METHOD FOR DETERMINING THE PAYLOAD MASS OF A VEHICLE

Abstract

A method for determining the payload mass resting on a wheel of a vehicle. In the method: using a level sensor system, in a time period in which the vehicle is moved, a time series of measured values is detected, which each indicate the vertical position of the vehicle body in relation to the wheel; a model is provided for the temporal development of the vertical position under the influence of the gravitational force of vehicle body and payload, an elastic suspension between the vehicle body and the wheel of the vehicle, and a damping of the vertical relative movement between the vehicle body and the wheel of the vehicle, the model being parameterized at least using the sought payload mass and the wheel of the vehicle and the connection of the wheel to the roadway being assumed to be rigid.

Claims

1. A method for determining a sought payload mass resting on a wheel of a vehicle, this vehicle having a level sensor system which is capable of detecting a vertical movement of the vehicle body in relation to the wheel, including the steps: detecting, using the level sensor system, in a time period in which the vehicle is moved, a time series of measured values, each of the measured values indicating a vertical position of the vehicle body in relation to the wheel; providing a model for a temporal development of the vertical position under influence of the gravitational force of vehicle body and the payload, an elastic suspension between the vehicle body and the wheel of the vehicle, and a damping of the vertical relative movement between the vehicle body and the wheel of the vehicle, the model being parameterized at least using the sought payload mass, and the wheel of the vehicle and a connection of the wheel to a roadway being assumed to be rigid; ascertaining a payload mass which brings the model optimally into accordance with the time series of measured values as the sought payload mass.

2. The method as recited in claim 1, wherein the model includes a balance of the forces acting on the vehicle body.

3. The method as recited in claim 1, wherein preparation of the model includes a discretization of the temporal development into time steps having step width Δt.

4. The method as recited in claim 3, wherein the ascertainment of the payload mass includes, preparing, based on the model and based on the temporal development of the vertical position between successive time steps, a system of differential equations in which the payload mass is an unknown.

5. The method as recited in claim 3, wherein a time derivative ż of at least one state z in a time step k is approximated by differential quotients of a state change z.sub.k+1−z.sub.k up to the time step k+1 and the step width Δt.

6. The method as recited in claim 1, wherein the model characterizes a state z=[z.sub.1,z.sub.2].sup.T of the vehicle body by way of the vertical position z.sub.1=z.sub.a−z.sub.r and by way of its time derivative z.sub.2=ż.sub.a−ż.sub.r.

7. The method as recited in claim 6, wherein the state z and parameters of the model are ascertained using at least one nonlinear observation algorithm.

8. The method as recited in claim 7, wherein the state z, or the parameters, are alternately predicted from temporally previous pieces of information and corrected based on more recent pieces of information using the nonlinear observation algorithm.

9. The method as recited in claim 8, wherein the correction of the parameters is carried out using the prediction of the state, and the prediction of the state is carried out using the prediction of the parameters.

10. The method as recited in claim 7, wherein the state z is ascertained using an Unscented Kalman Filter, and the parameters are ascertained using an Extended Kalman Filter.

11. The method as recited in claim 1, wherein: the model is adapted to the time series of the measured values by varying the parameters of the model; and the payload mass is ascertained from those parameters for which the model is best consistent with the time series of the measured values.

12. The method as recited in claim 1, wherein the ascertained payload mass is used to meter a braking force and/or acceleration force of the vehicle in a movement regulation for a longitudinal movement of the vehicle.

13. A non-transitory machine-readable data medium on which is stored a computer program for determining a sought payload mass resting on a wheel of a vehicle, the vehicle having a level sensor system which is capable of detecting a vertical movement of the vehicle body in relation to the wheel, the computer program, when executed by one or multiple computers, causing the one or multiple computers to perform the steps: detecting, using the level sensor system, in a time period in which the vehicle is moved, a time series of measured values, each of the measured values indicating a vertical position of the vehicle body in relation to the wheel; providing a model for a temporal development of the vertical position under influence of the gravitational force of vehicle body and the payload, an elastic suspension between the vehicle body and the wheel of the vehicle, and a damping of the vertical relative movement between the vehicle body and the wheel of the vehicle, the model being parameterized at least using the sought payload mass, and the wheel of the vehicle and a connection of the wheel to a roadway being assumed to be rigid; ascertaining a payload mass which brings the model optimally into accordance with the time series of measured values as the sought payload mass.

14. One or multiple computers configured to determine a sought payload mass resting on a wheel of a vehicle, this vehicle having a level sensor system which is capable of detecting a vertical movement of the vehicle body in relation to the wheel, the one or multiple computers configured to: detect, using the level sensor system, in a time period in which the vehicle is moved, a time series of measured values, each of the measured values indicating a vertical position of the vehicle body in relation to the wheel; provide a model for a temporal development of the vertical position under influence of the gravitational force of vehicle body and the payload, an elastic suspension between the vehicle body and the wheel of the vehicle, and a damping of the vertical relative movement between the vehicle body and the wheel of the vehicle, the model being parameterized at least using the sought payload mass, and the wheel of the vehicle and a connection of the wheel to a roadway being assumed to be rigid; ascertain a payload mass which brings the model optimally into accordance with the time series of measured values as the sought payload mass.

Description

BRIEF DESCRIPTION OF THE DRAWINGS

[0033] FIG. 1 shows an exemplary embodiment of method 100, according to the present invention.

[0034] FIG. 2 shows an illustration of simplified dynamic model 1 including only one spring constant k.sub.a and damping constants d.sub.a, according to an example embodiment of the present invention.

[0035] FIG. 3 shows a combination of an EKF for the estimation of parameters 1a and a UKF for the estimation of state z, according to an example embodiment of the present invention.

[0036] FIGS. 4A and 4B shows a comparison of various nonlinear observers in the simulation of a trip on a level route (FIG. 4A) and an uphill-downhill trip (FIG. 4B);

[0037] FIGS. 5A-5C show an observation of payload mass m.sub.a,zu and state z from measured values z.sub.a−z.sub.r of a real trip on a level route.

DETAILED DESCRIPTION OF EXAMPLE EMBODIMENTS

[0038] FIG. 1 is a schematic flowchart of an exemplary embodiment of method 100 for determining payload mass m.sub.a,zu resting on a wheel of a vehicle.

[0039] In step 110, a time series of measured values, which each indicate vertical position z.sub.a−z.sub.r of the vehicle body in relation to the wheel, is detected using the level sensor system of the vehicle in a time period in which the vehicle is moved.

[0040] In step 120, a model 1 for the temporal development of vertical position z.sub.a−z.sub.r under the influence of the gravitational force of vehicle body and payload, an elastic suspension between the vehicle body and the wheel of the vehicle, and a damping of the vertical relative movement between the vehicle body and the wheel of the vehicle is provided. This model 1 is parameterized at least using the sought payload mass m.sub.a,zu and assumes the wheel of the vehicle and the connection of the wheel to the roadway as rigid. Model 1 may additionally also be parameterized using arbitrary further parameters 1a.

[0041] In step 130, a payload mass m.sub.a,zu*, which brings model 1 optimally into accordance with the time series of measured values, is ascertained as the sought payload mass m.sub.a,zu.

[0042] In step 140, ascertained payload mass m.sub.a,zu is used to meter a braking force and/or acceleration force of the vehicle in a movement regulation for a longitudinal movement of the vehicle.

[0043] According to block 121, model 1 may include a balance of the forces acting on the vehicle body.

[0044] According to block 122, the preparation of model 1 may include the discretization of the temporal development in time steps having step width Δt. The ascertainment of payload mass m.sub.a,zu* may then in particular include, according to block 131, preparing a system of differential equations on the basis of model 1, on the one hand, and the temporal development of vertical position z.sub.a−z.sub.r between successive time steps k and k+1, on the other hand. In this system of differential equations, payload mass m.sub.a,zu* is an unknown.

[0045] According to block 122a, a time derivative ż of at least one state z in a time step k may be approximated by the differential quotient from state change z.sub.k+1−z.sub.k up to time step k+1 and step width Δt.

[0046] According to block 123, a model 1 may be selected which characterizes state z=[z.sub.1,z.sub.2].sup.T of the vehicle body by vertical position z.sub.1=z.sub.a−z.sub.r and by its temporal derivative z.sub.2=ż.sub.a−ż.sub.r. State z and parameters 1a of model 1 may then be ascertained according to block 132 using at least one nonlinear observation algorithm.

[0047] In particular, according to block 132a, state z, or parameters 1a, may alternately be predicted from temporally previous pieces of information and corrected on the basis of more recent pieces of information using the observation algorithm. According to block 132b, the correction of parameters 1a may be carried out using the prediction of state z, and the prediction of state z may be carried out using the prediction of parameters 1a.

[0048] According to block 132c, state z may be ascertained using an Unscented Kalman Filter (UKF), and parameters 1a may be ascertained using an Extended Kalman Filter (EKF).

[0049] According to block 124, model 1 may be adapted to the time series of the measured values by varying its parameters 1a. According to block 133, payload mass m.sub.a,zu* may be ascertained from those parameters 1a, for which model 1 is best consistent with the time series of the measured values.

[0050] FIG. 2 illustrates simplified dynamic model 1. The vehicle body having mass m.sub.a,l is coupled to road S via an elastic suspension having spring constant k.sub.a and via a damping (such as a shock absorber) having damping constant d.sub.a. The payload having mass m.sub.a,zu exerts a weight force F.sub.zu on the vehicle body.

[0051] The level sensor system measures vertical position z.sub.a−z.sub.r of the vehicle body in relation to the wheel of the vehicle (not shown in FIG. 2).

[0052] FIG. 3 illustrates a combination of an EKF for the estimation of parameters 1a and a UKF for the estimation of state z. Sensor data z.sub.a−z.sub.r are only supplied to the UKF.

[0053] The UKF includes a predictor P.sub.z, which outputs an estimation P.sub.z(k+1) of state z for point in time k+1 on the basis of the temporally previous pieces of information. The UKF additionally includes a corrector K.sub.z, which corrects this information P.sub.z(k+1) on the basis of the most updated pieces of information and outputs final result z.sub.k+1 for state z at point in time k+1.

[0054] Similarly, the EKF includes a predictor P.sub.1a, which outputs an estimation P.sub.1a(k+1) of parameters 1a for point in time k+1 on the basis of the temporally previous pieces of information. The EKF additionally includes a corrector K.sub.1a, which corrects this estimation P.sub.1a(k+1) on the basis of the most updated pieces of information and outputs final result 1a.sub.k+1 for parameters 1a at point in time k+1.

[0055] The main difference between the EKF and the UKF is that the EKF is primarily directed to a linearization of the observed behavior by Taylor development, while the UKF selects multiple sigma points and brings together the results obtained by processing of these sigma points with the nonlinear function to be observed.

[0056] Both in the UKF and in the EKF, correction K.sub.z(k+1) or K.sub.1a(k+1) supplied by corrector K.sub.z or K.sub.1a is fed back into associated predictor P.sub.z or P.sub.1a. In addition, prediction P.sub.z(k+1) from the UKF is fed into corrector K.sub.1a of the EKF. Furthermore, prediction P.sub.1a(k+1) from the EKF is fed into the predictor P.sub.z of the UKF.

[0057] FIG. 4A shows a comparison of various nonlinear observers in the simulation of a trip on a level route. The vehicle initially accelerates from 0 km/h to 10 km/h, before it is braked again after 10 m. Estimated total mass m.sub.g of the vehicle is plotted over driving distance D. This total mass m.sub.g results from the known and constant empty mass of the vehicle (here 1519 kg) and particular estimated payload mass m.sub.a,zu.

[0058] Actual total mass m.sub.g, which is ideally to be ascertained using estimation of m.sub.a,zu supplied in each case by the observers, is represented by line d. Curve a indicates total mass m.sub.g according to the estimation of m.sub.a,zu supplied by the combination illustrated in FIG. 3 of an EKF and a UKF (DEUKF). Curve b indicates total mass m.sub.g according to the estimation of m.sub.a,zu ascertained only using an EKF. Curve c indicates total mass m.sub.g according to the estimation of m.sub.a,zu ascertained only using a Luenberger observer. Lines d show a corridor of 3% percent deviation from actual total mass m.sub.g.

[0059] FIG. 4B shows a similar simulation for an uphill-downhill trip along the profile shown by curve f, for which the right scale of FIG. 4B shows particular height h.

[0060] Both during the trip on the level and also during the uphill-downhill trip, total mass m.sub.g ascertained according to the estimations ascertained using DEUKF converges very quickly to a final result which is close to actual total mass m.sub.g. The estimations are thus also usable for very short trips, as occur, for example, during parking and unparking.

[0061] FIGS. 5A-5C show an observation of payload mass m.sub.a,zu (FIG. 5A) and state z (FIGS. 5B and 5C) from measured values z.sub.a−z.sub.r of a real trip on a level route.

[0062] Line a in FIG. 5A shows actual payload mass m.sub.a,zu. Curve b plots the estimation of this payload mass m.sub.a,zu using the DEUKF model illustrated in FIG. 3 over driving distance D.

[0063] Curve c in FIG. 5B shows vertical position z.sub.a−z.sub.r taken from the observation of state z using the model. Curve d shows the measured values of the level sensor system, which are obviously reproduced very well by the observation.

[0064] Curve e in FIG. 5C shows time derivative ż.sub.a−ż.sub.r of vertical position z.sub.a−z.sub.r taken from the observation of state z using the DEUKF model. Curve f shows comparative values obtained by numeric differentiation of the measured values for time derivative ż.sub.a−ż.sub.r. At least qualitatively good correspondence with the observation is also shown here.