Vehicle weight determination method and device, storage medium and vehicle

Abstract

The present disclosure relates to a vehicle weight determination method and device, a storage medium and a vehicle, and relates to the field of vehicles to quickly and accurately calculate the vehicle weight. The vehicle weight determination method comprises: determining a driving mode according to driving state data of the vehicle; when the driving mode is a coasting mode, determining environment data of an environment in which the vehicle is located and a vehicle weight determined when the vehicle is in an accelerating mode, and determining a vehicle weight correction parameter according to the environment data, the driving state data and the vehicle weight, the vehicle weight correction parameter being used to represent a deviation between the vehicle weight determined by the vehicle weight determination method and an actual vehicle weight; when the driving mode of the vehicle is the accelerating mode, determining the environment data of the environment in which the vehicle is located and the vehicle weight determined when the vehicle was last in the accelerating mode, and determining the vehicle weight according to the environment data, the driving state data, the vehicle weight determined when the vehicle was last in the accelerating mode and the vehicle weight correction parameter determined when the vehicle is in the coasting mode.

Description

Technical Field

[0001] This disclosure relates to the field of vehicles, and more specifically, to methods, apparatus, storage media, and vehicles for determining vehicle weight. Background Technology

[0002] In related technologies, common methods for determining vehicle weight are based on the dynamic equations of the vehicle during its operation. This involves iteratively calculating the vehicle weight by acquiring real-time information such as torque, acceleration, and gradient, ultimately estimating the weight. However, due to the unbiased estimation nature of the algorithm, the calculated results often deviate from the actual situation during the iterative calculation process, leading to ill-conditioned ambiguities in the vehicle weight information matrix. This ill-conditioned information matrix can result in slow convergence of the calculation results, or even getting trapped in locally optimal vehicle weight data, making it impossible to accurately and quickly calculate the vehicle weight. Summary of the Invention

[0003] To overcome the problems existing in related technologies, this disclosure provides a method, apparatus, storage medium, and vehicle for determining vehicle weight.

[0004] According to a first aspect of the present disclosure, a method for determining vehicle weight is provided, comprising:

[0005] Determine the driving mode based on the vehicle's driving status data;

[0006] When the driving mode is coasting mode, the environmental data of the environment in which the vehicle is located and the vehicle weight determined in acceleration mode are determined. Based on the environmental data, the driving state data and the vehicle weight, a vehicle weight correction parameter is determined. The vehicle weight correction parameter is used to characterize the deviation between the vehicle weight determined by the vehicle weight determination method and the actual vehicle weight.

[0007] When the vehicle's driving mode is the acceleration mode, the environmental data of the vehicle's environment and the vehicle weight determined when the vehicle was last in the acceleration mode are determined. Based on the environmental data, the driving status data, the vehicle weight determined when the vehicle was last in the acceleration mode, and the vehicle weight correction parameter determined when the vehicle was in the coasting mode, the vehicle weight is determined.

[0008] Optionally, it also includes:

[0009] If the vehicle did not enter the coasting mode before entering the acceleration mode and this is not the first time it has entered the acceleration mode, then a preset vehicle weight correction parameter is obtained, and the vehicle weight is determined based on the environmental data, the driving status data, the preset vehicle weight correction parameter, and the vehicle weight determined when the vehicle was last in the acceleration mode.

[0010] If the vehicle enters the acceleration mode for the first time and has not entered the coasting mode before entering the acceleration mode, then the preset vehicle weight correction parameters and preset vehicle weight are obtained, and the vehicle weight is determined based on the environmental data, the driving status data, the preset vehicle weight correction parameters, and the preset vehicle weight.

[0011] Optionally, determining the vehicle weight correction parameters based on the environmental data, the driving status data, and the vehicle weight includes:

[0012] Based on the environmental data, the driving status data, and the vehicle weight, a correction parameter is determined as the initial target correction parameter, and the following process is executed iteratively:

[0013] The vehicle's driving status data is reacquired as the first target data, and the environmental data of the vehicle's environment is reacquired as the second target data.

[0014] Based on the first target data, the second target data, and the vehicle weight, a new correction parameter is determined, and the new correction parameter is added to the target correction parameter to obtain a new target correction parameter. This process continues until the vehicle exits the coasting mode, and the target correction parameter determined in the last cycle is used as the vehicle weight correction parameter.

[0015] Optionally, determining the vehicle weight correction parameters based on the environmental data, the driving status data, and the vehicle weight includes:

[0016] The actual wind resistance of the vehicle is determined based on the environmental data and the driving status data.

[0017] The calculated wind resistance of the vehicle is determined based on the driving status data and the vehicle weight.

[0018] The vehicle weight correction parameters are determined based on the actual wind resistance and the calculated wind resistance.

[0019] Optionally, the vehicle weight is determined based on the environmental data, the driving status data, the vehicle weight determined when the vehicle was last in the acceleration mode, and the vehicle weight correction parameter determined in the coasting mode, including:

[0020] Based on the sampling time of the driving status data and the environmental data, and the vehicle weight correction parameters determined by the vehicle in the coasting mode, a target weight value is determined, and the target weight value is inversely proportional to the vehicle weight correction parameters.

[0021] The vehicle weight is determined based on the target weight value, the environmental data, the driving status data, and the vehicle weight determined when the vehicle was last in the acceleration mode, using the recursive least squares method.

[0022] Optionally, determining the vehicle weight based on the target weight value, the environmental data, the driving state data, and the vehicle weight determined when the vehicle was last in the acceleration mode, using a recursive least squares method, includes:

[0023] Based on the target weight value, the environmental data, the driving status data, and the vehicle weight determined when the vehicle was last in the acceleration mode, a vehicle weight is determined as the initial target vehicle weight, and the following process is executed cyclically:

[0024] The vehicle's driving status data is reacquired as the third target data, and the environmental data of the vehicle's environment is reacquired as the fourth target data.

[0025] Based on the third target data, the fourth target data, the target weight value, and the target vehicle weight, a new target vehicle weight is determined until the vehicle exits the acceleration mode. The target vehicle weight determined in the last loop is then used as the vehicle weight.

[0026] Optionally, determining the driving mode based on the vehicle's driving status data includes:

[0027] The driving mode is determined to be acceleration mode when the vehicle meets any of the following judgment conditions: a first judgment condition, a second judgment condition, and a third judgment condition, wherein the first judgment condition is that the accelerator pedal of the vehicle is in an active state, the transmission system of the vehicle is in an engaged state, and the acceleration of the vehicle is greater than a preset acceleration threshold; the second judgment condition is that the cruise system of the vehicle is in an active state and the acceleration of the vehicle is greater than the preset acceleration threshold; and the third judgment condition is that the intelligent driving system of the vehicle is in an active state and the acceleration of the vehicle is greater than the preset acceleration threshold.

[0028] The vehicle is designated as coasting mode when all of the following conditions are met: the accelerator pedal is inactive, the braking system is inactive, the cruise control system is inactive, and the vehicle speed is greater than a preset speed threshold.

[0029] According to a second aspect of the present disclosure, a vehicle weight determination device is provided, comprising:

[0030] The first determining module is used to determine the driving mode based on the vehicle's driving status data;

[0031] The second determining module is used to determine the environmental data of the environment in which the vehicle is located and the vehicle weight determined in acceleration mode, and to determine the vehicle weight correction parameter based on the environmental data, the driving status data and the vehicle weight. The vehicle weight correction parameter is used to characterize the deviation between the vehicle weight determined by the vehicle weight determination method and the actual vehicle weight.

[0032] The third determining module is used to determine the environmental data of the vehicle's environment and the vehicle weight determined when the vehicle's driving mode is the acceleration mode, and to determine the vehicle weight based on the environmental data, the driving state data, the vehicle weight determined when the vehicle was in the acceleration mode, and the vehicle weight correction parameter determined when the vehicle is in the coasting mode.

[0033] According to a third aspect of the present disclosure, a non-transitory computer-readable storage medium is provided, on which a computer program is stored, which, when executed by a processor, implements the steps of any of the methods provided in the first aspect of the present disclosure.

[0034] According to a fourth aspect of the present disclosure, a vehicle is provided, comprising:

[0035] A processor and a memory, the memory storing machine-executable instructions executable by the processor, the processor executing the machine-executable instructions to implement any of the vehicle weight determination methods provided in the first aspect of this disclosure.

[0036] The technical solutions provided by the embodiments of this disclosure may include the following beneficial effects:

[0037] By calculating the vehicle weight in acceleration mode and the vehicle weight correction parameters in coasting mode, the vehicle weight calculated in acceleration mode can be corrected. This can prevent the vehicle weight calculation results from falling into the trap of large deviations, thus escaping the local optimum trap and ultimately enabling accurate and fast vehicle weight calculation.

[0038] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and are not intended to limit this disclosure.

[0039] Other features and advantages of this disclosure will be described in detail in the following detailed description section. Attached Figure Description

[0040] The accompanying drawings are provided to further illustrate the present disclosure and form part of the specification. They are used together with the following detailed description to explain the present disclosure, but do not constitute a limitation thereof. In the drawings:

[0041] Figure 1This is a flowchart illustrating a method for determining vehicle weight according to an exemplary embodiment.

[0042] Figure 2 This is a flowchart illustrating a method for determining vehicle weight correction parameters according to an exemplary embodiment.

[0043] Figure 3 This is a block diagram illustrating a vehicle weight determination device according to an exemplary embodiment.

[0044] Figure 4 This is a block diagram illustrating a vehicle weight determination device according to an exemplary embodiment. Detailed Implementation

[0045] The specific embodiments of this disclosure will be described in detail below with reference to the accompanying drawings. It should be understood that the specific embodiments described herein are for illustration and explanation only and are not intended to limit this disclosure.

[0046] In related technologies, common methods for determining vehicle weight are based on the dynamic equations of the vehicle during its operation. This involves iteratively calculating the vehicle weight by acquiring real-time information such as torque, acceleration, and gradient, ultimately estimating the weight. However, due to the unbiased estimation nature of the algorithm, the calculated results often deviate from the actual situation during the iterative calculation process, leading to ill-conditioned ambiguities in the vehicle weight information matrix. This ill-conditioned information matrix can result in slow convergence of the calculation results, or even getting trapped in locally optimal vehicle weight data, making it impossible to accurately and quickly calculate the vehicle weight.

[0047] In view of this, the present disclosure provides a method, apparatus, storage medium and vehicle for determining vehicle weight, which can prevent the vehicle weight calculation results from falling into the vehicle weight data with large deviations, thereby escaping the trap of local optima, and ultimately enabling accurate and fast calculation of vehicle weight.

[0048] Figure 1 This is a flowchart illustrating a vehicle weight determination method according to an exemplary embodiment, such as... Figure 1 As shown, the method for determining vehicle weight includes the following steps:

[0049] In step S101, the driving mode is determined based on the vehicle's driving status data;

[0050] In step S102, when the driving mode is coasting mode, the environmental data of the vehicle's environment and the vehicle weight determined in acceleration mode are determined. Based on the environmental data, driving status data and vehicle weight, the vehicle weight correction parameter is determined. The vehicle weight correction parameter is used to characterize the deviation between the vehicle weight determined by the vehicle weight determination method and the actual vehicle weight.

[0051] In step S103, when the vehicle driving mode is acceleration mode, the environmental data of the environment in which the vehicle is located and the vehicle weight determined when the vehicle was in acceleration mode last time are determined are determined. Based on the environmental data, driving status data, the vehicle weight determined when the vehicle was in acceleration mode last time and the vehicle weight correction parameters determined when the vehicle is in coasting mode, the vehicle weight is determined.

[0052] It should be understood that the vehicle weight determination method can be executed by a controller unit, such as a vehicle controller, an engine control unit, a transmission control unit, or a power pre-control unit, and this disclosure does not limit this. In specific embodiments of this disclosure, data sampling and data processing are performed synchronously, and the time interval is consistent with the execution step size of the vehicle weight determination method, such as 5ms, 10ms, or 20ms, and this disclosure does not limit this. Because the driving state data and environmental data change in real time as the vehicle's driving state changes, they can be processed in real time. The vehicle's driving state data may include at least one of: engine torque, motor torque, final drive ratio, mechanical efficiency of the transmission system, vehicle frontal area, tire equivalent rolling radius, vehicle speed, vehicle net weight, vehicle load threshold, and vehicle acceleration in the driving direction. Environmental data may include: air density and / or vehicle driving slope angle.

[0053] Driving modes are determined based on vehicle driving status data and can include coasting mode, acceleration mode, and other modes besides coasting and acceleration modes. Coasting mode refers to a state where the vehicle's engine or electric motor does not provide power to the vehicle, such as when the accelerator pedal is released. Conversely, acceleration mode refers to a state where the vehicle's engine or electric motor provides power to the vehicle, such as when the driver presses the accelerator pedal.

[0054] The calculations in coasting mode and acceleration mode are interrelated. In coasting mode, the vehicle weight correction parameters are calculated based on the vehicle weight calculated in acceleration mode; conversely, in acceleration mode, the vehicle weight is calculated based on the vehicle weight correction parameters calculated in coasting mode. These two modes form a loop: if the vehicle is currently in acceleration mode, the vehicle weight calculated using the result from the previous coasting mode is used for the next time the vehicle is in coasting mode; if the vehicle is currently in coasting mode, the vehicle weight correction parameters are calculated using the vehicle weight calculated using the previous acceleration mode, and this is used for the next time the vehicle is in acceleration mode. Furthermore, there is also a loop between acceleration modes: in the current acceleration mode, the vehicle weight calculated using the vehicle weight from the previous acceleration mode is used for the current time, and this is used for the next time the vehicle is in acceleration mode.

[0055] By calculating the vehicle weight in acceleration mode and the vehicle weight correction parameters in coasting mode, the vehicle weight calculated in acceleration mode can be corrected. This can prevent the vehicle weight calculation results from falling into the trap of large deviations in vehicle weight data, thus escaping the local optimum trap and ultimately enabling accurate and fast vehicle weight calculation.

[0056] In one possible approach, the vehicle weight determination method further includes:

[0057] If the vehicle has not entered coasting mode before entering acceleration mode and this is not the first time it has entered acceleration mode, then the preset vehicle weight correction parameters are obtained, and the vehicle weight is determined based on environmental data, driving status data, preset vehicle weight correction parameters, and the vehicle weight determined when the vehicle was last in acceleration mode.

[0058] If the vehicle enters acceleration mode for the first time and has not entered coasting mode before entering acceleration mode, the preset vehicle weight correction parameters and preset vehicle weight are obtained, and the vehicle weight is determined based on environmental data, driving status data, preset vehicle weight correction parameters and preset vehicle weight.

[0059] For example, the preset vehicle weight can be the vehicle's net weight, or the sum of the vehicle's net weight and the vehicle's load threshold, and the preset vehicle weight correction parameter can be 1.

[0060] It should be understood that if the vehicle enters acceleration mode without first entering coasting mode and this is not the first time it has entered acceleration mode (e.g., the vehicle enters acceleration mode for the second time and has never entered coasting mode before), meaning there is a mode other than coasting mode between the two acceleration modes, the vehicle weight correction parameters have never been calculated. In this case, the preset vehicle weight correction parameters are used when calculating the vehicle weight. Furthermore, if it is the second time entering acceleration mode, the vehicle weight calculated from the previous acceleration mode must also be used.

[0061] When a vehicle first enters acceleration mode without first entering coasting mode, such as entering acceleration mode immediately upon vehicle startup, preset vehicle weight correction parameters and a preset vehicle weight are used when calculating the vehicle weight.

[0062] It should also be understood that, except as described above, when the vehicle is in coasting mode, the vehicle weight determined in acceleration mode before entering coasting mode is determined as the vehicle weight in coasting mode; when the vehicle is in a mode other than coasting mode and acceleration mode, the vehicle weight determined in acceleration mode before entering other modes is determined as the vehicle weight in other modes.

[0063] In one possible approach, step S102 may include:

[0064] Based on environmental data, driving status data, and vehicle weight, a correction parameter is determined as the initial target correction parameter, and the following process is executed iteratively:

[0065] The vehicle's driving status data is reacquired as the first target data, and the environmental data of the vehicle's environment is reacquired as the second target data.

[0066] Based on the first target data, the second target data, and the vehicle weight, new correction parameters are determined, and the new correction parameters are added to the target correction parameters to obtain new target correction parameters. This process continues until the vehicle exits the coasting mode, and the target correction parameters determined in the last cycle are used as the vehicle weight correction parameters.

[0067] It should be understood that the time interval of each loop is consistent with the step size of the execution body of the vehicle weight determination method; that is, the vehicle weight correction parameter is calculated once each time environmental data and driving status data are collected. However, in this coasting mode, the vehicle weight used remains unchanged and is the vehicle weight calculated in the previous acceleration mode. The real-time vehicle weight correction parameter is calculated cyclically, but in the next loop, the intermediate result obtained from the previous loop is used until the vehicle exits the coasting mode. This calculation process is actually a summation of the vehicle weight correction parameters, divided by the time elapsed in this coasting mode, in order to obtain a more accurate vehicle weight correction parameter.

[0068] Figure 2 This is a flowchart illustrating a method for determining vehicle weight correction parameters according to an exemplary embodiment. In one possible approach, refer to... Figure 2 Step S102 may include the following steps:

[0069] In step S201, the actual wind resistance of the vehicle is determined based on environmental data and driving status data;

[0070] In step S202, the calculated wind resistance of the vehicle is determined based on the driving status data and vehicle weight;

[0071] In step S203, the vehicle weight correction parameters are determined based on the actual wind resistance and the calculated wind resistance.

[0072] Here, actual wind resistance represents the actual vehicle weight, while calculated wind resistance represents the calculated vehicle weight. The deviation between actual and calculated wind resistance represents the deviation between the actual and calculated vehicle weight. The actual vehicle weight can be obtained from the following dynamic equation:

[0073]

[0074] Where C DLet ρ be the drag coefficient, ρ be the air density, A be the vehicle's frontal area, and v be the vehicle's speed. The drag coefficient and air density are environmental data, while the vehicle's frontal area and speed are driving condition data. The negative sign in the above equation indicates that the vehicle's acceleration is opposite to its direction of travel. To simplify calculations, the expression on the left side of the equation represents the actual wind resistance, and the expression on the right side, m, represents the actual vehicle weight.

[0075] Accordingly, the calculated vehicle weight is characterized by the following formula for calculating wind resistance.

[0076]

[0077] Where g is the acceleration due to gravity. Let a be the slope angle of the vehicle. v f is the acceleration in the direction of vehicle travel. a f is a constant term in the velocity fitting of the rolling resistance coefficient. b The coefficients are the first-order terms for fitting the rolling resistance coefficient to the velocity. The vehicle's driving slope angle and acceleration in the driving direction are driving state data.

[0078] Substituting the actual and calculated wind resistance obtained above into any of the following formulas, we can obtain the vehicle weight correction parameters:

[0079] or

[0080]

[0081] Preferably, the vehicle weight correction parameters can be calculated using the following formula:

[0082]

[0083] Where, η c Here, T represents the vehicle weight correction parameter, and T represents the sampling time for environmental data and driving status data.

[0084] It should be understood that the calculation principle of the vehicle weight correction parameter is as follows: the actual wind resistance of the vehicle is used to characterize the vehicle weight calculated in real time under coasting mode; the calculated wind resistance of the vehicle is used to characterize the vehicle weight calculated under acceleration mode; the difference between the actual wind resistance and the calculated wind resistance is divided by the actual wind resistance or the calculated wind resistance to characterize the deviation between the vehicle weight calculated under acceleration mode and the actual vehicle weight; then, all the quotients obtained in real time are summed and divided by the sampling time to obtain the average value, which characterizes the final vehicle weight correction parameter obtained under this coasting mode.

[0085] For example, when the vehicle weight correction parameter is close to 0, it indicates that the vehicle weight calculated in acceleration mode is close to the actual vehicle weight. The larger the vehicle weight correction parameter is, the greater the difference between the vehicle weight calculated in acceleration mode and the actual vehicle weight.

[0086] In one possible approach, step S103 may include:

[0087] Based on the sampling time of driving status data and environmental data, as well as the vehicle weight correction parameters determined in coasting mode, the target weight value is determined, and the target weight value is inversely proportional to the vehicle weight correction parameters.

[0088] The vehicle weight is determined based on the target weight value, environmental data, driving status data, and the vehicle weight determined when the vehicle was in acceleration mode last time. The vehicle weight is determined using the recursive least squares method.

[0089] For example, by substituting the sampling time of the driving status data and environmental data, as well as the vehicle weight correction parameters determined in coasting mode, into the following formula, the target weight value can be obtained:

[0090] λ=1-0.001T-(0.005·η c )

[0091] Where λ is the target weight value, T is the sampling time for driving state data and environmental data, and η is the target weight value. c This is the vehicle weight correction parameter determined in coasting mode, with an initial value of 1.

[0092] It should be understood that when calculating vehicle weight using the recursive least squares method only in acceleration mode, the vehicle's longitudinal dynamics equations can be substituted into the recursive least squares parameter formula. The vehicle's longitudinal dynamics equations are as follows:

[0093]

[0094] Among them, T tq i represents engine torque or motor torque. g i0 is the current gear ratio of the transmission, i0 is the gear ratio of the main reducer, and η is the gear ratio of the gearbox. g R represents the mechanical efficiency of the transmission system. i C is the equivalent rolling radius of the tire. D Let ρ be the air drag coefficient, ρ be the air density, A be the vehicle's frontal area, v be the vehicle speed, m be the actual vehicle weight, and g be the acceleration due to gravity. f is the vehicle's driving slope angle. a f is a constant term in the velocity fitting of the rolling resistance coefficient. b a represents the first-order coefficient of the velocity fitting of the rolling resistance coefficient. v Acceleration in the direction of vehicle travel.

[0095] The recursive least squares formula addresses the process of deriving the parameter θ from Y = θX given observable X and Y. In this disclosure, Y is equivalent to force F, X is equivalent to acceleration a, and θ is equivalent to vehicle weight M.

[0096] In one possible approach, determining the vehicle's weight based on the target weight value, environmental data, driving status data, and the vehicle's weight determined when it was last in acceleration mode, using a recursive least squares method, may include the following steps:

[0097] Based on the target weight value, environmental data, driving status data, and the vehicle weight determined when the vehicle was in acceleration mode last time, a vehicle weight is determined as the initial target vehicle weight, and the following process is executed cyclically:

[0098] The vehicle's driving status data is reacquired as the third target data, and the environmental data of the vehicle's environment is reacquired as the fourth target data.

[0099] Based on the third target data, the fourth target data, the target weight value, and the target vehicle weight, a new target vehicle weight is determined until the vehicle exits the acceleration mode. The target vehicle weight determined in the last cycle is then used as the vehicle weight.

[0100] For example, by substituting the target weight value, environmental data, driving status data, and the vehicle's last acceleration state into the following least squares recursive formula, the vehicle's weight can be obtained:

[0101]

[0102]

[0103]

[0104] Where k is a natural number 0, 1, 2, 3, ... For vehicle weight, F k a is the resultant force on the vehicle, calculated by subtracting wind resistance from the traction force. k Let P1 be the resultant acceleration, λ be the target weight value determined for the vehicle in coasting mode, and I be the identity matrix. For a k+1 The transpose of .

[0105] F in the above formula k+1 It can be calculated using the following formula:

[0106]

[0107] Where k is a natural number 0, 1, 2, 3, ..., T tq i represents engine torque or motor torque. g i0 is the current gear ratio of the transmission, i0 is the gear ratio of the main reducer, and η is the gear ratio of the gearbox. g R represents the mechanical efficiency of the transmission system. i C is the equivalent rolling radius of the tire.D ρ is the air resistance coefficient, A is the vehicle's frontal area, and v is the vehicle's speed.

[0108] In the above formula, a k+1 It can be calculated using the following formula:

[0109]

[0110] Where k is a natural number 0, 1, 2, 3..., and g is the acceleration due to gravity. Let a be the slope angle of the vehicle. v f is the acceleration in the direction of vehicle travel. a f is a constant term in the velocity fitting of the rolling resistance coefficient. b These are the first-order coefficients for fitting the rolling resistance coefficient to the velocity. Since the vehicle weight is a specific value, i.e., a 1x1 matrix, therefore... and a k+1 equal.

[0111] The initial vehicle weight is a preset value, which can be the vehicle's net weight or the sum of the vehicle's net weight and the vehicle's load threshold. P0 is a preset value that ensures the calculation result during the iteration process is not negative, for example, 50000 or 100000. I is the identity matrix; since the vehicle weight is a specific value, i.e., a 1x1 matrix, I is the value 1.

[0112] The first iteration calculation process under this acceleration mode is as follows: 1) Calculate a1 based on driving state data and environmental data; calculate the target weight value λ based on the sampling time of driving state data and environmental data, and the vehicle weight correction parameters determined by the vehicle in coasting mode; substitute a1, P0 and λ into the above formula (3) to calculate the value of K0; 2) Calculate F1 based on driving state data and environmental data; Substituting K0, F1, and a1 into equation (1) above, we can calculate... The value at this time 3) Substitute λ, I, K0, a1 and P0 into the above formula (2) to calculate P1, which is used for the next iteration.

[0113] Perform an iterative calculation at one sampling interval and output the vehicle weight obtained in this iteration. Continue iterative calculations until exiting the current acceleration mode, and determine the vehicle weight obtained in the last iteration, which will be used as the vehicle weight for the next entry into acceleration mode or coasting mode.

[0114] It should be understood that when the vehicle weight correction parameter is large, the calculated vehicle weight in acceleration mode may differ significantly from the actual vehicle weight, indicating two possibilities: 1) The vehicle is trapped in a local optimum. In this case, the weight of historical vehicle weight data is reduced by increasing the target weight value to accelerate the avoidance of local traps and the iterative process toward the optimal solution. 2) The number of iterations is insufficient, and the vehicle weight has not yet converged to a stable value. In this case, the calculated vehicle weight is gradually converged to approach the actual vehicle weight through continuous iteration.

[0115] It should be understood that during the iterative process, as the historical vehicle weight increases continuously, the latest collected environmental and driving status data may not be able to change the impact of the historical vehicle weight on the latest vehicle weight. If the deviation of the historical vehicle weight is large, it will lead to a large error between the final calculated vehicle weight and the actual weight. Therefore, it is necessary to use a target weight value to change the weight of the historical vehicle weight in the latest vehicle weight. The target weight value is a number less than 1. The smaller the number, the lower the weight of the historical vehicle weight and the higher the weight of the latest collected environmental and driving status data.

[0116] In the target weight value of the least squares recursive formula, a vehicle weight correction parameter is added. This parameter represents the deviation between the calculated and actual vehicle weight values. A larger correction parameter indicates a larger deviation, resulting in a smaller target weight value. This reduces the weight of historical vehicle weights, thus accelerating the iteration process and moving away from historically inaccurate weight data. Conversely, a smaller correction parameter indicates a smaller deviation, resulting in a larger target weight value. This increases the weight of historical vehicle weights, slowing down the iteration process and maintaining a stable and relatively accurate vehicle weight calculation. This method helps avoid getting trapped in historically inaccurate weight data, escaping the local optimum trap and ultimately achieving accurate and rapid vehicle weight calculations.

[0117] In one possible approach, step S101 may include:

[0118] The driving mode is determined to be acceleration mode when the vehicle meets any of the following judgment conditions: first judgment condition, second judgment condition, and third judgment condition. The first judgment condition is that the accelerator pedal of the vehicle is active, the transmission system of the vehicle is engaged, and the acceleration of the vehicle is greater than a preset acceleration threshold. The second judgment condition is that the cruise system of the vehicle is active and the acceleration of the vehicle is greater than a preset acceleration threshold. The third judgment condition is that the intelligent driving system of the vehicle is active and the acceleration of the vehicle is greater than a preset acceleration threshold.

[0119] The vehicle is set to coasting mode when all of the following conditions are met: the accelerator pedal is inactive, the braking system is inactive, the cruise control system is inactive, and the vehicle speed is greater than a preset speed threshold.

[0120] It should be understood that a vehicle's braking system being inactive can be due to any of the following: brake pedal ineffective, auxiliary braking not activated, electronic braking system not activated, or intelligent driving braking signal not activated. Specifically, the inactive state of a vehicle's braking system is determined by the corresponding braking system status, based on the type of braking system used.

[0121] For example, the aforementioned acceleration threshold can be any one of 0.1 m / s², 0.2 m / s², 0.3 m / s², and 0.4 m / s², preferably set to 0.1 m / s². Setting an acceleration threshold can filter out acceleration fluctuations caused by environmental factors such as uneven road surfaces when the vehicle is traveling at a constant speed. The aforementioned coasting speed threshold can be any one of 20 km / h, 21 km / h, 22 km / h, and 23 km / h, preferably set to 20 km / h. Setting a coasting speed threshold can filter out speed fluctuations caused by environmental factors such as sloped road surfaces and vehicle inertia when the vehicle is moving slowly.

[0122] The system calculates the vehicle's weight based on its driving status during operation, ultimately achieving a fast and accurate weight calculation.

[0123] By using any of the above methods to determine vehicle weight, the calculation results can avoid falling into historical vehicle weight data with large deviations, thus escaping the trap of local optima and ultimately enabling accurate and rapid calculation of vehicle weight.

[0124] Figure 3 This is a block diagram illustrating a vehicle weight determination device according to an exemplary embodiment. (Refer to...) Figure 3 The device includes:

[0125] The first determining module 301 is used to determine the driving mode based on the vehicle's driving status data;

[0126] The second determining module 302 is used to determine the environmental data of the environment in which the vehicle is located and the vehicle weight determined in acceleration mode, and to determine the vehicle weight correction parameters based on the environmental data, driving status data and vehicle weight. The vehicle weight correction parameters are used to characterize the deviation between the vehicle weight determined by the vehicle weight determination method and the actual vehicle weight.

[0127] The third determining module 303 is used to determine the environmental data of the vehicle's environment and the vehicle weight determined when the vehicle's driving mode is acceleration mode, and to determine the vehicle weight based on the environmental data, driving status data, the vehicle weight determined when the vehicle was in acceleration mode and the vehicle weight correction parameters determined when the vehicle was in coasting mode.

[0128] Optionally, the vehicle weight determination device further includes:

[0129] The first initial determination module is used to obtain preset vehicle weight correction parameters when the vehicle has not entered coasting mode before entering acceleration mode and is not entering acceleration mode for the first time. Then, it determines the vehicle weight based on environmental data, driving status data, preset vehicle weight correction parameters and the vehicle weight determined when the vehicle was in acceleration mode last time.

[0130] The second initial determination module is used to obtain preset vehicle weight correction parameters and preset vehicle weight when the vehicle enters acceleration mode for the first time and has not entered coasting mode before entering acceleration mode, and to determine the vehicle weight based on environmental data, driving status data, preset vehicle weight correction parameters and preset vehicle weight.

[0131] Optionally, the second determining module 302 is used for:

[0132] Based on environmental data, driving status data, and vehicle weight, a correction parameter is determined as the initial target correction parameter, and the following process is executed iteratively:

[0133] The vehicle's driving status data is reacquired as the first target data, and the environmental data of the vehicle's environment is reacquired as the second target data.

[0134] Based on the first target data, the second target data, and the vehicle weight, new correction parameters are determined, and the new correction parameters are added to the target correction parameters to obtain new target correction parameters. This process continues until the vehicle exits the coasting mode, and the target correction parameters determined in the last cycle are used as the vehicle weight correction parameters.

[0135] Optionally, the second determining module 302 is used for:

[0136] The actual wind resistance of the vehicle is determined based on environmental data and driving status data;

[0137] The calculated wind resistance of the vehicle is determined based on driving status data and vehicle weight;

[0138] Based on the actual and calculated wind resistance, determine the vehicle weight correction parameters.

[0139] Optionally, the third determining module 303 includes:

[0140] The target weight determination module is used to determine the target weight value based on the sampling time of driving status data and environmental data, as well as the vehicle weight correction parameters determined by the vehicle in coasting mode. The target weight value is inversely proportional to the vehicle weight correction parameters.

[0141] The vehicle weight recursive determination module determines the vehicle weight based on the target weight value, environmental data, driving status data, and the vehicle weight determined when the vehicle was in acceleration mode last time, using the recursive least squares method.

[0142] Optionally, the vehicle weight recursive determination module is used for:

[0143] Based on the target weight value, environmental data, driving status data, and the vehicle weight determined when the vehicle was in acceleration mode last time, a vehicle weight is determined as the initial target vehicle weight, and the following process is executed cyclically:

[0144] The vehicle's driving status data is reacquired as the third target data, and the environmental data of the vehicle's environment is reacquired as the fourth target data.

[0145] Based on the third target data, the fourth target data, the target weight value, and the target vehicle weight, a new target vehicle weight is determined until the vehicle exits the acceleration mode. The target vehicle weight determined in the last cycle is then used as the vehicle weight.

[0146] Optionally, the first determining module 301 includes:

[0147] The first judgment module is used to determine the driving mode as acceleration mode when the vehicle meets any of the following judgment conditions: first judgment condition, second judgment condition, and third judgment condition, wherein the first judgment condition is that the accelerator pedal of the vehicle is in an active state, the transmission system of the vehicle is in an engaged state, and the acceleration of the vehicle is greater than a preset acceleration threshold; the second judgment condition is that the cruise system of the vehicle is in an active state and the acceleration of the vehicle is greater than a preset acceleration threshold; and the third judgment condition is that the intelligent driving system of the vehicle is in an active state and the acceleration of the vehicle is greater than a preset acceleration threshold.

[0148] The second judgment module is used to determine the driving mode as coasting mode when the vehicle meets all of the following conditions: the accelerator pedal of the vehicle is inactive, the braking system of the vehicle is inactive, the cruise system of the vehicle is inactive, and the vehicle speed is greater than a preset speed threshold.

[0149] Regarding the apparatus in the above embodiments, the specific manner in which each module performs its operation has been described in detail in the embodiments related to the method, and will not be elaborated upon here.

[0150] This disclosure also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of any of the vehicle weight determination methods provided in this disclosure.

[0151] This disclosure also provides a vehicle, including:

[0152] The processor and memory, the memory storing machine-executable instructions that can be executed by the processor, the processor executing the machine-executable instructions to implement any of the vehicle weight determination methods provided in the first aspect of this disclosure.

[0153] Figure 4 This is a block diagram illustrating a vehicle 400 according to an exemplary embodiment. (Refer to...) Figure 4 The vehicle 400 may include one or more of the following components: processing component 402, memory 404, power supply component 406, input / output interface 412, sensor component 414, and communication component 416.

[0154] Processing component 402 typically controls the overall operation of vehicle 400, such as operations associated with display, data communication, and recording. Processing component 402 may include one or more processors 420 to execute instructions to complete all or part of the steps of any of the vehicle weight determination methods described above. Furthermore, processing component 402 may include one or more modules to facilitate interaction between processing component 402 and other components. For example, processing component 402 may include sensor modules to facilitate interaction between sensor component 414 and processing component 402.

[0155] Memory 404 is configured to store various types of data to support operation on vehicle 400. Examples of this data include instructions for any application or method operating on vehicle 400. Memory 404 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk.

[0156] The power supply assembly 406 provides power to various components of the vehicle 400. The power supply assembly 406 may include a power management system, one or more power sources, and other components associated with generating, managing, and distributing power to the vehicle 400.

[0157] Input / output interface 412 provides an interface between processing component 402 and peripheral interface modules.

[0158] Sensor assembly 414 includes one or more sensors for providing status assessments of various aspects of vehicle 400. For example, sensor assembly 414 can detect the open / closed state of vehicle 400 and the relative positioning of components. Sensor assembly 414 may include a temperature sensor.

[0159] Communication component 416 is configured to facilitate wired or wireless communication between vehicle 400 and other devices. Vehicle 400 can access wireless networks based on communication standards, such as WiFi, 2G, or 3G, or combinations thereof. In one exemplary embodiment, communication component 416 receives broadcast signals or broadcast-related information from an external broadcast management system via a broadcast channel. In one exemplary embodiment, communication component 416 also includes a near-field communication (NFC) module to facilitate short-range communication. For example, the NFC module may be implemented based on radio frequency identification (RFID) technology, Infrared Data Association (IrDA) technology, ultra-wideband (UWB) technology, Bluetooth (BT) technology, and other technologies.

[0160] In an exemplary embodiment, the vehicle 400 may be implemented by one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field-programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors, or other electronic components to perform any of the above-described vehicle weight determination methods.

[0161] In an exemplary embodiment, a non-transitory computer-readable storage medium including instructions is also provided, such as a memory 404 including instructions, which can be executed by a processor 420 of a vehicle 400 to complete the vehicle weight determination method described above. For example, the non-transitory computer-readable storage medium may be a ROM, random access memory (RAM), CD-ROM, magnetic tape, floppy disk, and optical data storage device, etc.

[0162] In another exemplary embodiment, a computer program product is also provided, the computer program product comprising a computer program executable by a programmable device, the computer program having a code portion for performing any of the above-described vehicle weight determination methods when executed by the programmable device.

[0163] Other embodiments of this disclosure will readily occur to those skilled in the art upon consideration of the specification and practice of this disclosure. This disclosure is intended to cover any variations, uses, or adaptations of this disclosure that follow the general principles of this disclosure and include common knowledge or customary techniques in the art not disclosed herein. The specification and examples are to be considered exemplary only, and the true scope and spirit of this disclosure are indicated by the following claims.

[0164] It should be understood that this disclosure is not limited to the precise structures described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of this disclosure is limited only by the appended claims.

Claims

1. A method for determining vehicle weight, characterized in that, include: Determine the driving mode based on the vehicle's driving status data; When the driving mode is coasting mode, the environmental data of the environment in which the vehicle is located and the vehicle weight determined in acceleration mode are determined. Based on the environmental data, the driving state data and the vehicle weight, a vehicle weight correction parameter is determined. The vehicle weight correction parameter is used to characterize the deviation between the vehicle weight determined by the vehicle weight determination method and the actual vehicle weight. The step of determining the vehicle weight correction parameters based on the environmental data, the driving status data, and the vehicle weight includes: The actual wind resistance of the vehicle is determined based on the environmental data and the driving status data. The calculated wind resistance of the vehicle is determined based on the driving status data and the vehicle weight. The vehicle weight correction parameters are determined based on the actual wind resistance and the calculated wind resistance. When the vehicle's driving mode is the acceleration mode, the environmental data of the vehicle's environment and the vehicle weight determined when the vehicle was last in the acceleration mode are determined. Based on the environmental data, the driving status data, the vehicle weight determined when the vehicle was last in the acceleration mode, and the vehicle weight correction parameter determined when the vehicle was in the coasting mode, the vehicle weight is determined.

2. The method according to claim 1, characterized in that, The method further includes: If the vehicle did not enter the coasting mode before entering the acceleration mode and this is not the first time it has entered the acceleration mode, then a preset vehicle weight correction parameter is obtained, and the vehicle weight is determined based on the environmental data, the driving status data, the preset vehicle weight correction parameter, and the vehicle weight determined when the vehicle was last in the acceleration mode. If the vehicle enters the acceleration mode for the first time and has not entered the coasting mode before entering the acceleration mode, then the preset vehicle weight correction parameters and preset vehicle weight are obtained, and the vehicle weight is determined based on the environmental data, driving status data, the preset vehicle weight correction parameters and the preset vehicle weight.

3. The method according to claim 1, characterized in that, The step of determining the vehicle weight correction parameters based on the environmental data, the driving status data, and the vehicle weight includes: Based on the environmental data, the driving status data, and the vehicle weight, a correction parameter is determined as the initial target correction parameter, and the following process is executed iteratively: The vehicle's driving status data is reacquired as the first target data, and the environmental data of the vehicle's environment is reacquired as the second target data. Based on the first target data, the second target data, and the vehicle weight, a new correction parameter is determined, and the new correction parameter is added to the target correction parameter to obtain a new target correction parameter. This process continues until the vehicle exits the coasting mode, and the target correction parameter determined in the last cycle is used as the vehicle weight correction parameter.

4. The method according to any one of claims 1-3, characterized in that, The vehicle weight is determined based on the environmental data, the driving status data, the vehicle weight determined when the vehicle was last in the acceleration mode, and the vehicle weight correction parameters determined in the coasting mode, including: Based on the sampling time of the driving status data and the environmental data, and the vehicle weight correction parameters determined by the vehicle in the coasting mode, a target weight value is determined, and the target weight value is inversely proportional to the vehicle weight correction parameters. The vehicle weight is determined based on the target weight value, the environmental data, the driving status data, and the vehicle weight determined when the vehicle was last in the acceleration mode, using the recursive least squares method.

5. The method according to claim 4, characterized in that, The step of determining the vehicle weight based on the target weight value, the environmental data, the driving status data, and the vehicle weight determined when the vehicle was last in the acceleration mode, using a recursive least squares method, includes: Based on the target weight value, the environmental data, the driving status data, and the vehicle weight determined when the vehicle was last in the acceleration mode, a vehicle weight is determined as the initial target vehicle weight, and the following process is executed cyclically: The vehicle's driving status data is reacquired as the third target data, and the environmental data of the vehicle's environment is reacquired as the fourth target data. Based on the third target data, the fourth target data, the target weight value, and the target vehicle weight, a new target vehicle weight is determined until the vehicle exits the acceleration mode. The target vehicle weight determined in the last loop is then used as the vehicle weight.

6. The method according to any one of claims 1-3, characterized in that, Determining the driving mode based on vehicle driving status data includes: The driving mode is determined to be acceleration mode when the vehicle meets any of the following judgment conditions: a first judgment condition, a second judgment condition, and a third judgment condition, wherein the first judgment condition is that the accelerator pedal of the vehicle is in an active state, the transmission system of the vehicle is in an engaged state, and the acceleration of the vehicle is greater than a preset acceleration threshold; the second judgment condition is that the cruise system of the vehicle is in an active state and the acceleration of the vehicle is greater than the preset acceleration threshold; and the third judgment condition is that the intelligent driving system of the vehicle is in an active state and the acceleration of the vehicle is greater than the preset acceleration threshold. The vehicle is designated as coasting mode when all of the following conditions are met: the accelerator pedal is inactive, the braking system is inactive, the cruise control system is inactive, and the vehicle speed is greater than a preset speed threshold.

7. A vehicle weight determination device, characterized in that, The device includes: The first determining module is used to determine the driving mode based on the vehicle's driving status data; The second determining module is used to determine the environmental data of the vehicle's environment and the vehicle weight determined in acceleration mode when the driving mode is coasting mode; and to determine the actual wind resistance of the vehicle based on the environmental data and the driving state data; to determine the calculated wind resistance of the vehicle based on the driving state data and the vehicle weight; and to determine a vehicle weight correction parameter based on the actual wind resistance and the calculated wind resistance, wherein the vehicle weight correction parameter is used to characterize the deviation between the vehicle weight determined by the vehicle weight determining device and the actual vehicle weight. The third determining module is used to determine the environmental data of the vehicle's environment and the vehicle weight determined when the vehicle's driving mode is the acceleration mode, and to determine the vehicle weight based on the environmental data, the driving state data, the vehicle weight determined when the vehicle was in the acceleration mode, and the vehicle weight correction parameter determined when the vehicle is in the coasting mode.

8. A non-transitory computer storage medium storing a computer program thereon, characterized in that, When executed by a processor, the program implements the steps of the method described in any one of claims 1-6.

9. A vehicle, characterized in that, The vehicles include: A processor and a memory, the memory storing machine-executable instructions that can be executed by the processor, the processor executing the machine-executable instructions to implement the vehicle weight determination method according to any one of claims 1-6.

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