DE102019213749A1 - Method for maintaining an optimal speed specification for a distance to be covered by a vehicle - Google Patents

Abstract

In a method (V) for maintaining an optimal speed setting (vopt) for a distance (s) to be covered by a vehicle (1), on the basis of boundary conditions for stable driving behavior and boundary conditions for compliance with a maximum speed and a setting restriction with regard to Driving and braking force (u) formulates an objective function (J) that is minimized. Based on the result of the minimized objective function (J) and a previously formulated discrete equation of motion (E '), an optimal speed specification (vopt) for the vehicle (1) is determined. A drive system (2) of the vehicle (1) provides drive energy and braking energy in order to maintain the optimal speed setting (vopt) for the distance (s) to be covered.

Description

The present invention relates to a method for maintaining an optimal speed setting for a distance to be covered by a vehicle with the features according to claim 1, a control device with the features according to claim 3, a computer program product with the features according to claim 4 and a vehicle with the features according to claim 5.

Methods of model-based predictive control (in English: Model Predictive Control or MPC for short) are used in the field of trajectory control, in particular in the field of longitudinal dynamic control of motor vehicles. In his work "Energy-Efficient Driver Assistance System For Electric Vehicles Using Model-Predictive Control" (Schwickart, T., Universite du Luxembourg, dissertation, 2015), Schwickart proposes an approach for determining an energy-efficient speed specification based on a quadratic programming optimization problem in front.

Out EP 2610836 A1 an optimization of an energy management strategy on the basis of a forecast horizon and further environmental information by minimizing a cost function is also known. A neural network is created for use in the vehicle and the driver is modeled as well as a prediction of the speed profile he is likely to have chosen.

Also disclosed EP 1256476 B1 a strategy to reduce the energy consumption while driving and to increase the range. Information from the navigation device is used, namely a current vehicle position, road pattern, geography with date and time, change in altitude, speed restrictions, intersection density, traffic monitoring and the driver's driving pattern.

Also revealed DE 102016209984 A1 a method for estimating a probability distribution of the maximum coefficient of friction at a current and / or future waypoint of a vehicle.

It is therefore known to plan a speed profile that is as energy-efficient as possible. However, no approaches are known to expand this planning with regard to an optimization of comfort while at the same time maintaining driving dynamics limits, which in particular depend on the maximum existing coefficient of friction in front of the vehicle. Aspects of the prevention of motion sickness are also used to optimize comfort.

Based on the prior art, the present invention is based on the object of proposing an improved method by means of which an optimal speed specification for a distance to be covered by a vehicle can be determined.

Based on the aforementioned object, the present invention proposes a method for maintaining an optimal speed specification for a distance to be covered by a vehicle according to claim 1, a control device according to claim 3, a computer program product according to claim 4 and a vehicle according to claim 5. Further advantageous refinements and developments emerge from the subclaims.

In a method for maintaining an optimal speed specification for a route to be covered by a vehicle, an incline profile, a curve curvature, a maximum existing coefficient of friction and a reference speed for the route are determined. In addition, a mass and a projected frontal area of the vehicle are determined. Furthermore, a rolling resistance, an air resistance and a gradient resistance for the vehicle are determined. Using this data, a discrete equation of motion is formulated, taking into account a drive and braking force to be optimized. Furthermore, boundary conditions for a stable driving behavior for the route are formulated taking into account the curvature of the curve and the maximum existing coefficient of friction, these boundary conditions describing a convex approximation of the Kamm's circle by means of linear inequalities. In addition, boundary conditions for maintaining a maximum speed and a setting restriction for the route are formulated.

A target function is formulated on the basis of the boundary conditions for stable driving behavior and the boundary conditions for maintaining a maximum speed and a setting restriction with regard to drive and braking force. The objective function is then minimized. The drive or braking force over the distance to be covered is used as an optimization variable. Based on the result of the minimized objective function and the discrete equation of motion, an optimal speed specification for the vehicle is determined. Drive and braking energy is provided by a drive system of the vehicle in order to maintain the optimal speed specification for the distance to be covered.

The following criteria flow into the target function and provide the framework for optimization: the fastest possible execution of the distance to be covered, the smallest possible deviation from the reference speed, driving as smoothly as possible with minor changes in acceleration and the lowest possible potential for kinetosis. Furthermore, the following secondary conditions must be observed, which provide a further framework for the optimization: physical plausibility, which is achieved by the speed specification following the equation of motion in the longitudinal direction; stable driving behavior, which is achieved by not leaving Kamm's circle; compliance with a maximum permitted speed; compliance with a setting restriction with regard to the drive and braking force of the vehicle;

The vehicle can, for example, be a car, truck or other commercial vehicle, e.g. B. also be an agricultural or construction machine, or a rail vehicle. The vehicle can, for example, also be shaped in such a way that it is able to carry out driving functions from autonomy level 3 or higher. The autonomy level refers to the SAE J3016 classification.

The vehicle has a drive system which is designed to provide drive energy with which the vehicle can be driven. The drive system can have an energy source which z. B. can be formed as an electric motor or as an internal combustion engine or as another suitable energy source. The drive system can have a transmission which is suitable for translating the torque provided by the energy source.

The vehicle also has a control device which is connected to the drive system of the vehicle. This connection is such that data and signals can be exchanged. The connection can be wireless or wired. The control device is set up to control the drive system of the vehicle. Furthermore, the control device can be set up to enable the vehicle to carry out autonomous driving functions from level 3. The control device can, for. B. be designed as an ECU or domain ECU. The control device also has a communication unit by means of which it can communicate with external systems, for example with a central data memory, e.g. B. a cloud, or with other vehicles or an infrastructure via C2X communication. In addition, the control device can have a memory device.

In addition, the vehicle has an environment sensor system. This is set up to record the surroundings of the vehicle. The environment sensor system can have, for example, a radar sensor, a lidar sensor, a camera or a combination of these sensors or other suitable environment sensors. The environment sensor system is connected to the control device. This connection is such that data and signals can be exchanged. The connection can be wireless or wired.

The vehicle also has a position determination system. This is set up to determine a position of the vehicle. The position determination system can determine this position using GPS coordinates, for example. The position determination system can be integrated into a commercially available navigation device, for example. The position determination system is connected to the control device. This connection is such that data and signals can be exchanged. The connection can be wireless or wired.

The distance to be covered by the vehicle is the distance that is covered when the vehicle is in motion. The distance to be covered does not refer to the distance that the vehicle has to cover in the future, for example with a delay of several hours after the start of the journey. In other words, the route to be covered refers to the route that is directly in front of the vehicle and at which the vehicle is about to cover it. This distance to be covered can also be referred to as the horizon.

A gradient profile and a curve curvature are determined for this distance to be covered. These can e.g. B. by means of the environment sensors or by querying a database that is stored, for example, in map software of the navigation device or in the cloud. In addition, a route-dependent profile of a maximum existing coefficient of friction in front of the vehicle is determined. This can be estimated using a method known from the prior art. Alternatively, the profile of the maximum existing coefficient of friction can be determined by means of the environment sensor system or communicated to the control device, e.g. B. from vehicles in front or from a central facility.

In addition, a reference speed is determined for the distance to be covered. This reference speed can be known, for example, from previous journeys on the same route and can be present in the memory device of the control device. As an alternative to this, the reference speed can be present in a central data memory and can be called up from this.

Furthermore, a mass and a projected frontal area of the vehicle are determined. The mass of the vehicle and the projected frontal area of the vehicle can, for example, be stored in the storage device of the control device. The projected frontal area denotes the area that contributes to air resistance when driving the vehicle.

A gradient resistance, a rolling resistance and an air resistance for the vehicle are determined. The air resistance is determined using the following equation: $${\mathrm{F.}}_{\mathrm{A.}erO}=\frac{\rho c_{\mathrm{A.}erO}{\mathrm{A.}}_{\mathrm{A.}erO}}{m}\mathrm{E.}$$

Where p denotes the air density, C _Aero the drag coefficient of the vehicle, which can be stored in the storage device of the control device, A _aero the projected frontal area, m the vehicle mass and E the current kinetic energy of the vehicle.

The rolling resistance is calculated using the following equation: $${\mathrm{F.}}_{\mathrm{R.}Oll}\left(s\right)=c_{\mathrm{R.}Oll}mGcOs\left(\alpha \left(s\right)\right)$$

Here designated C _roll the rolling resistance, m the vehicle mass, g the acceleration due to gravity and α (s) the slope of the roadway at the route point s .

The incline resistance is calculated using the following equation: $${\mathrm{F.}}_{\mathrm{S.}teiG}\left(s\right)=mGsin\left(\alpha \left(s\right)\right)$$

Using this data, an initially continuous equation of motion is created over the path variable s formulated. This is as follows: $$\frac{d\mathrm{E.}}{ds}=\mathrm{E.}\text{'}=-{\mathrm{F.}}_{\mathrm{A.}erO}-{\mathrm{F.}}_{\mathrm{S.}teiGunG}\left(s\right)-{\mathrm{F.}}_{\mathrm{R.}Oll}\left(s\right)+u\left(s\right)$$

The variable u (s) denotes the drive and braking force made available by the drive system of the vehicle. An Euler discretization can then be applied to this equation of motion, which determines the step size H having between two discrete route sections. This leads to the following discrete equation of motion or state space representation for the state of kinetic energy E. of the vehicle: $${\mathrm{E.}}_{k+1}=\mathrm{L.}{\mathrm{E.}}_k+\mathrm{B.}{u}_k+Q_k$$

$$B=h$$

$$Q_k=-mg\left(\text{sin}\left({\alpha }_k\right)+c_{Roll}\text{cos}\left({\alpha }_k\right)\right)$$

L denotes a system matrix, B an input matrix for the input of the drive and braking force u , and Q the disturbance vector or the offset vector. The individual factors are calculated as follows: $$\mathrm{L.}=\left(1-H\frac{\rho c_{\mathrm{A.}erO}{\mathrm{A.}}_{\mathrm{A.}erO}}{m}\right)$$

$$\mathrm{B.}=H$$

$$Q_k=-mG\left(\text{sin}\left({\alpha }_k\right)+c_{\mathrm{R.}Oll}\text{cos}\left({\alpha }_k\right)\right)$$

Here, k each denotes a number of a discrete route section. If k = 1, this denotes a first route section. If k = 2, it denotes a second route section, etc. The vehicle always starts on the first route section (k = 1) and will then drive onto a second route section (k + 1) and then onto further route sections (k + i).

Furthermore, boundary conditions for stable driving behavior for the route are formulated, taking into account the curvature of the curve and the maximum existing coefficient of friction, these boundary conditions describing a convex approximation of the Kamm's circle. Accelerations in the transverse direction of the vehicle (y-direction) and in the longitudinal direction (x-direction) are included in these boundary conditions. The acceleration in the transverse direction of the vehicle is calculated as follows: $$a_{y,k+i}=\frac{2}{m}{\kappa }_{k+i}{\mathrm{E.}}_{k+i}$$

Here designated κ the curvature of the curve.

The acceleration in the longitudinal direction of the vehicle is calculated as follows: $$a_{x,k+i}=\frac{1}{m}{u}_{k+i}$$

Due to the Kamm's circle, the following non-linear inequality results: $$a_{x,k+\mathrm{<figure-callout\; id="2"\; label="i"\; filenames="DE102019213749A1\_0030.png"\; state="\{\{state\}\}">i</figure-callout>}}^2+a_{y,k+\mathrm{<figure-callout\; id="2"\; label="i"\; filenames="DE102019213749A1\_0030.png"\; state="\{\{state\}\}">i</figure-callout>}}^2\le {\left(G{\mu }_{k+i}\right)}^2$$

Here µ denotes the coefficient of friction of the respective route section.

This non-linear inequality can be convexly approximated by using suitable linear inequalities, e.g. B .: $$a_{y,k+i}\le G{\mu }_{k+i}-a_{x,k+i}$$

Corresponding: $$\frac{2}{m}{\kappa }_{k+i}{\mathrm{E.}}_{k+i}\le G{\mu }_{k+i}-\frac{1}{m}{u}_{k+i}$$

In addition, boundary conditions for compliance with the maximum speed and the setting restriction for the route are formulated. The maximum speed is the maximum speed that is permitted on the route. This admissibility can be specified, for example, by traffic rules or speed restrictions. Additionally or alternatively, the maximum speed can be specified by a route condition of the route to be covered, for example by a surface quality of the roadway which can only be safely driven on at a certain maximum speed. The maximum speed can e.g. B. communicated by means of C2X communication to the control device of the vehicle or recognized by means of the environment sensors of the vehicle, z. B. by means of traffic sign recognition or by means of coefficient of friction estimation with regard to the condition of the road surface. Such methods are known from the prior art. This leads to the following restrictions: $${\mathrm{E.}}_{k+i}\le {\mathrm{E.}}_{k+i}^{max}$$

Here max denotes a maximum.

$$u_{k+i}\ge u_{k+i}^{min}$$

The adjustment restriction with regard to the drive and braking force is specified by the restrictions that are established by the actuators of the drive system and the braking system of the vehicle. The setting restriction therefore specifies a limit that must be adhered to during the optimization. The setting restriction can be stored, for example, in the memory device of the control device. This leads to the following restrictions: $$u_{k+i}\le u_{k+i}^{max}$$

$$u_{k+i}\ge u_{k+i}^{min}$$

Min denotes a minimum.

A target function is formulated on the basis of the boundary conditions for stable driving behavior and the boundary conditions for maintaining a maximum speed and a setting restriction with regard to drive and braking force. The objective function is then minimized. The objective function is: $$\begin{array}{l}J={\mathrm{<figure-callout\; id="1"\; label="w"\; filenames="DE102019213749A1\_0030.png"\; state="\{\{state\}\}">w</figure-callout>}}_1{{\displaystyle \sum _{i=1}^n\left({\mathrm{E.}}_{k+i}-{\mathrm{E.}}_{k+i}^{ref}\right)}}^2+{\mathrm{<figure-callout\; id="2"\; label="w"\; filenames="DE102019213749A1\_0030.png"\; state="\{\{state\}\}">w</figure-callout>}}_2{\displaystyle \sum _{i=0}^{n-1}{u}_{k+\mathrm{<figure-callout\; id="2"\; label="i"\; filenames="DE102019213749A1\_0030.png"\; state="\{\{state\}\}">i</figure-callout>}}^2}+\\ +{\mathrm{<figure-callout\; id="3"\; label="w"\; filenames="DE102019213749A1\_0030.png"\; state="\{\{state\}\}">w</figure-callout>}}_3{\displaystyle \sum _{i=0}^{n-1}{\left(u_{k+i}-u_{k+i-1}\right)}^2+w_{\mathrm{4th}}{\displaystyle \sum _{i=1}^n\left(w_k{\left(\frac{1}{m}{u}_{k+i}\right)}^2+w_d{\left(\frac{2}{m}{\kappa }_{k+i}{\mathrm{E.}}_{k+i}\right)}^2\right)}}\end{array}$$

It is E ^ref the kinetic energy related to the reference speed. Are there w ₁ , w ₂ , w ₃ , w ₄ , w _k and w _d weighting factors in each case. These are always to be chosen greater than or equal to 0, depending on which of the summands the focus is to be placed on. The first summand represents the deviation from the reference speed over the distance to be covered: $${\displaystyle \sum _{i=1}^n{\left({\mathrm{E.}}_{k+i}-{\mathrm{E.}}_{k+i}^{ref}\right)}^2}$$

In the second summand, the amplitude of the driving and braking force is taken into account: $${\displaystyle \sum _{i=0}^{n-1}{u}_{k+\mathrm{<figure-callout\; id="2"\; label="i"\; filenames="DE102019213749A1\_0030.png"\; state="\{\{state\}\}">i</figure-callout>}}^2}$$

The third summand represents a jolt of the vehicle: $${\displaystyle \sum _{i=0}^{n-1}{\left(u_{k+i}-u_{k+i-1}\right)}^2}$$

The fourth summand represents a motion sickness dose value according to ISO 2631-1.

This describes what effects the vehicle movement has on a possible kinetosis of a vehicle occupant: $${\displaystyle \sum _{i=1}^n\left(w_k{\left(\frac{1}{m}{u}_{k+i}\right)}^2+w_d{\left(\frac{2}{m}{\kappa }_{k+i}{\mathrm{E.}}_{k+i}\right)}^2\right)}$$

The weighting factors w ₁ , w ₂ , w ₃ , w ₄ can be varied depending on the desired behavior of the specified speed to be determined. For example, if w ₁ >0; w ₂ = w ₃ = w ₄ = 0 only tries to keep the reference speed. Jerk or Motion Sickness Dose Value are then disregarded. However, mixed parameterizations are also conceivable in which all weights are positive. For example, these four weighting factors could be designed to be adaptive. This means that if kinetosis is detected, one Vehicle _{occupant w 4} > 0 is selected, but otherwise w4 = 0. The determination of the kinetosis can take place by means of methods known from the prior art, for example by means of a sensor system that monitors the vital parameters of the vehicle occupant and evaluates them with regard to indicators for a kinetosis.

Based on the result of the minimized objective function and the discrete equation of motion, the optimal speed specification for the vehicle is determined. The advantage of the minimization is that the kinetic energy of the vehicle is selected as the system state. As a result, the objective function only depends on the square of the driving and braking force, which represents the optimization variable. Thus, taking into account the discrete equation of motion, a quadratic optimization problem with linear boundary conditions results, which can be solved efficiently. Due to the convex nature of quadratic optimization problems, the determined optimal solution for the speed specification is always a global optimal solution for the speed specification.

Finally, drive energy and braking energy are provided by the drive system of the vehicle to the extent that the optimum speed specification for the distance to be covered is maintained. The drive energy is requested by means of the control device that controls the drive system of the vehicle. In other words, so much drive energy is provided that the vehicle travels the distance to be covered at the speed that has been determined as the optimal speed. The method has the advantage that an optimization can not only be carried out on the basis of a single parameter, but that several parameters that are important for a journey can flow into the same optimization. This means that several optimization problems do not have to be solved at the same time, compared with one another and weighted with one another. This represents a time saving and an energy saving, since less computing power has to be made available by the control device.

The control device for the vehicle can be connected to the drive system of the vehicle. Connectable means that the control device is connected to the drive system when the control device is used in a vehicle. The drive system can be controlled by means of the control device, as also already described. The control device can therefore control the drive system of the vehicle when the control device is used in the vehicle. The control device can be connected to other systems. This has also already been described. The control device has means to carry out the method which has already been described in the previous description. These means can include, for example, a computer program product.

The computer program product comprises instructions which, when the program is executed by the control device already described, execute the method that was described in the previous description. The computer program product can be embodied on a data carrier, for example on a CD, DVD, USB stick or the like, or as a downloadable data stream.

The vehicle has the drive system and the control device, as already described. The control device is connected to the drive system, as also already described. The vehicle can e.g. B. be able to perform autonomous driving functions from level 3.

• 1 eine schematische Darstellung eines Fahrzeugs auf einer Fahrbahn nach einem Ausführungsbeispiel,
• 2 eine schematische Darstellung eines Kammschen Kreises für das Fahrzeug aus dem Ausführungsbeispiel in 1,
• 3 eine schematische Darstellung eines Verfahrens für das Fahrzeug aus dem Ausführungsbeispiel in 1.

An exemplary embodiment and details of the invention are described in more detail with the aid of the figure explained below. Show it:

• 1 a schematic representation of a vehicle on a roadway according to an embodiment,
• 2 a schematic representation of a Kamm's circle for the vehicle from the embodiment in FIG 1 ,
• 3 a schematic illustration of a method for the vehicle from the exemplary embodiment in FIG 1 .

1 shows a schematic representation of a vehicle 1 on a roadway 6th according to an embodiment. The vehicle 1 has a control device 3 , a drive system 2 , an environment sensor system 4th and a positioning system 5 on. The control device 3 is with the propulsion system 2 , with the positioning system 5 and with the environment sensors 4th connected. All connections are designed in such a way that data and signals can be exchanged. The connections can be wireless or wired. The control device 3 also has a memory device, which is not shown here. Furthermore, the control device 3 a communication unit, by means of which it can communicate with external systems, for example with a central data memory, e.g. B. a cloud, or with other vehicles or an infrastructure via C2X communication. This is indicated by the dashed arrow.

The vehicle 1 moves in the direction of travel, which is indicated by the arrow, on the roadway 6th . The roadway 6th shows the route s on who the vehicle 1 departs. The distance s is in several Sections divided, which are numbered by means of the index k . The vehicle is at the point in time shown 1 on the start section S _k . In front of the vehicle in the direction of travel 1 further route sections S _{k + 1} to S _{k + n} lie by the vehicle 1 still have to be covered. For a better overview, only the final route section S _{k + n} and the first route section S _{k + 1 to be covered} are shown. The sections of the route in between are only indicated. Each route section S _{k + 1} ; S _{k + n} simultaneously forms a step size H out.

For each route section S _{k + 1} ; S _{k + n} are available to the control device 3 Information regarding a curve curvature, a gradient profile, a coefficient of friction, a permissible maximum speed and a reference speed are available. This information can be obtained by means of the environment sensors 4th are determined, and / or by means of C2X communication to the control device 3 are communicated, and / or in the memory device of the control device 3 be stored, and / or from a database, which is queried for example in a map software of a navigation device that is part of the position determination system.

In addition, the control device 3 Vehicle mass data 1 and to the projected face of the vehicle 1 to disposal. These are stored in the memory device.

To find the optimal speed specification for the vehicle 1 In order to be able to determine, data relating to air resistance, to rolling resistance, to climbing resistance are also necessary. These are from the control device 3 calculated as already explained in the previous description. In addition, the following constraints must be observed, which provide a framework for the optimization: physical plausibility, which is achieved by the speed specification following an equation of motion in the longitudinal direction; stable driving behavior, which is achieved because the Kamm circle should not be left; compliance with the maximum permitted speed; Compliance with the setting restriction with regard to the drive and braking force of the vehicle.

2 shows a schematic representation of a Kamm's circle 7th for the vehicle 1 from the embodiment in 1 . Compliance with Kamm's circle is necessary to ensure stable driving behavior for the vehicle 1 to reach. Shown here is a convex approximation of the Kamm's circle with linear inequalities that result in an acceleration a of the vehicle 1 describe. The acceleration a is plotted in a vehicle longitudinal direction and in a vehicle transverse direction for a route section with the number k + i.

The acceleration a _{y, k + i} in the transverse direction of the vehicle (y-direction) is calculated as follows: $$a_{y,k+i}=\frac{2}{m}{\kappa }_{k+i}{\mathrm{E.}}_{k+i}$$

Here designated κ the curvature of the curve, m the vehicle mass and E the kinetic energy of the vehicle 1 .

The acceleration a _{x, k + i} in the longitudinal direction of the vehicle (x-direction) is calculated as follows: $$a_{x,k+i}=\frac{1}{m}{u}_{k+i}$$

Here u denotes the driving and braking force.

The convex approximation of Kamm's circle results in the following inequality: $$a_{x,k+\mathrm{<figure-callout\; id="2"\; label="i"\; filenames="DE102019213749A1\_0030.png"\; state="\{\{state\}\}">i</figure-callout>}}^2+a_{y,k+\mathrm{<figure-callout\; id="2"\; label="i"\; filenames="DE102019213749A1\_0030.png"\; state="\{\{state\}\}">i</figure-callout>}}^2\le {\left(G{\mu }_{k+i}\right)}^2$$

Here µ denotes the coefficient of friction of the respective route section and g the acceleration due to gravity.

This non-linear inequality can be convexly approximated by using suitable linear inequalities: $$a_{y,k+i}\le G{\mu }_{k+i}-a_{x,k+i}$$

Corresponding: $$\frac{2}{m}{\kappa }_{k+i}{\mathrm{E.}}_{k+i}\le G{\mu }_{k+i}-\frac{1}{m}{u}_{k+i}$$

3 shows a schematic representation of a method V for the vehicle 1 from the embodiment in 1 . In a first step 101 become the slope profile α , the curve curvature κ , the maximum existing coefficient of friction µ and the reference speed V _ref for the route s determined. This is done according to at least one of the ways already described in the previous description.

In a second step 102 become the crowd m of the vehicle 1 and the projected frontal area A _aero of the vehicle 1 certainly. These data can e.g. B. in the memory device of the control device 3 be deposited.

In a third step 103 become the driving and braking force u , the rolling resistance F _roll , the air resistance F _Aero and the slope resistance F _climb for the vehicle 1 determined.

In a fourth step 104 is made using this data from the first three steps 101 , 102 , 103 a discrete equation of motion for the state of kinetic energy E. of the vehicle, which reads as follows: $${\mathrm{E.}}_{k+1}=\mathrm{L.}{\mathrm{E.}}_k+\mathrm{B.}{u}_k+Q_k$$

In a fifth step 105 become boundary conditions P. for a stable driving behavior for the route s taking into account the curvature of the curve κ and the maximum existing coefficient of friction µ formulated. In addition, there are boundary conditions P. for compliance with a maximum speed and a setting restriction with regard to drive and braking force for the route s formulated. For the sake of clarity, all boundary conditions P. shown as a block.

Based on the boundary conditions P. A target function is used for stable driving behavior and the boundary conditions for maintaining a maximum speed and a setting restriction J formulated. This is: $$\begin{array}{l}J={\mathrm{<figure-callout\; id="1"\; label="w"\; filenames="DE102019213749A1\_0030.png"\; state="\{\{state\}\}">w</figure-callout>}}_1{{\displaystyle \sum _{i=1}^n\left({\mathrm{E.}}_{k+i}-{\mathrm{E.}}_{k+i}^{ref}\right)}}^2+{\mathrm{<figure-callout\; id="2"\; label="w"\; filenames="DE102019213749A1\_0030.png"\; state="\{\{state\}\}">w</figure-callout>}}_2{\displaystyle \sum _{i=0}^{n-1}{u}_{k+\mathrm{<figure-callout\; id="2"\; label="i"\; filenames="DE102019213749A1\_0030.png"\; state="\{\{state\}\}">i</figure-callout>}}^2}+\\ +{\mathrm{<figure-callout\; id="3"\; label="w"\; filenames="DE102019213749A1\_0030.png"\; state="\{\{state\}\}">w</figure-callout>}}_3{\displaystyle \sum _{i=0}^{n-1}{\left(u_{k+i}-u_{k+i-1}\right)}^2+w_{\mathrm{4th}}{\displaystyle \sum _{i=1}^n\left(w_k{\left(\frac{1}{m}{u}_{k+i}\right)}^2+w_d{\left(\frac{2}{m}{\kappa }_{k+i}{\mathrm{E.}}_{k+i}\right)}^2\right)}}\end{array}$$

Are there w ₁ , w ₂ , w ₃ , w ₄ , w _k and w _d in each case weighting factors that are always to be chosen greater than or equal to 0, depending on which of the summands the focus is to be placed on. The first addend represents the deviation from the reference speed. The second addend shows the amplitude of the driving or braking force u considered. The third summand represents a jolt of the vehicle. The fourth summand simulates a motion sickness dose value ISO 2631-1 This describes what effects the vehicle movement has on a possible kinetosis of a vehicle occupant.

The weighting factors w ₁ , w ₂ , w ₃ , w ₄ can be varied depending on the desired behavior of the planning. For example, with w ₁ >0; w ₃ >0; w ₂ = w ₄ = 0 the attempt is made to maintain the reference speed and to keep the jerk low. The Motion Sickness Dose Value is ignored.

In a sixth step 106 becomes the objective function J minimized in order to achieve the optimum speed specification v _opt to obtain. All calculations and optimizations are carried out by means of the control device 3 carried out.

In a seventh step 107 is based on the optimal speed specification v _opt the drive system 2 of the vehicle 1 from the control device 3 controlled so that the drive system 2 so much energy that the vehicle provides 1 the optimal speed specification v _opt adheres to.

List of reference symbols

1

vehicle

2

Drive system

3

Control device

4th

Environment sensors

5

Positioning system

6th

roadway

7th

Kamm circle

101

first step

102

second step

103

Third step

104

fourth step

105

fifth step

106

sixth step

107

seventh step

a

acceleration

A _aero

projected face

B.

Input matrix

C _Aero

Drag coefficient

C _roll

Rolling resistance

E.

kinetic energy

E '

Derivation of the kinetic energy according to s

E ^ref

kinetic energy related to the reference speed

F _Aero

Air resistance

F _roll

Rolling resistance

F _climb

Incline resistance

G

Acceleration due to gravity

H

Increment

J

Objective function

k

Number of the route section

L.

System matrix

m

Mass of the vehicle

P

boundary conditions

Q

Disturbance vector

s

route

u

Driving and braking force

V

Procedure

v _ref

Reference speed

v _opt

optimal speed specification

w ₁ , w ₂ , w ₃ , w ₄ , w _d , w _k

Weighting factors

x

Vehicle longitudinal direction

y

Vehicle transverse direction

α

Slope profile

κ

Curve curvature

p

Airtightness

µ

Coefficient of friction

QUOTES INCLUDED IN THE DESCRIPTION

This list of the documents listed by the applicant was generated automatically and is included solely for the better information of the reader. The list is not part of the German patent or utility model application. The DPMA assumes no liability for any errors or omissions.

Patent literature cited

• EP 2610836 A1 [0003]
• EP 1256476 B1 [0004]
• DE 102016209984 A1 [0005]

Non-patent literature cited

• ISO 2631-1 [0074]

Claims (5)

Method (V) for maintaining an optimal speed _{setting (v opt} ) for a distance (s) to be covered by a vehicle (1), wherein - a gradient profile (a), a curve curvature (κ), a maximum existing coefficient of friction (µ) and a Reference speed (v _ref ) for the distance (s) can be determined, - a mass (m) and a projected frontal area (A _A ero) of the vehicle (1) are determined, - a rolling resistance (F _roll ), an air resistance ( F _Aero ) and a gradient resistance (F _Steig ) for the vehicle (1) are determined, - a discrete equation of motion (E ') is formulated using this data, taking into account a drive and braking force (u) to be optimized, - boundary conditions for a stable Driving behavior for the route (s) taking into account the curvature of the curve (κ) and the maximum existing coefficient of friction (µ) are formulated, whereby these boundary conditions describe a convex approximation of the Kamm's circle by means of linear inequalities, Boundary conditions for maintaining a maximum speed and a setting restriction for the distance (s) are formulated, - based on the boundary conditions for stable driving behavior and the boundary conditions for maintaining a maximum speed and a setting restriction with regard to drive and braking force (etc.) ) an objective function (J) is formulated, - the objective function (J) is minimized, with the drive and braking force (u) representing the optimization variable, - based on the result of the minimized objective function (J) and the discrete equation of motion (E ' ) an optimal speed _{specification (v opt} ) for the vehicle (1) is determined, - drive energy and braking energy are provided by a drive system (2) of the vehicle (1) in order to achieve the optimal speed specification (v _opt ) for the distance to be covered (s) to be observed. Procedure (V) according to Claim 1 , where the objective function (J) is: $$\begin{array}{l}J=w_1{{\displaystyle \sum _{i=1}^n\left({\mathrm{E.}}_{k+i}-{\mathrm{E.}}_{k+i}^{ref}\right)}}^2+w_2{\displaystyle \sum _{i=0}^{n-1}{u}_{k+i}^2}+\\ +w_3{\displaystyle \sum _{i=0}^{n-1}{\left(u_{k+i}-u_{k+i-1}\right)}^2+w_{\mathrm{4th}}{\displaystyle \sum _{i=1}^n\left(w_k{\left(\frac{1}{m}{u}_{k+i}\right)}^2+w_d{\left(\frac{2}{m}{\kappa }_{k+i}{\mathrm{E.}}_{k+i}\right)}^2\right)}}\end{array}$$

where w ₁ , w ₂ , w ₃ , w ₄ , w _k , w _d each denote weighting factors, where k denotes a number of a route section, where u denotes the driving force, where E denotes a kinetic energy, where E ^ref a kinetic energy referred to with respect to the reference speed (v _ref ). Control device (3) for a vehicle (1), the control device (3) being connectable to a drive system (2) of the vehicle (1), the drive system (2) being controllable by means of the control device (3), and the control device (3) comprises means for a method (V) according to one of the Claims 1 to 2 perform. Computer program product, comprising instructions which, when the program is executed by a control device (3) Claim 3 , the method (V) according to one of the Claims 1 to 2 To run. Vehicle (1) having a drive system (2) and a control device (3) according to Claim 3 , wherein the control device (3) is connected to the drive system (2).

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