METHOD FOR CONTROLLING A DRIVING ROBOT BY ANTICIPATION

Abstract

The invention relates to a method for controlling a driving robot for a motor vehicle powertrain, comprising: - a step of loading a template of raw speed commands (Cb) as a function of time associated with a maximum gross margin (Mgb_max) and a minimum gross margin (Mgb_min) to be respected, - a step of sampling the template of raw speed commands according to a sampling period, - a step of calculating a new maximum margin (Mgc_max) lower than the maximum gross constraint margin (Mgb_max) and a new minimum margin (Mgc_min) greater than the minimum gross constraint margin (Mgb_min), defining a level of driver anticipation, and - a step of calculating an anticipated speed command (Ca) from the maximum constraint margin (Mgc_max) and the minimum constraint margin (Mgc_min), a speed variation range, and an analysis horizon. Figure 5.

Description

Title of the invention: CONTROL METHOD OF AN ANTICIPATORY DRIVING ROBOT

[0001] The present invention relates to a method of piloting a driving robot by anticipation.

[0002] The invention finds a particularly advantageous application for the development of powertrains for internal combustion engine vehicles with gasoline and / or hybrid engines.

[0003] In a manner known per se, a driving robot used on a powertrain test bench is capable of autonomously and automatically carrying out powertrain homologation cycles, such as a worldwide harmonized test procedure for light vehicles known as the WLTP cycle (for "Worldwide Light Vehicles Test Procedures" according to Anglo-Saxon terminology).

[0004] To this end, the driving robot includes a control unit and actuators capable of mechanically controlling the brake pedal, the accelerator pedal, and the gear shift lever of the powertrain. The driving robot can thus control the acceleration and deceleration of the powertrain in order to follow a set of speed commands.

[0005] However, existing driving robots are not capable of automatically simulating the anticipatory behavior of a driver, making it possible to validate the robustness of settings for an energy optimization function of a powertrain.

[0006] The invention aims to effectively remedy this drawback by proposing a method for controlling a driving robot for a motor vehicle powertrain comprising: - a step of loading a template of raw speed instructions based on the time associated with a maximum gross margin and a minimum gross margin to be respected, - a step of sampling the template of raw speed instructions according to a sampling period, - a calculation step of a new maximum margin lower than the maximum gross stress margin and a new minimum margin higher than the minimum gross stress margin, referred to respectively as "maximum stress margin" and "minimum stress margin", defining a driver's level of anticipation, and - a step of calculating an anticipated speed setpoint from the maximum constraint margin and the minimum constraint margin, a speed variation range, and an analysis horizon.

[0007] The invention thus makes it possible to autonomously monitor a speed setpoint, simulating the anticipatory behavior of a driver, in order to validate the robustness of the settings of an energy optimization function. The invention also allows for a single setting, regardless of the powertrain being controlled and the initial speed setpoint, to produce a speed setpoint simulating the driver's anticipatory behavior.

[0008] According to one embodiment of the invention, the step of calculating the anticipated speed setpoint comprises: - a step of determining, for each speed variation in the range, a projection of the speed setpoint onto the analysis horizon that generates an exceedance of the maximum constraint margin or an exceedance of the minimum constraint margin, - a step of selecting a speed variation corresponding to a maximum observed exceedance duration, and - a step of calculating the anticipated speed setpoint equal to the selected speed variation multiplied by the sampling period added to a previous anticipated speed setpoint.

[0009] According to one embodiment of the invention, the speed variation range is by default between -4 km / h / s and +4 km / h / s.

[0010] According to one embodiment of the invention, the analysis horizon is between 300s and 700s and is preferably on the order of 500s.

[0011] According to one embodiment of the invention, said method further comprises a step for treating side effects related to a takeoff phase or a stopping phase of the motor vehicle.

[0012] According to one embodiment of the invention, below a parameterable speed threshold, the anticipated speed setpoint is equal to the raw setpoint during a takeoff phase of the motor vehicle.

[0013] According to an embodiment of the invention, below a parameterizable speed threshold, the anticipated speed setpoint (Ca) converges towards the raw speed setpoint during a stopping phase of the motor vehicle according to the function: Ca(n) = Ca(nl) + [(Cb(n) - Ca(nl)) / 2 ] - Ca(n) being a current anticipated speed setpoint, - Ca(nl) being a previous anticipated speed setpoint, - Cb(n) being a previous raw speed setpoint.

[0014] According to one embodiment of the invention, a transfer function applied to the maximum and minimum gross margins to calculate the maximum and minimum constrained margins is a parameterizable offset defined by a constraint value.

[0015] According to one embodiment of the invention, the maximum constrained margin is equal to the maximum gross margin less the constraint value.

[0016] According to one embodiment of the invention, the minimum constrained margin is equal to the minimum gross margin plus the constraint value.

[0017] The invention will be better understood upon reading the following description and examining the accompanying figures. These figures are given only to illustrate, but in no way limit, the invention.

[0018] [Fig-1] schematically represents a powertrain test bench in which a driving robot piloted according to the method of the invention is installed;

[0019] [Fig.2] is a graphical representation of a raw speed instruction template associated with maximum and minimum margins;

[0020] [Fig.3] illustrates the definition of maximum and minimum constraint margins during the implementation of the process according to the invention;

[0021] [Fig.4] illustrates the determination of an anticipated speed setpoint during the implementation of the method according to the invention;

[0022] [Fig.5] is a representation, on the same graph, of a template of raw speed instructions, maximum and minimum initial margins, minimum and maximum constrained margins, and anticipated speed instructions obtained following the implementation of the process according to the invention.

[0023] Figure 1 shows a test bench 10 of a powertrain 11 comprising a driving robot 12 controlled according to the method of the invention. The driving robot 12 comprises a control unit and actuators capable of mechanically controlling the brake pedal, the accelerator pedal, and the gear shift lever of the powertrain.

[0024] As explained below, the driving robot 12 is capable of developing a speed command from a template of raw speed commands which it will pass on to the powertrain 11 by imposing an acceleration Acc via the control of the brake pedal, the accelerator pedal and the gear shift lever.

[0025] The driving robot 12 communicates via a module 13 with a simulation environment 14, for example, of the dSpace-Simulink type (trade name). The module 13 manages, via the environment 14, a deceleration Dec_EE linked to a phase of electrical energy recovery. A drive system for the powertrain 15 manages a deceleration Dec_p linked to the road gradient.

[0026] The method of piloting the driving robot 12 according to the invention is described below with reference to figures 2 to 5.

[0027] A first step consists of loading a template of raw speed setpoints Cb as a function of time associated with a maximum gross margin Mgb_max and a minimum gross margin Mgb_min to be respected, as illustrated in [Fig.2]. The template The raw speed setpoints Cb and the raw margins Mgb_max, Mgb_min can be stored in a memory of the control unit of the driving robot 12.

[0028] The raw speed setpoint template Cb is sampled according to a sampling period T_ech. The sampling period T_ech is for example 0.1s.

[0029] A second step consists of calculating a new maximum margin Mgc_max, which is less than the maximum gross stress margin Mgb_max, and a new minimum margin Mgc_min, which is greater than the minimum gross stress margin Mgb_min. These are referred to respectively as the "maximum stress margin" and the "minimum stress margin." The maximum stress margin Mgb_max and minimum stress margin Mgb_min define a driver's level of anticipation. The minimum stress margin Mgc_min and maximum stress margin Mgc_max can be parameterized to limit or maximize the driver's anticipation.

[0030] A transfer function Fct is applied to the gross margins Mgb_min, Mgb_max to calculate the constrained margins, i.e., Mgcx = Fct(Mgbx), where x corresponds to the minimum or maximum margin depending on the calculation case. Preferably, this transfer function Fct is a parameterizable offset defined by a constraint value Ctr.

[0031] As illustrated in [Fig.3], the maximum constrained margin Mgc_max is equal to the maximum gross margin Mgb_max less the constraint value Ctr, i.e. Mgc_max = Mgb_max - Ctr.

[0032] The minimum constrained margin Mgc_min is equal to the minimum gross margin Mgb_min plus the constraint value Ctr, i.e. Mgc_min = Mgb_min + Ctr.

[0033] A third step consists of calculating an anticipated speed setpoint Ca from the maximum constraint margin Mgc_max and the minimum constraint margin Mgc_min, a speed variation range [Grd_min; Grd_max], and an analysis horizon Ha.

[0034] The speed variation range [Grd_min; Grd_max] is configurable. This range is, for example, set by default between -4 km / h / s and +4 km / h / s. The analysis horizon Ha can be set between 300s and 700s and is preferably on the order of 500s.

[0035] As illustrated in [Fig. 4], in order to calculate the anticipated speed setpoint Ca, the method includes a step of determining, for each speed variation Grd_x in the range [Grd_min, Grd_max], a projection of the speed setpoint onto the analysis horizon Ha that generates an overshoot of the maximum constraint margin Mgc_max or an overshoot of the minimum constraint margin Mgc_min. A time at which a corresponding overshoot occurs is denoted Tps_d.

[0036] A speed variation Grd_f corresponding to a maximum observed overshoot time Tps_dmax is selected. An anticipated speed setpoint Ca(n) is calculated, equal to the selected speed variation Grd_f multiplied by the period sampling rate T_ech added to a previous anticipated velocity setpoint (Ca(nl)), i.e., Ca (n) = Ca (n-1) + Grd_f * T_ech

[0037] The calculation is carried out on the whole of the speed setpoint template Cb starting from the initial raw setpoint CbO, i.e. Ca(l) = Cb(0).

[0038] A fourth step consists of addressing the side effects related to a takeoff or stop phase of the motor vehicle. It should be noted that a takeoff phase corresponds to a phase during which the vehicle changes from zero to a non-zero velocity. A stop phase corresponds to a phase during which the vehicle changes from a non-zero velocity to zero velocity.

[0039] As illustrated in [Fig. 5], below a configurable speed threshold Sv, the anticipated speed setpoint Ca is equal to the raw speed setpoint Cb during a takeoff phase of the motor vehicle. The speed threshold Sv is, for example, less than 20 km / h.

[0040] Below the configurable speed threshold Sv, the anticipated speed setpoint Ca converges towards the raw speed setpoint (Cb) during a stopping phase of the motor vehicle according to the function: Ca(n) = Ca(nl) + [(Cb(n) - Ca(nl)) / 2 ] - Ca(n) being the current anticipated speed setpoint, - Ca(nl) being the previous anticipated speed setpoint, and - Cb(n) being the previous raw speed setpoint.

Claims

Demands

1. A method for controlling a robot driving a motor vehicle powertrain, characterized in that said method comprises: - a step of loading a template of raw speed commands (Cb) as a function of time associated with a maximum gross margin (Mgb_max) and a minimum gross margin (Mgb_min) to be respected, - a step of sampling the template of raw speed commands according to a sampling period (P_ech), - a step of calculating a new maximum margin (Mgc_max) less than the maximum gross constraint margin (Mgb_max) and a new minimum margin (Mgc_min) greater than the minimum gross constraint margin (Mgb_min), respectively called "maximum constraint margin" and "minimum constraint margin", defining a level of anticipation of a driver, and - a step of calculating an anticipated speed command (Ca) from the maximum constraint margin (Mgc_max) and the minimum constraint margin (Mgc_min).of a speed variation range ([Grd_min; Grd_max]), and an analysis horizon (Ha), this step of calculating the anticipated speed setpoint includes: - a step of determining, for each speed variation (Grd_x) in the range ([Grd_min, Grd_max]), a projection of the speed setpoint onto the analysis horizon (Ha) which generates an exceedance of the maximum constraint margin (Mge max) or an exceedance of the minimum constraint margin (Mge min), - a step of selecting a speed variation (Grd_f) corresponding to a maximum observed exceedance time (Tps_dmax), and - a step of calculating the anticipated speed setpoint (Ca(n)) equal to the selected speed variation (Grd_f) multiplied by the sampling period (T_ech) added to a previous anticipated speed setpoint (Ca(nl)).

2. Method according to claim 1, characterized in that the speed variation range ([Grd_min, Grd_max]) is by default between -4km / h / s and +4km / h / s.

3. A method according to claim 1, characterized in that the analysis horizon (Ha) is between 300s and 700s and is preferably of the order of 500s.

4. A method according to any one of claims 1 to 3, characterized in that it comprises a step of treating side effects related to a takeoff phase or a stopping phase of the motor vehicle.

5. Method according to claim 4, characterized in that, below a parameterable speed threshold (Sv), the anticipated speed setpoint (Ca) is equal to the raw setpoint (Cb) during a takeoff phase of the motor vehicle.

6. Method according to claim 4, characterized in that, below a parameterable speed threshold (Sv), the anticipated speed setpoint (Ca) converges towards the gross speed setpoint (Cb) during a stopping phase of the motor vehicle according to the function: - Ca(n) = Ca(nl) + [(Cb(n) - Ca(nl)) / 2 ] - Ca(n) being a current anticipated speed setpoint, - Ca(nl) being a previous anticipated speed setpoint, - Cb(n) being a previous gross speed setpoint.

7. A method according to any one of claims 1 to 6, characterized in that a transfer function applied to the maximum and minimum gross margins (Mgb_max, Mgb_min) to calculate the maximum and minimum constrained margins (Mgc_max, Mgc_min) is a parameterizable offset defined by a constraint value (Ctr).

8. Method according to claim 7, characterized in that the maximum constrained margin (Mgc_max) is equal to the maximum gross margin (Mgb_max) less the constraint value (Ctr).

9. Method according to claim 7 or 8, characterized in that the minimum constrained margin (Mgc_min) is equal to the minimum gross margin (Mgb_min) plus the constraint value (Ctr).

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