New energy automobile shock absorber damping force control method based on road condition recognition

Abstract

The invention discloses a new energy automobile shock absorber damping force control method based on road condition recognition. The new energy automobile shock absorber damping force control method based on road condition recognition comprises the steps that wheel acceleration frequency characteristics and automobile body acceleration statistical characteristics are obtained; performing fuzzy cooperative reasoning on the wheel acceleration frequency characteristics, the vehicle body acceleration statistical characteristics and the current vehicle speed to obtain a fuzzy reasoning result, wherein the fuzzy reasoning result comprises a controllability tradeoff factor and a comfort tradeoff factor; modulating an initial target damping force based on the controllability trade-off factor and the comfort trade-off factor to obtain a target damping force; and generating a damping control instruction based on the target damping force. Therefore, the road condition is recognized in real time by selecting proper parameters, the controllability parameter and the comfort parameter are obtained through fuzzy reasoning, and then the damping force of the shock absorber capable of dynamically balancing the controllability and the comfort is obtained.

Description

Technical Field

[0001] The present application relates to the field of shock absorber damping control, and specifically to a method for controlling the damping force of a shock absorber of a new energy vehicle based on road condition recognition. Background Art

[0002] As the future direction of the automotive industry, new energy vehicles are attracting significant attention from both consumers and manufacturers regarding their ride comfort and handling stability. As a key component of a vehicle, the suspension system, with its core component, the shock absorber, is crucial for mitigating impacts caused by road irregularities, dampening vehicle body vibration, and maintaining good wheel-road contact. These factors directly impact a vehicle's ride comfort, handling stability, and tire grip.

[0003] With the rapid development of electronic technology and control theory, semi-active and active suspension systems have become a hot topic of research due to their ability to adjust shock absorber damping force or stiffness in real time based on driving conditions, thereby optimizing vehicle performance under a wider range of conditions. However, existing shock absorber damping force control methods for new energy vehicles still face many challenges in practical application, especially in terms of the accuracy of complex and changing road conditions, the intelligence of control strategies, and achieving a dynamic and precise balance between handling and comfort. These methods still have significant shortcomings and areas that need improvement.

[0004] Traditional shock absorber control strategies, such as passive adjustment based on fixed parameters or simple on-off control, are difficult to adapt to the dynamic needs of new energy vehicles under different loads, different speeds and diverse road conditions. When adjusting the inherently contradictory performance indicators of handling stability and ride comfort, they often sacrifice comfort to ensure handling under certain working conditions, or reduce handling to ensure comfort, making it impossible to achieve a globally optimal driving experience.

[0005] Therefore, how to design the shock absorber damping force control strategy to adapt to the dynamic and changeable road conditions and achieve a dynamic and delicate balance between handling and comfort is an issue that needs to be solved urgently. Summary of the Invention

[0006] One advantage of the present application is that it provides a new energy vehicle shock absorber damping force control method based on road condition identification, wherein the new energy vehicle shock absorber damping force control method based on road condition identification can identify road conditions in real time and dynamically balance handling and comfort by controlling the damping force of the shock absorber.

[0007] According to one aspect of the present application, a method for controlling the damping force of a shock absorber of a new energy vehicle based on road condition recognition is provided, which includes: Acquiring basic road condition recognition data and current vehicle speed, wherein the basic road condition recognition data includes wheel acceleration data and body acceleration data; Extracting key frequency features from the wheel acceleration data and performing statistical analysis on the vehicle body acceleration data to obtain wheel acceleration frequency features and vehicle body acceleration statistical features; performing fuzzy collaborative reasoning on the wheel acceleration frequency characteristics, the vehicle body acceleration statistical characteristics, and the current vehicle speed to obtain a fuzzy reasoning result, the fuzzy reasoning result including a maneuverability trade-off factor and a comfort trade-off factor; calculating an initial target damping force based on the wheel acceleration data and the vehicle body acceleration data, and modulating the initial target damping force based on the handling trade-off factor and the comfort trade-off factor to obtain a target damping force; A damping control instruction is generated based on the target damping force, wherein the damping control instruction is used to adjust the damping force of the shock absorber of the new energy vehicle.

[0008] In one embodiment of the new energy vehicle shock absorber damping force control method based on road condition identification described in the present application, fuzzy collaborative reasoning is performed on the wheel acceleration frequency characteristics, the body acceleration statistical characteristics and the current vehicle speed to obtain a fuzzy reasoning result, including: fuzzy processing is performed on the wheel acceleration frequency characteristics, the body acceleration statistical characteristics and the current vehicle speed to obtain wheel acceleration frequency language variables, body acceleration statistical value language variables and vehicle speed language variables; the wheel acceleration frequency language variables, the body acceleration statistical value language variables and the vehicle speed language variables are input into a preset rule base for rule reasoning to obtain the fuzzy reasoning result.

[0009] In one embodiment of the new energy vehicle shock absorber damping force control method based on road condition identification described in the present application, the wheel acceleration frequency linguistic variable, the body acceleration statistical value linguistic variable and the vehicle speed linguistic variable are input into a preset rule library for rule reasoning to obtain the fuzzy reasoning result, including: determining the membership of the wheel acceleration frequency linguistic variable, the body acceleration statistical value linguistic variable and the vehicle speed linguistic variable based on a preset membership function; calculating the activation strength of each preset rule through preset fuzzy logic and combining the membership of the wheel acceleration frequency linguistic variable, the body acceleration statistical value linguistic variable and the vehicle speed linguistic variable, and determining the activated target preset rule based on the activation strength; extracting the controllability requirement level conclusion and the comfort requirement level conclusion from the target preset rule; and converting the controllability requirement level conclusion and the comfort requirement level conclusion into the controllability trade-off factor and the comfort trade-off factor.

[0010] In one embodiment of the new energy vehicle shock absorber damping force control method based on road condition identification described in the present application, the controllability requirement level conclusion and the comfort requirement level conclusion are converted into the controllability trade-off factor and the comfort trade-off factor, including: performing preliminary normalization processing on the controllability requirement level conclusion and the comfort requirement level conclusion to obtain a preliminary controllability trade-off factor and a preliminary comfort trade-off factor; performing time consistency integration constraints on the preliminary controllability trade-off factor and the preliminary comfort trade-off factor to obtain a controllability trade-off factor and a comfort trade-off factor.

[0011] In one embodiment of the new energy vehicle shock absorber damping force control method based on road condition identification described in the present application, a preliminary maneuverability trade-off factor and a preliminary comfort trade-off factor are subjected to time-series consistency integration constraints to obtain a maneuverability trade-off factor and a comfort trade-off factor, including: integrating the wheel acceleration data and the body acceleration data respectively to obtain a wheel integral vector and a body integral vector; normalizing the Frobenius norm of the wheel integral vector correlation matrix and the Frobenius norm of the body integral vector correlation matrix to obtain a first dynamic constraint integration factor and a second dynamic constraint integration factor; correcting the preliminary maneuverability trade-off factor and the preliminary comfort trade-off factor by the wheel-body relative speed, the first dynamic constraint integration factor and the second dynamic constraint integration factor to obtain a maneuverability trade-off factor and a comfort trade-off factor; wherein the wheel integral vector correlation matrix is the Kronecker product of the transpose of the wheel integral vector and the wheel integral vector; and the body integral vector correlation matrix is the Kronecker product of the transpose of the body integral vector and the body integral vector.

[0012] In one embodiment of the new energy vehicle shock absorber damping force control method based on road condition recognition described in the present application, the wheel acceleration data and the body acceleration data are respectively integrated to obtain a wheel integral vector and a body integral vector, including: integrating the wheel acceleration data and the body acceleration data in the time dimension to obtain a preliminary wheel integral vector and a preliminary body integral vector; suppressing the long-term discretized time noise integral of the preliminary wheel integral vector and the preliminary body integral vector in the time dimension through an exponential decay kernel function to obtain a wheel integral vector and a body integral vector.

[0013] In one embodiment of the new energy vehicle shock absorber damping force control method based on road condition recognition described in the present application, the initial target damping force is calculated based on the wheel acceleration data and the body acceleration data, including: calculating the first initial target damping force that focuses on comfort based on the wheel speed and the first expected damping coefficient; calculating the second initial target damping force that focuses on handling based on the body speed and the second expected damping coefficient and the wheel-body relative speed and the third expected damping coefficient.

[0014] In one embodiment of the new energy vehicle shock absorber damping force control method based on road condition recognition described in the present application, the wheel speed, vehicle body speed and wheel-body relative speed are obtained, including: integrating the wheel acceleration data in the time dimension to obtain the wheel speed; integrating the vehicle body acceleration data in the time dimension to obtain the vehicle body speed; and calculating the difference between the wheel speed and the vehicle body speed as the wheel-body relative speed.

[0015] In one embodiment of the new energy vehicle shock absorber damping force control method based on road condition recognition described in the present application, the initial target damping force is modulated based on the maneuverability trade-off factor and the comfort trade-off factor to obtain the target damping force, including: using the maneuverability trade-off factor and the comfort trade-off factor as weights, calculating the weighted sum of the first initial target damping force and the second initial target damping force to obtain the target damping force.

[0016] Further objectives and advantages of the present application will be fully reflected through understanding of the following description and drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] The above and other purposes, features, and advantages of the present application will become more apparent through a more detailed description of the embodiments of the present application in conjunction with the accompanying drawings. The accompanying drawings are intended to provide a further understanding of the embodiments of the present application and constitute a part of the specification. Together with the embodiments of the present application, they are used to explain the present application and do not constitute a limitation of the present application. In the drawings, the same reference numerals generally represent the same components or steps.

[0018] Figure 1 The figure illustrates a flow chart of a method for controlling the damping force of a shock absorber of a new energy vehicle based on road condition recognition according to an embodiment of the present application.

[0019] Figure 2 The figure illustrates a flow chart of fuzzy reasoning of a new energy vehicle shock absorber damping force control method based on road condition recognition according to an embodiment of the present application.

[0020] Figure 3 The figure illustrates a rule reasoning flowchart of a new energy vehicle shock absorber damping force control method based on road condition recognition according to an embodiment of the present application.

[0021] Figure 4 The figure illustrates a flowchart for calculating a handling trade-off factor and a comfort trade-off factor in one implementation of a new energy vehicle shock absorber damping force control method based on road condition recognition according to an embodiment of the present application.

[0022] Figure 5The figure illustrates a detailed flow chart for calculating the handling trade-off factor and the comfort trade-off factor in one implementation of a new energy vehicle shock absorber damping force control method based on road condition recognition according to an embodiment of the present application. DETAILED DESCRIPTION

[0023] Below, the exemplary embodiments according to the present application will be described in detail with reference to the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments of the present application, and it should be understood that the present application is not limited to the exemplary embodiments described herein.

[0024] It is understood that the term "a" should be understood as "at least one" or "one or more", that is, in one embodiment, the number of an element may be one, while in another embodiment, the number of the element may be multiple, and the term "a" should not be understood as limiting the quantity. "Multiple" means greater than or equal to two.

[0025] Although ordinal numbers such as "first," "second," and the like will be used to describe various components, these are not intended to limit those components. The terms are used solely to distinguish one component from another. For example, a first component could be referred to as a second component, and similarly, a second component could be referred to as a first component without departing from the teachings of the present disclosure. As used herein, the term "and / or" includes any and all combinations of one or more of the associated listed items.

[0026] The terms used herein are for the purpose of describing various embodiments only and are not intended to be limiting. As used herein, the singular is intended to include the plural, unless the context clearly indicates otherwise. It will also be understood that the terms "including" and / or "having" when used in this specification specify the presence of a stated feature, number, operation, component, element, or combination thereof, and do not preclude the presence or addition of one or more other features, numbers, operations, components, elements, or combinations thereof.

[0027] As mentioned above, traditional shock absorber control strategies, such as passive adjustment based on fixed parameters or simple switch-type control, are difficult to adapt to the dynamic needs of new energy vehicles under different loads, different vehicle speeds and diverse road conditions. When adjusting the inherently contradictory performance indicators of handling stability and ride comfort, they often sacrifice comfort to ensure handling under certain working conditions, or reduce handling to ensure comfort, making it impossible to achieve a globally optimal driving experience.

[0028] Therefore, how to design the shock absorber damping force control strategy to adapt to the dynamic and changeable road conditions and achieve a dynamic and delicate balance between handling and comfort is an issue that needs to be solved urgently.

[0029] This application mainly solves the problem of shock absorber damping force control strategy from two aspects: on the one hand, real-time identification of current road conditions; on the other hand, control of shock absorber damping force based on current road conditions, and balance of handling and comfort.

[0030] It's worth noting that, in terms of real-time identification of current road conditions, this application selects parameters that reflect not only road condition information but also maneuverability and comfort, such as wheel acceleration data and vehicle body acceleration data. Specifically, wheel acceleration data not only reflects road conditions, such as road flatness, road surface roughness, and obstacle conditions, but also vehicle maneuverability; while vehicle body acceleration data not only reflects road conditions but also vehicle comfort. Selecting parameters with different physical meanings not only facilitates road condition identification but also provides data support for maneuverability and comfort adjustments.

[0031] It is also worth mentioning that the present application uses fuzzy logic to judge the current handling and current comfort, and calculates the target damping force based on the judgment results. Specifically, fuzzy algorithms are suitable for dealing with problems such as handling and comfort that are ambiguous, require expert experience, and are difficult to accurately model trade-offs. Fuzzy algorithms allow control strategies to be expressed using rules close to natural language. Compared with Model Predictive Control, fuzzy algorithms have less computational complexity and are easier to implement in real time; compared with simple weighted switching, fuzzy algorithms can achieve smoother and more intelligent continuous adjustment; compared with reinforcement learning, fuzzy algorithms are easier to design, debug, and understand their internal logic, and are highly interpretable, making them suitable for addressing the core challenges of dynamic trade-offs and adapting to changing road conditions.

[0032] Based on this, the present application proposes a new energy vehicle shock absorber damping force control method based on road condition identification, which includes: obtaining road condition identification basic data and current vehicle speed, the road condition identification basic data including wheel acceleration data and body acceleration data; extracting key frequency features from the wheel acceleration data and performing statistical analysis on the body acceleration data to obtain wheel acceleration frequency features and body acceleration statistical features; performing fuzzy collaborative reasoning on the wheel acceleration frequency features, the body acceleration statistical features and the current vehicle speed to obtain fuzzy reasoning results, the fuzzy reasoning results including a handling trade-off factor and a comfort trade-off factor; calculating an initial target damping force based on the wheel acceleration data and the body acceleration data, and modulating the initial target damping force based on the handling trade-off factor and the comfort trade-off factor to obtain a target damping force; generating a damping control instruction based on the target damping force, wherein the damping control instruction is used to adjust the damping force of the new energy vehicle shock absorber.

[0033] Accordingly, if Figures 1 to 5As shown, the damping force control method of the shock absorber of a new energy vehicle based on road condition recognition according to an embodiment of the present application is explained. Figure 1 As shown, the new energy vehicle shock absorber damping force control method based on road condition recognition includes: S110, obtaining road condition recognition basic data and current vehicle speed, the road condition recognition basic data including wheel acceleration data and body acceleration data; S120, extracting key frequency features from the wheel acceleration data and performing statistical analysis on the body acceleration data to obtain wheel acceleration frequency features and body acceleration statistical features; S130, performing fuzzy collaborative reasoning on the wheel acceleration frequency features, the body acceleration statistical features and the current vehicle speed to obtain a fuzzy reasoning result, the fuzzy reasoning result including a handling trade-off factor and a comfort trade-off factor; S140, calculating an initial target damping force based on the wheel acceleration data and the body acceleration data, and modulating the initial target damping force based on the handling trade-off factor and the comfort trade-off factor to obtain a target damping force; S150, generating a damping control instruction based on the target damping force, wherein the damping control instruction is used to adjust the damping force of the new energy vehicle shock absorber.

[0034] Specifically, in step S110, basic road condition identification data and current vehicle speed are obtained, wherein the basic road condition identification data includes wheel acceleration data and body acceleration data. As mentioned above, the present application selects parameters that can reflect not only road condition information but also controllability and comfort for road condition identification.

[0035] Specifically, as components in direct contact with the road, wheel acceleration provides the most direct and sensitive information about road surface roughness. For example, when a vehicle travels over bumps, potholes, speed bumps, or joints, the wheels experience dramatic vertical acceleration changes. These changes in wheel acceleration are key to identifying road roughness, impact type and magnitude, identifying specific obstacles, and determining handling. Vehicle body acceleration reflects the vibrations generated by the vehicle body (i.e., sprung mass) after road surface irregularities are transmitted and attenuated by the suspension system. It better reflects the overall body roll and low-frequency vibration characteristics, which are directly related to passenger comfort. Extracting these two types of features with distinct physical meanings provides richer and more representative input for subsequent road condition identification and control decisions.

[0036] Vehicle speed is a crucial parameter influencing the shock absorber's damping force control strategy. The same road surface roughness produces different impact and vibration frequencies at different speeds. For example, driving over a short, uneven surface at high speed will produce high-frequency vibrations, while driving at low speeds will produce lower frequencies. Therefore, vehicle speed information helps to more accurately interpret the actual road conditions represented by the acceleration signal.

[0037] Furthermore, wheel vertical acceleration and vehicle body vertical acceleration significantly impact road surface smoothness, handling, and comfort. Accordingly, in one embodiment of the present application, the wheel acceleration data may primarily be wheel vertical acceleration, and the vehicle body acceleration data may primarily be vehicle body vertical acceleration. It should be understood that in other embodiments, the wheel acceleration data may also be wheel acceleration in other directions, such as wheel lateral acceleration, and the vehicle body acceleration data may also be vehicle body acceleration in other directions, such as vehicle body lateral acceleration.

[0038] Wheel acceleration data comes from accelerometers mounted on the wheels, such as microelectromechanical system (MEMS) accelerometers mounted on the unsprung masses. Body acceleration data comes from accelerometers mounted on the vehicle body, such as MEMS accelerometers mounted on the sprung masses.

[0039] The frequency characteristics of wheel acceleration data directly reflect road surface details. Vehicle body acceleration reflects the vibrations directly felt by passengers, and its statistical characteristics are highly correlated with the ride experience. Accordingly, in step S120, key frequency characteristics are extracted from the wheel acceleration data, and statistical analysis is performed on the vehicle body acceleration data to obtain wheel acceleration frequency characteristics and vehicle body acceleration statistical characteristics.

[0040] Specifically, the wheels are in direct contact with the road surface, and their acceleration signals contain rich information about both micro- and macro-roughness of the road surface. Different road surface types (e.g., smooth asphalt, cement jointed, gravel, and pothole-prone) induce wheel vibrations in different frequency bands. High-frequency components primarily correspond to road texture, fine cracks, or very rough surfaces, such as gravel roads. Mid-frequency components primarily correspond to continuous joints, washboard surfaces, or medium-sized potholes. Low-frequency components primarily correspond to long-wavelength road undulations. Passing over speed bumps or single large potholes results in a concentration of energy within a specific frequency range. Compared to instantaneous time-domain peaks, frequency-domain features more stably characterize the persistent characteristics of a road section, reducing the interference of individual noise spikes. The statistical characteristics of vehicle body acceleration provide information about the vehicle's response to the current road surface, such as whether it is relatively smooth or somewhat bumpy, and can indirectly reflect the passenger experience.

[0041] In the process of extracting key frequency features from the wheel acceleration data, the spectrum can be divided into several key frequency bands, such as: low frequency band (1-5 Hz), medium-low frequency band (5-15 Hz), medium-high frequency band (15-30 Hz), and high frequency band (30-60 Hz); statistical data within each frequency band, such as total energy, average energy, spectrum centroid, etc., are calculated to obtain the wheel acceleration frequency features.

[0042] In the process of statistically analyzing the vehicle body acceleration data, the root mean square value, peak value, variance, etc. of the vibration energy may be calculated to obtain the statistical characteristics of the vehicle body acceleration.

[0043] Road condition descriptions—for example, flat, rough, somewhat bumpy, very bumpy, or relatively smooth—are inherently fuzzy and difficult to define with precise mathematical thresholds. The mapping relationships between wheel acceleration data, body acceleration data, and vehicle speed are not simple linear or step-like relationships. Handling and comfort requirements are also subjective and ambiguous. For example, when driving at a moderate speed on a slightly bumpy road, there's no absolute definitive answer as to whether to prioritize handling or comfort; the ultimate decision relies heavily on experience and intuition. Fuzzy reasoning can transform natural language rules, which rely on expert experience, into computer-implementable rules that effectively reflect complex nonlinear mapping relationships. Fuzzy reasoning also provides a natural framework for coordinating data from diverse sources and properties, linking them through fuzzy rules to jointly determine the final output. Compared to traditional logic control based on hard thresholds, fuzzy reasoning utilizes the concept of membership (an input can partially belong to multiple fuzzy sets), resulting in smooth output changes. This avoids sudden changes in control output caused by small input changes crossing thresholds, thereby improving control smoothness and robustness.

[0044] Accordingly, in step S130, fuzzy collaborative reasoning is performed on the wheel acceleration frequency characteristics, the body acceleration statistical characteristics, and the current vehicle speed to obtain a fuzzy reasoning result. The fuzzy reasoning result includes a handling trade-off factor and a comfort trade-off factor. Specifically, the wheel acceleration frequency characteristics, the body acceleration statistical characteristics, and the current vehicle speed are first fuzzified to obtain fuzzy linguistic variables. The fuzzy linguistic variables are then input into a preset rule base for rule reasoning to obtain the fuzzy reasoning result.

[0045] Accordingly, if Figure 2 As shown, step S130 includes: S131, fuzzy processing is performed on the wheel acceleration frequency characteristics, the body acceleration statistical characteristics and the current vehicle speed to obtain wheel acceleration frequency linguistic variables, body acceleration statistical value linguistic variables and vehicle speed linguistic variables; S132, the wheel acceleration frequency linguistic variables, the body acceleration statistical value linguistic variables and the vehicle speed linguistic variables are input into a preset rule base for rule reasoning to obtain the fuzzy reasoning result.

[0046] In step S131, the wheel acceleration frequency characteristics, the body acceleration statistical characteristics, and the current vehicle speed are fuzzified to obtain wheel acceleration frequency linguistic variables, body acceleration statistical value linguistic variables, and vehicle speed linguistic variables. Specifically, the fuzzy set for the wheel acceleration frequency characteristics is {low, medium, high}; the fuzzy set for the body acceleration statistical characteristics is {small, medium, large}; and the fuzzy set for the current vehicle speed is {slow, medium, fast}. The wheel acceleration frequency linguistic variables include: low wheel acceleration frequency, medium wheel acceleration frequency, and high wheel acceleration frequency; the body acceleration statistical value linguistic variables include: small body acceleration statistical value, medium body acceleration statistical value, and large body acceleration statistical value; and the vehicle speed linguistic variables include: slow vehicle speed, medium vehicle speed, and fast vehicle speed.

[0047] like Figure 3 As shown, step S132 includes: S1321, determining the membership of the wheel acceleration frequency linguistic variable, the body acceleration statistical value linguistic variable and the vehicle speed linguistic variable based on a preset membership function; S1322, calculating the activation strength of each preset rule by preset fuzzy logic and combining the membership of the wheel acceleration frequency linguistic variable, the body acceleration statistical value linguistic variable and the vehicle speed linguistic variable, and determining the activated target preset rule based on the activation strength; S1323, extracting the controllability requirement level conclusion and the comfort requirement level conclusion from the target preset rule; S1324, converting the controllability requirement level conclusion and the comfort requirement level conclusion into the controllability trade-off factor and the comfort trade-off factor.

[0048] In step S1321, the membership of the wheel acceleration frequency linguistic variable, the body acceleration statistical value linguistic variable, and the vehicle speed linguistic variable is determined based on a preset membership function. For example, the wheel acceleration frequency characteristic value is 0.7, the body acceleration statistical value is 0.3, and the current vehicle speed is 80 km / h. According to the membership function, the following can be obtained: the membership of the low wheel acceleration frequency (wheel acceleration frequency eigenvalue = 0.7) is 0.0; the membership of the medium wheel acceleration frequency (wheel acceleration frequency eigenvalue = 0.7) is 0.6; the membership of the high wheel acceleration frequency (wheel acceleration frequency eigenvalue = 0.7) is 0.4; the membership of the small body acceleration statistical value (body acceleration statistical value = 0.3) is 0.7; the membership of the medium body acceleration statistical value (body acceleration statistical value = 0.3) is 0.2; the membership of the large body acceleration statistical value (body acceleration statistical value = 0.3) is 0.1; the membership of the slow vehicle speed (current vehicle speed = 80 km / h) is 0.0; the membership of the medium vehicle speed (current vehicle speed = 80 km / h) is 0.3; and the membership of the fast vehicle speed (current vehicle speed = 80 km / h) is 0.7.

[0049] In step S1322, the activation strength of each preset rule is calculated using preset fuzzy logic and combined with the membership of the wheel acceleration frequency linguistic variable, the body acceleration statistical value linguistic variable, and the vehicle speed linguistic variable. Based on the activation strength, the target preset rule to be activated is determined. Each preset rule in the preset rule library includes a premise and a conclusion. The premise is a precondition regarding the wheel acceleration frequency linguistic variable, the body acceleration statistical value linguistic variable, and / or the vehicle speed linguistic variable, and the conclusion is a conclusion regarding the required handling level or the required comfort level.

[0050] For example, the preset rules in the rule library include: Rule 1: If the wheel acceleration frequency characteristic value is high, the body acceleration statistical value is large, and the current vehicle speed is fast, then the required handling level is high and the required comfort level is low; Rule 2: If the wheel acceleration frequency characteristic value is low, the body acceleration statistical value is small, and the current vehicle speed is slow, then the required handling level is low and the required comfort level is high; Rule 3: If the wheel acceleration frequency characteristic value is medium, the body acceleration statistical value is medium, and the current vehicle speed is medium or fast, then the required handling level is medium and the required comfort level is medium. It should be understood that the rule library may also include more rules.

[0051] For each rule, the activation strength of the rule premise is calculated using fuzzy logic operators based on the membership of the input variables. For example, for Rule 1, the activation strength is the minimum of the membership of the high wheel acceleration frequency characteristic value, the high vehicle body acceleration statistical value, and the high current vehicle speed. Accordingly, the activation strength of Rule 1 is: When the activation strength of a rule is not 0, the rule is activated and the rule is preset for the target.

[0052] In step S1323, the controllability requirement level conclusion and the comfort requirement level conclusion are extracted from the target preset rule. Specifically, in one embodiment of the present application, after calculating the activation strengths of multiple rules, the target preset rule with the greatest activation strength is determined, and the controllability requirement level conclusion and the comfort requirement level conclusion in the target preset rule with the greatest activation strength are determined. For example, the target preset rule with the greatest activation strength is Rule 3, and the controllability requirement level conclusion and the comfort requirement level conclusion in Rule 3 are: medium controllability requirement level and medium comfort requirement level.

[0053] In step S1324, the maneuverability requirement level conclusion and the comfort requirement level conclusion are converted into the maneuverability tradeoff factor and the comfort tradeoff factor. Specifically, the maneuverability requirement level conclusion and the comfort requirement level conclusion may be normalized to obtain the maneuverability tradeoff factor and the comfort tradeoff factor. For example, a medium maneuverability requirement level and a medium comfort requirement level may be converted into a maneuverability tradeoff factor of 0.5 and a comfort tradeoff factor of 0.5.

[0054] It is worth mentioning that in the present application, the wheel acceleration frequency characteristics, the body acceleration statistical characteristics, and the current vehicle speed are fuzzified to obtain corresponding linguistic variables, and then the handling trade-off factor and the comfort trade-off factor are obtained based on logical fuzzy reasoning and semantic normalization based on preset rules. In this process, since the wheel acceleration data and body acceleration data in the time dimension reflect the temporal instantaneous change characteristics, they will also be reflected as the fuzzy semantic time step change rate in the fuzzy semantic logic space. Therefore, if the influence of the rule-constrained normalization factor of the time dimension integral data on the linguistic variables, that is, the temporal consistency constraint of the handling trade-off factor and the comfort trade-off factor, is ignored, the accuracy of the handling trade-off factor and the comfort trade-off factor will obviously be affected. Therefore, the present application can perform temporal consistency integration constraints on the rule-constrained normalization factor of the time dimension integral data on the linguistic variables on the handling trade-off factor and the comfort trade-off factor obtained above to obtain more accurate handling trade-off factors and comfort trade-off factors.

[0055] Accordingly, in one embodiment of the present application, Figure 4 As shown, step S1324 includes: S13241, performing preliminary normalization processing on the controllability requirement level conclusion and the comfort requirement level conclusion to obtain a preliminary controllability trade-off factor and a preliminary comfort trade-off factor; S13242, performing temporal consistency integration constraints on the preliminary controllability trade-off factor and the preliminary comfort trade-off factor to obtain a controllability trade-off factor and a comfort trade-off factor.

[0056] Specifically, in step S13241, the preliminary maneuverability trade-off factor and the preliminary comfort trade-off factor refer to the maneuverability trade-off factor and the comfort trade-off factor obtained without considering the influence of the time-series dimension integral data on the rule-constrained normalization factor of the language variable. For example, the above converts the medium maneuverability requirement level and the medium comfort requirement level into a maneuverability trade-off factor of 0.5 and a comfort trade-off factor of 0.5.

[0057] In step S13242, a temporal consistency integration constraint is applied to the preliminary handling and comfort trade-off factors to obtain the handling and comfort trade-off factors. Specifically, considering the influence of the temporal dimension integral data on the rule-constrained normalization factors of the linguistic variables, in step S13242, the wheel acceleration data and the body acceleration data are first integrated to obtain wheel integral vectors and body integral vectors, respectively. Then, the parameters influencing the wheel integral vectors and body integral vectors on the normalization factors are calculated to obtain the final handling and comfort trade-off factors.

[0058] Accordingly, if Figure 5 As shown, step S13242 includes: S11, integrating the wheel acceleration data and the body acceleration data respectively to obtain a wheel integral vector and a body integral vector; S12, normalizing the Frobenius norm of the wheel integral vector correlation matrix and the Frobenius norm of the body integral vector correlation matrix to obtain a first dynamic constraint integration factor and a second dynamic constraint integration factor; S13, correcting the preliminary controllability trade-off factor and the preliminary comfort trade-off factor by means of the wheel-body relative speed, the first dynamic constraint integration factor and the second dynamic constraint integration factor to obtain a controllability trade-off factor and a comfort trade-off factor.

[0059] In step S11, the wheel acceleration data and the vehicle body acceleration data are integrated to obtain a wheel integral vector and a vehicle body integral vector, respectively. Considering the presence of nonlinear noise in the time series dimension during the integration process, after obtaining the wheel acceleration data and the vehicle body acceleration data in the time series dimension, the integrals of the wheel acceleration data and the vehicle body acceleration data in the time series dimension need to be corrected.

[0060] Accordingly, in one embodiment of the present application, step S11 includes: S1101, performing time dimension integration on the wheel acceleration data and the vehicle body acceleration data respectively to obtain a preliminary wheel integral vector and a preliminary vehicle body integral vector; S1102, suppressing the long-term discretized time noise integral of the preliminary wheel integral vector and the preliminary vehicle body integral vector in the time dimension through an exponential decay kernel function to obtain a wheel integral vector and a vehicle body integral vector.

[0061] In step S1101, the wheel acceleration data and the vehicle body acceleration data are respectively integrated in time series dimension to obtain a preliminary wheel integral vector and the preliminary body integral vector .

[0062] In the preliminary wheel integral vector The initial wheel integral value is: ;in, represents the preliminary wheel integral value, i.e. the preliminary wheel speed; represents the wheel acceleration; Indicates time.

[0063] Integral vector in the preliminary body The initial vehicle body integral value is: ;in, represents the initial vehicle body integral value, i.e. the initial vehicle body speed; Indicates vehicle acceleration.

[0064] In step S1102, the wheel integral vector In the wheel integral value for: ; Body integral vector Vehicle body integral value for: ; Among them, the exponential decay kernel function 、 It can suppress the accumulation of long-term discretized temporal noise in the temporal dimension, so that the integral representation can be used for nonlinear activation distribution under temporal-semantic projection; is a frequency parameter related to the integral density in the time dimension, for example, it can be simplified as . It is set according to demand. The longer the time period selected by the points, The larger the time period, the shorter the The smaller the value, for example, when calculating the wheel integral value and the body integral value with a 5-second integral time period is 5.

[0065] In step S12, the Frobenius norms of the wheel-integrated vector correlation matrix and the body-integrated vector correlation matrix are normalized to obtain a first dynamic constraint integration factor and a second dynamic constraint integration factor. Specifically, the wheel-integrated vector and the body-integrated vector are row vectors. The wheel-integrated vector correlation matrix is the Kronecker product of the transpose of the wheel-integrated vector and the wheel-integrated vector; the body-integrated vector correlation matrix is the Kronecker product of the transpose of the body-integrated vector and the body-integrated vector. The Frobenius norm of the matrix is used to ensure vector adaptive distribution adaptation under temporal-semantic space alignment during dynamic constraint integration.

[0066] Accordingly, the first dynamic constraint integration factor and the second dynamic constraint integration factor are calculated by the following formula: ;in, represents the first dynamic constraint integration factor; represents the second dynamic constraint integration factor; represents the wheel integral vector; represents the transpose of the wheel integral vector; represents the Kronecker product of the transpose of the wheel integral vector and the wheel integral vector; represents the Frobenius norm of the wheel-integrated vector correlation matrix formed by the transpose of the wheel-integrated vector and the Kronecker product of the wheel-integrated vector; represents the vehicle body integral vector; represents the transpose of the vehicle body integral vector; represents the Kronecker product of the transpose of the body integral vector and the body integral vector; Represents the Frobenius norm of the body integral vector correlation matrix formed by the transpose of the body integral vector and the Kronecker product of the body integral vector.

[0067] In step S13, the preliminary handling trade-off factor and preliminary comfort trade-off factor are modified using the wheel-body relative speed, the first dynamic constraint integration factor, and the second dynamic constraint integration factor to obtain a handling trade-off factor and a comfort trade-off factor. Specifically, a modification factor is calculated based on the wheel-body relative speed, the first dynamic constraint integration factor, and the second dynamic constraint integration factor. The modification factor is equal to the difference between 1 and a constant e raised to a predetermined power; the predetermined power is equal to the product of the difference between the wheel-body relative speed and the second dynamic constraint integration factor and the inverse of the first dynamic constraint integration factor. The handling trade-off factor is equal to the ratio of the preliminary handling trade-off factor to the modification factor; the comfort trade-off factor is equal to the ratio of the preliminary comfort trade-off factor to the modification factor.

[0068] Accordingly, the controllability trade-off factor and comfort trade-off factor are calculated by the following formula: ;in, represents the controllability trade-off factor; represents the preliminary manipulability trade-off factor; Indicates the relative speed between the wheel and the vehicle body; it is equal to the difference between the wheel speed and the vehicle body speed; represents the comfort trade-off factor; represents the preliminary comfort trade-off factor.

[0069] Based on the dynamic integration of dynamic constraints as activation distribution factors, this application performs temporal consistency integration constraints on the rule-constrained normalization factors of the time-dimensional integral data for language variables, thereby improving the accuracy of the controllability trade-off factor and the comfort trade-off factor under the normalization of predetermined temporal semantic fuzzy logic rules.

[0070] In step S140, the initial target damping force is calculated based on the wheel acceleration data and the vehicle body acceleration data, and the initial target damping force is modulated based on the maneuverability trade-off factor and the comfort trade-off factor to obtain the target damping force. Specifically, the present application calculates a damping force that is biased towards comfort and a damping force that is biased towards operability, and modulates the damping force that is biased towards comfort and the damping force that is biased towards operability by the maneuverability trade-off factor and the comfort trade-off factor to obtain the target damping force. When the demand is more inclined towards controllability, the maneuverability trade-off factor is larger, and the final target damping force is more likely to meet the controllability demand; when the demand is more inclined towards comfort, the comfort trade-off factor is larger, and the final target damping force is more likely to meet the comfort demand.

[0071] Furthermore, the damping force is usually proportional to the speed. Therefore, in the process of calculating the damping force that focuses on comfort and the damping force that focuses on operability, it is necessary to first obtain the speed data, for example, integrate the wheel acceleration data in the time dimension to obtain the wheel speed; integrate the vehicle body acceleration data in the time dimension to obtain the vehicle body speed; calculate the difference between the wheel speed and the vehicle body speed as the wheel-body relative speed; then, obtain the damping force that focuses on comfort and the damping force that focuses on operability through the damping coefficient and speed data.

[0072] Accordingly, the initial target damping force is calculated based on the wheel acceleration data and the vehicle body acceleration data, including: S141, calculating the first initial target damping force that focuses on comfort based on the wheel speed and the first expected damping coefficient; S142, calculating the second initial target damping force that focuses on handling based on the vehicle body speed and the second expected damping coefficient and the wheel-body relative speed and the third expected damping coefficient.

[0073] In one embodiment of the present application, the wheel vertical velocity is 0.3 m / s; the vehicle body vertical velocity is 0.1 m / s; and the wheel-body relative velocity is 0.2 m / s. The first desired damping coefficient, the second desired damping coefficient, and the third desired damping coefficient can be obtained through testing. The first desired damping coefficient is 1200 Ns / m; the second desired damping coefficient is 1000 Ns / m; and the third desired damping coefficient is 1800 Ns / m. The first initial target damping force is equal to the product of the wheel vertical velocity of 0.3 m / s and the first desired damping coefficient of 1200 Ns / m, which is 0.3 m / s * 1200 Ns / m = 360 N. The second initial target damping force is equal to the sum of the body damping force and the relative damping force. The body damping force is equal to the product of the body vertical velocity of 0.1 m / s and the second desired damping coefficient of 1000 Ns / m. The relative damping force is equal to the product of the wheel-body relative velocity of 0.2 m / s and the third desired damping coefficient of 1800 Ns / m. The second initial target damping force is 0.1 m / s*1000 Ns / m+0.2 m / s*1800 Ns / m=460 N.

[0074] In the process of modulating the initial target damping force based on the controllability trade-off factor and the comfort trade-off factor to obtain the target damping force, the controllability trade-off factor and the comfort trade-off factor are used as weights to calculate the weighted sum of the first initial target damping force and the second initial target damping force to obtain the target damping force.

[0075] In one embodiment of the present application, the handling trade-off factor is 0.7; the comfort trade-off factor is 0.3; the first initial target damping force is 360 N; the second initial target damping force is 460 N; and the weighted sum of the first initial target damping force and the second initial target damping force is 460 N*0.7+360 N*0.3=430 N.

[0076] In step S150, a damping control command is generated based on the target damping force, wherein the damping control command is used to adjust the damping force of the new energy vehicle's shock absorber. Specifically, the damping control command is used to adjust the electrical signal of the new energy vehicle's shock absorber, thereby adjusting the damping force of the new energy vehicle's shock absorber. In one embodiment of the present application, the damping force is controlled by adjusting the operating current of the new energy vehicle's shock absorber.

[0077] In summary, the road condition recognition-based shock absorber damping force control method for new energy vehicles has been described. This method can identify road conditions in real time by selecting appropriate parameters and, through a suitable algorithm (fuzzy reasoning), obtain controllability and comfort parameters, thereby achieving a shock absorber damping force that dynamically balances controllability and comfort.

[0078] The above description of the present application and its embodiments is non-limiting. The drawings show only one embodiment of the present application, and the actual structure is not limited thereto. In short, if a person skilled in the art is inspired by this and, without departing from the inventive purpose of this application, designs a structure and embodiment similar to this technical solution without creatively designing, they shall fall within the scope of protection of this application.

Claims

1. A new energy vehicle shock absorber damping force control method based on road condition recognition, characterized in that: include: Acquiring basic road condition recognition data and current vehicle speed, wherein the basic road condition recognition data includes wheel acceleration data and body acceleration data; Extracting key frequency features from the wheel acceleration data and performing statistical analysis on the vehicle body acceleration data to obtain wheel acceleration frequency features and vehicle body acceleration statistical features; performing fuzzy collaborative reasoning on the wheel acceleration frequency characteristics, the vehicle body acceleration statistical characteristics, and the current vehicle speed to obtain a fuzzy reasoning result, the fuzzy reasoning result including a maneuverability trade-off factor and a comfort trade-off factor; calculating an initial target damping force based on the wheel acceleration data and the vehicle body acceleration data, and modulating the initial target damping force based on the handling trade-off factor and the comfort trade-off factor to obtain a target damping force; A damping control instruction is generated based on the target damping force, wherein the damping control instruction is used to adjust the damping force of the shock absorber of the new energy vehicle.

2. The method for controlling the damping force of a shock absorber of a new energy vehicle based on road condition recognition according to claim 1, characterized in that: Performing fuzzy collaborative reasoning on the wheel acceleration frequency characteristics, the vehicle body acceleration statistical characteristics, and the current vehicle speed to obtain a fuzzy reasoning result, including: Performing fuzzy processing on the wheel acceleration frequency feature, the body acceleration statistical feature, and the current vehicle speed to obtain a wheel acceleration frequency linguistic variable, a body acceleration statistical value linguistic variable, and a vehicle speed linguistic variable; The wheel acceleration frequency linguistic variable, the vehicle body acceleration statistical value linguistic variable and the vehicle speed linguistic variable are input into a preset rule library for rule reasoning to obtain the fuzzy reasoning result.

3. The method for controlling the damping force of a shock absorber of a new energy vehicle based on road condition recognition according to claim 2 is characterized in that: Inputting the wheel acceleration frequency linguistic variable, the vehicle body acceleration statistical value linguistic variable, and the vehicle speed linguistic variable into a preset rule library for rule reasoning to obtain the fuzzy reasoning result includes: Determining the membership of the wheel acceleration frequency linguistic variable, the vehicle body acceleration statistical value linguistic variable, and the vehicle speed linguistic variable based on a preset membership function; By using preset fuzzy logic and combining the membership of the wheel acceleration frequency linguistic variable, the body acceleration statistical value linguistic variable, and the vehicle speed linguistic variable, the activation strength of each preset rule is calculated, and a target preset rule to be activated is determined based on the activation strength; Extracting a controllability demand level conclusion and a comfort demand level conclusion from the target preset rules; The maneuverability demand level conclusion and the comfort demand level conclusion are converted into the maneuverability trade-off factor and the comfort trade-off factor.

4. The method for controlling the damping force of a shock absorber of a new energy vehicle based on road condition recognition according to claim 3 is characterized in that: Converting the maneuverability requirement level conclusion and the comfort requirement level conclusion into the maneuverability trade-off factor and the comfort trade-off factor includes: performing preliminary normalization processing on the maneuverability demand level conclusion and the comfort demand level conclusion to obtain a preliminary maneuverability trade-off factor and a preliminary comfort trade-off factor; Temporal consistency integration constraints are applied to the preliminary maneuverability trade-off factor and the preliminary comfort trade-off factor to obtain the maneuverability trade-off factor and the comfort trade-off factor.

5. The method for controlling the damping force of a shock absorber of a new energy vehicle based on road condition recognition according to claim 4 is characterized in that: Temporal consistency integration constraints are applied to the preliminary maneuverability trade-off factor and the preliminary comfort trade-off factor to obtain the maneuverability trade-off factor and the comfort trade-off factor, including: Integrating the wheel acceleration data and the vehicle body acceleration data to obtain a wheel integral vector and a vehicle body integral vector respectively; Normalizing the Frobenius norm of the wheel integral vector correlation matrix and the Frobenius norm of the vehicle body integral vector correlation matrix to obtain a first dynamic constraint integration factor and a second dynamic constraint integration factor; Correcting the preliminary maneuverability trade-off factor and the preliminary comfort trade-off factor by using the wheel-body relative speed, the first dynamic constraint integration factor, and the second dynamic constraint integration factor to obtain a maneuverability trade-off factor and a comfort trade-off factor; Among them, the wheel integral vector correlation matrix is the transpose of the wheel integral vector and the Kronecker product of the wheel integral vector; the body integral vector correlation matrix is the transpose of the body integral vector and the Kronecker product of the body integral vector.

6. The method for controlling the damping force of a shock absorber of a new energy vehicle based on road condition recognition according to claim 5 is characterized in that: Integrating the wheel acceleration data and the vehicle body acceleration data to obtain a wheel integral vector and a vehicle body integral vector, respectively, includes: Performing time-series integration on the wheel acceleration data and the vehicle body acceleration data to obtain a preliminary wheel integral vector and a preliminary vehicle body integral vector; The long-term discretized time series noise integral of the preliminary wheel integral vector and the preliminary body integral vector in the time series dimension is suppressed by the exponential decay kernel function to obtain the wheel integral vector and the body integral vector.

7. The method for controlling the damping force of a shock absorber of a new energy vehicle based on road condition recognition according to claim 1, characterized in that: Calculating an initial target damping force based on the wheel acceleration data and the vehicle body acceleration data includes: Calculating a first initial target damping force that prioritizes comfort based on the wheel speed and the first desired damping coefficient; A second initial target damping force that prioritizes maneuverability is calculated based on the vehicle body speed and the second desired damping coefficient as well as the wheel-body relative speed and the third desired damping coefficient.

8. The method for controlling the damping force of a shock absorber of a new energy vehicle based on road condition recognition according to claim 7 is characterized in that: Obtaining wheel speed, vehicle body speed, and wheel-body relative speed, including: integrating the wheel acceleration data in a time dimension to obtain the wheel speed; integrating the vehicle body acceleration data in a time dimension to obtain the vehicle body speed; The difference between the wheel speed and the vehicle body speed is calculated as the wheel-body relative speed.

9. The method for controlling the damping force of a shock absorber of a new energy vehicle based on road condition recognition according to claim 7, characterized in that: Modulating the initial target damping force based on the maneuverability trade-off factor and the comfort trade-off factor to obtain a target damping force includes: The target damping force is obtained by calculating a weighted sum of the first initial target damping force and the second initial target damping force using the maneuverability trade-off factor and the comfort trade-off factor as weights.

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