Vehicle mass real-time estimation method

Abstract

The invention provides a vehicle mass estimation method based on a dynamic forgetting factor recursive least square method (DFFLS for short) in order to improve the rapidity and the stability of a vehicle mass estimation process by aiming at the conditions that the vehicle mass is frequently changed and measurement errors exist in considerable measurement, and aiming at solving the problems that the vehicle mass is frequently changed and the measurement errors exist in considerable measurement, the invention provides the vehicle mass estimation method based on the DFFLS for short. The method comprises the following steps: determining the system output quantity and observable measurement of a vehicle mass estimation recursive least square method (RLS for short) according to a vehicle longitudinal dynamics principle; and by analyzing the influence of the forgetting factor on the estimation result of the recursive least square method and the change rule of the vehicle operation condition and the vehicle mass, the forgetting factor is improved into a dynamic change mode associated with the vehicle speed and the parking time point. Matlab / Simulink is used to establish a 10.5 m whole vehicle simulation test environment, and FFRLS and DFFRLS vehicle mass estimation comparison verification is carried out. The estimation time of the DFFLS is within 2 s, the fluctuation ratio of the vehicle mass estimation value is superior to the estimation time of the FFRLS, and the rapidity and stability of the vehicle mass estimation of the DFFLS are superior to those of the FFRLS. Therefore, the DFFRLS can quickly and stably estimate the mass of the vehicle.

Description

Technical Field

[0001] The invention belongs to the technical field of automobile control, and in particular relates to a real-time estimation method for vehicle mass. Background Art

[0002] At present, great progress has been made in the research on vehicle mass estimation based on the recursive least squares method and its improved methods. However, there are relatively few studies that can take into account both the speed and stability of the bus mass estimation process when the bus mass changes frequently and there are measurement errors in the observed quantities.

[0003] Vehicle mass is an important parameter for intelligent vehicle control and decision-making. As the main source of rolling resistance and ramp resistance, it affects the shifting strategy of the automatic transmission, and thus affects the power and economy of the vehicle. Combining sensor measurement technology with parameter estimation algorithm to accurately obtain vehicle mass in real time has important significance and application value for vehicle driving decision-making. At present, the common method for vehicle mass estimation is the recursive least squares method and its improved method.

[0004] The literature Vehicle mass and road slope estimation considering lateral motion [J / OL] uses acceleration to correct the vehicle dynamics model, and adopts the least squares algorithm with forgetting factor to enhance new data and adapt to the time-varying characteristics of the vehicle system to estimate the vehicle mass; the literature Joint estimation of freight vehicle mass and road slope under complex working conditions [J / OL] aims to solve the problem of mass estimation stability during the acceleration process of freight vehicles, explores the influence of different acceleration types on mass estimation, proposes a joint estimation method of M estimation and recursive least squares method based on forgetting factor, and realizes robust estimation of freight vehicle mass; the literature Electric vehicle ramp recognition integrating vehicle mass estimation [J / OL] establishes a vehicle longitudinal dynamics model, analyzes the signal characteristics of the speed sensor under actual vehicle conditions, and constructs a method with forgetting factor. The least squares vehicle mass estimation strategy of the paper realizes the direct acquisition of the vehicle mass under starting conditions; the literature vehicle mass estimation based on longitudinal frequency response characteristics [J] obtains longitudinal acceleration and wheel speed information, and based on the vehicle longitudinal dynamics model, derives the amplitude-frequency function relationship between the acceleration and wheel speed signals, and obtains the amplitude ratio between the acceleration and wheel speed at the vehicle center of mass at different frequencies, and then obtains the vehicle mass through least squares fitting; the literature vehicle mass and road slope estimation based on interactive multi-models [J] sets the working conditions suitable for accurate mass estimation, and proposes a mass estimation confidence factor calculation algorithm based on fuzzy rules, and then designs a recursive least squares vehicle mass estimation algorithm based on confidence factor to realize online mass estimation. Summary of the invention

[0005] The purpose of the present invention is to propose a real-time vehicle mass estimation method based on a dynamic forgetting factor to solve the problems existing in the prior art, such as the rapidity and stability of vehicle mass estimation.

[0006] In order to achieve the above object, the present invention provides the following technical solutions:

[0007] A method for real-time estimation of vehicle mass comprises the following steps:

[0008] Constructing the vehicle longitudinal dynamics model:

[0009] T dr / r tire -(ρ·A·C w ·v^2) / 2-ε·mg·cos(α)-mg·sin(α)=ma

[0011] Where r_tire is the tire radius, in m; ρ is the air density, which is 1.29kg / m^3; A is the frontal area, in m^2; C_W is the drag coefficient; V is the vehicle speed, in m / s; ε is the rolling resistance coefficient; m is the vehicle mass, in kg; g is the acceleration due to gravity, which is 9.8N / kg; α is the slope, which is 0 to 1; a is the acceleration, in m / s^2.

[0012] Preferably, the vehicle longitudinal dynamics model is converted into the following form to facilitate mass calculation:

[0013]

[0014] In this formula, the data to be solved in real time is the mass of the vehicle being tested, and the known parameters are the vehicle tire radius, air density, windward area of ​​the vehicle being tested, test environment drag coefficient, rolling resistance coefficient of the vehicle being tested, and gravitational acceleration; the parameters collected in real time are slope, vehicle speed, acceleration, and driving torque.

[0015] Preferably, white noise is introduced to simulate the measurement errors of vehicle speed, acceleration and driving torque, thereby increasing the robustness of the experiment.

[0016] Construct a least squares model of vehicle mass:

[0017]

[0018] The incremental matrix update formula is:

[0019]

[0020] The covariance matrix update formula is:

[0021]

[0022] Where M(k) represents the estimated quality of the k-th step, K(k) represents the gain of the k-th step, and E F (k) represents the output of the k-th step kinetic equation, E α (k) represents the observation at the k-th step, k represents the k-th step, M(k-1) represents the quality of the k-1-th step, model P(k-1) represents the covariance of the k-1-th step, P(k) represents the covariance of the k-th step, and λ represents the forgetting factor.

[0023] When k = 0, the following values ​​are available:

[0024]

[0025] α is a sufficiently large positive real number, ε is a zero vector or a sufficiently small positive real vector, and E is the identity matrix.

[0026] According to the vehicle longitudinal dynamics analysis, the system observable quantity E can be obtained: α Model:

[0027] E α =a+ε·g·cos(·α)+g·sin(·α)

[0028] According to the vehicle longitudinal dynamics analysis, the system output E can be obtained: F Model:

[0029]

[0030] Determine the value range and changing trend of the dynamic forgetting factor.

[0031] When the optimal forgetting factor is in the range of 0.8 to 1, the quality calculation stability and real-time performance are high. In the early stage of calculation, it is set to 0.8. As the system running time increases, the forgetting factor approaches 1.

[0032] According to the value range and change trend of the dynamic forgetting factor, the calculation formula of the dynamic forgetting factor is expressed using the exponential increasing formula, and the result is:

[0033]

[0034] Among them, Δt is the iteration period, which is 0.1s; k0 is the number of iterations when the vehicle restarts;

[0035] Try to find the appropriate values ​​of dynamic factor parameters A, B, and C by exhaustive method;

[0036] Bring the dynamic forgetting factor into the least squares model of quality and obtain the quality at time k.

[0037] The present invention also provides a system for assisting vehicle mass estimation, characterized in that it comprises the following modules:

[0038] Vehcle module for calculating vehicle speed, acceleration, friction, torque, and slope;

[0039] Mass_RLS module for estimating vehicle mass;

[0040] The Mass_RLS module adopts any one of the vehicle mass real-time estimation methods of claims 1-6, and the Mass_RLS module includes:

[0041] The lam module for calculating the dynamic forgetting factor;

[0042] EF_Cal module for calculating system observations;

[0043] EA_Cal module for calculating system output;

[0044] FFRLS module of recursive least squares method;

[0045] The FFRLS module includes:

[0046] FFRLS_Init module used to record K-1 time parameters.

[0047] The present invention also provides a device for real-time mass estimation on a vehicle, comprising the following modules: a data acquisition module, a device for collecting vehicle operation data; a data transmission module, which transmits the collected data to a CAN network in a Matlab algorithm system; and a mass calculation module, a system used for mass estimation as described in claim 7.

[0048] Traditional vehicle mass estimation methods mostly rely on fixed parameter models and are easily affected by changes in working conditions, such as changes in road conditions and load, which leads to increased mass estimation errors. However, the present invention introduces a dynamic forgetting factor to adaptively adjust the model's parameter update rate according to the real-time vehicle operating status, thereby improving the stability and real-time performance of mass estimation. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification.

[0050] The examples are used to explain the present invention, but are not intended to limit the present invention.

[0051] Figure 1 CCBC bus operation status

[0052] Figure 2 The vehicle weight changes under the set conditions

[0053] Figure 3 is the estimated value of vehicle mass with a forgetting factor of 0.80

[0054] Figure 4 is the estimated value of vehicle mass with a forgetting factor of 0.95

[0055] Figure 5 is the estimated value of vehicle mass with a forgetting factor of 0.99

[0056] Figure 6 is the estimated value of vehicle mass with dynamic forgetting factor

[0057] Figure 7 Algorithm module diagram

[0058] Figure 8 The structure diagram of the algorithm for simulating bus parameters calculation

[0059] Fig. 9 is the system output E F Calculation algorithm structure diagram

[0060] Fig.10 is the observable quantity E of the system α Calculation algorithm structure diagram

[0061] Fig.11 Dynamic forgetting factor calculation algorithm structure diagram

[0062] Fig.12 The structure diagram of the vehicle mass calculation algorithm

[0063] Fig.13 The structure diagram of the FFRLS forgetting factor recursive least squares algorithm

[0064] Fig.14 The algorithm structure diagram of FFRLS_Init module

[0065] Fig.15 Schematic diagram of the vehicle mass measurement device DETAILED DESCRIPTION

[0066] The present invention is described in detail below in conjunction with the accompanying drawings and embodiments. The following embodiments are only used to more clearly illustrate the technical solution of the present invention, and are therefore only used as examples, and cannot be used to limit the protection scope of the present invention.

[0067] Unless otherwise specified, the technical terms or scientific terms used in this application should be understood by those skilled in the art. The terms "first", "second", etc. used in this specification, claims and drawings are used to distinguish similar objects and do not indicate a specific order or priority. It is understood that these data can be interchanged where appropriate to implement the vehicle mass real-time estimation method described in the embodiment of the present invention. In addition, the terms "including" and "having" and any variations thereof are intended to indicate non-exclusive inclusion, and the term "plurality" indicates two or more possibilities unless otherwise specified. In the embodiment of the present invention, the character " / " is used to indicate that the front and back objects have an "or" relationship, for example, sensor A / sensor B means sensor A or sensor B. The term "and / or" is used to describe the association relationship of objects, and can represent three relationships, for example, sensor A and / or sensor B means sensor A, sensor B, or sensor A and sensor B exist at the same time. The term "corresponding" refers to an association relationship or binding relationship, for example, the vehicle load data corresponds to the vehicle mass estimation model, which means that there is an association or binding relationship between the two.

[0068] This embodiment provides a bus mass estimation example of a vehicle mass real-time estimation method, wherein the steps include:

[0069] Constructing the vehicle longitudinal dynamics model:

[0070]

[0071] In this formula, the data to be solved in real time is the mass of the vehicle being tested, the known parameters are the vehicle tire radius, air density, the windward area of ​​the vehicle being tested, the test environment drag coefficient, the rolling resistance coefficient of the vehicle being tested, and the acceleration of gravity, and the parameters collected in real time are slope, vehicle speed, acceleration, and driving torque.

[0072] In Matlab / Simulink environment, a 10.5-meter bus model is built to run CCBC (China's typical urban bus cycle) working conditions. The main parameters of the bus are as follows

[0073] parameter Numeric Vehicle equipment weight / kg 11800 Air resistance coefficient 0.65 Windward area / m^2 7.25 Rolling resistance coefficient 0.018 Tire radius / m 0.463

[0074] Simulated bus parameters

[0075] Bus driving conditions Figure 1 ;

[0076] Eliminate the influence of slope on vehicle mass estimation and set the vehicle to travel on a flat road with a slope of zero.

[0077] According to the vehicle speed, acceleration, slope setting and vehicle longitudinal dynamics formula set in CCBC working condition, the driving torque is set as:

[0078]

[0079] The maximum number of passengers a bus can carry is 90, and the average weight per person is 70 kg (including passenger weight and carry-on luggage). The weight changes are as follows: Figure 2 .

[0080] The simulation of vehicle speed, acceleration and driving torque measurement errors is achieved by introducing white noise. In this example, the noise power of the vehicle speed white noise is set to 0.001, and the sampling time is 0.01s; the noise power of the acceleration white noise is set to 0.000001, and the sampling time is 0.01s; the noise power of the driving torque white noise is set to 0.01, and the sampling time is 0.02s.

[0081] Construct a vehicle mass calculation method based on the least squares method of the forgetting factor:

[0082]

[0083] Where M(k) represents the estimated quality of the k-th step, K(k) represents the gain of the k-th step, and E F (k) represents the output of the k-th step kinetic equation, E α (k) represents the observation at step k, k represents the kth step, and M(k-1) represents the quality of step k-1;

[0084] The incremental matrix update formula is:

[0085]

[0086] The covariance matrix update formula is:

[0087]

[0088] P(k-1) represents the covariance at the k-1th step, P(k) represents the covariance at the kth step, and λ represents the forgetting factor.

[0089] When k = 0, the following values ​​are available:

[0090]

[0091] α is a sufficiently large positive real number, ε is a zero vector or a sufficiently small positive real vector, and E is the unit matrix.

[0092] According to the vehicle longitudinal dynamics analysis, the system observable quantity E can be obtained: α Model:

[0093] E α =a+ε·g·cos(α)+g·sin(·α)

[0094] According to the vehicle longitudinal dynamics analysis, the system output E can be obtained: F Model:

[0095]

[0096] Determine the value range of the dynamic forgetting factor. The value range of the forgetting factor is generally 0.8 to 1 (not 1). When first estimating, it is set to 0.8. As the system running time increases, the forgetting factor approaches 1.

[0097] According to the setting method of the value range and change trend of the forgetting factor, the calculation formula of the dynamic forgetting factor is expressed using the exponential increasing formula, and the result is:

[0098]

[0099] Among them, Δt is the iteration period, which is 0.1s; k0 is the number of iterations when the vehicle restarts;

[0100] Try to find the appropriate values ​​of dynamic factor parameters A, B, and C by exhaustive method;

[0101] The formula obtained in this example is:

[0102]

[0103] Bring the dynamic forgetting factor into the least squares model of quality and obtain the quality at time k.

[0104] The present invention can achieve fast and stable vehicle weight estimation. The vehicle weight estimation results with forgetting factors of 0.8, 0.95, 0.99 and dynamic form are compared. Figure 3 Figure 4 Figure 5 Figure 6 .

[0105] In order to measure the rapidity of the bus mass estimation process, the time from when the bus starts running from the stop to when the difference between the estimated value of the vehicle mass and the true value is within 1% is set as the rapidity measurement standard.

[0106] In order to improve the stability of the bus mass estimation process, V is set as the estimated value volatility after the error between the estimated value and the true value of the bus mass is less than 1%, in units of (kg / m), and the calculation method is as follows:

[0107]

[0108] The smaller the volatility, the smaller the fluctuation of the estimated value and the better the stability; the larger the volatility, the greater the fluctuation of the estimated value and the worse the stability.

[0109] From the four sets of comparison data, we can see that the smaller the forgetting factor, the shorter the estimation time and the greater the fluctuation of the estimation result; the larger the forgetting factor, the longer the estimation time and the smaller the fluctuation of the estimation result. The dynamic forgetting factor has a fast and stable estimation effect.

[0110]

[0111] When the forgetting factor is a fixed value of 0.80 or 0.95, the estimated value of the bus mass fluctuates greatly. When the forgetting factor is a fixed value of 0.99 or a dynamically changing value, the estimated value of the bus mass fluctuates less.

[0112] For FFRLS, when the forgetting factor is 0.99, the volatility is the smallest, which is 35.8kg / s and 35.7kg / s; when the DFFRLS method is used for estimation, its volatility is smaller than the volatility when the FFRLS forgetting factor is 0.99, which is 10kg / s and 23.8kg / s.

[0113] When the forgetting factor is 0.80, the estimated time is shorter; when the forgetting factor is 0.99, the estimated volatility is lower, but the forgetting factor is not the same value; and the forgetting factor of DFFRLS changes dynamically, and its estimated time is equivalent to the FFRLS estimated time with a forgetting factor of 0.80, and its estimated volatility is lower than the FFRLS estimated volatility with a forgetting factor of 0.99.

[0114] In a second aspect, the present invention provides a system that can help estimate vehicle mass. The system can be made into a software module and put into a transmission controller for operation. The algorithm can be developed based on Matlab / Simulink. The algorithm module is as follows: Figure 7 As shown;

[0115] First, the CCBC bus operation status, passenger number, gravity acceleration and other data are input into the Figure 8 In the algorithm shown, the current vehicle speed vVehSpdMeas, driving torque trqDrv, vehicle acceleration aAccVehMeas, and sideslip angle phiSlp can be obtained. The simulation of the measurement errors of vehicle speed, acceleration, and driving torque is realized by introducing white noise. In this example, the noise power of the vehicle speed white noise is set to 0.001, and the sampling time is 0.01s; the noise power of the acceleration white noise is set to 0.000001, and the sampling time is 0.01s; the noise power of the driving torque white noise is set to 0.01, and the sampling time is 0.02s;

[0116] The current speed and drive torque are introduced into Fig. 9 In the algorithm shown, the air density, drag coefficient, frontal area, and tire radius are input, and the algorithm can output the system output E F ;

[0117] Introducing vehicle acceleration and sideslip angle Fig.10 In the algorithm shown, gravity acceleration and friction are input, and the algorithm can output the system observation E a ;

[0118] CCBC and K0 input Fig.11 In the algorithm shown, the algorithm can output a dynamic forgetting factor lam.

[0119] Lam, E F , Eα Introduced Fig.12 In the algorithm shown, the initial mass, maximum mass and minimum mass of the vehicle are introduced, and the algorithm can output the vehicle mass M_k at time K.

[0120] Fig.12 The detailed algorithm of FFRLS algorithm is as follows Fig.13 As shown, the fcn function is:

[0121]

[0122] The detailed algorithm of FFRLS_Init algorithm in FFRLS algorithm is as follows Fig.14 As shown, it is used to record the vehicle value at the last moment.

[0123] According to a third aspect, the present invention provides a device capable of measuring vehicle data in real time. The device can be mounted on a vehicle to perform real-time vehicle mass estimation.

[0124] There are the following steps, such as Fig.15 As shown:

[0125] (1) Use a diagnostic instrument to measure the current driving condition of the vehicle. The measurement data includes: vehicle speed V, current road slope α, and the combined torque Tdr formed by the vehicle's accelerator and brake pedal control.

[0126] (2) By connecting the CAN communication card to the vehicle's CAN network interface, the CAN network data on the vehicle can be read in real time.

[0127] (3) Matlab / Simulink uses the above system to estimate the real-time vehicle mass based on the real-time read data.

Claims

1. A method for real-time estimation of vehicle mass, characterized in that: The following steps are involved: Constructing the vehicle longitudinal dynamics model: T dr / r tire -(p·A·C W ·v^2) / 2-ε·mg·cos(α)-mg·sin(α)=ma Collect vehicle operation data; determine the parameters as vehicle tire radius, air density, windward area of ​​the vehicle under test, test environment drag coefficient, test vehicle rolling resistance coefficient, and gravity acceleration; collect slope, vehicle speed, acceleration, and driving torque in real time; Construct a vehicle mass calculation method based on the least squares method of the forgetting factor: Where M(k) represents the estimated quality of the k-th step, K(k) represents the gain of the k-th step, and E F (k) represents the output of the k-th step kinetic equation, E α (k) represents the observation at step k, k represents the kth step, and M(k-1) represents the quality of step k-1; According to the analysis of vehicle longitudinal dynamics, the model of system observable quantity Eα can be obtained: E α =a+ε·g·cos(α)+g·sin(α) According to the vehicle longitudinal dynamics analysis, the system output E can be obtained: F Model: Determine the value range and change trend of the dynamic forgetting factor; According to the value range and change trend of the dynamic forgetting factor, the calculation formula of the dynamic forgetting factor is expressed using the exponential increasing formula, and the result is: Among them, Δt is the iteration period, which is 0.1s; k0 is the number of iterations when the vehicle restarts; Try to find the appropriate values ​​of dynamic factor parameters A, B, and C by exhaustive method; Bring the dynamic forgetting factor into the least squares model of quality and obtain the quality at time k.

2. A vehicle mass real-time estimation method according to claim 1, characterized in that: The vehicle longitudinal dynamics model is converted to: Where r_tire is the tire radius, in m; ρ is the air density, which is 1.29kg / m^3; A is the frontal area, in m^2; C_W is the drag coefficient; V is the vehicle speed, in m / s; ε is the rolling resistance coefficient; m is the vehicle mass, in kg; g is the acceleration due to gravity, which is 9.8N / kg; α is the slope, which is 0 to 1; a is the acceleration, in m / s^2.

3. A vehicle mass real-time estimation method according to claim 1, characterized in that: The method for setting the dynamic forgetting factor: the value range of the forgetting factor is 0.8 to 1 (not 1).

4. A vehicle mass real-time estimation method according to claim 1, characterized in that: The least squares model incremental matrix update formula for the quality is: The covariance matrix update formula is: P(k-1) represents the covariance at the k-1th step, P(k) represents the covariance at the kth step, and λ represents the forgetting factor.

5. A system for assisting vehicle mass estimation, characterized in that: The system includes the following modules: Vehcle module for calculating vehicle speed, acceleration, torque, and slope; Mass_RLS module for estimating vehicle mass; The Mass_RLS module adopts any one of the vehicle mass real-time estimation methods of claims 1-6, and the Mass_RLS module includes: Iam module for calculating dynamic forgetting factor; EF_Cal module for calculating system observations; EA_Cal module for calculating system output; FFRLS module of recursive least squares method; The FFRLS module includes: FFRLS_Init module used to record K-1 time parameters.

6. A device for real-time mass estimation on a vehicle, characterized in that: It includes the following modules: data acquisition module, a device for collecting vehicle operation data; Data transmission module, which transmits the collected data to the CAN network in the computer software Matlab; The mass calculation module is established in Matlab software, and is the system used for mass estimation as described in claim 5.

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