Vehicle mass and wheel load distribution estimation method and device based on steady-state signal decoupling

Abstract

The application discloses a vehicle mass and wheel load distribution estimation method and device based on steady-state signal decoupling, comprising: obtaining test calibration data of a plurality of suspension type test vehicles, establishing a partial load reference database and a partial load solving rule library covering a plurality of suspension types according to the test calibration data; matching corresponding target partial load reference data and target partial load solving rules in the partial load reference database and the partial load solving rule library according to a target suspension type of a target vehicle; obtaining a standard sensor signal of the target vehicle in a steady-state straight running state, determining a partial load estimation result and an estimation accuracy identifier of the target vehicle according to the standard sensor signal, the target partial load reference data and the target partial load solving rules; and outputting the partial load estimation result and the estimation accuracy identifier to a vehicle-mounted control system of the target vehicle. The application improves the reliability of vehicle mass and wheel load distribution estimation and can be applied to the technical field of vehicle control.

Description

Technical Field

[0001] This invention relates to the field of vehicle control technology, and in particular to a method and apparatus for estimating vehicle mass and wheel load distribution based on steady-state signal decoupling. Background Technology

[0002] The vehicle's actual gross weight, center of gravity position, and four-wheel load distribution are core parameters of the chassis control system. Usage scenarios such as passenger boarding and alighting, and luggage loading and unloading can cause these parameters to change significantly relative to the unloaded state (i.e., "uneven load"), directly affecting the performance of systems such as suspension control, brake distribution, and drive force distribution.

[0003] Currently, methods for obtaining vehicle mass and wheel load distribution can be mainly divided into the following three categories: 1) Fixed parameter method: This method directly uses the vehicle's mass, center of gravity, and wheel load parameters under no-load conditions as control inputs. While simple, this method completely ignores the effects of off-center loading in actual use, leading to decreased control accuracy.

[0004] 2) Air Spring Load Estimation Method: Utilizing the pressure and height sensors standard on air spring suspension systems, the vertical load on each wheel is calculated using the gas state equation and effective area characteristic curve, thereby obtaining the vehicle's mass and center of gravity position. This method is only applicable to high-end models equipped with air suspension and cannot cover the vast majority of electronically controlled passive suspension models and entry-level passive suspension models in the market.

[0005] 3) Additional sensor measurement method: By installing additional load sensors or six-component force sensors, the vertical force of each wheel is directly measured. This method has high accuracy, but it is costly and complex to install, making it unsuitable for widespread use in mass-produced vehicles.

[0006] The above problems urgently need to be addressed. Summary of the Invention

[0007] The purpose of this invention is to at least partially solve one of the technical problems existing in the prior art.

[0008] Therefore, one objective of this invention is to provide a method for estimating vehicle mass and wheel load distribution based on steady-state signal decoupling. This method establishes an off-center load reference database and an off-center load calculation rule library covering different suspension types based on test calibration data. It matches the corresponding off-center load reference data and off-center load calculation rules according to the suspension type of the target vehicle, and solves the off-center load estimation results of the corresponding accuracy level by combining the standard sensor signals of the target vehicle in steady-state execution state. This achieves graded estimation of vehicle mass and wheel load distribution accuracy and improves the reliability of vehicle mass and wheel load distribution estimation.

[0009] Another objective of this invention is to provide a vehicle mass and wheel load distribution estimation device based on steady-state signal decoupling.

[0010] To achieve the above-mentioned technical objectives, the technical solutions adopted in the embodiments of the present invention include: On one hand, embodiments of the present invention provide a method for estimating vehicle mass and wheel load distribution based on steady-state signal decoupling, comprising the following steps: Acquire test calibration data of test vehicles with multiple suspension types, and establish an off-center load benchmark database and an off-center load calculation rule library covering multiple suspension types based on the test calibration data; According to the target suspension type of the target vehicle, the corresponding target off-center load reference data and target off-center load calculation rules are matched in the off-center load reference database and the off-center load calculation rule base; Acquire the standard sensor signals of the target vehicle in steady-state straight driving condition, and determine the off-center load estimation result and estimation accuracy indicator of the target vehicle based on the standard sensor signals, the target off-center load reference data and the target off-center load calculation rules; The off-center load estimation result and the estimation accuracy indicator are output to the vehicle control system of the target vehicle; The off-center load estimation results include the estimated total vehicle mass, the estimated center of gravity position, and the estimated wheel load distribution of the four wheels.

[0011] Furthermore, in one embodiment of the present invention, the suspension type includes an air spring suspension, an electronically controlled passive suspension with a height sensor, and a passive suspension without a height sensor. The test calibration data includes the vehicle mass parameter measurement results, center of gravity height measurement results, and wheel load distribution measurement results of the test vehicle under different loading conditions. When the suspension type is an air spring suspension, the test calibration data also includes airbag pressure measurement results and suspension height measurement results. When the suspension type is an electronically controlled passive suspension with a height sensor, the test calibration data also includes suspension height measurement results.

[0012] Furthermore, in one embodiment of the present invention, the step of establishing an off-center load reference database and an off-center load calculation rule base covering multiple suspension types based on the test calibration data specifically includes: Based on the test calibration data, determine the reference values ​​for the total vehicle mass, center of gravity position, and four-wheel load distribution of the test vehicles for each suspension type under no-load conditions. The off-center load reference database is constructed based on the vehicle total mass reference value, the center of gravity position reference value, and the four-wheel load distribution reference value. For air spring suspension, a first mapping relationship between air spring pressure, suspension height and wheel vertical load is determined based on the corresponding test calibration data, and a first off-center load calculation rule is determined based on the first mapping relationship. For an electronically controlled passive suspension with a height sensor, a second mapping relationship between the suspension height change and the wheel vertical load change is determined based on the corresponding test calibration data, and a second off-center load calculation rule is determined based on the second mapping relationship. For passive suspensions without height sensors, the third off-center load calculation rule is determined based on the vehicle's longitudinal dynamics equations. The off-center load calculation rule library is constructed based on the first off-center load calculation rule, the second off-center load calculation rule, and the third off-center load calculation rule.

[0013] Furthermore, in one embodiment of the present invention, the step of matching the corresponding target off-center load reference data and target off-center load calculation rules in the off-center load reference database and the off-center load calculation rule base according to the target suspension type of the target vehicle specifically includes: Obtain the target suspension type of the target vehicle; According to the target suspension type, the corresponding target off-center load reference data is obtained by matching the off-center load reference database. The target off-center load reference data includes the target vehicle total mass reference value, the target center of gravity position reference value, and the target four-wheel load distribution reference value. According to the target suspension type, the corresponding target off-center load calculation rule is obtained by matching in the off-center load calculation rule library. The target off-center load calculation rule is one of the first off-center load calculation rule, the second off-center load calculation rule, and the third off-center load calculation rule.

[0014] Furthermore, in one embodiment of the present invention, when the target suspension type is an air spring suspension, the standard sensor signals include an airbag pressure signal and a suspension height signal; when the target suspension type is an electronically controlled passive suspension with a height sensor, the standard sensor signals include a suspension height signal; and when the target suspension type is a passive suspension without a height sensor, the standard sensor signals include a longitudinal acceleration signal and a driving force / braking force signal.

[0015] Furthermore, in one embodiment of the present invention, the step of determining the off-center load estimation result and estimation accuracy identifier of the target vehicle based on the standard sensor signal, the target off-center load reference data, and the target off-center load calculation rule specifically includes: When the target suspension type is an air spring suspension, the real-time wheel vertical load of each wheel is determined according to the airbag pressure signal, the suspension height signal and the first mapping relationship. The estimated value of the four-wheel load distribution is determined according to the real-time wheel vertical load. The estimated value of the total vehicle mass is determined according to the sum of the real-time wheel vertical loads. The estimated value of the center of gravity position is determined according to the load distribution of the real-time wheel vertical loads, and the corresponding estimation accuracy is identified as high accuracy. When the target suspension type is an electronically controlled passive suspension with a height sensor, the real-time vertical load change of each wheel is determined according to the suspension height signal and the second mapping relationship. The real-time wheel vertical load of each wheel is determined according to the target four-wheel load distribution reference value and the real-time vertical load change. The four-wheel load distribution estimate is determined according to the real-time wheel vertical load. The total mass estimate of the vehicle is determined according to the sum of the real-time wheel vertical loads. The center of gravity position estimate is determined according to the load distribution of the real-time wheel vertical loads, and the corresponding estimation accuracy is identified as high accuracy. When the target suspension type is a passive suspension without a height sensor, the estimated total mass of the vehicle is determined based on the longitudinal acceleration signal and the driving force / braking force signal. The estimated center of gravity position and the estimated four-wheel load distribution are determined based on the target center of gravity position reference value and the target four-wheel load distribution reference value, and the corresponding estimation accuracy is identified as low accuracy.

[0016] Furthermore, in one embodiment of the present invention, the off-center load estimation result and the estimation accuracy indicator are used to input the suspension control system for damping and stiffness pre-adjustment, input the braking control system for braking force distribution correction, input the driving force distribution system for driving torque adjustment, or input the wheel center input force calculation system for dynamic updating of the vehicle resultant force balance verification benchmark.

[0017] On the other hand, embodiments of the present invention provide a vehicle mass and wheel load distribution estimation device based on steady-state signal decoupling, comprising: The test data processing module is used to acquire test calibration data of test vehicles with multiple suspension types, and to establish an off-center load benchmark database and an off-center load calculation rule library covering multiple suspension types based on the test calibration data. The calculation rule matching module is used to match the corresponding target off-center load reference data and target off-center load calculation rules in the off-center load reference database and the off-center load calculation rule library according to the target suspension type of the target vehicle. The off-center load estimation module is used to acquire the standard sensor signals of the target vehicle in steady-state straight driving, and determine the off-center load estimation result and estimation accuracy indicator of the target vehicle based on the standard sensor signals, the target off-center load reference data and the target off-center load calculation rules. The result output module is used to output the off-center load estimation result and the estimation accuracy identifier to the vehicle control system of the target vehicle. The off-center load estimation results include the estimated total vehicle mass, the estimated center of gravity position, and the estimated wheel load distribution of the four wheels.

[0018] On the other hand, embodiments of the present invention provide an electronic device, including: At least one processor; At least one memory for storing at least one program; When the at least one program is executed by the at least one processor, the at least one processor implements the above-described method for estimating vehicle mass and wheel load distribution based on steady-state signal decoupling.

[0019] On the other hand, embodiments of the present invention also provide a computer-readable storage medium storing a processor-executable computer program that, when executed by a processor, implements the above-described method for estimating vehicle mass and wheel load distribution based on steady-state signal decoupling.

[0020] On the other hand, embodiments of the present invention also provide a computer program product, including a computer program that, when executed by a processor, implements the above-described method for estimating vehicle mass and wheel load distribution based on steady-state signal decoupling.

[0021] The advantages and beneficial effects of the present invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention: This invention acquires test calibration data from test vehicles with multiple suspension types. Based on the test calibration data, it establishes an off-center load reference database and an off-center load calculation rule library covering multiple suspension types. According to the target suspension type of the target vehicle, it matches the corresponding target off-center load reference data and target off-center load calculation rules in the off-center load reference database and the off-center load calculation rule library. It acquires the standard sensor signals of the target vehicle in steady-state straight driving. Based on the standard sensor signals, target off-center load reference data, and target off-center load calculation rules, it determines the off-center load estimation result and estimation accuracy identifier of the target vehicle, and outputs the off-center load estimation result and estimation accuracy identifier to the vehicle's onboard control system. This invention establishes an off-center load reference database and an off-center load calculation rule library covering different suspension types based on test calibration data, matches the corresponding off-center load reference data and off-center load calculation rules according to the suspension type of the target vehicle, and combines the standard sensor signals of the target vehicle in steady-state execution to solve for the off-center load estimation result of the corresponding accuracy level. This achieves graded estimation of vehicle mass and wheel load distribution accuracy, improving the reliability of vehicle mass and wheel load distribution estimation. Attached Figure Description

[0022] To more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings used in the embodiments of the present invention are described below. It should be understood that the drawings described below are only for the convenience of clearly describing some embodiments of the technical solutions of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0023] Figure 1 A flowchart illustrating the steps of a vehicle mass and wheel load distribution estimation method based on steady-state signal decoupling provided in an embodiment of the present invention; Figure 2 A structural block diagram of a vehicle mass and wheel load distribution estimation device based on steady-state signal decoupling provided in an embodiment of the present invention; Figure 3 This is a structural block diagram of an electronic device provided in an embodiment of the present invention. Detailed Implementation

[0024] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention. In the following description, when referring to the accompanying drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the embodiments of this invention; they are merely examples of apparatuses and methods consistent with some aspects of the embodiments of this invention as detailed in the appended claims.

[0025] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains. The terminology used herein is for the purpose of describing embodiments of the invention only and is not intended to limit the invention.

[0026] The vehicle mass and wheel load distribution estimation method based on steady-state signal decoupling provided in this invention can be applied to a terminal, a server, or software running on either a terminal or a server. In some embodiments, the terminal can be a smartphone, tablet, laptop, desktop computer, smart speaker, smartwatch, or in-vehicle terminal, but is not limited to these. The server can be configured as an independent physical server, a server cluster or distributed system composed of multiple physical servers, or a cloud server providing basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, CDN, and big data and artificial intelligence platforms. The server can also be a node server in a blockchain network. The software can be an application implementing the vehicle mass and wheel load distribution estimation method based on steady-state signal decoupling, but is not limited to the above forms.

[0027] This invention can be used in a wide variety of general-purpose or special-purpose computer system environments or configurations. Examples include: personal computers, server computers, handheld or portable devices, tablet devices, multiprocessor systems, microprocessor-based systems, set-top boxes, programmable consumer electronics, network PCs, minicomputers, mainframe computers, and distributed computing environments including any of the above systems or devices. This invention can be described in the general context of computer-executable instructions, such as program modules, that are executed by a computer. Generally, program modules include routines, programs, objects, components, data structures, etc., that perform specific tasks or implement specific abstract data types. This invention can also be practiced in distributed computing environments where tasks are performed by remote processing devices connected via a communication network. In distributed computing environments, program modules can reside in local and remote computer storage media, including storage devices.

[0028] It should be noted that in various specific embodiments of the present invention, when processing data related to user identity or characteristics, such as user information, user behavior data, user historical data, and user parking space location information, user permission or consent is obtained first. Furthermore, the collection, use, and processing of this data comply with relevant laws, regulations, and standards. In addition, when embodiments of the present invention require access to sensitive personal information of users, separate permission or consent from the user is obtained through pop-ups or redirection to a confirmation page. Only after obtaining the user's separate permission or consent is the necessary user-related data for the normal operation of the embodiments of the present invention acquired.

[0029] Existing vehicle mass and wheel load distribution estimation schemes have the following problems: 1) Insufficient coverage across all vehicle models: The air spring load estimation method is only applicable to high-end models equipped with air suspension, and cannot cover the vast majority of electronically controlled passive suspension models and entry-level passive suspension models. For entry-level models without height sensors, existing technology does not provide a feasible mass estimation solution.

[0030] 2) Lack of accuracy grading mechanism: Existing technologies output estimation results with a single accuracy level for all vehicle models, without grading accuracy based on differences in sensor configuration. For vehicle models with weaker sensor capabilities, existing technologies are either impossible to implement or may output insufficiently accurate estimates, affecting the reliability of downstream control systems.

[0031] 3) Reliance on specific calibration: Existing technologies usually require specific calibration tests for specific vehicle models, which increases development costs and time.

[0032] 4) Functional isolation: Existing technologies are all implemented as independent mass estimation functions, without forming a synergy with the vehicle dynamics calculation system (such as the wheel center input force calculation system), and cannot provide a unified verification benchmark for the latter.

[0033] This invention does not rely on direct measurement from a single high-precision sensor. Instead, it constructs a system-level framework covering the entire vehicle model spectrum, automatically matching calculation schemes according to sensor configurations and outputting estimation results in a tiered manner. Specifically, it establishes an off-center load benchmark database based on national standard mandatory test data, achieving zero incremental calibration cost; it triggers decoupling under steady-state straight-line driving conditions to ensure the reliability of the estimation results; it automatically matches calculation schemes with corresponding accuracy levels according to vehicle suspension type and standard sensor configuration; for models equipped with suspension load / height sensors, it uses their standard sensors to output precise estimates; for models not equipped with the above sensors, it uses the ESP standard signal to output coarse estimates, clearly indicating their accuracy level; and it outputs the estimation results to multiple vehicle control systems, while simultaneously providing dynamically updated verification benchmarks for the wheel center input force calculation system.

[0034] Reference Figure 1 This invention provides a method for estimating vehicle mass and wheel load distribution based on steady-state signal decoupling, specifically including the following steps: S101. Obtain test calibration data of test vehicles with multiple suspension types, and establish an off-center load benchmark database and an off-center load calculation rule library covering multiple suspension types based on the test calibration data. S102. Match the corresponding target off-center load reference data and target off-center load calculation rules in the off-center load reference database and off-center load calculation rule library according to the target suspension type of the target vehicle. S103. Obtain the standard sensor signals of the target vehicle in steady-state straight driving state, and determine the off-center load estimation result and estimation accuracy indicator of the target vehicle based on the standard sensor signals, target off-center load reference data and target off-center load calculation rules. S104. Output the off-center load estimation result and estimation accuracy indicator to the on-board control system of the target vehicle; The off-center load estimation results include the estimated total vehicle mass, the estimated center of gravity location, and the estimated wheel load distribution among the four wheels.

[0035] A complete implementation flow of the vehicle mass and wheel load distribution estimation method based on steady-state signal decoupling according to an embodiment of the present invention is as follows: 1) Constructing the off-center load benchmark database and solution rule base Before mass production of a new model, a whole vehicle mass-center of gravity-wheel load distribution off-center load reference database and a full model off-center load calculation rule library are established based on the existing results of the national standard mandatory test for new vehicle development. The national standard mandatory test includes whole vehicle mass parameter measurement test and wheel load distribution measurement test. The establishment of the off-center load reference database and the calculation rule library can reuse 100% of the existing data from the national standard mandatory test without adding any bench tests and calibration work.

[0036] 2) Real vehicle steady-state off-center load pre-decoupling When the vehicle is in operation, based on the steady-state straight-line signal collected by the standard sensors of the mass-produced vehicle, combined with the established off-center load reference database and calculation rule base, the vehicle's current total mass, center of gravity position and the real wheel load reference of the four wheels are calculated in real time.

[0037] The pre-decoupling of the off-center load automatically matches the corresponding accuracy level of the calculation scheme according to the vehicle suspension type and standard sensor configuration: for models equipped with suspension load sensors or height sensors, it outputs a fine estimate; for models not equipped with suspension load sensors and height sensors, it outputs a coarse estimate; the pre-decoupling of the off-center load does not depend on the wheel center input force calculation results throughout the process.

[0038] 3) Output the results of graded off-center load decoupling. The calculated vehicle's current total mass, center of gravity position, and estimated actual wheel load of the four wheels, along with the corresponding accuracy level identifier, are output to the vehicle control system to provide a real-time parameter reference for at least one of the suspension control, braking control, and drive force distribution systems.

[0039] It can be recognized that the embodiments of the present invention establish an off-center load reference database and an off-center load calculation rule library covering different suspension types based on test calibration data. According to the suspension type of the target vehicle, the corresponding off-center load reference data and off-center load calculation rules are matched. Combined with the standard sensor signals of the target vehicle in steady-state execution state, the off-center load estimation results of the corresponding accuracy level are solved, thereby realizing the accuracy-level estimation of vehicle mass and wheel load distribution and improving the reliability of vehicle mass and wheel load distribution estimation.

[0040] As an optional implementation, the suspension type includes air spring suspension, electronically controlled passive suspension with height sensor, and passive suspension without height sensor. The test calibration data includes the measurement results of the vehicle mass parameters, center of gravity height, and wheel load distribution of the test vehicle under different loading conditions. When the suspension type is air spring suspension, the test calibration data also includes airbag pressure measurement results and suspension height measurement results. When the suspension type is electronically controlled passive suspension with height sensor, the test calibration data also includes suspension height measurement results.

[0041] Specifically, the test calibration data of this invention covers three types of suspensions: air spring suspension, electronically controlled passive suspension with a height sensor, and passive suspension without a height sensor. The test calibration data can be readily available results from mandatory national standards tests for new vehicle development, including vehicle mass parameter measurement results specified in GB / T 12534, wheel load distribution measurement results specified in GB 7258, and center of gravity height measurement results in vehicle announcement certification. Furthermore, when the suspension type is air spring suspension, the test calibration data also includes airbag pressure measurement results and suspension height measurement results, used to subsequently establish the mapping relationship between airbag pressure, suspension height, and wheel vertical load. When the suspension type is electronically controlled passive suspension with a height sensor, the test calibration data also includes suspension height measurement results, used to subsequently establish the mapping relationship between changes in suspension height and changes in wheel vertical load.

[0042] As a further optional implementation, an off-center load reference database and an off-center load calculation rule base are established based on test calibration data, covering multiple suspension types. Specifically, this includes: S1011. Based on the test calibration data, determine the reference values ​​of the total vehicle mass, center of gravity position, and four-wheel load distribution of the test vehicle under no-load conditions for each suspension type. S1012. Construct an off-center load reference database based on the vehicle's total mass reference value, center of gravity position reference value, and four-wheel load distribution reference value; S1013. For air spring suspension, determine the first mapping relationship between air spring pressure, suspension height and wheel vertical load based on the corresponding test calibration data, and determine the first off-center load calculation rule based on the first mapping relationship. S1014. For an electronically controlled passive suspension with a height sensor, determine the second mapping relationship between the suspension height change and the wheel vertical load change based on the corresponding test calibration data, and determine the second off-center load calculation rule based on the second mapping relationship. S1015. For passive suspensions without height sensors, the third off-center load calculation rule is determined based on the vehicle's longitudinal dynamics equations. S1016. Construct an off-center load calculation rule library based on the first off-center load calculation rule, the second off-center load calculation rule, and the third off-center load calculation rule.

[0043] Specifically, three types of national standard test data completed during the new vehicle development phase are extracted: measurement results of parameters such as vehicle curb weight and maximum gross weight as specified in GB / T 12534; measurement results of front and rear axle load distribution under no-load and fully-load conditions as specified in GB 7258; and measurement results of center of gravity height in vehicle announcement certification. These data are categorized and organized by vehicle model to establish a "Vehicle Weight-Center of Gravity-Wheel Load Distribution Off-center Load Reference Database." Each vehicle model corresponds to a set of reference parameters under no-load conditions, including gross weight, center of gravity coordinates, four-wheel load values, and axle load ratio. For different suspension types (air spring suspension, electronically controlled passive suspension, and conventional passive suspension) and sensor configurations, corresponding off-center load calculation logic is developed, forming a full-vehicle off-center load calculation rule library, clarifying the type of calculation scheme matched to different vehicle models.

[0044] It should be noted that for air spring suspension vehicles and electronically controlled suspension vehicles with height sensors, this invention uses their standard sensors for precise estimation. The process of constructing the mapping relationship between "sensor signal → wheel vertical load" is not the focus of this invention. It can be obtained through various methods such as existing formulas, curve fitting, and machine learning models, and will not be elaborated here.

[0045] As a further optional implementation, the corresponding target off-center load reference data and target off-center load calculation rules are matched in the off-center load reference database and the off-center load calculation rule base according to the target suspension type of the target vehicle. Specifically, this includes: S1021. Obtain the target suspension type of the target vehicle; S1022. Match the corresponding target off-center load reference data in the off-center load reference database according to the target suspension type. The target off-center load reference data includes the target vehicle total mass reference value, the target center of gravity position reference value, and the target four-wheel load distribution reference value. S1023. Match the corresponding target off-center load calculation rule in the off-center load calculation rule library according to the target suspension type. The target off-center load calculation rule is one of the first off-center load calculation rule, the second off-center load calculation rule, and the third off-center load calculation rule.

[0046] In some optional embodiments, the target vehicle is determined to be in a steady-state straight-line driving state when the following conditions are met simultaneously: the absolute value of the steering wheel angle is less than a preset threshold, the absolute value of the yaw rate is less than a preset threshold, and the rate of change of longitudinal acceleration is less than a preset threshold. If the above conditions continue for more than a preset time window, the vehicle is determined to have entered a steady-state straight-line driving state, triggering the pre-decoupling of the off-center load.

[0047] As an optional implementation, when the target suspension type is an air spring suspension, the standard sensor signals include airbag pressure signal and suspension height signal; when the target suspension type is an electronically controlled passive suspension with a height sensor, the standard sensor signals include suspension height signal; and when the target suspension type is a passive suspension without a height sensor, the standard sensor signals include longitudinal acceleration signal and driving force / braking force signal.

[0048] As a further optional implementation, the target vehicle's off-center load estimation result and estimation accuracy indicator are determined based on the standard sensor signals, target off-center load reference data, and target off-center load calculation rules. Specifically, this includes: S1031. When the target suspension type is an air spring suspension, determine the real-time wheel vertical load of each wheel based on the airbag pressure signal, suspension height signal and the first mapping relationship, determine the estimated value of the four-wheel load distribution based on the real-time wheel vertical load, determine the estimated value of the total vehicle mass based on the sum of the real-time wheel vertical loads, determine the estimated value of the center of gravity position based on the load distribution of the real-time wheel vertical loads, and determine the corresponding estimation accuracy as high accuracy. S1032. When the target suspension type is an electronically controlled passive suspension with a height sensor, determine the real-time vertical load change of each wheel based on the suspension height signal and the second mapping relationship. Determine the real-time wheel vertical load of each wheel based on the target four-wheel load distribution reference value and the real-time vertical load change. Determine the estimated value of the four-wheel load distribution based on the real-time wheel vertical load. Determine the estimated value of the total vehicle mass based on the sum of the real-time wheel vertical loads. Determine the estimated value of the center of gravity position based on the load distribution of the real-time wheel vertical loads, and mark the corresponding estimation accuracy as high accuracy. S1033. When the target suspension type is a passive suspension without a height sensor, determine the estimated total mass of the vehicle based on the longitudinal acceleration signal and the driving force / braking force signal, determine the estimated center of gravity position and the estimated four-wheel load distribution based on the target center of gravity position reference value and the target four-wheel load distribution reference value, and determine the corresponding estimation accuracy as low accuracy.

[0049] Specifically, for air spring suspension models, the real-time pressure of each airbag is obtained based on the standard air spring load sensor. Combined with the real-time suspension height obtained by the height sensor, the real-time vertical load of each wheel is calculated. The sum of the vertical loads of the four wheels yields the current total mass. The center of gravity position is calculated through the load distribution difference, and finally, the precise estimates of the total mass, center of gravity coordinates, and wheel loads of the four wheels are output. For electronically controlled passive suspension models with height sensors, the real-time signals of the four vehicle height sensors are collected, the difference between the current vehicle height and the unloaded reference height is calculated, and the change in vertical load of each wheel is calculated using the second mapping relationship. The unloaded wheel load values ​​in the reference database are then superimposed to obtain the current true wheel loads of the four wheels, and then the calculation is performed. The system provides precise estimates of total mass and center of gravity position. For vehicles with passive suspension and no height sensor, the longitudinal acceleration and braking pressure signals of the ESP system are collected when the vehicle is in a starting acceleration or braking deceleration state. Under the condition that the longitudinal acceleration of the vehicle during starting acceleration or braking deceleration is perceptible, the system substitutes these signals into the vehicle's longitudinal dynamics equation (total mass = driving force / longitudinal acceleration, or total mass = braking force / longitudinal deceleration) to calculate a coarse estimate of the current total mass of the vehicle. This coarse estimate can be used for braking force distribution correction of the braking system and feedforward adjustment of the driving force distribution system. However, the four-wheel load distribution and center of gravity lateral position are not output as precise estimates; only the default distribution values ​​based on the unloaded wheel load ratio are output.

[0050] It should be noted that the estimation accuracy of the off-center load estimation results obtained by the vehicle models with air spring suspension and electronically controlled passive suspension with height sensor is marked as high accuracy, while the estimation accuracy of the off-center load estimation results obtained by the passive suspension without height sensor is marked as low accuracy. The accuracy level is output synchronously with the off-center load estimation results, so that the downstream vehicle control system can adjust the confidence and gain of the control strategy according to the accuracy level.

[0051] As an optional implementation, the off-center load estimation results and estimation accuracy indicators are used to input the suspension control system for damping and stiffness pre-adjustment, input the braking control system for braking force distribution correction, input the driving force distribution system for driving torque adjustment, or input the wheel center input force calculation system for dynamic updating of the vehicle resultant force balance verification benchmark.

[0052] Specifically, the actual wheel loads of the four wheels are output to the suspension control system, which pre-adjusts damping and stiffness based on the actual wheel loads to improve comfort and handling stability. The current total mass of the vehicle is output to the braking control system, which uses the current total mass to correct the braking force distribution to avoid wheel lock-up or insufficient braking force during braking. The center of gravity position is output to the drive force distribution system, which adjusts the driving torque of the front and rear, and left and right wheels based on the center of gravity position to optimize the power performance during start-up and acceleration. In addition, the off-center load estimation results are also used to dynamically update the vehicle resultant force balance verification benchmark of the wheel center input calculation system. The wheel center input calculation system locks the non-core directional force variables based on dependency-free pre-identification rules, and calculates the wheel center input after transforming the underdetermined equations into positive definite equations.

[0053] The method steps of the embodiments of the present invention have been described above. It can be understood that the embodiments of the present invention establish an off-center load reference database and an off-center load calculation rule library covering different suspension types based on experimental calibration data. According to the suspension type of the target vehicle, the corresponding off-center load reference data and off-center load calculation rules are matched. Combined with the standard sensor signals of the target vehicle under steady-state execution, the off-center load estimation results of corresponding accuracy levels are solved, realizing the accuracy-level estimation of vehicle mass and wheel load distribution, and improving the reliability of vehicle mass and wheel load distribution estimation.

[0054] Compared with the prior art, the embodiments of the present invention have the following advantages: 1) Full vehicle model coverage, filling a market gap: This invention is the first to propose an off-center load decoupling framework covering the entire vehicle model spectrum, including air spring suspension, electronically controlled passive suspension, and entry-level passive suspension. In particular, for entry-level models without height sensors, it provides a feasible coarse-grained mass estimation scheme, filling a gap in the existing technology.

[0055] 2) Accuracy Grading for True Reliability: This invention introduces an accuracy grading mechanism for the first time in the field of vehicle mass estimation, clearly indicating the accuracy level corresponding to the sensor configuration of each vehicle model. For entry-level vehicles with limited sensor capabilities, it outputs realistic coarse estimates rather than false high-precision values, avoiding misleading downstream control systems and meeting the functional safety requirements for signal confidence.

[0056] 3) Zero incremental calibration cost: This invention reuses existing data from national standard mandatory tests, without the need for any additional bench tests or calibration work, which greatly reduces the implementation threshold and vehicle model adaptation cost of the solution.

[0057] 4) Deep collaboration with the wheel center calculation system: This invention uses the off-center load decoupling results to dynamically update the vehicle resultant force balance verification benchmark of the wheel center input force calculation system, realizing closed-loop collaboration between mass estimation and dynamic calculation, and improving the accuracy and robustness of the overall system.

[0058] Reference Figure 2This invention provides a vehicle mass and wheel load distribution estimation device based on steady-state signal decoupling, comprising: The test data processing module is used to acquire test calibration data of test vehicles with multiple suspension types, and to establish an off-center load benchmark database and an off-center load calculation rule library covering multiple suspension types based on the test calibration data. The calculation rule matching module is used to match the corresponding target off-center load reference data and target off-center load calculation rules in the off-center load reference database and the off-center load calculation rule library according to the target suspension type of the target vehicle. The off-center load estimation module is used to acquire the standard sensor signals of the target vehicle in steady-state straight driving, and determine the off-center load estimation result and estimation accuracy of the target vehicle based on the standard sensor signals, target off-center load reference data and target off-center load calculation rules. The result output module is used to output the off-center load estimation result and estimation accuracy indicator to the on-board control system of the target vehicle. The off-center load estimation results include the estimated total vehicle mass, the estimated center of gravity location, and the estimated wheel load distribution among the four wheels.

[0059] It is understood that the content of the above method embodiments is applicable to the present device embodiments. The specific functions implemented by the present device embodiments are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0060] Reference Figure 3 This invention provides an electronic device, comprising: At least one processor; At least one memory for storing at least one program; When the above-mentioned at least one program is executed by the above-mentioned at least one processor, the above-mentioned at least one processor implements the above-mentioned method for estimating vehicle mass and wheel load distribution based on steady-state signal decoupling.

[0061] It is understood that the content of the above method embodiments is applicable to this device embodiment. The specific functions implemented by this device embodiment are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0062] This invention also provides a computer-readable storage medium storing a processor-executable computer program that, when executed by a processor, implements the above-described method for estimating vehicle mass and wheel load distribution based on steady-state signal decoupling.

[0063] This invention provides a computer-readable storage medium that can execute a vehicle mass and wheel load distribution estimation method based on steady-state signal decoupling provided in the method embodiments of this invention. It can execute any combination of the implementation steps of the method embodiments and has the corresponding functions and beneficial effects of the method.

[0064] This invention also provides a computer program product, including a computer program that, when executed by a processor, implements the above-described method for estimating vehicle mass and wheel load distribution based on steady-state signal decoupling.

[0065] It is understood that the content of the above method embodiments is applicable to the embodiments of this program product. The specific functions implemented by the embodiments of this program product are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0066] Memory, as a non-transitory computer-readable storage medium, can be used to store non-transitory software programs and non-transitory computer-executable programs. Furthermore, memory may include high-speed random access memory, and may also include non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid-state storage device. In some embodiments, memory may optionally include memory remotely located relative to the processor, and these remote memories can be connected to the processor via a network. Examples of such networks include, but are not limited to, the Internet, intranets, local area networks, mobile communication networks, and combinations thereof.

[0067] The embodiments described in this invention are for the purpose of more clearly illustrating the technical solutions of the embodiments of this invention, and do not constitute a limitation on the technical solutions provided by the embodiments of this invention. As those skilled in the art will know, with the evolution of technology and the emergence of new application scenarios, the technical solutions provided by the embodiments of this invention are also applicable to similar technical problems.

[0068] The terms "first," "second," "third," "fourth," etc. (if present) in the specification and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0069] In some alternative embodiments, the functions / operations mentioned in the block diagrams may not occur in the order shown in the operation diagrams. For example, depending on the functions / operations involved, two consecutively shown blocks may actually be executed substantially simultaneously, or the aforementioned blocks may sometimes be executed in reverse order. Furthermore, the embodiments presented and described in the flowcharts of this invention are provided by way of example to provide a more comprehensive understanding of the technology. The disclosed methods are not limited to the operations and logic flows presented herein. Alternative embodiments are contemplated in which the order of various operations is changed and sub-operations described as part of a larger operation are executed independently.

[0070] Furthermore, although the invention has been described in the context of functional modules, it should be understood that, unless otherwise stated, one or more of the aforementioned functions and / or features may be integrated into a single physical device and / or software module, or one or more functions and / or features may be implemented in a separate physical device or software module. It is also understood that a detailed discussion of the actual implementation of each module is unnecessary for understanding the invention. Rather, given the properties, functions, and internal relationships of the various functional modules in the apparatus disclosed herein, the actual implementation of the module will be understood within the scope of conventional skill of an engineer. Therefore, those skilled in the art can implement the invention as set forth in the claims using ordinary techniques without excessive experimentation. It is also understood that the specific concepts disclosed are merely illustrative and not intended to limit the scope of the invention, which is determined by the full scope of the appended claims and their equivalents.

[0071] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0072] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-including system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device.

[0073] More specific examples (a non-exhaustive list) of computer-readable media include: electrical connections (electronic devices) having one or more wires, portable computer disk drives (magnetic devices), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Furthermore, computer-readable media can even be paper or other suitable media on which the aforementioned program can be printed, because the aforementioned program can be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, interpreting, or otherwise processing as necessary, and then stored in computer memory.

[0074] It should be understood that various parts of the present invention can be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0075] In the foregoing description of this specification, references to terms such as "one embodiment," "another embodiment," or "some embodiments" indicate that a specific feature, structure, material, or characteristic described in connection with an embodiment or example is included in at least one embodiment or example of the present invention. In this specification, illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0076] Although embodiments of the invention have been shown and described, those skilled in the art will understand that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the claims and their equivalents.

[0077] The above is a detailed description of the preferred embodiments of the present invention. However, the present invention is not limited to the above embodiments. Those skilled in the art can make various equivalent modifications or substitutions without departing from the spirit of the present invention. All such equivalent modifications or substitutions are included within the scope defined by the claims of the present invention.

Claims

1. A method for estimating vehicle mass and wheel load distribution based on steady-state signal decoupling, characterized in that, Includes the following steps: Acquire test calibration data of test vehicles with multiple suspension types, and establish an off-center load benchmark database and an off-center load calculation rule library covering multiple suspension types based on the test calibration data; According to the target suspension type of the target vehicle, the corresponding target off-center load reference data and target off-center load calculation rules are matched in the off-center load reference database and the off-center load calculation rule base; Acquire the standard sensor signals of the target vehicle in steady-state straight driving condition, and determine the off-center load estimation result and estimation accuracy indicator of the target vehicle based on the standard sensor signals, the target off-center load reference data and the target off-center load calculation rules; The off-center load estimation result and the estimation accuracy indicator are output to the vehicle control system of the target vehicle; The off-center load estimation results include the estimated total vehicle mass, the estimated center of gravity position, and the estimated wheel load distribution of the four wheels.

2. The method for estimating vehicle mass and wheel load distribution based on steady-state signal decoupling according to claim 1, characterized in that, The suspension types include air spring suspension, electronically controlled passive suspension with a height sensor, and passive suspension without a height sensor. The test calibration data includes the vehicle mass parameters, center of gravity height, and wheel load distribution measurements of the test vehicle under different loading conditions. When the suspension type is air spring suspension, the test calibration data also includes airbag pressure measurement results and suspension height measurement results. When the suspension type is electronically controlled passive suspension with a height sensor, the test calibration data also includes suspension height measurement results.

3. The method for estimating vehicle mass and wheel load distribution based on steady-state signal decoupling according to claim 2, characterized in that, The step of establishing an off-center load benchmark database and an off-center load calculation rule library covering multiple suspension types based on the test calibration data specifically includes: Based on the test calibration data, determine the reference values ​​for the total vehicle mass, center of gravity position, and four-wheel load distribution of the test vehicles for each suspension type under no-load conditions. The off-center load reference database is constructed based on the vehicle total mass reference value, the center of gravity position reference value, and the four-wheel load distribution reference value. For air spring suspension, a first mapping relationship between air spring pressure, suspension height and wheel vertical load is determined based on the corresponding test calibration data, and a first off-center load calculation rule is determined based on the first mapping relationship. For an electronically controlled passive suspension with a height sensor, a second mapping relationship between the suspension height change and the wheel vertical load change is determined based on the corresponding test calibration data, and a second off-center load calculation rule is determined based on the second mapping relationship. For passive suspensions without height sensors, the third off-center load calculation rule is determined based on the vehicle's longitudinal dynamics equations. The off-center load calculation rule library is constructed based on the first off-center load calculation rule, the second off-center load calculation rule, and the third off-center load calculation rule.

4. The method for estimating vehicle mass and wheel load distribution based on steady-state signal decoupling according to claim 3, characterized in that, The step of matching the corresponding target off-center load reference data and target off-center load calculation rules in the off-center load reference database and the off-center load calculation rule base according to the target suspension type of the target vehicle specifically includes: Obtain the target suspension type of the target vehicle; According to the target suspension type, the corresponding target off-center load reference data is obtained by matching the off-center load reference database. The target off-center load reference data includes the target vehicle total mass reference value, the target center of gravity position reference value, and the target four-wheel load distribution reference value. According to the target suspension type, the corresponding target off-center load calculation rule is obtained by matching in the off-center load calculation rule library. The target off-center load calculation rule is one of the first off-center load calculation rule, the second off-center load calculation rule, and the third off-center load calculation rule.

5. The method for estimating vehicle mass and wheel load distribution based on steady-state signal decoupling according to claim 4, characterized in that, When the target suspension type is an air spring suspension, the standard sensor signals include airbag pressure signal and suspension height signal. When the target suspension type is an electronically controlled passive suspension with a height sensor, the standard sensor signals include suspension height signal. When the target suspension type is a passive suspension without a height sensor, the standard sensor signals include longitudinal acceleration signal and driving force / braking force signal.

6. The method for estimating vehicle mass and wheel load distribution based on steady-state signal decoupling according to claim 5, characterized in that, The step of determining the off-center load estimation result and estimation accuracy indicator of the target vehicle based on the standard sensor signal, the target off-center load reference data, and the target off-center load calculation rule specifically includes: When the target suspension type is an air spring suspension, the real-time wheel vertical load of each wheel is determined according to the airbag pressure signal, the suspension height signal and the first mapping relationship. The estimated value of the four-wheel load distribution is determined according to the real-time wheel vertical load. The estimated value of the total vehicle mass is determined according to the sum of the real-time wheel vertical loads. The estimated value of the center of gravity position is determined according to the load distribution of the real-time wheel vertical loads, and the corresponding estimation accuracy is identified as high accuracy. When the target suspension type is an electronically controlled passive suspension with a height sensor, the real-time vertical load change of each wheel is determined according to the suspension height signal and the second mapping relationship. The real-time wheel vertical load of each wheel is determined according to the target four-wheel load distribution reference value and the real-time vertical load change. The four-wheel load distribution estimate is determined according to the real-time wheel vertical load. The total mass estimate of the vehicle is determined according to the sum of the real-time wheel vertical loads. The center of gravity position estimate is determined according to the load distribution of the real-time wheel vertical loads, and the corresponding estimation accuracy is identified as high accuracy. When the target suspension type is a passive suspension without a height sensor, the estimated total mass of the vehicle is determined based on the longitudinal acceleration signal and the driving force / braking force signal. The estimated center of gravity position and the estimated four-wheel load distribution are determined based on the target center of gravity position reference value and the target four-wheel load distribution reference value, and the corresponding estimation accuracy is identified as low accuracy.

7. A method for estimating vehicle mass and wheel load distribution based on steady-state signal decoupling according to any one of claims 1 to 6, characterized in that, The off-center load estimation result and the estimation accuracy indicator are used to input the suspension control system for damping and stiffness pre-adjustment, input the braking control system for braking force distribution correction, input the driving force distribution system for driving torque adjustment, or input the wheel center input force calculation system for dynamic updating of the vehicle resultant force balance verification benchmark.

8. A vehicle mass and wheel load distribution estimation device based on steady-state signal decoupling, characterized in that, include: The test data processing module is used to acquire test calibration data of test vehicles with multiple suspension types, and to establish an off-center load benchmark database and an off-center load calculation rule library covering multiple suspension types based on the test calibration data. The calculation rule matching module is used to match the corresponding target off-center load reference data and target off-center load calculation rules in the off-center load reference database and the off-center load calculation rule library according to the target suspension type of the target vehicle. The off-center load estimation module is used to acquire the standard sensor signals of the target vehicle in steady-state straight driving, and determine the off-center load estimation result and estimation accuracy indicator of the target vehicle based on the standard sensor signals, the target off-center load reference data and the target off-center load calculation rules. The result output module is used to output the off-center load estimation result and the estimation accuracy identifier to the vehicle control system of the target vehicle. The off-center load estimation results include the estimated total vehicle mass, the estimated center of gravity position, and the estimated wheel load distribution of the four wheels.

9. An electronic device, characterized in that, include: At least one processor; At least one memory for storing at least one program; When the at least one program is executed by the at least one processor, the at least one processor implements a vehicle mass and wheel load distribution estimation method based on steady-state signal decoupling as described in any one of claims 1 to 7.

10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by the processor, it implements a vehicle mass and wheel load distribution estimation method based on steady-state signal decoupling as described in any one of claims 1 to 7.

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