Vehicle weight determination method and device, storage medium and vehicle

Abstract

The invention relates to a vehicle weight determination method and device, a storage medium and a vehicle, relates to the field of vehicles, and aims to quickly and accurately calculate the vehicle weight. The vehicle weight determination method comprises the following steps: determining a driving mode according to driving state data of a vehicle; when the driving mode is the sliding mode, environment data of the environment where the vehicle is located and the vehicle weight determined by the vehicle in the acceleration mode are determined, vehicle weight correction parameters are determined according to the environment data, the driving state data and the vehicle weight, and the vehicle weight correction parameters are used for representing the deviation between the vehicle weight determined through the vehicle weight determination method and the actual vehicle weight; when the running mode of the vehicle is the acceleration mode, environment data of the environment where the vehicle is located and the vehicle weight determined when the vehicle is in the acceleration mode last time are determined, and the vehicle weight of the vehicle is determined according to the environment data, the running state data, the vehicle weight determined when the vehicle is in the acceleration mode last time and vehicle weight correction parameters determined when the vehicle is in the sliding mode.

Description

Technical Field

[0001] The present disclosure relates to the field of vehicles, and in particular, to a vehicle weight determination method, device, storage medium and vehicle. Background Art

[0002] In the related art, the common method for determining vehicle weight is based on the dynamic equation of the vehicle during driving, and the vehicle weight is iteratively calculated by obtaining information such as the vehicle's real-time torque, acceleration, and slope, and finally the vehicle weight is estimated. However, during the iterative calculation process, due to the unbiased estimation characteristics of the algorithm, there is often a deviation between the calculation result and the actual situation, resulting in pathological ambiguous points in the vehicle weight information matrix. The pathological information matrix may cause the calculation result to converge slowly, or even fall into the vehicle weight data with large deviations, that is, the trap of local optimality, and the vehicle weight cannot be calculated accurately and quickly. Summary of the invention

[0003] In order to overcome the problems existing in the related art, the present disclosure provides a vehicle weight determination method, device, storage medium and vehicle.

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

[0005] Determine a driving mode according to the driving state data of the vehicle;

[0006] When the driving mode is the gliding mode, determining environmental data of the environment in which the vehicle is located and a vehicle weight determined in an acceleration mode, and determining a vehicle weight correction parameter according to the environmental data, the driving state data and the vehicle weight, the vehicle weight correction parameter being used to characterize a deviation between the vehicle weight determined by the vehicle weight determination method and the actual vehicle weight;

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

[0008] Optionally, it also includes:

[0009] If the vehicle has not entered the coasting mode before entering the acceleration mode and it is not the first time for the vehicle to enter the acceleration mode, obtaining a preset vehicle weight correction parameter, and determining the vehicle weight of the vehicle according to the environmental data, the driving state data, the preset vehicle weight correction parameter, and the vehicle weight determined when the vehicle was in the acceleration mode last time;

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

[0011] Optionally, determining the vehicle weight correction parameter according to the environmental data, the driving state data and the vehicle weight includes:

[0012] According to the environmental data, the driving state data and the vehicle weight, a correction parameter is determined as an initial target correction parameter, and the following process is performed cyclically:

[0013] Reacquiring the driving state data of the vehicle as the first target data, and reacquiring the environmental data of the environment in which the vehicle is located as the second target data;

[0014] A new correction parameter is determined based on the first target data, the second target data and the vehicle weight, and the new correction parameter is added to the target correction parameter to obtain a new target correction parameter, until the vehicle exits the gliding mode, and the target correction parameter determined during the last cycle is used as the vehicle weight correction parameter.

[0015] Optionally, determining the vehicle weight correction parameter according to the environmental data, the driving state data and the vehicle weight includes:

[0016] determining an actual wind resistance of the vehicle according to the environmental data and the driving state data;

[0017] determining a calculated wind resistance of the vehicle according to the driving state data and the vehicle weight;

[0018] The vehicle weight correction parameter is determined according to the actual wind resistance and the calculated wind resistance.

[0019] Optionally, determining the vehicle weight of the vehicle according to the environmental data, the driving state data, the vehicle weight determined when the vehicle was in the acceleration mode last time, and the vehicle weight correction parameter determined when the vehicle was in the coasting mode includes:

[0020] Determining a target weight value according to the sampling time of the driving state data and the environmental data, and the vehicle weight correction parameter determined by the vehicle in the gliding mode, wherein the target weight value is inversely proportional to the vehicle weight correction parameter;

[0021] The vehicle weight of the vehicle is determined based on a recursive least square method according to the target weight value, the environmental data, the driving state data and the vehicle weight determined when the vehicle was in the acceleration mode last time.

[0022] Optionally, determining the vehicle weight of the vehicle based on a recursive least squares method according to 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 includes:

[0023] According to the target weight value, the environmental data, the driving state data and the vehicle weight determined when the vehicle was in the acceleration mode last time, a vehicle weight is determined as the initial target vehicle weight, and the following process is performed cyclically:

[0024] reacquiring the driving state data of the vehicle as the third target data, and reacquiring the environmental data of the environment in which the vehicle is located as the fourth target data;

[0025] A new target vehicle weight is determined based on the third target data, the fourth target data, the target weight value and the target vehicle weight until the vehicle exits the acceleration mode, and the target vehicle weight determined during the last cycle is used as the vehicle weight.

[0026] Optionally, determining the driving mode according to the driving state data of the vehicle includes:

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

[0028] When the vehicle meets all of the following conditions, the driving mode is determined to be the gliding mode: the accelerator pedal of the vehicle is in an inactive state, the braking system of the vehicle is in an inactive state, the cruise system of the vehicle is in an inactive state, and the speed of the vehicle is greater than a preset speed threshold.

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

[0030] A first determination module, used to determine a driving mode according to driving state data of the vehicle;

[0031] a second determination module, configured to determine environmental data of an environment in which the vehicle is located and a vehicle weight determined by the vehicle in an acceleration mode, and to determine a vehicle weight correction parameter based on the environmental data, the driving state data and the vehicle weight, wherein the vehicle weight correction parameter is used to characterize a deviation between the vehicle weight determined by the vehicle weight determination method and the actual vehicle weight;

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

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

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

[0035] A processor and a memory, wherein the memory stores machine executable instructions that can be executed by the processor, and the processor is used to execute the machine executable instructions to implement any of the vehicle weight determination methods provided in the first aspect of the present disclosure.

[0036] The technical solution provided by the embodiments of the present disclosure may have the following beneficial effects:

[0037] By calculating the vehicle weight in the acceleration mode and calculating the vehicle weight correction parameters in the gliding mode, the vehicle weight calculated in the acceleration mode can be corrected. This can prevent the vehicle weight calculation result from falling into vehicle weight data with large deviations, thereby jumping out of the local optimal trap, and ultimately the vehicle weight can be calculated accurately and quickly.

[0038] It is to be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the present disclosure.

[0039] Other features and advantages of the present disclosure will be described in detail in the following detailed description. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] The accompanying drawings are used to provide a further understanding of the present disclosure and constitute a part of the specification. Together with the following specific embodiments, they are used to explain the present disclosure but do not constitute a limitation of the present disclosure. In the accompanying drawings:

[0041] Figure 1The figure is a flow chart of a method for determining vehicle weight according to an exemplary embodiment.

[0042] Figure 2 The figure is a flow chart of a method for determining a vehicle weight correction parameter according to an exemplary embodiment.

[0043] Figure 3 The figure is a block diagram of a vehicle weight determination device according to an exemplary embodiment.

[0044] Figure 4 The invention is a block diagram of a device for determining vehicle weight according to an exemplary embodiment. DETAILED DESCRIPTION

[0045] The specific implementation of the present disclosure is described in detail below in conjunction with the accompanying drawings. It should be understood that the specific implementation described herein is only used to illustrate and explain the present disclosure, and is not used to limit the present disclosure.

[0046] In the related art, the common method for determining vehicle weight is based on the dynamic equation of the vehicle during driving, and the vehicle weight is iteratively calculated by obtaining information such as the vehicle's real-time torque, acceleration, and slope, and finally the vehicle weight is estimated. However, during the iterative calculation process, due to the unbiased estimation characteristics of the algorithm, there is often a deviation between the calculation result and the actual situation, resulting in pathological ambiguous points in the vehicle weight information matrix. The pathological information matrix may cause the calculation result to converge slowly, or even fall into the vehicle weight data with large deviations, that is, the trap of local optimality, and the vehicle weight cannot be calculated accurately and quickly.

[0047] In view of this, the present disclosure provides a vehicle weight determination method, device, storage medium and vehicle, which can prevent the vehicle weight calculation result from falling into vehicle weight data with large deviations, thereby jumping out of the local optimal trap, and ultimately calculating the vehicle weight accurately and quickly.

[0048] Figure 1 is a flow chart of a method for determining vehicle weight according to an exemplary embodiment. Figure 1 As shown, the vehicle weight determination method includes the following steps:

[0049] In step S101, a driving mode is determined according to the driving state data of the vehicle;

[0050] In step S102, when the driving mode is the gliding mode, environmental data of the vehicle environment and the vehicle weight determined in the acceleration mode are determined, and a vehicle weight correction parameter is determined based on the environmental data, the driving state data and the vehicle weight, the vehicle weight correction parameter being 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 the acceleration mode, the environmental data of the vehicle's environment and the vehicle weight determined when the vehicle was in the acceleration mode last are determined, and the vehicle weight is determined based on the environmental data, driving status data, the vehicle weight determined when the vehicle was in the acceleration mode last and the vehicle weight correction parameters determined when the vehicle is in the gliding mode.

[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 gearbox control unit, or a power pre-control unit, and the present disclosure does not limit this. The data sampling and data processing in the specific embodiment of the present disclosure are performed synchronously, and the time interval is consistent with the execution step length of the vehicle weight determination method, such as 5ms, 10ms or 20ms, and the present disclosure embodiment does not limit this. Because the driving state data and environmental data change in real time with the change of the driving state of the vehicle, they can be processed in real time. The driving state data of the vehicle may include: engine torque, motor torque, main reducer transmission ratio, mechanical efficiency of the transmission system, windward area of ​​the vehicle, tire equivalent rolling radius, vehicle driving speed, vehicle net weight, vehicle load threshold and at least one of the vehicle driving direction acceleration. Environmental data may include: air density and / or vehicle driving slope angle.

[0053] The driving mode is determined based on the driving state data of the vehicle, and may include a coasting mode, an acceleration mode, and other modes except the coasting mode and the acceleration mode. The coasting mode refers to a state in which the vehicle engine or motor does not provide power to the vehicle, such as when the vehicle is in a state in which the accelerator is released. Correspondingly, the acceleration mode refers to a state in which the vehicle engine or motor provides power to the vehicle, such as when the driver steps on the accelerator pedal.

[0054] The calculations in the coasting mode and the acceleration mode are interrelated. In the coasting mode, the vehicle weight correction parameters are calculated based on the vehicle weight calculated in the acceleration mode; in the acceleration mode, the vehicle weight is calculated based on the vehicle weight correction parameters calculated in the coasting mode. These two modes form a loop. If the vehicle is currently in the acceleration mode, the vehicle weight is calculated using the result of the last coasting mode calculation, and will be used the next time the vehicle is in the coasting mode; if the vehicle is currently in the coasting mode, the vehicle weight calculated using the last acceleration mode calculation is used to calculate the vehicle weight correction parameters, and will be used the next time the vehicle is in the acceleration mode. In addition, a loop is also formed between the acceleration modes, that is, in this acceleration mode, the vehicle weight is calculated based on the vehicle weight calculated in the last acceleration mode, and will be used the next time the vehicle is in the acceleration mode.

[0055] By calculating the vehicle weight in the acceleration mode and calculating the vehicle weight correction parameters in the gliding mode, the vehicle weight calculated in the acceleration mode can be corrected. This can prevent the vehicle weight calculation result from falling into vehicle weight data with large deviations, thereby jumping out of the local optimal trap and ultimately calculating the vehicle weight accurately and quickly.

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

[0057] If the vehicle has not entered the coasting mode before entering the acceleration mode and it is not the first time for the vehicle to enter the acceleration mode, a preset vehicle weight correction parameter is obtained, and the vehicle weight of the vehicle is determined based on the environmental data, the driving state data, the preset vehicle weight correction parameter, and the vehicle weight determined when the vehicle was in the acceleration mode last time;

[0058] If the vehicle enters the acceleration mode for the first time and has not entered the coasting mode before entering the acceleration mode, the preset vehicle weight correction parameter and the 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 parameter and the preset vehicle weight.

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

[0060] It should be understood that when the vehicle enters the acceleration mode before entering the coasting mode and it is not the first time to enter the acceleration mode, such as the second time the vehicle enters the acceleration mode and has never entered the coasting mode. That is, there is a mode other than the coasting mode and the acceleration mode between the two acceleration modes. At this time, the vehicle weight correction parameter has never been calculated, and the preset vehicle weight correction parameter is used when calculating the vehicle weight. And if it is the second time to enter the acceleration mode, it is also necessary to use the vehicle weight calculated in the last acceleration mode.

[0061] When the vehicle enters the acceleration mode for the first time and has not entered the coasting mode before entering the acceleration mode, such as when the vehicle starts and immediately enters the acceleration mode, the preset vehicle weight correction parameter and the preset vehicle weight are used when calculating the vehicle weight.

[0062] It should also be understood that, in addition to the above situation, when the vehicle's driving mode is the gliding mode, the vehicle weight determined in the acceleration mode before the vehicle enters the gliding mode is determined as the vehicle's weight in the gliding mode; when the vehicle's driving mode is any mode other than the gliding mode and the acceleration mode, the vehicle weight determined in the acceleration mode before the vehicle enters the other mode is determined as the vehicle's weight in the other mode.

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

[0064] According to the environmental data, driving state data and vehicle weight, a correction parameter is determined as the initial target correction parameter, and the following process is executed cyclically:

[0065] Reacquiring the driving state data of the vehicle as the first target data, and reacquiring the environmental data of the environment in which the vehicle is located as the second target data;

[0066] According to 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, until the vehicle exits the gliding mode, and the target correction parameter determined in the last cycle is used as the vehicle weight correction parameter.

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

[0068] Figure 2 is a flow chart of a method for determining a vehicle weight correction parameter according to an exemplary embodiment. Figure 2 , step S102 may include the following steps:

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

[0070] In step S202, the calculated wind resistance of the vehicle is determined according to the driving state data and the vehicle weight;

[0071] In step S203, a vehicle weight correction parameter is determined according to the actual wind resistance and the calculated wind resistance.

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

[0073]

[0074] Among them C Dis the air resistance coefficient, ρ is the air density, A is the frontal area of ​​the vehicle, and v is the vehicle speed. The air resistance coefficient and air density are environmental data, and the frontal area of ​​the vehicle and the vehicle speed are driving state data. The negative sign in the above equation indicates that the acceleration of the vehicle is opposite to the driving direction of the vehicle. In order to simplify the calculation, the formula on the left side of the equal sign in the above equation is used as the actual wind resistance to represent the m in the formula on the right side of the equal sign, which is the actual vehicle weight.

[0075] Accordingly, the following formula for calculating wind resistance is used to represent the calculated vehicle weight:

[0076]

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

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

[0079] or

[0080]

[0081] Preferably, the following formula can be used to calculate the vehicle weight correction parameter:

[0082]

[0083] Among them, η c is the vehicle weight correction parameter, and T is the sampling time of environmental data and driving status data.

[0084] It should be understood that the calculation principle of the vehicle weight correction parameter is: use the actual wind resistance of the vehicle to characterize the vehicle weight calculated in real time in the gliding mode; use the calculated wind resistance of the vehicle to characterize the vehicle weight calculated in the acceleration mode; use the difference between the actual wind resistance and the calculated wind resistance, divided by the actual wind resistance or the calculated wind resistance, to characterize the deviation between the vehicle weight calculated in the acceleration mode and the actual vehicle weight; then sum up all the quotients calculated in real time, divide them by the sampling time to obtain the average value, to characterize the final vehicle weight correction parameter obtained in this gliding mode.

[0085] For example, when the vehicle weight correction parameter is close to 0, it indicates that the vehicle weight calculated in the acceleration mode is close to the actual vehicle weight. When the vehicle weight correction parameter is larger, it indicates that the vehicle weight calculated in the acceleration mode is significantly different from the actual vehicle weight.

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

[0087] Determine a target weight value according to the sampling time of the driving state data and the environmental data, and a vehicle weight correction parameter determined by the vehicle in the gliding mode, wherein the target weight value is inversely proportional to the vehicle weight correction parameter;

[0088] The vehicle weight is determined based on a recursive least square method according to the target weight value, environmental data, driving state data and the vehicle weight determined when the vehicle was last in an acceleration mode.

[0089] For example, the target weight value can be obtained by substituting the sampling time of the driving state data and the environmental data, and the vehicle weight correction parameter determined in the gliding mode into the following formula:

[0090] λ=1-0.001T-(0.005·η c )

[0091] Where λ is the target weight value, T is the sampling time of driving state data and environmental data, η c It is the vehicle weight correction parameter determined in the gliding mode, and the initial value can be 1.

[0092] It should be understood that the recursive least squares method is only used to calculate the vehicle weight in the acceleration mode, and the vehicle longitudinal dynamics equation can be substituted into the recursive least squares method parameter formula. The vehicle longitudinal dynamics equation is as follows:

[0093]

[0094] Among them, T tq is the engine torque or motor torque, i g is the current gear ratio of the transmission device, i 0 is the main reducer transmission ratio, η g is the mechanical efficiency of the transmission system, R i is the equivalent rolling radius of the tire, C D is the air resistance coefficient, ρ is the air density, A is the frontal area of ​​the vehicle, v is the vehicle speed, m is the actual vehicle weight, g is the gravitational acceleration, is the vehicle driving slope angle, f a is the constant term of the rolling resistance coefficient velocity fitting, f b is the first-order coefficient of the rolling resistance coefficient speed fitting, a v is the vehicle's acceleration in the direction of travel.

[0095] The recursive formula of the recursive least square method solves the process of deriving the parameter θ when Y=θX is known and observable X and Y are known. In the present 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 manner, determining the vehicle weight of the vehicle based on a recursive least squares method according to the target weight value, the environment data, the driving state data, and the vehicle weight determined when the vehicle was last in the acceleration mode may include the following steps:

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

[0098] Reacquiring the driving state data of the vehicle as the third target data, and reacquiring the environmental data of the environment in which the vehicle is located as the fourth target data;

[0099] A new target vehicle weight is determined based on the third target data, the fourth target data, the target weight value and the target vehicle weight until the vehicle exits the acceleration mode, and the target vehicle weight determined during the last cycle is used as the vehicle weight.

[0100] For example, the target weight value, environmental data, driving status data, and the last time the vehicle was in acceleration mode are substituted into the following least squares recursive formula to obtain the vehicle weight:

[0101]

[0102]

[0103]

[0104] Where k is a natural number 0, 1, 2, 3, etc. is the vehicle weight, F k is the resultant force on the vehicle, which is the traction force minus the wind resistance. k is the total acceleration, P 1 is the preset initial value, λ is the target weight value determined by the vehicle in the gliding mode, I is the unit matrix, for a k+1 The transpose of .

[0105] In the above formula, F k+1 It can be calculated by the following formula:

[0106]

[0107] Where k is a natural number 0, 1, 2, 3, ..., T tq is the engine torque or motor torque, ig is the current gear ratio of the transmission device, i 0 is the main reducer transmission ratio, η g is the mechanical efficiency of the transmission system, R i is the equivalent rolling radius of the tire, C D is the air resistance coefficient, ρ is the air density, A is the frontal area of ​​the vehicle, and v is the vehicle speed.

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

[0109]

[0110] Where k is a natural number 0, 1, 2, 3, ..., g is the acceleration due to gravity, is the vehicle driving slope angle, a v is the vehicle's acceleration in the direction of travel, f a is the constant term of the rolling resistance coefficient velocity fitting, f b is the first-order coefficient of the rolling resistance coefficient speed fitting. Since the vehicle weight is a specific value, that is, a 1×1 matrix, and a k+1 equal.

[0111] is the preset initial value of vehicle weight, which can be the vehicle net weight, or the sum of the vehicle net weight and the vehicle load threshold. 0 is a preset value, which needs to ensure that the calculation result in the iteration process is not negative, such as 50000 or 100000. I is the unit matrix. Since the vehicle weight is a specific value, that is, a 1×1 matrix, I is 1.

[0112] The first iteration calculation process in this acceleration mode is as follows: 1) Calculate a based on the driving state data and environmental data 1 ; Calculate the target weight value λ according to the sampling time of driving state data and environmental data, and the vehicle weight correction parameter determined in the gliding mode; 1 , P 0 Substituting and λ into the above formula (3), we can calculate K 0 2) Calculate F based on driving status data and environmental data 1 ;Will K 0 , F 1 and a 1 Substituting into the above formula (1), we can calculate The value of is the vehicle weight of the first iteration in this acceleration mode, and the calculated vehicle weight is output in real time; 3) λ, I, K 0, a 1 and P 0 Substituting into the above formula (2), we can calculate P 1 , P 1 Used for the next iteration.

[0113] Perform an iterative calculation every sampling time and output the vehicle weight calculated by this iterative calculation. Continue iterative calculation until exiting the current acceleration mode, and determine the vehicle weight calculated by the last iterative calculation as the vehicle weight used when entering the next acceleration mode or coasting mode.

[0114] It should be understood that when the vehicle weight correction parameter is large, it can be considered that the vehicle weight calculated in the acceleration mode is significantly different from the actual vehicle weight, indicating two situations: 1) the state of falling into the local optimal solution. In this case, the weight of the historical vehicle weight data is reduced by increasing the target weight value to accelerate the avoidance of the local trap and the iterative process to the optimal solution. 2) The number of iterations is insufficient and the vehicle weight has not converged to a stable value. In this case, the calculated vehicle weight is gradually converged to the actual vehicle weight through continuous iteration.

[0115] It should be understood that in the process of continuous iteration, due to the continuous increase of historical vehicle weight in iteration, the latest collected environmental data and driving status data may not be able to change the impact of historical vehicle weight on the latest vehicle weight. If the deviation of historical vehicle weight is large, it will cause a large error between the final calculated vehicle weight and the actual vehicle weight. It is necessary to use the target weight value to change the weight of 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 historical vehicle weight and the higher the weight of the latest collected environmental data and driving status data.

[0116] In the target weight value of the least squares recursive formula, a vehicle weight correction parameter is added. The vehicle weight correction parameter is a parameter that characterizes the deviation between the calculated vehicle weight and the actual value. When the vehicle weight correction parameter is large, it means that the deviation between the result and the actual value is large, and the calculated target weight value is small. The weight of the historical vehicle weight is reduced, thereby speeding up the iteration speed and accelerating away from the historical vehicle weight data with large deviations. When the vehicle weight correction parameter is small, it means that the deviation between the result and the actual value is small, and the calculated target weight value is large. The weight of the historical vehicle weight is increased, and the iteration speed is reduced, thereby maintaining a stable and more accurate vehicle weight calculation result. Through the above method, the vehicle weight calculation result can avoid falling into the historical vehicle weight data with large deviations, thereby jumping out of the local optimal trap, and finally the vehicle weight can be calculated accurately and quickly.

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

[0118] When the vehicle meets any of the following judgment conditions, the driving mode is determined to be the acceleration mode: the first judgment condition, the second judgment condition, and the third judgment condition, wherein the first judgment condition is that the accelerator pedal of the vehicle is in an activated state, the transmission system of the vehicle is in a coupled 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 activated 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 activated state and the acceleration of the vehicle is greater than the preset acceleration threshold;

[0119] When the vehicle meets all of the following conditions, the driving mode is determined to be the gliding mode: the vehicle's accelerator pedal is in an inactive state, the vehicle's braking system is in an inactive state, the vehicle's cruise system is in an inactive state, and the vehicle's speed is greater than a preset speed threshold.

[0120] It should be understood that the vehicle's brake system is in an inactive state, which may be any of the following: the brake pedal is invalid, the auxiliary brake is not activated, the electronic brake system is not activated, and the intelligent driving brake signal is not activated. Specifically, according to the type of the vehicle's brake system, the corresponding brake system state is met to determine that the vehicle's brake system is in an inactive state.

[0121] For example, the acceleration threshold may be any one of 0.1m / s, 0.2m / s, 0.3m / s and 0.4m / s, preferably 0.1m / s. By setting the acceleration threshold, it is possible to filter out acceleration fluctuations caused by environmental factors such as uneven road surface when the vehicle is in a uniform driving state. The coasting speed threshold may be any one of 20km / h, 21km / h, 22km / h and 23km / h, preferably 20km / h. By setting the coasting speed threshold, it is possible to filter out speed fluctuations caused by environmental factors such as a sloped road surface and vehicle inertia when the vehicle is in a slow moving state.

[0122] The vehicle weight is calculated according to the vehicle's driving status during the driving process, and ultimately the vehicle weight can be calculated quickly and accurately.

[0123] By using any of the above-mentioned vehicle weight determination methods, the vehicle weight calculation result can avoid falling into historical vehicle weight data with large deviations, thereby jumping out of the local optimal trap, and ultimately the vehicle weight can be calculated accurately and quickly.

[0124] Figure 3 is a block diagram of a vehicle weight determination device according to an exemplary embodiment. Figure 3 , the device comprises:

[0125] A first determination module 301 is used to determine a driving mode according to driving state data of the vehicle;

[0126] A second determination module 302 is used to determine environmental data of the environment in which the vehicle is located and a vehicle weight determined by the vehicle in an acceleration mode, and determine a vehicle weight correction parameter according to the environmental data, the driving state data and the vehicle weight, wherein the vehicle weight correction parameter is used to characterize a deviation between the vehicle weight determined by the vehicle weight determination method and the actual vehicle weight;

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

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

[0129] a first initial determination module, configured to obtain a preset vehicle weight correction parameter when the vehicle does not enter the coasting mode before entering the acceleration mode and it is not the first time for the vehicle to enter the acceleration mode, and determine the vehicle weight of the vehicle according to the environmental data, the driving state data, the preset vehicle weight correction parameter and the vehicle weight determined when the vehicle was in the acceleration mode last time;

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

[0131] Optionally, the second determining module 302 is configured to:

[0132] According to the environmental data, driving state data and vehicle weight, a correction parameter is determined as the initial target correction parameter, and the following process is executed cyclically:

[0133] Reacquiring the driving state data of the vehicle as the first target data, and reacquiring the environmental data of the environment in which the vehicle is located as the second target data;

[0134] According to 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, until the vehicle exits the gliding mode, and the target correction parameter determined in the last cycle is used as the vehicle weight correction parameter.

[0135] Optionally, the second determining module 302 is configured to:

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

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

[0138] Determine the vehicle weight correction parameters based on the actual wind resistance and calculated wind resistance.

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

[0140] A target weight determination module is used to determine a target weight value according to the sampling time of the driving state data and the environmental data, and a vehicle weight correction parameter determined by the vehicle in the gliding mode, wherein the target weight value is inversely proportional to the vehicle weight correction parameter;

[0141] The vehicle weight recursive determination module determines the vehicle weight of the vehicle based on the recursive least square method according to the target weight value, environmental data, driving state data and the vehicle weight determined when the vehicle was in the acceleration mode last time.

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

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

[0144] Reacquiring the driving state data of the vehicle as the third target data, and reacquiring the environmental data of the environment in which the vehicle is located as the fourth target data;

[0145] A new target vehicle weight is determined based on the third target data, the fourth target data, the target weight value and the target vehicle weight until the vehicle exits the acceleration mode, and the target vehicle weight determined during the last cycle is used as the vehicle weight.

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

[0147] A first judgment module, used for determining that the driving mode is the 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 activated state, the transmission system of the vehicle is in a coupled 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 activated 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 activated state and the acceleration of the vehicle is greater than the preset acceleration threshold;

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

[0149] Regarding the device in the above embodiment, the specific manner in which each module performs operations has been described in detail in the embodiment of the method, and will not be elaborated here.

[0150] The present 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 vehicle weight determination method provided by the present disclosure.

[0151] The present disclosure also provides a vehicle, comprising:

[0152] A processor and a memory, the memory storing machine executable instructions that can be executed by the processor, and the processor is used to execute the machine executable instructions to implement any of the vehicle weight determination methods provided in the first aspect of the present disclosure.

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

[0154] The processing component 402 generally controls the overall operation of the vehicle 400, such as operations associated with display, data communication, and recording operations. The 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 above-mentioned vehicle weight determination methods. In addition, the processing component 402 may include one or more modules to facilitate the interaction between the processing component 402 and other components. For example, the processing component 402 may include a sensor module to facilitate the interaction between the sensor component 414 and the processing component 402.

[0155] The memory 404 is configured to store various types of data to support operations on the vehicle 400. Examples of such data include instructions for any application or method operating on the vehicle 400. The memory 404 may be implemented by any type of volatile or non-volatile storage device or 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 memory, 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 supplies, and other components associated with generating, managing, and distributing power to the vehicle 400.

[0157] The input / output interface 412 provides an interface between the processing component 402 and the peripheral interface module.

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

[0159] The communication component 416 is configured to facilitate wired or wireless communication between the vehicle 400 and other devices. The vehicle 400 can access a wireless network based on a communication standard, such as WiFi, 2G or 3G, or a combination thereof. In an exemplary embodiment, the communication component 416 receives a broadcast signal or broadcast-related information from an external broadcast management system via a broadcast channel. In an exemplary embodiment, the communication component 416 also includes a near field communication (NFC) module to facilitate short-range communication. For example, the NFC module can 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-mentioned 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, and the instructions can be executed by a processor 420 of the vehicle 400 to complete the above-mentioned vehicle weight determination method. For example, the non-transitory computer-readable storage medium can be a ROM, a random access memory (RAM), a CD-ROM, a magnetic tape, a floppy disk, an optical data storage device, etc.

[0162] In another exemplary embodiment, a computer program product is also provided. The computer program product includes a computer program executable by a programmable device. The computer program has a code portion for executing any of the above vehicle weight determination methods when executed by the programmable device.

[0163] Those skilled in the art will readily appreciate other embodiments of the present disclosure after considering the specification and practicing the present disclosure. The present disclosure is intended to cover any variations, uses, or adaptations of the present disclosure that follow the general principles of the present disclosure and include common knowledge or customary technical means in the art that are not disclosed in the present disclosure. The description and examples are to be considered as exemplary only, and the true scope and spirit of the present disclosure are indicated by the following claims.

[0164] It should be understood that the present disclosure is not limited to the exact structures that have been described above and shown in the drawings, and that various modifications and changes may be made without departing from the scope thereof. The scope of the present disclosure is limited only by the appended claims.

Claims

1. A method for determining vehicle weight, characterized in that: include: Determine a driving mode according to the driving state data of the vehicle; When the driving mode is the gliding mode, determining environmental data of the environment in which the vehicle is located and a vehicle weight determined in an acceleration mode, and determining a vehicle weight correction parameter according to the environmental data, the driving state data and the vehicle weight, the vehicle weight correction parameter being used to characterize a deviation between the vehicle weight determined by the vehicle weight determination method and the actual vehicle weight; When the vehicle driving mode is the acceleration mode, the environmental data of the vehicle's environment and the vehicle weight determined when the vehicle was in the acceleration mode last are determined, and the vehicle weight of the vehicle is determined based on the environmental data, the driving status data, the vehicle weight determined when the vehicle was in the acceleration mode last, and the vehicle weight correction parameter determined when the vehicle is in the gliding mode.

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

3. The method according to claim 1, characterized in that The determining of the vehicle weight correction parameter according to the environmental data, the driving state data and the vehicle weight includes: According to the environmental data, the driving state data and the vehicle weight, a correction parameter is determined as an initial target correction parameter, and the following process is performed cyclically: Reacquiring the driving state data of the vehicle as the first target data, and reacquiring the environmental data of the environment in which the vehicle is located as the second target data; A new correction parameter is determined based on the first target data, the second target data and the vehicle weight, and the new correction parameter is added to the target correction parameter to obtain a new target correction parameter, until the vehicle exits the gliding mode, and the target correction parameter determined during the last cycle is used as the vehicle weight correction parameter.

4. The method according to claim 1, characterized in that: The determining of the vehicle weight correction parameter according to the environmental data, the driving state data and the vehicle weight includes: determining an actual wind resistance of the vehicle according to the environmental data and the driving state data; determining a calculated wind resistance of the vehicle according to the driving state data and the vehicle weight; The vehicle weight correction parameter is determined according to the actual wind resistance and the calculated wind resistance.

5. The method according to any one of claims 1 to 4, characterized in that: Determining the vehicle weight of the vehicle according to the environmental data, the driving state data, the vehicle weight determined when the vehicle was in the acceleration mode last time, and the vehicle weight correction parameter determined when the vehicle was in the coasting mode, comprises: Determining a target weight value according to the sampling time of the driving state data and the environmental data, and the vehicle weight correction parameter determined by the vehicle in the gliding mode, wherein the target weight value is inversely proportional to the vehicle weight correction parameter; The vehicle weight of the vehicle is determined based on a recursive least square method according to the target weight value, the environmental data, the driving state data and the vehicle weight determined when the vehicle was in the acceleration mode last time.

6. The method according to claim 5, characterized in that The determining the vehicle weight of the vehicle based on a recursive least square method according to the target weight value, the environmental data, the driving state data and the vehicle weight determined when the vehicle was in the acceleration mode last time includes: According to the target weight value, the environmental data, the driving state data and the vehicle weight determined when the vehicle was in the acceleration mode last time, a vehicle weight is determined as the initial target vehicle weight, and the following process is performed cyclically: reacquiring the driving state data of the vehicle as the third target data, and reacquiring the environmental data of the environment in which the vehicle is located as the fourth target data; A new target vehicle weight is determined based on the third target data, the fourth target data, the target weight value and the target vehicle weight until the vehicle exits the acceleration mode, and the target vehicle weight determined during the last cycle is used as the vehicle weight.

7. The method according to any one of claims 1 to 4, characterized in that: Determining the driving mode according to the driving state data of the vehicle includes: When the vehicle meets any of the following judgment conditions, the driving mode is determined to be the acceleration mode: the first judgment condition, the second judgment condition, and the third judgment condition, wherein the first judgment condition is that the accelerator pedal of the vehicle is in an activated state, the transmission system of the vehicle is in a coupled 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 activated 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 activated state and the acceleration of the vehicle is greater than the preset acceleration threshold; When the vehicle meets all of the following conditions, the driving mode is determined to be the gliding mode: the accelerator pedal of the vehicle is in an inactive state, the braking system of the vehicle is in an inactive state, the cruise system of the vehicle is in an inactive state, and the speed of the vehicle is greater than a preset speed threshold.

8. A vehicle weight determination device, characterized in that: The device comprises: A first determination module, used to determine a driving mode according to driving state data of the vehicle; a second determination module, configured to determine environmental data of an environment in which the vehicle is located and a vehicle weight determined by the vehicle in an acceleration mode, and to determine a vehicle weight correction parameter based on the environmental data, the driving state data and the vehicle weight, wherein the vehicle weight correction parameter is used to characterize a deviation between the vehicle weight determined by the vehicle weight determination method and the actual vehicle weight; The third determination module is used to determine the environmental data of the vehicle's environment and the vehicle weight determined when the vehicle was in the acceleration mode last time when the vehicle's driving mode is the acceleration mode, and determine the vehicle weight of the vehicle based on the environmental data, the driving status data, the vehicle weight determined when the vehicle was in the acceleration mode last time and the vehicle weight correction parameter determined when the vehicle is in the gliding mode.

9. A non-transitory computer storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the steps of the method described in any one of claims 1 to 7 are implemented.

10. A vehicle, characterized in that: The vehicle comprises: A processor and a memory, wherein the memory stores machine executable instructions that can be executed by the processor, and the processor is used to execute the machine executable instructions to implement the vehicle weight determination method described in any one of claims 1-7.

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