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

Abstract

The invention relates to a vehicle mass estimation method and device, electronic equipment, a storage medium and a product, and the method comprises the steps: obtaining a plurality of operation parameters of a current vehicle; when it is judged that the current vehicle meets the preset vehicle mass estimation condition, vehicle mass estimation operation is executed, vehicle longitudinal force is calculated through a pre-constructed vehicle longitudinal dynamic model, vehicle longitudinal acceleration is calculated through a preset difference algorithm, and a vehicle longitudinal force result and a vehicle longitudinal acceleration result are obtained; and performing iterative calculation on the vehicle mass and the longitudinal force offset through a pre-established two-dimensional Kalman filter to obtain a vehicle mass state prediction result and a longitudinal force offset state prediction result, and finally obtaining a vehicle mass estimation result. Therefore, the problems of relatively low vehicle mass estimation precision and efficiency, relatively poor robustness and the like in related technologies are solved, the vehicle mass estimation precision and efficiency are improved, and the vehicle mass estimation robustness is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of vehicle engineering, and particularly relates to a vehicle mass estimation method, device, electronic device, storage medium and product. Background Art

[0002] In the field of vehicle engineering, accurate vehicle mass estimation is crucial for vehicle performance optimization, safety control and energy-saving management. At present, vehicle mass estimation mainly uses a vehicle longitudinal mechanical model combined with corresponding estimation algorithms. It mainly includes methods such as the least squares method and the Kalman filter to estimate the mass of the whole vehicle.

[0003] In the related art, there are mainly two methods to achieve vehicle mass estimation: (1) A method for estimating vehicle mass based on engine torque: Using fuzzy logic rules to determine the ideal working condition, and performing mass estimation based on the recursive least squares method. And a confidence evaluation mechanism is constructed to determine the effectiveness of the result, and (2) A vehicle mass estimation method based on adaptive extended Kalman filter: Learning the non-linear relationship between vehicle states through a neural network to reduce the influence caused by model mismatch. Embedding the mass pre-estimation of the neural network model into the adaptive Kalman filter, and correcting the vehicle mass prediction output by the neural network model based on the weight factor.

[0004] However, in the related art, for method (1), the method for estimating vehicle mass based on engine torque, the method of using a vehicle longitudinal mechanical model combined with corresponding estimation algorithms is used to complete the mass estimation of the whole vehicle. When constructing a mathematical model, only single-state estimation is performed, and other key factors during vehicle driving are not fully considered. Facing a complex driving environment, a single state is easily interfered by noise and has weak anti-interference ability, resulting in insufficient estimation accuracy. And for models without an acceleration sensor, it is difficult to obtain the acceleration, and it is difficult to perform mass estimation. For method (2), the training effect of the model depends to a large extent on sample features, and the estimation process is complex, covering multiple links such as neural network training, weight factor calculation, and embedding of adaptive Kalman filter. It not only takes a long time and has a high computational complexity, but also requires a large amount of computing resources, and faces problems of cost and efficiency in practical applications, which need to be solved urgently. Summary of the Invention

[0005] The present invention provides a vehicle mass estimation method, device, electronic device, storage medium and product to solve the problems of low accuracy and efficiency of vehicle mass estimation and poor robustness in the related art, improve the accuracy and efficiency of vehicle mass estimation, and enhance the robustness of vehicle mass estimation.

[0006] An embodiment of the first aspect of the present invention provides a vehicle mass estimation method, including the following steps: obtaining a plurality of operating parameters of the current vehicle; when it is determined that the current vehicle meets a preset vehicle mass estimation condition based on the plurality of operating parameters, performing a vehicle mass estimation operation, calculating the vehicle longitudinal force through a pre-constructed vehicle longitudinal dynamics model, and calculating the vehicle longitudinal acceleration through a preset difference algorithm to obtain a vehicle longitudinal force result and a vehicle longitudinal acceleration result; based on the vehicle longitudinal force result and the vehicle longitudinal acceleration result, through a pre-established two-dimensional Kalman filter, iteratively calculating the vehicle mass and the longitudinal force offset to obtain a vehicle mass state prediction result and a longitudinal force offset state prediction result, and obtaining a vehicle mass estimation result based on the vehicle mass state prediction result and the longitudinal force offset state prediction result.

[0007] Further, in some embodiments, after obtaining the plurality of operating parameters of the current vehicle, it further includes: calculating each parameter in the plurality of operating parameters based on a preset verification algorithm to obtain an independent verification factor result for each parameter; if the minimum value among the independent verification factor results of the plurality of parameters is greater than a preset threshold, it is determined that the current vehicle meets the preset vehicle mass estimation condition.

[0008] Further, in some embodiments, calculating the vehicle longitudinal force through a pre-constructed vehicle longitudinal dynamics model and calculating the vehicle longitudinal acceleration through a preset difference algorithm to obtain a vehicle longitudinal force result and a vehicle longitudinal acceleration result includes: constructing a longitudinal dynamics model of the current vehicle and calculating the vehicle longitudinal force result based on the longitudinal dynamics model; processing the speed signal of the current vehicle through a preset difference algorithm to obtain the vehicle longitudinal acceleration result.

[0009] Further, in some embodiments, based on the vehicle longitudinal force result and the vehicle longitudinal acceleration result, the vehicle mass and the longitudinal force offset are iteratively calculated through a pre-established two-dimensional Kalman filter to obtain a vehicle mass state prediction result and a longitudinal force offset state prediction result, and a vehicle mass estimation result is obtained based on the vehicle mass state prediction result and the longitudinal force offset state prediction result. It includes: establishing a state equation with the vehicle mass as the first state and the longitudinal force offset as the second state; predicting the first state and the second state based on the state equation to obtain a prediction result of the first state and a prediction result of the second state; performing covariance estimation and prediction on the first state and the second state based on the state equation to obtain a prediction result of the covariance of the first state and a prediction result of the covariance of the second state; updating the prediction result of the first state, the prediction result of the second state, the prediction result of the covariance of the first state, and the prediction result of the covariance of the second state based on a preset Kalman coefficient to obtain an updated prediction result of the first state, an updated prediction result of the second state, an updated prediction result of the covariance of the first state, and an updated prediction result of the covariance of the second state. The vehicle mass estimation result is obtained based on the updated prediction result of the first state, the updated prediction result of the second state, the updated prediction result of the covariance of the first state, and the updated prediction result of the covariance of the second state.

[0010] Further, in some embodiments, after obtaining the vehicle mass estimation result based on the updated prediction result of the first state, the updated prediction result of the second state, the updated prediction result of the covariance of the first state, and the updated prediction result of the covariance of the second state, it further includes: obtaining an output estimation result of the acceleration sensor of the current vehicle; and correcting the vehicle mass estimation result based on the output estimation result.

[0011] According to the vehicle mass estimation method provided by the embodiments of the present invention, based on the vehicle real-time operation parameters, the vehicle longitudinal force is calculated through a vehicle longitudinal dynamics model, the vehicle longitudinal acceleration is calculated through a difference algorithm, and the vehicle mass and the longitudinal force offset are iteratively calculated through a two-dimensional Kalman filter to obtain a vehicle mass estimation result, which solves the problems of low accuracy and efficiency of vehicle mass estimation and poor robustness in the related art, improves the accuracy and efficiency of vehicle mass estimation, and enhances the robustness of vehicle mass estimation.

[0012] In a second aspect embodiment of the present invention, a vehicle mass estimation device is provided, including: an acquisition module for acquiring a plurality of operating parameters of the current vehicle; a data processing module for, when determining that the current vehicle meets a preset vehicle mass estimation condition based on the plurality of operating parameters, performing a vehicle mass estimation operation, calculating the vehicle longitudinal force through a pre-constructed vehicle longitudinal dynamics model, and calculating the vehicle longitudinal acceleration through a preset difference algorithm to obtain a vehicle longitudinal force result and a vehicle longitudinal acceleration result; an estimation module for, based on the vehicle longitudinal force result and the vehicle longitudinal acceleration result, performing iterative calculations on the vehicle mass and the longitudinal force offset through a pre-established two-dimensional Kalman filter to obtain a vehicle mass state prediction result and a longitudinal force offset state prediction result, and obtaining a vehicle mass estimation result based on the vehicle mass state prediction result and the longitudinal force offset state prediction result.

[0013] Further, in some embodiments, after acquiring the plurality of operating parameters of the current vehicle, the data processing module is further configured to: calculate an independent verification factor result for each parameter among the plurality of operating parameters based on a preset verification algorithm; and determine that the current vehicle meets the preset vehicle mass estimation condition if the minimum value among the independent verification factor results of the plurality of parameters is greater than a preset threshold.

[0014] Further, in some embodiments, the data processing module is further configured to: construct a longitudinal dynamics model of the current vehicle, and calculate the vehicle longitudinal force result based on the longitudinal dynamics model; and process the speed signal of the current vehicle through a preset difference algorithm to obtain the vehicle longitudinal acceleration result.

[0015] Further, in some embodiments, the estimation module is specifically configured to: establish a state equation with the vehicle mass as the first state and the longitudinal force offset as the second state; based on the state equation, predict the first state and the second state to obtain a prediction result of the first state and a prediction result of the second state; based on the state equation, perform covariance estimation and prediction on the first state and the second state to obtain a prediction result of the covariance of the first state and a prediction result of the covariance of the second state; based on a preset Kalman coefficient, update the prediction result of the first state, the prediction result of the second state, the prediction result of the covariance of the first state, and the prediction result of the covariance of the second state to obtain an updated prediction result of the first state, an updated prediction result of the second state, an updated prediction result of the covariance of the first state, and an updated prediction result of the covariance of the second state. Based on the updated prediction result of the first state, the updated prediction result of the second state, the updated prediction result of the covariance of the first state, and the updated prediction result of the covariance of the second state, obtain the vehicle mass estimation result.

[0016] Further, in some embodiments, after obtaining the vehicle mass estimation result based on the updated prediction result of the first state, the updated prediction result of the second state, the updated prediction result of the covariance of the first state, and the updated prediction result of the covariance of the second state, the estimation module is further configured to: obtain an output estimation result of the acceleration sensor of the current vehicle; based on the output estimation result, correct the vehicle mass estimation result.

[0017] According to the vehicle mass estimation device provided by the embodiments of the present invention, based on the real-time operating parameters of the vehicle, the longitudinal force of the vehicle is calculated through a vehicle longitudinal dynamics model, and the longitudinal acceleration of the vehicle is calculated through a difference algorithm. Through a two-dimensional Kalman filter, iterative calculation is performed on the vehicle mass and the longitudinal force offset to obtain the vehicle mass estimation result, which solves the problems of low estimation accuracy and efficiency and poor robustness in the related art, improves the vehicle mass estimation accuracy and efficiency, and enhances the robustness of the vehicle mass estimation.

[0018] An embodiment of the third aspect of the present invention provides an electronic device, including: a memory, a processor, and a computer program stored on the memory and executable on the processor, where the processor executes the program to implement the above vehicle mass estimation method.

[0019] An embodiment of the fourth aspect of the present invention provides a computer-readable storage medium, on which a computer program is stored, and the program is executed by a processor to implement the vehicle mass estimation method as described in the above embodiments.

[0020] An embodiment of the fifth aspect of the present invention provides a computer program product, including a computer program, which is executed to implement the vehicle mass estimation method as described in any one of the above.

[0021] Additional aspects and advantages of the present invention will be given in part in the following description, become apparent in part from the following description, or be understood through the practice of the present invention. Brief Description of the Drawings

[0022] The above and / or additional aspects and advantages of the present invention will become apparent and be readily understood from the following description of embodiments in conjunction with the drawings, where:

[0023] Figure 1 is a flowchart of the vehicle mass estimation method provided according to an embodiment of the present invention;

[0024] Figure 2 is a schematic diagram of the two-dimensional Kalman filter mass estimation result provided according to a specific embodiment of the present invention;

[0025] Figure 3 is a schematic diagram of the curve results of the differential acceleration method and the acceleration sensor provided according to a specific embodiment of the present invention;

[0026] Figure 4 is a flowchart of the vehicle mass estimation method provided according to a specific embodiment of the present invention;

[0027] Figure 5 is a schematic block diagram of the vehicle mass estimation device provided according to an embodiment of the present invention. Detailed Description of the Embodiments

[0028] Embodiments of the present invention will be described in detail below. Examples of the embodiments are shown in the drawings, where the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below by referring to the drawings are exemplary and are intended to explain the present invention and should not be construed as limiting the present invention.

[0029] The vehicle mass estimation method, device, electronic device, storage medium and product according to the embodiments of the present invention will be described below with reference to the accompanying drawings. In view of the problems of low accuracy and efficiency of vehicle mass estimation and poor robustness mentioned in the above background art, the present invention provides a vehicle mass estimation method. Based on the real-time operating parameters of the vehicle, the longitudinal force of the vehicle is calculated through a vehicle longitudinal dynamics model, and the longitudinal acceleration of the vehicle is calculated through a difference algorithm. Then, through a two-dimensional Kalman filter, the vehicle mass and the longitudinal force offset are iteratively calculated to obtain the vehicle mass estimation result, solving the problems of low accuracy and efficiency of vehicle mass estimation and poor robustness in the related art, improving the accuracy and efficiency of vehicle mass estimation, and enhancing the robustness of vehicle mass estimation.

[0030] Specifically, Figure 1 FIG. is a flowchart of a vehicle mass estimation method according to an embodiment of the present invention.

[0031] As Figure 1 shown, the vehicle mass estimation method includes the following steps:

[0032] In step S101, a plurality of operating parameters of the current vehicle are obtained;

[0033] Among them, the plurality of operating parameters of the vehicle are parameters reflecting the vehicle performance and operating state during the vehicle driving process, such as: engine torque, transmission ratio, final drive ratio, transmission efficiency, air resistance coefficient, frontal area, air density, rolling resistance coefficient, rotating mass conversion coefficient, gravitational acceleration and slope.

[0034] Specifically, the engine torque, transmission ratio, final drive ratio, transmission efficiency, air resistance coefficient, frontal area, air density, rolling resistance coefficient, rotating mass conversion coefficient, gravitational acceleration and slope can be obtained through sensors provided in the vehicle or through parameter calibration experiments.

[0035] It should be noted that the above-mentioned obtaining methods of the plurality of operating parameters of the vehicle are only exemplary and do not limit the present invention. Those skilled in the art can obtain them according to the actual situation. To avoid redundancy, no detailed description will be given here.

[0036] In step S102, when it is determined that the current vehicle meets the preset vehicle mass estimation condition based on the plurality of operating parameters, the vehicle mass estimation operation is executed. The longitudinal force of the vehicle is calculated through a pre-constructed vehicle longitudinal dynamics model, and the longitudinal acceleration of the vehicle is calculated through a preset difference algorithm to obtain the vehicle longitudinal force result and the vehicle longitudinal acceleration result.

[0037] Among them, the preset vehicle mass estimation condition is the judgment condition for performing the vehicle mass estimation operation. If the preset vehicle mass estimation condition is satisfied, the vehicle mass estimation operation is performed; if the preset vehicle mass estimation condition is not satisfied, the vehicle mass estimation operation is not performed. The vehicle longitudinal force refers to the force acting on the vehicle in the vehicle driving direction. The vehicle longitudinal acceleration is a physical quantity that describes how fast the speed of the vehicle changes in the longitudinal direction (i.e., the vehicle driving direction). The vehicle longitudinal force result is the model prediction value of the vehicle longitudinal force calculated according to the pre-constructed vehicle longitudinal dynamics model. The vehicle longitudinal acceleration result is the predicted value of the vehicle longitudinal acceleration result calculated according to the preset difference algorithm.

[0038] As a possible implementation manner, in some embodiments, after obtaining multiple operating parameters of the current vehicle, it further includes: based on a preset verification algorithm, calculating each parameter among the multiple operating parameters to obtain the independent verification factor result of each parameter; if the minimum value among the independent verification factor results of the multiple parameters is greater than the preset threshold, it is determined that the current vehicle satisfies the preset vehicle mass estimation condition.

[0039] Among them, for the preset vehicle mass estimation condition, the independent verification factor of each parameter can be calculated by two-dimensional look-up table interpolation, and the minimum value is taken as the credibility degree of the final mass estimation. Dynamic correction is performed on the sign consistency of the acceleration signal and the longitudinal force signal, and the verification result is subjected to first-order filtering to form a stable verification value. When the filtered verification value exceeds the threshold, the vehicle mass estimation operation is performed; when it is lower than the threshold, the vehicle mass estimation operation is not performed.

[0040] It should be noted that in the embodiments of the present invention, a multi-dimensional verification module performs hierarchical verification on vehicle speed, lateral acceleration, dynamic change of steering wheel angle, engine speed fluctuation, transmission ratio working condition, driving torque credibility, longitudinal force amplitude, and acceleration signal. The independent verification factor of each parameter is calculated by two-dimensional look-up table interpolation, and the credibility of the mass estimation is output in combination with the verification factor.

[0041] Specifically, the longitudinal speed interval is defined as [5 6 30 35] m / s, then the longitudinal speed verification factor is [0 1 10]. When the vehicle speed is less than or equal to 5 m / s or greater than or equal to 35 m / s, the verification factor takes the value of 0; when the vehicle speed is between 6 m / s and 30 m / s, the verification factor takes the value of 1; when the vehicle speed is between 5 m / s and 6 m / s, linear interpolation is performed on 0 and 1 to calculate the verification factor; when the vehicle speed is between 30 m / s and 35 m / s, linear interpolation is performed on 1 and 0 to calculate the verification factor. For example, when the vehicle speed is 5.5 m / s, the verification factor takes the value of 0.5; when the vehicle speed is 32 m / s, the verification factor takes the value of 0.4.

[0042] Further, define the steering wheel angle signal interval as [10 15 20 25] degrees, and the corresponding verification factor as [1 0 0 0]; define the engine torque working interval as [30 40 50 60] N·m, and the corresponding verification factor as [0 1 1 1]; define the lateral acceleration signal interval as [-2 -1 1 2] m / s 2 , and the corresponding verification factor as [0 1 1 0]; define the longitudinal force interval as [1200 1500 1800 2100] N, and the corresponding verification factor as [0 1 1 1]; define the acceleration signal interval as [0.2 0.4 0.6 1] m / s 2 , and the corresponding verification factor as [0 1 1 1]; perform interpolation calculation on the verification factor according to the above method.

[0043] It should be noted that during the vehicle operation, the vehicle state is monitored in real time, including the braking state, the shifting process, the traction control state, etc. If systems such as braking, shifting, and TCS are involved, reset the verification factor and stop judging the vehicle mass estimation conditions.

[0044] Further, in some embodiments, calculate the vehicle longitudinal force through a pre-constructed vehicle longitudinal dynamics model, and calculate the vehicle longitudinal acceleration through a preset difference algorithm to obtain the vehicle longitudinal force result and the vehicle longitudinal acceleration result, including: constructing the longitudinal dynamics model of the current vehicle and calculating the vehicle longitudinal force result based on the longitudinal dynamics model; processing the speed signal of the current vehicle through the preset difference algorithm to obtain the vehicle longitudinal acceleration result.

[0045] Specifically, the longitudinal dynamics model of the vehicle is:

[0046]

[0047] Among them, T e is the engine torque, with the unit of Nm, i g is the transmission ratio, dimensionless, i0 is the final drive ratio, dimensionless, η t is the transmission efficiency, dimensionless, C D is the air resistance coefficient, dimensionless, A is the frontal area, with the unit of m 2 , ρ is the air density, kg / m, f is the rolling resistance coefficient, dimensionless, δ is the rotating mass conversion coefficient, dimensionless, g is the gravitational acceleration, with the unit of m / s 2 , and θ is the slope, with the unit of rad.

[0048] Thus, the vehicle longitudinal force calculation formula is:

[0049]

[0050] where m k-1 is the weight estimated at the previous moment, with the unit of kg.

[0051] Furthermore, the preset difference algorithm is the Kalman filter algorithm, and the difference model is:

[0052]

[0053] where A x is the acceleration, with the unit of m / s 2 , and V X is the input longitudinal speed, with the unit of m / s.

[0054] The predicted state is:

[0055] V x,pre = dt * A x + V x,pre ;

[0056] where V x,pre ——predicted vehicle speed, with the unit of m / s, dt——operation period, dimensionless

[0057] The state update equation is:

[0058]

[0059] where K1 is the gain coefficient of the acceleration, dimensionless, and K2 is the gain coefficient of the longitudinal speed, dimensionless.

[0060] Thus, the vehicle longitudinal force result is calculated through the longitudinal force dynamics model, and the vehicle longitudinal acceleration result is obtained through the Kalman filter. It should be noted that the Kalman filter algorithm can minimize the influence of noise on the speed difference operation result, thereby improving the accuracy of the speed difference calculation.

[0061] In step S103, based on the vehicle longitudinal force result and the vehicle longitudinal acceleration result, through the pre-established two-dimensional Kalman filter, iterative calculations are performed on the vehicle mass and the longitudinal force offset, and the predicted results of the vehicle mass state and the longitudinal force offset state are obtained. Based on the predicted results of the vehicle mass state and the longitudinal force offset state, the vehicle mass estimation result is obtained.

[0062] Among them, the pre-established two-dimensional Kalman filter is the constructed mass-force offset coupling model, which realizes high-precision real-time mass estimation. The longitudinal force offset refers to the offset distance between the action line of the longitudinal force acting on the vehicle and the longitudinal symmetry axis of the vehicle during driving.

[0063] Specifically, the offset force is modeled with a dynamic decay coefficient to characterize the time-varying environmental resistance, and the system model is constructed based on the dynamic equation F = m * a. Through the prediction-correction two-stage iteration, in the prediction stage, physical limit constraints are imposed on the mass and the process noise covariance matrix is introduced to update the state prediction value. In the update stage, the Kalman gain is dynamically calculated based on the residual between the longitudinal force measurement value and the model prediction value, realizing the joint optimization of mass estimation and force offset compensation. A covariance matrix out-of-bounds detection and adaptive reset mechanism is designed. When the covariance element exceeds the threshold or an initialization signal is received, the filter state is automatically reset and the preset initial value is loaded to ensure the stability of the algorithm.

[0064] Further, in some embodiments, based on the vehicle longitudinal force result and the vehicle longitudinal acceleration result, through a pre-established two-dimensional Kalman filter, iterative calculations are performed on the vehicle mass and the longitudinal force offset amount to obtain the vehicle mass state prediction result and the longitudinal force offset amount state prediction result. Based on the vehicle mass state prediction result and the longitudinal force offset amount state prediction result, the vehicle mass estimation result is obtained. It includes: establishing a state equation with the vehicle mass as the first state and the longitudinal force offset amount as the second state; based on the state equation, predicting the first state and the second state to obtain the prediction results of the first state and the second state; based on the state equation, performing covariance estimation and prediction on the first state and the second state to obtain the prediction results of the covariance of the first state and the covariance of the second state; based on the preset Kalman coefficient, updating the prediction results of the first state, the prediction results of the second state, the prediction results of the covariance of the first state, and the prediction results of the covariance of the second state to obtain the updated prediction results of the first state, the updated prediction results of the second state, the updated prediction results of the covariance of the first state, and the updated prediction results of the covariance of the second state. Based on the updated prediction results of the first state, the updated prediction results of the second state, the updated prediction results of the covariance of the first state, and the updated prediction results of the covariance of the second state, the vehicle mass estimation result is obtained.

[0065] As a possible implementation, with the vehicle mass as the first state and the longitudinal force offset amount as the second state, the state equation is:

[0066]

[0067] where dm is the mass increment, with the unit kg, dF offset is the offset force increment, with the unit N, F offset is the offset force, with the unit N, C1 is the dynamic coefficient, dimensionless, F pre is the longitudinal force predicted value, with the unit N, m is the estimated vehicle mass, with the unit kg, a is the longitudinal acceleration, with the unit m / s 2 .

[0068] Furthermore, the state prediction equation is:

[0069]

[0070] The covariance prediction equation is:

[0071]

[0072] According to the state prediction equation and the covariance prediction equation, the prediction results of the first state, the prediction results of the second state, the prediction results of the covariance of the first state, and the prediction results of the covariance of the second state are obtained, where m pre is the predicted mass, with the unit kg, F updateoffset is the updated offset force, with the unit N, p pre，m is the predicted mass covariance, with the unit kg 2 , p m is the updated mass covariance, with the unit kg 2 , p noise，m is the process noise covariance related to mass, with the unit kg 2 , p pre，F is the predicted longitudinal force covariance, with the unit N 2 , p F is the updated longitudinal force covariance, with the unit N 2 , p noise,F is the process noise covariance related to longitudinal force, with the unit N 2 .

[0073] As a possible implementation, in some embodiments, the prediction results of the first state, the prediction results of the second state, the prediction results of the covariance of the first state, and the prediction results of the covariance of the second state are updated, and the Kalman coefficients are selected:

[0074]

[0075] The prediction results of the first state and the prediction results of the second state are updated according to the state update equation:

[0076]

[0077] And the prediction results of the covariance of the first state and the prediction results of the covariance of the second state are updated according to the covariance update equation:

[0078]

[0079] where k m is the Kalman coefficient for mass estimation, dimensionless, k F is the Kalman coefficient for offset force estimation, dimensionless, P RFor measuring the noise covariance, unit N 2 and F error is the error between the measured value Fx of the driving force and the predicted value F pre output.

[0080] Thus, based on the real-time operating parameters of the vehicle, the longitudinal force of the vehicle is calculated through the vehicle longitudinal dynamics model, and the longitudinal acceleration of the vehicle is calculated through the differential algorithm. Through the two-dimensional Kalman filter, the vehicle mass and the longitudinal force offset are iteratively calculated to obtain the vehicle mass estimation result.

[0081] Figure 2 FIG. is a schematic diagram of the two-dimensional Kalman filter mass estimation result provided according to a specific embodiment of the present invention. As Figure 2 shown, Figure 2 the dotted line in is the actual vehicle mass curve, Figure 2 and the solid line in is the estimated mass curve. After multiple iterative update calculations, the vehicle estimation result of the two-dimensional Kalman filter algorithm of the embodiment of the present invention is very close to the actual vehicle mass.

[0082] Further, in some embodiments, after obtaining the vehicle mass estimation result based on the prediction results of the updated first state, the prediction results of the updated second state, the prediction results of the updated first state covariance, and the prediction results of the updated second state covariance, it further includes: obtaining the output estimation result of the acceleration sensor of the current vehicle; and correcting the vehicle mass estimation result based on the output estimation result.

[0083] Wherein, the output estimation result of the acceleration sensor of the current vehicle can be obtained after analyzing the signal collected by the acceleration sensor. Correcting the vehicle mass estimation result means correcting the vehicle mass estimation result according to the longitudinal acceleration output by the sensor and the acceleration obtained by speed difference to obtain the corrected vehicle mass estimation result.

[0084] Specifically, the embodiment of the present invention can analyze the credibility of the estimation variances of the two mass estimations and combine the credibility based on the number of estimation periods and the credibility based on the difference between the two estimated masses to determine the weighted fusion strategy and obtain the final mass estimation value.

[0085] As a possible implementation manner, in some embodiments, first analyze the credibility of the estimation variances of the two mass estimations:

[0086]

[0087] Wherein, Var is the estimated variance, n is the number of estimation periods, mass is the mass calculated from the sensor acceleration or the differential acceleration of the vehicle speed, and massF is the mass after fusion weighting at the previous moment.

[0088] Define the variance intervals of the two mass estimations as [100 700], and the corresponding verification factors as [1 0.1], and perform interpolation calculation on the verification factors. Secondly, analyze the credibility based on the number of estimation periods, define the number of estimation periods interval as [1 15], and the corresponding verification factors as [0.5 1], and perform interpolation calculation on the verification factors. Then analyze the credibility of the difference between the two estimated masses:

[0089]

[0090] Wherein, MassFactor is the calculated mass difference coefficient, m ax is the mass calculated using the sensor acceleration, m dvx is the mass calculated using the differential acceleration of the speed, and MassloadMax is the maximum value of the loadable mass of the whole vehicle. Define the mass difference coefficient interval as [0.1 0.6], and the corresponding verification factors as [1 0.2], and perform interpolation calculation on the verification factors.

[0091] It should be noted that the credibility of the mass output calculated by the sensor acceleration method, conf_dvx, and the credibility of the mass output calculated by the differential acceleration of the speed method, conf_ax, both take the minimum value among the corresponding credibility based on the estimated variance, the credibility based on the number of estimation periods, and the credibility of the difference between the two estimated masses.

[0092] Furthermore, if the credibility after the fusion of the two methods is not zero, that is, conf_dvx and conf_ax are not zero, then the calculation formula for the final mass is:

[0093] m = 0.5 * m ax + 0.5 * 0.5 * m dvx ;

[0094] If one of conf_dvx and conf_ax is zero, then the calculation formula for the final mass is:

[0095]

[0096] Wherein, Figure 3 is the schematic diagram of the curve results of the differential acceleration method and the acceleration sensor according to a specific embodiment of the present invention, as Figure 3 shown, Figure 3 the dashed line in is the curve measured by the differential acceleration method, Figure 3The solid line in the figure is the curve measured by the sensor acceleration. Among them, the differential acceleration method is very similar to the acceleration value obtained by the acceleration sensor, indicating the effectiveness of the differential acceleration method. Therefore, for vehicles equipped with acceleration sensors, dual mass estimation can be performed to improve stability and enhance the generality of vehicle mass estimation.

[0097] To enable relevant technical personnel in the field to better understand the vehicle mass estimation method of the embodiments of the present invention, the following will be described in conjunction with specific embodiments.

[0098] Figure 4 It is a flowchart of the vehicle mass estimation method provided according to a specific embodiment of the present invention.

[0099] As Figure 4 shown, the vehicle mass estimation method includes the following steps:

[0100] In step S401, vehicle signals and parameters are obtained, and the longitudinal force is estimated using the longitudinal dynamics model.

[0101] In step S402, the acceleration is obtained through the sensor, and the longitudinal speed is obtained through vehicle speed difference.

[0102] In step S403, the working condition is discriminated, a two-dimensional interpolation lookup table is used, and the 0-1 verification factor evaluation result is output, and it is decided whether to release the function.

[0103] In step S404, for the mass-force offset coupling model, combined with the two-dimensional Kalman filter, mass estimate 1 and mass estimate 2 are output. For vehicles without an acceleration sensor, only mass estimate 1 is output.

[0104] In step S405, the credibility of mass estimate 1 and mass estimate 2 is obtained according to the verification factor, and combined with the credibility of the two estimation mass differences of the estimation variance and the estimation period based on mass estimate 1 and mass estimate 2, the fusion scheme of the final mass estimate is selected.

[0105] According to the vehicle mass estimation method provided by the embodiments of the present invention, based on the real-time operating parameters of the vehicle, the longitudinal force of the vehicle is calculated through the vehicle longitudinal dynamics model, the longitudinal acceleration of the vehicle is calculated through the differential algorithm, and through the two-dimensional Kalman filter, the vehicle mass and the longitudinal force offset are iteratively calculated to obtain the vehicle mass estimation result, which solves the problems of low accuracy and efficiency of vehicle mass estimation and poor robustness in the related art, improves the accuracy and efficiency of vehicle mass estimation, and enhances the robustness of vehicle mass estimation.

[0106] Secondly, a vehicle mass estimation device proposed according to an embodiment of the present invention will be described with reference to the accompanying drawings.

[0107] Figure 5Schematic block diagram of a vehicle mass estimation device provided according to an embodiment of the present invention.

[0108] As Figure 5 shown, the vehicle mass estimation device 10 includes: an acquisition module 100, a data processing module 200, and an estimation module 300.

[0109] Among them, the acquisition module 100 is used to acquire multiple operating parameters of the current vehicle; the data processing module 200 is used to perform a vehicle mass estimation operation when it is determined that the current vehicle meets the preset vehicle mass estimation conditions based on the multiple operating parameters, calculate the vehicle longitudinal force through a pre-constructed vehicle longitudinal dynamics model, and calculate the vehicle longitudinal acceleration through a preset difference algorithm to obtain a vehicle longitudinal force result and a vehicle longitudinal acceleration result; the estimation module 300 is used to iteratively calculate the vehicle mass and the longitudinal force offset based on the vehicle longitudinal force result and the vehicle longitudinal acceleration result through a pre-established two-dimensional Kalman filter to obtain a vehicle mass state prediction result and a longitudinal force offset state prediction result, and obtain a vehicle mass estimation result based on the vehicle mass state prediction result and the longitudinal force offset state prediction result.

[0110] Further, in some embodiments, after acquiring the multiple operating parameters of the current vehicle, the data processing module 200 is further used to: calculate each parameter in the multiple operating parameters based on a preset verification algorithm to obtain an independent verification factor result for each parameter; if the minimum value among the independent verification factor results of the multiple parameters is greater than a preset threshold, it is determined that the current vehicle meets the preset vehicle mass estimation conditions.

[0111] Further, in some embodiments, the data processing module 200 is further used to: construct a longitudinal dynamics model of the current vehicle, and calculate a vehicle longitudinal force result based on the longitudinal dynamics model; process the speed signal of the current vehicle through a preset difference algorithm to obtain a vehicle longitudinal acceleration result.

[0112] Further, in some embodiments, the estimation module 300 is specifically configured to: establish a state equation with the vehicle mass as the first state and the longitudinal force offset as the second state; based on the state equation, predict the first state and the second state to obtain a prediction result of the first state and a prediction result of the second state; perform covariance estimation and prediction on the first state and the second state based on the state equation to obtain a prediction result of the covariance of the first state and a prediction result of the covariance of the second state; update the prediction result of the first state, the prediction result of the second state, the prediction result of the covariance of the first state, and the prediction result of the covariance of the second state based on a preset Kalman coefficient to obtain an updated prediction result of the first state, an updated prediction result of the second state, an updated prediction result of the covariance of the first state, and an updated prediction result of the covariance of the second state. Based on the updated prediction result of the first state, the updated prediction result of the second state, the updated prediction result of the covariance of the first state, and the updated prediction result of the covariance of the second state, obtain a vehicle mass estimation result.

[0113] Further, in some embodiments, after obtaining the vehicle mass estimation result based on the updated prediction result of the first state, the updated prediction result of the second state, the updated prediction result of the covariance of the first state, and the updated prediction result of the covariance of the second state, the estimation module 300 is further configured to: obtain an output estimation result of the acceleration sensor of the current vehicle; correct the vehicle mass estimation result based on the output estimation result.

[0114] It should be noted that the above explanation of the embodiments of the vehicle mass estimation method also applies to the vehicle mass estimation device of this embodiment, and will not be elaborated here.

[0115] According to the vehicle mass estimation device proposed by the embodiments of the present invention, based on the real-time operating parameters of the vehicle, the longitudinal force of the vehicle is calculated through a vehicle longitudinal dynamics model, and the longitudinal acceleration of the vehicle is calculated through a difference algorithm. Through a two-dimensional Kalman filter, iterative calculations are performed on the vehicle mass and the longitudinal force offset to obtain a vehicle mass estimation result, which solves the problems of low accuracy and efficiency of vehicle mass estimation and poor robustness in the related art, improves the accuracy and efficiency of vehicle mass estimation, and enhances the robustness of vehicle mass estimation.

[0116] In addition, an embodiment of the present invention further provides a computer-readable storage medium, on which a computer program is stored, and when the program is executed by a processor, the above vehicle mass estimation method is implemented.

[0117] In addition, an embodiment of the present invention further provides a computer program product, including a computer program, and the computer program is executed to implement the vehicle mass estimation method as described in any one of the above.

[0118] In the description of this specification, the descriptions referring to terms such as "one embodiment", "some embodiments", "examples", "specific examples", or "some examples", etc., mean that the specific features, structures, materials, or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials, or characteristics described can be combined in any one or N embodiments or examples in a suitable manner. In addition, without contradiction, those skilled in the art can combine and combine the different embodiments or examples described in this specification and the features of different embodiments or examples.

[0119] In addition, the terms "first" and "second" are used for descriptive purposes only and cannot be construed as indicating or implying relative importance or implicitly specifying the quantity of the indicated technical features. Thus, the features defined with "first" and "second" may explicitly or implicitly include at least one of these features. In the description of the present invention, the meaning of "N" is at least two, such as two, three, etc., unless otherwise specifically defined.

[0120] Any process or method description shown in the flowchart or described in other ways herein can be understood to represent a module, segment, or portion of code including one or more N executable instructions for implementing a customized logical function or process, and the scope of the preferred embodiments of the present invention includes additional implementations, where the functions can be executed in a substantially simultaneous manner or in a reverse order according to the involved functions, rather than in the order shown or discussed, which should be understood by those skilled in the art to which the embodiments of the present invention pertain.

[0121] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware, or a combination thereof. In the above embodiments, the N steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, any one or a combination of the following well-known technologies in the art can be used: discrete logic circuits having logic gate circuits for implementing logical functions on data signals, application-specific integrated circuits having appropriate combinational logic gate circuits, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0122] Those of ordinary skill in the technical field can understand that all or part of the steps carried by the method of implementing the above embodiments can be completed by instructing relevant hardware through a program, and the program can be stored in a computer-readable storage medium. When the program is executed, it includes one or a combination of the steps of the method embodiments.

Claims

1. A vehicle mass estimation method, characterized in that: The following steps are involved: Get multiple operating parameters of the current vehicle; When it is determined based on the multiple operating parameters that the current vehicle meets a preset vehicle mass estimation condition, a vehicle mass estimation operation is performed, the vehicle longitudinal force is calculated using a pre-constructed vehicle longitudinal dynamics model, and the vehicle longitudinal acceleration is calculated using a preset differential algorithm to obtain a vehicle longitudinal force result and a vehicle longitudinal acceleration result; Based on the vehicle longitudinal force result and the vehicle longitudinal acceleration result, the vehicle mass and the longitudinal force offset are iteratively calculated through a pre-established two-dimensional Kalman filter to obtain a vehicle mass state prediction result and a longitudinal force offset state prediction result, and a vehicle mass estimation result is obtained based on the vehicle mass state prediction result and the longitudinal force offset state prediction result.

2. The vehicle mass estimation method according to claim 1, characterized in that: After obtaining the multiple operating parameters of the current vehicle, the method further includes: Based on a preset verification algorithm, each of the multiple operating parameters is calculated to obtain an independent verification factor result for each parameter; If the minimum value of the independent verification factor results of the multiple parameters is greater than a preset threshold, it is determined that the current vehicle meets the preset vehicle mass estimation condition.

3. The vehicle mass estimation method according to claim 1, characterized in that: The vehicle longitudinal force is calculated by using a pre-built vehicle longitudinal dynamics model, and the vehicle longitudinal acceleration is calculated by using a preset differential algorithm to obtain a vehicle longitudinal force result and a vehicle longitudinal acceleration result, including: Constructing a longitudinal dynamics model of the current vehicle, and calculating the longitudinal force result of the vehicle based on the longitudinal dynamics model; The speed signal of the current vehicle is processed by a preset differential algorithm to obtain the longitudinal acceleration result of the vehicle.

4. The vehicle mass estimation method according to claim 1, characterized in that: The method of iteratively calculating the vehicle mass and the longitudinal force offset based on the vehicle longitudinal force result and the vehicle longitudinal acceleration result through a pre-established two-dimensional Kalman filter to obtain a vehicle mass state prediction result and a longitudinal force offset state prediction result, and obtaining a vehicle mass estimation result based on the vehicle mass state prediction result and the longitudinal force offset state prediction result, includes: Taking the vehicle mass as the first state and the longitudinal force offset as the second state, a state equation is established; Based on the state equation, the first state and the second state are predicted to obtain a prediction result of the first state and a prediction result of the second state; based on the state equation, the first state and the second state are predicted by covariance estimation to obtain a prediction result of the covariance of the first state and a prediction result of the covariance of the second state; Based on a preset Kalman coefficient, the prediction result of the first state, the prediction result of the second state, the prediction result of the covariance of the first state, and the prediction result of the covariance of the second state are updated to obtain an updated prediction result of the first state, an updated prediction result of the second state, an updated prediction result of the covariance of the first state, and an updated prediction result of the covariance of the second state; The vehicle mass estimation result is obtained based on the updated prediction result of the first state, the updated prediction result of the second state, the updated prediction result of the first state covariance and the updated prediction result of the second state covariance.

5. The vehicle mass estimation method according to claim 4, characterized in that: After obtaining the vehicle mass estimation result based on the updated prediction result of the first state, the updated prediction result of the second state, the updated prediction result of the first state covariance, and the updated prediction result of the second state covariance, the method further includes: Obtaining an output estimation result of an acceleration sensor of the current vehicle; Based on the output estimation result, the vehicle mass estimation result is corrected.

6. A vehicle mass estimation device, characterized in that: The device comprises: An acquisition module is used to acquire multiple operating parameters of the current vehicle; a data processing module, configured to, when it is determined based on the plurality of operating parameters that the current vehicle satisfies a preset vehicle mass estimation condition, perform a vehicle mass estimation operation, calculate the vehicle longitudinal force by using a pre-constructed vehicle longitudinal dynamics model, and calculate the vehicle longitudinal acceleration by using a preset differential algorithm, to obtain a vehicle longitudinal force result and a vehicle longitudinal acceleration result; An estimation module is used to iteratively calculate the vehicle mass and the longitudinal force offset based on the vehicle longitudinal force result and the vehicle longitudinal acceleration result through a pre-established two-dimensional Kalman filter, so as to obtain a vehicle mass state prediction result and a longitudinal force offset state prediction result, and obtain a vehicle mass estimation result based on the vehicle mass state prediction result and the longitudinal force offset state prediction result.

7. The vehicle mass estimation device according to claim 6, characterized in that: After acquiring the multiple operating parameters of the current vehicle, the data processing module is further used to: Based on a preset verification algorithm, each of the multiple operating parameters is calculated to obtain an independent verification factor result for each parameter; If the minimum value of the independent verification factor results of the multiple parameters is greater than a preset threshold, it is determined that the current vehicle meets the preset vehicle mass estimation condition.

8. An electronic device, characterized in that: include: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the vehicle mass estimation method according to any one of claims 1 to 5.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: The program is executed by a processor to implement the vehicle mass estimation method as described in any one of claims 1 to 5.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the vehicle mass estimation method according to any one of claims 1 to 5 is implemented.

Images