Vehicle mass estimation method and device, storage medium and computer program product
By performing longitudinal force analysis on the vehicle as a whole and combining recursive least squares with Kalman gain, the problem of low accuracy in vehicle mass estimation was solved, achieving accurate mass estimation under different driving conditions and improving the precision of vehicle control.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- SAIC MOTOR
- Filing Date
- 2024-12-18
- Publication Date
- 2026-06-19
Smart Images
Figure CN122232638A_ABST
Abstract
Description
Technical Field
[0001] This application relates to the field of vehicles, and more specifically, to a method and apparatus for estimating the weight of a vehicle, a storage medium, and a computer program product. Background Technology
[0002] In model-based vehicle acceleration control strategies, accurate vehicle mass estimation is crucial for ensuring precise execution of the control strategy. The accuracy of vehicle mass estimation significantly impacts vehicle dynamics, handling stability, ride comfort, and active safety control systems. Current vehicle mass estimation methods primarily employ sensor-based and longitudinal dynamics-based approaches. Sensor-based methods directly measure the slope angle by installing sensors such as tilt sensors, inertial navigation systems, and GPS, then calculate the vehicle mass. However, this method is generally unsuitable for real-world applications due to either inaccurate slope estimation or high cost. Longitudinal dynamics-based methods utilize the vehicle's longitudinal dynamics model and existing CAN signals to estimate the vehicle mass. Related technologies also employ recursive least squares for mass estimation and design time-varying forgetting factors based on fuzzy control principles to make the mass estimation model more applicable to time-varying conditions. However, when using forgetting factors for online estimation, low signal-to-noise ratios can occur when longitudinal acceleration values are small, leading to significant fluctuations in estimation errors.
[0003] There is no effective solution to the problem of low accuracy in existing vehicle weight estimation methods. Summary of the Invention
[0004] This application provides a vehicle weight estimation method and apparatus, storage medium, and computer program product to at least solve the problem of low accuracy of weight estimation results in existing vehicle weight estimation methods in related technologies.
[0005] According to one aspect of the embodiments of this application, a method for estimating the mass of a vehicle is provided, comprising: performing a longitudinal force analysis on a target vehicle; establishing a longitudinal dynamic equation of the target vehicle based on the force analysis results; determining whether the target vehicle meets the mass estimation enabling condition; and, if the target vehicle meets the mass estimation enabling condition, estimating the mass of the target vehicle using the recursive least squares method based on the longitudinal dynamic equation of the vehicle to obtain the mass of the target vehicle.
[0006] In an exemplary embodiment, determining whether the target vehicle meets the mass estimation enabling condition includes at least one of the following: if the absolute value of the longitudinal acceleration of the target vehicle is less than a first threshold, determining that the target vehicle does not meet the mass estimation enabling condition; if mechanical braking is applied to the target vehicle, determining that the target vehicle does not meet the mass estimation enabling condition; if the vehicle mass estimation result of the target vehicle exceeds a preset estimation range, determining that the target vehicle does not meet the mass estimation enabling condition; if the driving gradient value of the target vehicle is greater than a second threshold, determining that the target vehicle does not meet the mass estimation enabling condition.
[0007] In an exemplary embodiment, the vehicle mass of the target vehicle is estimated using the recursive least squares method based on the vehicle's longitudinal dynamics equations to obtain the target vehicle's total mass. This includes: an estimation error calculation step: calculating the estimation error of the longitudinal acceleration of the target vehicle in the k-th total mass estimation based on the k-th measured value and the k-th estimated value of the target vehicle's longitudinal acceleration; and a Kalman gain calculation step: using the formula... Kalman gain calculation is performed to obtain the Kalman gain matrix K(k) for the k-th vehicle mass estimation, where P(k-1) is the covariance matrix for the (k-1)-th vehicle mass estimation, λ is the forgetting factor, and H(k-1) is the Kalman gain matrix. T Let H(k-1) be the transpose of H(k-1), where H(k-1) is the system vector corresponding to the (k-1)th vehicle mass estimation. H(k-1) is determined based on the driving slope value of the target vehicle and the longitudinal dynamic equation of the vehicle. The update steps are as follows: update the estimation parameter vector x(k-1) corresponding to the (k-1)th vehicle mass estimation based on the estimation error and the Kalman gain matrix K(k), and update the covariance matrix P(k-1) based on the Kalman gain matrix K(k). m is the total vehicle mass, β is the driving gradient value, and c r The target vehicle's rolling resistance coefficient is given, and the updated estimated parameter vector x(k) includes the vehicle mass obtained from the kth vehicle mass estimation. If the target vehicle meets the mass estimation enabling condition, the estimation error calculation step, the Kalman gain calculation step, and the update step are executed cyclically.
[0008] In an exemplary embodiment, after estimating the vehicle mass of the target vehicle using the recursive least squares method based on the vehicle's longitudinal dynamics equations, the method further includes: averaging m(k) and M(k-1) to obtain M(k), where m(k) is the vehicle mass obtained from the k-th vehicle mass estimation, and M(k-1) is the vehicle mass estimation result obtained from the (k-1)-th vehicle mass estimation; updating vehicle control parameters based on M(k), wherein the vehicle control parameters are used to indicate the vehicle mass information of the target vehicle; and performing vehicle acceleration control on the target vehicle within a target time period based on the vehicle control parameters, wherein the start time of the target time period is the first time of this update of the vehicle control parameters, and the end time of the target time period is the second time of the next update of the vehicle control parameters.
[0009] In an exemplary embodiment, after averaging m(k) and M(k-1) to obtain M(k), the method further includes: calculating the rate of change of M(k-1) and M(k); determining whether the rate of change exceeds a preset range; and if the rate of change exceeds the preset range, prohibiting the updating of the vehicle control parameters based on M(k).
[0010] In an exemplary embodiment, after updating the vehicle control parameters according to M(k), the method further includes: statistically analyzing multiple consecutive historical values of the vehicle control parameters; determining whether the vehicle mass estimation result has converged based on the multiple historical values; and if it is determined that the vehicle mass estimation result has converged, prohibiting further updates to the vehicle control parameters until the target vehicle stops and the door is opened or the target vehicle is powered on again.
[0011] In an exemplary embodiment, before prohibiting further updates to the vehicle control parameters, the method further includes: determining whether the target vehicle satisfies the golden iteration condition during multiple vehicle mass estimations corresponding to multiple historical values, wherein the golden iteration condition includes at least one of the following: the target vehicle does not experience wheel slippage, the target vehicle's maximum yaw rate is less than a third threshold, the target vehicle's maximum steering angle is less than a fourth threshold, and the target vehicle's driving slope value is less than a fifth threshold; if the target vehicle is determined to satisfy the golden iteration condition, further updates to the vehicle control parameters are prohibited.
[0012] According to another aspect of the embodiments of this application, a vehicle mass estimation device is also provided, comprising: an establishment module for performing longitudinal force analysis on a target vehicle and establishing a longitudinal dynamic equation of the target vehicle based on the force analysis results; a determination module for determining whether the target vehicle meets the mass estimation enabling conditions; and a vehicle mass estimation module for estimating the vehicle mass of the target vehicle by using the recursive least squares method based on the longitudinal dynamic equation of the target vehicle, when it is determined that the target vehicle meets the mass estimation enabling conditions, to obtain the vehicle mass of the target vehicle.
[0013] According to another aspect of the embodiments of this application, a computer-readable storage medium is also provided, wherein a computer program is stored in the computer program, and the computer program is configured to execute the above-mentioned data file analysis method or data file transmission method when running.
[0014] According to another aspect of the embodiments of this application, an electronic device is also provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the data file analysis method or the data file transmission method described above through the computer program.
[0015] According to another aspect of the embodiments of this application, a computer program product is also provided, including a computer program that, when executed by a processor, implements the steps of the methods described in various embodiments of this application.
[0016] In this embodiment, a longitudinal force analysis of the target vehicle is first performed, and the longitudinal dynamic equation of the target vehicle is established based on the force analysis results. It is then determined whether the target vehicle meets the mass estimation enabling condition. If the target vehicle meets the mass estimation enabling condition, the vehicle mass is estimated using the recursive least squares method based on the longitudinal dynamic equation, thus obtaining the vehicle mass. This approach proposes a method for estimating vehicle mass based on the recursive least squares method, achieving accurate estimation of the vehicle mass. This solves the problem of low accuracy in existing vehicle mass estimation methods in related technologies. Attached Figure Description
[0017] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.
[0018] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on these drawings without creative effort.
[0019] Figure 1 This is a flowchart of an optional vehicle weight estimation method according to an embodiment of this application;
[0020] Figure 2 This is a schematic diagram of an optional longitudinal dynamics model of a vehicle according to an embodiment of this application;
[0021] Figure 3 A structural block diagram of an optional vehicle weight estimation device according to an embodiment of this application. Detailed Implementation
[0022] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present application, and not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present application.
[0023] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.
[0024] To address the technical problems existing in related technologies, this embodiment provides a method for estimating vehicle weight. Figure 1 This is a flowchart of an optional vehicle weight estimation method according to an embodiment of this application, the process including the following steps S102-S106:
[0025] Step S102: Perform longitudinal force analysis on the target vehicle and establish the longitudinal dynamic equation of the target vehicle based on the force analysis results.
[0026] Specifically, the established longitudinal dynamics model of the target vehicle is as follows: Figure 2 As shown, based on this model, a longitudinal force analysis of the target vehicle is performed, and the analysis formula is as follows:
[0027]
[0028] F roll =mg·c r ·cosβ;
[0029] F climb =mg·sinβ;
[0030] F brake =c b ·p b .
[0031] In the formula, T represents the output driving torque of the drive motor, i is the reduction ratio, η is the transmission system efficiency, r is the effective radius of the wheel, and J is the wheel moment of inertia. c is the angular acceleration of the wheel. w A represents the air drag coefficient and the frontal area, respectively; β represents the slope (rad); c r The rolling resistance coefficient is given. The longitudinal dynamic equation is written as a linear system model, where y is the vehicle's longitudinal acceleration: y = H·x, and the system vector H is: H = [F...]. long -F aero -F brake The estimated parameter vector X is: F brake For the braking force of the whole vehicle, c b p is the pressure coefficient of the brake master cylinder. b Main cylinder pressure.
[0032] Due to the estimated parameters and β+c r Since the two estimated parameters have different rates of change, we can set forgetting factors λ = diag[λ1, λ2] respectively. The closer the forgetting factor is to 1, the less sensitive the parameter changes are. Considering that the quality changes are not sensitive and the estimated slope is not a concern, the model with a single forgetting factor λ≈1 can be used in the actual algorithm.
[0033] Step S104: Determine whether the target vehicle meets the mass estimation enabling conditions;
[0034] Step S106: If it is determined that the target vehicle meets the mass estimation enabling condition, the vehicle mass is estimated by recursive least squares method according to the vehicle longitudinal dynamics equation to obtain the vehicle mass of the target vehicle.
[0035] Through the above steps, a longitudinal force analysis of the target vehicle is first performed, and the longitudinal dynamic equation of the target vehicle is established based on the force analysis results. It is then determined whether the target vehicle meets the mass estimation enabling condition. If the target vehicle meets the mass estimation enabling condition, the vehicle mass is estimated using the recursive least squares method based on the longitudinal dynamic equation, thus obtaining the vehicle mass. This approach proposes a method for estimating vehicle mass based on the recursive least squares method, achieving accurate estimation of the vehicle mass. This solves the problem of low accuracy in existing vehicle mass estimation methods in related technologies.
[0036] In an exemplary embodiment, determining whether the target vehicle meets the mass estimation enabling condition includes at least one of the following: if the absolute value of the longitudinal acceleration of the target vehicle is less than a first threshold, determining that the target vehicle does not meet the mass estimation enabling condition; if mechanical braking is applied to the target vehicle, determining that the target vehicle does not meet the mass estimation enabling condition; if the vehicle mass estimation result of the target vehicle exceeds a preset estimation range, determining that the target vehicle does not meet the mass estimation enabling condition; if the driving gradient value of the target vehicle is greater than a second threshold, determining that the target vehicle does not meet the mass estimation enabling condition.
[0037] The use of a forgetting factor is common in online quality estimation, but when the longitudinal acceleration value is small, a low signal-to-noise ratio can easily occur, while older information is "forgotten." This leads to an increase in the covariance matrix P, causing fluctuations in the estimated parameter values. Therefore, a quality estimation enabling condition judgment is proposed:
[0038] When the absolute value of longitudinal acceleration When the first threshold mentioned above is reached or mechanical braking is applied, the quality estimation update is paused.
[0039] Setting upper and lower limits for estimated vehicle weight: (i.e., the aforementioned preset estimation range);
[0040] When the slope value and slope derivative value calculated based on kinematics are too large (greater than the second threshold), the quality estimation update is paused.
[0041] If the quality estimation enabling condition is not met, the covariance matrix of the previous time step is maintained to prevent the estimated slope from jumping when the estimable condition is met.
[0042] In an exemplary embodiment, the vehicle mass of the target vehicle is estimated using the recursive least squares method based on the vehicle's longitudinal dynamics equations to obtain the target vehicle's total mass. This includes: an estimation error calculation step: calculating the estimation error of the longitudinal acceleration of the target vehicle in the k-th total mass estimation based on the k-th measured value and the k-th estimated value of the target vehicle's longitudinal acceleration; and a Kalman gain calculation step: using the formula... Kalman gain calculation is performed to obtain the Kalman gain matrix K(k) for the k-th vehicle mass estimation, where P(k-1) is the covariance matrix for the (k-1)-th vehicle mass estimation, λ is the forgetting factor, and H(k-1) is the Kalman gain matrix. T Let H(k-1) be the transpose of H(k-1), where H(k-1) is the system vector corresponding to the (k-1)th vehicle mass estimation. H(k-1) is determined based on the driving slope value of the target vehicle and the longitudinal dynamic equation of the vehicle. The update steps are as follows: update the estimation parameter vector x(k-1) corresponding to the (k-1)th vehicle mass estimation based on the estimation error and the Kalman gain matrix K(k), and update the covariance matrix P(k-1) based on the Kalman gain matrix K(k). m is the total vehicle mass, β is the driving gradient value, and c r The target vehicle's rolling resistance coefficient is given, and the updated estimated parameter vector x(k) includes the vehicle mass obtained from the kth vehicle mass estimation. If the target vehicle meets the mass estimation enabling condition, the estimation error calculation step, the Kalman gain calculation step, and the update step are executed cyclically.
[0043] When k=0, initialize the values of the parameters to be estimated (i.e. the parameter vector) and the covariance matrix P;
[0044] When k = 1, 2, 3..., the following iterative operation is performed:
[0045] 1. Steps for calculating estimation error: In step k, use the system matrix H corresponding to the optimal estimate from step k-1 to calculate the predicted system input, and subtract the measured input value y(k) from the estimated value from the estimated value from step k to obtain the error.
[0046] ε(k)=y(k)-H(k)x(k-1);
[0047] 2. Kalman gain calculation steps: Using the covariance matrix P(k-1) from step k-1 and the system matrix H, calculate the Kalman gain matrix K(k) from step k, considering the forgetting factor coefficients:
[0048]
[0049] 3. Optimal estimate update step and covariance matrix update step (i.e., the update step mentioned above): Update the optimal estimated parameter value of step k-1 using the calculated Kalman gain K(k) and estimation error: x(k) = x(k-1) + K(k)ε(k); and update the covariance matrix using the covariance matrix and system matrix from the previous step, along with the previously calculated Kalman gain, considering the influence of the forgetting factor, and then continue the logical loop: P(k) = λ -1 ·[P(k-1)-K(k)H T P(k-1)].
[0050] Based on the above steps, after estimating the vehicle mass of the target vehicle using the recursive least squares method according to the vehicle longitudinal dynamics equation, the method further includes: calculating the average value of m(k) and M(k-1) to obtain M(k), where m(k) is the vehicle mass obtained from the k-th vehicle mass estimation, and M(k-1) is the vehicle mass estimation result obtained from the (k-1)-th vehicle mass estimation; updating the vehicle control parameters according to M(k), wherein the vehicle control parameters are used to indicate the vehicle mass information of the target vehicle; and performing vehicle acceleration control on the target vehicle within a target time period according to the vehicle control parameters, wherein the start time of the target time period is the first time of the current update of the vehicle control parameters, and the end time of the target time period is the second time of the next update of the vehicle control parameters.
[0051] After each new mass estimate is calculated using the recursive least squares method, it needs to be averaged with the old vehicle mass estimate to obtain a new vehicle mass estimate. Then, the new vehicle mass estimate M(k) is used to update the vehicle control parameters. The vehicle can use the vehicle mass indicated in the vehicle control parameters to assist in vehicle acceleration control. The effective duration of M(k) is the target time period from the current update to the next update.
[0052] Optionally, after averaging m(k) and M(k-1) to obtain M(k), the method further includes: calculating the rate of change of M(k-1) and M(k); determining whether the rate of change exceeds a preset range; and if the rate of change exceeds the preset range, prohibiting the updating of the vehicle control parameters based on M(k).
[0053] To prevent abrupt changes in quality estimation, a Rate Limiter is set to ensure a smooth transition in output quality. After calculating M(k), the rate of change between M(k-1) and M(k) is calculated. It is then determined whether this rate of change exceeds the preset range (i.e., the aforementioned preset range). If it does, the use of M(k) is prohibited.
[0054] Optionally, this application embodiment may also set a maximum update limit. If the vehicle quality reaches the maximum update limit (e.g., 20 times), quality updates will no longer be allowed until the vehicle stops.
[0055] In an exemplary embodiment, after updating the vehicle control parameters according to M(k), the method further includes: statistically analyzing multiple consecutive historical values of the vehicle control parameters; determining whether the vehicle mass estimation result has converged based on the multiple historical values; and if it is determined that the vehicle mass estimation result has converged, prohibiting further updates to the vehicle control parameters until the target vehicle stops and the door is opened or the target vehicle is powered on again.
[0056] Considering that the mass estimation can quickly converge to a relatively accurate value under high-throttle acceleration on a straight road, subsequent updates to the mass may introduce errors in the estimation under complex operating conditions. Therefore, when a relatively ideal mass update is identified, this estimated mass should be maintained until the vehicle stops again and a door is opened or closed. This requires statistically analyzing multiple consecutive historical values corresponding to the vehicle control parameters. Based on these historical values, it is determined whether the vehicle mass estimation result has converged. If converged, the estimated mass is maintained and no further updates are made until the vehicle stops and a door is opened or the target vehicle is powered on again.
[0057] Optionally, before prohibiting further updates to the vehicle control parameters, the method further includes: determining whether the target vehicle meets the golden iteration condition during multiple vehicle mass estimations corresponding to multiple historical values, wherein the golden iteration condition includes at least one of the following: the target vehicle does not experience wheel slippage, the target vehicle's maximum yaw rate is less than a third threshold, the target vehicle's maximum steering angle is less than a fourth threshold, and the target vehicle's driving slope value is less than a fifth threshold; if the target vehicle is determined to meet the golden iteration condition, further updates to the vehicle control parameters are prohibited.
[0058] After determining that the vehicle mass estimation results have converged, it is also necessary to determine whether the current iteration process conforms to the golden iteration. To determine whether the current mass update process belongs to the "golden iteration", the following conditions must be met: the driver did not make large steering inputs during the current iteration, and the vehicle maintained a straight driving as much as possible (i.e., the maximum steering angle mentioned above is less than the fourth threshold); no large yaw rate occurred during the current iteration (i.e., the maximum yaw rate mentioned above is less than the third threshold); no obvious wheel slippage was detected during the iteration; and the road gradient was small and changed smoothly during the iteration (i.e., the driving gradient value mentioned above is less than the fifth threshold), avoiding mass estimation errors introduced by inaccurate gradient estimation.
[0059] Through the above embodiments, the overall vehicle mass can be effectively estimated based on the vehicle model without the need for additional sensors or cameras, which is of guiding significance for improving the vehicle's power performance, handling stability, ride comfort, and active safety control systems.
[0060] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods according to the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) and includes several instructions to cause a terminal device (which may be a mobile phone, computer, server, or network device, etc.) to execute the methods of the various embodiments of this application.
[0061] This application also provides a vehicle weight estimation device, such as... Figure 3 As shown, Figure 3 This is a structural block diagram of an optional vehicle weight estimation device according to an embodiment of this application. The device includes:
[0062] Module 32 is established to perform longitudinal force analysis on the target vehicle and establish the longitudinal dynamic equation of the target vehicle based on the force analysis results.
[0063] Determining module 34 is used to determine whether the target vehicle meets the mass estimation enabling conditions;
[0064] The vehicle mass estimation module 36 is used to estimate the vehicle mass of the target vehicle by recursive least squares method according to the vehicle longitudinal dynamics equation, when it is determined that the target vehicle meets the mass estimation enabling condition, so as to obtain the vehicle mass of the target vehicle.
[0065] Using the aforementioned device, a longitudinal force analysis of the target vehicle is first performed, and the longitudinal dynamic equation of the target vehicle is established based on the force analysis results. It is then determined whether the target vehicle meets the mass estimation enabling condition. If the target vehicle meets the mass estimation enabling condition, the vehicle mass is estimated using the recursive least squares method based on the longitudinal dynamic equation, thus obtaining the vehicle mass. This approach proposes a method for estimating vehicle mass based on the recursive least squares method, achieving accurate estimation of the vehicle mass. This solves the problem of low accuracy in existing vehicle mass estimation methods in related technologies.
[0066] In an exemplary embodiment, the determining module 34 is further configured to determine whether the target vehicle meets the mass estimation enabling condition by at least one of the following methods: if the absolute value of the longitudinal acceleration of the target vehicle is less than a first threshold, determine that the target vehicle does not meet the mass estimation enabling condition; if mechanical braking is applied to the target vehicle, determine that the target vehicle does not meet the mass estimation enabling condition; if the vehicle mass estimation result of the target vehicle exceeds a preset estimation range, determine that the target vehicle does not meet the mass estimation enabling condition; if the driving slope value of the target vehicle is greater than a second threshold, determine that the target vehicle does not meet the mass estimation enabling condition.
[0067] The use of a forgetting factor is common in online quality estimation, but when the longitudinal acceleration value is small, a low signal-to-noise ratio can easily occur, while older information is "forgotten." This leads to an increase in the covariance matrix P, causing fluctuations in the estimated parameter values. Therefore, a quality estimation enabling condition judgment is proposed:
[0068] When the absolute value of longitudinal acceleration When the first threshold mentioned above is reached or mechanical braking is applied, the quality estimation update is paused.
[0069] Setting upper and lower limits for estimated vehicle weight: (i.e., the aforementioned preset estimation range);
[0070] When the slope value and slope derivative value calculated based on kinematics are too large (greater than the second threshold), the quality estimation update is paused.
[0071] If the quality estimation enabling condition is not met, the covariance matrix of the previous time step is maintained to prevent the estimated slope from jumping when the estimable condition is met.
[0072] In an exemplary embodiment, the vehicle mass estimation module 36 described above is further configured to include an estimation error calculation step: The estimation error calculation step involves calculating the estimation error of the longitudinal acceleration of the target vehicle in the k-th vehicle mass estimation based on the k-th measured value and the k-th estimated value of the longitudinal acceleration; the Kalman gain calculation step involves calculating the Kalman gain using the formula... Kalman gain calculation is performed to obtain the Kalman gain matrix K(k) for the k-th vehicle mass estimation, where P(k-1) is the covariance matrix for the (k-1)-th vehicle mass estimation, λ is the forgetting factor, and H(k-1) is the Kalman gain matrix. TLet H(k-1) be the transpose of H(k-1), where H(k-1) is the system vector corresponding to the (k-1)th vehicle mass estimation. H(k-1) is determined based on the driving slope value of the target vehicle and the longitudinal dynamic equation of the vehicle. The update steps are as follows: update the estimation parameter vector x(k-1) corresponding to the (k-1)th vehicle mass estimation based on the estimation error and the Kalman gain matrix K(k), and update the covariance matrix P(k-1) based on the Kalman gain matrix K(k). m is the total vehicle mass, β is the driving gradient value, and c r The target vehicle's rolling resistance coefficient is given, and the updated estimated parameter vector x(k) includes the vehicle mass obtained from the kth vehicle mass estimation. If the target vehicle meets the mass estimation enabling condition, the estimation error calculation step, the Kalman gain calculation step, and the update step are executed cyclically.
[0073] When k=0, initialize the values of the parameters to be estimated (i.e. the parameter vector) and the covariance matrix P;
[0074] When k = 1, 2, 3..., the following iterative operation is performed:
[0075] l. Steps for calculating estimation error: In step k, use the system matrix H corresponding to the optimal estimate from step k-1 to calculate the predicted system input, and subtract the measured input value y(k) from the estimated value from the estimated value from step k to obtain the error:
[0076] ε(k)=y(k)-H(k)x(k-1);
[0077] 2. Kalman gain calculation steps: Using the covariance matrix P(k-1) from step k-1 and the system matrix H, calculate the Kalman gain matrix K(k) from step k, considering the forgetting factor coefficients:
[0078]
[0079] 3. Optimal estimate update step and covariance matrix update step (i.e., the update step mentioned above): Update the optimal estimated parameter value of step k-1 using the calculated Kalman gain K(k) and estimation error: x(k) = x(k-1) + K(k)ε(k); and update the covariance matrix using the covariance matrix and system matrix from the previous step, along with the previously calculated Kalman gain, considering the influence of the forgetting factor, and then continue the logical loop: P(k) = λ -1 ·[P(k-1)-K(k)H T P(k-1)].
[0080] Furthermore, the aforementioned vehicle mass estimation module 36 is also used to calculate the average value of m(k) and M(k-1) to obtain M(k), where m(k) is the vehicle mass obtained from the k-th vehicle mass estimation, and M(k-1) is the vehicle mass estimation result obtained from the (k-1)-th vehicle mass estimation; update the vehicle control parameters according to M(k), wherein the vehicle control parameters are used to indicate the vehicle mass information of the target vehicle; and perform vehicle acceleration control on the target vehicle within a target time period according to the vehicle control parameters, wherein the start time of the target time period is the first time of the current update of the vehicle control parameters, and the end time of the target time period is the second time of the next update of the vehicle control parameters.
[0081] After each new mass estimate is calculated using the recursive least squares method, it needs to be averaged with the old vehicle mass estimate to obtain a new vehicle mass estimate. Then, the new vehicle mass estimate M(k) is used to update the vehicle control parameters. The vehicle can use the vehicle mass indicated in the vehicle control parameters to assist in vehicle acceleration control. The effective duration of M(k) is the target time period from the current update to the next update.
[0082] Optionally, the vehicle mass estimation module 36 described above is also used to calculate the rate of change of M(k-1) and M(k); determine whether the rate of change exceeds a preset range; and if the rate of change exceeds the preset range, prohibit updating the vehicle control parameters based on M(k).
[0083] To prevent abrupt changes in quality estimation, a Rate Limiter is set to ensure a smooth transition in output quality. After calculating M(k), the rate of change between M(k-1) and M(k) is calculated. It is then determined whether this rate of change exceeds the preset range (i.e., the aforementioned preset range). If it does, the use of M(k) is prohibited.
[0084] Optionally, this application embodiment may also set a maximum update limit. If the vehicle quality reaches the maximum update limit (e.g., 20 times), quality updates will no longer be allowed until the vehicle stops.
[0085] In an exemplary embodiment, the vehicle weight estimation module 36 is further configured to statistically analyze multiple consecutive historical values of the vehicle control parameters; determine whether the vehicle weight estimation result has converged based on the multiple historical values; and, if the vehicle weight estimation result has converged, prohibit further updates to the vehicle control parameters until the target vehicle stops and the door is opened or the target vehicle is powered on again.
[0086] Considering that the mass estimation can quickly converge to a relatively accurate value under high-throttle acceleration on a straight road, subsequent updates to the mass may introduce errors in the estimation under complex operating conditions. Therefore, when a relatively ideal mass update is identified, this estimated mass should be maintained until the vehicle stops again and a door is opened or closed. This requires statistically analyzing multiple consecutive historical values corresponding to the vehicle control parameters. Based on these historical values, it is determined whether the vehicle mass estimation result has converged. If converged, the estimated mass is maintained and no further updates are made until the vehicle stops and a door is opened or the target vehicle is powered on again.
[0087] Optionally, the aforementioned vehicle mass estimation module 36 is further configured to determine whether the target vehicle meets the golden iteration condition during multiple vehicle mass estimations corresponding to multiple historical values, wherein the golden iteration condition includes at least one of the following: the target vehicle does not experience wheel slippage, the target vehicle's maximum yaw rate is less than a third threshold, the target vehicle's maximum steering angle is less than a fourth threshold, and the target vehicle's driving slope value is less than a fifth threshold; if it is determined that the target vehicle meets the golden iteration condition, updating the vehicle control parameters again is prohibited.
[0088] After determining that the vehicle mass estimation results have converged, it is also necessary to determine whether the current iteration process conforms to the golden iteration. To determine whether the current mass update process belongs to the "golden iteration", the following conditions must be met: the driver did not make large steering inputs during the current iteration, and the vehicle maintained a straight driving as much as possible (i.e., the maximum steering angle mentioned above is less than the fourth threshold); no large yaw rate occurred during the current iteration (i.e., the maximum yaw rate mentioned above is less than the third threshold); no obvious wheel slippage was detected during the iteration; and the road gradient was small and changed smoothly during the iteration (i.e., the driving gradient value mentioned above is less than the fifth threshold), avoiding mass estimation errors introduced by inaccurate gradient estimation.
[0089] Embodiments of this application also provide a storage medium including a stored program, wherein the program executes any of the methods described above when it is run.
[0090] Optionally, in this embodiment, the storage medium may be configured to store program code for performing the following steps:
[0091] S1, Perform longitudinal force analysis on the target vehicle and establish the longitudinal dynamic equation of the target vehicle based on the force analysis results;
[0092] S2, determine whether the target vehicle meets the mass estimation enabling conditions;
[0093] S3, if it is determined that the target vehicle meets the mass estimation enabling condition, the vehicle mass is estimated by recursive least squares method according to the vehicle longitudinal dynamics equation to obtain the vehicle mass of the target vehicle.
[0094] Embodiments of this application also provide an electronic device including a memory and a processor, wherein the memory stores a computer program and the processor is configured to run the computer program to perform the steps in any of the above method embodiments.
[0095] Optionally, the electronic device may further include a transmission device and an input / output device, wherein the transmission device is connected to the processor and the input / output device is connected to the processor.
[0096] Optionally, in this embodiment, the processor can be configured to perform the following steps via a computer program:
[0097] S1, Perform longitudinal force analysis on the target vehicle and establish the longitudinal dynamic equation of the target vehicle based on the force analysis results;
[0098] S2, determine whether the target vehicle meets the mass estimation enabling conditions;
[0099] S3, if it is determined that the target vehicle meets the mass estimation enabling condition, the vehicle mass is estimated by recursive least squares method according to the vehicle longitudinal dynamics equation to obtain the vehicle mass of the target vehicle.
[0100] Optionally, in this embodiment, the storage medium may include, but is not limited to, various media capable of storing program code, such as USB flash drives, read-only memory (ROM), random access memory (RAM), portable hard drives, magnetic disks, or optical disks.
[0101] Embodiments of this application also provide a computer program product, including a non-volatile computer-readable storage medium storing the computer program product, wherein the computer program, when executed by a processor, implements the steps of the methods described in various embodiments of this application.
[0102] Optionally, in this embodiment, the computer program described above can be configured to perform the following steps when executed by a processor:
[0103] S1, Perform longitudinal force analysis on the target vehicle and establish the longitudinal dynamic equation of the target vehicle based on the force analysis results;
[0104] S2, determine whether the target vehicle meets the mass estimation enabling conditions;
[0105] S3, if it is determined that the target vehicle meets the mass estimation enabling condition, the vehicle mass is estimated by recursive least squares method according to the vehicle longitudinal dynamics equation to obtain the vehicle mass of the target vehicle.
[0106] Optionally, specific examples in this embodiment can refer to the examples described in the above embodiments and optional implementations, and will not be repeated here.
[0107] Obviously, those skilled in the art should understand that the modules or steps of this application described above can be implemented using general-purpose computing devices. They can be centralized on a single computing device or distributed across a network of multiple computing devices. Optionally, they can be implemented using computer-executable program code, thereby storing them in a storage device for execution by a computing device. In some cases, the steps shown or described can be performed in a different order than those presented here, or they can be fabricated as separate integrated circuit modules, or multiple modules or steps can be fabricated as a single integrated circuit module. Thus, this application is not limited to any particular combination of hardware and software.
[0108] The above description is merely a preferred embodiment of this application and is not intended to limit this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the principles of this application should be included within the protection scope of this application.
Claims
1. A method for estimating the weight of a vehicle, characterized in that, include: Perform a longitudinal force analysis on the target vehicle and establish the longitudinal dynamic equation of the target vehicle based on the force analysis results; Determine whether the target vehicle meets the mass estimation enabling conditions; If the target vehicle meets the mass estimation enabling condition, the vehicle mass is estimated by recursive least squares method according to the vehicle longitudinal dynamics equation to obtain the vehicle mass.
2. The vehicle weight estimation method according to claim 1, characterized in that, Determining whether the target vehicle meets the mass estimation enabling conditions includes at least one of the following: If the absolute value of the longitudinal acceleration of the target vehicle is less than a first threshold, it is determined that the target vehicle does not meet the mass estimation enabling condition. When mechanical braking is applied to the target vehicle, it is determined that the target vehicle does not meet the mass estimation enabling condition; If the estimated vehicle mass of the target vehicle exceeds the preset estimation range, it is determined that the target vehicle does not meet the mass estimation enabling condition. If the driving gradient of the target vehicle is greater than the second threshold, it is determined that the target vehicle does not meet the mass estimation enabling condition.
3. The vehicle weight estimation method according to claim 1, characterized in that, Based on the longitudinal dynamics equations of the vehicle, the vehicle mass of the target vehicle is estimated using the recursive least squares method to obtain the vehicle mass, including: Estimation error calculation steps: Calculate the estimation error of the longitudinal acceleration of the target vehicle in the kth vehicle mass estimation based on the kth measurement value and the kth estimation value of the longitudinal acceleration of the target vehicle; Kalman gain calculation steps: using the formula Kalman gain calculation is performed to obtain the Kalman gain matrix K(k) for the k-th vehicle mass estimation, where P(k-1) is the covariance matrix for the (k-1)-th vehicle mass estimation, λ is the forgetting factor, and H(k-1) is the Kalman gain matrix. T Let H(k-1) be the transpose of H(k-1), where H(k-1) is the system vector corresponding to the (k-1)th vehicle mass estimation. H(k-1) is determined based on the driving slope value of the target vehicle and the longitudinal dynamic equation of the vehicle. Update steps: Update the estimated parameter vector x(k-1) corresponding to the (k-1)th vehicle mass estimation based on the estimation error and the Kalman gain matrix K(k), and update the covariance matrix P(k-1) based on the Kalman gain matrix K(k), wherein, m is the total vehicle mass, β is the driving gradient value, and c r The rolling resistance coefficient of the target vehicle is given, and the updated estimated parameter vector x(k) includes the vehicle mass obtained from the kth vehicle mass estimation. If the target vehicle is determined to meet the mass estimation enabling condition, the estimation error calculation step, the Kalman gain calculation step, and the update step are executed cyclically.
4. The vehicle weight estimation method according to claim 1, characterized in that, After estimating the vehicle mass of the target vehicle using the recursive least squares method based on the vehicle's longitudinal dynamics equations, the method further includes: The average value of m(k) and M(k-1) is calculated to obtain M(k), where m(k) is the vehicle mass obtained by the k-th vehicle mass estimation, and M(k-1) is the vehicle mass estimation result obtained by the (k-1)-th vehicle mass estimation. The vehicle control parameters are updated according to M(k), wherein the vehicle control parameters are used to indicate the overall vehicle mass information of the target vehicle; The vehicle acceleration is controlled according to the vehicle control parameters within a target time period, wherein the start time of the target time period is the first moment of the current update of the vehicle control parameters, and the end time of the target time period is the second moment of the next update of the vehicle control parameters.
5. The vehicle weight estimation method according to claim 4, characterized in that, After averaging m(k) and M(k-1) to obtain M(k), the method further includes: Calculate the rate of change of M(k-1) and M(k); Determine whether the rate of change exceeds a preset range; If the rate of change is determined to exceed the preset range, updating the vehicle control parameters based on M(k) is prohibited.
6. The vehicle weight estimation method according to claim 4, characterized in that, After updating the vehicle control parameters based on M(k), the method further includes: Statistically analyze multiple consecutive historical values of the vehicle control parameters; Determine whether the vehicle mass estimation result converges based on multiple historical values; Once the vehicle weight estimation result is determined to be converged, the vehicle control parameters shall not be updated again until the target vehicle stops and the door is opened or the target vehicle is powered on again.
7. The vehicle weight estimation method according to claim 6, characterized in that, Before prohibiting further updates to the vehicle control parameters, the method further includes: Determine whether the target vehicle meets the golden iteration condition during multiple vehicle mass estimations corresponding to multiple historical values, wherein the golden iteration condition includes at least one of the following: the target vehicle does not experience wheel slippage, the target vehicle's maximum yaw rate is less than a third threshold, the target vehicle's maximum steering angle is less than a fourth threshold, and the target vehicle's driving slope value is less than a fifth threshold. If the target vehicle is determined to meet the golden iteration condition, updating the vehicle control parameters again is prohibited.
8. A vehicle weight estimation device, characterized in that, include: A module is established to perform longitudinal force analysis on the target vehicle and to establish the longitudinal dynamic equations of the target vehicle based on the force analysis results. A determination module is used to determine whether the target vehicle meets the mass estimation enabling conditions; The vehicle mass estimation module is used to estimate the vehicle mass of the target vehicle by recursive least squares method based on the vehicle longitudinal dynamics equation, when it is determined that the target vehicle meets the mass estimation enabling conditions, so as to obtain the vehicle mass of the target vehicle.
9. A computer-readable storage medium, characterized in that, The computer-readable storage medium includes a stored program, wherein the program, when executed, performs the method described in any one of claims 1 to 7.
10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by a processor, it implements the method of any one of claims 1 to 7.