A performance optimization method and device for a vehicle semi-active suspension, a medium and a vehicle

Abstract

The present application relates to the technical field of automobile semi-active suspension, and particularly relates to a performance optimization method and device of a vehicle semi-active suspension, a medium and a vehicle. According to state information, it is judged whether the vehicle is in a specific vehicle motion state requiring suspension performance optimization. If the vehicle is in the specific vehicle motion state requiring suspension performance optimization, the working condition of the vehicle is classified to obtain a working condition category of the vehicle, and a semi-active suspension optimization strategy matched with the working condition category is selected. In the present application, whether the vehicle is in the specific vehicle motion state requiring suspension performance optimization is judged through state information of the vehicle. If the vehicle is in the specific vehicle motion state requiring suspension performance optimization, corresponding optimization strategies are selected according to different working condition categories, so that the semi-active suspension performance is adaptively optimized, the problem of driving working conditions that are difficult to cover in suspension calibration and adjustment is solved, and the comfort performance of the vehicle is improved.

Description

A method, device, medium, and vehicle for optimizing the performance of a semi-active vehicle suspension. Technical Field

[0001] This invention relates to the field of automotive semi-active suspension technology, and in particular to a method, device, medium, and vehicle for optimizing the performance of a vehicle semi-active suspension. Background Technology

[0002] As a crucial component of the vehicle chassis system, the suspension system not only connects the vehicle body and wheels but also buffers and dampens impact loads transmitted from the road surface to the body, thereby reducing irregular vibrations of the wheels and body and improving ride smoothness and comfort. To date, suspension systems primarily consist of elastic elements, guiding mechanisms, and shock absorbers. However, traditional suspension systems cannot adjust to the actual road conditions, failing to provide consistent vibration damping performance across various road surfaces. Therefore, semi-active suspensions have been developed based on traditional suspensions. These semi-active suspensions can adjust the stiffness or damping according to road conditions, achieving better ride comfort under different road conditions.

[0003] The existing semi-active suspension works mainly by integrating calibration parameters into the controller after calibration, adjustment, and acceptance. However, due to the limited road surface types at the test track for suspension adjustment, it is impossible to cover all daily driving conditions of vehicles. Moreover, the vehicle users are diverse, with different driving needs. The traditional calibration and adjustment process of semi-active suspension is difficult to cover the usage needs of multiple groups of people, resulting in a low comfort experience for users. Therefore, improving the comfort experience of users during the process of adjusting the stiffness or damping of semi-active suspension has become an urgent problem to be solved. Summary of the Invention

[0004] Therefore, it is necessary to provide a method, device, medium, and vehicle for optimizing the performance of a semi-active suspension to address the aforementioned technical problems, thereby resolving the issue of low user comfort during the adjustment of suspension stiffness or damping in a semi-active suspension.

[0005] A first aspect of this application provides a method for optimizing the performance of a vehicle semi-active suspension, the performance optimization method comprising:

[0006] Obtain vehicle status information, and based on the status information, determine whether the vehicle is in a specific vehicle motion state that requires suspension performance optimization;

[0007] If the vehicle is in a specific vehicle motion state that requires suspension performance optimization, then the environmental information and geographical location information of the vehicle are collected.

[0008] Based on the status information, the environmental information, and the geographical location information, the working conditions of the vehicle are classified to obtain the working condition category of the vehicle.

[0009] Select a semi-active suspension optimization strategy that matches the operating condition category, and use the semi-active suspension optimization strategy to optimize the performance of the semi-active suspension.

[0010] A second aspect of this application provides a performance optimization device for a vehicle semi-active suspension, the performance optimization device comprising:

[0011] The judgment module is used to obtain the vehicle's status information and, based on the status information, determine whether the vehicle is in a specific vehicle motion state that requires suspension performance optimization.

[0012] The data acquisition module is used to acquire environmental and geographical location information of the vehicle if the vehicle is in a specific vehicle motion state that requires suspension performance optimization.

[0013] The classification module is used to classify the working conditions of the vehicle based on the status information, the environmental information, and the geographical location information, so as to obtain the working condition category of the vehicle.

[0014] An optimization module is used to select a semi-active suspension optimization strategy that matches the operating condition category, and to optimize the performance of the semi-active suspension using the semi-active suspension optimization strategy.

[0015] A third aspect of this application provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the performance optimization method for a vehicle semi-active suspension as described in the first aspect.

[0016] A fourth aspect of this application provides a vehicle that includes the performance optimization device as described in the second aspect.

[0017] The advantages of this invention compared to the prior art are:

[0018] This application acquires vehicle status information and, based on this information, determines whether the vehicle is in a specific motion state requiring suspension performance optimization. If so, it collects environmental and geographical location information about the vehicle. Based on this information, the application categorizes the vehicle's operating conditions, determining the appropriate operating condition category. A semi-active suspension optimization strategy matching this category is then selected, and the semi-active suspension performance is optimized using this strategy. In this application, the vehicle's status information is used to determine whether the vehicle is in a specific motion state requiring suspension performance optimization. If so, a corresponding optimization strategy is selected based on different operating condition categories, enabling adaptive optimization of the semi-active suspension performance. This addresses the problem of driving conditions that are difficult to cover through suspension calibration and tuning, thereby improving vehicle comfort. Attached Figure Description

[0019] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the description of the embodiments of the present invention will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0020] Figure 1 is a flowchart illustrating a performance optimization method for a semi-active vehicle suspension provided in Embodiment 1 of the present invention;

[0021] Figure 2 is a flowchart illustrating a performance optimization method for a semi-active vehicle suspension provided in Embodiment 2 of the present invention;

[0022] Figure 3 is a flowchart illustrating a performance optimization method for a semi-active vehicle suspension provided in Embodiment 3 of the present invention;

[0023] Figure 4 is a flowchart illustrating a performance optimization method for a semi-active vehicle suspension provided in Embodiment 4 of the present invention.

[0024] Figure 5 is a flowchart illustrating a performance optimization method for a semi-active vehicle suspension provided in Embodiment 5 of the present invention;

[0025] Figure 6 is a flowchart illustrating a performance optimization method for a semi-active vehicle suspension provided in Embodiment 6 of the present invention;

[0026] Figure 7 is a flowchart illustrating a performance optimization method for a vehicle semi-active suspension provided in Embodiment 7 of the present invention;

[0027] Figure 8 is a structural block diagram of a vehicle semi-active suspension performance optimization device provided in Embodiment 8 of the present invention;

[0028] Figure 9 is a schematic diagram of the structure of a computer device provided in Embodiment 9 of the present invention;

[0029] Figure 10 is a schematic diagram of the structure of a vehicle provided in Embodiment 10 of the present invention. Detailed Implementation

[0030] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0031] It should be understood that, when used in this specification and the appended claims, the term "comprising" indicates the presence of the described features, integrals, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components and / or collections thereof.

[0032] It should also be understood that the term “and / or” as used in this specification and the appended claims refers to any combination of one or more of the associated listed items and all possible combinations, and includes such combinations.

[0033] As used in this specification and the appended claims, the term "if" may be interpreted, depending on the context, as "when," "once," "in response to determination," or "in response to detection." Similarly, the phrase "if determined" or "if [described condition or event] is detected" may be interpreted, depending on the context, as meaning "once determined," "in response to determination," "once [described condition or event] is detected," or "in response to detection of [described condition or event]."

[0034] Furthermore, in the description of this invention and the appended claims, the terms "first," "second," "third," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0035] References to "one embodiment" or "some embodiments" as described in this specification mean that one or more embodiments of the invention include a specific feature, structure, or characteristic described in connection with that embodiment. Therefore, the phrases "in one embodiment," "in some embodiments," "in other embodiments," "in still other embodiments," etc., appearing in different parts of this specification do not necessarily refer to the same embodiment, but rather mean "one or more, but not all, embodiments," unless otherwise specifically emphasized. The terms "comprising," "including," "having," and variations thereof mean "including but not limited to," unless otherwise specifically emphasized.

[0036] It should be understood that the sequence number of each step in the following embodiments does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0037] To illustrate the technical solution of the present invention, specific embodiments are described below.

[0038] Referring to Figure 1, it is a flowchart illustrating a performance optimization method for a semi-active vehicle suspension provided in Embodiment 1 of the present invention. As shown in Figure 1, the performance optimization method for a semi-active vehicle suspension may include the following steps.

[0039] S101: Obtain vehicle status information and determine whether the vehicle is in a specific vehicle motion state that requires suspension performance optimization based on the status information.

[0040] In step S101, the vehicle's status information is obtained. The vehicle's status can be used to collect relevant motion information, fault information, parameter information, etc., which can directly reflect the vehicle's status. Based on the status information, it is determined whether the vehicle is in a specific vehicle motion state that requires suspension performance optimization. The vehicle's status information is information that characterizes the vehicle's driving state. The specific vehicle motion state that requires suspension performance optimization is a state that makes the occupants uncomfortable during vehicle driving.

[0041] In this embodiment, the vehicle's state information is acquired. This state information may include the vehicle's wheel speed information, vehicle speed information, vehicle pitch information, vehicle roll information, vehicle vertical displacement, vehicle vertical velocity, vehicle vertical acceleration, vehicle lateral displacement, vehicle lateral velocity, vehicle longitudinal acceleration, vehicle longitudinal velocity, vehicle longitudinal acceleration, brake pedal depth information, wheel-end braking torque information, master cylinder pressure information, drive torque information, accelerator pedal opening information, steering wheel angle information, and angular velocity information.

[0042] It should be noted that vehicle status information can be obtained directly from the corresponding sensors. For example, vehicle speed information can be obtained directly from the speed sensor, or it can be obtained from the signals of existing sensors through estimation algorithms or information fusion.

[0043] It should be noted that the status information collected by the sensors may also include tire pressure, load, vehicle malfunction status, vehicle maintenance status (such as whether parts have been replaced), and the service life of the vehicle and key components.

[0044] Based on the status information, it is determined whether the vehicle is in a specific motion state that requires suspension performance optimization, that is, whether the vehicle is in a state that would cause discomfort to the occupants. Determining whether the vehicle is in a specific motion state requiring suspension performance optimization can be done by judging whether the vehicle is in a state of large undulation motion, whether it is in a state of swaying motion at low frequencies, or whether it is in a state of large impact, etc.

[0045] It should be noted that when determining whether a vehicle is in a state of large undulation motion, the judgment can be made based on the displacement information and acceleration information of the semi-active suspension, and the vehicle's motion information. For example, when the displacement information of the active suspension is large, it is considered that the vertical fluctuation of the vehicle is greater, and the vehicle body is in a state of large undulation motion; when the acceleration information of the semi-active suspension is large, it is considered that the vertical fluctuation of the vehicle per unit time is greater, and the vehicle body is in a state of large undulation motion. Alternatively, if the displacement information, acceleration information, and motion information of the semi-active suspension meet other conditions, the vehicle is considered to be in a state of large undulation motion, but this embodiment does not impose any limitations.

[0046] It should be noted that when determining whether a vehicle is in a low-frequency swaying motion state, the judgment can be made based on the pitch and roll information of the vehicle body within a preset frequency range. For example, the preset frequency range is a corresponding low-frequency range, such as the 2Hz-3Hz frequency range. If the pitch value in the pitch information is greater than a preset pitch threshold and the roll value in the roll information is greater than a preset roll threshold within the 2Hz-3Hz frequency range, the vehicle is considered to be in a low-frequency swaying motion state. Alternatively, if the pitch and roll information of the vehicle body within the preset frequency range meets other conditions, the vehicle is considered to be in a low-frequency swaying motion state. This embodiment does not impose any limitations on this.

[0047] It should be noted that when determining whether a vehicle is in a state of large impact, the judgment can be made based on the vehicle's acceleration information, the steering wheel's angular velocity information, and the steering wheel's angular acceleration information. For example, when the acceleration values ​​in these three information exceed a preset acceleration threshold, the vehicle is considered to be in a state of large impact. Alternatively, if the vehicle's acceleration information, the steering wheel's angular velocity information, and the steering wheel's angular acceleration information meet other conditions, the vehicle is considered to be in a state of large impact at low frequencies. This embodiment does not impose any limitations on this.

[0048] When the vehicle is in a state of large undulation, a state of swaying at low frequencies, or a state of large impact, it is determined that the vehicle is in a specific vehicle motion state that requires suspension performance optimization.

[0049] In another embodiment, the system can also determine whether the vehicle is in a specific motion state requiring suspension performance optimization based on the collected information of the occupants. For example, facial, motion, and voice information of the occupants can be collected. For instance, if the collected facial information shows a painful expression, the occupants are considered to be in an uncomfortable state, and the vehicle is determined to be in a specific motion state requiring suspension performance optimization. Similarly, if the collected voice information from the occupants includes keywords such as "uncomfortable" or "too stiff," the occupants are considered to be in an uncomfortable state, and the vehicle is determined to be in a specific motion state requiring suspension performance optimization.

[0050] Referring to Figure 2, which is a flowchart illustrating a method for optimizing the performance of a semi-active vehicle suspension according to Embodiment 2 of the present invention, as shown in Figure 2, the step of determining whether the vehicle is in a specific vehicle motion state requiring suspension performance optimization based on state information includes:

[0051] S201: Calculate vehicle comfort based on acceleration in each direction;

[0052] S202: Based on vehicle comfort, determine whether the vehicle is in a specific vehicle motion state that requires suspension performance optimization.

[0053] In this embodiment, the state information includes acceleration in each direction within the vehicle coordinate system. Based on the state information, it is determined whether the vehicle is in a specific vehicle motion state requiring suspension performance optimization. Then, the vehicle's comfort level is calculated based on the acceleration in each direction within the vehicle coordinate system. In the vehicle coordinate system, the X direction is the vehicle's driving direction, the Y direction is perpendicular to the driving direction, and the Z direction is the vertical direction. The root mean square value of the total weighted acceleration is calculated based on the acceleration in each direction. A smaller root mean square value indicates better vehicle ride comfort and smoother ride. Conversely, a larger root mean square value indicates poorer ride comfort and smoother ride.

[0054] Based on vehicle comfort, it is determined whether the vehicle is in a specific vehicle motion state that requires suspension performance optimization. The better the comfort, the less likely the vehicle is to be in a specific vehicle motion state that requires suspension performance optimization; conversely, the worse the comfort, the more likely the vehicle is to be in a specific vehicle motion state that requires suspension performance optimization.

[0055] S102: If the vehicle is in a specific vehicle motion state that requires suspension performance optimization, then collect the environmental information and geographical location information of the vehicle.

[0056] In step S102, after determining that the vehicle is in a specific vehicle motion state requiring suspension performance optimization, the specific vehicle motion state requiring suspension performance optimization is optimized. The optimization is based on the vehicle's state information, the vehicle's environmental information, and its geographical location information. Therefore, it is also necessary to collect the vehicle's environmental information and geographical location information. Among them, the environmental information refers to the vehicle's driving environment, and the geographical location information refers to the vehicle's accurate location.

[0057] In this embodiment, after determining that the vehicle is in a specific motion state requiring suspension performance optimization, environmental and geographical location information of the vehicle is collected. The environmental information characterizes the vehicle's driving environment and can be extracted based on camera images, radar point cloud data, road surface measurement data (such as road roughness level, road elevation information, and international road roughness index), temperature, wind speed, and slope information. For example, camera images can be processed to determine if there are speed bumps ahead of the vehicle. Geographical location information reflects the vehicle's precise location; the higher the accuracy of the location information, the more accurate the adjustments based on geographical information, and the better the effect.

[0058] It should be noted that among the methods for collecting environmental information about the vehicle, the preferred approach is to combine multiple types of vehicle-mounted information. This is because it leverages the strengths and weaknesses of each sensor to cope with complex and harsh real-world physical environments. For example, LiDAR sensors have strong anti-interference capabilities and can present the surrounding environment in 3D, providing more accurate perception of pedestrians and vehicles. However, their accuracy is affected by fog and rain. Cameras are less expensive, have a high object recognition rate, and strong spatial detection capabilities. Road surface instruments can accurately measure the road surface condition, compensating for the poor accuracy of camera measurements of road surface height and effectively estimating the severity of the road surface. Temperature and wind information can be used to estimate the potential impact of the external environment on the vehicle.

[0059] Referring to Figure 3, which is a flowchart illustrating a method for optimizing the performance of a semi-active vehicle suspension according to Embodiment 3 of the present invention, as shown in Figure 3, after determining whether the vehicle is in a specific vehicle motion state requiring suspension performance optimization, the method further includes:

[0060] S301: If the vehicle is in a specific vehicle motion state that requires suspension performance optimization, then collect the environmental information, geographical location information, and the occupant status information of the vehicle.

[0061] S302: Based on status information, environmental information, geographical location information, and personnel status information, perform the step of classifying the working condition of the vehicle.

[0062] In this embodiment, after determining that the vehicle is in a specific vehicle motion state requiring suspension performance optimization, environmental information, geographical location information, and occupant status information can be collected. The environmental information characterizes the vehicle's driving environment and can be extracted from camera images, radar point cloud information, road surface measurement information (such as road roughness level, road elevation information, and international road roughness index), temperature information, wind information, and slope information. For example, camera images can be processed to determine if there are speed bumps ahead of the vehicle. Geographical location information reflects the vehicle's accurate position; higher accuracy allows for more accurate and effective adjustments based on geographic information. Occupant status information may include, but is not limited to, gender, age, weight, and height. This reflects the physiological state of the occupants, and their vehicle usage needs can be assessed based on this physiological state. Therefore, suspension performance can also be optimized based on the occupant status information.

[0063] Based on status information, environmental information, geographical location information, and personnel status information, the steps of classifying the working conditions of the vehicle are performed.

[0064] In another embodiment, if the vehicle is in a specific motion state requiring suspension performance optimization, environmental information, geographical location information, occupant status information, and interaction information between the vehicle and occupants can be collected. The interaction information can reflect whether the semi-active suspension has requested the activation of semi-active suspension damping, and determine whether the current suspension stiffness responds to the request. The vehicle's operating condition can also be classified based on the environmental information, geographical location information, occupant status information, and interaction information between the vehicle and occupants.

[0065] S103: Based on status information, environmental information, and geographical location information, classify the operating conditions of the vehicle to obtain the operating condition category of the vehicle.

[0066] In step S103, the operating conditions of the vehicle are classified according to the status information, environmental information and geographical location information, that is, the precise operating conditions corresponding to the vehicle are classified so as to select the corresponding optimization strategy.

[0067] In this embodiment, the vehicle's operating conditions are classified based on state information, environmental information, and geographical location information. During classification, features can be extracted from the state information, environmental information, and geographical location information to obtain corresponding feature information. This feature information may include extreme values, variance, standard deviation, root mean square (RMS) value, and frequency. Extreme values ​​effectively characterize the magnitude of the signal amplitude; for example, the maximum and minimum values ​​of vehicle speed indicate the minimum and maximum speeds during the vehicle's movement in the time domain. Variance and standard deviation effectively reflect the dispersion of the signal, the RMS value reflects the magnitude of the signal's effective energy, and frequency is strongly correlated with vehicle dynamics, effectively reflecting the input-output transmission relationship.

[0068] Classification can be performed based on the corresponding feature information, and can be done through clustering methods, such as K-means clustering, Gaussian mixture clustering (GMM), hierarchical clustering, DBSCAN density clustering, spectral clustering, etc. This embodiment does not limit the methods.

[0069] In another embodiment, if the vehicle is in a specific motion state requiring suspension performance optimization, environmental information, geographical location information, and occupant status information are collected. Based on these information, the vehicle's operating condition is classified. During classification, features can be extracted from these information to obtain corresponding feature information. This feature information may include extreme values, variance, standard deviation, root mean square (RMS) value, and frequency. Extreme values ​​effectively characterize the magnitude of the signal amplitude; for example, the maximum and minimum vehicle speeds indicate the minimum and maximum speeds during the vehicle's movement in the time domain. Variance and standard deviation effectively reflect the signal's dispersion, the RMS value reflects the effective energy of the signal, and frequency is strongly correlated with vehicle dynamics, effectively reflecting the input-output transmission relationship.

[0070] In another embodiment, if the vehicle is in a specific motion state requiring suspension performance optimization, environmental information, geographical location information, occupant status information, and interaction information between the vehicle and occupants are collected. Based on the status information, environmental information, geographical location information, occupant status information, and interaction information, the vehicle's operating condition is classified. During classification, features can be extracted from the status information, environmental information, geographical location information, occupant status information, and interaction information to obtain corresponding feature information. This feature information may include extreme values, variance, standard deviation, root mean square (RMS) value, and frequency. Extreme values ​​effectively characterize the magnitude of signal amplitude; for example, the maximum and minimum values ​​of vehicle speed indicate the minimum and maximum speeds during vehicle travel in the time domain. Variance and standard deviation effectively reflect the dispersion of the signal, the RMS value reflects the effective energy of the signal, and frequency is strongly correlated with vehicle dynamics and effectively reflects the input-output transmission relationship.

[0071] Classification can be performed based on the corresponding feature information, and can be done through clustering methods, such as K-means clustering, Gaussian mixture clustering (GMM), hierarchical clustering, DBSCAN density clustering, spectral clustering, etc. This embodiment does not limit the methods.

[0072] S104: Select a semi-active suspension optimization strategy that matches the operating condition category, and use the semi-active suspension optimization strategy to optimize the performance of the semi-active suspension.

[0073] In step S104, a semi-active suspension optimization strategy matching the working condition category is selected, wherein the semi-active suspension optimization strategy is an optimization method to optimize the performance of the semi-active suspension.

[0074] In this embodiment, different operating condition categories correspond to different semi-active suspension optimization strategies. Therefore, based on the corresponding operating condition category, a semi-active suspension optimization strategy matching the operating condition category can be selected. Once the corresponding operating condition category is determined, the corresponding semi-active suspension optimization strategy is triggered to optimize the performance of the semi-active suspension. The semi-active suspension optimization strategy may include controlling damping parameters and controlling the damping of the semi-active suspension.

[0075] For example, when the operating condition is high-speed, large-amplitude driving, the corresponding semi-active suspension optimization strategy is to adjust the damping control parameters; when the operating condition is speed bump driving, the corresponding semi-active suspension optimization strategy is to adjust the damping of the semi-active suspension.

[0076] In this embodiment, the performance of the semi-active suspension is optimized by selecting the corresponding semi-active suspension optimization strategy, so as to make different optimizations according to different working conditions and improve the comfort of the occupants.

[0077] Referring to Figure 4, which is a flowchart illustrating a method for optimizing the performance of a semi-active suspension in a vehicle according to Embodiment 4 of the present invention, as shown in Figure 4, the steps of optimizing the performance of the semi-active suspension using a semi-active suspension optimization strategy include:

[0078] S401: Obtain the mapping relationship between vehicle status information and damping control parameters;

[0079] S402: Based on the vehicle's status information and the mapping relationship between the vehicle status and the damping control parameters, determine the target value of the damping control parameters and adjust the damping control parameters to the target value.

[0080] In this embodiment, the mapping relationship between vehicle state information and damping control parameters is obtained. The vehicle state information includes wheel speed, vehicle speed, vehicle pitch, vehicle roll, vertical displacement, vertical velocity, vertical acceleration, lateral displacement, lateral velocity, longitudinal acceleration, longitudinal velocity, longitudinal acceleration, brake pedal depth, wheel-end braking torque, master cylinder pressure, driving torque, accelerator pedal opening, steering wheel angle, and angular velocity. The damping control parameters are algorithm parameters used to control vehicle damping, such as parameters from the ceiling algorithm. Based on the vehicle state information and the mapping relationship between the vehicle state and the damping control parameters, a target value for the damping control parameters is determined, and the damping control parameters are adjusted to this target value. This allows the magnitude of the semi-active suspension damping to be changed by adjusting the damping control parameters, enabling the occupants to be in a comfortable state or the vehicle to be in a specific motion state where suspension performance optimization is not required.

[0081] For example, if the operating condition is a high-speed, large-amplitude vehicle condition, the vehicle speed is relatively high, but does not exceed the range of the corresponding vehicle speed in the mapping relationship between the vehicle's state information and the damping control parameters. In this case, the target value of the damping control parameters can be determined by combining the mapping relationship between the vehicle's state information and the damping control parameters, and the damping control parameters can be adjusted to the target value.

[0082] It should be noted that when the value in the state information of the mapping relationship fails to cover the value in the vehicle's state information, that is, when the value in the vehicle's state information exceeds the value in the state information of the mapping relationship, the damping control parameters are adjusted until the vehicle is in a specific vehicle motion state that does not require suspension performance optimization. Then, the state information of this condition and the corresponding damping control parameters are added to the mapping relationship between the vehicle's state information and the damping control parameters, so that when the corresponding condition occurs in the future, the target value of the damping control parameters can be directly determined according to the corresponding mapping relationship, and the damping control parameters can be adjusted to the target value.

[0083] For example, if the operating condition is a high-speed, large-amplitude vehicle condition, and the vehicle speed is too high, exceeding the range of the corresponding speed in the mapping relationship between the vehicle's state information and the damping control parameters, then the range of the corresponding speed in the mapping relationship between the vehicle's state information and the damping control parameters can be expanded.

[0084] Referring to Figure 5, which is a flowchart illustrating a method for optimizing the performance of a semi-active suspension in a vehicle according to Embodiment 5 of the present invention, as shown in Figure 5, the step of optimizing the performance of the semi-active suspension using a semi-active suspension optimization strategy further includes:

[0085] S501: Obtain the mapping relationship between the vehicle's environmental information and the damping of the semi-active suspension;

[0086] S502: Based on the vehicle's environmental information and the mapping relationship between the vehicle's environmental information and the damping of the semi-active suspension, determine the target damping of the semi-active suspension and control the damping of the semi-active suspension to be adjusted to the target damping.

[0087] In this embodiment, the mapping relationship between the vehicle's environmental information and the damping of the semi-active suspension is obtained. The vehicle's environmental information may include camera image information, radar point cloud information, road surface measurement information (such as road roughness level, road elevation information, international road roughness index information, etc.), temperature information, wind force information, and slope information. The damping of the semi-active suspension can be adjusted arbitrarily. Based on the vehicle's environmental information and the mapping relationship between the vehicle's environmental information and the damping of the semi-active suspension, the target damping of the semi-active suspension is determined, and the damping of the semi-active suspension is adjusted to the target damping. This allows the damping magnitude to be changed, enabling the occupants to be in a comfortable state or the vehicle to be in a specific vehicle motion state that does not require suspension performance optimization.

[0088] For example, when the operating condition is a common one, such as speed bumps or large convex bumps, the target damping of the semi-active suspension is determined by combining the mapping relationship between the vehicle's environmental information and the damping of the semi-active suspension, and the damping of the semi-active suspension is adjusted to the target damping.

[0089] Referring to Figure 6, which is a flowchart illustrating a method for optimizing the performance of a semi-active suspension in a vehicle according to Embodiment 6 of the present invention, as shown in Figure 6, the steps of using a semi-active suspension optimization strategy to optimize the performance of the semi-active suspension also include:

[0090] S601: Obtain the mapping relationship between the vehicle's geographical location information and the damping of the semi-active suspension;

[0091] S602: Obtain the vehicle's geographical location information, and based on the vehicle's geographical location information and the mapping relationship between the vehicle's geographical location information and the damping of the semi-active suspension, determine the target damping of the semi-active suspension, and control the damping of the semi-active suspension to be adjusted to the target damping.

[0092] In this embodiment, after adjusting the damping control parameters based on the vehicle's state information and adjusting the damping of the semi-active suspension based on the vehicle's environmental information, the vehicle is still in a specific motion state requiring suspension performance optimization. Therefore, the performance of the semi-active suspension can be optimized based on geographical location information. Because geographical locations are unique and easily distinguishable, the damping of the semi-active suspension can be determined based on different geographical location information to adapt to that geographical environment. For example, if the geographical location information is a mountainous area, the target damping for the semi-active suspension to adapt to the mountainous area is determined, and the damping of the semi-active suspension is adjusted to the target damping.

[0093] Therefore, the mapping relationship between the vehicle's geographical location information and the damping of the semi-active suspension is obtained. Different geographical location information in the mapping relationship corresponds to a damping value. Based on the vehicle's geographical location information and the mapping relationship between the vehicle's geographical location information and the damping of the semi-active suspension, the target damping of the semi-active suspension is determined, and the damping of the semi-active suspension is adjusted to the target damping.

[0094] Referring to Figure 7, which is a flowchart illustrating a performance optimization method for a semi-active suspension of a vehicle according to Embodiment 7 of the present invention, as shown in Figure 7, the steps of using a semi-active suspension optimization strategy to optimize the performance of the semi-active suspension also include:

[0095] S701: Obtain the mapping relationship between personnel status information and the damping compensation of the semi-active suspension;

[0096] S702: Based on the personnel status information and the mapping relationship between the personnel status information and the damping compensation of the semi-active suspension, determine the target damping compensation of the semi-active suspension, and use the target damping compensation to compensate the damping of the semi-active suspension.

[0097] In this embodiment, a mapping relationship is obtained between occupant status information and the damping compensation of the semi-active suspension. Occupant status information includes, but is not limited to, gender, age, weight, and height. Combining multi-dimensional information allows for a more detailed analysis of the occupant group, accurately identifying user needs and providing compensation accordingly. The damping compensation of the semi-active suspension can be determined based on its control structure. For example, if the control structure is current-controlled, the damping can be compensated by controlling the current; if it is electromagnetic-controlled, the damping can be compensated by controlling the electromagnetic field. Different occupant status information corresponds to a compensation value in the mapping relationship. Based on the occupant status information and the mapping relationship between occupant status information and the damping compensation of the semi-active suspension, a target damping compensation for the semi-active suspension is determined and used to compensate for the damping. By using the collected occupant status information for damping compensation of the semi-active suspension, the damping adjustment of the semi-active suspension can be adjusted to the target damping based on the needs of occupants.

[0098] This application acquires vehicle status information and, based on this information, determines whether the vehicle is in a specific motion state requiring suspension performance optimization. If so, it collects environmental and geographical location information about the vehicle. Based on this information, the application categorizes the vehicle's operating conditions, determining the appropriate operating condition category. A semi-active suspension optimization strategy matching this category is then selected, and the semi-active suspension performance is optimized using this strategy. In this application, the vehicle's status information is used to determine whether the vehicle is in a specific motion state requiring suspension performance optimization. If so, a corresponding optimization strategy is selected based on different operating condition categories, enabling adaptive optimization of the semi-active suspension performance. This addresses the problem of driving conditions that are difficult to cover through suspension calibration and tuning, thereby improving vehicle comfort.

[0099] Referring to Figure 8, which is a structural block diagram of a vehicle semi-active suspension performance optimization device according to Embodiment 8 of the present invention, only the parts related to the embodiment of the present invention are shown for ease of explanation. Referring to Figure 8, the performance optimization device 80 includes: a judgment module 81, a data acquisition module 82, a classification module 83, and an optimization module 84.

[0100] The judgment module 81 is used to obtain the vehicle's status information and, based on the status information, determine whether the vehicle is in a specific vehicle motion state that requires suspension performance optimization.

[0101] The data acquisition module 82 is used to collect environmental and geographical location information of the vehicle if the vehicle is in a specific vehicle motion state that requires suspension performance optimization.

[0102] The classification module 83 is used to classify the working conditions of the vehicle based on status information, environmental information and geographical location information, and obtain the working condition category of the vehicle.

[0103] The optimization module 84 is used to select a semi-active suspension optimization strategy that matches the operating condition category, and to optimize the performance of the semi-active suspension using the semi-active suspension optimization strategy.

[0104] Optionally, the above-mentioned judgment module 81 includes:

[0105] The calculation unit is used to calculate the vehicle's comfort level based on the acceleration in each direction.

[0106] The judgment unit is used to determine whether the vehicle is in a specific vehicle motion state that requires suspension performance optimization, based on the vehicle's comfort level.

[0107] Optionally, the performance optimization device 80 further includes:

[0108] The second data acquisition module is used to collect environmental information, geographical location information, and occupant status information of the vehicle if the vehicle is in a specific vehicle motion state requiring suspension performance optimization. The second classification module is used to classify the vehicle's operating condition based on the status information, environmental information, geographical location information, and occupant status information.

[0109] Optionally, the above optimization module 84 includes:

[0110] The first acquisition unit is used to acquire the mapping relationship between the vehicle's state information and the damping control parameters.

[0111] The first control unit is used to determine the target value of the damping control parameters based on the vehicle's status information and the mapping relationship between the vehicle status and the damping control parameters, and to control the damping control parameters to be adjusted to the target value.

[0112] Optionally, the optimization module 84 mentioned above also includes:

[0113] The second acquisition unit is used to acquire the mapping relationship between the vehicle's environmental information and the damping of the semi-active suspension.

[0114] The second control unit is used to determine the target damping of the semi-active suspension based on the vehicle's environmental information and the mapping relationship between the vehicle's environmental information and the damping of the semi-active suspension, and to control the damping of the semi-active suspension to be adjusted to the target damping.

[0115] Optionally, the optimization module 84 mentioned above also includes:

[0116] The third acquisition unit is used to acquire the mapping relationship between the vehicle's geographical location information and the damping of the semi-active suspension.

[0117] The third control unit is used to acquire the vehicle's geographical location information, and based on the vehicle's geographical location information and the mapping relationship between the vehicle's geographical location information and the damping of the semi-active suspension, determines the target damping of the semi-active suspension and controls the damping of the semi-active suspension to be adjusted to the target damping.

[0118] Optionally, the optimization module 84 mentioned above also includes:

[0119] The fourth acquisition unit is used to acquire the mapping relationship between personnel status information and the damping compensation of the semi-active suspension.

[0120] The fourth control unit is used to determine the target damping compensation of the semi-active suspension based on the personnel status information and the mapping relationship between the personnel status information and the damping compensation of the semi-active suspension, and to use the target damping compensation to compensate the damping of the semi-active suspension.

[0121] It should be noted that the information interaction and execution process between the above modules are based on the same concept as the method embodiments of the present invention. For details on their specific functions and technical effects, please refer to the method embodiments section, which will not be repeated here.

[0122] Figure 9 is a schematic diagram of the structure of a computer device provided in Embodiment 7 of the present invention. As shown in Figure 9, the computer device of this embodiment includes: at least one processor (only one is shown in Figure 9), a memory, and a computer program stored in the memory and executable on at least one processor. When the processor executes the computer program, it implements the performance optimization methods for any of the above-mentioned vehicle semi-active suspensions, as well as the steps in the calculation method embodiment.

[0123] The computer device may include, but is not limited to, a processor and memory. Those skilled in the art will understand that Figure 9 is merely an example of a computer device and does not constitute a limitation thereof. The computer device may include more or fewer components than illustrated, or a combination of certain components, or different components; for example, it may also include a network interface.

[0124] The processor referred to can be a CPU, but it can also be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor can be a microprocessor or any conventional processor.

[0125] Memory includes readable storage media, internal memory, etc., wherein internal memory can be the RAM of a computer device, providing an environment for the operation of the operating system and computer-readable instructions stored in the readable storage media. The readable storage media can be the hard drive of a computer device, or in other embodiments, it can be an external storage device of the computer device, such as a plug-in hard drive, Smart Media Card (SMC), Secure Digital (SD) card, or Flash Card. Furthermore, memory can include both internal storage units and external storage devices of a computer device. Memory is used to store the operating system, applications, bootloader, data, and other programs, such as program code for computer programs. Memory can also be used to temporarily store data that has been output or will be output.

[0126] Those skilled in the art will understand that, for the sake of convenience and brevity, the above-described division of functional units and modules is used as an example. In practical applications, the functions described above can be assigned to different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiments can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit. Furthermore, the specific names of the functional units and modules are only for easy differentiation and are not intended to limit the scope of protection of this invention. The specific working process of the units and modules in the above device can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here. If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the present invention can implement all or part of the processes in the methods of the above embodiments by instructing related hardware through a computer program. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the above method embodiments. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. A computer-readable medium can include at least: any entity or device capable of carrying computer program code, a recording medium, a computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signals, telecommunication signals, and software distribution media. Examples include USB flash drives, portable hard drives, magnetic disks, or optical disks. In some jurisdictions, according to legislation and patent practice, computer-readable media cannot be electrical carrier signals or telecommunication signals.

[0127] The present invention can implement all or part of the processes in the methods of the above embodiments, or it can be accomplished by a computer program product. When the computer program product is run on a computer device, the computer device executes the steps in the above method embodiments.

[0128] In the above embodiments, the descriptions of each embodiment have different focuses. For parts that are not described in detail or recorded in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0129] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this invention.

[0130] In the embodiments provided by this invention, it should be understood that the disclosed apparatus / computer devices and methods can be implemented in other ways. For example, the apparatus / computer device embodiments described above are merely illustrative. For instance, the division of modules or units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the mutual coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.

[0131] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0132] The above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be included within the protection scope of the present invention.

[0133] Referring to Figure 10, Figure 10 shows a vehicle 100 provided in Embodiment 10 of the present invention, the vehicle including the performance optimization device described above.

Claims

1. A method for optimizing the performance of a vehicle semi-active suspension, characterized in that, The performance optimization method includes: acquiring vehicle state information; determining, based on the state information, whether the vehicle is in a specific vehicle motion state requiring suspension performance optimization; if the vehicle is in a specific vehicle motion state requiring suspension performance optimization, collecting environmental and geographical location information of the vehicle; classifying the vehicle's operating conditions based on the state information, environmental information, and geographical location information to obtain the operating condition category of the vehicle; the operating condition category includes high-speed, large-amplitude vehicle operating conditions; selecting a semi-active suspension optimization strategy matching the operating condition category, and applying the semi-active suspension optimization strategy... The optimization strategy optimizes the performance of the semi-active suspension. If the operating condition is a high-speed, large-amplitude vehicle condition, and the vehicle speed does not exceed the range of the corresponding speed in the mapping relationship between the vehicle's state information and the damping control parameters, then the target value of the damping control parameters is determined based on the mapping relationship between the vehicle's state information and the damping control parameters, and the damping control parameters are adjusted to the target value. If the operating condition is a high-speed, large-amplitude vehicle condition, and the vehicle speed exceeds the range of the corresponding speed in the mapping relationship between the vehicle's state information and the damping control parameters, then the range of the corresponding speed in the mapping relationship between the vehicle's state information and the damping control parameters is expanded.

2. The performance optimization method as described in claim 1, characterized in that, The state information includes acceleration in each direction in the vehicle coordinate system; Based on the state information, determine whether the vehicle is in a specific vehicle motion state that requires suspension performance optimization, including: calculating the vehicle's comfort level based on the acceleration in each direction; Based on the vehicle's comfort level, determine whether the vehicle is in a specific vehicle motion state that requires suspension performance optimization.

3. The performance optimization method as described in claim 1, characterized in that, After determining whether the vehicle is in a specific vehicle motion state that requires suspension performance optimization, the method further includes: if the vehicle is in a specific vehicle motion state that requires suspension performance optimization, collecting environmental information, geographical location information, and personnel status information of the vehicle; and performing a step of classifying the operating conditions of the vehicle based on the status information, environmental information, geographical location information, and personnel status information.

4. The performance optimization method as described in claim 1, characterized in that, The optimization of the semi-active suspension performance using the semi-active suspension optimization strategy includes: obtaining the mapping relationship between vehicle state information and damping control parameters; determining the target value of the damping control parameters based on the vehicle state information and the mapping relationship between the vehicle state information and the damping control parameters; and controlling the damping control parameters to be adjusted to the target value.

5. The performance optimization method as described in claim 1, characterized in that, The optimization of the semi-active suspension performance using the semi-active suspension optimization strategy further includes: obtaining the mapping relationship between the vehicle's environmental information and the damping of the semi-active suspension; determining the target damping of the semi-active suspension based on the vehicle's environmental information and the mapping relationship between the vehicle's environmental information and the damping of the semi-active suspension; and controlling the damping of the semi-active suspension to be adjusted to the target damping.

6. The performance optimization method as described in claim 1, characterized in that, The optimization of the semi-active suspension performance using the semi-active suspension optimization strategy further includes: obtaining the mapping relationship between the vehicle's geographical location information and the damping of the semi-active suspension; obtaining the vehicle's geographical location information, determining the target damping of the semi-active suspension based on the vehicle's geographical location information and the mapping relationship between the vehicle's geographical location information and the damping of the semi-active suspension, and controlling the damping of the semi-active suspension to be adjusted to the target damping.

7. The performance optimization method as described in claim 3, characterized in that, The optimization of the semi-active suspension performance using the semi-active suspension optimization strategy includes: obtaining the mapping relationship between personnel status information and the damping compensation of the semi-active suspension; determining the target damping compensation of the semi-active suspension based on the personnel status information and the mapping relationship between the personnel status information and the damping compensation of the semi-active suspension; and using the target damping compensation to compensate the damping of the semi-active suspension.

8. A performance optimization device for a vehicle semi-active suspension, characterized in that, The performance optimization device includes: a judgment module, used to acquire vehicle status information and, based on the status information, determine whether the vehicle is in a specific vehicle motion state requiring suspension performance optimization; a collection module, used to collect environmental and geographical location information of the vehicle if it is in a specific vehicle motion state requiring suspension performance optimization; a classification module, used to classify the operating conditions of the vehicle based on the status information, environmental information, and geographical location information to obtain the operating condition category of the vehicle; the operating condition category includes high-speed, large-amplitude vehicle operating conditions; and an optimization module, used to select a semi-active suspension optimization method that matches the operating condition category. The optimization strategy uses the semi-active suspension optimization strategy to optimize the performance of the semi-active suspension. If the operating condition is a high-speed, large-amplitude condition, and the vehicle speed does not exceed the range of the corresponding speed in the mapping relationship between the vehicle's state information and the damping control parameters, then the target value of the damping control parameters is determined based on the mapping relationship between the vehicle's state information and the damping control parameters, and the damping control parameters are adjusted to the target value. If the operating condition is a high-speed, large-amplitude condition, and the vehicle speed exceeds the range of the corresponding speed in the mapping relationship between the vehicle's state information and the damping control parameters, then the range of the corresponding speed in the mapping relationship between the vehicle's state information and the damping control parameters is expanded.

9. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the performance optimization method as described in any one of claims 1 to 7.

10. A vehicle, characterized in that, The vehicle includes the performance optimization device as described in claim 8.

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