Method, device and equipment for determining vehicle load and storage medium

Abstract

The embodiment of the invention discloses a method, device and equipment for determining the load of a vehicle and a storage medium. The method comprises the steps that a data simulation model is constructed; wherein the data simulation model is used for calculating the equivalent rotational inertia of the vehicle; the equivalent rotational inertia is the rotational inertia after the transmission system of the vehicle is equivalent to the equivalent system; the vehicle is controlled to run in a no-load mode under M conditions, and corresponding M sets of parameter values are obtained; each condition is an operation mode and an operation gear in one-to-one correspondence; inputting the M groups of parameter values into a data simulation model to obtain M groups of equivalent rotational inertia; determining a target equivalent moment of inertia under the target condition according to the M groups of equivalent moment of inertia; the target condition is a corresponding target operation mode and a target operation gear; and calculating the target load of the vehicle according to the target equivalent moment of inertia and the wheel moment of inertia. The accuracy of estimating the load of a hybrid vehicle is improved.

Description

Technical Field

[0001] The present application relates to the field of vehicle control technology, and in particular to a method, device, equipment and storage medium for determining vehicle load. Background Art

[0002] At present, the estimation of hybrid vehicle load is mostly based on Newton's second law of mechanics, and the calculation results are processed by fitting methods such as least squares method and Kalman filter to estimate the load. However, the current load estimation needs to consider the influence of vehicle driving resistance such as air resistance, slope resistance, rolling resistance, acceleration resistance, etc. During the driving of hybrid vehicles, the rotational inertia required for the calculation of acceleration resistance is difficult to accurately calculate or collect, resulting in errors in the calculation of vehicle body force, which in turn leads to the load estimation result is still not accurate after processing. Summary of the invention

[0003] Embodiments of the present application provide a method, device, apparatus and storage medium for determining vehicle load, so as to improve the accuracy of estimating the load of a hybrid vehicle.

[0004] In a first aspect, an embodiment of the present application provides a method for determining a vehicle load, comprising:

[0005] Constructing a data simulation model; wherein the data simulation model is used to calculate the equivalent moment of inertia of the vehicle; the equivalent moment of inertia is the moment of inertia after the transmission system of the vehicle is equivalent to the equivalent system;

[0006] Control the vehicle to run without load in M ​​situations, and obtain corresponding M groups of parameter values; each situation has a one-to-one corresponding operation mode and operation gear; M is an integer greater than 1;

[0007] Input M groups of parameter values ​​into the data simulation model to obtain M groups of equivalent moments of inertia;

[0008] Determine the target equivalent moment of inertia under the target condition according to the M group equivalent moment of inertia; wherein the target condition is the corresponding target operating mode and target operating gear;

[0009] The target load of the vehicle is calculated based on the target equivalent moment of inertia and the wheel moment of inertia.

[0010] In a second aspect, an embodiment of the present application provides a device for determining a vehicle load, comprising:

[0011] The processing unit is used to: construct a data simulation model; wherein the data simulation model is used to calculate the equivalent moment of inertia of the vehicle; the equivalent moment of inertia is the moment of inertia after the transmission system of the vehicle is equivalent to the equivalent system;

[0012] A control unit, for controlling the vehicle to run without load in M ​​situations, and obtaining corresponding M groups of parameter values; wherein each situation has a one-to-one corresponding operation mode and operation gear; and M is an integer greater than 1;

[0013] The processing unit is further used to: input the M groups of parameter values ​​into the data simulation model to obtain the M groups of equivalent moments of inertia;

[0014] The processing unit is further used to: determine a target equivalent moment of inertia under a target condition according to the M group of equivalent moments of inertia; wherein the target condition is a corresponding target operating mode and a target operating gear;

[0015] The processing unit is further used to calculate the target load of the vehicle according to the target equivalent moment of inertia and the wheel moment of inertia.

[0016] In a third aspect, an embodiment of the present application provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of any of the above methods when executing the computer program.

[0017] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium having computer program instructions stored thereon, which implement the steps of any of the above methods when executed by a processor.

[0018] In the embodiment of the present application, considering that the transmission system of the vehicle is relatively complex, the transmission system of the vehicle is equivalent to an equivalent system, and then a data simulation model is constructed to calculate the equivalent moment of inertia of the vehicle. The operating mode and the operating gear affect the transmission relationship. Therefore, the one-to-one corresponding operating mode and operating gear are called a situation. The vehicle is controlled to run at no load in M ​​situations, and the corresponding M groups of parameter values ​​are obtained. The M groups of parameter values ​​are input into the data simulation model to obtain M groups of equivalent moments of inertia. The actual situation of the vehicle when it is running is called the target situation. Therefore, the target equivalent moment of inertia in the target situation can be determined based on the M groups of equivalent moments of inertia; the target load of the vehicle is calculated based on the target equivalent moment of inertia and the wheel moment of inertia. Such a design improves the accuracy of estimating the load of the hybrid vehicle. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required for use in the embodiments of the present application will be briefly introduced below. Obviously, the drawings introduced below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0020] Figure 1 A flow chart of a method for determining vehicle load provided in one embodiment of the present application;

[0021] Figure 2 A flowchart of a complete method for determining vehicle load provided in one embodiment of the present application;

[0022] Figure 3 A schematic diagram of the structure of a device for determining vehicle load provided in one embodiment of the present application;

[0023] Figure 4 A schematic diagram of the structure of an electronic device provided in one embodiment of the present application. DETAILED DESCRIPTION

[0024] In order to make the purpose, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application.

[0025] For ease of understanding, the terms involved in the embodiments of the present application are explained below:

[0026] (1) Equivalent moment of inertia: Under the condition that kinetic energy and potential energy are equal, the complex transmission system is simplified into an equivalent system, and the moment of inertia is calculated by treating it as a simple rotating body centered on a certain rotating shaft. For the transmission system, when its energy mode and gear position are fixed, the transmission relationship is fixed, and its moment of inertia can be simplified into an equivalent system for calculation using the concept of equivalent moment of inertia. In the embodiment of the present application, the concept of equivalent moment of inertia is adopted, and the transmission system is regarded as an equivalent system. The moment of inertia of each component is converted to the transmission shaft, and after it is converted to the wheel end through the torque and transmission relationship, it is solved by the principle of mechanics.

[0027] Any number of elements in the drawings is for illustration and not limitation, and any naming is for distinction only and does not have any limiting meaning.

[0028] At present, the estimation of the load of hybrid vehicles is mostly based on Newton's second law of mechanics, and the least square method, Kalman filter and other fitting methods are used to process the calculation results to estimate the load. The current load estimation needs to consider the influence of vehicle driving resistance such as air resistance, slope resistance, rolling resistance, acceleration resistance, etc. During the driving of hybrid vehicles, the acceleration resistance needs to be calculated by the moment of inertia of the wheel end. In addition to the wheel itself, the moment of inertia converted to the wheel end by the transmission system needs to be considered. The moment of inertia required for the calculation of the acceleration resistance during the driving of hybrid vehicles is difficult to accurately calculate or collect, resulting in the inability to accurately calculate the acceleration resistance, causing errors in the calculation of the body force, and then causing the load estimation results to be inaccurate after processing.

[0029] To this end, the present application provides a method for determining vehicle load, which estimates the vehicle load based on mechanics and rotational inertia, and calculates it through equivalent rotational inertia conversion and mechanical principle fitting, thereby solving the problem of inaccurate vehicle load estimation caused by inaccurate acceleration resistance.

[0030] To further illustrate the technical solution provided by the embodiment of the present application, this is described in detail below in conjunction with the accompanying drawings and specific implementation methods. Although the embodiment of the present application provides the method operation steps shown in the following embodiments or drawings, more or fewer operation steps may be included in the method based on routine or no creative labor. In the steps where there is no necessary causal relationship logically, the execution order of these steps is not limited to the execution order provided in the embodiment of the present application.

[0031] The technical solution provided by the embodiment of the present application is described. Figure 1 The present invention provides a method for determining vehicle load, comprising the following steps:

[0032] S101: Build a data simulation model.

[0033] S102: Control the vehicle to run without load in M ​​situations, and obtain corresponding M groups of parameter values.

[0034] S103: Inputting M groups of parameter values ​​into a data simulation model to obtain M groups of equivalent moments of inertia.

[0035] S104: Determine a target equivalent moment of inertia under a target condition according to the M group of equivalent moments of inertia.

[0036] S105: Calculate the target load of the vehicle according to the target equivalent moment of inertia and the wheel moment of inertia.

[0037] In the embodiment of the present application, considering that the transmission system of the vehicle is relatively complex, the transmission system of the vehicle is equivalent to an equivalent system, and then a data simulation model is constructed to calculate the equivalent moment of inertia of the vehicle. The operating mode and the operating gear affect the transmission relationship. Therefore, the one-to-one corresponding operating mode and operating gear are called a situation. The vehicle is controlled to run at no load in M ​​situations, and the corresponding M groups of parameter values ​​are obtained. The M groups of parameter values ​​are input into the data simulation model to obtain M groups of equivalent moments of inertia. The actual situation of the vehicle when it is running is called the target situation. Therefore, the target equivalent moment of inertia in the target situation can be determined based on the M groups of equivalent moments of inertia; the target load of the vehicle is calculated based on the target equivalent moment of inertia and the wheel moment of inertia. Such a design improves the accuracy of estimating the load of the hybrid vehicle.

[0038] In S101, the transmission system of the vehicle can be equivalent to an equivalent system, in which the moment of inertia is called equivalent moment of inertia. In the embodiment of the present application, a data simulation model is constructed, and the data simulation model can be used to calculate the equivalent moment of inertia of the vehicle.

[0039] The principles of building a data simulation model are as follows:

[0040] The moment of inertia of the whole vehicle is also called the moment of inertia of the wheel end, which includes the moment of inertia of the wheel and the moment of inertia of the transmission system. Among them, the wheel has a regular shape and uniform mass distribution, and the moment of inertia of the wheel can be directly calculated by the mass and radius of the wheel. However, the shapes and mass distributions of the components in the transmission system are different, and it is difficult to directly calculate the moment of inertia through the formula. Therefore, an equivalent moment of inertia is first assumed, that is, the total moment of inertia of the components in the transmission system converted to the transmission shaft, and it is assumed that the moment of inertia is converted to the moment of inertia of the wheel end through the torque (torque = moment of inertia * angular acceleration) and the transmission relationship, and then the value is obtained by reverse deduction through the mechanical formula.

[0041] The data simulation model is constructed in combination with the above principle of constructing a data simulation model. Optionally, the process of constructing a data simulation model can be implemented through steps A1-A4:

[0042] A1: Obtain the first relationship between acceleration resistance and wheel end driving force, air resistance, ramp resistance and rolling resistance.

[0043] The first relationship is: acceleration resistance = wheel end driving force - air resistance - ramp resistance - rolling resistance. Optionally, the formula of the first relationship is as follows:

[0044] F Acc =(T Eng +T MT )*i0*i g *η T / r-(1 / 2)*CρS*v 2 -mgsinα-mgfcosα

[0045] Among them, F Acc is the acceleration resistance, T Eng is the engine torque, T MT is the motor torque, m is the curb weight, i g is the gear ratio, η T is the transmission system efficiency, i0 is the main reducer speed ratio, r is the tire radius, C is the air resistance coefficient, ρ is the air density, S is the frontal area, v is the vehicle speed, g is the acceleration of gravity, α is the road slope angle, and f is the rolling resistance coefficient.

[0046] A2: Determine the second relationship between wheel end moment of inertia and acceleration resistance, curb mass, acceleration and tire radius.

[0047] Among them, the formula of the second relationship is expressed as follows:

[0048] J1=(F Acc / am)*r 2

[0049] Among them, J1 is the wheel end moment of inertia and a is the acceleration.

[0050] A3: Determine the third relationship between wheel moment of inertia, tire mass, and tire radius.

[0051] Among them, the formula of the third relationship is expressed as follows:

[0052] J2=∑m0r 2

[0053] Among them, J2 is the wheel moment of inertia, and m0 is the mass of one wheel.

[0054] A4: According to the first relationship, the second relationship and the third relationship, determine the N parameters that affect the equivalent moment of inertia and the relationship between the N parameters.

[0055] Optionally, according to the first relationship, the second relationship and the third relationship, the formula of the data simulation model can be determined as follows: J = J1-J2 =

[0056] {[(T Eng +T MT )*i0*i g *η T / r-(1 / 2)*CρS*v 2 -mgsinα-mgfcosα] / am}*(r 2 )-∑m0r 2

[0057] Wherein, J is the wheel end moment of inertia in the form of equivalent moment of inertia.

[0058] Therefore, it can be seen that the relationship between N parameters and N parameters constitutes a data simulation model. The N parameters are vehicle configuration parameters, operating parameters or environmental parameters; vehicle configuration parameters include part or all of curb weight, gear ratios, transmission system efficiency, main reducer ratio and tire radius; operating parameters include part or all of vehicle speed, acceleration, engine torque or motor torque; environmental parameters include part or all of air resistance coefficient, air density, windward area, gravity acceleration, road slope angle or rolling resistance coefficient. As mentioned above, the actual meaning of each parameter can be found in the aforementioned embodiment, which will not be repeated here.

[0059] Regarding S102, all parameters mentioned in the process of building the data simulation model are variables and do not involve specific parameter values.

[0060] After the data simulation model is built, the real vehicle data simulation is performed to obtain the real-time moment of inertia. In addition, due to different transmission relationships, the equivalent moment of inertia converted from each component of the transmission system is also different. Therefore, the one-to-one corresponding operation mode and operation gear can be considered as one situation. The vehicle is controlled to run without load in M ​​situations to obtain the corresponding M groups of parameter values. Among them, M is an integer greater than 1.

[0061] For example, the operating modes include electric mode, engine mode and hybrid mode, and the operating gear includes four gears, then M can be 12. Each group of parameters includes multiple values ​​of N parameters. For example, by adjusting the vehicle speed, acceleration and road slope angle, different parameter values ​​in each group of parameters can be obtained. In a specific example, it is assumed that each group of parameters includes 10 values ​​corresponding to each of the N parameters.

[0062] In S103, M groups of parameter values ​​are input into the data simulation model to obtain M groups of equivalent moments of inertia. In combination with the above example, it can be seen that if M is 12, each group of parameters includes 10 values ​​corresponding to N parameters, then 12 groups of equivalent moments of inertia can be obtained, each group of equivalent moments of inertia includes 10 equivalent moments of inertia, and each group of equivalent moments of inertia is a value under a fixed operating mode and a fixed operating gear.

[0063] In order to improve the accuracy of estimating the vehicle load, after obtaining the M groups of equivalent moments of inertia, the equivalent moments of inertia that do not meet the first screening rule based on driving conditions, the second screening rule based on acceleration conditions, or the third screening rule based on the error of equivalent moments of inertia can be removed from the M groups of equivalent moments of inertia. The driving conditions include part or all of the gear conditions, curve conditions, braking conditions, mode conditions, and speed conditions.

[0064] In a specific example, the conditions are as follows:

[0065] Driving conditions: gear in gear, not on a curve, not braking, not in mode switching, speed>10km / h, no wheel slip; acceleration conditions: when the acceleration is small, the error fluctuation is large, and the resulting rotational inertia error will also be large, so the acceleration a can be limited to ≥0.5 (m / s 2 ); Error range condition: take the average value J of each group of rotational inertia obtained N1 , if its moment of inertia J satisfies the condition |JJ N1 | / J N1 If the value is ≤15%, it will be retained; if it does not meet the value, it will be screened out.

[0066] In S104, during the actual operation of the vehicle, for example, the current operation situation is called the target situation, and the target situation is the corresponding target operation mode and target operation gear, for example, electric mode + gear 1. At this time, the target equivalent rotational inertia of the target situation can be determined based on the M groups of equivalent rotational inertia or at least the screened M groups of equivalent rotational inertia.

[0067] Optionally, the process can be implemented through steps B1-B2:

[0068] B1: Among the M groups of equivalent moments of inertia, determine a group of equivalent moments of inertia corresponding to the target situation.

[0069] Among them, M groups of equivalent rotational inertia correspond to M situations (operating mode+operating gear), therefore, a group of equivalent rotational inertia corresponding to the target situation (target operating mode+target operating gear) can be found.

[0070] B1: Determine the average value of a set of equivalent moments of inertia as the target equivalent moment of inertia under the target condition.

[0071] Optionally, an average value of the set of equivalent moments of inertia is calculated as a target equivalent moment of inertia under the target condition.

[0072] In S105, a target load of the vehicle is calculated based on the target equivalent moment of inertia and the wheel moment of inertia.

[0073] Optionally, the process can be implemented through steps C1-C2:

[0074] C1: Calculate the target wheel end moment of inertia based on the target equivalent moment of inertia and wheel moment of inertia.

[0075] Among them, the target wheel end moment of inertia = target equivalent moment of inertia + wheel moment of inertia, so the formula for the target wheel end moment of inertia is expressed as:

[0076] J'1=J N2 +J2=J N2 +∑m0r 2

[0077] Where J'1 is the target wheel end moment of inertia, J N2 is the target equivalent moment of inertia, that is, the moment of inertia of the transmission system.

[0078] Applying the wheel end moment of inertia calculated in this way to calculate the acceleration resistance can effectively reduce the calculation error of the acceleration resistance, thereby reducing the error in calculating the resultant force acting on the vehicle body, and thereby reducing the error in the estimated vehicle load.

[0079] C2: Calculate the target load of the vehicle using the target wheel end moment of inertia, as well as the target wheel end driving force, target air resistance, target ramp resistance, and target rolling resistance determined under the target conditions.

[0080] Among them, the formula of target acceleration resistance is expressed as follows:

[0081] F' Acc =δ*m1a'=(1+J1' / r 2 / m1)*m1a'

[0082] Where J'1 is the target wheel end moment of inertia, δ is the rotational mass conversion factor, m1 is the target load of the vehicle to be calculated (unknown in this expression), F' Acc Target acceleration resistance (since m1 is unknown, the target acceleration resistance is also unknown)

[0083] Optionally, the vehicle dynamics equation is as follows: target wheel-end driving force = target air resistance + target ramp resistance + target rolling resistance + target acceleration resistance. The vehicle dynamics equation is expressed as follows:

[0084] (T' Eng +T' MT )*i'0*i' g *η' T / r=

[0085] (1 / 2)*C'ρ'S'*v' 2 +m1gsinα'+m1gf'cosα'+(1+J'1 / r 2 / m1)*m1a'.

[0086] Among them, (T' Eng +T' MT )*i'0*i' g *η' T / r is the target wheel end driving force, (1 / 2)*C'ρ'S'*v' 2 is the target air resistance, m1gsinα' is the target ramp resistance, m1gf'cosα' is the target rolling resistance, (1+J'1 / r 2 / m1)*m1a' is the target acceleration resistance. T' Eng is the target engine torque, T' MT is the target motor torque, i' g is the target gear ratio, η' T is the target transmission system efficiency, i'0 is the target final reducer speed ratio, C' is the target air resistance coefficient, ρ' is the target air density, S' is the target frontal area, v'target is the vehicle speed, α' is the target road slope angle, f' is the target rolling resistance coefficient, and a' is the target acceleration.

[0087] In addition, according to the expression of the vehicle dynamics equation, the expression of the target load of the vehicle to be calculated can be determined as follows:

[0088] m1=[(T' Eng +T' MT )*i'0*i' g *η' T / r-(1 / 2)*C'ρ'S'*v' 2 -J'1 / r 2 *a'] / [gsinα'+gf'cosα'+a']

[0089] It should be noted that “target” refers to the values ​​of each parameter under the target condition, which is to distinguish the values ​​of each parameter in the model construction stage.

[0090] In the embodiment of the present application, the rotational mass conversion coefficient δ is calculated from the moment of inertia under different vehicle driving energy modes, current gears, and transmission relationships, and the vehicle load under different working conditions is corrected. Compared with the rotational mass conversion coefficient in the prior art which is a fixed static parameter, it has higher accuracy.

[0091] In the actual calculation process, in order to improve the accuracy of estimating the vehicle load, the vehicle can be controlled to run for a period of time, during which the possible operating modes and operating gears will change. Therefore, when there are multiple target conditions within a period of time, that is, the target loads corresponding to the multiple target conditions can be obtained, then fitting methods such as recursive least squares method or Kalman filtering can be applied to process each target load to obtain the final target moment of inertia of the vehicle.

[0092] In order to make the technical solution of this application easier to understand, Figure 2 A complete flow chart of a method for determining vehicle load provided in an embodiment of the present application is provided. Figure 2 At least the following steps are included:

[0093] S201: Obtaining a first relationship between acceleration resistance and wheel end driving force, air resistance, ramp resistance, and rolling resistance.

[0094] S202: Determine a second relationship between the wheel end moment of inertia and the acceleration resistance, the curb mass, the acceleration, and the tire radius.

[0095] S203: Determine a third relationship between the wheel rotational inertia, the tire mass, and the tire radius.

[0096] S204: Determine the relationship between N parameters that affect the equivalent moment of inertia according to the first relationship, the second relationship, and the third relationship; the relationship between the N parameters and the N parameters constitute a data simulation model.

[0097] S205: Control the vehicle to run without load in M ​​situations, and obtain corresponding M groups of parameter values.

[0098] S206: Inputting M groups of parameter values ​​into the data simulation model to obtain M groups of equivalent moments of inertia.

[0099] S207: Remove the equivalent moments of inertia in the M groups that do not meet the first screening rule based on the driving condition, the second screening rule based on the acceleration condition, or the third screening rule based on the equivalent moment of inertia error.

[0100] S208: Determine a group of equivalent moments of inertia corresponding to the target situation from among the M groups of equivalent moments of inertia.

[0101] S209: Determine an average value of a group of equivalent moments of inertia as a target equivalent moment of inertia under a target condition.

[0102] S210: Calculate the target wheel end moment of inertia according to the target equivalent moment of inertia and the wheel moment of inertia.

[0103] S211: Calculate the target load by applying the target wheel end moment of inertia, and the target wheel end driving force, target air resistance, target ramp resistance, and target rolling resistance determined in the target situation.

[0104] S212: If there are multiple target conditions, determine the target load of the vehicle according to the target loads corresponding to the multiple target conditions.

[0105] The implementation method of each step can be found in the above-mentioned embodiment and will not be repeated here.

[0106] The embodiment of the present application, through the conversion of equivalent moment of inertia and the fitting calculation method of mechanical principles, reversely calculates and calibrates the moment of inertia through driving data, and then corrects the hybrid vehicle load estimation process based on mechanics, solves the problem of vehicle weight estimation error caused by inaccurate acceleration resistance, improves the accuracy of load estimation, and realizes continuous monitoring of load conditions during driving. The estimation result can be used for starting gear decision, gear shifting control and other methods to improve economy and power.

[0107] like Figure 3 As shown, based on the same inventive concept as the above-mentioned method for determining vehicle load, an embodiment of the present application also provides a device for determining vehicle load, including a processing unit 31 and a control unit 32.

[0108] The processing unit 31 is used to: construct a data simulation model; wherein the data simulation model is used to calculate the equivalent moment of inertia of the vehicle; the equivalent moment of inertia is the moment of inertia after the transmission system of the vehicle is equivalent to the equivalent system;

[0109] The control unit 32 is used to: control the vehicle to run without load in M ​​situations, and obtain corresponding M groups of parameter values; wherein each situation has a one-to-one corresponding operation mode and operation gear; M is an integer greater than 1;

[0110] The processing unit 31 is further used to: input M groups of parameter values ​​into the data simulation model to obtain M groups of equivalent moments of inertia;

[0111] The processing unit 31 is further used to: determine a target equivalent moment of inertia under a target condition according to the M group of equivalent moments of inertia; wherein the target condition is a corresponding target operating mode and a target operating gear;

[0112] The processing unit 31 is further used to calculate a target load of the vehicle according to the target equivalent moment of inertia and the wheel moment of inertia.

[0113] In an optional implementation manner, the processing unit 31 is specifically configured to:

[0114] Calculate the target wheel end moment of inertia according to the target equivalent moment of inertia and the wheel moment of inertia;

[0115] Calculating a target load of the vehicle using the target wheel end moment of inertia, and the determined target wheel end driving force, target air resistance, target ramp resistance, and target rolling resistance under the target condition;

[0116] In an optional implementation, if there are multiple target situations, the processing unit 31 is further configured to:

[0117] The target load of the vehicle is determined according to the target loads corresponding to the multiple target situations.

[0118] In an optional implementation, after obtaining the M groups of equivalent moments of inertia, the processing unit 31 is further used to:

[0119] Remove the equivalent moments of inertia in the M group that do not meet the first screening rule based on driving conditions, the second screening rule based on acceleration conditions, or the third screening rule based on the equivalent moment of inertia error; wherein the driving conditions include part or all of the gear conditions, curve conditions, braking conditions, mode conditions and speed conditions.

[0120] In an optional implementation manner, the processing unit 31 is specifically configured to:

[0121] Among the M groups of equivalent moments of inertia, a group of equivalent moments of inertia corresponding to the target situation is determined;

[0122] An average value of a set of equivalent moments of inertia is determined as a target equivalent moment of inertia under a target condition.

[0123] In an optional implementation manner, the processing unit 31 is specifically configured to:

[0124] Obtaining a first relationship between acceleration resistance and wheel end driving force, air resistance, ramp resistance and rolling resistance;

[0125] Determining a second relationship between the wheel end moment of inertia and the acceleration resistance, curb mass, acceleration, and tire radius;

[0126] Determine the third relationship between the moment of inertia of the wheel and the mass and radius of the tire;

[0127] Determine the relationship between N parameters and the N parameters that affect the equivalent moment of inertia according to the first relationship, the second relationship, and the third relationship; wherein the relationship between the N parameters and the N parameters constitutes a data simulation model; and N is an integer greater than 1;

[0128] The N parameters are vehicle configuration parameters, operating parameters or environmental parameters; vehicle configuration parameters include part or all of curb weight, gear ratios, transmission system efficiency, main reducer ratio and tire radius; operating parameters include part or all of vehicle speed, acceleration, engine torque or motor torque; environmental parameters include part or all of air resistance coefficient, air density, frontal area, gravitational acceleration, road slope angle or rolling resistance coefficient.

[0129] In an optional implementation, the operating mode includes an electric mode, an engine mode, or a hybrid mode.

[0130] The device for determining vehicle load proposed in the embodiment of the present application adopts the same inventive concept as the method for determining vehicle load mentioned above, and can achieve the same beneficial effects, which will not be repeated here.

[0131] Based on the same inventive concept as the above-mentioned method for determining vehicle load, the embodiment of the present application further provides an electronic device, which can be a control device or control system arranged inside the vehicle, or other external device that communicates with the vehicle, such as a desktop computer, a portable computer, a smart phone, a tablet computer, a personal digital assistant (PDA), a server, etc. Figure 4 As shown, the electronic device may include a processor 41 and a memory 42 .

[0132] The processor 41 may be a general-purpose processor, such as a central processing unit (CPU), a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field programmable gate array (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components, and may implement or execute the methods, steps and logic block diagrams disclosed in the embodiments of the present application. A general-purpose processor may be a microprocessor or any conventional processor, etc. The steps of the method disclosed in the embodiments of the present application may be directly embodied as being executed by a hardware processor, or may be executed by a combination of hardware and software modules in the processor.

[0133] The memory 42 is a non-volatile computer-readable storage medium that can be used to store non-volatile software programs, non-volatile computer executable programs and modules. The memory may include at least one type of storage medium, such as flash memory, hard disk, multimedia card, card-type memory, random access memory (Random Access Memory, RAM), static random access memory (Static Random Access Memory, SRAM), programmable read-only memory (Programmable Read Only Memory, PROM), read-only memory (Read Only Memory, ROM), electrically erasable programmable read-only memory (Electrically Erasable Programmable Read-Only Memory, EEPROM), magnetic memory, disk, optical disk, etc. The memory is any other medium that can be used to carry or store the desired program code in the form of instructions or data structures and can be accessed by a computer, but is not limited thereto. The memory 42 in the embodiment of the present application can also be a circuit or any other device that can realize a storage function, for storing program instructions and / or data.

[0134] A person of ordinary skill in the art can understand that: all or part of the steps of implementing the above method embodiments can be completed by hardware related to program instructions, and the aforementioned program can be stored in a computer-readable storage medium. When the program is executed, it executes the steps of the above method embodiments; the above computer storage medium can be any available medium or data storage device that can be accessed by a computer, including but not limited to: mobile storage devices, random access memory (RAM, Random Access Memory), magnetic storage (such as floppy disks, hard disks, magnetic tapes, magneto-optical disks (MO)), optical storage (such as CD, DVD, BD, HVD, etc.), and semiconductor storage (such as ROM, EPROM, EEPROM, non-volatile memory (NAND FLASH), solid-state drive (SSD)) and other media that can store program codes.

[0135] Alternatively, if the above-mentioned integrated unit of the present application is implemented in the form of a software function module and sold or used as an independent product, it can also be stored in a computer-readable storage medium. Based on such an understanding, the technical solution of the embodiment of the present application is essentially or the part that contributes to the prior art can be embodied in the form of a software product, which is stored in a storage medium, including several instructions to enable a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the methods of each embodiment of the present application. The aforementioned storage medium includes: a mobile storage device, a random access memory (RAM, Random Access Memory), a magnetic storage device (such as a floppy disk, a hard disk, a magnetic tape, a magneto-optical disk (MO), etc.), an optical storage device (such as CD, DVD, BD, HVD, etc.), and a semiconductor memory (such as ROM, EPROM, EEPROM, non-volatile memory (NAND FLASH), a solid-state drive (SSD)) and other various media that can store program codes.

[0136] The above embodiments are only used to introduce the technical solutions of the present application in detail, but the description of the above embodiments is only used to help understand the methods of the embodiments of the present application and should not be understood as limiting the embodiments of the present application. Any changes or substitutions that can be easily thought of by technicians in this technical field should be included in the protection scope of the embodiments of the present application.

Claims

1. A method for determining vehicle load, characterized in that: include: Constructing a data simulation model; wherein the data simulation model is used to calculate the equivalent moment of inertia of the vehicle; the equivalent moment of inertia is the moment of inertia after the transmission system of the vehicle is equivalent to the equivalent system; Control the vehicle to run without load in M ​​situations, and obtain corresponding M groups of parameter values; wherein each situation has a one-to-one corresponding operation mode and operation gear; and M is an integer greater than 1; Inputting the M groups of parameter values ​​into the data simulation model to obtain M groups of equivalent moments of inertia; Determining a target equivalent moment of inertia under a target condition according to the M group of equivalent moments of inertia; wherein the target condition is a corresponding target operating mode and a target operating gear; The target load of the vehicle is calculated according to the target equivalent moment of inertia and the wheel moment of inertia.

2. The method according to claim 1, characterized in that The calculating the target load of the vehicle according to the target equivalent moment of inertia and the wheel moment of inertia comprises: Calculating a target wheel end moment of inertia according to the target equivalent moment of inertia and the wheel moment of inertia; The target wheel end moment of inertia, as well as the determined target wheel end driving force, target air resistance, target ramp resistance and target rolling resistance under the target condition are used to calculate the target load of the vehicle.

3. The method according to claim 1 or 2, characterized in that: If there are multiple target situations, the method further includes: The target load of the vehicle is determined according to the target loads corresponding to the multiple target situations.

4. The method according to claim 1, characterized in that After obtaining the M groups of equivalent moments of inertia, the method further includes: The equivalent moments of inertia of the M group that do not meet the first screening rule based on driving conditions, the second screening rule based on acceleration conditions, or the third screening rule based on the equivalent moment of inertia error are removed; wherein the driving conditions include part or all of the gear conditions, curve conditions, braking conditions, mode conditions and speed conditions.

5. The method according to claim 1, characterized in that Determining a target equivalent moment of inertia under a target condition according to the M groups of equivalent moments of inertia includes: Among the M groups of equivalent moments of inertia, determining a group of equivalent moments of inertia corresponding to the target situation; An average value of the set of equivalent moments of inertia is determined as a target equivalent moment of inertia under the target condition.

6. The method according to claim 1, characterized in that The constructing of the data simulation model comprises: Obtaining a first relationship between acceleration resistance and wheel end driving force, air resistance, ramp resistance and rolling resistance; Determining a second relationship between the wheel end moment of inertia and the acceleration resistance, curb mass, acceleration, and tire radius; Determining a third relationship between the moment of inertia of the wheel and the mass and radius of the tire; Determine, according to the first relationship, the second relationship and the third relationship, the relationship between N parameters affecting the equivalent moment of inertia and the N parameters; wherein the relationship between the N parameters and the N parameters constitutes the data simulation model; N is an integer greater than 1; The N parameters are vehicle configuration parameters, operating parameters or environmental parameters; the vehicle configuration parameters include part or all of the curb weight, gear ratios, transmission system efficiency, main reducer ratio and tire radius; the operating parameters include part or all of the vehicle speed, the acceleration, engine torque or motor torque; the environmental parameters include part or all of the air resistance coefficient, air density, frontal area, gravity acceleration, road slope angle or rolling resistance coefficient.

7. The method according to any one of claims 1 to 2 or 4 to 6, characterized in that: The operating modes include electric mode, engine mode or hybrid mode.

8. A device for determining vehicle load, characterized in that: include: A processing unit, used to: construct a data simulation model; wherein the data simulation model is used to calculate the equivalent moment of inertia of the vehicle; the equivalent moment of inertia is the moment of inertia after the transmission system of the vehicle is equivalent to the equivalent system; A control unit, used to: control the vehicle to run without load in M ​​situations, and obtain corresponding M groups of parameter values; wherein each situation has a one-to-one corresponding operation mode and operation gear; and M is an integer greater than 1; The processing unit is further used to: input the M groups of parameter values ​​into the data simulation model to obtain M groups of equivalent moments of inertia; The processing unit is further used to: determine a target equivalent moment of inertia under a target condition according to the M groups of equivalent moments of inertia; wherein the target condition is a corresponding target operating mode and a target operating gear; The processing unit is further used to calculate the target load of the vehicle according to the target equivalent moment of inertia and the wheel moment of inertia.

9. 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, the steps of the method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having computer program instructions stored thereon, characterized in that: When the computer program instructions are executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

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