Vehicle mass estimation method and device, electronic equipment and storage medium

Abstract

The invention relates to the technical field of vehicles, and discloses a whole vehicle mass estimation method and device, electronic equipment and a storage medium. The method comprises the steps of obtaining a multi-source vehicle-mounted data set and executing preprocessing operation to obtain a preprocessed data set; executing energy item collection calculation according to the preprocessed data set so as to at least determine multiple vehicle energy item powers and working parameters; integrating all vehicle energy item power and work parameters to construct a vehicle energy balance equation in stages so as to at least obtain a driving stage balance equation and a braking stage balance equation; a driving stage mass estimation formula and a braking stage mass estimation formula are output at least according to the driving stage balance equation and the braking stage balance equation, and then vehicle mass estimation parameters are obtained at least based on all the mass estimation formulas. On the basis of not increasing extra vehicle cost, a high-precision vehicle quality real-time estimation scheme adaptive to multiple working conditions and multiple scenes is provided, and the actual use requirement of vehicle intelligent operation can be met.

Description

Technical Field

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

[0002] The safety and stability of autonomous vehicles during operation require multiple factors to ensure, and actual operating quality (including cargo weight) is a core parameter affecting vehicle power performance, energy consumption, braking safety, and operational efficiency. Accurately obtaining real-time vehicle operating quality is crucial for optimizing operational strategies, reducing safety risks, and improving energy management.

[0003] Currently, there are at least the following technical problems with quality inspection during vehicle operation:

[0004] 1. Traditional detection methods require the addition of dedicated sensors (such as weight sensors), which not only increases equipment costs and installation complexity, but is also easily affected by working conditions and environment, resulting in insufficient detection stability;

[0005] 2. Existing estimation methods mostly rely on data from a single operating condition (such as only based on the driving or braking phase), which are easily affected by factors such as slope and resistance fluctuations, and the estimation accuracy is difficult to meet the actual operational needs.

[0006] 3. Some technical solutions have limited applicability to various scenarios, and cannot be compatible with typical road conditions such as highways, mountain roads, and mining construction sites. They are also difficult to adapt to the customized needs of different vehicle models and sub-sectors. Summary of the Invention

[0007] The purpose of this invention is to provide a method, device, electronic device and storage medium for estimating vehicle weight, which provides a high-precision real-time vehicle weight estimation scheme that utilizes the vehicle's original data channels and is adaptable to multiple working conditions and scenarios, without modifying the vehicle's external equipment or increasing the vehicle's additional costs, thus meeting the actual needs of intelligent vehicle operation.

[0008] To address the aforementioned technical problems, in a first aspect, the present invention provides a method for estimating the weight of a vehicle, comprising at least:

[0009] A multi-source vehicle-mounted dataset is acquired, and a preprocessing operation is performed on the multi-source vehicle-mounted dataset to obtain a preprocessed dataset;

[0010] Energy term aggregation calculations are performed based on the preprocessed dataset to determine at least several vehicle energy term power and work parameters;

[0011] By integrating all the vehicle energy terms, power and work parameters, the vehicle energy balance equation is constructed in stages to obtain at least the balance equation for the driving stage and the balance equation for the braking stage.

[0012] Based at least on the driving phase balance equation and the braking phase balance equation, output the driving phase mass estimation formula and the braking phase mass estimation formula, and then obtain the vehicle mass estimation parameters based on at least all of the mass estimation formulas.

[0013] Optionally, the vehicle mass estimation parameters include at least driving phase mass parameters and braking phase mass parameters;

[0014] After outputting the drive phase mass estimation formula and the braking phase mass estimation formula based at least on the drive phase balance equation and the braking phase balance equation, and then obtaining the vehicle mass estimation parameters based at least on all of the mass estimation formulas, the method further includes at least:

[0015] A joint estimation equation system is established, and an objective function is constructed by using the driving stage mass parameters and the braking stage mass parameters as mutual constraints.

[0016] The objective function is solved using a preset optimization algorithm to output optimized quality estimation parameters.

[0017] Optionally, after solving the objective function using a preset optimization algorithm to output optimized quality estimation parameters, the method further includes at least:

[0018] Based on the actual driving conditions of the vehicle, the mass parameters of the driving phase and the mass parameters of the braking phase are updated in real time, and the dynamic correction of the optimized mass estimation parameters is achieved iteratively to at least ensure the real-time performance and accuracy of the vehicle mass estimation.

[0019] Optionally, the power and work parameters of the plurality of vehicle energy items include at least the work parameters of driving force, rotational kinetic energy change parameters, braking force, air resistance, gradient resistance, rolling resistance, and vehicle kinetic energy change parameters.

[0020] Based on the same concept, in a second aspect, the present invention also provides a vehicle weight estimation apparatus for performing the vehicle weight estimation method described in any one of the first aspects;

[0021] The vehicle mass estimation device includes at least:

[0022] The data processing module is used to acquire multi-source vehicle-mounted datasets and perform preprocessing operations on the multi-source vehicle-mounted datasets to obtain preprocessed datasets.

[0023] The parameter determination module is used to perform energy term aggregation calculation based on the preprocessed dataset to determine at least multiple vehicle energy term power and work parameters.

[0024] The equation construction module is used to integrate all the vehicle energy terms, power and work parameters to construct the vehicle energy balance equation in stages, so as to obtain at least the driving stage balance equation and the braking stage balance equation.

[0025] The mass estimation module is used to output a driving phase mass estimation formula and a braking phase mass estimation formula based at least on the driving phase balance equation and the braking phase balance equation, and then obtain vehicle mass estimation parameters based at least on all of the mass estimation formulas.

[0026] Optionally, the vehicle mass estimation parameters include at least driving phase mass parameters and braking phase mass parameters;

[0027] The vehicle mass estimation device further includes at least an estimation optimization module, which is at least used for:

[0028] A joint estimation equation system is established, and an objective function is constructed by using the driving stage mass parameters and the braking stage mass parameters as mutual constraints.

[0029] The objective function is solved using a preset optimization algorithm to output optimized quality estimation parameters.

[0030] Optionally, the vehicle weight estimation device further includes at least a dynamic correction module, which is used for at least:

[0031] Based on the actual driving conditions of the vehicle, the mass parameters of the driving phase and the mass parameters of the braking phase are updated in real time, and the dynamic correction of the optimized mass estimation parameters is achieved iteratively to at least ensure the real-time performance and accuracy of the vehicle mass estimation.

[0032] Optionally, the power and work parameters of the plurality of vehicle energy items include at least the work parameters of driving force, rotational kinetic energy change parameters, braking force, air resistance, gradient resistance, rolling resistance, and vehicle kinetic energy change parameters.

[0033] Based on the same concept, in a third aspect, the present invention also provides an electronic device including a memory and a processor, the memory storing a computer program executable on the processor, the processor executing the program to implement the steps in the vehicle weight estimation method of any one of the first aspects.

[0034] Based on the same concept, in a fourth aspect, the present invention also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps in the vehicle weight estimation method of any one of the first aspects.

[0035] The technical solution provided by this invention first acquires a multi-source vehicle-mounted dataset and performs preprocessing operations on the dataset to obtain a preprocessed dataset. Further, energy term aggregation calculations are performed based on the preprocessed dataset to determine at least several vehicle energy term power and work parameters. Further, all vehicle energy term power and work parameters are integrated to construct vehicle energy balance equations in stages, to obtain at least driving stage balance equations and braking stage balance equations. Finally, driving stage mass estimation formulas and braking stage mass estimation formulas are output based on at least the driving stage balance equations and braking stage balance equations, thereby obtaining vehicle mass estimation parameters based on at least all mass estimation formulas. Therefore, this invention provides a high-precision real-time vehicle mass estimation solution that utilizes the vehicle's existing data channels and adapts to multiple operating conditions and scenarios, without requiring modifications to external vehicle equipment or increasing additional vehicle costs, thus meeting the practical needs of intelligent vehicle operation. Attached Figure Description

[0036] Figure 1 This is a flowchart of a vehicle weight estimation method provided in an embodiment of the present invention;

[0037] Figure 2 This is a flowchart of another vehicle weight estimation method provided in an embodiment of the present invention;

[0038] Figure 3 This is a schematic diagram of the structure of a vehicle weight estimation device provided in an embodiment of the present invention;

[0039] Figure 4 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention. Detailed Implementation

[0040] To make the objectives, technical solutions, and advantages of this application clearer, the application will be further described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0041] The terminology used in the embodiments of this application is for the purpose of describing particular embodiments only and is not intended to limit the application. The singular forms “a,” “said,” and “the” used in the embodiments of this application and the appended claims are also intended to include the plural forms, and “multiple” generally includes at least two unless the context clearly indicates otherwise.

[0042] It should be understood that the term "and / or" used in this article is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, and B existing alone. Additionally, the character " / " in this article generally indicates that the preceding and following related objects have an "or" relationship.

[0043] It should be understood that although the terms first, second, third, etc., may be used in the embodiments of this application, these descriptions should not be limited to these terms. These terms are only used to distinguish the descriptions. For example, first may also be referred to as second without departing from the scope of the embodiments of this application, and similarly, second may also be referred to as first.

[0044] Depending on the context, the words “if” or “suppose” as used here can be interpreted as “when” or “in response to determination” or “in response to detection.” Similarly, depending on the context, the phrases “if determination” or “if detection (of the stated condition or event)” can be interpreted as “when determination” or “in response to determination” or “when detection (of the stated condition or event)” or “in response to detection (of the stated condition or event).”

[0045] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that an article or device that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such an article or device. Without further limitation, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the article or device that includes said element.

[0046] It should be noted that any symbols and / or numbers present in the specification that are not marked in the accompanying drawings are not reference numerals.

[0047] Figure 1 This is a flowchart of a vehicle weight estimation method provided by an embodiment of the present invention. This embodiment is applicable to at least any real-time weight detection and estimation scenario of a vehicle during driving. The vehicle weight estimation method can be, but is not limited to, executed by the vehicle weight estimation device in this embodiment of the present invention as the execution subject, which can be implemented in software and / or hardware. Figure 1 As shown, the vehicle weight estimation method includes at least the following steps:

[0048] S1. Obtain multi-source vehicle-mounted datasets and perform preprocessing operations on the multi-source vehicle-mounted datasets to obtain preprocessed datasets.

[0049] The multi-source vehicle-mounted dataset can be at least one type of dynamic and environmental data collected in real time by the vehicle's original onboard data acquisition unit or equipment (compatible with vehicle communication protocols such as CAN and Ethernet), including but not limited to engine output torque (T). eng ), engine output shaft speed (n eng ), vehicle speed (v) h ), wheel braking force (F) brk ), slope value (θ) measured by slope sensor, wheel speed (n) whl )wait.

[0050] For example, the preprocessing operation can be specifically as follows:

[0051] 1. Divide the multi-source vehicle dataset into segments, such as the driving and braking phases of vehicle operation, and define the data analysis intervals for each phase.

[0052] 2. Perform further processing on the partitioned data, including but not limited to outlier filtering and smoothing, to at least eliminate interference data generated by scenarios such as gear shifting, rapid acceleration / deceleration, etc., and ensure the validity of the data in the preprocessed dataset.

[0053] S2. Perform energy term aggregation calculations based on the preprocessed dataset to determine at least several vehicle energy term power and work parameters.

[0054] The specific quantities of vehicle energy items power and work parameters can be adapted to the actual vehicle use, and this embodiment of the invention does not limit this.

[0055] In one specific implementation, optionally, the power and work parameters of the multiple vehicle energy items include at least the work parameters of driving force, rotational kinetic energy change, braking force, air resistance, gradient resistance, rolling resistance, and vehicle kinetic energy change.

[0056] Specifically, the parameters for the work done by the driving force can be based on the engine output torque (in Nm) and the engine output shaft speed (in rpm), using the formula P. eng =(T eng ×n eng The power is calculated by 9549, and then the total engine output power is obtained by integrating over time.

[0057] Regarding the rotational kinetic energy change parameter, when the vehicle speed changes, the corresponding change in wheel angular velocity will cause a change in wheel rotational kinetic energy. By combining the number of wheels, moment of inertia, and changes in vehicle speed / rotational speed, the change in rotational kinetic energy of the engine flywheel and wheels can be calculated.

[0058] Changes in engine rotational kinetic energy:

[0059] ;

[0060] Changes in the kinetic energy of the rotating wheel:

[0061] ;

[0062] Among them, E rote J represents the change in the rotational kinetic energy of the engine. eng Let n be the engine's rotational inertia. eng0 Let n be the engine speed at the initial moment. engN E represents the engine speed at the moment of termination. rotr N represents the change in the rotational kinetic energy of the wheel. s J represents the number of wheels. rot The moment of inertia of each wheel, r whl This is the radius of the wheel's rotation.

[0063] The parameter for braking force work can refer to the work done by the braking force provided by the braking system, with a power of P. brk .

[0064] ;

[0065] Where, N s1 F represents the total number of wheels involved in braking. brk Indicates the braking force of the wheel, n whl The wheel speed is given. The braking force can be obtained by integrating the braking power over time.

[0066] Regarding the work done by air resistance, the power generated by air resistance during vehicle movement is P. air .

[0067] ;

[0068] Among them, F air For air resistance, v h ρ is the vehicle's speed. a C represents air density. D A is the drag coefficient of the vehicle. D The frontal area of ​​the vehicle.

[0069] Regarding the work parameters related to slope resistance, during vehicle operation, the power of the driving resistance due to the slope is P, which is generated under different terrain conditions. slp .

[0070] P slp =-F g v h =-mgsin(θ-θ0)v h ≈-mg(θ-θ0)vh ;

[0071] Among them, F g V represents slope resistance. h Let θ represent the vehicle speed, θ0 represent the initial slope value caused by the installation of the slope sensor, and θ represent the slope value measured by the slope sensor. Assuming the vehicle is traveling on a structured road with a relatively small slope, then sinθ≈tanθ≈θ.

[0072] Regarding the parameters of work done by rolling resistance, the work done by rolling resistance has a power of P. r .

[0073] P r =-F r v h =-C rr mgv h cosθ≈-C rr mgv h ;

[0074] Among them, C rr This represents the rolling resistance coefficient.

[0075] Regarding the vehicle kinetic energy change parameters, under the action of driving force / braking force, the kinetic energy change of the whole vehicle in the time domain [0,N] is as follows:

[0076] ;

[0077] Among them, v h0 v represents the initial speed of the vehicle. hN This represents the vehicle speed in the terminal time domain.

[0078] S3. Integrate all vehicle energy parameters, including power and work parameters, to construct vehicle energy balance equations in stages, so as to obtain at least the balance equations for the driving stage and the braking stage.

[0079] S4. Output the mass estimation formulas for the driving stage and the braking stage based at least on the balance equations for the driving stage and the balance equations for the braking stage, and then obtain the vehicle mass estimation parameters based at least on all the mass estimation formulas.

[0080] Among them, the vehicle energy balance equation can be constructed in stages based on the actual vehicle use and by integrating the power and work parameters of all vehicle energy items.

[0081] For the equilibrium equations of the driving phase, N can be collected from the historical time series during the continuous driving phase. t Data from 1 sampling point, with a sampling interval of ∆t, are combined with the energy terms to obtain the equilibrium relationship:

[0082] E rote +E rotr +E kin=E eng -E air -E slp -E roll ;

[0083] Substituting into the above equation, we get:

[0084]

[0085] Therefore, the expression for the vehicle's mass during travel is:

[0086] m=F Z / F M ;

[0087] in,

[0088]

[0089] .

[0090] Considering that the installation of the slope sensor will affect the initial slope value, and assuming that the rolling resistance coefficient remains constant during a certain driving period, the integral term of the vehicle speed during this process can be expressed as the driving distance. Assuming the gradient remains constant over the travel distance, the above formula can be further simplified.

[0091] make:

[0092]

[0093] ;

[0094] F k =C rr g-gθ0.

[0095] Therefore, the quality estimation formula (i.e., the quality estimation formula for the driving phase) can be written as m1=B k / (Z k +F k S k m1 is the quality parameter of the driving stage.

[0096] For the equilibrium equations during the braking phase, by simultaneously solving the energy terms during the braking phase, we can obtain the equilibrium relationship:

[0097] E rotr +E kin =-E brk -E air -E slp -E roll .

[0098] Similar to the continuous driving phase, the mass estimation formula for the braking phase (i.e., the braking phase mass estimation formula) can be obtained as follows:

[0099] m2=B kb / (Z kb +F kb S kb );

[0100] in,

[0101]

[0102] ;

[0103] F kb =C rr g-gθ0; m2 is the mass parameter during the braking phase; m1 and m2 are collectively referred to as the vehicle mass estimation parameters.

[0104] The technical solution provided in this embodiment first acquires a multi-source vehicle-mounted dataset and performs preprocessing operations on the dataset to obtain a preprocessed dataset. Further, it performs energy term aggregation calculations based on the preprocessed dataset to determine at least several vehicle energy term power and work parameters. Further, it integrates all vehicle energy term power and work parameters to construct vehicle energy balance equations in stages, obtaining at least driving stage balance equations and braking stage balance equations. Finally, it outputs driving stage mass estimation formulas and braking stage mass estimation formulas based at least on the driving stage balance equations and braking stage balance equations, thereby obtaining vehicle mass estimation parameters based at least on all mass estimation formulas. Therefore, this embodiment provides a high-precision real-time vehicle mass estimation solution that utilizes the vehicle's existing data channels and adapts to multiple operating conditions and scenarios, without requiring modifications to external vehicle equipment or increasing additional vehicle costs, thus meeting the practical needs of intelligent vehicle operation.

[0105] Based on the above embodiments or implementation methods, in order to further improve the estimation accuracy and reliability, this embodiment performs joint optimization on the vehicle mass estimation parameters (i.e., the aforementioned m1 and m2) of the driving and braking phases.

[0106] In yet another specific implementation, the vehicle mass estimation parameters may optionally include at least driving phase mass parameters and braking phase mass parameters;

[0107] After outputting the drive phase mass estimation formula and the braking phase mass estimation formula based at least on the drive phase balance equation and the braking phase balance equation, and then obtaining the vehicle mass estimation parameters based at least on all the mass estimation formulas, it also includes at least:

[0108] Establish a joint estimation equation set, and construct an objective function by using the mass parameters of the driving stage and the mass parameters of the braking stage as mutual constraints.

[0109] The objective function is solved using a pre-defined optimization algorithm to output optimized quality estimation parameters.

[0110] In yet another specific implementation, optionally, after solving the objective function using a preset optimization algorithm to output optimized quality estimation parameters, at least the following is included:

[0111] Based on the actual driving conditions of the vehicle, the mass parameters during the driving and braking phases are updated in real time, and the dynamic correction of the optimized mass estimation parameters is achieved through iterative processes to ensure at least the real-time performance and accuracy of the overall vehicle mass estimation.

[0112] Based on this Figure 2 This is a flowchart of another vehicle weight estimation method provided in an embodiment of the present invention, such as... Figure 2 As shown, the vehicle weight estimation method includes at least the following steps:

[0113] S1. Obtain multi-source vehicle-mounted datasets and perform preprocessing operations on the multi-source vehicle-mounted datasets to obtain preprocessed datasets.

[0114] S2. Perform energy term aggregation calculations based on the preprocessed dataset to determine at least several vehicle energy term power and work parameters.

[0115] S3. Integrate all vehicle energy parameters, including power and work parameters, to construct vehicle energy balance equations in stages, so as to obtain at least the balance equations for the driving stage and the braking stage.

[0116] S4. Output the mass estimation formulas for the driving stage and the braking stage based at least on the balance equations for the driving stage and the balance equations for the braking stage, and then obtain the vehicle mass estimation parameters based at least on all the mass estimation formulas.

[0117] S5. Establish a joint estimation equation set, and construct an objective function by using the driving stage quality parameters and braking stage quality parameters as mutual constraints.

[0118] The objective function can be specifically defined as follows:

[0119] minJ=ω1(m-m1) 2 +ω2(m-m2) 2 ;

[0120] In the above formula, ω1 and ω2 are weighting coefficients, and each weighting coefficient can be dynamically adjusted according to the reliability of the two-stage data.

[0121] S6. Use a preset optimization algorithm to solve the objective function and output the optimized quality estimation parameters.

[0122] The preset optimization algorithm may include, but is not limited to, least squares method, Kalman filtering, etc.

[0123] S7. Based on the actual driving conditions of the vehicle, the mass parameters of the driving phase and the mass parameters of the braking phase are updated in real time, and the dynamic correction of the optimized mass estimation parameters is achieved iteratively to ensure at least the real-time performance and accuracy of the whole vehicle mass estimation.

[0124] It can also perform data processing and standardized storage for optimized quality estimation parameters, driving phase quality parameters, braking phase quality parameters, etc., and display real-time quality and related operating status through a visual interface. At the same time, it can start log recording and anomaly monitoring mechanisms.

[0125] In another specific implementation, a travel distance threshold S is set, the vehicle travel distance is estimated based on vehicle speed information, and two adjacent data segments with a distance closest to S are selected; the distances of the two travel segments are respectively set as S. k1 and S k2 Then, within their respective driving ranges, we have:

[0126] m(Z k1 +F k1 S k1 )=B k1 ;

[0127] m(Z k2 +F k2 S k2 )=B k2 .

[0128] Since the threshold S is very small, the slope is approximately the same in both regions, therefore:

[0129] F k1 =C rr g-gθ0=F k1 →F k .

[0130] We can obtain:

[0131] F k =(B k2 Z k1 -B k1 Z k2 ) / (B k1 S k2 -B k2 S k1 ).

[0132] Substituting into the mass calculation formula, we finally obtain:

[0133] m=(B k1 S k2-B k2 S k1 ) / (Z k1 S k2 -Z k2 S k1 ).

[0134] In summary, the technical solution provided in this embodiment can achieve at least the following beneficial effects:

[0135] 1. No external sensor modification is required. Multi-source dynamics and environmental data are collected using the vehicle's original data channels. Mass estimation is achieved through the principle of energy balance, reducing the threshold for deployment and application.

[0136] 2. Construct dual energy balance equations for the driving and braking stages, and derive a unified mass expression by combining simplified parameters, laying the foundation for joint estimation;

[0137] 3. A quality estimation process that eliminates the influence of slope can be designed. By combining data from two consecutive braking phases with similar distances, the interference caused by the initial error of the slope sensor and slope changes can be effectively avoided.

[0138] 4. A joint optimization method for the driving and braking stages is proposed, which can use the least squares method or Kalman filtering to fuse the estimation results of the two stages, eliminate the estimation error of a single stage, and improve the estimation accuracy and robustness.

[0139] 5. It is compatible with multiple vehicle types, multiple vehicle communication protocols, and multiple industry scenarios, supports efficient data integration with third-party platforms and data analysis tools, and has good scalability;

[0140] 6. Integrates full-process functions such as data processing, result storage, visualization output, and log monitoring to achieve real-time online estimation of quality parameters and operational support.

[0141] Figure 3 This is a schematic diagram of a vehicle weight estimation device provided in an embodiment of the present invention. This embodiment is applicable to at least any real-time weight detection and estimation scenario of a vehicle during driving. The vehicle weight estimation device can be implemented in software and / or hardware. Figure 3 As shown, the vehicle weight estimation device includes at least:

[0142] The data processing module 110 is used to acquire multi-source vehicle-mounted datasets and perform preprocessing operations on the multi-source vehicle-mounted datasets to obtain preprocessed datasets.

[0143] The parameter determination module 120 is used to perform energy term aggregation calculations based on the preprocessed dataset to determine at least multiple vehicle energy term power and work parameters.

[0144] Equation building module 130 is used to integrate all vehicle energy terms, power and work parameters to build vehicle energy balance equations in stages, so as to obtain at least the driving stage balance equation and the braking stage balance equation.

[0145] The mass estimation module 140 is used to output the mass estimation formula for the driving stage and the mass estimation formula for the braking stage based at least on the balance equation for the driving stage and the balance equation for the braking stage, and then obtain the vehicle mass estimation parameters based at least on all the mass estimation formulas.

[0146] Optionally, the vehicle mass estimation parameters include at least the mass parameters during the driving phase and the mass parameters during the braking phase;

[0147] The vehicle weight estimation device also includes at least an estimation optimization module 150, which is used for at least:

[0148] Establish a joint estimation equation set, and construct an objective function by using the mass parameters of the driving stage and the mass parameters of the braking stage as mutual constraints.

[0149] The objective function is solved using a pre-defined optimization algorithm to output optimized quality estimation parameters.

[0150] Optionally, the vehicle weight estimation device may further include at least a dynamic correction module 160, which is used for at least:

[0151] Based on the actual driving conditions of the vehicle, the mass parameters during the driving and braking phases are updated in real time, and the dynamic correction of the optimized mass estimation parameters is achieved through iterative processes to ensure at least the real-time performance and accuracy of the overall vehicle mass estimation.

[0152] Optionally, the power and work parameters of the multiple vehicle energy items include at least the work parameters of driving force, rotational kinetic energy change, braking force, air resistance, gradient resistance, rolling resistance, and vehicle kinetic energy change.

[0153] The technical solution provided in this embodiment firstly acquires multi-source vehicle-mounted datasets through a data processing module and performs preprocessing operations on these datasets to obtain a preprocessed dataset. Further, a parameter determination module performs energy term aggregation calculations based on the preprocessed dataset to determine at least several vehicle energy term power and work parameters. Further, an equation construction module integrates all vehicle energy term power and work parameters to construct vehicle energy balance equations in stages, obtaining at least driving stage balance equations and braking stage balance equations. Finally, a mass estimation module outputs driving stage mass estimation formulas and braking stage mass estimation formulas based at least on the driving stage balance equations and braking stage balance equations, thereby obtaining the overall vehicle mass estimation parameters based at least on all mass estimation formulas. Therefore, this embodiment provides a high-precision real-time vehicle mass estimation solution that utilizes the vehicle's existing data channels and adapts to multiple operating conditions and scenarios, without requiring modifications to external vehicle equipment or increasing additional vehicle costs, thus meeting the practical needs of intelligent vehicle operation.

[0154] This embodiment provides an electronic device. Figure 3 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention. See also: Figure 3 The electronic device 1000 includes a processor 1001 and a memory 1002. The memory 1002 stores computer-readable instructions. When the computer-readable instructions are executed by the processor 1001, the steps in any of the above-mentioned vehicle weight estimation methods are performed. Through the above technical solution, the processor 1001 and the memory 1002 are interconnected and communicate with each other through a communication bus and / or other forms of connection mechanism (not shown). The memory 1002 stores a computer program executable by the processor. When the electronic device 1000 is running, the processor 1001 executes the computer program to execute the vehicle mass estimation method in any optional implementation of the above embodiments, so as to achieve at least the following functions: acquiring multi-source vehicle datasets and performing preprocessing operations on the multi-source vehicle datasets to obtain preprocessed datasets; performing energy term aggregation calculations based on the preprocessed datasets to determine at least multiple vehicle energy term power and work parameters; integrating all vehicle energy term power and work parameters to construct vehicle energy balance equations in stages to obtain at least driving stage balance equations and braking stage balance equations; outputting driving stage mass estimation formulas and braking stage mass estimation formulas based at least on the driving stage balance equations and braking stage balance equations, and then obtaining vehicle mass estimation parameters based at least on all mass estimation formulas.

[0155] This embodiment provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the vehicle mass estimation method provided in all embodiments of this application: acquiring a multi-source vehicle dataset and performing preprocessing operations on the multi-source vehicle dataset to obtain a preprocessed dataset; performing energy term aggregation calculations based on the preprocessed dataset to determine at least multiple vehicle energy term power and work parameters; integrating all vehicle energy term power and work parameters to construct vehicle energy balance equations in stages to obtain at least a driving stage balance equation and a braking stage balance equation; outputting driving stage mass estimation formulas and braking stage mass estimation formulas based at least on the driving stage balance equations and braking stage balance equations, and then obtaining vehicle mass estimation parameters based at least on all mass estimation formulas.

[0156] Any combination of one or more computer-readable media may be used. A computer-readable medium can be a computer-readable signal medium or a computer-readable storage medium. A computer-readable storage medium can be, for example—but not limited to—an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples (a non-exhaustive list) of computer-readable storage media include: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In this document, a computer-readable storage medium can be any tangible medium that contains or stores a program that can be used by or in connection with an instruction execution system, apparatus, or device.

[0157] Computer-readable signal media may include data signals propagated in baseband or as part of a carrier wave, carrying computer-readable program code. Such propagated data signals may take various forms, including—but not limited to—electromagnetic signals, optical signals, or any suitable combination thereof. Computer-readable signal media may also be any computer-readable medium other than computer-readable storage media, capable of transmitting, propagating, or transmitting programs for use by or in connection with an instruction execution system, apparatus, or device.

[0158] The program code contained on a computer-readable medium may be transmitted using any suitable medium, including—but not limited to—wireless, wire, optical fiber, RF, etc., or any suitable combination thereof.

[0159] Computer program code for performing the operations of this invention can be written in one or more programming languages ​​or a combination thereof. Programming languages ​​include object-oriented programming languages—such as Java, Smalltalk, and C++—as well as conventional procedural programming languages—such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0160] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for estimating the weight of a vehicle, characterized in that, At least including: A multi-source vehicle-mounted dataset is acquired, and a preprocessing operation is performed on the multi-source vehicle-mounted dataset to obtain a preprocessed dataset; Energy term aggregation calculations are performed based on the preprocessed dataset to determine at least several vehicle energy term power and work parameters; By integrating all the vehicle energy terms, power and work parameters, the vehicle energy balance equation is constructed in stages to obtain at least the balance equation for the driving stage and the balance equation for the braking stage. Based at least on the driving phase balance equation and the braking phase balance equation, output the driving phase mass estimation formula and the braking phase mass estimation formula, and then obtain the vehicle mass estimation parameters based on at least all of the mass estimation formulas.

2. The vehicle weight estimation method according to claim 1, characterized in that, The vehicle mass estimation parameters include at least driving phase mass parameters and braking phase mass parameters; After outputting the drive phase mass estimation formula and the braking phase mass estimation formula based at least on the drive phase balance equation and the braking phase balance equation, and then obtaining the vehicle mass estimation parameters based at least on all of the mass estimation formulas, the method further includes at least: A joint estimation equation system is established, and an objective function is constructed by using the driving stage mass parameters and the braking stage mass parameters as mutual constraints. The objective function is solved using a preset optimization algorithm to output optimized quality estimation parameters.

3. The vehicle weight estimation method according to claim 2, characterized in that, After solving the objective function using a preset optimization algorithm to output optimized quality estimation parameters, the method further includes at least: Based on the actual driving conditions of the vehicle, the mass parameters of the driving phase and the mass parameters of the braking phase are updated in real time, and the dynamic correction of the optimized mass estimation parameters is achieved iteratively to at least ensure the real-time performance and accuracy of the vehicle mass estimation.

4. The vehicle weight estimation method according to claim 1, characterized in that, The power and work parameters of the multiple vehicle energy items include at least the work parameters of driving force, rotational kinetic energy change parameters, braking force, air resistance, gradient resistance, rolling resistance, and vehicle kinetic energy change parameters.

5. A vehicle weight estimation device, characterized in that, Used to perform the vehicle weight estimation method according to any one of claims 1-4; The vehicle mass estimation device includes at least: The data processing module is used to acquire multi-source vehicle-mounted datasets and perform preprocessing operations on the multi-source vehicle-mounted datasets to obtain preprocessed datasets. The parameter determination module is used to perform energy term aggregation calculation based on the preprocessed dataset to determine at least multiple vehicle energy term power and work parameters. The equation construction module is used to integrate all the vehicle energy terms, power and work parameters to construct the vehicle energy balance equation in stages, so as to obtain at least the driving stage balance equation and the braking stage balance equation. The mass estimation module is used to output a driving phase mass estimation formula and a braking phase mass estimation formula based at least on the driving phase balance equation and the braking phase balance equation, and then obtain vehicle mass estimation parameters based at least on all of the mass estimation formulas.

6. The vehicle weight estimation device according to claim 5, characterized in that, The vehicle mass estimation parameters include at least driving phase mass parameters and braking phase mass parameters; The vehicle mass estimation device further includes at least an estimation optimization module, which is at least used for: A joint estimation equation system is established, and an objective function is constructed by using the driving stage mass parameters and the braking stage mass parameters as mutual constraints. The objective function is solved using a preset optimization algorithm to output optimized quality estimation parameters.

7. The vehicle weight estimation device according to claim 6, characterized in that, The vehicle mass estimation device further includes at least a dynamic correction module, which is used for at least: Based on the actual driving conditions of the vehicle, the mass parameters of the driving phase and the mass parameters of the braking phase are updated in real time, and the dynamic correction of the optimized mass estimation parameters is achieved iteratively to at least ensure the real-time performance and accuracy of the vehicle mass estimation.

8. The vehicle weight estimation device according to claim 5, characterized in that, The power and work parameters of the multiple vehicle energy items include at least the work parameters of driving force, rotational kinetic energy change parameters, braking force, air resistance, gradient resistance, rolling resistance, and vehicle kinetic energy change parameters.

9. An electronic device comprising a memory and a processor, the memory storing a computer program executable on the processor, characterized in that, When the processor executes the program, it implements the steps in the vehicle weight estimation method according to any one of claims 1-4.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps in the vehicle weight estimation method according to any one of claims 1-4.

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