Vehicle mass estimation method and device, equipment and storage medium

Abstract

The invention discloses a vehicle mass estimation method, device and equipment and a storage medium, relates to the technical field of vehicles, and is used for improving the speed and accuracy of determining an estimation result of the vehicle mass. The method comprises the steps that vehicle driving data are acquired, and the acceleration and driving force of a vehicle at the current moment are determined based on the vehicle driving data; determining a mass estimation value of the vehicle at the current moment based on a least square method carrying a forgetting factor and the acceleration and the driving force of the vehicle at the current moment; wherein the forgetting factor is determined according to the mass estimation value of the vehicle at the previous moment.

Description

Technical Field

[0001] This invention relates to the field of vehicle technology, and in particular to a method, apparatus, device, and storage medium for estimating vehicle mass. Background Technology

[0002] In recent years, with the rapid development of the automotive industry, users have become increasingly concerned about driving safety. For vehicles, mass affects factors such as vehicle attitude and braking distance; therefore, estimating mass can help determine driving safety.

[0003] In related technologies, vehicle mass can be estimated based on sensors and vehicle dynamics models. Sensor-based vehicle mass estimation estimates the mass by measuring the deformation of the vehicle's load-bearing structure due to gravity after the vehicle is loaded. This method is simple but very costly. Vehicle mass estimation based on vehicle dynamics models estimates the vehicle mass based on the vehicle's dynamics during operation. This method only requires acquiring the vehicle's operating data to estimate the mass; however, the vehicle's operating data is affected by many factors, resulting in inaccurate vehicle mass estimates.

[0004] Therefore, how to accurately estimate vehicle weight while reducing estimation costs is a pressing technical problem that needs to be solved. Summary of the Invention

[0005] The purpose of this invention is to provide a method, apparatus, device, and storage medium for estimating vehicle mass, aiming to solve the technical problem in related technologies that cannot simultaneously consider the cost of vehicle mass estimation and the accuracy of vehicle mass estimation results.

[0006] To achieve the above objectives, the present invention adopts the following technical solution:

[0007] In a first aspect, this application provides a method for estimating vehicle mass, comprising: acquiring vehicle driving data, determining the vehicle's acceleration and driving force at the current moment based on the vehicle driving data; determining an estimated mass value of the vehicle at the current moment based on the least squares method with a forgetting factor, and the vehicle's acceleration and driving force at the current moment; wherein the forgetting factor is determined based on the estimated mass value of the vehicle at the previous moment.

[0008] The vehicle mass estimation method provided in this application determines the estimated mass of the vehicle at the current moment based on the least squares method with a forgetting factor, according to the vehicle's acceleration and driving force at the current moment. The forgetting factor in this application changes as the estimated mass changes, specifically determined based on the estimated mass of the vehicle at the previous moment. Therefore, based on the least squares method with a forgetting factor, the driving force is used as the true value, the error between the product of the estimated mass and acceleration is calculated, and the error is corrected by introducing the forgetting factor, thereby achieving an approximation of the true value. Compared to vehicle mass estimation based on sensors, this application can directly acquire vehicle driving data and determine the estimated mass based on the vehicle's acceleration and driving force, resulting in lower mass estimation costs. Compared to vehicle mass estimation based on vehicle dynamics models, this application, by introducing the least squares method with a forgetting factor, achieves faster mass estimation, and the forgetting factor, changing according to the estimated mass, improves the accuracy of the mass estimation results.

[0009] In some embodiments, determining the estimated mass of a vehicle at the current moment based on the least squares method with a forgetting factor and the vehicle's acceleration and driving force at the current moment includes: determining the estimated mass of a vehicle based on the least squares method with a forgetting factor and the vehicle's acceleration and driving force at the current moment, provided that the vehicle's operating characteristics satisfy the iteration start condition.

[0010] In some embodiments, the above iteration start conditions include at least one of the following: the driving force of the vehicle at the current moment is greater than the driving force threshold, the acceleration is greater than the acceleration threshold, the accelerator pedal depth is greater than the accelerator pedal depth threshold, the brake pedal depth is greater than the brake pedal depth threshold, the absolute value of the steering wheel angle is less than the angle threshold, and the absolute value of the slope is less than the slope threshold.

[0011] In some embodiments, if the estimated mass of the vehicle at the previous time step is less than the heavy load threshold, the iteration start condition is a first iteration start condition; or, if the estimated mass of the vehicle at the previous time step is greater than or equal to the heavy load threshold, the iteration start condition is a second iteration start condition; wherein the driving force threshold in the first iteration start condition is less than the driving force threshold in the second iteration start condition, and the acceleration threshold in the first iteration start condition is less than the acceleration threshold in the second iteration start condition.

[0012] In some embodiments, determining the estimated mass of the vehicle at the current moment based on the least squares method with a forgetting factor, and the vehicle's acceleration and driving force at the current moment includes: determining the incremental value of the vehicle at the current moment based on the vehicle's forgetting factor and acceleration at the current moment, and the error covariance at the previous moment; determining the error covariance of the vehicle at the current moment based on the vehicle's forgetting factor, acceleration, and incremental value at the current moment, and the error covariance at the previous moment; and determining the estimated mass of the vehicle at the current moment based on the vehicle's acceleration, driving force, and incremental value at the current moment, and the estimated mass of the vehicle at the previous moment.

[0013] In some embodiments, the above-described method of determining the vehicle's mass estimate at the current moment based on the least squares method with a forgetting factor and the vehicle's acceleration and driving force at the current moment includes: iteratively estimating the vehicle's mass based on the least squares method with a forgetting factor, the vehicle's acceleration and driving force at the current moment, until the iteration stopping condition is met and the vehicle's mass estimate is output.

[0014] In some embodiments, the above-mentioned iteration stopping condition includes: the number of iterations reaches a preset threshold number.

[0015] In some embodiments, if the estimated mass of the vehicle at the previous moment is less than the overload threshold, the preset number of times threshold is a first value; or, if the estimated mass of the vehicle at the previous moment is greater than or equal to the overload threshold, the preset number of times threshold is a second value; the first value is less than the second value.

[0016] In some embodiments, the forgetting factor is determined based on the vehicle's mass estimate at the previous time step, including: adjusting the forgetting factor at the current time step from the first forgetting factor to the second forgetting factor if the vehicle's mass estimate at the previous time step is greater than or equal to a heavy load threshold, and the vehicle's mass estimate at the previous time step is greater than or equal to a first mass threshold and less than a second mass threshold; or, adjusting the forgetting factor at the current time step from the second forgetting factor to the first forgetting factor if the vehicle's mass estimate at the previous time step is greater than or equal to a heavy load threshold, and the vehicle's mass estimate at the previous time step is greater than a second mass threshold; wherein the second forgetting factor is less than the first forgetting factor.

[0017] Secondly, this application provides a vehicle mass estimation device, including a determining unit and a processing unit; the determining unit is used to acquire vehicle driving data and determine the vehicle's acceleration and driving force at the current moment based on the vehicle driving data; the processing unit is used to determine the vehicle's mass estimate at the current moment based on the least squares method with a forgetting factor and the vehicle's acceleration and driving force at the current moment; wherein, the forgetting factor is determined based on the vehicle's mass estimate at the previous moment.

[0018] In some embodiments, the processing unit described above is specifically used to determine an estimated value of the vehicle mass based on the least squares method with a forgetting factor and the vehicle's acceleration and driving force at the current moment, provided that the vehicle's operating characteristics satisfy the iteration start condition.

[0019] In some embodiments, the above iteration start conditions include at least one of the following: the driving force of the vehicle at the current moment is greater than the driving force threshold, the acceleration is greater than the acceleration threshold, the accelerator pedal depth is greater than the accelerator pedal depth threshold, the brake pedal depth is greater than the brake pedal depth threshold, the absolute value of the steering wheel angle is less than the angle threshold, and the absolute value of the slope is less than the slope threshold.

[0020] In some embodiments, if the estimated mass of the vehicle at the previous time step is less than the heavy load threshold, the iteration start condition is a first iteration start condition; or, if the estimated mass of the vehicle at the previous time step is greater than or equal to the heavy load threshold, the iteration start condition is a second iteration start condition; wherein the driving force threshold in the first iteration start condition is less than the driving force threshold in the second iteration start condition, and the acceleration threshold in the first iteration start condition is less than the acceleration threshold in the second iteration start condition.

[0021] In some embodiments, the processing unit is specifically configured to: determine the incremental value of the vehicle at the current moment based on the vehicle's forgetting factor and acceleration at the current moment, and the error covariance at the previous moment; determine the error covariance of the vehicle at the current moment based on the vehicle's forgetting factor, acceleration, and incremental value at the current moment, and the error covariance at the previous moment; and determine the estimated mass of the vehicle at the current moment based on the vehicle's acceleration, driving force, and incremental value at the current moment, and the estimated mass value at the previous moment.

[0022] In some embodiments, the processing unit is specifically used to iteratively estimate the mass of the vehicle based on the least squares method with a forgetting factor, the vehicle's acceleration and driving force at the current moment, until the iteration stopping condition is met and the estimated mass value of the vehicle is output.

[0023] In some embodiments, the above-mentioned iteration stopping condition includes: the number of iterations reaches a preset threshold number.

[0024] In some embodiments, if the estimated mass of the vehicle at the previous moment is less than the overload threshold, the preset number of times threshold is a first value; or, if the estimated mass of the vehicle at the previous moment is greater than or equal to the overload threshold, the preset number of times threshold is a second value; the first value is less than the second value.

[0025] In some embodiments, the processing unit is specifically configured to adjust the forgetting factor at the current moment from the first forgetting factor to the second forgetting factor when the estimated mass value of the vehicle at the previous moment is greater than or equal to the heavy load threshold, and the estimated mass value of the vehicle at the previous moment is greater than or equal to the first mass threshold and less than the second mass threshold; or, when the estimated mass value of the vehicle at the previous moment is greater than or equal to the heavy load threshold, and the estimated mass value of the vehicle at the previous moment is greater than the second mass threshold, adjust the forgetting factor at the current moment from the second forgetting factor to the first forgetting factor; the second forgetting factor is less than the first forgetting factor.

[0026] Thirdly, this application provides an apparatus comprising: a processor and a memory configured to store processor-executable instructions; wherein the processor is configured to execute the instructions to implement any of the optional vehicle mass estimation methods described in the first aspect above.

[0027] Fourthly, this application provides a computer-readable storage medium storing instructions that, when executed by a device, enable the device to perform any of the optional vehicle mass estimation methods described in the first aspect.

[0028] Fifthly, this application provides a computer program product including computer instructions that, when executed on a processor of a device, enable the device to perform any of the optional vehicle mass estimation methods described in the first aspect above. Attached Figure Description

[0029] To more clearly illustrate the technical solutions of the embodiments of this application, the drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0030] Figure 1 A flowchart illustrating a method for estimating vehicle mass provided in an embodiment of this application;

[0031] Figure 2 A flowchart illustrating another method for estimating vehicle mass provided in this application embodiment;

[0032] Figure 3 A flowchart illustrating another method for estimating vehicle mass provided in this application embodiment;

[0033] Figure 4 A flowchart illustrating another method for estimating vehicle mass provided in this application embodiment;

[0034] Figure 5 A schematic diagram of the architecture of a vehicle mass estimation method provided in an embodiment of this application;

[0035] Figure 6 A flowchart illustrating another method for estimating vehicle mass provided in this application embodiment;

[0036] Figure 7 A schematic diagram of a vehicle mass estimation device provided in an embodiment of this application;

[0037] Figure 8 This is a schematic diagram of the structure of a device provided in an embodiment of this application. Detailed Implementation

[0038] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0039] In the description of this invention, it should be understood that the terms "upper," "lower," "left," "right," "front," "rear," "inner," and "outer," etc., indicate the orientation or positional relationship based on the orientation or relative positional relationship shown in the accompanying drawings. They are used only for the convenience of describing the invention and for simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on the invention. Unless otherwise specified, the above-described orientation can be flexibly set in practical applications, provided that the relative positional relationship shown in the accompanying drawings is satisfied.

[0040] The terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of this invention, unless otherwise stated, "a plurality of" means two or more.

[0041] In embodiments of the invention, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, article, or apparatus that includes that element.

[0042] In embodiments of the present invention, the terms "exemplary" or "for example" are used to indicate that something is an example, illustration, or description. Any embodiment or design described as "exemplary" or "for example" in embodiments of the present invention should not be construed as being more preferred or advantageous than other embodiments or designs. Rather, the use of the terms "exemplary" or "for example" is intended to present the relevant concepts in a specific manner.

[0043] In the description of this specification, specific features, structures, materials, or characteristics may be combined in any suitable manner in one or more embodiments or examples.

[0044] In recent years, with the rapid development of the automotive industry, users have become increasingly concerned about driving safety.

[0045] For vehicles, the mass of the vehicle is related to the vehicle's operating posture, braking distance, etc. Therefore, the safety of a vehicle can be determined by estimating its mass.

[0046] In related technologies, vehicle mass estimation based on sensors is achieved by measuring the physical deformation of the vehicle's load-bearing structure due to gravity after the vehicle is loaded using sensing devices. This method provides high accuracy and fast vehicle mass estimation, but adding sensing devices increases manufacturing costs and vehicle weight, which is not conducive to vehicle lightweighting and has a certain impact on the vehicle body structure.

[0047] Alternatively, vehicle mass estimation based on vehicle dynamics models involves estimating the vehicle's mass while it is in motion. This method uses Newton's second law to model the physical state of the vehicle and solve for the mass. While this method only requires acquiring the vehicle's operational data, this data is influenced by various factors, leading to inaccurate mass estimates.

[0048] Therefore, how to accurately estimate vehicle weight while reducing estimation costs is a pressing technical problem that needs to be solved.

[0049] Based on this, embodiments of this application provide a method, apparatus, device, and storage medium for estimating vehicle mass. By using a least squares method with a forgetting factor, the estimated mass of the vehicle at the current moment is determined based on the vehicle's acceleration and driving force. The forgetting factor in this embodiment changes as the estimated mass changes, specifically determined based on the estimated mass of the vehicle at the previous moment. Therefore, using the least squares method with a forgetting factor, the driving force is taken as the true value, and the error between the product of the estimated mass and acceleration is calculated. The error is corrected by introducing a forgetting factor, thereby achieving an approximation of the true value. Compared to vehicle mass estimation based on sensors, this application can directly acquire vehicle driving data and determine the estimated mass based on the vehicle's acceleration and driving force, resulting in lower mass estimation costs. Compared to vehicle mass estimation based on vehicle dynamics models, this application, by introducing a least squares method with a forgetting factor, achieves faster mass estimation, and the forgetting factor, changing according to the estimated mass, improves the accuracy of the mass estimation results.

[0050] It should be noted that the vehicle weight estimation method provided in this application is applied to a vehicle weight estimation device, which can be a personal computer (PC), laptop computer, mobile device, tablet computer, or other similar device. This application does not limit the specific form of the electronic device. Alternatively, the vehicle weight estimation device can be a single server or a server cluster consisting of multiple servers. In some implementations, the server cluster can be a distributed cluster server. This application does not impose any limitations in this regard.

[0051] like Figure 1 As shown, the method for estimating the vehicle's mass includes S101-S102.

[0052] S101. Obtain vehicle driving data and determine the vehicle's acceleration and driving force at the current moment based on the vehicle driving data.

[0053] The vehicle driving data includes at least one of the following: wheel torque, vehicle speed, accelerator pedal depth, brake pedal depth, steering wheel angle, and slope.

[0054] It is important to understand that a vehicle contains multiple sensors that monitor vehicle driving data in real time and send this data to a vehicle mass estimation device. For example, an angle sensor is installed on the steering wheel, which monitors the steering wheel's rotation angle at every moment during vehicle operation and sends this angle to the vehicle mass estimation device.

[0055] As one possible implementation, the process of determining the vehicle's acceleration at the current moment based on vehicle driving data can be as follows: The vehicle mass estimation device can determine the vehicle's acceleration at the current moment based on the vehicle's speed at the current moment and the speed at the previous moment. Since the vehicle is also affected by resistance during driving, it is also necessary to subtract the gradient resistance and rolling resistance acceleration from the vehicle's acceleration at the current moment. The gradient resistance and rolling resistance acceleration can be preset based on the vehicle's historical experience values.

[0056] As another possible implementation, the process of determining the vehicle's driving force at the current moment based on vehicle driving data can be as follows: a vehicle mass estimation device can determine the driving force at the current moment based on the wheel-end torque. Since the vehicle is also affected by wind resistance during driving, it is also necessary to subtract the wind resistance from the vehicle's acceleration at the current moment. Since the frontal area of ​​the same type of vehicle is the same, the wind resistance can be preset based on historical experience values.

[0057] It is important to understand that vehicle driving data is easily affected by high-frequency noise signals, resulting in large errors in the acceleration and driving force obtained using the above method. Therefore, after obtaining the vehicle's acceleration and driving force at the current moment, filtering can be performed on the acceleration and driving force. For example, a second-order low-pass filter is performed on the acceleration and driving force. The calculation process of the second-order filter can be shown in formula (1):

[0058] V0(n)=αV i Formula (1) is: (n) + βV0(n-1) + εV0(n-2)

[0059] Where V0(n) is the filter value at the current time n, V i (n) represents the input data, V0(n-1) represents the filtered value at time n-1, V0(n-2) represents the filtered value at time n-2, and α, β, and ε are the filtering coefficients.

[0060] Wherein, α, β and ε are determined according to formulas (2), (3) and (4) respectively:

[0061]

[0062]

[0063] Among them, T s F is the sampling frequency. c This is the cutoff frequency.

[0064] S102. Based on the least squares method with a forgetting factor, and the vehicle's acceleration and driving force at the current moment, determine the estimated mass of the vehicle at the current moment.

[0065] The forgetting factor is determined based on the vehicle's mass estimate at the previous moment.

[0066] It's important to understand that the forgetting factor least squares method is a commonly used algorithm in signal processing and control system design. This algorithm starts with initial values ​​and adaptively updates the fitting parameters in time-varying systems to optimize the results. By introducing a forgetting factor, the influence of historical data on the current fitting results is reduced, thus achieving a better fit to new data.

[0067] The forgetting factor is a value between 0 and 1, typically ranging from 0.95 to 0.99. Introducing the forgetting factor can reduce the influence of old data while enhancing the impact of new data in parameter estimation, thus preventing the algorithm from losing its ability to correct parameters due to over-reliance on old data.

[0068] As one possible implementation, the vehicle mass estimation device can determine the vehicle's increment value at the current moment based on the vehicle's forgetting factor and acceleration at the current moment, and the error covariance at the previous moment; determine the vehicle's error covariance at the current moment based on the vehicle's forgetting factor, acceleration, and increment value at the current moment, and the error covariance at the previous moment; and determine the vehicle's estimated mass value at the current moment based on the vehicle's acceleration, driving force, and increment value at the current moment, and the estimated mass value at the previous moment.

[0069] The vehicle mass estimation device can determine the estimated value of the vehicle mass according to the following formulas (5)-(7):

[0070] m(n)=m(n-1)+K(n)[F(n)-a(n)m(n-1)] Formula (5)

[0071]

[0072] Where m(n) represents the mass estimate at the current moment, m(n-1) represents the mass estimate at the previous moment, F(n) represents the driving force at the current moment, a(n) represents the acceleration at the current moment, K(n) represents the increment at the current moment, P(n) represents the error covariance at the current moment, P(n-1) represents the error covariance at the previous moment, and μ(n) represents the forgetting factor at the current moment.

[0073] Compared to other mass estimation methods, this application uses the least squares method with a forgetting factor, which can optimize the mass estimation process by determining the error of the product of the driving force and the mass estimate and acceleration, thereby improving the accuracy of the mass estimation results.

[0074] In this embodiment, the driving force is used as the true value in the least squares method with a forgetting factor. The error between the driving force and the product of the mass estimate and acceleration is calculated, and the error is corrected by introducing a forgetting factor, thereby achieving an approximation of the true value. Compared to vehicle mass estimation based on sensors, this application can directly obtain vehicle driving data and determine the vehicle mass estimate based on the vehicle's acceleration and driving force, resulting in lower mass estimation costs. Compared to vehicle mass estimation based on vehicle dynamics models, this application, by introducing a least squares method with a forgetting factor, achieves faster mass estimation. Furthermore, the forgetting factor changes according to the mass estimate, improving the accuracy of the mass estimation results.

[0075] Combination Figure 1 ,like Figure 2 As shown, in one implementation of this application embodiment, the above-mentioned determination of the vehicle's mass estimate at the current moment based on the least squares method with a forgetting factor, and the vehicle's acceleration and driving force at the current moment may specifically include S201.

[0076] S201. If the vehicle's operating characteristics meet the iteration start condition, determine the estimated value of the vehicle's mass based on the least squares method with a forgetting factor, and the vehicle's acceleration and driving force at the current moment.

[0077] It should be understood that the operating characteristics of a vehicle include: the vehicle's driving data, as well as the vehicle's acceleration and driving force.

[0078] The above iteration start conditions include at least one of the following: the driving force of the vehicle at the current moment is greater than the driving force threshold, the acceleration is greater than the acceleration threshold, the accelerator pedal depth is greater than the accelerator pedal depth threshold, the brake pedal depth is greater than the brake pedal depth threshold, the absolute value of the steering wheel angle is less than the angle threshold, and the absolute value of the slope is less than the slope threshold.

[0079] It is understandable that a vehicle exists in two states during operation: heavily loaded and unloaded. Compared to the unloaded state, a heavily loaded vehicle requires a greater driving force to achieve the same acceleration. Therefore, if the iteration start conditions corresponding to the unloaded state are used to determine the estimated vehicle mass when the vehicle is in a heavily loaded state, it will lead to a deviation in the estimated vehicle mass. Therefore, this application distinguishes between the heavily loaded and unloaded states, thereby improving the accuracy of the estimated vehicle mass.

[0080] Therefore, if the vehicle's estimated mass at the previous time step is less than the heavy load threshold, the iteration starts under the first iteration start condition. Alternatively, if the vehicle's estimated mass at the previous time step is greater than or equal to the heavy load threshold, the iteration starts under the second iteration start condition.

[0081] Wherein, the driving force threshold in the first iteration start condition is less than the driving force threshold in the second iteration start condition, and the acceleration threshold in the first iteration start condition is less than the acceleration threshold in the second iteration start condition.

[0082] For example, the first iteration start condition may include at least one of the following: the driving force of the vehicle at the current moment is greater than a first driving force threshold, the acceleration is greater than a first acceleration threshold, the accelerator pedal depth is greater than an accelerator pedal depth threshold, the brake pedal depth is greater than a brake pedal depth threshold, the absolute value of the steering wheel angle is less than an angle threshold, and the absolute value of the slope is less than a slope threshold.

[0083] The conditions for starting the second iteration may include at least one of the following: the vehicle's driving force at the current moment is greater than a second driving force threshold, the acceleration is greater than a second acceleration threshold, the accelerator pedal depth is greater than an accelerator pedal depth threshold, the brake pedal depth is greater than a brake pedal depth threshold, the absolute value of the steering wheel angle is less than an angle threshold, and the absolute value of the slope is less than a slope threshold. The second driving force threshold is greater than the first driving force threshold, and the second acceleration threshold is greater than the first acceleration threshold.

[0084] In this embodiment of the application, by setting different iteration start conditions, the acceleration and driving force of the vehicle in the non-heavy load state and the heavy load state are distinguished, thereby improving the accuracy of the vehicle mass estimation value.

[0085] Combination Figure 2 ,like Figure 3 As shown, in one implementation of this application embodiment, the above-mentioned determination of the vehicle's mass estimate at the current moment based on the least squares method with a forgetting factor and the vehicle's acceleration and driving force at the current moment may further include S301.

[0086] S301. Based on the least squares method with a forgetting factor, the vehicle's acceleration and driving force at the current moment, the mass of the vehicle is iteratively estimated until the iteration stopping condition is met, at which point the estimated mass value of the vehicle is output.

[0087] The iteration stopping condition includes: the number of iterations reaches a preset threshold.

[0088] Understandably, least squares with a forgetting factor is a recursive algorithm. Therefore, when using least squares with a forgetting factor to determine the estimated mass of a vehicle, it is necessary to set an iteration termination condition for least squares with a forgetting factor to terminate the algorithm.

[0089] It should be noted that the vehicle mass estimation device can determine whether the vehicle is under heavy load based on the vehicle's mass estimation value at the previous moment. In this case, the least squares method with forgetting factor has started iterative calculation, and the number of iterations has also started counting. In order to ensure the accuracy of the final output vehicle mass estimation value, the preset number threshold needs to be increased.

[0090] Therefore, if the vehicle's estimated mass at the previous moment is less than the overload threshold, the preset number of attempts threshold is the first value. If the vehicle's estimated mass at the previous moment is greater than or equal to the overload threshold, the preset number of attempts threshold is the second value. The first value is less than the second value.

[0091] In this embodiment, the non-heavy load state and the heavy load state correspond to different iteration start conditions, that is, the non-heavy load state and the heavy load state of the vehicle also need to correspond to different iteration stop conditions. When the vehicle is in the heavy load state, increasing the preset number threshold can improve the accuracy of the vehicle mass estimation value.

[0092] It should be noted that, compared to a non-heavy-load state, a heavily loaded vehicle requires a greater driving force to achieve the same acceleration. Furthermore, the acceleration itself contains errors, leading to an underestimation of the final mass. To address this issue, this application introduces a forgetting factor to reduce the bias caused by errors. However, the presence of the forgetting factor causes the final estimation result to oscillate within a certain range around the true value; the amplitude of this oscillation can be limited by controlling the magnitude of the forgetting factor.

[0093] Based on this, in one implementation of the present application, the determination of the forgetting factor based on the vehicle's mass estimate at the previous moment may specifically include S401 and S402.

[0094] S401. If the estimated mass of the vehicle at the previous moment is greater than or equal to the heavy load threshold, and the estimated mass of the vehicle at the previous moment is greater than or equal to the first mass threshold and less than the second mass threshold, the forgetting factor at the current moment is adjusted from the first forgetting factor to the second forgetting factor.

[0095] The second forgetting factor is smaller than the first forgetting factor, and the first and second forgetting factors can be non-fixed values.

[0096] Understandably, in least squares with a forgetting factor, the choice of the forgetting factor has a significant impact on the algorithm's convergence speed and stability. A smaller forgetting factor will lead to faster convergence but may result in unstable results. Conversely, a larger forgetting factor will make the algorithm more stable but may reduce the convergence speed.

[0097] Therefore, when a vehicle is under heavy load, if the mass estimate at the previous moment is greater than or equal to the first mass threshold, or less than the second mass threshold, appropriately reducing the value of the forgetting factor can reduce the impact of the mass estimate at the previous moment on the mass estimate at the current moment, thereby accelerating the speed of iterative convergence.

[0098] S402. If the estimated mass of the vehicle at the previous moment is greater than or equal to the heavy load threshold, and the estimated mass of the vehicle at the previous moment is greater than the second mass threshold, the forgetting factor at the current moment is adjusted from the second forgetting factor to the first forgetting factor.

[0099] It is understandable that if the mass estimate at the previous moment is greater than the second mass threshold, appropriately increasing the forgetting factor can reduce the mass estimation error caused by the oscillations brought about by the forgetting factor.

[0100] In this embodiment of the application, by adjusting the size of the forgetting factor according to the vehicle's mass estimation value, it is possible not only to accelerate the convergence speed of the least squares method of the forgetting factor by appropriately reducing the forgetting factor, but also to reduce the error of the mass estimation value by appropriately increasing the forgetting factor.

[0101] The following example illustrates the vehicle mass estimation method provided in this application embodiment.

[0102] For example, such as Figure 4 As shown, the methods for estimating the vehicle's mass include S501-S506.

[0103] S501, Collect vehicle driving data.

[0104] S502, calculate acceleration and driving force.

[0105] S503. Based on the vehicle driving data, acceleration, and driving force, determine whether the iteration start condition is met.

[0106] S504. If the conditions for starting the first iteration are met, determine the estimated value of the vehicle mass based on the least squares method with a forgetting factor, and the vehicle's acceleration and driving force at the current moment.

[0107] S505. If the estimated mass of the vehicle at the previous moment is greater than or equal to the heavy load threshold, adjust the first iteration start condition to the second iteration start condition.

[0108] S506. The mass of the vehicle is iteratively estimated based on the least squares method with a forgetting factor until the iteration stopping condition is met, at which point the estimated mass value of the vehicle is output.

[0109] It should be noted that the iteration stopping conditions include: the number of iterations reaching a preset threshold. If the vehicle's estimated mass at the previous moment is less than the heavy load threshold (the preset threshold is a first value), and if the vehicle's estimated mass at the previous moment is greater than or equal to the heavy load threshold, the first value is adjusted to a second value. The first value is less than the second value.

[0110] The following example illustrates the architecture of the vehicle mass estimation method provided in this application embodiment.

[0111] like Figure 5 As shown, the methods for estimating the vehicle's mass include S601-S603.

[0112] S601 limits vehicle speed, accelerator pedal depth, brake pedal depth, gradient, steering wheel angle, acceleration, and wheel torque.

[0113] It should be noted that when the driving force is low, the acceleration is also low, and the acceleration is filtered, resulting in a certain delay, which can lead to inaccurate mass estimation. When going downhill or turning, the acceleration will be higher, and during braking, the braking force is provided by the braking system. Therefore, this application limits torque and acceleration to ensure the reliability of subsequent calculations. Considering that using non-heavy-load constraints under heavy loads would lead to calculation errors, the thresholds for torque and acceleration need to be increased to obtain better calculation results.

[0114] S602, Solve for acceleration and driving force.

[0115] It should be noted that acceleration consists of three quantities: vehicle speed change, gradient resistance acceleration, and rolling resistance acceleration. Vehicle speed change is calculated by averaging the current vehicle speed and the previous vehicle speed, while also considering the effects of gradient and rolling resistance on acceleration. The driving force of the entire vehicle is obtained by subtracting wind resistance from the wheel-end torque. Finally, to reduce the influence of high-frequency noise on acceleration and driving force, and to make the calculated acceleration and driving force more reliable, low-pass filtering is applied to the calculated driving force and acceleration. However, the vehicle's mass also affects the driving force and acceleration values. For example, under heavy load, the vehicle conversion factor is smaller than under no-load conditions, thus affecting the acceleration calculation. Therefore, the estimated mass value at the current moment must also be considered when solving for acceleration and driving force.

[0116] S603. The mass of the vehicle is iteratively estimated based on the least squares method with a forgetting factor.

[0117] like Figure 6 As shown, the methods for estimating the vehicle's mass include S701-S704.

[0118] S701. Assign initial values ​​to the quality, increment value, and error covariance.

[0119] It should be noted that the initial value of the mass is the mass of the vehicle in an unloaded state, and the incremental value and error covariance can be preset based on historical experience values.

[0120] S702. Update the increment value and error covariance at the current time step based on the increment value and error covariance at the previous time step.

[0121] S703. Update the current mass estimate based on the mass estimate from the previous moment.

[0122] S704. When the number of iterations meets the condition, output the current quality estimate.

[0123] The condition for the number of iterations is that the number of iterations in the above embodiments reaches a preset threshold.

[0124] The foregoing mainly describes the solutions provided by the embodiments of this application from a methodological perspective. To achieve the above functions, the vehicle mass estimation device includes hardware structures and / or software modules corresponding to the execution of each function. Those skilled in the art should readily recognize that, based on the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein, this application can be implemented in hardware or a combination of hardware and computer software. Whether a function is executed in hardware or by computer software driving hardware depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0125] This application embodiment can, according to the above method, exemplarily divide the vehicle weight estimation device into functional modules. For example, the vehicle weight estimation device may include functional modules corresponding to each functional division, or two or more functions may be integrated into one processing module. The integrated module can be implemented in hardware or as a software functional module. It should be noted that the module division in this application embodiment is illustrative and only represents one logical functional division; in actual implementation, there may be other division methods.

[0126] Figure 7 A schematic diagram of a possible structure of the vehicle mass estimation device involved in the above embodiments is shown. (Refer to...) Figure 7 The vehicle mass estimation device 800 includes: a determination unit 801 and a processing unit 802.

[0127] The determining unit 801 is used to acquire vehicle driving data and determine the vehicle's acceleration and driving force at the current moment based on the vehicle driving data.

[0128] Processing unit 802 is used to determine the mass estimate of the vehicle at the current moment based on the least squares method carrying a forgetting factor, and the vehicle's acceleration and driving force at the current moment; wherein the forgetting factor is determined based on the mass estimate of the vehicle at the previous moment.

[0129] In some embodiments, the processing unit 802 is specifically used to determine an estimated value of the vehicle mass based on the least squares method with a forgetting factor and the vehicle's acceleration and driving force at the current moment, when the vehicle's operating characteristics meet the iteration start condition.

[0130] In some embodiments, the above-mentioned iteration start conditions include at least one of the following: the driving force, acceleration, accelerator pedal depth, brake pedal depth, steering wheel angle, and slope of the vehicle at the current moment; wherein, the driving force of the vehicle at the current moment is greater than a driving force threshold, the acceleration is greater than an acceleration threshold, the accelerator pedal depth is greater than an accelerator pedal depth threshold, the brake pedal depth is greater than a brake pedal depth threshold, the absolute value of the steering wheel angle is less than an angle threshold, and the absolute value of the slope is less than a slope threshold.

[0131] In some embodiments, if the estimated mass of the vehicle at the previous time step is less than the heavy load threshold, the iteration start condition is a first iteration start condition; or, if the estimated mass of the vehicle at the previous time step is greater than or equal to the heavy load threshold, the iteration start condition is a second iteration start condition; wherein the driving force threshold in the first iteration start condition is less than the driving force threshold in the second iteration start condition, and the acceleration threshold in the first iteration start condition is less than the acceleration threshold in the second iteration start condition.

[0132] In some embodiments, the processing unit 802 is specifically configured to: determine the incremental value of the vehicle at the current moment based on the vehicle's forgetting factor and acceleration at the current moment, and the error covariance at the previous moment; determine the error covariance of the vehicle at the current moment based on the vehicle's forgetting factor, acceleration, and incremental value at the current moment, and the error covariance at the previous moment; and determine the estimated mass of the vehicle at the current moment based on the vehicle's acceleration, driving force, and incremental value at the current moment, and the estimated mass value at the previous moment.

[0133] In some embodiments, the processing unit 802 is specifically used to iteratively estimate the mass of the vehicle based on the least squares method with a forgetting factor, the vehicle's acceleration and driving force at the current moment, until the iteration stopping condition is met and the estimated mass value of the vehicle is output.

[0134] In some embodiments, the above-mentioned iteration stopping condition includes: the number of iterations reaches a preset threshold number.

[0135] In some embodiments, if the estimated mass of the vehicle at the previous moment is less than the overload threshold, the preset number of times threshold is a first value; or, if the estimated mass of the vehicle at the previous moment is greater than or equal to the overload threshold, the preset number of times threshold is a second value; the first value is less than the second value.

[0136] In some embodiments, the processing unit 802 is specifically configured to adjust the forgetting factor at the current moment from the first forgetting factor to the second forgetting factor when the estimated mass value of the vehicle at the previous moment is greater than or equal to the heavy load threshold, and the estimated mass value of the vehicle at the previous moment is greater than or equal to the first mass threshold and less than the second mass threshold; or, when the estimated mass value of the vehicle at the previous moment is greater than or equal to the heavy load threshold, and the estimated mass value of the vehicle at the previous moment is greater than the second mass threshold, adjust the forgetting factor at the current moment from the second forgetting factor to the first forgetting factor; the second forgetting factor is less than the first forgetting factor.

[0137] Regarding the apparatus in the above embodiments, the specific manner in which each module performs its operation has been described in detail in the embodiments related to the method, and will not be elaborated upon here.

[0138] Figure 8 A schematic diagram of the device involved in the above embodiments is shown. For example... Figure 8 As shown, device 900 includes, but is not limited to, processor 901 and memory 902.

[0139] The memory 902 described above is used to store the executable instructions of the processor 901. It is understood that the processor 901 is configured to execute instructions to implement the obstacle detection method in the above embodiments.

[0140] It should be noted that those skilled in the art will understand that Figure 8 The device structure shown does not constitute a limitation on the device; the device may include, but is not limited to, other types of devices. Figure 8 This may indicate more or fewer components, or combinations of certain components, or different component arrangements.

[0141] The processor 901 is the control center of the device, connecting various parts of the device through various interfaces and lines. It performs various functions and processes data by running or executing software programs and / or modules stored in the memory 902, and by calling data stored in the memory 902, thereby providing overall monitoring of the device. The processor 901 may include one or more processing units. Optionally, the processor 901 may integrate an application processor and a modem processor. The application processor mainly handles the operating system, user interface, and applications, while the modem processor mainly handles wireless communication. It is understood that the modem processor may not be integrated into the processor 901.

[0142] The memory 902 can be used to store software programs and various data. The memory 902 may primarily include a program storage area and a data storage area. The program storage area may store the operating system, application programs required by at least one functional module (such as a determination unit, processing unit, etc.), etc. Furthermore, the memory 902 may include high-speed random access memory, and may also include non-volatile memory, such as at least one disk storage device, flash memory device, or other volatile solid-state storage device.

[0143] In an exemplary embodiment, this application also provides a vehicle including the aforementioned device.

[0144] In an exemplary embodiment, a computer-readable storage medium including instructions is also provided, such as a memory 902 including instructions, which can be executed by a processor 901 of the device 900 to implement the methods in the above embodiments.

[0145] In actual implementation, Figure 7 The determining unit 801 and the processing unit 802 can both be determined by... Figure 8 The processor 901 calls the computer program stored in the memory 902 to implement the process. The specific execution process can be found in the description of the method section in the previous embodiment, and will not be repeated here.

[0146] Optionally, the computer-readable storage medium may be a non-transitory computer-readable storage medium, such as a read-only memory (ROM), random access memory (RAM), CD-ROM, magnetic tape, floppy disk, and optical data storage device.

[0147] In an exemplary embodiment, this application also provides a computer program product including one or more instructions, which can be executed by the processor 901 of the device to perform the methods described above.

[0148] It should be noted that when one or more instructions in the computer-readable storage medium or computer program product are executed by the processor of the device, they implement the various processes of the above method embodiments and achieve the same technical effect as the above method. To avoid repetition, they will not be described again here.

[0149] Through the above description of the embodiments, those skilled in the art can clearly understand that, for the sake of convenience and brevity, only the division of the above functional modules is used as an example. In actual applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above.

[0150] In the several embodiments provided in this application, it should be understood that the disclosed apparatus and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of modules or units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another apparatus, or some features may be ignored or not executed. Furthermore, the mutual coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.

[0151] The units described as separate components may or may not be physically separate. A component shown as a unit can be one or more physical units; that is, it can be located in one place or distributed in multiple different locations. Some or all of the classified units can be selected to achieve the purpose of this embodiment, depending on actual needs.

[0152] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0153] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a readable storage medium. Based on this understanding, the technical solution of the embodiments of this application, essentially, or the part that contributes to the prior art, or a complete or partial classification of the technical solution, can be embodied in the form of a software product. This software product is stored in a storage medium and includes several instructions to cause a device (which may be a microcontroller, chip, etc.) or processor to execute all or part of the steps of the methods of the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, ROM, RAM, magnetic disks, or optical disks.

[0154] The above are merely specific embodiments of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A method for estimating vehicle mass, characterized in that, The method includes: Acquire vehicle driving data, and determine the vehicle's acceleration and driving force at the current moment based on the vehicle driving data; Based on the least squares method with a forgetting factor, and the vehicle's acceleration and driving force at the current moment, the estimated mass of the vehicle at the current moment is determined; wherein the forgetting factor is determined based on the estimated mass of the vehicle at the previous moment.

2. The method according to claim 1, characterized in that, The determination of the vehicle's estimated mass at the current moment, based on the least squares method with a forgetting factor and the vehicle's acceleration and driving force at the current moment, includes: If the vehicle's operating characteristics satisfy the iteration start condition, an estimated value of the vehicle's mass is determined based on the least squares method with a forgetting factor, and the vehicle's acceleration and driving force at the current moment.

3. The method according to claim 2, characterized in that, The iteration start condition includes at least one of the following: The vehicle's driving force at the current moment is greater than the driving force threshold, acceleration is greater than the acceleration threshold, accelerator pedal depth is greater than the accelerator pedal depth threshold, brake pedal depth is greater than the brake pedal depth threshold, the absolute value of the steering wheel angle is less than the angle threshold, and the absolute value of the slope is less than the slope threshold.

4. The method according to claim 3, characterized in that, If the estimated mass of the vehicle at the previous time step is less than the heavy load threshold, the iteration start condition is the first iteration start condition; or, if the estimated mass of the vehicle at the previous time step is greater than or equal to the heavy load threshold, the iteration start condition is the second iteration start condition. Wherein, the driving force threshold in the first iteration start condition is less than the driving force threshold in the second iteration start condition, and the acceleration threshold in the first iteration start condition is less than the acceleration threshold in the second iteration start condition.

5. The method according to any one of claims 1 to 4, characterized in that, The determination of the vehicle's estimated mass at the current moment, based on the least squares method with a forgetting factor and the vehicle's acceleration and driving force at the current moment, includes: The increment value of the vehicle at the current moment is determined based on the forgetting factor and acceleration of the vehicle at the current moment, and the error covariance at the previous moment. The error covariance of the vehicle at the current moment is determined based on the forgetting factor, acceleration, and increment value of the vehicle at the current moment, as well as the error covariance of the previous moment. The estimated mass of the vehicle at the current moment is determined based on the vehicle's acceleration, driving force, and increment at the current moment, as well as the estimated mass at the previous moment.

6. The method according to claim 5, characterized in that, The determination of the vehicle's estimated mass at the current moment, based on the least squares method with a forgetting factor and the vehicle's acceleration and driving force at the current moment, includes: Based on the least squares method with a forgetting factor, the vehicle's acceleration and driving force at the current moment, the mass of the vehicle is iteratively estimated until the iteration stops and the estimated mass value of the vehicle is output.

7. The method according to claim 6, characterized in that, The iteration stopping condition includes: the number of iterations reaches a preset threshold.

8. The method according to claim 7, characterized in that, If the estimated mass of the vehicle at the previous moment is less than the overload threshold, the preset number of times threshold is a first value; or, if the estimated mass of the vehicle at the previous moment is greater than or equal to the overload threshold, the preset number of times threshold is a second value; the first value is less than the second value.

9. The method according to any one of claims 1-8, characterized in that, The forgetting factor is determined based on the vehicle's mass estimate at the previous moment, including: If the estimated mass of the vehicle at the previous moment is greater than or equal to the heavy load threshold, and the estimated mass of the vehicle at the previous moment is greater than or equal to the first mass threshold and less than the second mass threshold, the forgetting factor at the current moment will be adjusted from the first forgetting factor to the second forgetting factor. Alternatively, if the estimated mass of the vehicle at the previous moment is greater than or equal to the heavy load threshold, and the estimated mass of the vehicle at the previous moment is greater than the second mass threshold, the forgetting factor at the current moment is adjusted from the second forgetting factor to the first forgetting factor; the second forgetting factor is less than the first forgetting factor.

10. A vehicle mass estimation device, characterized in that, Includes a first determining unit and a processing unit: The first determining unit is used to acquire vehicle driving data and determine the vehicle's acceleration and driving force at the current moment based on the vehicle driving data; The processing unit is configured to determine the mass estimate of the vehicle at the current moment based on the least squares method with a forgetting factor, and the vehicle's acceleration and driving force at the current moment; wherein the forgetting factor is determined based on the mass estimate of the vehicle at the previous moment.

11. A device, characterized in that, include: processor; Memory used to store the processor's executable instructions; The processor is configured to execute the instructions to implement the method as described in any one of claims 1 to 9.

12. A computer-readable storage medium, characterized in that, When the computer-executable instructions stored in the computer-readable storage medium are executed by the processor of the device, the device is capable of performing the method as described in any one of claims 1 to 9.

13. A computer program product, said computer program product comprising computer instructions, characterized in that, When the computer instructions are executed on the processor of the device, the device is enabled to perform the method as described in any one of claims 1 to 9.

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