Patent ID: 12233884

DETAILED DESCRIPTION

Reference will now be made to embodiments of the invention, one or more examples of which are shown in the drawings. Each embodiment is provided by way of explanation of the invention, and not as a limitation of the invention. For example, features illustrated or described as part of one embodiment can be combined with another embodiment to yield still another embodiment. It is intended that the present invention include these and other modifications and variations to the embodiments described herein.

FIG.1shows a motor vehicle1, for example, a passenger car. The motor vehicle1includes a system2for the model predictive control of multiple components of the motor vehicle1. The system2in the exemplary embodiment shown includes a processor unit3, a memory unit4, a communication interface5, and a detection unit6, in particular for detecting state data related to the motor vehicle1. The motor vehicle1also includes a drive train7, which can include, for example, an electric machine8, which can be operated as a motor and as a generator, a battery9, and a transmission10. The electric machine8, in the motor mode, can drive wheels of the motor vehicle1via the transmission10, which can have, for example, a constant ratio. The battery9can provide the electrical energy necessary therefor. The battery9can be charged by the electric machine8when the electric machine8is operated in the generator mode (recuperation). Optionally, the battery9can also be charged at an external charging station. Likewise, the drive train of the motor vehicle1can optionally include an internal combustion engine17, which, alternatively or in addition to the electric machine8, can drive the motor vehicle1. The internal combustion engine17can also drive the electric machine8in order to charge the battery9.

The motor vehicle1also includes multiple components that are relevant for the efficiency of the operation of the motor vehicle1(“efficiency-relevant components”), in particular when the motor vehicle1is operated in an autonomous traveling mode. These components are not arranged exclusively in the drive train7of the motor vehicle1. InFIG.1, purely by way of example, a first component18and a second component19are represented, although the motor vehicle1still includes a number of further efficiency-relevant components. In the exemplary embodiment according toFIG.1, a first component is represented in the form of a system18for the level control of the motor vehicle1and a second component is represented in the form of a braking system19of the motor vehicle1. For example, the electric machine8, the battery9, the transmission10, and the internal combustion engine17can also be construed as efficiency-relevant components of the motor vehicle1.

FIG.2shows that the system18for the level control of the motor vehicle1can be operated with different values of a first operating parameter20(first degree of freedom). For example, the first operating parameter20can be the set height of a chassis21of the motor vehicle1. Purely by way of example, the set height of the chassis21of the motor vehicle1can assume a first value h1, a second value h2, and a third value h3. The different heights h1, h2, and h3of the chassis21can result in a different level of a drag force of the motor vehicle1in each case. This can be represented by the longitudinal model14of the drive train7of the motor vehicle1described further below.

The system18for the level control of the motor vehicle1can be construed as an actuator of the motor vehicle1. In addition, the system18for the level control of the motor vehicle1itself can include at least one actuator22(for example, a hydraulic cylinder or a pneumatic cylinder or a hydro-pneumatic shock absorber), which the system18actuates for the level control of the motor vehicle1. The first actuator22can be operated with different actuator values23, and so the different values h1, h2, and h3result for the system18for the level control of the motor vehicle1. For example, a first actuator value x1(for example, a first pressure value for a hydraulic cylinder) yields the first height h1of the chassis21, a second actuator value x2yields the second height h2of the chassis21, and a third actuator value x3yields the third height h3of the chassis21.

FIG.3shows that the braking system19can be operated with different values of a second operating parameter24(second degree of freedom). For example, the second operating parameter24can be the braking force of the braking system19. Purely by way of example, the braking force can assume a first value y1, a second value y2, and a third value y3. The different levels of the braking forces y1, y2, and y3of the braking system19can result in a different level of a traction force, in each case, that is exerted by the braking system upon the wheels of the motor vehicle1. This can be represented by the longitudinal model14of the drive train7of the motor vehicle1described further below.

The braking system19can be construed as an actuator of the motor vehicle1. In addition, the braking system19itself can include an actuator25(for example, a hydraulic cylinder), which actuates the braking system19. The second actuator25can be operated with different actuator values26, and so the different values y1, y2, and y3result for the braking system19. For example, a first actuator value z1yields the first braking force y1, a second actuator value z2yields the second braking force y2, and a third actuator value z3yields the third braking force y3.

A computer program product11can be stored on the memory unit4. The computer program product11can be run on the processor unit3, for the purpose of which the processor unit3and the memory unit4are connected to each other by the communication interface5. When the computer program product11is run on the processor unit3, it instructs the processor unit3to perform the functions described in conjunction with the drawing and/or to carry out method steps.

The computer program product11includes an MPC algorithm13. The MPC algorithm13includes a dynamic model of the motor vehicle, namely a longitudinal dynamic model14of the drive train7of the motor vehicle1in the exemplary embodiment shown. In addition, the MPC algorithm13includes a cost function15to be minimized, wherein a first term cefficiencyof the cost function15represents the overall loss of the motor vehicle1. Therefore, the cost function15to be minimized can be expressed mathematically as follows:
min(cefficiency+ctime+ccomfort)Wherein:cefficiencyrepresents the parameterizable costs with respect to efficiency,ctimerepresents the parameterizable costs with respect to the travel time, andccomfortrepresents the parameterizable costs with respect to comfort.

The longitudinal dynamic model14includes a loss model27of the motor vehicle1. The loss model27describes the operating behavior of the efficiency-relevant components18,19with respect to their efficiency and with respect to their loss. This yields the overall loss of the motor vehicle1. The processor unit3executes the MPC algorithm13and, in so doing, predicts a behavior of the motor vehicle1based on the longitudinal dynamic model14, wherein the cost function15is minimized.

The overall loss of the motor vehicle1depends on a combination of operating values. The combination of operating values includes a first value of the first operating parameter and a second value of the second operating parameter. In the simplified exemplary embodiment shown, there are six possible combinations of operating values. A first combination of operating values includes the first height h1of the chassis21and the first braking force y1of the braking system19. The first combination of operating values yields a first overall loss of the motor vehicle1. A second combination of operating values includes the first height h1of the chassis21and the second braking force y2of the braking system19. The second combination of operating values yields a second overall loss of the motor vehicle1. A third combination of operating values includes the first height h1of the chassis21and the third braking force y3of the braking system19. The third combination of operating values yields a third overall loss of the motor vehicle1. A fourth combination of the operating values includes the second height h2of the chassis21and the first braking force y1of the braking system19. The fourth combination of operating values yields a fourth overall loss of the motor vehicle1. A fifth combination of operating values includes the second height h2of the chassis21and the third braking force y3of the braking system19. The fifth combination of operating values yields a fifth overall loss of the motor vehicle1. A sixth combination of operating values includes the third height h3of the chassis21and the third braking force y3of the braking system19. The sixth combination of operating values yields a sixth overall loss of the motor vehicle1.

The processor unit3can determine the aforementioned six combinations of operating values by executing the MPC algorithm13as a function of the loss model14. The processor unit3can compare the overall losses resulting from the six different combinations of operating values with one another. The processor unit3can establish, for example, that the third combination of operating values (h1; y3) results in the lowest overall loss of the motor vehicle1. The processor unit3can select the third combination of operating values and output the appropriate values, for example, at a target generator, which can be integrated as a software module, the MPC algorithm. Alternatively, the target generator can also be included, for example, as a software module in a driver assistance system16. Based on the determined combination of operating values (h1; y3), the first component18can be regulated to the first value h1of the first operating parameter20and the second component19can be regulated to the third value y3of the second operating parameter24, in particular by the target generator. In addition, the processor unit3can also regulate the first actuator22to the first actuator value x1, and so the first value h1of the first operating parameter20sets in for the first component18. In a similar way, the processor unit3can regulate the second actuator25to the third actuator value z3, and so the third value y3value of the second operating parameter24sets in for the second component19.

In addition, an optimal rotational speed and an optimal torque of the electric machine8for calculated points in the prediction horizon can result as the output of the optimization by the MPC algorithm13. For this purpose, the processor unit3can determine an input variable for the electric machine8, and so the optimal rotational speed and the optimal torque set in. The processor unit3can control, by way of an open-loop system, the electric machine8based on the determined input variable. In addition, this can also be carried out by the driver assistance system16, however.

The detection unit6can measure current state variables of the motor vehicle1, record appropriate data, and supply these to the MPC algorithm13. In addition, route data from an electronic map can be updated, in particular cyclically, for a prediction horizon (for example, four hundred meters (400 m)) ahead of the motor vehicle1. The route data can include, for example, uphill grade information, curve information, and information regarding speed limits. Moreover, a curve curvature can be converted, via a maximum permissible lateral acceleration, into a speed limit for the motor vehicle1. In addition, a position finding of the motor vehicle can be carried out by the detection unit6, in particular via a signal generated by a GNSS sensor12for the precise localization on the electronic map. Moreover, the detection unit for detecting the external surroundings of the motor vehicle1can include, for example, a radar sensor, a camera system, and/or a LIDAR sensor. The processor unit3can access information of the aforementioned elements, for example, via the communication interface5. This information can be incorporated into the longitudinal model14of the motor vehicle1, in particular as restrictions or constraints.

The longitudinal dynamic model14of the motor vehicle1can be expressed mathematically as follows:

d⁢v⁡(t)d⁢t=(Ftrac(t)-Fr(α⁡(t))-Fg⁢r(α⁡(t))-Fd(v⁡(t)))/me⁢qWherein:v is the speed of the motor vehicle;Ftracis the tractive force exerted by the prime mover or the brakes upon the wheels of the motor vehicle, for example, influenced by the above-described different levels of braking forces y1, y2, and y3of the braking system19;Fris the rolling resistance, which is an effect of the deformation of the tires during rolling and depends on the load of the wheels (on the normal force between the wheel and the road) and, thus, on the inclination angle of the road;Fgris the gradient resistance, which describes the longitudinal component of gravity, which acts upon the vehicle during operation uphill or downhill, depending on the inclination of the roadway;Fdis the drag force of the motor vehicle, for example, influenced by the above-described different heights h1, h2, and h3of the chassis21; andmeqis the equivalent mass of the motor vehicle; the equivalent mass includes, in particular, the inertia of the turned parts of the drive train, which are subjected to the acceleration of the motor vehicle (prime mover, transmission input shafts, wheels).

By converting time dependence into distance dependence

dds=ddt*dtds=ddt*1v
and coordinate transformation in order to eliminate the quadratic speed term in the aerodynamic drag with

ek⁢i⁢n=12*me⁢q*v⁡(t)2,
the result is

dekinds=Ftrac(s)-Fr(α⁡(s))-Fg⁢r(α⁡(s))-Fd(ekin(s)).

In order to ensure that the problem is quickly and easily solvable by the MPC algorithm13, the dynamic equation of the longitudinal dynamic model14can be linearized, in that the speed is expressed, via coordinate transformation, by kinetic energy dekin. As a result, the quadratic term for calculating the aerodynamic drag Fdis replaced by a linear term and, simultaneously, the longitudinal dynamic model14of the motor vehicle1is no longer described as a function of time, as usual, but rather as a function of distance. This fits well with the optimization problem, since the predictive information of the electric horizon is based on distance.

In addition to the kinetic energy, there are two further state variables, which, within the scope of a simple optimization problem, can also be described in a linear and distance-dependent manner. On the one hand, the electrical energy consumption of the drive train7is usually described in the form of a characteristic map as a function of torque and prime mover speed. In the exemplary embodiment shown, the motor vehicle1has a fixed ratio between the electric machine8and the road, on which the motor vehicle1moves. As a result, the rotational speed of the electric machine8can be directly converted into a speed of the motor vehicle1or even into a kinetic energy of the motor vehicle1. In addition, the electrical power of the electric machine8can be converted into energy consumption per meter via division by the appropriate speed. As a result, the characteristic map of the electric machine8obtains the form shown inFIG.4. In order to be able to utilize this characteristic map for the optimization, it is linearly approximated: EnergyperMeter≥ai*ekinbi*Ftracfor all i.

In detail, the cost function15to be minimized can be expressed mathematically as follows:

min(⁠cefficiency-wBat·(sE)+wTime·T⁡(sE)+wTem·∑s=1sE-1(FA(s)-FA(s-1)Δ⁢s)2+wTemStart·(FA(s1)-FA(s0))2+∑s=1sE-1wSlack·Varslack)Wherein:cefficiencyrepresents the parameterizable efficiency costs of the efficiency-relevant components, for example, the above-described components19,20,wBatis the weighting factor for the energy consumption of the batteryEBatis the energy consumption of the batteryS is the distanceSE-1is the distance one time step before the end of the prediction horizonFAis the drive force that is provided by the electric machine, transmitted by a transmission at a constant ratio, and applied at a wheel of the motor vehicleWTemis the weighting factor for torque gradientsWTemStartis the weighting factor for torque surgesT is the time that the vehicle needs in order to cover the entire distance predicted within the prediction horizonwTimeis the weighting factor for the time TSEis the distance to the end of the horizonwSlackis the weighting factor for the slack variableVarSlackis the slack variable

The cost function15has exclusively linear and quadratic terms. As a result, the overall problem has the form of a quadratic optimization with linear constraints and a convex problem results, which can be solved well and quickly.

The cost function15includes, in addition to the above-described parameterizable efficiency costs of the components19,20, as one further term, an electrical energy EBatweighted with a first weighting factor wBatand predicted according to the longitudinal dynamic model, which is provided within a prediction horizon by the battery9of the drive train7for driving the electric machine8. The battery9and the electric machine8can be construed as efficiency-relevant components of the motor vehicle1, similarly to the above-described system18for level control and the above-described braking system19. Correspondingly, the electrical energy EBatweighted with the first weighting factor wBatand predicted according to the longitudinal dynamic model can also be incorporated into the term cefficiency.

The cost function15includes, as one further term, a driving time T weighted with a second weighting factor WTimeand predicted according to the longitudinal dynamic model14, which the motor vehicle1needs in order to cover the predicted distance. As a result, depending on the selection of the weighting factors, a low speed cannot always be evaluated as optimal and, thus, the problem no longer exists that the resultant speed is always at the lower edge of the permitted speed.

The energy consumption and the driving time can both be evaluated and weighted at the end of the horizon. These terms are therefore active only for the last point of the horizon.

Excessively high torque gradients within the horizon are disadvantageous. Therefore, torque gradients are already penalized in the cost function15, namely by the term

wTem·∑s=1sE-1(FA(s)-FA(s-1)Δ⁢s)2.
The quadratic deviation of the drive force per meter is weighted with a weighting factor WTemand minimized in the cost function. Alternatively to the drive force FAper meter, the torque MEMprovided by the electric machine8can also be utilized and weighted with the weighting factor WTem, and so the alternative term

wTem·∑s=1sE-1(MEM(s)-MEM(s-1)Δ⁢s)2
results. Due to the constant ratio of the transmission10, the drive force and the torque are directly proportional to one another.

In order to ensure comfortable driving, one further term is introduced in the cost function15for penalizing torque surges, namely wTemStart·(FA(s1)−FA(s0))2. Alternatively to the drive force FA, the torque MEMprovided by the electric machine8can also be utilized here, and so the alternative term wTemStart·(MEM(s1)−MEM(s0))2results. For the first point in the prediction horizon, the deviation from the most recently set torque can be evaluated as negative and weighted with a weighting factor wTemStartin order to ensure that there is a seamless and smooth transition during the change-over between an old trajectory and a new trajectory.

Speed limits are hard limits for the optimization that are not permitted to be exceeded. A slight exceedance of the speed limits is always permissible in reality and tends to be the normal case primarily during transitions from one speed zone into a second zone. In dynamic surroundings, where speed limits shift from one computing cycle to the next computing cycle, it can happen, in the case of very hard limits, that a valid solution for a speed profile can no longer be found. In order to increase the stability of the computational algorithm, a soft constraint is introduced into the cost function15. A slack variable VarSlackweighted with a weighting factor WSlackbecomes active in a predefined narrow range before the hard speed limit is reached. Solutions that are situated very close to this speed limit are evaluated as poorer, i.e, solutions, the speed trajectory of which maintains a certain distance to the hard limit.

Modifications and variations can be made to the embodiments illustrated or described herein without departing from the scope and spirit of the invention as set forth in the appended claims. In the claims, reference characters corresponding to elements recited in the detailed description and the drawings may be recited. Such reference characters are enclosed within parentheses and are provided as an aid for reference to example embodiments described in the detailed description and the drawings. Such reference characters are provided for convenience only and have no effect on the scope of the claims. In particular, such reference characters are not intended to limit the claims to the particular example embodiments described in the detailed description and the drawings.

REFERENCE CHARACTERS

h1first value of first operating parameterh2second value of first operating parameterh3third value of first operating parameterx1first actuator value of the first actuatorx2second actuator value of the first actuatorx3third actuator value of the first actuatory1first value of second operating parametery2second value of second operating parametery3third value of second operating parameterz1first actuator value of the second actuatorz2second actuator value of the second actuatorz3third actuator value of the second actuator1vehicle2system3processor unit4memory unit5communication interface6detection unit7drive train8electric machine9battery10transmission11computer program product12GNSS sensor13MPC algorithm14longitudinal dynamic model15cost function16driver assistance system17internal combustion engine18system for the level control19braking system20first operating parameter21chassis22first actuator23actuator values of the first actuator24second operating parameter25second actuator26actuator values of the second actuator27loss model