Active self-adaptive vehicle damping control method and system

Abstract

The embodiment of the invention relates to the technical field of vehicle damping, in particular to an active self-adaptive vehicle damping control method and system. In the vehicle damping control method, stage optimization of a fuzzy control algorithm is realized through a genetic algorithm and stage driving parameter information capable of reflecting the stage driving stability condition of a tested vehicle, and stage optimization of the fuzzy control algorithm is realized by utilizing the global search capability of the genetic algorithm and combining the stage driving parameter information, so that the stability of the vehicle is improved. Therefore, the fuzzy control algorithm which is finally applied to vehicle suspension control to achieve vehicle damping can better integrate the real-time values corresponding to the multiple vehicle driving parameters to output the real-time values corresponding to the multiple suspension control parameters, a better vehicle real-time damping control effect is achieved, and the vehicle driving stability is improved.

Description

Technical Field

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

[0002] A vehicle suspension is a device that connects the vehicle body and the wheels. Its main function is to absorb road impacts, reduce vehicle body vibrations, and improve ride comfort and safety. Through the cooperation of springs and shock absorbers, the suspension can effectively filter out bumps from the road surface and make the vehicle travel more smoothly.

[0003] Currently, existing solutions use a fuzzy control algorithm to output suspension control parameters. The fuzzy control algorithm is based on rule reasoning and does not require a complex mathematical model. Therefore, it can quickly process input information and generate an output. This enables it to respond quickly in a dynamic environment and is suitable for scenarios with high real-time requirements such as suspension control during vehicle driving. In addition, the fuzzy control algorithm has strong adaptability to system uncertainties and nonlinearities and can quickly adjust corresponding control parameters in such complex scenarios during vehicle driving, thereby improving the stability of the system.

[0004] However, in practical applications, the performance of the fuzzy control algorithm is greatly affected by the settings of the membership function, fuzzy inference rules, and defuzzification function. If the membership function, fuzzy inference rules, and defuzzification function are not reasonably set, the output control parameters may not meet the actual requirements. Especially for the vehicle suspension control scenario, in this scenario, due to the large number of vehicle driving parameters and suspension control parameters, the requirements for setting the membership function, fuzzy inference rules, and defuzzification function are higher. Summary of the Invention

[0005] 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, and further achieve a better vehicle shock absorption control effect and improve vehicle driving stability.

[0006] The technical solution is as follows: Embodiments of this specification provide an active adaptive vehicle shock absorption control method, including: Obtain an initial fuzzy control algorithm; Construct a test section for a test vehicle to conduct a driving test; The test vehicle conducts a driving test on the test section. During the driving test, based on the real-time values of multiple vehicle driving parameters of the test vehicle respectively and the real-time fuzzy control algorithm, the real-time values of multiple suspension control parameters of the test vehicle are obtained in real time, and the test vehicle suspension is controlled in real time based on the real-time values of multiple suspension control parameters respectively. During the driving test, stage driving parameter information that can reflect the stage driving stability of the test vehicle is obtained periodically. During the process of periodically obtaining the stage driving parameter information, the fuzzy control algorithm is optimized periodically based on the genetic algorithm and the periodically obtained stage driving parameter information until the preset requirements are met to obtain the target fuzzy control algorithm. Based on the target fuzzy control algorithm and the real-time values of multiple vehicle driving parameters of the driving vehicle during actual driving, the real-time values of multiple suspension control parameters of the driving vehicle are obtained, and the suspension of the driving vehicle is controlled in real time based on the real-time values of multiple suspension control parameters of the driving vehicle.

[0007] As a preferred solution, the test section includes multiple sub-sections with different road conditions. During the driving test, one complete drive of the test vehicle through the test section is regarded as one stage, and each complete drive of the test vehicle on the test section follows the same preset driving plan.

[0008] As a preferred solution, the real-time fuzzy control algorithm includes multiple membership function sets corresponding to multiple vehicle driving parameters respectively, multiple fuzzy inference rule sets corresponding to multiple suspension control parameters respectively, and multiple defuzzification functions corresponding to multiple fuzzy inference rule sets respectively. The real-time obtaining of the real-time values of multiple suspension control parameters of the test vehicle based on the real-time values of multiple vehicle driving parameters of the test vehicle and the real-time fuzzy control algorithm includes: Based on the real-time values of multiple vehicle driving parameters of the test vehicle and multiple membership function sets corresponding to multiple vehicle driving parameters respectively, the parameter membership information corresponding to multiple vehicle driving parameters is obtained. Based on the parameter membership information corresponding to multiple vehicle driving parameters and multiple fuzzy inference rule sets corresponding to multiple suspension control parameters respectively, the rule membership information corresponding to multiple fuzzy inference rule sets is obtained. Based on the rule membership information corresponding to multiple fuzzy inference rule sets and multiple defuzzification functions corresponding to multiple fuzzy inference rule sets respectively, the real-time values of multiple suspension control parameters of the test vehicle are obtained in real time.

[0009] As a preferred solution, during the process of periodically obtaining the stage driving parameter information, only the multiple membership function sets corresponding to multiple vehicle driving parameters in the fuzzy control algorithm are optimized periodically based on the genetic algorithm and the periodically obtained stage driving parameter information.

[0010] As a preferred solution, during the driving test, based on the sky-ground hybrid control algorithm, the real-time value of the total damping force is calculated by combining the real-time sprung mass speed and the real-time unsprung mass speed of the test vehicle, and the real-time damping force output control of the suspension shock absorber of the test vehicle is performed based on the calculated real-time value of the total damping force; Among the multiple suspension control parameters, there is a weight control parameter for controlling the weight distribution of the skyhook control and the groundhook control when calculating the real-time value of the total damping force based on the sky-ground hybrid control algorithm.

[0011] As a preferred solution, among the multiple suspension control parameters, there are also a suspension stiffness control parameter and a suspension height control parameter.

[0012] As a preferred solution, multiple vehicle driving parameters include vehicle driving speed, vehicle driving acceleration, sprung mass speed, unsprung mass speed, and road surface undulation.

[0013] As a preferred solution, the phased optimization of the fuzzy control algorithm based on the genetic algorithm and the phased obtained phased driving parameter information includes: Calculating a phased fitness value based on the phased obtained phased driving parameter information; Performing phased optimization of the fuzzy control algorithm based on the phased fitness value; Among them, the phased driving parameter information includes the sprung mass speed and the unsprung mass speed corresponding to each moment within the phase.

[0014] As a preferred solution, the calculation of the phased fitness value based on the phased obtained phased driving parameter information includes: Obtaining the total change in the sprung mass speed based on the sprung mass speed corresponding to each moment within the phase; Obtaining the total change in the unsprung mass speed based on the unsprung mass speed corresponding to each moment within the phase; Calculating the phased fitness value based on the total change in the sprung mass speed and the total change in the unsprung mass speed.

[0015] In a second aspect, an embodiment of this specification provides an active adaptive vehicle shock absorption control system, including: A first acquisition module that acquires the real-time values corresponding to multiple vehicle driving parameters of a driving vehicle during actual driving; A second acquisition module that, based on the target fuzzy control algorithm obtained from the first aspect of the above-described embodiment of the active adaptive vehicle shock absorption control method and the real-time values corresponding to multiple vehicle driving parameters of a driving vehicle during actual driving, acquires the real-time values corresponding to multiple suspension control parameters of the driving vehicle; A control module performs real-time control on the suspension of a moving vehicle based on the real-time values of multiple suspension control parameters of the moving vehicle respectively.

[0016] In a third aspect, an embodiment of this specification provides an electronic device, including 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.

[0017] In a fourth aspect, an embodiment of this specification provides a computer storage medium, which stores multiple instructions, and the instructions are suitable for being loaded and executed by a processor to execute the steps described in the first aspect of the above embodiment.

[0018] The beneficial effects brought by the technical solutions provided by some embodiments of this specification at least include: During the driving test process, stage driving parameter information reflecting the stage driving stability of the test vehicle is obtained periodically, and during the process of periodically obtaining the stage driving parameter information, the fuzzy control algorithm is optimized periodically based on the genetic algorithm and the stage driving parameter information obtained periodically until the preset requirements are met, so as to obtain the target fuzzy control algorithm. The periodic optimization of the fuzzy control algorithm is realized through the genetic algorithm and the stage driving parameter information reflecting the stage driving stability of the test vehicle. By using the global search ability of the genetic algorithm and combining the stage driving parameter information, the periodic optimization of the fuzzy control algorithm is realized, so that the fuzzy control algorithm finally applied to vehicle suspension control to realize vehicle shock absorption can better synthesize the real-time values of multiple vehicle driving parameters respectively and output the real-time values of multiple suspension control parameters respectively, thereby realizing a better vehicle real-time shock absorption control effect and improving vehicle driving stability. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the following drawings are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0020] Figure 1 It is a schematic flow chart of an active adaptive vehicle shock absorption control method provided by an embodiment of this specification.

[0021] Figure 2 It is a schematic structural diagram of an active adaptive vehicle shock absorption control system provided by an embodiment of this specification.

[0022] Figure 3 It is a schematic structural diagram of an electronic device provided by an embodiment of this specification. Specific implementation manners

[0023] Next, the technical solutions in the embodiments of this specification will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of this specification.

[0024] The terms "first", "second", "third", etc. in the specification, claims and the above-mentioned drawings of this specification are used to distinguish different objects, rather than to describe a specific order. In addition, the terms "include" and "have" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device that includes a series of steps or units is not limited to the listed steps or units, but optionally further includes steps or units not listed, or optionally further includes other steps or units inherent to these processes, methods, products or devices.

[0025] The following description provides examples and does not limit the scope, applicability or examples set forth in the claims. Changes can be made to the functions and arrangements of the described elements without departing from the scope of the content of this specification. Each example can appropriately omit, substitute or add various processes or components. For example, the described method can be executed in a different order than the described order, and various steps can be added, omitted or combined. In addition, the features described in some examples can be combined into other examples.

[0026] Referring to Figure 1 as shown Figure 1 It is a schematic flowchart of an active adaptive vehicle shock absorption control method provided by an embodiment of this specification, and may at least include: Step 102, obtain an initial fuzzy control algorithm (which can be obtained through preliminary manual setting); Step 104, construct a test section for testing the vehicle to perform a driving test; Step 106, the test vehicle performs a driving test on the test section. During the driving test, based on the real-time values of multiple vehicle driving parameters of the test vehicle respectively, the real-time values of multiple suspension control parameters of the test vehicle are obtained in real time by the real-time fuzzy control algorithm, and the test vehicle suspension is controlled in real time based on the real-time values of the multiple suspension control parameters respectively; Among them, during the driving test, stage driving parameter information that can reflect the stage driving stability of the test vehicle is obtained periodically. During the process of periodically obtaining the stage driving parameter information, the fuzzy control algorithm is optimized periodically based on the genetic algorithm and the obtained stage driving parameter information (that is, each time the stage driving parameter information is obtained, the fuzzy control algorithm is optimized once) until the preset requirements are met (the preset requirements can be that the number of stages reaches the preset total number of stages or the stage driving parameter information meets the preset requirements, and at this time, the driving test of the test vehicle on the test section can be ended), so as to obtain the target fuzzy control algorithm; Step 108: Based on the target fuzzy control algorithm and the real-time values of multiple vehicle driving parameters corresponding to the driving vehicle during actual driving, obtain the real-time values of multiple suspension control parameters corresponding to the driving vehicle, and perform real-time control on the suspension of the driving vehicle based on the real-time values of multiple suspension control parameters corresponding to the driving vehicle.

[0027] It can be understood that in the embodiments of this specification, the periodic optimization of the fuzzy control algorithm is realized through the genetic algorithm and the stage driving parameter information that can reflect the stage driving stability of the test vehicle. Utilizing the global search ability of the genetic algorithm and combining the stage driving parameter information, the periodic optimization of the fuzzy control algorithm is realized, so that the fuzzy control algorithm finally applied to vehicle suspension control to achieve vehicle shock absorption can better comprehensively output the real-time values of multiple suspension control parameters corresponding to the real-time values of multiple vehicle driving parameters, thereby achieving a better vehicle real-time shock absorption control effect and improving vehicle driving stability.

[0028] In some embodiments of this specification, the multiple vehicle driving parameters include vehicle driving speed, vehicle driving acceleration, sprung mass speed, unsprung mass speed, and road surface undulation (it should be noted that during the driving test stage, the road surface undulation can be obtained in advance when constructing the test section, and when applying the target fuzzy control algorithm to control the suspension of the driving vehicle during actual driving, the road surface undulation needs to be detected by corresponding detection devices arranged on the vehicle, such as cameras, lidar, and other types of sensors).

[0029] It can be understood that the vehicle driving speed, vehicle driving acceleration, and road surface undulation will all affect the vehicle vibration condition, and thus affect the driving stability. Therefore, these parameters need to be considered during the process of controlling the vehicle suspension.

[0030] Sprung mass speed: It refers to the vertical movement speed of the vehicle body part, which can reflect the vibration of the vehicle body part; Unsprung mass speed: It refers to the vertical movement speed of components such as wheels, which can reflect the vibration of the vehicle wheel part. Therefore, both of them reflect the vehicle driving stability situation. Therefore, these two parameters also need to be considered during the control of the vehicle suspension.

[0031] In some embodiments of this specification, during the driving test, based on the sky-ground hybrid control algorithm, the real-time value of the total damping force is calculated by combining the real-time sprung mass speed and the real-time unsprung mass speed of the test vehicle, and the real-time damping force output control of the shock absorber of the test vehicle suspension is carried out based on the calculated real-time value of the total damping force; Among the multiple suspension control parameters, there is a weight control parameter for controlling the weight distribution of the sky control and the ground control when calculating the real-time value of the total damping force based on the sky-ground hybrid control algorithm.

[0032] It can be understood that the calculation formula of the sky-ground hybrid control algorithm is: F = aF sky + (1 - a)F ground ; Wherein, F represents the real-time value of the total damping force, a represents the weight control parameter, 1 ≥ a ≥ 0, F sky represents the real-time damping force of the sky control, and the calculation of F sky is related to the real-time sprung mass speed, F ground represents the real-time damping force of the ground control, and the calculation of F ground is related to the real-time unsprung mass speed. Among them, the calculation processes of the real-time damping force of the sky control and the real-time damping force of the ground control are not elaborated here, as they are prior arts.

[0033] It can be understood that through the target fuzzy control algorithm optimized by the embodiments of this specification, when calculating the real-time value of the total damping force based on the sky-ground hybrid control algorithm, the weight distribution of the sky control and the ground control can be reasonably controlled, thereby comprehensively damping the vehicle and further improving the vehicle driving stability.

[0034] In some embodiments of this specification, among the multiple suspension control parameters, there are also a suspension stiffness control parameter and a suspension height control parameter.

[0035] In some embodiments of this specification, the test section includes multiple sub-sections with different road conditions; During the driving test, taking one complete driving of the test vehicle through the test section as one stage, and each complete driving of the test vehicle on the test section follows the same preset driving scheme.

[0036] In order to enable the finally obtained target fuzzy control algorithm to comprehensively achieve better shock absorption effects under different road conditions, so as to comprehensively improve the driving stability of the vehicle. In the embodiments of this specification, multiple sub-sections with different road conditions are set in the test section. And in order to ensure the comparability between the stage driving parameter information that can reflect the stage driving stability of the test vehicle obtained in each stage, so as to provide more meaningful guidance for the stage optimization of the fuzzy control algorithm based on the genetic algorithm, in the embodiments of this specification, a complete driving of the test vehicle through the test section is regarded as one stage, and each complete driving of the test vehicle on the test section follows the same preset driving plan. The preset driving plan may 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, and so on.

[0037] In some embodiments of this specification, the real-time fuzzy control algorithm includes multiple membership function sets corresponding to multiple vehicle driving parameters respectively, multiple fuzzy inference rule sets corresponding to multiple suspension control parameters respectively, and multiple defuzzification functions corresponding to multiple fuzzy inference rule sets respectively; Based on the real-time values respectively corresponding to the multiple vehicle driving parameters of the test vehicle, the real-time values respectively corresponding to the multiple suspension control parameters of the test vehicle are obtained in real time by the real-time fuzzy control algorithm, including: Based on the real-time values respectively corresponding to the multiple vehicle driving parameters of the test vehicle and the multiple membership function sets corresponding to the multiple vehicle driving parameters respectively, the parameter membership information respectively corresponding to the multiple vehicle driving parameters is obtained; Based on the parameter membership information respectively corresponding to the multiple vehicle driving parameters and the multiple fuzzy inference rule sets corresponding to the multiple suspension control parameters respectively, the rule membership information respectively corresponding to the multiple fuzzy inference rule sets is obtained; Based on the rule membership information respectively corresponding to the multiple fuzzy inference rule sets and the multiple defuzzification functions corresponding to the multiple fuzzy inference rule sets respectively, the real-time values respectively corresponding to the multiple suspension control parameters of the test vehicle are obtained in real time.

[0038] It can be understood that for the output of a suspension control parameter in the fuzzy control algorithm, a fuzzy inference rule set, a defuzzification function, and multiple parameter membership function sets corresponding to multiple vehicle driving parameters respectively need to be set. The following is an example for illustration: Suppose the fuzzy control algorithm needs to output the suspension stiffness control parameter, and the multiple vehicle driving parameters are the vehicle driving speed and the vehicle driving acceleration.

[0039] First, a set of parameter membership functions needs to be set for the vehicle driving speed, and a set of parameter membership functions also needs to be set for the vehicle driving acceleration. Each set of parameter membership functions includes multiple parameter membership functions, and each parameter membership function corresponds to a fuzzy division interval. Based on the multiple parameter membership functions, the membership degree values of the vehicle driving parameters in each fuzzy division interval can be obtained. For example: A set of parameter membership functions is set for the vehicle driving speed. Three parameter membership functions are set in this set of parameter membership functions, corresponding to the fuzzy division intervals of low speed, medium speed, and high speed respectively. Based on these three parameter membership functions, the membership degree value of the vehicle driving speed in the low-speed fuzzy division interval, the membership degree value in the medium-speed fuzzy division interval, and the membership degree value in the high-speed fuzzy division interval can be obtained. The same applies to the vehicle driving acceleration, which will not be elaborated here. Then, the parameter membership information corresponding to the vehicle driving speed and the vehicle driving acceleration respectively is obtained.

[0040] Furthermore, a set of fuzzy inference rules needs to be set, which includes multiple fuzzy inference rules. For example: Rule 1: If the vehicle driving speed is low and the vehicle driving acceleration is slightly accelerating, then the suspension stiffness is soft; Rule 2: If the vehicle driving speed is low and the vehicle driving acceleration is moderately accelerating, then the suspension stiffness is medium; Rule 3: If the vehicle driving speed is low and the vehicle driving acceleration is strongly accelerating, then the suspension stiffness is hard; Rule 4: If the vehicle driving speed is medium and the vehicle driving acceleration is slightly accelerating, then the suspension stiffness is medium; Rule 5: If the vehicle driving speed is medium and the vehicle driving acceleration is moderately accelerating, then the suspension stiffness is medium; Rule 6: If the vehicle driving speed is medium and the vehicle driving acceleration is strongly accelerating, then the suspension stiffness is hard; Rule 7: If the vehicle driving speed is high and the vehicle driving acceleration is slightly accelerating, then the suspension stiffness is medium; Rule 8: If the vehicle driving speed is high and the vehicle driving acceleration is moderately accelerating, then the suspension stiffness is hard; Rule 9: If the vehicle driving speed is high and the vehicle driving acceleration is strongly accelerating, then the suspension stiffness is very hard; And corresponding numerical intervals need to be set for soft, medium, hard, and very hard.

[0041] After obtaining the parameter membership information corresponding to each vehicle driving parameter, the minimum operation method can be used, but not limited to, to calculate the rule membership degree values of each rule. For example: Suppose the membership values of the vehicle driving speed in the fuzzy partition intervals of low speed, medium speed, and high speed are 0.3, 0.5, and 0.7 respectively; the membership values of the vehicle driving acceleration in the fuzzy partition intervals of slight acceleration, medium acceleration, and strong acceleration are 0.3, 0.5, and 0.7 respectively; Then the rule membership degrees corresponding to Rule 1, Rule 2, Rule 3, Rule 4, Rule 5, Rule 6, Rule 7 are 0.3, 0.3, 0.3, 0.3, 0.5, 0.5, 0.3 respectively; the rule membership degrees corresponding to Rule 8, Rule 9 are 0.5, 0.7.

[0042] Finally, a defuzzification function needs to be set to output the specific value of the suspension stiffness control parameter. It can but is not limited to using the center weighted average method. Continuing with the above example for illustration: Further assume that the numerical intervals corresponding to soft, medium, hard, and very hard are 0 N / m - 100 N / m, 100 N / m - 200 N / m, 200 N / m - 300 N / m, 400 N / m - 500 N / m respectively. Therefore, the corresponding center values are 50 N / m, 150 N / m, 250 N / m, 350 N / m respectively; Then, based on the rule membership degree values 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: (0.3 * 50 N / m + 0.3 * 150 N / m + 0.3 * 250 N / m + 0.3 * 150 N / m + 0.5 * 150 N / m + 0.5 * 250 N / m + 0.3 * 150 N / m + 0.5 * 250 N / m + 0.7 * 350 N / m) / (0.3 + 0.3 + 0.3 + 0.3 + 0.5 + 0.5 + 0.3 + 0.5 + 0.7) ≈ 215 N / m.

[0043] The principle of obtaining other suspension control parameters is the same as the principle of obtaining the suspension stiffness control parameter described above, so it will not be elaborated here. However, it should be noted that in multiple embodiments of this specification, for the output of the real-time values corresponding to multiple suspension control parameters, using the parameter membership degree information corresponding to the same multiple vehicle driving parameters, there is no need to repeat the calculation of membership values, thereby reducing the calculation amount, improving the calculation efficiency, and further enhancing the overall performance and response speed of the suspension control system, providing a more real-time and accurate suspension control effect for the vehicle.

[0044] In some embodiments of the present specification, during the process of periodically obtaining stage driving parameter information, only based on the genetic algorithm and the periodically obtained stage driving parameter information, multiple membership function sets corresponding to multiple vehicle driving parameters in the fuzzy control algorithm are periodically optimized.

[0045] It can be understood that since the fuzzy inference rules are usually formulated based on expert experience or physical laws and have clear meanings and stability, the selection of the defuzzification function also often depends on specific performance requirements, and the above-mentioned center weighted average method or other methods such as the centroid 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 behaviors. Therefore, in the embodiments of the present specification, focusing on the optimization of the membership function ensures the rationality of the optimization, reduces the computational complexity in the optimization process, and improves the optimization efficiency.

[0046] Among them, in the fuzzy control algorithm, the membership function is used to quantify the degree to which an element belongs to a fuzzy set. The membership function can map an accurate input value to a value between 0 and 1, indicating the degree to which the input value belongs to a certain fuzzy set. Common types of membership functions include Gaussian membership functions, triangular membership functions, trapezoidal membership functions, and so on.

[0047] In some embodiments of the present specification, before periodically optimizing multiple membership function sets corresponding to multiple vehicle driving parameters in the fuzzy control algorithm based on the genetic algorithm and the periodically obtained stage driving parameter information, it includes: Pre-determining the membership function type information corresponding to each of the multiple membership function sets; During the process of periodically optimizing multiple membership function sets corresponding to multiple vehicle driving parameters in the fuzzy control algorithm based on the genetic algorithm and the periodically obtained stage driving parameter information, the membership function type information corresponding to each of the multiple membership function sets does not change.

[0048] That is, in some embodiments of the present specification, the type of the membership function is not optimized by the genetic algorithm.

[0049] It can be understood that in theory, the type of the membership function can also be optimized by the genetic algorithm, but doing so will make the optimization problem more complex. Therefore, in some embodiments of the present specification, it is possible but not limited to first select a suitable membership function type according to expert experience, and then optimize its function parameters based on the determined function type by the genetic algorithm.

[0050] It is understandable that, especially in the field of automotive suspension control, there are already mature engineering practices for automotive suspension control. Engineers can usually select suitable types of membership functions based on expert experience. For example, triangular functions or trapezoidal functions are widely adopted because of their simple calculation and easy interpretation, and are more suitable for real-time systems such as automotive suspension control.

[0051] In some embodiments of this specification, the step of stagewise optimizing the fuzzy control algorithm based on the genetic algorithm and the stage driving parameter information obtained periodically includes: Calculating a stage fitness value based on the stage driving parameter information obtained periodically; Stagewise optimizing the fuzzy control algorithm based on the stage fitness value; Wherein, the stage driving parameter information includes the sprung mass velocity and the unsprung mass velocity corresponding to each moment within the stage (note: the time intervals between each two adjacent moments within the stage are the same).

[0052] Specifically: The step of calculating a stage fitness value based on the stage driving parameter information obtained periodically includes: Obtaining the total change amount of the sprung mass velocity based on the sprung mass velocity corresponding to each moment within the stage; Obtaining the total change amount of the unsprung mass velocity based on the unsprung mass velocity corresponding to each moment within the stage; Calculating a stage fitness value based on the total change amount of the sprung mass velocity and the total change amount of the unsprung mass velocity.

[0053] The step of obtaining the total change amount of the sprung mass velocity based on the sprung mass velocity corresponding to each moment within the stage includes: Obtaining the change amount of the sprung mass velocity corresponding to each two adjacent moments within the stage based on the sprung mass velocity corresponding to each moment within the stage; Obtaining the total change amount of the sprung mass velocity based on the change amount of the sprung mass velocity corresponding to each two adjacent moments within the stage.

[0054] The step of obtaining the total change amount of the unsprung mass velocity based on the unsprung mass velocity corresponding to each moment within the stage includes: Obtaining the change amount of the unsprung mass velocity corresponding to each two adjacent moments within the stage based on the unsprung mass velocity corresponding to each moment within the stage; Obtaining the total change amount of the unsprung mass velocity based on the change amount of the unsprung mass velocity corresponding to each two adjacent moments within the stage.

[0055] It can be understood that for the same stage, the smaller the total change in the sprung mass speed within the stage, the smaller the body vibration, the better the passenger riding comfort, and the more stable the vehicle; the smaller the total change in the unsprung mass speed within the stage, the smaller the wheel vibration, the better the vehicle handling and safety, and the more stable the vehicle. Therefore, when optimizing the fuzzy control algorithm stage by stage based on the genetic algorithm, the fitness function adopted simultaneously considers the total change in the sprung mass speed and the total change in the unsprung mass speed, and the calculation formula can be but is not limited to the following formula: ; ; ; where, F n represents the stage fitness value corresponding to the nth stage, represents the change in the sprung mass speed corresponding to the th moment and the th moment within the nth stage, represents the sprung mass speed corresponding to the th moment within the nth stage, represents the change in the unsprung mass speed corresponding to the th moment and the th moment within the nth stage, represents the unsprung mass speed corresponding to the th moment within the nth stage, and N represents the total number of moments within the nth stage.

[0056] It should be noted that the genetic algorithm is a search heuristic algorithm that simulates natural selection and genetics and is used to solve optimization and search problems. In the genetic algorithm, each potential solution is represented as a "chromosome", and the fitness value calculated based on the fitness function can be 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: Step 1. Initialize the population: Objective: Create the starting point of the algorithm; Method: Randomly generate a set of solutions (called individuals or chromosomes), and these solutions 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.

[0057] Step 2. Evaluate the fitness: Objective: Evaluate the quality of each individual in the population; Method: Use a fitness function to calculate the fitness value of each individual. This function maps an individual (solution) to a numerical value that reflects how "good" the solution is 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: in the embodiments of this specification, it is a minimization problem). In the embodiments of this specification, the fitness function is the above-mentioned: .

[0058] Step 3. Selection: Goal: Select individuals according to fitness for generating the next generation, simulating the natural selection process of "survival of the fittest". Method: Select individuals from the current population with a certain probability. Individuals with higher fitness have a greater probability of being selected, but individuals with lower fitness also have a certain probability of being selected (to maintain population diversity). Common selection methods include: roulette wheel selection, tournament selection, ranking selection, etc.

[0059] Step 4. Generation of new solutions: Goal: Combine the information of two parent individuals to generate new offspring individuals and explore new solution spaces. Method: From the individuals (parents) selected in the selection stage, pair them with a certain crossover probability. Randomly select one or more crossover points on the paired individuals and 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.

[0060] Step 5. Mutation: Goal: Introduce small random changes in the genes of offspring individuals to maintain population diversity, prevent the algorithm from prematurely converging to a local optimal solution, and help explore new regions. Method: For the offspring individuals generated by crossover, randomly change some bits (genes) in the individual encoding with a very low mutation probability. The mutation operation simulates gene mutations in biological evolution. Common mutation methods include: bit-flip mutation, swap mutation, etc.

[0061] Step 6. Formation of the new generation population: Goal: Replace some or all of the old individuals with the new individuals (offspring) generated through selection, crossover, and mutation operations to form the next generation population. Method: Usually, the newly generated offspring individuals are added to the next generation population, or they replace the individuals with lower fitness in the parent population. Sometimes, some of the best individuals from the previous generation are also retained to ensure that the quality of the solution does not degrade.

[0062] Step 7. Judgment of termination conditions: Goal: Determine when the algorithm stops. 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 being lower than a certain minimum value, reaching the specified running time, etc.; If the termination condition is met, the algorithm ends and outputs the optimal solution found currently; If the termination condition is not met, return to continue the iterative evolution process.

[0063] In some embodiments of this specification, before calculating the stage fitness value based on the total change in sprung mass velocity and the total change in unsprung mass velocity, it further includes: Obtain vehicle driving preference information (for example: preferring ride comfort or preferring driving handling); Based on the vehicle driving preference information, set respective adjustment coefficients for the total change in sprung mass velocity and the total change in unsprung mass velocity; The calculation of the stage fitness value based on the total change in sprung mass velocity and the total change in unsprung mass velocity includes: Calculate the stage fitness value based on the total change in sprung mass velocity, the total change in unsprung mass velocity, and the respective adjustment coefficients corresponding to the total change in sprung mass velocity and the total change in unsprung mass velocity.

[0064] It can be understood that the total change in sprung mass velocity affects the ride comfort of passengers, and the total change in unsprung mass velocity affects the driving handling and safety of the vehicle. Therefore, different adjustment coefficients can be set for the two based on the vehicle driving preference information, so that the finally optimized target fuzzy control algorithm can adapt to the corresponding vehicle driving preference.

[0065] And on this basis, the target fuzzy control algorithms corresponding to different vehicle driving preferences can be obtained in advance. When the vehicle is actually driving later, the corresponding target fuzzy control algorithm can be selected according to the driving mode selected by the driver, so that the suspension control conforms to the driver's driving preference and adapts to different driving requirements.

[0066] In the vehicle shock absorption control method disclosed in the embodiments of this specification: It can adaptively adjust the suspension height, stiffness, and shock absorber damping force output to suppress vehicle vibration and improve vehicle driving stability. The real-time values of vehicle driving parameters can be detected by corresponding sensors. The functions of data analysis, processing, and sending are executed by the ECU, and the actuator controls the suspension, thereby forming a closed-loop control, realizing the full-dimensional dynamic precise adjustment of the suspension performance, and being able to meet the personalized scenarios of users, improving comfort, handling, and stability.

[0067] The above description is of specific embodiments of this specification. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recited in the claims may be performed in a different order than in the embodiments and still achieve the desired result. Additionally, the processes depicted in the drawings do not necessarily require the specific order or sequential order shown to achieve the desired result. In certain embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0068] Next, please refer to Figure 2 , Figure 2 which shows a schematic structural diagram of an active adaptive vehicle shock absorption control system provided by an embodiment of this specification, and may at least include: A first acquisition module that acquires the real-time values corresponding to multiple vehicle driving parameters of a driving vehicle during actual driving; A second acquisition module that, based on the target fuzzy control algorithm obtained from the above-described active adaptive vehicle shock absorption control method and the real-time values corresponding to multiple vehicle driving parameters of a driving vehicle during actual driving, acquires the real-time values corresponding to multiple suspension control parameters of the driving vehicle; A control module that performs real-time control on the suspension of the driving vehicle based on the real-time values corresponding to multiple suspension control parameters of the driving vehicle.

[0069] In some embodiments of this specification, the vehicle shock absorption control system further includes a shock absorber control module; The shock absorber control module calculates the real-time value of the total damping force based on the sky-ground hybrid control algorithm in combination with the real-time sprung mass speed and the real-time unsprung mass speed of the driving vehicle, and performs real-time damping force output control on the shock absorbers of the suspension of the driving vehicle based on the calculated real-time value of the total damping force; Among the multiple suspension control parameters of the driving vehicle acquired by the second acquisition module, there is a weight control parameter for controlling the weight distribution of the skyhook control and the groundhook control when calculating the real-time value of the total damping force based on the sky-ground hybrid control algorithm.

[0070] Each embodiment in this specification is described in a progressive manner. For the parts that are the same or similar among the embodiments, reference can be made to each other. The key point of each embodiment is to illustrate the differences from other embodiments. In particular, for the embodiments of the vehicle shock absorption control system, since it is basically similar to the embodiments of the vehicle shock absorption control method, the description is relatively simple, and for the relevant parts, reference can be made to the partial description of the embodiments of the vehicle shock absorption control method.

[0071] Please refer to Figure 3 which shows a schematic structural diagram of an electronic device provided by an embodiment of this specification.

[0072] As Figure 3 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.

[0073] Among them, the communication bus 302 can be used to realize the connection and communication of the above-mentioned components.

[0074] Among them, the user interface 303 may include buttons, and the optional user interface may further include a standard wired interface and a wireless interface.

[0075] Among them, the network interface 304 may include, but is not limited to, a Bluetooth module, an NFC module, a Wi-Fi module, etc.

[0076] Among them, the processor 301 may include one or more processing cores. The processor 301 uses various interfaces and lines to connect all parts within the entire electronic device 300, and by running or executing instructions, programs, code sets, or instruction sets stored in the memory 305, as well as calling data stored in the memory 305, it executes various functions of the electronic device 300 and processes data. Optionally, the processor 301 may be implemented in at least one hardware form of DSP, FPGA, or PLC. The processor 301 may integrate one or several combinations of CPU, GPU, and modem, etc. Among them, the CPU mainly processes the operating system, user interface, application programs, etc.; the GPU is responsible for rendering and drawing the content to be displayed on the display screen; the modem is used to process wireless communication. It can be understood that the above-mentioned modem may not be integrated into the processor 301 and may be implemented separately by a single chip.

[0077] Among them, the memory 305 may include RAM and may also include ROM. Optionally, the memory 305 includes a non-transitory computer-readable medium. The memory 305 can be used to store instructions, programs, code, code sets, or instruction sets. The memory 305 may include a program storage area and a data storage area. Among them, the program storage area may store instructions for implementing the operating system, instructions for at least one function (such as touch function, sound playback function, image playback function, etc.), instructions for implementing the above-mentioned method embodiments, etc.; the data storage area may store data involved in the above-mentioned method embodiments. Optionally, the memory 305 may also be at least one storage device located far from the aforementioned processor 301. As a computer storage medium, the memory 305 may include an operating system, a network communication module, a user interface module, and a vehicle shock absorption control application program. The processor 301 may be used to call the vehicle shock absorption control program stored in the memory 305 and execute the steps of the vehicle shock absorption control method mentioned in the foregoing embodiments.

[0078] The embodiments of this specification also provide a computer-readable storage medium. Instructions are stored in this computer-readable storage medium. When they run on a computer or a processor, the computer or the processor is caused to execute one or more steps in the embodiments of the above vehicle shock absorption control method. If the respective component modules of the above electronic device are implemented in the form of software functional units and sold or used as independent products, they can be stored in the computer-readable storage medium.

[0079] In the above embodiments, they can be implemented in whole or in part by software, hardware, firmware, or any combination thereof. When implemented using software, they can be implemented in whole or in part 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, the processes or functions described in the embodiments of this specification are generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions can be stored in a computer-readable storage medium or transmitted through the computer-readable storage medium. The computer instructions can be transmitted from a website, a computer, a server, or a data center to another website, a computer, a server, or a data center in a wired manner (such as coaxial cable, optical fiber, Digital Subscriber Line (DSL)) or a wireless manner (such as infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server, a data center, etc. that integrates one or more available media. The available medium can be a magnetic medium (such as a floppy disk, a hard disk, a magnetic tape), an optical medium (such as a Digital Versatile Disc (DVD)), or a semiconductor medium (such as a Solid State Disk (SSD)), etc.

[0080] Those of ordinary skill in the art can understand that all or part of the processes of implementing the methods in the above embodiments can be completed by instructing relevant hardware through a computer program. The program can be stored in a computer-readable storage medium. When the program is executed, it can include the processes of the embodiments of the above respective methods. The foregoing storage medium includes various media that can store program codes such as ROM, RAM, magnetic disks, or optical discs. Without conflict, the technical features in this embodiment and the implementation solutions can be combined arbitrarily.

[0081] The embodiments described above are merely described as the preferred embodiments of this specification, and do not limit the scope of this specification. Without departing from the design spirit of this specification, various deformations and improvements made by those of ordinary skill in the art to the technical solutions of this specification shall fall within the protection scope determined by the claims of this specification.

Claims

1. An active adaptive vehicle shock absorption control method, characterized in that Including: Obtain an initial fuzzy control algorithm; Construct a test section for the test vehicle to conduct a driving test; The test vehicle conducts a driving test on the test section. During the driving test, based on the real-time values of multiple vehicle driving parameters of the test vehicle respectively and the real-time fuzzy control algorithm, the real-time values of multiple suspension control parameters of the test vehicle are obtained in real time, and the suspension of the test vehicle is controlled in real time based on the real-time values of multiple suspension control parameters; Among them, during the driving test, the stage driving parameter information reflecting the stage driving stability of the test vehicle is obtained periodically. And during the process of periodically obtaining the stage driving parameter information, the fuzzy control algorithm is optimized periodically based on the genetic algorithm and the periodically obtained stage driving parameter information until the preset requirements are met to obtain the target fuzzy control algorithm; Based on the target fuzzy control algorithm and the real-time values of multiple vehicle driving parameters of the driving vehicle during actual driving respectively, the real-time values of multiple suspension control parameters of the driving vehicle are obtained, and the suspension of the driving vehicle is controlled in real time based on the real-time values of multiple suspension control parameters of the driving vehicle.

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

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

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

5. The active adaptive vehicle shock absorption control method according to claim 1, characterized in that During the driving test, based on the sky-ground hybrid control algorithm, the real-time value of the total damping force is calculated by combining the real-time sprung mass speed and the real-time unsprung mass speed of the test vehicle, and the real-time damping force output control of the suspension shock absorber of the test vehicle is carried out based on the calculated real-time value of the total damping force; Among the multiple suspension control parameters, there is a weight control parameter for controlling the weight distribution of the sky control and the ground control when calculating the real-time value of the total damping force based on the sky-ground hybrid control algorithm.

6. An active adaptive vehicle shock absorption control method according to claim 5, characterized in that, Among the multiple suspension control parameters, there are also a suspension stiffness control parameter and a suspension height control parameter.

7. An active adaptive vehicle shock absorption control method according to claim 1, characterized in that The multiple vehicle driving parameters include vehicle driving speed, vehicle driving acceleration, sprung mass speed, unsprung mass speed, and road surface undulation.

8. An active adaptive vehicle shock absorption control method according to claim 1, characterized in that, The step-by-step optimization of the fuzzy control algorithm based on the genetic algorithm and the step-by-step obtained step driving parameter information includes: Calculating a step fitness value based on the step-by-step obtained step driving parameter information; Carrying out step-by-step optimization of the fuzzy control algorithm based on the step fitness value; Among them, the step driving parameter information includes the sprung mass speed and the unsprung mass speed corresponding to each moment within the step.

9. The active adaptive vehicle shock absorption control method according to claim 8, characterized in that, The calculation of the step fitness value based on the step-by-step obtained step driving parameter information includes: Based on the sprung mass speed corresponding to each moment within the step, obtaining the total change amount of the sprung mass speed; Based on the unsprung mass speed corresponding to each moment within the step, obtaining the total change amount of the unsprung mass speed; Calculating the step fitness value based on the total change amount of the sprung mass speed and the total change amount of the unsprung mass speed.

10. An active adaptive vehicle shock absorption control system, characterized in that, Including: A first acquisition module that acquires the real-time values corresponding to multiple vehicle driving parameters of a driving vehicle during actual driving; A second acquisition module that, based on the target fuzzy control algorithm obtained from any one of claims 1 to 9 and the real-time values corresponding to multiple vehicle driving parameters of a driving vehicle during actual driving, acquires the real-time values corresponding to multiple suspension control parameters of the driving vehicle; A control module that performs real-time control on the suspension of the driving vehicle based on the real-time values corresponding to multiple suspension control parameters of the driving vehicle.

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