple vehicle driving parameters of the test vehicle and the real-time fuzzy control algorithm, and the suspension of the test vehicle is controlled in real time based on the real-time values ​​of the multiple suspension control parameters.

[0011] During the driving test, stage-by-stage driving parameter information that can reflect the stage-by-stage driving stability of the test vehicle is obtained. In the process of obtaining the stage-by-stage driving parameter information, the fuzzy control algorithm is optimized in stages based on the genetic algorithm and the stage-by-stage driving parameter information until the preset requirements are met, thereby obtaining a target fuzzy control algorithm.

[0012] Based on the target fuzzy control algorithm and the real-time values ​​corresponding to multiple vehicle driving parameters of the moving vehicle during actual driving, the real-time values ​​corresponding to multiple suspension control parameters of the moving vehicle are obtained, and the suspension of the moving vehicle is controlled in real time based on the real-time values ​​corresponding to the multiple suspension control parameters of the moving vehicle.

[0013] As a preferred solution, the test section includes a plurality of sub-sections with different road conditions;

[0014] During the driving test, one stage is defined as the test vehicle driving a complete test section, and each complete driving of the test vehicle on the test section follows the same preset driving plan.

[0015] As a preferred solution, the real-time fuzzy control algorithm includes a plurality of membership function sets corresponding to a plurality of vehicle driving parameters, a plurality of fuzzy inference rule sets corresponding to a plurality of suspension control parameters, and a plurality of defuzzification functions corresponding to the plurality of fuzzy inference rule sets.

[0016] The method of acquiring the real-time values ​​of the multiple suspension control parameters of the test vehicle in real time based on the real-time values ​​of the multiple vehicle driving parameters of the test vehicle and the real-time fuzzy control algorithm includes:

[0017] Obtaining parameter membership information corresponding to each of the plurality of vehicle driving parameters based on real-time values ​​corresponding to each of the plurality of vehicle driving parameters of the test vehicle and a plurality of membership function sets corresponding to the plurality of vehicle driving parameters;

[0018] Based on parameter membership information corresponding to each of the plurality of vehicle driving parameters and a plurality of fuzzy inference rule sets corresponding to the plurality of suspension control parameters, obtaining rule membership information corresponding to each of the plurality of fuzzy inference rule sets;

[0019] Based on the rule membership information corresponding to each of the multiple fuzzy inference rule sets and the multiple defuzzification functions corresponding to the multiple fuzzy inference rule sets, the real-time values ​​corresponding to each of the multiple suspension control parameters of the test vehicle are obtained in real time.

[0020] As a preferred solution, in the process of obtaining stage-by-stage driving parameter information, multiple membership function sets corresponding to multiple vehicle driving parameters in the fuzzy control algorithm are optimized in stages based only on the genetic algorithm and the stage-by-stage driving parameter information obtained in stages.

[0021] As a preferred solution, during the driving test, a real-time total damping force value is calculated based on a ceiling-ground-shelve hybrid control algorithm combined with the real-time sprung mass velocity and real-time unsprung mass velocity of the test vehicle. Based on the calculated real-time total damping force value, real-time damping force output control is performed on the test vehicle's suspension shock absorber.

[0022] The multiple suspension control parameters include a weight control parameter for controlling the weight distribution of skyhook control and groundhook control when the real-time value of the total damping force is calculated based on the skyhook-groundhook hybrid control algorithm.

[0023] As a preferred solution, the multiple suspension control parameters also include a suspension stiffness control parameter and a suspension height control parameter.

[0024] As a preferred solution, the multiple vehicle driving parameters include vehicle speed, vehicle acceleration, sprung mass speed, unsprung mass speed, and road surface undulations.

[0025] As a preferred solution, the phased optimization of the fuzzy control algorithm based on the genetic algorithm and the phased driving parameter information obtained in phases includes:

[0026] The stage fitness value is calculated based on the stage driving parameter information obtained in stages;

[0027] Perform phased optimization of the fuzzy control algorithm based on the phase fitness value;

[0028] The stage driving parameter information includes the sprung mass speed and the unsprung mass speed corresponding to each moment in the stage.

[0029] As a preferred solution, the stage fitness value is calculated based on the stage driving parameter information obtained in stages, including:

[0030] Based on the sprung mass velocity corresponding to each moment in the stage, the total sprung mass velocity change is obtained;

[0031] Based on the unsprung mass speed corresponding to each moment in the stage, the total change of the unsprung mass speed is obtained;

[0032] The stage fitness value is calculated based on the total change in sprung mass velocity and the total change in unsprung mass velocity.

[0033] In a second aspect, the embodiments of this specification provide an active adaptive vehicle shock absorption control system, comprising:

[0034] A first acquisition module acquires real-time values ​​corresponding to a plurality of vehicle driving parameters of the vehicle during actual driving;

[0035] a second acquisition module, which acquires real-time values ​​corresponding to a plurality of suspension control parameters of the moving vehicle based on the target fuzzy control algorithm obtained from the active adaptive vehicle damping control method described in the first aspect of the embodiment and real-time values ​​corresponding to a plurality of vehicle driving parameters of the moving vehicle during actual driving;

[0036] The control module controls the suspension of the traveling vehicle in real time based on real-time values ​​corresponding to a plurality of suspension control parameters of the traveling vehicle.

[0037] In a third aspect, an embodiment of this specification provides an electronic device comprising a processor and a memory; the processor is connected to the memory; the memory is used to store executable program code; the processor runs a program corresponding to the executable program code by reading the executable program code stored in the memory, so as to execute the steps described in the first aspect of the above embodiment.

[0038] In a fourth aspect, an embodiment of this specification provides a computer storage medium, wherein the computer storage medium stores a plurality of instructions, wherein the instructions are suitable for being loaded by a processor and executing the steps described in the first aspect of the above embodiment.

[0039] The beneficial effects of the technical solutions provided by some embodiments of this specification include at least:

[0040] During the driving test, stage-by-stage driving parameter information that can reflect the stage-by-stage driving stability of the test vehicle is obtained. In the process of obtaining the stage-by-stage driving parameter information, the fuzzy control algorithm is optimized in stages based on the genetic algorithm and the stage-by-stage driving parameter information obtained in stages until preset requirements are met to obtain a target fuzzy control algorithm. The fuzzy control algorithm is optimized in stages using the genetic algorithm and the stage-by-stage driving parameter information that can reflect the stage-by-stage driving stability of the test vehicle. The global search capability of the genetic algorithm is utilized in combination with the stage-by-stage driving parameter information to achieve stage-by-stage optimization of the fuzzy control algorithm, so that the fuzzy control algorithm ultimately applied to vehicle suspension control to achieve vehicle shock absorption can better integrate the real-time values ​​corresponding to multiple vehicle driving parameters to output the real-time values ​​corresponding to multiple suspension control parameters, thereby achieving better real-time vehicle shock absorption control effects and improving vehicle driving stability. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0042] Figure 1 This is a flow chart of an active adaptive vehicle shock absorption control method provided in an embodiment of this specification.

[0043] Figure 2 This is a schematic structural diagram of an active adaptive vehicle shock absorption control system provided in an embodiment of this specification.

[0044] Figure 3 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this specification. DETAILED DESCRIPTION

[0045] The technical solutions in the embodiments of this specification will be described clearly and completely below in conjunction with the drawings in the embodiments of this specification.

[0046] Throughout this specification, the claims, and the accompanying drawings, the terms "first," "second," "third," and the like are used to distinguish between different items, not to describe a particular order. Furthermore, the terms "including," "having," and any variations thereof, are intended to cover non-exclusive inclusions. For example, a process, method, system, product, or apparatus comprising a series of steps or elements is not limited to the listed steps or elements but may optionally include steps or elements not listed, or may include other steps or elements inherent to the process, method, product, or apparatus.

[0047] The following description provides examples and does not limit the scope, applicability, or examples set forth in the claims. Changes may be made to the functions and arrangements of the elements described without departing from the scope of this specification. Various examples may appropriately omit, replace, or add various processes or components. For example, the described methods may be performed in an order different from the order described, and various steps may be added, omitted, or combined. Furthermore, features described with respect to some examples may be combined in other examples.

[0048] Reference Figure 1 As shown, Figure 1 A flowchart of an active adaptive vehicle shock absorption control method provided in one embodiment of this specification may at least include:

[0049] Step 102: Obtain an initial fuzzy control algorithm (which can be obtained through preliminary manual settings);

[0050] Step 104: construct a test road section for the test vehicle to perform a driving test;

[0051] Step 106: The test vehicle performs a driving test on the test road section. During the driving test, real-time values ​​corresponding to multiple suspension control parameters of the test vehicle are obtained in real time based on the real-time values ​​corresponding to multiple vehicle driving parameters of the test vehicle and the real-time fuzzy control algorithm, and the test vehicle suspension is controlled in real time based on the real-time values ​​corresponding to the multiple suspension control parameters.

[0052] During the driving test, stage-by-stage driving parameter information that can reflect the stage-by-stage driving stability of the test vehicle is obtained in stages. In the process of obtaining the stage-by-stage driving parameter information, the fuzzy control algorithm is optimized in stages based on the genetic algorithm and the stage-by-stage driving parameter information obtained in stages (i.e., the fuzzy control algorithm is optimized once each time the stage-by-stage driving parameter information is obtained) until a preset requirement is met (the preset requirement may be that the number of stages reaches a preset total number of stages or the stage-by-stage driving parameter information meets the preset requirement, at which point the driving test of the test vehicle on the test road section can be terminated), so as to obtain a target fuzzy control algorithm.

[0053] Step 108: Based on the target fuzzy control algorithm and the real-time values ​​corresponding to the multiple vehicle driving parameters of the moving vehicle during actual driving, the real-time values ​​corresponding to the multiple suspension control parameters of the moving vehicle are obtained, and the suspension of the moving vehicle is controlled in real time based on the real-time values ​​corresponding to the multiple suspension control parameters of the moving vehicle.

[0054] It can be understood that the embodiments of this specification achieve phased optimization of the fuzzy control algorithm through genetic algorithms and phased driving parameter information that can reflect the phased driving stability of the test vehicle. The global search capability of the genetic algorithm is utilized in combination with the phased driving parameter information to achieve phased optimization of the fuzzy control algorithm, so that the fuzzy control algorithm ultimately applied to vehicle suspension control to achieve vehicle shock absorption can better integrate the real-time values ​​corresponding to multiple vehicle driving parameters and output the real-time values ​​corresponding to multiple suspension control parameters, thereby achieving better real-time vehicle shock absorption control effects and improving vehicle driving stability.

[0055] In some embodiments of the present specification, the multiple vehicle driving parameters include vehicle speed, vehicle acceleration, sprung mass speed, unsprung mass speed, and road surface undulation (it should be noted that during the driving test phase, the road surface undulation can be acquired in advance when constructing the test section, while when applying the target fuzzy control algorithm to control the suspension of the moving vehicle during actual driving, the road surface undulation needs to be detected by corresponding detection devices provided on the vehicle, such as cameras, lidars, and other types of sensors).

[0056] It is understandable that vehicle speed, vehicle acceleration, and road conditions will all affect vehicle vibration and, in turn, driving stability. Therefore, these parameters need to be considered when controlling the vehicle suspension.

[0057] Sprung mass velocity refers to the vertical speed of the vehicle body and reflects its vibration. Unsprung mass velocity refers to the vertical speed of components such as the wheels and reflects their vibration. Both parameters reflect vehicle stability and are therefore important to consider when controlling the vehicle's suspension.

[0058] In some embodiments of the present specification, during a driving test, a real-time total damping force value is calculated based on a sky-ground-shelve hybrid control algorithm in combination with the real-time sprung mass velocity and the real-time unsprung mass velocity of the test vehicle, and real-time damping force output control of the test vehicle's suspension shock absorber is performed based on the calculated real-time total damping force value;

[0059] The multiple suspension control parameters include a weight control parameter for controlling the weight distribution of skyhook control and groundhook control when the real-time value of the total damping force is calculated based on the skyhook-groundhook hybrid control algorithm.

[0060] It can be understood that the calculation formula of the sky-ground-shed hybrid control algorithm is:

[0061] F=aF sky + (1-a) F ground ;

[0062] Among them, F represents the real-time value of the total damping force, a represents the weight control parameter, 1≥a≥0, F sky Indicates the real-time damping force of the ceiling control, and F sky The calculation is related to the real-time sprung mass velocity, F ground Indicates the real-time damping force of the ground shed control, and F ground The calculation of is related to the real-time unsprung mass velocity. The calculation process of the real-time damping force of the skyhook control and the real-time damping force of the groundhook control is not described in detail, as it is a prior art.

[0063] It can be understood that the target fuzzy control algorithm optimized by the embodiments of this specification can reasonably control the weight distribution of skyhook control and groundhook control when the real-time value of the total damping force is calculated based on the skyhook-groundhook hybrid control algorithm, and then perform comprehensive shock absorption on the vehicle, so that the vehicle driving stability is further improved.

[0064] In some embodiments of the present specification, the plurality of suspension control parameters further include a suspension stiffness control parameter and a suspension height control parameter.

[0065] In some embodiments of this specification, the test section includes a plurality of sub-sections with different road conditions;

[0066] During the driving test, one stage is defined as the test vehicle driving a complete test section, and each complete driving of the test vehicle on the test section follows the same preset driving plan.

[0067] In order to make the final target fuzzy control algorithm achieve a better shock absorption effect under different road conditions, so as to comprehensively improve the vehicle driving stability. In the embodiment of this specification, a plurality of sub-sections with different road conditions are set in the test section. And in order to ensure that the stage driving parameter information obtained in each stage that can reflect the stage driving stability of the test vehicle is comparable, so as to provide more meaningful guidance for the stage-by-stage optimization of the fuzzy control algorithm based on the genetic algorithm, the embodiment of this specification takes the test vehicle's complete driving of the test section as one stage, and each complete driving of the test vehicle on the test section is in accordance with the same preset driving plan. The preset driving plan can include the driving speed information of the vehicle at different road section positions, the driving acceleration information of the vehicle at different road section positions, etc.

[0068] In some embodiments of the present specification, the real-time fuzzy control algorithm includes a plurality of membership function sets corresponding to a plurality of vehicle driving parameters, a plurality of fuzzy inference rule sets corresponding to a plurality of suspension control parameters, and a plurality of defuzzification functions corresponding to the plurality of fuzzy inference rule sets;

[0069] The method of acquiring the real-time values ​​of the multiple suspension control parameters of the test vehicle in real time based on the real-time values ​​of the multiple vehicle driving parameters of the test vehicle and the real-time fuzzy control algorithm includes:

[0070] Obtaining parameter membership information corresponding to each of the plurality of vehicle driving parameters based on real-time values ​​corresponding to each of the plurality of vehicle driving parameters of the test vehicle and a plurality of membership function sets corresponding to the plurality of vehicle driving parameters;

[0071] Based on parameter membership information corresponding to each of the plurality of vehicle driving parameters and a plurality of fuzzy inference rule sets corresponding to the plurality of suspension control parameters, obtaining rule membership information corresponding to each of the plurality of fuzzy inference rule sets;

[0072] Based on the rule membership information corresponding to each of the multiple fuzzy inference rule sets and the multiple defuzzification functions corresponding to the multiple fuzzy inference rule sets, the real-time values ​​corresponding to each of the multiple suspension control parameters of the test vehicle are obtained in real time.

[0073] It is understandable that in the fuzzy control algorithm, for the output of a suspension control parameter, it is necessary to set a fuzzy inference rule set, a defuzzification function, and multiple parameter membership function sets corresponding to multiple vehicle driving parameters. The following example illustrates this:

[0074] Assume that the fuzzy control algorithm needs to output suspension stiffness control parameters, and the multiple vehicle driving parameters are vehicle speed and vehicle acceleration.

[0075] First, it is necessary to set a parameter membership function set for vehicle speed and a parameter membership function set for vehicle acceleration. Each parameter membership function set includes multiple parameter membership functions, and each parameter membership function corresponds to a fuzzy partition interval. Based on multiple parameter membership functions, the membership value of the vehicle driving parameter in each fuzzy partition interval can be obtained, for example:

[0076] A parameter membership function set is set for vehicle speed. This parameter membership function set includes three parameter membership functions, corresponding to the fuzzy partition interval low speed, the fuzzy partition interval medium speed, and the fuzzy partition interval high speed. Based on these three parameter membership functions, the vehicle speed membership value in the fuzzy partition interval low speed, the fuzzy partition interval medium speed, and the fuzzy partition interval high speed can be obtained. The same principle applies to vehicle acceleration, which will not be elaborated here. The parameter membership information corresponding to the vehicle speed and vehicle acceleration can then be obtained.

[0077] Furthermore, it is necessary to set a fuzzy inference rule set, which includes multiple fuzzy inference rules, such as:

[0078] Rule 1: If the vehicle is traveling at a low speed and the vehicle is accelerating slightly, the suspension stiffness is soft;

[0079] Rule 2: If the vehicle is traveling at a low speed and at a moderate acceleration, the suspension stiffness is moderate;

[0080] Rule 3: If the vehicle is traveling at a low speed and the vehicle is accelerating strongly, the suspension stiffness is hard;

[0081] Rule 4: If the vehicle is traveling at a moderate speed and with a slight acceleration, the suspension stiffness is moderate;

[0082] Rule 5: If the vehicle is traveling at a medium speed and at a medium acceleration, the suspension stiffness is medium;

[0083] Rule 6: If the vehicle is traveling at a moderate speed and the vehicle is accelerating strongly, the suspension stiffness is hard;

[0084] Rule 7: If the vehicle is traveling at a high speed and the vehicle is accelerating slightly, the suspension stiffness is medium;

[0085] Rule 8: If the vehicle is traveling at high speed and the vehicle is traveling at medium acceleration, the suspension stiffness is hard;

[0086] Rule 9: If the vehicle is traveling at high speed and the vehicle is accelerating strongly, the suspension stiffness is very stiff;

[0087] And for soft, medium, hard, and very hard, corresponding numerical ranges need to be set.

[0088] After obtaining the parameter membership information corresponding to each vehicle driving parameter, the rule membership value of each rule can be calculated using, but not limited to, a minimum value operation method, for example:

[0089] Assume that the vehicle speed has a membership value of 0.3 in the fuzzy partition interval of low speed, a membership value of 0.5 in the fuzzy partition interval of medium speed, and a membership value of 0.7 in the fuzzy partition interval of high speed; the vehicle acceleration has a membership value of 0.3 in the fuzzy partition interval of slight acceleration, a membership value of 0.5 in the fuzzy partition interval of medium acceleration, and a membership value of 0.7 in the fuzzy partition interval of strong acceleration;

[0090] The rule membership corresponding to rule 1 is 0.3, the rule membership corresponding to rule 2 is 0.3, the rule membership corresponding to rule 3 is 0.3, the rule membership corresponding to rule 4 is 0.3, the rule membership corresponding to rule 5 is 0.5, the rule membership corresponding to rule 6 is 0.5, and the rule membership corresponding to rule 7 is 0.3; the rule membership corresponding to rule 8 is 0.5, and the rule membership corresponding to rule 9 is 0.7.

[0091] Finally, a defuzzification function needs to be set to output the specific value of the suspension stiffness control parameter. This can be done, but is not limited to, by using the center-weighted average method. Continuing with the above example, we will illustrate:

[0092] Assuming further that the numerical ranges for soft, medium, hard, and very hard are 0N / m-100N / m, 100N / m-200N / m, 200N / m-300N / m, and 400N / m-500N / m, respectively, the corresponding central values ​​are 50N / m, 150N / m, 250N / m, and 350N / m, respectively.

[0093] Based on the membership value of each rule and the defuzzification function of the center weighted average method, the output value corresponding to the suspension stiffness control parameter can be obtained as follows:

[0094] (0.3*50N / m+0.3*150N / m+0.3*250N / m+0.3*150N / m+0.5*150N / m+0.5*250N / m+0.3*15 0N / m+0.5*250N / m+0.7*350N / m) / (0.3+0.3+0.3+0.3+0.5+0.5+0.3+0.5+0.7)≈215N / m.

[0095] The principles for obtaining other suspension control parameters are consistent with those for obtaining the suspension stiffness control parameters described above and will not be elaborated upon here. However, it should be noted that in various embodiments of this specification, the output of real-time values ​​corresponding to multiple suspension control parameters utilizes the parameter membership information corresponding to the same multiple vehicle driving parameters, eliminating the need for repeated membership value calculations. This reduces the amount of computation and improves computational efficiency, thereby enhancing the overall performance and response speed of the suspension control system and providing more real-time and precise suspension control for the vehicle.

[0096] In some embodiments of the present specification, in the process of obtaining stage-by-stage driving parameter information, multiple membership function sets corresponding to multiple vehicle driving parameters in the fuzzy control algorithm are optimized in stages based only on the genetic algorithm and the stage-by-stage driving parameter information obtained in stages.

[0097] It is understandable that since fuzzy inference rules are usually formulated based on expert experience or physical laws and have clear meanings and stability, the selection of defuzzification functions is often based on specific performance requirements, and the above-mentioned center weighted average method or other methods such as the center of gravity method will be directly selected to formulate the defuzzification function. Therefore, optimizing these parts may lead to the loss of the original physical meaning or logical structure, and even introduce unreasonable control behavior. Therefore, in the embodiments of this specification, we focus on the optimization of the membership function, which ensures the rationality of the optimization, reduces the computational complexity of the optimization process, and improves the optimization efficiency.

[0098] In fuzzy control algorithms, membership functions are used to quantify the degree to which an element belongs to a fuzzy set. A membership function maps a precise input value to a value between 0 and 1, representing the degree to which the input value belongs to the fuzzy set. Common membership functions include Gaussian, triangular, and trapezoidal membership functions.

[0099] In some embodiments of the present specification, before performing phased optimization on a plurality of membership function sets corresponding to a plurality of vehicle driving parameters in a fuzzy control algorithm based on a genetic algorithm and phased driving parameter information obtained in phases, the method includes:

[0100] Predetermining membership function type information corresponding to each of a plurality of membership function sets;

[0101] During the process of performing phased optimization on the multiple membership function sets corresponding to the multiple vehicle driving parameters in the fuzzy control algorithm based on the genetic algorithm and the phased driving parameter information obtained in phases, the membership function type information corresponding to each of the multiple membership function sets does not change.

[0102] That is, in some embodiments of this specification, the type of membership function is not optimized by a genetic algorithm.

[0103] It is understood that, in theory, the type of membership function can be optimized using a genetic algorithm, but doing so would complicate the optimization problem. Therefore, in some embodiments of this specification, it is possible, but not limited to, to first select an appropriate membership function type based on expert experience, and then optimize its function parameters using a genetic algorithm based on the determined function type.

[0104] This is understandable, especially in the field of automotive suspension control, where mature engineering practices exist. Engineers can often select the appropriate membership function based on expert experience. For example, triangular or trapezoidal functions are widely used due to their computational simplicity and ease of interpretation, making them more suitable for real-time systems like automotive suspension control.

[0105] In some embodiments of this specification, the phased optimization of the fuzzy control algorithm based on the genetic algorithm and the phased driving parameter information obtained in phases includes:

[0106] The stage fitness value is calculated based on the stage driving parameter information obtained in stages;

[0107] Perform phased optimization of the fuzzy control algorithm based on the phase fitness value;

[0108] The stage driving parameter information includes the sprung mass speed and unsprung mass speed corresponding to each moment in the stage (note: the time interval between each adjacent moment in the stage is the same).

[0109] Specifically:

[0110] The stage fitness value is calculated based on the stage driving parameter information obtained in stages, including:

[0111] Based on the sprung mass velocity corresponding to each moment in the stage, the total sprung mass velocity change is obtained;

[0112] Based on the unsprung mass speed corresponding to each moment in the stage, the total change of the unsprung mass speed is obtained;

[0113] The stage fitness value is calculated based on the total change in sprung mass velocity and the total change in unsprung mass velocity.

[0114] The total sprung mass velocity change is obtained based on the sprung mass velocity corresponding to each moment in the stage, including:

[0115] Based on the sprung mass velocity corresponding to each moment in the stage, obtaining the sprung mass velocity change corresponding to each two adjacent moments in the stage;

[0116] Based on the sprung mass velocity changes corresponding to each two adjacent moments in the stage, the total sprung mass velocity change is obtained.

[0117] The total change in the unsprung mass velocity is obtained based on the unsprung mass velocity corresponding to each moment in the stage, including:

[0118] Based on the unsprung mass speed corresponding to each moment in the stage, obtaining the unsprung mass speed change corresponding to each two adjacent moments in the stage;

[0119] The total unsprung mass velocity change is obtained based on the unsprung mass velocity change corresponding to each two adjacent moments in the stage.

[0120] It can be understood that, for the same stage, the smaller the total amount of sprung mass velocity change within the stage, the less body vibration, better passenger comfort, and more stable vehicle; the smaller the total amount of unsprung mass velocity change within the stage, the less wheel vibration, better vehicle handling and safety, and more stable vehicle. Therefore, when optimizing the fuzzy control algorithm based on the genetic algorithm, the fitness function used considers both the total amount of sprung mass velocity change and the total amount of unsprung mass velocity change, and the calculation formula can be, but is not limited to, the following formula:

[0121] ;

[0122] ;

[0123] ;

[0124] Among them, F n Indicates the stage fitness value corresponding to the nth stage, Indicates the first The moment and The change in sprung mass velocity corresponding to the moment is, Indicates the first The sprung mass velocity corresponding to the moment is Indicates the first The moment and The change in unsprung mass velocity corresponding to the moment is: Indicates the first is the unsprung mass velocity corresponding to the moment, and N is the total number of moments in the nth stage.

[0125] It should be noted that a genetic algorithm is a search heuristic algorithm that simulates natural selection and genetics and is used to solve optimization and search problems. In a genetic algorithm, each potential solution is represented as a "chromosome," and the fitness value calculated based on the fitness function is used to evaluate the fitness of each chromosome, that is, the quality of the solution. The specific steps of the genetic algorithm can be referred to as follows:

[0126] Step 1: Initialize the population:

[0127] Goal: Create a starting point for the algorithm;

[0128] Method: A set of solutions (called individuals or chromosomes) are randomly generated, which together form the first generation population; the size of the population is a key parameter that determines the breadth of the search, and each individual represents a potential solution to the problem.

[0129] Step 2: Evaluate fitness:

[0130] Goal: Evaluate the quality of each individual in the population;

[0131] Method: A fitness function is used to calculate the fitness value of each individual. This function maps the individual (solution) to a numerical value that reflects the degree of "goodness" of the solution relative to the optimization goal. For maximization problems, the higher the fitness value, the better; for minimization problems, the lower the fitness value, the better (Note: the embodiment of this manual is a minimization problem). In this embodiment of the manual, the fitness function is the above-mentioned:

[0132] .

[0133] Step 3. Select:

[0134] Goal: Select individuals based on fitness to produce the next generation, simulating the natural selection process of "survival of the fittest";

[0135] Method: Individuals are selected from the current population by probability. Individuals with high fitness are more likely to be selected, but individuals with low fitness also have a certain probability of being selected (to maintain population diversity). Common selection methods include: roulette selection, tournament selection, sorting selection, etc.

[0136] Step 4: New solution generation:

[0137] Goal: Combine the information of two parent individuals to generate new offspring individuals and explore new solution spaces;

[0138] Method: Individuals (parents) selected in the selection phase are paired with a certain crossover probability. One or more crossover points are randomly selected on the paired individuals to exchange some genes of the two parents to generate one or two new offspring individuals. Common crossover methods include: single-point crossover, multi-point crossover, uniform crossover, etc.

[0139] Step 5: Mutation:

[0140] Goal: Introduce small random changes in the genes of offspring individuals to maintain population diversity, prevent the algorithm from converging to a local optimal solution prematurely, and facilitate exploration of new areas;

[0141] Method: For the offspring individuals produced by crossover, some bits (genes) in the individual code are randomly changed with a very low mutation probability. The mutation operation simulates the gene mutation in biological evolution. Common mutation methods include: bit flip mutation, exchange mutation, etc.

[0142] Step 6: Forming a new generation of population:

[0143] Goal: Replace some or all of the old individuals with new individuals (offspring) generated through selection, crossover, and mutation operations to form the next generation population;

[0144] Method: Usually, the newly generated offspring individuals are added to the next generation population, or they are used to replace the individuals with lower fitness in the parent population. Sometimes some of the best individuals of the previous generation are retained to ensure that the quality of the solution does not degrade.

[0145] Step 7: Termination condition judgment:

[0146] Goal: Decide when the algorithm should stop;

[0147] Method: Check whether the preset termination conditions are met. Common termination conditions include: reaching the maximum number of iterations, the fitness value of individuals in the population reaching or approaching a predetermined threshold, the improvement of the population fitness falling below a certain minimum value, reaching a specified running time, etc.

[0148] If the termination condition is met, the algorithm ends and the currently found optimal solution is output;

[0149] If the termination condition is not met, the process returns to continue the iterative evolution process.

[0150] In some embodiments of the present specification, before calculating the stage fitness value based on the total amount of sprung mass velocity change and the total amount of unsprung mass velocity change, the method further includes:

[0151] Obtain vehicle driving preference information (for example, preference for ride comfort or preference for driving control);

[0152] Based on the vehicle driving preference information, corresponding adjustment coefficients are set for the total amount of sprung mass speed change and the total amount of unsprung mass speed change;

[0153] The stage fitness value is calculated based on the total amount of sprung mass velocity change and the total amount of unsprung mass velocity change, including:

[0154] The stage fitness value is calculated based on the total amount of sprung mass velocity change, the total amount of unsprung mass velocity change, and the adjustment coefficients corresponding to the total amount of sprung mass velocity change and the total amount of unsprung mass velocity change.

[0155] It is understandable that the total amount of sprung mass velocity change affects passenger ride comfort, while the total amount of unsprung mass velocity change affects vehicle handling and safety. Therefore, different adjustment coefficients can be set for the two based on vehicle driving preference information so that the final optimized target fuzzy control algorithm adapts to the corresponding vehicle driving preference.

[0156] On this basis, the target fuzzy control algorithms corresponding to different vehicle driving preferences can be obtained in advance. Subsequent vehicles can select the corresponding target fuzzy control algorithm according to the driving mode selected by the driver during actual driving, so that the suspension control conforms to the driver's driving preferences and adapts to different driving needs.

[0157] In the vehicle shock absorption control method disclosed in the embodiments of this specification:

[0158] The system adaptively adjusts suspension height, stiffness, and shock absorber damping force to suppress vehicle vibration and improve driving stability. Real-time vehicle driving parameters are detected by appropriate sensors. The ECU analyzes, processes, and transmits this data, while actuators control the suspension, creating a closed-loop control system. This allows for precise, dynamic adjustment of suspension performance across all dimensions, tailored to individual user scenarios and enhancing comfort, handling, and stability.

[0159] The foregoing description of this specification describes specific embodiments. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recited in the claims can be performed in an order different from that described in the embodiments and still achieve the desired results. Furthermore, the processes depicted in the accompanying drawings do not necessarily require the specific order shown or the sequential order to achieve the desired results. In certain embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0160] See next Figure 2 , Figure 2 The structure diagram of an active adaptive vehicle shock absorption control system provided by an embodiment of this specification is shown, which may include at least:

[0161] A first acquisition module acquires real-time values ​​corresponding to a plurality of vehicle driving parameters of the vehicle during actual driving;

[0162] a second acquisition module, which acquires real-time values ​​corresponding to a plurality of suspension control parameters of the moving vehicle based on the target fuzzy control algorithm obtained in the active adaptive vehicle damping control method described in the above embodiment and the real-time values ​​corresponding to a plurality of vehicle driving parameters of the moving vehicle during actual driving;

[0163] The control module controls the suspension of the traveling vehicle in real time based on real-time values ​​corresponding to a plurality of suspension control parameters of the traveling vehicle.

[0164] In some embodiments of this specification, the vehicle shock absorption control system further includes a shock absorber control module;

[0165] The shock absorber control module calculates a real-time total damping force value based on a sky-ground-shelve hybrid control algorithm and the real-time sprung mass velocity and the real-time unsprung mass velocity of the moving vehicle, and performs real-time damping force output control on the suspension shock absorber of the moving vehicle based on the calculated real-time total damping force value;

[0166] The second acquisition module acquires multiple suspension control parameters of the moving vehicle, including a weight control parameter for controlling the weight distribution of skyhook control and groundhook control when the real-time value of the total damping force is calculated based on the skyhook-groundhook hybrid control algorithm.

[0167] The various embodiments in this specification are described in a progressive manner. References to the common and similar parts between the various embodiments are sufficient. Each embodiment focuses on the differences between the other embodiments. In particular, the vehicle shock absorption control system embodiment is generally similar to the vehicle shock absorption control method embodiment, so its description is relatively simple. For relevant details, refer to the vehicle shock absorption control method embodiment.

[0168] See also Figure 3 A schematic structural diagram of an electronic device provided in an embodiment of this specification is shown.

[0169] like Figure 3 As shown, the electronic device 300 may include: at least one processor 301 , at least one network interface 304 , a user interface 303 , a memory 305 and at least one communication bus 302 .

[0170] The communication bus 302 may be used to implement the connection and communication between the above components.

[0171] The user interface 303 may include buttons, and the optional user interface may also include a standard wired interface or a wireless interface.

[0172] The network interface 304 may include, but is not limited to, a Bluetooth module, an NFC module, a Wi-Fi module, and the like.

[0173] Among them, the processor 301 may include one or more processing cores. The processor 301 uses various interfaces and lines to connect the various parts of the entire electronic device 300, and executes various functions of the electronic device 300 and processes data by running or executing instructions, programs, code sets or instruction sets stored in the memory 305, and calling data stored in the memory 305. Optionally, the processor 301 can be implemented in at least one hardware form of DSP, FPGA, and PLC. The processor 301 can integrate one or a combination of CPU, GPU, and modem. Among them, the CPU mainly processes the operating system, user interface, and application programs; the GPU is responsible for rendering and drawing the content to be displayed on the display screen; and the modem is used to handle wireless communications. It is understandable that the above-mentioned modem may not be integrated into the processor 301, but may be implemented separately through a chip.

[0174] Memory 305 may include either RAM or ROM. Optionally, memory 305 may include non-transitory computer-readable media. Memory 305 may be used to store instructions, programs, codes, code sets, or instruction sets. Memory 305 may include a program storage area and a data storage area. The program storage area may store instructions for implementing an operating system, instructions for at least one function (such as touch control, sound playback, image playback, etc.), and instructions for implementing the aforementioned method embodiments. The data storage area may store data related to the aforementioned method embodiments. Memory 305 may also optionally be at least one storage device located remotely from the processor 301. Memory 305, as a computer storage medium, may include an operating system, a network communication module, a user interface module, and a vehicle shock absorption control application. Processor 301 may be configured to invoke the vehicle shock absorption control program stored in memory 305 and execute the steps of the vehicle shock absorption control method described in the aforementioned embodiments.

[0175] The embodiments of this specification also provide a computer-readable storage medium storing instructions that, when executed on a computer or processor, cause the computer or processor to perform one or more steps of the aforementioned vehicle shock absorption control method embodiment. If the various component modules of the aforementioned electronic device are implemented as software functional units and sold or used as independent products, they may be stored in the computer-readable storage medium.

[0176] In the above embodiments, all or part of the embodiments can be implemented using software, hardware, firmware, or any combination thereof. When implemented using software, all or part of the embodiments can be implemented in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, all or part of the processes or functions described in the embodiments of this specification are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted via the computer-readable storage medium. The computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., coaxial cable, optical fiber, Digital Subscriber Line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that can be accessed by a computer, or a data storage device such as a server or data center that integrates one or more available media. The available medium may be a magnetic medium (eg, a floppy disk, a hard disk, a magnetic tape), an optical medium (eg, a digital versatile disc (DVD)), or a semiconductor medium (eg, a solid state drive (SSD)).

[0177] Those skilled in the art will appreciate that all or part of the processes in the above-described method embodiments can be implemented by instructing the relevant hardware through a computer program. The program can be stored in a computer-readable storage medium. When executed, the program can include the processes of the above-described method embodiments. The aforementioned storage medium includes various media capable of storing program code, such as ROM, RAM, magnetic disks, or optical disks. The technical features of this embodiment and the implementation scheme can be combined in any manner unless they conflict.

[0178] The embodiments described above are merely preferred embodiments of this specification and are not intended to limit the scope of this specification. Without departing from the design spirit of this specification, various modifications and improvements made to the technical solutions of this specification by ordinary technicians in this field should fall within the scope of protection determined by the claims of this specification.

Claims

1. An active adaptive vehicle shock absorption control method, characterized in that: include: Obtain the initial fuzzy control algorithm; Constructing a test road section for testing vehicles to conduct driving tests; The test vehicle is driven on a test road section. During the driving test, the real-time values ​​of the multiple suspension control parameters of the test vehicle are respectively obtained based on the real-time values ​​of the multiple vehicle driving parameters of the test vehicle and the real-time fuzzy control algorithm, and the suspension of the test vehicle is controlled in real time based on the real-time values ​​of the multiple suspension control parameters. During the driving test, stage-by-stage driving parameter information that can reflect the stage-by-stage driving stability of the test vehicle is obtained. In the process of obtaining the stage-by-stage driving parameter information, the fuzzy control algorithm is optimized in stages based on the genetic algorithm and the stage-by-stage driving parameter information until the preset requirements are met, thereby obtaining a target fuzzy control algorithm. Based on the target fuzzy control algorithm and the real-time values ​​of the plurality of vehicle driving parameters corresponding to the vehicle during actual driving, the real-time values ​​of the plurality of suspension control parameters corresponding to the vehicle are obtained, and the suspension of the vehicle is controlled in real time based on the real-time values ​​of the plurality of suspension control parameters corresponding to the vehicle; During the driving test, the real-time total damping force value is calculated based on the sky-ground-shelve hybrid control algorithm combined with the real-time sprung mass velocity and real-time unsprung mass velocity of the test vehicle. Based on the calculated real-time total damping force value, the real-time damping force output of the test vehicle's suspension shock absorber is controlled. The plurality of suspension control parameters include a weight control parameter for controlling the weight distribution of skyhook control and groundhook control when the real-time value of the total damping force is calculated based on the skyhook-groundhook hybrid control algorithm; The step of optimizing the fuzzy control algorithm based on the genetic algorithm and the stage-by-stage driving parameter information obtained in stages includes: The stage fitness value is calculated based on the stage driving parameter information obtained in stages; Perform phased optimization of the fuzzy control algorithm based on the phase fitness value; The stage driving parameter information includes the sprung mass speed and unsprung mass speed corresponding to each moment in the stage; The stage fitness value is calculated based on the stage driving parameter information obtained in stages, including: Based on the sprung mass velocity corresponding to each moment in the stage, the total sprung mass velocity change is obtained; Based on the unsprung mass speed corresponding to each moment in the stage, the total change of the unsprung mass speed is obtained; Based on the vehicle driving preference information, corresponding adjustment coefficients are set for the total amount of sprung mass speed change and the total amount of unsprung mass speed change; The stage fitness value is calculated based on the total amount of sprung mass velocity change, the total amount of unsprung mass velocity change, and the adjustment coefficients corresponding to the total amount of sprung mass velocity change and the total amount of unsprung mass velocity change.

2. The active adaptive vehicle shock absorption control method according to claim 1, characterized in that: The test section includes a plurality of sub-sections with different road conditions; During the driving test, one stage is defined as the test vehicle driving a complete test section, and each complete driving of the test vehicle on the test section follows the same preset driving plan.

3. The active adaptive vehicle shock absorption control method according to claim 1, characterized in that: The real-time fuzzy control algorithm includes a plurality of membership function sets corresponding to a plurality of vehicle driving parameters, a plurality of fuzzy inference rule sets corresponding to a plurality of suspension control parameters, and a plurality of defuzzification functions corresponding to the plurality of fuzzy inference rule sets. The method of acquiring the real-time values ​​of the multiple suspension control parameters of the test vehicle in real time based on the real-time values ​​of the multiple vehicle driving parameters of the test vehicle and the real-time fuzzy control algorithm includes: Obtaining parameter membership information corresponding to each of the plurality of vehicle driving parameters based on real-time values ​​corresponding to each of the plurality of vehicle driving parameters of the test vehicle and a plurality of membership function sets corresponding to the plurality of vehicle driving parameters; Based on parameter membership information corresponding to each of the plurality of vehicle driving parameters and a plurality of fuzzy inference rule sets corresponding to the plurality of suspension control parameters, obtaining rule membership information corresponding to each of the plurality of fuzzy inference rule sets; Based on the rule membership information corresponding to each of the multiple fuzzy inference rule sets and the multiple defuzzification functions corresponding to the multiple fuzzy inference rule sets, the real-time values ​​corresponding to each of the multiple suspension control parameters of the test vehicle are obtained in real time.

4. The active adaptive vehicle shock absorption control method according to claim 3, characterized in that: In the process of obtaining stage-by-stage driving parameter information, multiple membership function sets corresponding to multiple vehicle driving parameters in the fuzzy control algorithm are optimized in stages based only on the genetic algorithm and the stage-by-stage driving parameter information obtained in stages.

5. The active adaptive vehicle shock absorption control method according to claim 1, characterized in that: The multiple suspension control parameters also include a suspension stiffness control parameter and a suspension height control parameter.

6. The active adaptive vehicle shock absorption control method according to claim 1, characterized in that: The plurality of vehicle driving parameters include vehicle speed, vehicle acceleration, sprung mass speed, unsprung mass speed, and road surface undulations.

7. An active adaptive vehicle shock absorption control system, characterized in that: include: A first acquisition module acquires real-time values ​​corresponding to a plurality of vehicle driving parameters of the vehicle during actual driving; a second acquisition module, configured to acquire real-time values ​​corresponding to a plurality of suspension control parameters of the moving vehicle based on a target fuzzy control algorithm obtained in the active adaptive vehicle damping control method according to any one of claims 1 to 6 and real-time values ​​corresponding to a plurality of vehicle driving parameters of the moving vehicle during actual driving; The control module controls the suspension of the traveling vehicle in real time based on real-time values ​​corresponding to a plurality of suspension control parameters of the traveling vehicle.

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