Vehicle weight and slope calculation method, device and equipment

Abstract

The present application relates to the technical field of vehicles, and an embodiment thereof provides a vehicle weight and slope calculation method, device and equipment. One of the vehicle weight calculation methods comprises: obtaining a corresponding relationship between an acceleration value and a vehicle weight under one combination of a slope value and a motion state; during a loading process or an unloading process starting from a given vehicle weight, determining whether the vehicle is in the combination of the slope value and the motion state according to whether the acceleration value at the start of the loading process or the unloading process conforms to the corresponding relationship between the acceleration value and the vehicle weight; and if it is determined that the vehicle is not in the combination of the slope value and / or the motion state, selecting to correct the acceleration value before mapping and / or correct the vehicle weight obtained by mapping. The vehicle weight estimation method provided in the embodiment has the advantages of simple and fast operation and high accuracy.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of vehicles, in particular to a vehicle weight calculation method, a vehicle weight estimation device, a vehicle slope calculation method, a vehicle slope calculation device, an electronic device and a computer readable storage medium. BACKGROUND

[0002] Vehicle weight and road slope are key parameters of vehicle dynamics, which are of great significance to gear selection, energy recovery control and automatic driving speed following, and seriously affect the safety of vehicle operation. The self-unloading vehicle has the characteristics of large range of cargo quality change, and it is very important to accurately estimate the current vehicle weight and the slope of the slope where the vehicle is located. The prior art perceives vehicle weight and slope by load sensors or Recursive Least Square (RLS) least square method, Kalman Filter (KF) Kalman filter and Model Prediction Control (MPC) model prediction algorithm. The former has high cost and complex installation; the latter has slow convergence speed, large amount of calculation, and cannot estimate vehicle weight and slope in the parking state.

[0003] The prior art perceives vehicle weight and slope by load sensors or Recursive Least Square (RLS) least square method, Kalman Filter (KF) Kalman filter and Model Prediction Control (MPC) model prediction algorithm. The former has high cost and complex installation; the latter has slow convergence speed due to the difficulty in accurately obtaining the initial value, large error in the early stage of calculation or low speed, and cannot estimate vehicle weight and slope in the parking state. Even if the preset proportion of the full load value of the vehicle is used as the vehicle weight estimation value, the estimated value of the whole vehicle weight is only the full load quality and the half load quality corresponding to the preset proportion, which cannot be accurately estimated according to the actual situation. If the vehicle is loaded or unloaded during parking, it will lead to inaccurate vehicle weight and slope data, causing slope start failure or impact phenomenon, which will cause danger. SUMMARY

[0004] The purpose of the embodiments of the present application is to provide a vehicle weight calculation method, device and equipment to solve some problems in the prior art.

[0005] In order to achieve the above object, the first aspect of the present application provides a vehicle weight calculation method, comprising: obtaining a corresponding relationship between an acceleration value and a vehicle weight under a combination of a slope value and a motion state of a vehicle; determining whether the vehicle is in the combination of the slope value and the motion state according to whether the acceleration value at the beginning of a loading process or an unloading process from a given vehicle weight conforms to the corresponding relationship between the acceleration value and the vehicle weight; if it is determined that the vehicle is not in the combination of the slope value and / or the motion state, selecting to correct the acceleration value before mapping or to correct the vehicle weight obtained by mapping according to the acceleration value during the loading process or the unloading process and the corresponding relationship between the acceleration value and the vehicle weight.

[0006] Preferably, the method further comprises: if it is determined that the vehicle is in the combination of the slope value and the motion state, obtaining the vehicle weight according to the acceleration value during the loading process or the unloading process through the corresponding relationship between the acceleration value and the vehicle weight.

[0007] Preferably, the combination of the slope value and the motion state comprises: the slope value is 0 and the motion state is static.

[0008] Preferably, the vehicle is a self-unloading vehicle, and the method further comprises: determining that the vehicle is in the loading process or the unloading process according to the type of the action execution instruction obtained by the vehicle; determining that the loading process is in one of the following states: loading start, loading and loading completion according to the loading signal obtained from the loading device cooperating with the vehicle; determining that the unloading process is in one of the following states: unloading start, unloading and unloading completion according to the power take-off state of the power take-off device of the vehicle and the inclination angle state of the cargo box.

[0009] Preferably, the determination of whether the vehicle is in the combination of the slope value and the motion state according to whether the acceleration value at the beginning of the loading process or the unloading process conforms to the corresponding relationship between the acceleration value and the vehicle weight comprises: obtaining an acceleration value as an acceleration reference value according to the given vehicle weight and the corresponding relationship between the acceleration value and the vehicle weight; calculating the difference between the acceleration value at the beginning of the loading process or the unloading process and the acceleration reference value, and determining that at least one of the slope value and the motion state of the vehicle is not in the combination of the slope value and the motion state if the calculated difference is greater than a preset threshold value.

[0010] Preferably, the correction of the acceleration value before mapping comprises: obtaining the acceleration value during the loading process or the unloading process; correcting the obtained acceleration value by using the difference to obtain a corrected acceleration value; and the corrected acceleration value is used to obtain the vehicle weight through the corresponding relationship between the acceleration value and the vehicle weight.

[0011] Preferably, the mapping obtained vehicle weight is corrected, comprising: selecting the motion speed of the vehicle, the mapping obtained vehicle weight and the slope value as system state variables, and constructing state equation and observation equation respectively; based on the state equation and the observation equation, obtaining the corrected vehicle weight of the vehicle through an adaptive extended Kalman filtering algorithm.

[0012] Preferably, the slope value is obtained through the following steps: obtaining the vehicle weight and the acceleration value; calculating the acceleration value according to the corresponding relationship between the vehicle weight and the acceleration value; calculating the difference between the obtained acceleration value and the calculated acceleration value; and calculating the slope value of the vehicle according to the trigonometric function relationship between the difference and the gravitational acceleration.

[0013] Preferably, the trigger condition for the obtained vehicle weight after correction as the vehicle weight comprises: the vehicle is not in the motion state in the combination; the motion speed of the vehicle is higher than a preset speed threshold; the steering wheel angle of the vehicle is less than a preset angle threshold; and the gearbox of the vehicle is not in the shifting process.

[0014] Preferably, the method further comprises: data fusion of the obtained vehicle weights in different motion states, and taking the fused vehicle weight as the vehicle weight.

[0015] In the second aspect of the present application, a method for calculating the slope value of a vehicle is also provided, comprising: obtaining the corresponding relationship between the acceleration value and the vehicle weight of the vehicle in a combination of the slope value and the motion state; obtaining the vehicle weight and the acceleration value of the vehicle; calculating the acceleration value according to the corresponding relationship between the vehicle weight and the acceleration value; calculating the difference between the obtained acceleration value and the calculated acceleration value; and calculating the slope value of the vehicle according to the trigonometric function relationship between the difference and the gravitational acceleration.

[0016] Preferably, the method further comprises: data fusion of the obtained slope values in different motion states, and taking the fused slope value as the slope value of the vehicle.

[0017] In a third aspect of the present application, a vehicle weight calculation device is also provided, which comprises: a correspondence relationship storage module configured to obtain a correspondence relationship between an acceleration value and a vehicle weight of a vehicle under a combination of a slope value and a motion state; a vehicle state determination module configured to determine whether the vehicle is in the combination of the slope value and the motion state according to whether the acceleration value at the beginning of a loading process or an unloading process from a given vehicle weight conforms to the correspondence relationship between the acceleration value and the vehicle weight; and a vehicle weight calculation module configured to correct the acceleration value before mapping and / or correct the vehicle weight after mapping if it is determined that the vehicle is not in the combination of the slope value and / or the motion state in a process of calculating the vehicle weight of the vehicle according to the acceleration value in the loading process or the unloading process and the correspondence relationship between the acceleration value and the vehicle weight.

[0018] In a fourth aspect of the present application, a slope calculation device is also provided, which comprises: a correspondence relationship storage module configured to obtain a correspondence relationship between an acceleration value and a vehicle weight of a vehicle under a combination of a slope value and a motion state; a parameter acquisition module configured to obtain the vehicle weight and the acceleration value of the vehicle; an acceleration calculation module configured to calculate the acceleration value according to the vehicle weight and the correspondence relationship between the acceleration value and the vehicle weight; a difference calculation module configured to calculate a difference between the obtained acceleration value and the calculated acceleration value; and a slope calculation module configured to calculate the slope value of the vehicle according to a trigonometric function relationship between the difference and the gravitational acceleration.

[0019] In a fifth aspect of the present application, an electronic device is also provided, which comprises a memory, a processor, and a computer program stored in the memory and executable on the processor, and the processor implements the steps of the vehicle weight calculation method when executing the computer program.

[0020] In a sixth aspect of the present application, a computer readable storage medium is also provided, which stores instructions, and the instructions make the computer execute the steps of the vehicle weight calculation method when the computer readable storage medium is executed on the computer.

[0021] In a seventh aspect of the present application, a computer program product is also provided, which comprises a computer program, and the computer program implements the vehicle weight calculation method when executed on the processor.

[0022] The above technical solution has at least the following beneficial effects:

[0023] (1) Compared with the Recursive Least Square (RLS) least square method, Kalman Filter (KF) Kalman filter and Model Prediction Control (MPC) model prediction algorithm in the prior art, the vehicle weight and slope estimation method based on the existing acceleration sensor of the vehicle in the embodiment of the application has high precision, simple and fast operation, can save the cost of the vehicle, and can effectively estimate the vehicle weight in the static state of the vehicle.

[0024] (2) The loading and unloading state recognition method provided in the embodiment of the application can realize real-time estimation of the vehicle weight during the loading and unloading process based on the loading and unloading state recognition.

[0025] (3) The adaptive extended Kalman filter estimation method provided in the embodiment of the application uses the obtained vehicle weight and slope as the initial value of calculation, and selects different vehicle speeds and covariance initial values according to different initial conditions to realize the parameter adaptation of the algorithm, which can effectively solve the problem that the convergence speed is slow and the estimation accuracy is low in the initial calculation of the Kalman filter algorithm due to the difficulty in obtaining the initial value, and effectively expand the use scenario of the Kalman filter, so that it has good adaptability to the working conditions of the self-unloading vehicle with large weight change and complex and changeable road conditions.

[0026] (4) The vehicle weight and slope estimation method provided in the embodiment of the application effectively integrates the calculation results of the static weighing method and the adaptive Kalman filter estimation method through a data fusion method based on credibility, and the data fusion rule is automatically adjusted according to the change of the working condition of the vehicle, which can further improve the estimation result accuracy.

[0027] Other features and advantages of the embodiments of the application will be described in detail in the following specific embodiments. BRIEF DESCRIPTION OF DRAWINGS

[0028] The accompanying drawings are included to provide a further understanding of the embodiments of the application, and constitute a part of the specification, and are used together with the following specific embodiments to explain the embodiments of the application, but do not constitute a limitation on the embodiments of the application. In the drawings:

[0029] Figure 1 A schematic diagram of the steps of the vehicle weight calculation method according to the embodiment of the application is shown schematically;

[0030] Figure 2 A relationship diagram of vehicle weight slope estimation and loading and unloading state according to the embodiment of the application is shown schematically;

[0031] Figure 3 A vehicle weight estimation principle diagram according to the embodiment of the application is shown schematically;

[0032] Figure 4A vehicle ramp driving longitudinal force analysis diagram according to an embodiment of the present application is schematically shown;

[0033] Figure 5 A structure diagram of a vehicle weight estimation device according to an embodiment of the present application is schematically shown. DETAILED DESCRIPTION

[0034] The specific embodiments of the embodiments of the present application are described in detail below with reference to the accompanying drawings. It should be understood that the specific embodiments described herein are merely intended to illustrate and explain the embodiments of the present application, and are not intended to limit the embodiments of the present application.

[0035] Figure 1 A step diagram of a vehicle weight calculation method according to an embodiment of the present application is schematically shown. As shown in the figure, Figure 1 A vehicle weight calculation method, comprising:

[0036] S01, obtaining the corresponding relationship between the acceleration value and the vehicle weight of the vehicle under a combination of the slope value and the motion state; for example, for the convenience of calculation, the slope value of 0, i.e. the horizontal road surface, and the motion state of 0, i.e. the static state, can be selected as the combination. Under the condition of the combination, the vehicle is increased from the empty load to the full load in the unit weight increment manner, and the acceleration sensor values under different vehicle weights are recorded. The unit weight can be selected as 5 tons. In this way, the corresponding relationship between the vehicle weight and the acceleration sensor value under the horizontal road surface static working condition is obtained, and the corresponding relationship can be stored in the form of a query table. In the subsequent use, the corresponding vehicle weight or acceleration value can be quickly determined through the obtained acceleration value or vehicle weight.

[0037] S02, in the loading process or unloading process starting from a given vehicle weight, according to whether the acceleration value at the start of the loading process or unloading process meets the corresponding relationship between the acceleration value and the vehicle weight, determine whether the vehicle is in the combination of slope value and motion state. The vehicle weight changes during the loading process or unloading process, and the frame angle changes due to the change of the vehicle weight. The acceleration sensor is arranged at the corresponding position of the frame or axle, and can detect the change of the angle of the frame or axle. Therefore, when the vehicle is stationary, the acceleration sensor signal contains slope information and vehicle weight information; when the vehicle is running, the acceleration sensor signal contains slope information, vehicle weight information and real vehicle acceleration information. And the change trend of the vehicle weight during the loading process is from light to heavy, and the change trend during the unloading process is the opposite. The vehicle weight at the start of the loading process or unloading process is the given vehicle weight. If the acceleration value at this time meets the corresponding relationship between the acceleration value and the vehicle weight, it can be basically determined that the slope value and the motion state meet the set conditions of the corresponding relationship. In the subsequent process, the vehicle weight can be obtained directly through the acceleration value and the corresponding relationship. However, in most scenarios, the slope value and the motion state do not completely match the set conditions of the corresponding relationship, so corresponding adjustment needs to be made in the subsequent step to obtain the accurate vehicle weight.

[0038] S03, if it is determined that the vehicle is not in the combination of slope value and / or motion state, in the process of mapping the vehicle weight of the vehicle according to the acceleration value in the loading process or unloading process and the corresponding relationship between the acceleration value and the vehicle weight, the acceleration value before mapping is corrected and / or the mapped vehicle weight is corrected. In actual scenarios, the dump truck is usually not in the preset slope and / or motion state, for example, it is not on a horizontal road. At this time, the acceleration value obtained will be affected by the current slope and will cause errors in the estimation of the vehicle weight. Therefore, when the dump truck is not in the preset slope, i.e. the query table cannot be applied, the acceleration value needs to be corrected to meet the application conditions of the query table. The adjustment in this step includes the following three adjustment methods: 1. first correct the acceleration, and then map the vehicle weight according to the corrected acceleration and the corresponding relationship between the acceleration value and the vehicle weight; 2. first map the vehicle weight according to the acceleration and the corresponding relationship between the acceleration value and the vehicle weight, and then adjust the mapped vehicle weight; 3. first obtain the vehicle weight by the first adjustment method, and then adjust the mapped vehicle weight, i.e. correct or adjust twice.

[0039] Through the above embodiments, the existing acceleration sensor of the vehicle is used to realize high-precision vehicle weight and slope estimation, the operation is simple and fast, the whole vehicle cost can be saved, and the vehicle weight and slope can be effectively estimated in the stationary state of the vehicle.

[0040] In some embodiments, the combination of the slope value and the motion state includes: the slope value is 0 and the motion state is static. The reason for selecting the above condition as the combination is that it belongs to the most common working scenario of the vehicle, and the vehicle is mostly in the state of the slope value being 0 and the motion state being static. And it is convenient for the collection of the corresponding relationship and the subsequent calculation.

[0041] Figure 2 The vehicle weight and slope estimation and loading and unloading state relationship diagram according to the embodiment of the application is schematically shown; as shown in the figure, Figure 2 In the embodiment, the execution stages of the loading process include: no loading, loading start, loading in progress and loading completion; the execution stages of the unloading process include: no unloading, unloading start, unloading in progress and unloading completion, Figure 2 The vehicle weight and the change trend of the vehicle weight in different execution stages are also shown in the figure. Before the vehicle is loaded for the first time, the vehicle weight is the off-line factory value G1. After the loading starts, the vehicle weight is estimated in real time by using the aforementioned method until the loading is completed (assuming that the vehicle weight after the loading is completed is G2'). Since the preset motion state in the aforementioned method is preferably 0, the aforementioned vehicle weight estimation method is also called the static weighing method. The motion state changes during the transportation process, at which time the static weighing method cannot be applied, and therefore the adaptive extended Kalman filtering is used for vehicle weight estimation, and the vehicle weight is corrected from G2' to G2. The details of the adaptive extended Kalman filtering will be described later. After the unloading starts, the vehicle weight is estimated in real time by using the static weighing method until the unloading is completed (assuming that the vehicle weight after the unloading is completed is G3'); the vehicle weight is estimated during the return process by using the adaptive extended Kalman filtering, and the vehicle weight is corrected from G3' to G3' until the next loading. The slope α of the loading point and the unloading point remains unchanged, and is calculated by using the static weighing method from the acceleration value A and the estimated value G of the vehicle weight, and the real acceleration a of the vehicle. The adaptive extended Kalman filtering algorithm is used to calculate in real time during the vehicle driving process as the road surface changes. Since the "no loading" and "no unloading" states coincide with other states, for example, the "no unloading" state is the "loading completion" state of the previous loading process. Therefore, in the actual scenario, the two states can not be identified separately.

[0042] In some embodiments provided by the present application, the execution stage of a dump truck is determined by the following method. According to the type of action execution instruction obtained by the dump truck, it is determined whether the dump truck is in a loading process or an unloading process. For example, after the vehicle stops at a loading point, the V2X (vehicle-to-everything) loading side device (such as a excavator) is connected to the vehicle through V2X wireless communication, and sends a loading state signal to the dump truck, so that it is determined that the dump truck is in the loading process. When the dump truck reaches the preset unloading position, it can be determined that the dump truck can be unloaded according to the judgment condition, and the instruction of unloading start is generated, so that it is determined that the dump truck is in the unloading process. After the vehicle starts unloading, the unloading side device has not loaded yet, and the unloading start state is sent when the unloading side device is ready to pour the goods into the dump truck. The loading start state is sent during the loading process, and the loading completion is sent after the loading is completed. According to the power take-off state of the power take-off device of the dump truck and the inclination angle state of the cargo box, the execution stage in the loading process or the unloading process is determined, including: the vehicle identifies the unloading state according to the power take-off state fed back by the power take-off position sensor and the cargo box rotation angle fed back by the cargo box inclination angle sensor, and identifies the unloading state after the vehicle starts loading; the unloading start state is identified when the vehicle reaches the unloading point and the power take-off state is connected; the unloading state is identified from the start of lifting of the cargo box to the disconnection of the power take-off; and the unloading completion is identified after the power take-off is disconnected. In this embodiment, a loading and unloading state identification method is provided, and based on the loading and unloading state identification, real-time estimation of the vehicle weight during the loading and unloading process can be realized. Figure 3 The vehicle weight estimation principle diagram according to the embodiment of the present application is schematically shown. As shown in Figure 3 The change of the vehicle weight is obtained by mapping the change of the acceleration value in the loading process and the unloading process.

[0043] In some embodiments provided by the present application, whether the vehicle is in the combination of the slope value and the motion state is determined according to whether the acceleration value at the start of the loading process or the unloading process meets the corresponding relationship between the acceleration value and the vehicle weight, including: obtaining the acceleration value as an acceleration reference value according to the given vehicle weight and the corresponding relationship between the acceleration value and the vehicle weight; calculating the difference between the acceleration value at the start of the loading process or the unloading process and the acceleration reference value, and if the calculated difference is greater than a preset threshold, determining that at least one of the slope value and the motion state of the vehicle is not in the combination of the slope value and the motion state. Specifically, taking the loading process as an example, when the vehicle is loaded or unloaded completely for the first time, the current vehicle weight is obtained as a reference vehicle weight, and the acceleration reference value corresponding to the reference vehicle weight is taken as a reference value. When the vehicle enters the loading start state again, the change starts from the reference vehicle weight, i.e., the given vehicle weight. The first acceleration value is obtained by the acceleration sensor. If the absolute value of the first acceleration value minus the reference value is greater than a certain set value, it is judged that the vehicle is on a slope, and the slope equivalent acceleration value is (the first acceleration value minus the reference value). At this time, the current acceleration value needs to be corrected, which is corrected as: current acceleration value minus (the first acceleration value minus the reference value); that is, the acceleration value of the vehicle weight G2' on the horizontal road. Then, according to the corrected acceleration value, the corresponding relationship between the vehicle weight and the acceleration reference value is queried to obtain the vehicle weight G2' after the loading is completed. During the transportation process of the vehicle (from the completion of the loading to the start of the unloading), the adaptive extended Kalman filtering algorithm is used to correct the vehicle weight to G2. Similarly, taking the unloading process as an example, the corresponding acceleration value is obtained according to the vehicle weight when the unloading is not completed, which is recorded as the acceleration reference value. When the vehicle reaches the unloading point and enters the unloading start state, the acceleration value is obtained by the acceleration sensor, which is recorded as the second acceleration value. If the absolute value of the second acceleration value minus the reference value is greater than a certain set value, it is judged that the vehicle is on a slope. The subsequent processing process is similar to that of the loading process, which will not be described here.

[0044] In some embodiments provided by the present application, a method for calculating the slope value of the vehicle is also provided. Specifically, according to whether the vehicle is in the preset motion state, the slope value is calculated according to the first algorithm and the second algorithm respectively.

[0045] When the vehicle is stationary, the acceleration value obtained by the acceleration sensor signal contains the slope information and the vehicle weight information. According to the vehicle weight estimation value G, the corresponding relationship between the vehicle weight and the acceleration value is queried to obtain the equivalent acceleration value A of the vehicle weight G G , and the slope value α (percentage) is calculated according to the following formula:

[0046] where g is the acceleration of gravity.

[0047] When the vehicle is running, the acceleration sensor signal contains slope information, vehicle weight information and vehicle real acceleration information, and the slope value α calculation formula is: Wherein, wherein g is the acceleration of gravity, a is the vehicle real acceleration, and the vehicle real acceleration a can be obtained by derivation of actual vehicle speed v with respect to time t, that is, a = dv / dt.

[0048] In some embodiments provided by the application, the obtained vehicle weight needs to be corrected as the vehicle weight. The vehicle weight of the dump truck changes in a large range, and the road conditions of the working road are complex and changeable. The existing technology uses Kalman filter (KF) to estimate the vehicle weight and the road slope of the dump truck, and there are problems such as difficulty in determining the initial value, poor decoupling effect of the vehicle weight and the road slope, large ECU calculation load and the like. Key, Kalman filter cannot estimate the whole vehicle weight and the road slope in the vehicle stationary state, which seriously limits its application scenarios. In order to solve the above problems, the initial value of the vehicle speed v veh , the vehicle weight m veh , the road slope θ and the covariance P is effectively selected, and different initial values are selected according to different calculation initial conditions, which can effectively expand the use scenarios of Kalman filter, so that it has good self-adaptability to the working conditions of the dump truck weight change and the complex and changeable road conditions of the driving road, and the iteration formula is discretized according to the discretization calculation characteristics of the whole vehicle controller, so the vehicle weight also needs to be adjusted accordingly.

[0049] In some embodiments provided by the application, the adaptive extended Kalman filter algorithm is used to correct the obtained vehicle weight of the dump truck, including: selecting the motion speed of the dump truck, the obtained vehicle weight and the slope value as system state variables, and constructing state equation and observation equation respectively; based on the state equation and the observation equation, the adaptive extended Kalman filter algorithm is used to obtain the corrected vehicle weight of the dump truck. Figure 4 A vehicle slope driving longitudinal force analysis diagram according to an embodiment of the application is schematically shown. As shown in the figure, Figure 4 F j , F w , F f , F i , F t are acceleration resistance, wind resistance, rolling resistance, slope resistance and driving force respectively. The relationship between them is shown in formula (1):

[0050] F t = F j +F w +F f +F i (1)

[0051] In combination with the embodiments of the present application, the specific expression of formula (1) is determined by formula (2):

[0052]

[0053] wherein, v veh is the vehicle speed, i is the transmission ratio of the transmission system, η is the efficiency of the powertrain, F mot is the motor driving force or the motor braking force, F brk is the brake braking force, m veh is the mass of the vehicle, f is the rolling resistance coefficient, θ is the road slope, C D is the wind resistance coefficient, A is the windward area, ρ is the air density, v is the vehicle speed, and g is the gravitational acceleration.

[0054] The solving process of the adaptive extended Kalman filter is shown in formula (3)-(8):

[0055] First, formula (2) is discretized and set to be small in the AEKF iterative solving process, then the change difference of the vehicle mass and the slope is:

[0056]

[0057] wherein, the subscripts k and k-1 respectively represent the values of the variables at the current step and the previous calculation step, △t step represents the controller calculation step length.

[0058] Then, the vehicle speed v veh , the vehicle mass m veh , and the road slope θ are selected as the system state variables, and the system state equation and the observation equation are:

[0059]

[0060]

[0061] wherein, W k and V k are the system noise and the observation noise, respectively, V veh,ob and θ ,ob are the vehicle speed and the road slope measured by the sensor. Next, the adaptive extended Kalman filter is used for calculation. The Kalman filter gain K and the state variable estimation value are as follows:

[0062]

[0063]

[0064] wherein, the required Jacobian matrix can be obtained by taking the partial derivative of the system state equation of formula (3) with respect to each state variable.

[0065] Furthermore, to address the problems of difficulty in determining the initial value and slow convergence speed of Kalman filtering, the embodiments of this invention address the three state variables of the system (vehicle speed v). veh Vehicle weight (m) veh And road slope θ) and covariance P, the initial values ​​are selected from four aspects: vehicle speed v veh The initial value is determined based on a comprehensive assessment of the brake pedal opening, powertrain driving force, and powertrain braking force.

[0066]

[0067] For the vehicle weight m veh The aforementioned implementation method can provide a relatively accurate vehicle weight m for Kalman filtering. veh Estimating initial values ​​is crucial for ensuring the convergence speed and accuracy of the Kalman filter. It also facilitates the decoupling of vehicle weight and gradient. Similarly, the initial values ​​for calculating the road gradient θ can be clearly defined by combining the aforementioned implementation methods of this invention. For the covariance P, a three-dimensional lookup table module is pre-compiled based on the possible operating conditions of the actual vehicle, and then the initial values ​​are calculated based on the vehicle speed v. veh Vehicle weight (m) veh By looking up the initial value of the road slope θ in a table, we can obtain the initial value of the covariance, which has good calculation results.

[0068] In some embodiments provided by this invention, entry or activation conditions are set for the aforementioned adaptive extended Kalman filter algorithm. These include: the vehicle is not in the motion state described in the combination; the vehicle's speed is higher than a preset speed threshold; the vehicle's steering wheel angle is less than a preset angle threshold; and the vehicle's transmission is not in the process of shifting gears. Specifically, the adaptive Kalman filter algorithm is based on the vehicle dynamics equations. When the vehicle is at low speed, turning, or shifting gears, the algorithm's estimation accuracy is low. Therefore, this embodiment sets an entry condition Flag_Enable, whose triggering conditions include: 1. The vehicle speed is higher than a certain calibration value (V1); 2. The vehicle's steering wheel angle is less than a certain value (not turning); 3. The transmission is not in the process of shifting gears. When all the above conditions are met simultaneously, Flag_Enable = 1, i.e., estimation begins. Otherwise, Flag_Enable = 0, i.e., estimation exits.

[0069] In some embodiments provided by the present application, the method further comprises: performing data fusion on the vehicle weight obtained under different motion states to obtain the fused vehicle weight of the dump truck; or performing data fusion on the slope values obtained under different motion states to obtain the fused slope value. The calculation methods of the vehicle weight and the slope value under different working conditions are different. In order to further improve the estimation accuracy, the calculation results of the two methods under different working conditions are fused in the embodiment. The calculation method of the fusion result can be selected from the existing data processing method. Here, the weighted fusion is taken as an example: different weight coefficients are set according to the reliability of different calculation results, and the estimation results are fused:

[0070]

[0071] wherein Rmix is the fusion result (final estimation value), which can be the vehicle weight or the slope value, Rsensor is the estimation result of the static weighing method, R AEKF is the estimation result of the adaptive extended Kalman filter, V is the vehicle speed, and V1 and V2 are the calibrated vehicle speeds.

[0072] In some embodiments provided by the present application, the vehicle weight calculation method comprises the following steps:

[0073] Step 1: calibrate the acceleration sensor so that the reading of the acceleration sensor is 0 when the vehicle is on a horizontal road and is stationary (the reading of the acceleration sensor only contains the vehicle weight information);

[0074] Step 2: when the vehicle is on a horizontal road and is stationary, increase the vehicle weight from empty to full load in 5-ton units, record the acceleration sensor values under different vehicle weights, obtain the corresponding relationship, and record it as a correspondence query table T1 of vehicle weight and acceleration sensor value under the horizontal road and stationary working condition.

[0075] Step 3: set the return vehicle weight as G3, if the unloading is complete or the vehicle is loaded for the first time, G3=G1, query the correspondence query table T1 of vehicle weight and acceleration sensor value under the horizontal road and stationary working condition according to the vehicle weight G3 to obtain the sixth acceleration value A6 corresponding to G3; when the vehicle enters the loading start state again, obtain the first acceleration value A1 through the acceleration sensor; after the loading is completed, obtain the second acceleration value A2 through the acceleration sensor;

[0076] Step 4: if the absolute value of A1-A6 is less than or equal to a certain set value, it is judged that the vehicle is on a horizontal road, then the vehicle weight G2' after loading is completed is obtained by querying the correspondence query table T1 of vehicle weight and acceleration sensor value under the horizontal road and stationary working condition according to the second acceleration value A2;

[0077] Step 5: If the absolute value of A1-A6 is greater than a certain set value, it is determined that the vehicle is on a slope, and the slope equivalent acceleration value is A1-A6, at this time the third acceleration value A3=A2-(A1-A6) is the acceleration value of the vehicle weight G2' on the horizontal road, and then the vehicle weight G2' after loading is obtained by querying the "correspondence table T1 of vehicle weight and acceleration sensor value under the static condition of the horizontal road" according to the third slope value A3;

[0078] Step 6: During the transportation process (after loading is completed to before unloading starts), the vehicle weight is corrected to G2 by using the adaptive extended Kalman filtering algorithm;

[0079] Step 7: During the loading process, the real-time acceleration sensor value A loading is read, and the real-time vehicle weight G loading during the loading process is obtained by querying the "correspondence table T1 of vehicle weight and acceleration sensor value under the static condition of the horizontal road" according to A loading -(A1-A6);

[0080] Step 8: When the vehicle arrives at the unloading point and enters the unloading start, the fourth acceleration value A4 is obtained by the acceleration sensor, and after the unloading is completed, the fifth acceleration value A5 is obtained by the acceleration sensor;

[0081] Step 9: If the absolute value of A4-A3 is less than or equal to a certain set value, it is determined that the vehicle is on a horizontal road, and then the vehicle weight G3' after unloading is obtained by querying the "correspondence table T1 of vehicle weight and acceleration sensor value under the static condition of the horizontal road" according to the fourth acceleration value A4;

[0082] Step 10: If the absolute value of A4-A3 is greater than a certain set value, it is determined that the vehicle is on a slope, and the slope equivalent acceleration value is A4-A3, at this time the sixth acceleration value A6=A5-(A4-A3) is the acceleration value of the vehicle weight G3' on the horizontal road, and then the vehicle weight G3' after loading is obtained by querying the "correspondence table T1 of vehicle weight and acceleration sensor value under the static condition of the horizontal road" according to the sixth slope value A6;

[0083] Step 11: During the return process (after unloading is completed to before loading starts), the vehicle weight is corrected to G3 by using the adaptive extended Kalman filtering algorithm;

[0084] Step 12: During the unloading process, the real-time acceleration sensor value A unloading is read, and the real-time vehicle weight G unloading during the unloading process is obtained by querying the "correspondence table T1 of vehicle weight and acceleration sensor value under the static condition of the horizontal road" according to A unloading -(A4-A3);

[0085] Step 13: when the vehicle is powered off, A1, A2, A4, A5, A loading , A unloading , the estimated vehicle weight G and the current loading and unloading state are stored in the non-volatile storage module of the controller, and the corresponding stored values are read from the non-volatile storage module of the controller when powered on again, so as to ensure that the vehicle estimation function is normally implemented. In this step, the relevant quantities required for estimation are stored when the vehicle is powered off, and are read when powered on next time, so as to ensure that the vehicle weight and slope can be obtained as soon as the vehicle is powered on, which improves the real-time performance of estimation compared with the existing algorithm which needs to accumulate certain data to perceive the vehicle weight and slope.

[0086] Based on the same inventive concept, the application also provides a vehicle weight estimation device. Figure 5 The structure of the vehicle weight estimation device according to the embodiment of the application is schematically shown. As shown in the figure, a vehicle weight estimation device comprises a corresponding relationship storage module, a vehicle state determination module and a vehicle weight calculation module. Figure 5 The corresponding relationship storage module is configured to obtain the corresponding relationship between the acceleration value and the vehicle weight of the vehicle under a combination of the slope value and the motion state. The vehicle state determination module is configured to determine whether the vehicle is in the combination of the slope value and the motion state according to whether the acceleration value at the start of the loading process or the unloading process conforms to the corresponding relationship between the acceleration value and the vehicle weight. The vehicle weight calculation module is configured to select to correct the acceleration value before mapping and / or correct the vehicle weight obtained by mapping if it is determined that the vehicle is not in the combination of the slope value and / or the motion state in the process of obtaining the vehicle weight of the vehicle according to the acceleration value in the loading process or the unloading process and the corresponding relationship between the acceleration value and the vehicle weight.

[0087] In some optional embodiments of the application, the device further comprises: if it is determined that the vehicle is in the combination of the slope value and the motion state, obtaining the vehicle weight of the vehicle according to the acceleration value in the loading process or the unloading process through the corresponding relationship between the acceleration value and the vehicle weight.

[0088] In some optional embodiments of the application, the vehicle is a self-unloading vehicle, and the device further comprises: determining that the vehicle is in the loading process or the unloading process according to the type of the action execution instruction obtained by the vehicle; determining that the loading process is in one of the following states: loading start, loading and loading completion according to the loading signal obtained from the loading device cooperating with the vehicle; and determining that the unloading process is in one of the following states: unloading start, unloading and unloading completion according to the power take-off state of the power take-off device of the vehicle and the inclination angle state of the cargo box.

[0089] In some optional embodiments of the present application, determining whether the vehicle is in the combination of the slope value and the motion state according to whether the acceleration value at the start of the loading process or the unloading process meets the corresponding relationship between the acceleration value and the vehicle weight comprises: obtaining an acceleration value as an acceleration reference value according to the given vehicle weight and the corresponding relationship between the acceleration value and the vehicle weight; calculating a difference between the acceleration value at the start of the loading process or the unloading process and the acceleration reference value, and determining that at least one of the slope value and the motion state of the vehicle is not in the combination of the slope value and the motion state if the calculated difference is greater than a preset threshold.

[0090] In some optional embodiments of the present application, the acceleration value before mapping is corrected, comprising: obtaining the acceleration value in the loading process or the unloading process; correcting the obtained acceleration value by using the difference to obtain a corrected acceleration value; and the corrected acceleration value is used to obtain the vehicle weight of the vehicle by using the corresponding relationship between the acceleration value and the vehicle weight.

[0091] In some optional embodiments of the present application, the mapped vehicle weight is corrected, comprising: selecting the motion speed of the vehicle, the mapped vehicle weight and the slope value as system state variables, and constructing a state equation and an observation equation respectively; and obtaining the corrected vehicle weight of the vehicle by using an adaptive extended Kalman filtering algorithm based on the state equation and the observation equation.

[0092] In some optional embodiments of the present application, the slope value is obtained by the following steps: obtaining the vehicle weight and the acceleration value of the vehicle; obtaining the acceleration value according to the corresponding relationship between the vehicle weight and the acceleration value; calculating the difference between the obtained acceleration value and the calculated acceleration value; and obtaining the slope value of the vehicle according to the trigonometric function relationship between the difference and the gravitational acceleration.

[0093] In some optional embodiments of the present application, the trigger condition for correcting the obtained vehicle weight as the vehicle weight of the vehicle comprises: the vehicle is not in the combination of the motion state; the motion speed of the vehicle is higher than a preset speed threshold; the steering wheel angle of the vehicle is less than a preset angle threshold; and the gearbox of the vehicle is not in the shifting process.

[0094] In some optional embodiments of the present application, the device further comprises: fusing the obtained vehicle weights in different motion states to obtain a fused vehicle weight as the vehicle weight of the vehicle.

[0095] The specific definitions of each functional module in the vehicle weight estimation device described above can refer to the definitions of the vehicle weight calculation method described above, which will not be repeated here. Each module in the device described above can be implemented in whole or in part by software, hardware, and combinations thereof. Each module described above can be embedded in or independent of the processor in the computer device in hardware form, or can be stored in the memory in the computer device in software form, so that the processor can call and execute the corresponding operations of each module described above.

[0096] In some embodiments, a vehicle slope calculation device is provided, a correspondence relationship storage module is configured to store a correspondence relationship between an acceleration value and a vehicle weight of the vehicle under a combination of a slope value and a motion state; a parameter acquisition module is configured to acquire the vehicle weight and the acceleration value of the vehicle; an acceleration calculation module is configured to calculate the acceleration value according to the vehicle weight and the correspondence relationship between the acceleration value and the vehicle weight; a difference calculation module is configured to calculate a difference between the acquired acceleration value and the calculated acceleration value; and a slope calculation module is configured to calculate the slope value of the vehicle according to a trigonometric function relationship between the difference and the gravitational acceleration.

[0097] In some optional embodiments, the device further comprises: data fusion of the slope values obtained under different motion states, and the slope value after the fusion is taken as the slope value of the vehicle.

[0098] Similarly, the specific definitions of each functional module in the vehicle slope calculation device described above can refer to the definitions of the vehicle slope calculation method described above, which will not be repeated here. Each module in the device described above can be implemented in whole or in part by software, hardware, and combinations thereof. Each module described above can be embedded in or independent of the processor in the computer device in hardware form, or can be stored in the memory in the computer device in software form, so that the processor can call and execute the corresponding operations of each module described above.

[0099] In some embodiments provided by the present application, an electronic device is also provided, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor, and the processor implements the steps of the vehicle weight calculation method and / or the slope calculation method of the vehicle when executing the computer program. The processor has the functions of numerical calculation and logical operation, and has at least a central processing unit CPU with data processing capability, a random access memory RAM, a read-only memory ROM, various I / O ports, an interrupt system, and the like. The processor includes a core, and the core retrieves corresponding program units from the memory. The core can be one or more, and the aforementioned method is realized by adjusting core parameters. The memory can include a non-permanent memory in a computer readable medium, a random access memory (RAM), and / or a non-volatile memory such as a read-only memory (ROM) or a flash memory (flash RAM), and the memory includes at least one memory chip.

[0100] In an embodiment of the present application, a computer readable storage medium is also provided, and the storage medium stores instructions, which, when executed on a computer, cause the processor to be configured to perform the steps of the vehicle weight calculation method and / or the slope calculation method of the vehicle when the instructions are executed by the processor.

[0101] In an embodiment provided by the present application, a computer program product is provided, which includes a computer program, and the computer program implements the steps of the vehicle weight calculation method and / or the slope calculation method of the vehicle when executed by the processor.

[0102] Those skilled in the art should understand that the embodiments of the present application can be provided as a method, a system, or a computer program product. Therefore, the present application can take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present application can take the form of a computer program product implemented on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROMs, optical storage devices, etc.) containing computer-usable program code.

[0103] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each flow and / or block in the flowcharts and / or block diagrams, and the combination of the flows and / or blocks in the flowcharts and / or block diagrams can be implemented by computer program instructions. These computer program instructions can be provided to a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to produce a machine, so that the instructions executed by the computer or other programmable data processing devices generate a means for implementing the functions specified in the flowcharts and / or block diagrams. Figure 1one or more processes and / or blocks Figure 1 an apparatus with a functionality for performing the steps of the one or more processes and / or blocks

[0104] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a Figure 1 one or more processes and / or blocks Figure 1 an apparatus with a functionality for performing the steps of the one or more processes and / or blocks

[0105] These computer program instructions can also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the Figure 1 one or more processes and / or blocks Figure 1 an apparatus with a functionality for performing the steps of the one or more processes and / or blocks

[0106] In one typical configuration, the computing device includes one or more processors (CPUs), input / output interfaces, network interfaces, and memory.

[0107] The memory can include non-persistent memory and / or volatile memory, such as random access memory (RAM) about which the processor can execute instructions. The memory can also include non-volatile memory, such as read only memory (ROM), electrically programmable read only memory (EPROM), electrically erasable programmable read only memory (EEPROM), programmable read only memory (PROM), erasable programmable read only memory (EPROM), flash memory, or a combination of non-volatile memories in different types. The memory can also include a storage device, such as a hard disk drive or a solid state drive. The memory can include a combination of memory devices in different types. The memory is an example of computer readable storage media.

[0108] Computer readable media includes permanent and non-permanent, removable and non-removable media implemented in any method or technology for storage of information such as computer readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read only memory (ROM), electrically programmable read only memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), digital versatile disc (DVD), or other optical storage, magnetic cassette, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other non-transmission medium that can be used to store information accessible to computing devices. According to the definition herein, computer readable media does not include transitory media, such as modulated data signals and carrier waves.

[0109] It should also be noted that the terms "comprising", "comprises" or other variations thereof are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but can also include other elements not expressly listed or inherent to such process, method, article, or apparatus.

[0110] The above embodiments are only used to illustrate the present application, but not to limit it. Instead of the above, various modifications and changes can be made to the application by those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the application shall fall into the scope of the claims of the application.

Claims

1. A method for calculating the weight of a vehicle, characterized in that, The method includes: Obtain the relationship between the vehicle's acceleration value and vehicle weight under a combination of slope value and motion state; During the loading or unloading process starting from a given vehicle weight, the vehicle is determined to be in the slope value and motion state of the combination based on whether the acceleration value at the beginning of the loading or unloading process conforms to the correspondence between the acceleration value and the vehicle weight. If it is determined that the vehicle is not in the slope value and / or motion state of the combination, in the process of mapping the vehicle weight according to the acceleration value and the correspondence between the acceleration value and the vehicle weight during the loading or unloading process, the acceleration value before mapping and / or the vehicle weight obtained by mapping are corrected. The mapped vehicle weight is corrected, including: The vehicle's speed, the mapped vehicle weight, and the slope are selected as system state variables, and state equations and observation equations are constructed respectively. Based on the state equation and observation equation, the corrected vehicle weight is obtained using the adaptive extended Kalman filter algorithm.

2. The method according to claim 1, characterized in that, The method further includes: If the slope value and motion state of the vehicle in the combination are determined, the vehicle weight is obtained by the correspondence between the acceleration value and the vehicle weight based on the acceleration value during the loading or unloading process.

3. The method according to claim 1 or 2, characterized in that, One combination of the slope value and motion state includes: a slope value of 0 and a motion state of being stationary.

4. The method according to claim 1, characterized in that, The vehicle is a dump truck, and the method further includes: The type of action execution command obtained by the vehicle determines whether the vehicle is in a loading or unloading process; The loading process is determined to be in one of the following states based on the loading signal obtained from the loading equipment that cooperates with the vehicle: loading started, loading in progress, and loading completed; Based on the power take-off status of the vehicle's power take-off unit and the tilt angle of the cargo box, the unloading process is determined to be in one of the following states: unloading begins, unloading in progress, and unloading complete.

5. The method according to claim 1, characterized in that, Based on whether the acceleration value at the start of the loading or unloading process conforms to the correspondence between the acceleration value and the vehicle weight, determine whether the vehicle is in the slope value and motion state of the combination, including: Based on the given vehicle weight and the correspondence between the acceleration value and the vehicle weight, the acceleration value is obtained as the acceleration reference value; Calculate the difference between the acceleration value at the start of the loading or unloading process and the acceleration reference value. If the calculated difference is greater than a preset threshold, it is determined that at least one of the vehicle's slope value and motion state is not in the combination of slope value and motion state.

6. The method according to claim 5, characterized in that, Correcting the acceleration values ​​before mapping includes: Obtain the acceleration value during the loading or unloading process; The obtained acceleration value is corrected using the difference to obtain a corrected acceleration value; the corrected acceleration value is used to obtain the vehicle weight through the correspondence between the acceleration value and the vehicle weight.

7. The method according to claim 1, characterized in that, The slope value is obtained through the following steps: Obtain the vehicle's weight and acceleration values; The acceleration value is calculated based on the vehicle weight and the corresponding relationship between the acceleration value and the vehicle weight; Calculate the difference between the obtained acceleration value and the calculated acceleration value; The slope value of the vehicle is calculated based on the trigonometric function relationship between the difference and gravitational acceleration.

8. The method according to claim 1, characterized in that, The triggering conditions for using the corrected vehicle weight as the vehicle weight include: The vehicle is not in the motion state of the combination; The vehicle's speed is higher than a preset speed threshold; The steering wheel angle of the vehicle is less than a preset angle threshold; and The vehicle's transmission was not in the process of shifting gears.

9. The method according to claim 1, characterized in that, The method further includes: The vehicle weights obtained under different motion states are fused, and the fused vehicle weight is taken as the vehicle weight.

10. A method for calculating the slope of a vehicle, characterized in that, Used in conjunction with the vehicle weight calculation method according to any one of claims 1 to 9, the method includes: Obtain the relationship between the vehicle's acceleration value and vehicle weight under a combination of slope value and motion state; Obtain the vehicle's weight and acceleration values; The acceleration value is calculated based on the vehicle weight and the corresponding relationship between the acceleration value and the vehicle weight; Calculate the difference between the obtained acceleration value and the calculated acceleration value; The slope value of the vehicle is calculated based on the trigonometric function relationship between the difference and gravitational acceleration.

11. The method according to claim 10, characterized in that, The method further includes: The slope values ​​obtained under different motion states are fused, and the fused slope value is used as the slope value of the vehicle.

12. A vehicle weight calculation device, characterized in that, The device includes: The correspondence storage module is used to obtain the correspondence between the vehicle's acceleration value and vehicle weight under a combination of slope value and motion state; The vehicle state determination module is used to determine whether the vehicle is in the slope value and motion state of the combination during the loading or unloading process starting from a given vehicle weight, based on whether the acceleration value at the start of the loading or unloading process conforms to the correspondence between the acceleration value and the vehicle weight; and The vehicle weight calculation module is used to, if it is determined that the vehicle is not in the slope value and / or motion state of the combination, in the process of mapping the vehicle weight according to the acceleration value during the loading or unloading process and the correspondence between the acceleration value and the vehicle weight, select to correct the acceleration value before mapping and / or correct the vehicle weight obtained by mapping. The mapped vehicle weight is corrected, including: The vehicle's speed, the mapped vehicle weight, and the slope are selected as system state variables, and state equations and observation equations are constructed respectively. Based on the state equation and observation equation, the corrected vehicle weight is obtained using the adaptive extended Kalman filter algorithm.

13. A device for calculating the slope of a vehicle, characterized in that, Used in conjunction with the vehicle weight calculation device of claim 12, the device comprises: The correspondence storage module obtains the correspondence between the vehicle's acceleration value and vehicle weight under a combination of slope value and motion state; The parameter acquisition module is used to acquire the vehicle's weight and acceleration values; An acceleration calculation module is used to calculate the acceleration value based on the vehicle weight and the correspondence between the acceleration value and the vehicle weight. The difference calculation module is used to calculate the difference between the acquired acceleration value and the calculated acceleration value; and The slope calculation module is used to calculate the slope value of the vehicle based on the trigonometric function relationship between the difference and gravitational acceleration.

14. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the vehicle weight calculation method according to any one of claims 1 to 9 and / or the steps of the vehicle slope calculation method according to claim 10 or 11.

15. A computer-readable storage medium storing instructions that, when executed on a computer, cause the computer to perform the steps of the method for calculating the vehicle weight of any one of claims 1 to 9 and / or the steps of the method for calculating the slope of the vehicle as described in claim 10 or 11.

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