Method and device for estimating vehicle mass and road slope

Abstract

The present application discloses a method and device for estimating the mass of a car and the slope of a road surface. The method comprises the following steps: after the car is powered on, the driving condition of the car in any time period, as well as the vehicle speed and wheel-end torque at each moment in each time period are obtained; for the driving condition in each time period, when it is determined that the driving condition meets the preset enabling conditions, the vehicle speed and wheel-end torque at each moment in the time period are input into the estimation model to obtain the estimation result output by the estimation model. The method calculates the mass of the car and the slope of the road surface at each moment in the time period to which the driving condition belongs by using the vehicle speed, wheel-end torque and the estimation model when it is determined that the driving condition meets the preset enabling conditions. The method can accurately estimate the mass of the car and the slope of the road surface under the driving condition without increasing the hardware cost of the car, so as to improve the accuracy of the algorithm control of the braking system.

Description

Technical Field

[0001] The present application relates to the field of vehicle technology, and in particular to a method and device for estimating vehicle mass and road slope. Background Art

[0002] Vehicle mass and road slope are currently inputs to many braking system algorithms, such as braking force calculation and transmission shift control, but the dynamic vehicle mass and road slope cannot be directly obtained.

[0003] In the prior art, the half-loaded mass of the car and the road slope with a preset fixed value are usually used as inputs of the software control algorithm. However, the half-loaded mass of the car is not consistent with the actual car mass, and the road slope has always been set to a fixed value, which does not conform to the actual driving conditions at all, resulting in unsatisfactory control effects of the braking system. If sensors are added to obtain the car mass and road slope, the production cost of the car will increase.

[0004] Therefore, how to estimate the mass of the vehicle and the road slope under driving conditions without increasing the cost of vehicle hardware to improve the accuracy of the braking system algorithm control has become an urgent problem to be solved in this field. Summary of the invention

[0005] The present application provides a method and device for estimating the mass of a vehicle and the slope of a road surface, the purpose of which is to accurately estimate the mass of a vehicle and the slope of a road surface under driving conditions without increasing the hardware cost of the vehicle, so as to improve the accuracy of the algorithm control of the braking system.

[0006] In order to achieve the above objectives, this application provides the following technical solutions:

[0007] A method for estimating vehicle mass and road slope, comprising:

[0008] After the vehicle is powered on, the driving condition of the vehicle in any time period, as well as the vehicle speed and wheel end torque at each moment in each time period are obtained;

[0009] For the driving condition in each of the time periods, when it is determined that the driving condition meets the preset enabling conditions, the vehicle speed and wheel-end torque at each moment in the time period are input into the estimation model to obtain the estimation result output by the estimation model; wherein the preset enabling conditions include: the vehicle is not in any of the braking condition, steering condition, starting condition, sudden acceleration condition, sudden deceleration condition, and wheel slip condition; the estimation result includes the vehicle mass and road surface slope at each moment in the time period; the estimation model is pre-constructed based on the vehicle dynamics model; the vehicle dynamics model is used to indicate the correlation between the vehicle speed, wheel-end torque, vehicle mass and road surface slope.

[0010] Optionally, the process of pre-building the estimation model based on the vehicle dynamics model includes:

[0011] Create the state equation of road slope and the state equation of vehicle mass in advance;

[0012] Generate a Jacobian matrix of acceleration based on a vehicle dynamics model, a state equation of the road slope, and a state equation of the vehicle mass;

[0013] Calibrate initial values ​​for parameters in the Jacobian matrix;

[0014] Constructing a Kalman filter gain coefficient based on a preset priori error covariance matrix and the Jacobian matrix;

[0015] The estimation model is constructed based on the measurement error of acceleration, a preset a posteriori error covariance matrix, and the Kalman filter gain coefficient; wherein the measurement error indicates the difference between the first acceleration and the second acceleration; the first acceleration is the acceleration calculated based on the vehicle speed; and the second acceleration is the acceleration calculated based on the Kalman filter gain coefficient.

[0016] Optionally, also include:

[0017] When it is determined that the driving condition does not meet the preset enabling condition, the mass of the vehicle at the last moment in the previous time period is used as the mass of the vehicle at each moment in the time period, and the road surface slope at the last moment in the previous time period is used as the road surface slope at each moment in the time period.

[0018] Optionally, also include:

[0019] When it is determined that the driving condition does not meet the preset enabling conditions and the time period is the first time period experienced by the car after power-on, the preset initial value of the car mass is used as the car mass at each moment in the time period, and the preset initial value of the road surface slope is used as the road surface slope at each moment in the time period.

[0020] A device for estimating vehicle mass and road slope, comprising:

[0021] A working condition acquisition unit, used to acquire the driving working condition of the vehicle in any time period, as well as the vehicle speed and wheel end torque at each moment in each time period after the vehicle is powered on;

[0022] An estimation unit is used for, for the driving condition in each time period, upon determining that the driving condition satisfies a preset enabling condition, inputting the vehicle speed and wheel-end torque at each moment in the time period into an estimation model to obtain an estimation result output by the estimation model; wherein the preset enabling condition includes: the vehicle is not in any of the braking condition, steering condition, starting condition, rapid acceleration condition, rapid deceleration condition, and wheel slip condition; the estimation result includes the vehicle mass and road surface slope at each moment in the time period; the estimation model is pre-constructed based on a vehicle dynamics model; the vehicle dynamics model is used to indicate the correlation between the vehicle speed, wheel-end torque, vehicle mass, and road surface slope.

[0023] Optionally, the estimation unit is specifically used for:

[0024] Create the state equation of road slope and the state equation of vehicle mass in advance;

[0025] Generate a Jacobian matrix of acceleration based on a vehicle dynamics model, a state equation of the road slope, and a state equation of the vehicle mass;

[0026] Calibrate initial values ​​for parameters in the Jacobian matrix;

[0027] Constructing a Kalman filter gain coefficient based on a preset priori error covariance matrix and the Jacobian matrix;

[0028] The estimation model is constructed based on the measurement error of the acceleration, a preset a posteriori error covariance matrix, and the Kalman filter gain coefficient; wherein the measurement error indicates the difference between the first acceleration and the second acceleration; the first acceleration is the acceleration calculated based on the vehicle speed; and the second acceleration is the acceleration calculated based on the Kalman filter gain coefficient.

[0029] Optionally, the estimation unit is further used for:

[0030] When it is determined that the driving condition does not meet the preset enabling condition, the mass of the vehicle at the last moment in the previous time period is used as the mass of the vehicle at each moment in the time period, and the road surface slope at the last moment in the previous time period is used as the road surface slope at each moment in the time period.

[0031] Optionally, the estimation unit is further used for:

[0032] When it is determined that the driving condition does not meet the preset enabling conditions and the time period is the first time period experienced by the car after power-on, the preset initial value of the car mass is used as the car mass at each moment in the time period, and the preset initial value of the road surface slope is used as the road surface slope at each moment in the time period.

[0033] A computer-readable storage medium includes a stored program, wherein the program executes the method for estimating the vehicle mass and road slope.

[0034] A vehicle, comprising: a processor, a memory and a bus; the processor and the memory are connected via the bus;

[0035] The memory is used to store programs, and the processor is used to run the programs, wherein the method for estimating the vehicle mass and the road slope is executed when the programs are run.

[0036] The technical solution provided by the present application obtains the driving conditions of the car in any time period, as well as the vehicle speed and wheel-end torque at each moment in each time period, after the car is powered on. For the driving conditions in each time period, when it is determined that the driving conditions meet the preset enabling conditions, the vehicle speed and wheel-end torque at each moment in the time period are input into the estimation model to obtain the estimation results output by the estimation model. Among them, the preset enabling conditions include: the car is not in any of the braking conditions, steering conditions, starting conditions, rapid acceleration conditions, rapid deceleration conditions, and wheel slip conditions. The estimation results include the car mass and road slope at each moment in the time period. The estimation model is pre-constructed based on the vehicle dynamics model, and the vehicle dynamics model is used to indicate the correlation between the vehicle speed, wheel-end torque, car mass, and road slope. The present application scheme, by determining that the driving condition meets the preset enabling conditions, uses the vehicle speed, wheel-end torque and estimation model to calculate the vehicle mass and road slope at each moment in the time period to which the driving condition belongs. This can accurately estimate the vehicle mass and road slope under the driving condition without increasing the vehicle hardware cost, thereby improving the accuracy of the braking system algorithm control. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described 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 paying creative work.

[0038] Figure 1A schematic flow chart of a method for estimating vehicle mass and road slope provided in an embodiment of the present application;

[0039] Figure 2 A schematic diagram of a process of constructing an estimation model provided in an embodiment of the present application;

[0040] Figure 3 A schematic diagram of the architecture of a device for estimating vehicle mass and road slope provided in an embodiment of the present application. DETAILED DESCRIPTION

[0041] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0042] like Figure 1 FIG. 1 is a flow chart of a method for estimating a vehicle mass and a road surface slope provided in an embodiment of the present application, comprising the following steps:

[0043] S101: After the vehicle is powered on, the driving conditions of the vehicle in any time period, as well as the vehicle speed and wheel-end torque at each moment in each time period are obtained.

[0044] Among them, the driving condition, vehicle speed, and wheel-end torque of the vehicle can all be collected in real time by the original sensors on the vehicle, which are common knowledge familiar to technical personnel in this field and will not be elaborated here.

[0045] It should be noted that the specific duration of the time period shown in the embodiment of the present application can be set by the technician according to the actual situation. In addition, the sampling interval of each moment contained in any time period, that is, the sampling moment of the vehicle speed and the wheel-end torque, can be set by the technician according to the actual situation. Specifically, it can be set to sample once every 2S, that is, in a 1min time period, the vehicle speed and wheel-end torque are sampled every 2S, and a total of 30 samples (that is, a combination of vehicle speed and wheel-end torque) are sampled in a 1min time period. Correspondingly, each sample represents the vehicle speed and wheel-end torque at each sampling moment in the time period.

[0046] S102: For the driving condition in each time period, when it is determined that the driving condition meets the preset enabling condition, the vehicle speed and wheel end torque at each moment in the time period are input into the estimation model to obtain the estimation result output by the estimation model.

[0047] The preset enabling conditions include: the vehicle is not in any of the braking, steering, starting, rapid acceleration, rapid deceleration, and wheel slip conditions. The estimation results include the vehicle mass and road slope at each moment in the time period.

[0048] It should be noted that the estimation model is pre-constructed based on a vehicle dynamics model, and the vehicle dynamics model is used to indicate the correlation between vehicle speed, wheel end torque, vehicle mass and road slope.

[0049] Optionally, the estimation model construction process can be found in Figure 2 The steps are shown, with explanations of the steps.

[0050] Optionally, when it is determined that the driving condition does not meet the preset enabling conditions, the vehicle mass at the last moment in the previous time period is used as the vehicle mass at each moment in the time period, and the road surface slope at the last moment in the previous time period is used as the road surface slope at each moment in the time period.

[0051] Optionally, when it is determined that the driving condition does not meet the preset enabling conditions and the time period is the first time period experienced after the car is powered on, the preset initial value of the car mass is used as the car mass at each moment in the time period, and the preset initial value of the road surface slope is used as the road surface slope at each moment in the time period.

[0052] To summarize, when it is determined that the driving condition meets the preset enabling conditions, the vehicle speed, wheel-end torque and estimation model are used to calculate the vehicle mass and road slope at each moment in the time period to which the driving condition belongs. The vehicle mass and road slope under the driving condition can be accurately estimated without increasing the vehicle hardware cost, thereby improving the accuracy of the braking system algorithm control.

[0053] like Figure 2 FIG. 1 is a flow chart of a process of building an estimation model according to an embodiment of the present application, which includes the following steps:

[0054] S201: Pre-create the state equation of the road slope and the state equation of the vehicle mass.

[0055] The state equation of the road slope is shown in formula (1), and the state equation of the vehicle mass is shown in formula (2).

[0056]

[0057]

[0058] In formula (1) and formula (2), x1 represents the road slope and its value range is x2 represents the mass of the car, represents the predicted value of the road slope at time t, represents the predicted value of the car mass at time t, is the prior estimate of the road slope at time t-1, is the prior estimate of the car mass at time t-1, mod represents the remainder function, sign represents the sign function, max represents the maximum value function, min represents the minimum value function, Mass max Represents the probability mass function for the maximum value, Mass min represents the probability mass function for minimization.

[0059] In the embodiment of the present application, formula (1) and formula (2) can be integrated into formula (3).

[0060]

[0061] S202: Generate a Jacobian matrix of acceleration based on the vehicle dynamics model, the state equation of the road slope, and the state equation of the vehicle mass.

[0062] The vehicle dynamics model is used to indicate the correlation between vehicle speed, wheel end torque, vehicle mass and road slope. Specifically, the specific expression of the vehicle dynamics model is shown in formula (4).

[0063]

[0064] In formula (4), WhlForce Powertrain Represents driving force, WhlForce Aerodynamic Represents air resistance, WhlForce Road Represents the sum of rolling resistance and ramp resistance.

[0065] Specifically, the calculation principle of the driving force is shown in formula (5), the calculation principle of the air resistance is shown in formula (6), and the calculation principle of the sum of the rolling resistance and the slope resistance is shown in formula (7).

[0066]

[0067]

[0068] WhlForce Road =m·g·[Resistance Rolling ·cosα+sinα] (7)

[0069] In formulas (5), (6) and (7), Torque Wheel Represents wheel end torque, RadiusWheel represents the wheel radius, ρ air Represents the preset air density, C x Represents the preset air resistance coefficient, S f Represents the preset frontal area of ​​the car, Speed Vehicle represents the speed of the vehicle, m represents the mass of the vehicle, g represents the acceleration of gravity, and resistance Rolling represents the rolling resistance coefficient, and α represents the road slope.

[0070] Based on the above formulas (4), (5), (6) and (7), the calculation formula of acceleration is derived. Specifically, the calculation formula of acceleration is shown in formula (8).

[0071]

[0072] In formula (8), a(t) represents the acceleration at any time, m(t) represents the mass of the car at any time, and Torque Wheel (t) represents the wheel end torque at any time, Speed Vehicle (t) represents the vehicle speed at any time, and α(t) represents the road slope at any time.

[0073] Furthermore, based on formula (8) and formula (3), we can derive formula (9).

[0074]

[0075] Furthermore, the car mass and the road slope in formula (9) are derived to generate the Jacobian matrix of acceleration. Specifically, the Jacobian matrix of acceleration is shown in formula (10).

[0076]

[0077] It should be noted that the principle of derivation of the vehicle mass and the road slope in formula (9) is common knowledge familiar to those skilled in the art and will not be elaborated here.

[0078] S203: Calibrate initial values ​​for parameters in the Jacobian matrix of acceleration.

[0079] The parameters in the Jacobian matrix include the predicted value of road slope, the predicted value of vehicle mass, the process noise covariance matrix, the error covariance matrix, the measurement noise covariance matrix, the coefficient matrix, and the step time. The purpose of calibrating the initial values ​​of the parameters in the Jacobian matrix is ​​to use the Jacobian matrix to perform the Kalman filter update iterative process in the future, so as to improve the efficiency and accuracy of the update iterative process.

[0080] Generally speaking, the initial value of the road slope prediction value is denoted by The initial value of the predicted vehicle mass is recorded as The initial value of the process noise covariance matrix is ​​recorded as Q(0), the initial value of the error covariance matrix is ​​recorded as P(0), the initial value of the measurement noise covariance matrix is ​​recorded as R(0), the initial value of the coefficient matrix is ​​recorded as F, and the initial value of the step time is recorded as W. In the embodiment of the present application, the coefficient matrix and the step time are both fixed values.

[0081] Specifically, R(0)=[R 11 ], Among them, Q 11 , Q 22 , P 11 , P 22 , R 11 and T s Both can be set by technicians according to actual conditions.

[0082] S204: Constructing Kalman filter gain coefficients based on a preset priori error covariance matrix and Jacobian matrix.

[0083] The prior error covariance matrix is ​​expressed as shown in formula (11), and the Kalman filter gain coefficient is expressed as shown in formula (12).

[0084] P(t)=F·P(t-1)·F'+W·Q·W' (11)

[0085]

[0086] In formulas (11) and (12), P(t) represents the prior error covariance matrix, F' represents the inverse matrix of the coefficient matrix, W' represents the inverse matrix of the step time, K(t) represents the Kalman filter gain coefficient, H(t) represents the Jacobian matrix, H(t)' represents the inverse matrix of the Jacobian matrix, and R represents the preset measurement noise covariance matrix.

[0087] S205: Constructing an estimation model based on the acceleration measurement error, the preset a posteriori error covariance matrix, and the Kalman filter gain coefficient.

[0088] Wherein, the measurement error indicates the difference between the first acceleration and the second acceleration, the first acceleration is the acceleration calculated based on the vehicle speed, and the second acceleration is the acceleration calculated based on the Kalman filter gain coefficient. Specifically, the calculation process of the acceleration measurement error is shown in formula (13), and the expression form of the posterior error covariance matrix is ​​shown in formula (14).

[0089]

[0090]

[0091] In formulas (13) and (14), Residual(t) represents the measurement error of acceleration, a Actual (t) represents the acceleration calculated based on the vehicle speed, represents the acceleration calculated based on the Kalman filter gain coefficient, Specifically, it is shown in formula (9). In the embodiment of the present application, the measurement error of the acceleration can be used to correct the Kalman filter gain coefficient to improve the accuracy of the Kalman filter gain coefficient.

[0092] It should be noted that the estimation results output by the estimation model are usually recorded as It can also be called the a posteriori estimate of the state variable at time t, which is based on the Kalman gain coefficient and measurement error at time t, and the state variable at time t-1 Specifically, the calculation process of the posterior estimation value is shown in formula (15).

[0093]

[0094] It can be seen from formula (15) that the estimation result output by the estimation model not only takes into account the correction of the vehicle mass and road slope by the Kalman gain coefficient, but also calibrates the Kalman gain coefficient based on the measurement error of the acceleration, so that the corrected vehicle mass and road slope are more accurate.

[0095] In summary, by using the solution shown in this embodiment, an estimation model based on Kalman filtering can be effectively constructed.

[0096] Corresponding to the method for estimating the vehicle mass and the road surface slope provided in the above-mentioned embodiment of the present application, the embodiment of the present application also provides a device for estimating the vehicle mass and the road surface slope.

[0097] like Figure 3 FIG. 1 is a schematic diagram of the architecture of a device for estimating vehicle mass and road slope provided in an embodiment of the present application, including:

[0098] The operating condition acquisition unit 100 is used to acquire the driving condition of the vehicle in any time period after the vehicle is powered on, as well as the vehicle speed and wheel end torque at each moment in each time period.

[0099] The estimation unit 200 is used for inputting the vehicle speed and wheel-end torque at each moment in the time period into the estimation model for the driving condition in each time period, upon determining that the driving condition meets the preset enabling conditions, to obtain the estimation result output by the estimation model; wherein the preset enabling conditions include: the vehicle is not in any of the braking conditions, steering conditions, starting conditions, rapid acceleration conditions, rapid deceleration conditions, and wheel slip conditions; the estimation result includes the vehicle mass and the road surface slope at each moment in the time period; the estimation model is pre-constructed based on the vehicle dynamics model; the vehicle dynamics model is used to indicate the correlation between the vehicle speed, wheel-end torque, vehicle mass, and road surface slope.

[0100] Optionally, the estimation unit 200 is specifically used to: pre-create a state equation for road slope and a state equation for vehicle mass; generate a Jacobian matrix of acceleration based on the vehicle dynamics model, the state equation for road slope, and the state equation for vehicle mass; calibrate initial values ​​for parameters in the Jacobian matrix; construct a Kalman filter gain coefficient based on a preset prior error covariance matrix and the Jacobian matrix; construct an estimation model based on the measurement error of acceleration, a preset a posteriori error covariance matrix, and a Kalman filter gain coefficient; wherein the measurement error indicates the difference between the first acceleration and the second acceleration; the first acceleration is the acceleration calculated based on the vehicle speed; and the second acceleration is the acceleration calculated based on the Kalman filter gain coefficient.

[0101] The estimation unit 200 is also used to: when it is determined that the driving condition does not meet the preset enabling conditions, use the vehicle mass at the last moment in the previous time period as the vehicle mass at each moment in the time period, and use the road surface slope at the last moment in the previous time period as the road surface slope at each moment in the time period.

[0102] The estimation unit 200 is also used for: when it is determined that the driving condition does not meet the preset enabling condition, and the time period is the first time period experienced by the car after power-on, using the preset initial value of the car mass as the car mass at each moment in the time period, and using the preset initial value of the road surface slope as the road surface slope at each moment in the time period.

[0103] To summarize, when it is determined that the driving condition meets the preset enabling conditions, the vehicle speed, wheel-end torque and estimation model are used to calculate the vehicle mass and road slope at each moment in the time period to which the driving condition belongs. The vehicle mass and road slope under the driving condition can be accurately estimated without increasing the vehicle hardware cost, thereby improving the accuracy of the braking system algorithm control.

[0104] The present application also provides a computer-readable storage medium, which includes a stored program, wherein the program executes the method for estimating the vehicle mass and road slope provided by the present application.

[0105] The present application also provides a vehicle, including: a processor, a memory and a bus. The processor and the memory are connected via a bus, the memory is used to store a program, and the processor is used to run the program, wherein when the program is run, the method for estimating the vehicle mass and the road surface slope provided by the present application is executed, including the following steps:

[0106] After the vehicle is powered on, the driving condition of the vehicle in any time period, as well as the vehicle speed and wheel end torque at each moment in each time period are obtained;

[0107] For the driving condition in each of the time periods, when it is determined that the driving condition meets the preset enabling conditions, the vehicle speed and wheel-end torque at each moment in the time period are input into the estimation model to obtain the estimation result output by the estimation model; wherein the preset enabling conditions include: the vehicle is not in any of the braking condition, steering condition, starting condition, sudden acceleration condition, sudden deceleration condition, and wheel slip condition; the estimation result includes the vehicle mass and road surface slope at each moment in the time period; the estimation model is pre-constructed based on the vehicle dynamics model; the vehicle dynamics model is used to indicate the correlation between the vehicle speed, wheel-end torque, vehicle mass and road surface slope.

[0108] Specifically, based on the above embodiment, the process of pre-building the estimation model based on the vehicle dynamics model includes:

[0109] Create the state equation of road slope and the state equation of vehicle mass in advance;

[0110] Generate a Jacobian matrix of acceleration based on a vehicle dynamics model, a state equation of the road slope, and a state equation of the vehicle mass;

[0111] Calibrate initial values ​​for parameters in the Jacobian matrix;

[0112] Constructing a Kalman filter gain coefficient based on a preset priori error covariance matrix and the Jacobian matrix;

[0113] The estimation model is constructed based on the measurement error of acceleration, a preset a posteriori error covariance matrix, and the Kalman filter gain coefficient; wherein the measurement error indicates the difference between the first acceleration and the second acceleration; the first acceleration is the acceleration calculated based on the vehicle speed; and the second acceleration is the acceleration calculated based on the Kalman filter gain coefficient.

[0114] Specifically, based on the above embodiment, it also includes:

[0115] When it is determined that the driving condition does not meet the preset enabling condition, the mass of the vehicle at the last moment in the previous time period is used as the mass of the vehicle at each moment in the time period, and the road surface slope at the last moment in the previous time period is used as the road surface slope at each moment in the time period.

[0116] Specifically, based on the above embodiment, it also includes:

[0117] When it is determined that the driving condition does not meet the preset enabling conditions and the time period is the first time period experienced by the car after power-on, the preset initial value of the car mass is used as the car mass at each moment in the time period, and the preset initial value of the road surface slope is used as the road surface slope at each moment in the time period.

[0118] If the functions described in the method of the embodiment of the present application are implemented in the form of software functional units and sold or used as independent products, they can be stored in a storage medium readable by a computing device. Based on this understanding, the part of the embodiment of the present application that contributes to the prior art or the part of the technical solution can be embodied in the form of a software product, which is stored in a storage medium and includes several instructions for a computing device (which can be a personal computer, server, mobile computing device or network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes: various media that can store program codes, such as USB flash drives, mobile hard disks, read-only memories, random access memories, magnetic disks or optical disks.

[0119] The various embodiments in this specification are described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the various embodiments can be referenced to each other.

[0120] The above description of the disclosed embodiments enables those skilled in the art to implement or use the present application. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application will not be limited to the embodiments shown herein, but will conform to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for estimating vehicle mass and road slope, characterized in that: include: After the vehicle is powered on, the driving condition of the vehicle in any time period, as well as the vehicle speed and wheel end torque at each moment in each time period are obtained; For the driving condition in each of the time periods, when it is determined that the driving condition meets the preset enabling conditions, the vehicle speed and wheel-end torque at each moment in the time period are input into the estimation model to obtain the estimation result output by the estimation model; wherein the preset enabling conditions include: the vehicle is not in any of the braking condition, steering condition, starting condition, rapid acceleration condition, rapid deceleration condition, and wheel slip condition; the estimation result includes the vehicle mass and the road surface slope at each moment in the time period; the construction process of the estimation model is: pre-creating the state equation of the road surface slope and the state equation of the vehicle mass; generating the Jacobian matrix of the acceleration based on the vehicle dynamics model, the state equation of the road surface slope, and the state equation of the vehicle mass Represents the wheel end torque at time t, Radius Wheel represents the wheel radius, ρ air Represents the preset air density, C x Represents the preset air resistance coefficient, S f Represents the preset frontal area of ​​the car, Speed Vehicle (t) represents the vehicle speed at time t, g represents the acceleration due to gravity, and Resistance Rolling represents the rolling resistance coefficient, represents the predicted value of the road slope at time t, Represents the predicted value of the vehicle mass at time t; calibrates the initial values ​​of the parameters in the Jacobian matrix; constructs the Kalman filter gain coefficient based on the preset prior error covariance matrix and the Jacobian matrix; constructs the estimation model based on the measurement error of acceleration, the preset a posteriori error covariance matrix, and the Kalman filter gain coefficient; wherein the measurement error indicates the difference between the first acceleration and the second acceleration; the first acceleration is the acceleration calculated based on the vehicle speed; the second acceleration is the acceleration calculated based on the Kalman filter gain coefficient; the vehicle dynamics model is used to indicate the correlation between the vehicle speed, wheel end torque, vehicle mass and road slope.

2. The method according to claim 1, characterized in that Also includes: When it is determined that the driving condition does not meet the preset enabling condition, the mass of the vehicle at the last moment in the previous time period is used as the mass of the vehicle at each moment in the time period, and the road surface slope at the last moment in the previous time period is used as the road surface slope at each moment in the time period.

3. The method according to claim 1, characterized in that Also includes: When it is determined that the driving condition does not meet the preset enabling conditions and the time period is the first time period experienced by the car after power-on, the preset initial value of the car mass is used as the car mass at each moment in the time period, and the preset initial value of the road surface slope is used as the road surface slope at each moment in the time period.

4. A device for estimating vehicle mass and road slope, characterized in that: include: A working condition acquisition unit, used to acquire the driving working condition of the vehicle in any time period, as well as the vehicle speed and wheel end torque at each moment in each time period after the vehicle is powered on; An estimation unit is used for, for each of the driving conditions in the time period, upon determining that the driving conditions meet the preset enabling conditions, inputting the vehicle speed and wheel-end torque at each moment in the time period into an estimation model to obtain an estimation result output by the estimation model; wherein the preset enabling conditions include: the vehicle is not in any of the braking conditions, steering conditions, starting conditions, rapid acceleration conditions, rapid deceleration conditions, and wheel slip conditions; the estimation result includes the vehicle mass and the road surface slope at each moment in the time period; the construction process of the estimation model is: pre-creating a state equation for the road surface slope and a state equation for the vehicle mass; generating a Jacobian matrix of acceleration based on the vehicle dynamics model, the state equation for the road surface slope, and the state equation for the vehicle mass. Represents the wheel end torque at time t, Radius Wheel represents the wheel radius, ρ air Represents the preset air density, C x Represents the preset air resistance coefficient, S f Represents the preset frontal area of ​​the car, Speed Vehicle (t) represents the vehicle speed at time t, g represents the acceleration due to gravity, and Resistance Rolling represents the rolling resistance coefficient, represents the predicted value of the road slope at time t, Represents the predicted value of the vehicle mass at time t; calibrates the initial values ​​of the parameters in the Jacobian matrix; constructs the Kalman filter gain coefficient based on the preset prior error covariance matrix and the Jacobian matrix; constructs the estimation model based on the measurement error of acceleration, the preset a posteriori error covariance matrix, and the Kalman filter gain coefficient; wherein the measurement error indicates the difference between the first acceleration and the second acceleration; the first acceleration is the acceleration calculated based on the vehicle speed; the second acceleration is the acceleration calculated based on the Kalman filter gain coefficient; the vehicle dynamics model is used to indicate the correlation between the vehicle speed, wheel end torque, vehicle mass and road slope.

5. The device according to claim 4, characterized in that The estimation unit is also used for: When it is determined that the driving condition does not meet the preset enabling condition, the mass of the vehicle at the last moment in the previous time period is used as the mass of the vehicle at each moment in the time period, and the road surface slope at the last moment in the previous time period is used as the road surface slope at each moment in the time period.

6. The device according to claim 4, characterized in that The estimation unit is also used for: When it is determined that the driving condition does not meet the preset enabling conditions and the time period is the first time period experienced by the car after power-on, the preset initial value of the car mass is used as the car mass at each moment in the time period, and the preset initial value of the road surface slope is used as the road surface slope at each moment in the time period.

7. A computer-readable storage medium, characterized in that: The computer-readable storage medium includes a stored program, wherein the program executes the method for estimating the vehicle mass and road slope as described in any one of claims 1-3.

8. A vehicle, characterized in that: include: processor, memory, and bus; The processor is connected to the memory via the bus; The memory is used to store programs, and the processor is used to run programs, wherein the program, when running, executes the method for estimating the vehicle mass and the road slope as described in any one of claims 1-3.

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