Model-Based Predictive Adjustment of Motor Vehicles

Abstract

The invention relates to a model predictive adjustment of a motor vehicle (1), wherein the motor vehicle (1) comprises a coolant pump (28) which can be operated with different pump operating values of at least one operating parameter. A processor unit (3) is configured to execute a model predictive control (MPC) algorithm (13) for the model predictive adjustment of the motor vehicle (1), wherein the MPC algorithm (13) comprises a longitudinal dynamics model (14) of the motor vehicle (1) and a cost function (15) to be minimized, wherein the cost function (15) contains a plurality of terms, and wherein the first term represents the power of the coolant pump (28). Additionally, the processor unit (3) is configured to determine, by executing the MPC algorithm (13) as a function of the longitudinal dynamics model (14), a speed trajectory of the motor vehicle (1) within a prediction horizon and simultaneously a pump operating value trajectory within the prediction horizon, such that the first term of the cost function (15) is minimized.

Description

Field of the Invention

[0001] The present invention relates to a motor vehicle adjusted based on model prediction. In particular, a processor unit, a motor vehicle, a method, and a computer program product are claimed in combination herewith. Background Art

[0002] The warming and cooling of the powertrain, the battery, and different other components can significantly affect the efficiency of an (electric) vehicle. Although the degree of the thermal influence significantly depends on the respective vehicle, generally, an improvement in the driving efficiency can be achieved by optimizing the thermal management. Here, depending on the operating conditions, the components have various different cooling requirements. Depending on the temperature and the operating point, different efficiency levels are obtained.

[0003] One type of variable cooling is cooling by means of one or more coolant pumps. The control of the coolant pumps for adjusting the optimal component temperature is known, wherein so-called demand-based cooling can be employed. Demand-based cooling is either controlled by a regulator and a target temperature or follows an adjustment-based logic. Thus, the cooling power of the pump can be controlled by a closed-loop adjustment. The regulator here attempts to minimize the deviation from a given target temperature. However, this method can only partially meet the requirements for efficiency optimization. This is mainly due to two effects. On the one hand, the optimal component temperature is not the same in every operating condition. On the other hand, the cooling power implies pump power that must in turn be regarded as a loss.

[0004] DE 10 2018 005 948 A1 discloses a vehicle model for calculating predictive vehicle parameters, in particular vehicle speed. The vehicle model outputs predictive vehicle parameters, which are transmitted as input values to a coolant circuit model. The coolant circuit model is processed after the vehicle model. The coolant circuit model calculates the predictive temperatures of the coolant before and after each component of the coolant circuit system. The coolant circuit model outputs predictive coolant circuit parameters, which are processed into output parameters, such as the rotational speed and / or the adjustment setting of the coolant pump.

[0005] DE 10 2013 110 346 A1 teaches a method for operating a powertrain of a vehicle that can be driven by at least one electric drive machine, wherein a prediction of the future temperature of at least one preferably electrical component in the powertrain is created and the powertrain is operated depending on the predicted temperature. To improve efficiency, it is proposed to assign to each component in the powertrain an optimal temperature operating range for optimal operating efficiency, select a driving route and assign to this driving route an initial speed profile for a speed target value, create a prediction of the future load and future operating temperature of the components along the driving route based on this speed profile and adapt the speed profile of the vehicle to the boundary conditions for the driving route, so as to maintain the optimal operating temperature range for each component during the selection of the driving route.

[0006] Furthermore, a method for restricting the search space of a model-based online optimization method is known from EP 3 072 769 A2, which determines a predicted optimal first curve of vehicle state parameters for a forward section within a predetermined search space. The outstanding feature of this method is that a predicted second curve of the state parameters is determined along the forward section depending on the section information of the forward section, wherein the search space is restricted depending on the determined predicted second curve of the state parameters. Summary of the Invention

[0007] The object of the present invention can be seen as providing an especially efficient regulation of at least one coolant pump of a motor vehicle. This object is solved by a processor unit for model predictive adjustment of a motor vehicle according to the invention, a motor vehicle according to the invention, a method for model predictive adjustment of a motor vehicle according to the invention and a computer program product for model predictive adjustment of a motor vehicle according to the invention. Advantageous embodiments are the subject of the following description and the drawings.

[0008] According to the invention, it is proposed to integrate the control of the coolant pump as a degree of freedom into the overall efficiency optimization with the help of an MPC solver. One or more coolant pumps can also be operated by a forward-looking longitudinal driving strategy. At least one coolant pump can be controlled by an MPC-based logic, thereby achieving an optimization of efficiency at the system level. Thus, the activation of one or more coolant pumps can be planned in such a way that as little energy as possible is lost. In other words, a combination of one or more controllable (coolant) pumps and a forward-looking driving strategy is proposed. Here, the subject of the present invention is to integrate the control of the pump into an MPC-based driving strategy. Here, the MPC solver can minimize a cost function that includes multiple terms. One of these terms can describe the power of at least one pump. Another term can, for example, describe the losses of other components with respect to their power.

[0009] To achieve this, the driving strategy uses one or more pumps as additional degrees of freedom. The driving strategy can plan an optimal speed trajectory for the upcoming section of the route taking into account the route topology, traffic, and other environmental information. The speed trajectory can now be planned in combination with the new degrees of freedom. In this sense, the strategy no longer only plans the trajectory, but also adjusts the pump power (= power of the pump) along this trajectory. In this way, an integrated optimization of the various degrees of freedom is carried out, which results in a more efficient driving behavior overall. It should be noted here that the optimization of the individual degrees of freedom is carried out simultaneously. The optimal degrees of freedom planning can then be passed on to a target generator, which can be implemented in particular by a software module.

[0010] In this sense, according to a first aspect of the invention, there is provided a processor unit for model predictive adjustment of a motor vehicle, wherein the motor vehicle includes a coolant pump that can be operated with different pump operating values of at least one operating parameter. The processor unit is configured to execute a model predictive control (MPC) algorithm for model predictive adjustment of the motor vehicle, wherein the MPC algorithm includes a longitudinal dynamics model of the motor vehicle and a cost function to be minimized. The cost function in turn includes a plurality of terms, the first of which represents the power of the coolant pump. The processor unit determines, by executing the MPC algorithm and depending on the longitudinal dynamics model, the speed trajectory of the motor vehicle within the prediction horizon. This can be achieved in particular by minimizing the cost function as a whole. In addition, the processor unit, by executing the MPC algorithm, is configured to also determine the pump operating value trajectory within the prediction horizon while calculating the speed trajectory, so as to minimize the first term of the cost function.

[0011] According to a second aspect of the invention, there is provided a motor vehicle. The motor vehicle includes a processor unit according to the first aspect of the invention. In addition, the motor vehicle further includes a driver assistance system, a powertrain, and a coolant pump that can be operated with different pump operating values of at least one operating parameter. The driver assistance system is configured to access the speed trajectory of the motor vehicle within the prediction horizon determined by the processor unit. The driver assistance system is also configured to access the pump operating value trajectory within the prediction horizon determined simultaneously by the processor unit. In addition, the driver assistance system is further configured to control the powertrain of the motor vehicle based on the speed trajectory of the motor vehicle and to control the coolant pump based on the pump operating value trajectory.

[0012] A motor vehicle is a vehicle driven by a motor, such as an automobile (e.g., a sedan with a weight of less than 3.5 t), a motorcycle, a scooter, a moped, a bicycle, an electric bicycle, a bus or a truck (e.g., a truck with a weight of more than 3.5 t), or a rail vehicle, a ship, an aircraft (such as a helicopter or an airplane). The present invention can also be applied to a small and lightweight electric vehicle with micromobility, where these motor vehicles are particularly used in urban traffic and for the first and last mile in rural areas. The first and last mile can be understood as all sections and distances within the first and last links of the mobility chain. For example, it is the journey from home to the train station or the section from the train station to the workplace. In other words, the present invention can be applied to all fields of transportation means such as automobiles, aviation, navigation, and aerospace. The motor vehicle can, for example, belong to a vehicle fleet.

[0013] According to a third aspect of the present invention, there is provided a method for model predictive adjustment of a motor vehicle, wherein the motor vehicle includes a coolant pump that can be operated with different pump operating values of at least one operating parameter. The method includes the steps of:

[0014] - Performing a model predictive control (MPC) algorithm for model predictive adjustment of the motor vehicle, wherein the MPC algorithm includes a longitudinal dynamics model of the motor vehicle and a cost function to be minimized, and wherein the cost function includes a plurality of terms, and the first term thereof represents the power of the coolant pump,

[0015] - Obtaining, by performing the MPC algorithm and depending on the longitudinal dynamics model, the speed trajectory of the motor vehicle within the prediction horizon, which can be implemented in particular by minimizing the cost function as a whole; and

[0016] - Obtaining, by performing the MPC algorithm, the pump operating value trajectory within the prediction horizon so as to minimize the first term of the cost function,

[0017] wherein the obtaining of the speed trajectory and the pump operating value trajectory is performed simultaneously.

[0018] According to a fourth aspect of the present invention, there is provided a computer program product for model predictive adjustment of a motor vehicle, wherein the motor vehicle includes a coolant pump that can be operated with different pump operating values of at least one operating parameter, and when the computer program product is executed on a processor unit, it instructs the processor unit to execute a model predictive control (MPC) algorithm for model predictive adjustment of the motor vehicle. The MPC algorithm includes a longitudinal dynamics model of the motor vehicle and a cost function to be minimized, wherein the cost function includes a plurality of terms, and the first of which represents the power of the coolant pump. In addition, when the computer program product is executed on a processor unit, it instructs the processor unit to obtain, depending on the longitudinal dynamics model by executing the MPC algorithm, the speed trajectory of the motor vehicle within a prediction range and simultaneously obtain the pump operating value trajectory within the prediction range, so as to minimize the first term of the cost function. The obtaining of the speed trajectory can be implemented by minimizing the cost function as a whole.

[0019] Next, the specific details, definitions and embodiments of the present invention will be mainly described in connection with the processor unit according to the first aspect of the present invention. These embodiments are meaningfully also applicable to the corresponding embodiments of the motor vehicle according to the second aspect of the present invention, the corresponding embodiments of the method according to the third aspect of the present invention and the corresponding embodiments of the computer program product according to the fourth aspect of the present invention.

[0020] The feature "trajectory" can be understood as the path that the motor vehicle should follow in the future within a prediction range, for example, within the next few seconds. A speed curve of the motor vehicle can be assigned to this path, and the speed curve can specify a target speed for the motor vehicle for each point along the path. This assignment of the path and the speed results in the speed trajectory of the motor vehicle. In addition, the trajectory can also describe the trend of the pump operating value, which is assigned to the operating parameter of the coolant pump and should be adjusted in the case of the pump within the prediction range. This trajectory can be a pump operating value trajectory. Exemplary operating parameters of the coolant pump are its delivery volume (especially the volume flow that can be delivered by the coolant pump) and the pressure that can be built up by the pump, and the corresponding pump operating values are different delivery volume values, volume flow values and pressure values.

[0021] The pump operating value, such as the delivery volume, can be converted into pump power in the longitudinal model of the motor vehicle, especially the following vehicle loss model, which can in turn be directly processed as loss power in the cost function. From a mathematical point of view, it can also be advantageous, for example, to plan the rotational speed of the coolant pump. In this case, the vehicle model needs to be matched so that the influence of the plan on the cost function to be minimized can be represented mathematically.

[0022] Furthermore, the invention is not limited to obtaining and implementing the operating value trajectory of the sole cooling pump of a motor vehicle. Instead, two, three or more cooling pumps of a motor vehicle can be planned and controlled as described above. Thus, if the "cooling pump" is mentioned, it can be assumed to be "at least one" cooling pump.

[0023] In order to find the optimal solution of the so-called "driving efficiency" driving function that should provide an efficient driving mode in any situation under given boundary conditions and restrictions, a method based on model predictive control (MPC) has been selected. The MPC method is based on a system model that describes the behavior of the system (in the present invention, the system model consists of a longitudinal dynamics model). In addition, the MPC method is also based on an objective function or cost function that describes and determines the optimization problem, and the objective function or cost function should minimize the state parameters.

[0024] The longitudinal dynamics model of a motor vehicle and a vehicle model including vehicle parameters and powertrain losses (partially approximated characteristic curves). In particular, at least one cooling pump can be reflected, calculated or simulated through the longitudinal dynamics model by using different pump operating values of at least one operating parameter. Information about the terrain of the road section ahead (such as curves and slopes) can also be incorporated into the longitudinal dynamics model of the motor vehicle. In addition, information about the speed limit of the road section ahead can also be incorporated into the longitudinal dynamics model of the powertrain.

[0025] The current state parameters can be measured, the corresponding data can be recorded and transmitted to the MPC algorithm. In particular, the road section data within the forward-looking range or prediction range (such as 400 m) for the motor vehicle ahead from the electronic map can be updated or upgraded periodically. These road section data can include, for example, slope information, curve information and information about the speed limit. In addition, the curve curvature can be converted into the speed limit of the motor vehicle through the maximum allowable lateral acceleration. In addition, the motor vehicle can also be oriented, especially accurately positioned on the electronic map through GNSS signals.

[0026] The MPC algorithm can include an MPC solver in the form of a software module in order to obtain the pump operating value trajectory and the speed trajectory of the motor vehicle simultaneously. The MPC solver can include instructions or program codes, thereby instructing the processor unit to optimize the mentioned trajectories depending on the longitudinal dynamics model, so as to minimize the cost function or its first term. In this way, the present invention can select and implement the optimal trajectory combination while considering the available degrees of freedom.

[0027] The opposite of simultaneous optimization is sequential optimization. For example, a speed profile can be planned, and only then the volumetric flow of the coolant pump. However, it may happen that in the first step, a speed profile is selected that can only be achieved at high coolant costs. In this way, the originally seemingly optimal speed profile becomes clearly meaningless at the end of the sequential consideration. However, in the case of simultaneously obtaining (the speed profile and the pump operating value profile) according to the invention, two degrees of freedom (i.e., the speed of the motor vehicle and the operating parameters of at least one coolant pump) are optimized in the same step while taking into account the respective causal relationships (the vehicle model or the longitudinal dynamics model of the motor vehicle). Thereby, it can be ensured that the MPC solver finds the global optimum.

[0028] The feature that the cost function is minimized "as a whole" can be understood as minimizing the sum of the individual costs. In other words, the cost function includes a plurality of terms, each of which represents an individual cost or cost block. One of these terms is the first term, which describes the power of at least one coolant pump (pump power, such as the consumed power or the loss power). By adding up all the terms of the cost function, the entire cost function or the cost function "as a whole" is obtained.

[0029] The invention can in particular be used for the autonomous driving function of a motor vehicle. The autonomous driving function can enable the motor vehicle to drive autonomously, that is, without the vehicle occupants controlling the motor vehicle. The driver has transferred the control to the driver assistance system. Thus, the autonomous driving function includes: the motor vehicle is in particular configured by means of a central processing unit to, for example, perform steering, flashing, accelerating and braking operations without human intervention and in particular to control external lights and signals, such as the turn signals of the motor vehicle. The autonomous driving function can also be a driving function that supports the motor vehicle driver when controlling the motor vehicle, in particular during steering, flashing, accelerating and braking operations, wherein the driver still controls the motor vehicle.

[0030] The processor unit can send the optimized pump operating value profile and the optimized speed profile of the motor vehicle to a software module ("target generator"). With the aid of this software module, the processor unit can convert the mathematical optimal planning of the available degrees of freedom, in particular those of at least one coolant pump and at least one component of the powertrain (in order to adjust the speed of the motor vehicle), into practically usable component signals. For example, the speed profile of the vehicle can be optimally planned for the next 5000 m by means of MPC. In this case, the target generator "converts" the first (= currently required) speed value of this profile, for example, into the required torque of the electric motor of the motor vehicle. The component software can work with this value and set the desired speed.

[0031] The MPC functionality described in the present invention is not limited to specific components or specific pumps, but can be applied to all fields capable of planning volume flow or pump power through the MPC algorithm. The more sensitive the loss of the cooled component is to temperature, the more obvious the effect will be. Here, the loss power to be minimized is usually obtained by adding the pump power and the additional loss caused by the deviation from the ideal temperature of the component to be cooled. Here, the interior space of a motor vehicle is an exception, which is cooled by a cooling pump to provide a corresponding coolant volume flow. The loss power is obtained only from the cooling pump power here.

[0032] Since the MPC algorithm generally not only minimizes losses, but also, for example, minimizes comfort, comfort costs can be added to the loss power costs of the cooling pump in the cost function. Here, the comfort cost is the deviation between the actual temperature and the set target temperature. The optimal compromise between the temporary deviation of the ideal temperature and the energy saved thereby can be obtained by means of the MPC algorithm. For example, the interior temperature of the vehicle can be reduced in advance before going uphill by means of the MPC algorithm, so that the air conditioner can be switched off during uphill driving and full power can be provided for uphill driving.

[0033] In one embodiment, at least one cooling pump is arranged in or on the powertrain of a motor vehicle in order to cool the motor and / or transmission and / or power electronics and / or battery of the powertrain. The mentioned components of the powertrain can be cooled together, that is to say, a common cooling pump conveys coolant through a common cooling circuit (which is guided along the motor, transmission, power electronics and battery of the powertrain), so that the mentioned components can dissipate heat to the coolant conveyed by the cooling pump through the cooling circuit. This embodiment can achieve cooling with particularly low equipment costs.

[0034] The mentioned components of the powertrain can also be cooled separately, whereby particularly high heat output and thus particularly efficient cooling can be achieved. Here, a first cooling pump can convey coolant through a first cooling circuit guided along the motor of the powertrain, so that the motor can dissipate heat to the coolant conveyed by the first cooling pump through the first cooling circuit. A second cooling pump can convey coolant through a second cooling circuit guided along the transmission of the powertrain, so that the transmission can dissipate heat to the coolant conveyed by the second cooling pump through the second cooling circuit. A third cooling pump can convey coolant through a third cooling circuit guided along the power electronics of the powertrain, so that the power electronics can dissipate heat to the coolant conveyed by the third cooling pump through the third cooling circuit. A fourth cooling pump can convey coolant through a fourth cooling circuit guided along the battery of the powertrain, so that the battery can dissipate heat to the coolant conveyed by the fourth cooling pump through the fourth cooling circuit.

[0035] In addition, two components of the mentioned components of the powertrain can be cooled jointly. Six combinations are obtained thereby. A common first coolant pump can convey coolant through a common first coolant circuit leading along the electric motor and the transmission of the powertrain, so that the mentioned components can dissipate heat to the coolant conveyed by the common first coolant pump through the common first coolant circuit. A common second coolant pump can convey coolant through a common second coolant circuit leading along the power electronics and the battery of the powertrain, so that the mentioned components can dissipate heat to the coolant conveyed by the common second coolant pump through the common second coolant circuit. A common third coolant pump can convey coolant through a common third common coolant circuit leading along the electric motor and the power electronics of the powertrain, so that the mentioned components can dissipate heat to the coolant conveyed by the common third coolant pump through the common third coolant circuit. A common fourth coolant pump can convey coolant through a common fourth coolant circuit leading along the transmission and the battery of the powertrain, so that the mentioned components can dissipate heat to the coolant conveyed by the common fourth coolant pump through the common fourth coolant circuit. A common fifth coolant pump can convey coolant through a common fifth coolant circuit leading along the electric motor and the battery of the powertrain, so that the mentioned components can dissipate heat to the coolant conveyed by the common fifth coolant pump through the common fifth coolant circuit. A common sixth coolant pump can convey coolant through a common sixth coolant circuit leading along the transmission and the power electronics of the powertrain, so that the mentioned components can dissipate heat to the coolant conveyed by the common sixth coolant pump through the common sixth coolant circuit.

[0036] Furthermore, three of the mentioned components of the powertrain can be cooled jointly. Four combinations are obtained thereby. A common first coolant pump can convey coolant through a common first coolant circuit leading along the transmission, the power electronics and the battery of the powertrain, so that the mentioned components can dissipate heat to the coolant conveyed by the common first coolant pump through the common first coolant circuit. A common second coolant pump can convey coolant through a common second coolant circuit leading along the electric motor, the power electronics and the battery of the powertrain, so that the mentioned components can dissipate heat to the coolant conveyed by the common second coolant pump through the common second coolant circuit. A common third coolant pump can convey coolant through a common third coolant circuit leading along the electric motor, the transmission and the battery of the powertrain, so that the mentioned components can dissipate heat to the coolant conveyed by the common third coolant pump through the common third coolant circuit. A common fourth coolant pump can convey coolant through a common fourth coolant circuit leading along the electric motor, the transmission and the power electronics of the powertrain, so that the mentioned components can dissipate heat to the coolant conveyed by the common fourth coolant pump through the common fourth coolant circuit.

[0037] Furthermore, at least one cooling pump can be arranged within the cooling unit for cooling the interior space or on the on-vehicle charger for cooling during charging at the charging station. In this sense, in one embodiment, the cooling pump is arranged to convey a coolant through a cooling circuit, wherein the cooling circuit cools the interior space or the on-vehicle charger of the motor vehicle. Additionally, at least one cooling pump can also be arranged within the transmission, in particular within the automatic transmission of the motor vehicle, wherein the cooling pump is in particular a vane pump. The cooling pump, which is in particular a vane pump, can be arranged to convey a coolant through a cooling circuit, wherein the cooling circuit cools the components of the transmission.

[0038] In addition to the cooling pump, the motor vehicle can also have a first component and a second component. The first component can be operated with different values of a first operating parameter, while the second component can be operated with different values of a second operating parameter. The first component with different values of the first operating parameter and the second component with different values of the second operating parameter can be reflected, calculated, or simulated through a longitudinal dynamics model.

[0039] The longitudinal dynamics model can also include a loss model of the motor vehicle, wherein the loss model illustrates the total losses of the motor vehicle. The loss model can, for example, include a list of efficiency-related components (such as a height-adjustable vehicle or a system provided therefor) and degrees of freedom (such as the adjustment height of the chassis), which encompasses various different parts of the entire vehicle system. Thus, the loss model illustrates the total losses of the motor vehicle. Here, the degrees of freedom are the operating parameters of the components. According to the present invention, a "component" is, for example, an actuator for steering and damping of the motor vehicle. Additionally, the braking facility and the powertrain can in particular constitute components according to the present invention. In addition to the first component and the second component, the motor vehicle also includes other components, and their degrees of freedom can be planned and adjusted in the same manner as detailed for the first component and the second component.

[0040] The cost function includes a first term (c pump ), which represents the power of the cooling pump. The cost function can also include a second term ("c efficiency "), which represents the total losses (remaining) or component losses of the motor vehicle. The overall losses of the motor vehicle depend in particular on the operating value combination, which includes a first value of the first operating parameter and a second value of the second operating parameter. Additionally, the cost function can include additional terms ("c time ", "c comfort "), and some of the advantages thereof will be further elaborated below. The processor unit is arranged to obtain, by executing the MPC algorithm and depending on the loss model, at least the operating value combination that minimizes the second term ("c efficiency ") of the cost function. Additionally, through the obtained operating value combination, the entire cost function (min(f cost= c pump + c efficieny + c time + c comfort )) Minimize.

[0041] The MPC solver can determine the operating value combination depending on the loss model as follows, i.e., minimize the cost function or at least its second term (“c efficiency ”). In this way, the present invention can enable the selection and implementation of an optimal operating value combination or actuator combination of the available degrees of freedom. The processor unit can send the optimized operating value combination to the aforementioned software module (“target generator”). With the help of this software module, the processor unit can convert the mathematically optimal plan of all available degrees of freedom into practically available component signals.

[0042] In another embodiment, the processor unit can be configured to drive the first actuator of the first component by executing the conversion software module as follows, i.e., the first actuator operates with the first actuator value, whereby the first component operates with the first value of the first operating parameter of the operating value combination that at least minimizes the first term of the cost function. In addition, in this embodiment, the processor unit is configured to drive the second actuator of the second component by executing the conversion software module as follows, i.e., the second actuator operates with the second actuator value, whereby the second component operates with the second value of the second operating parameter of the operating value combination that at least minimizes the first term of the cost function. This embodiment can achieve controlling these actuators as follows, i.e., enabling the components to operate as efficiently as possible. In other words, an optimal actuator value combination can be determined and implemented so that the components and thus the motor vehicle operate as energy-efficiently as possible.

[0043] As already mentioned, the present invention is not limited to the first and second components. In a modern vehicle, various different efficiency-related components are installed, and they are not necessarily only in the powertrain. All these components with their degrees of freedom can be reflected by the loss model. In one embodiment, in particular, in addition to other efficiency-related components, the first component is a system for horizontally adjusting the motor vehicle, and the second component can be a braking facility. By intelligently adjusting the body height and / or by intelligently activating the brakes (which is achieved by determining the optimal operating value combination), the total loss of the motor vehicle can be reduced.

[0044] According to another embodiment, the second term includes electrical energy weighted by a first weighting factor and predicted according to a longitudinal dynamics model, and the electrical energy is provided by a battery of a powertrain within a prediction range to drive an electric motor. In addition, the cost function includes a travel time weighted by a second weighting factor and predicted according to the longitudinal dynamics model as a third term, and the motor vehicle needs this travel time to drive through the entire predicted distance within the prediction range. The processor unit can be configured to obtain input parameters for the electric motor depending on the first term, depending on the second term, and depending on the third term by executing an MPC algorithm, so as to minimize the cost function.

[0045] Therefore, state variables for the driving function for driving efficiency can be, for example, vehicle speed or kinetic energy, remaining battery charge, and travel time. Optimization of energy consumption and travel time can be performed, for example, based on the slope of the road ahead and restrictions on driving force and speed, and based on the current system state. By means of an objective function or a cost function of a driving strategy for driving efficiency, in addition to total losses or energy consumption, the travel time can also be minimized. This results in that, depending on the choice of weighting factors, a low speed is not always evaluated as optimal and there is no longer a problem that the resulting speed is always at the lower limit of the permitted speed. It can be achieved that the driver influence is no longer related to the energy consumption and travel time of the motor vehicle, because the electric motor can be controlled by the processor unit based on input parameters, and the input parameters are obtained by executing the MPC algorithm. By means of the input parameters, in particular, the optimal motor operating point of the electric motor can be adjusted. Thereby, a direct adjustment of the optimal speed of the motor vehicle can be achieved.

[0046] The cost function can in particular have only linear terms and quadratic terms. Thereby, the entire problem has the form of a quadratic optimization with linear constraints, and a convex problem that can be solved well and quickly is obtained. The objective function or cost function can be assigned with weighting (weighting factors), wherein in particular energy efficiency, travel time, and driving comfort are calculated and weighted. An energy-optimal speed trajectory can be calculated online on the processor unit for the forward range, and the processor unit can in particular form part of a central controller of the motor vehicle. By applying the MPC method, the target speed of the motor vehicle can also be recalculated periodically based on the current vehicle state and road segment information ahead. Description of the Drawings

[0047] Next, embodiments of the present invention will be further described in conjunction with schematic diagrams, wherein the same or similar elements are denoted by the same reference numerals. Among them:

[0048] Figure 1 is a schematic diagram of a motor vehicle, which includes a coolant pump, a first component related to efficiency, and a second component related to efficiency,

[0049] Figure 2 is according to Figure 1the degree of freedom of the first component related to efficiency,

[0050] Figure 3 is based on Figure 1 the degree of freedom of the second component related to efficiency,

[0051] Figure 4 is based on Figure 1 the degree of freedom of the coolant pump,

[0052] Figure 5 is based on Figure 1 the characteristic curve clusters of the electric motor of the vehicle. Detailed implementation

[0053] Figure 1 A motor vehicle 1, such as a sedan, is shown. The motor vehicle 1 includes a system 2 for model-based predictive adjustment of the motor vehicle 1. In the illustrated embodiment, the system 2 also includes a processor unit 3, a memory unit 4, a communication interface 5, and a detection unit 6 for detecting state data related to the motor vehicle 1 in particular. In addition, the motor vehicle 1 includes a powertrain 7, which may include, for example, an electric motor 8 (which can operate as a motor and a generator), a battery 9, a transmission 10, and power electronics 34. The electric motor 8 can drive the wheels of the motor vehicle 1 through the transmission 10 during motor operation, and the transmission may have a constant transmission ratio, for example. The battery 9 can provide the required electrical energy. When the electric motor 8 operates in generator mode (regeneration), the battery 9 can be charged through the electric motor 8. Optionally, the battery 9 can also be charged at an external charging station. Similarly, the powertrain of the motor vehicle 1 optionally has an internal combustion engine 17, which can drive the motor vehicle 1 alternatively or in addition to the electric motor 8. The internal combustion engine 17 can also drive the electric motor 8 to charge the battery 9.

[0054] The motor vehicle 1 also includes a coolant pump 28, which conveys coolant through a coolant circuit 29. In the illustrated embodiment, the coolant circuit 29 extends through the electric motor 8, the battery 9, the transmission 10, and the power electronics 34, so that these components 8, 9, 10, 34 of the powertrain 7 can output heat to the coolant conveyed through the coolant circuit 29 by the coolant pump 28. Then, the coolant can be cooled again in a heat exchanger 35, for example.

[0055] In addition to the coolant pump 28, the motor vehicle 1 may also include other coolant pumps (not shown, such as a vane pump in the automatic transmission of the motor vehicle, a coolant pump in the coolant unit for cooling the interior space of the motor vehicle 1, or a coolant pump on the on-board charger for cooling when the battery 9 is charged at a charging station), which can be programmed and adjusted in a similar manner as further described below. Furthermore, the motor vehicle 1 includes a plurality of components that are related to the efficiency of the operation of the motor vehicle 1 ("efficiency-related components"), especially when the motor vehicle 1 is operating in the autonomous driving mode. These components are not only arranged within the powertrain 7 of the motor vehicle 1. Although the motor vehicle 1 also includes a series of other efficiency-related components, however Figure 1 only the first component 18 and the second component 19 are shown by way of example. In accordance with Figure 1 the embodiment shown, a first component in the form of a system 18 for horizontally adjusting the motor vehicle 1 and a second component in the form of the braking facility 19 of the motor vehicle 1 are shown. For example, the electric motor 8, the battery 9, the transmission 10, and the internal combustion engine 17 can also be referred to as efficiency-related components of the motor vehicle 1.

[0056] Figure 2 It is shown that the system 18 for horizontally adjusting the motor vehicle 1 can operate with different values of a first operating parameter 20 (first degree of freedom). The first operating parameter 20 can, for example, be the set height of the chassis 21 of the motor vehicle 1. The set height of the chassis 21 of the motor vehicle 1 can be assumed, by way of example only, to be a first value h1, a second value h2, and a third value h3. Different heights h1, h2, and h3 of the chassis 21 result in different magnitudes of air resistance for the motor vehicle 1. This can be described by a further longitudinal model 14 of the powertrain 7 of the motor vehicle 1 as described below.

[0057] The system 18 for horizontally adjusting the motor vehicle 1 can be referred to as an actuator of the motor vehicle 1. Furthermore, the system 18 for horizontally adjusting the motor vehicle 1 itself can have at least one actuator 22 (such as a hydraulic cylinder or a pneumatic cylinder or a hydropneumatic shock absorber), which is operated to horizontally adjust the system 18 of the motor vehicle 1. The first actuator 22 can operate with different actuator values 23, so as to obtain different values h1, h2, and h3 for the system 18 for horizontally adjusting the motor vehicle 1. For example, in the case of a first actuator value x1 (such as a first pressure value of a hydraulic cylinder), a first height h1 of the chassis 21 is obtained, in the case of a second actuator value x2, a second height h2 of the chassis 21 is obtained, and in the case of a third actuator value x3, a third height h3 of the chassis 21 is obtained.

[0058] Figure 3It is shown that the braking facility 19 can operate with different values of a second operating parameter 24 (second degree of freedom). The second operating parameter 20 can be, for example, the braking force of the braking facility 19. The braking force can be assumed, purely by way of example, to be a first value y1, a second value y2 and a third value y3. Different magnitudes of the braking force y1, y2 and y3 of the braking facility 19 result in different magnitudes of the traction force which is applied to the wheels of the motor vehicle 1 by the braking facility. This can be described by other longitudinal models 14 of the powertrain 7 of the motor vehicle 1 described below.

[0059] The braking facility 19 can be referred to as an actuator of the motor vehicle 1. In addition, the braking facility 19 itself can have an actuator 25 (for example a hydraulic cylinder) which operates the braking facility 19. The second actuator 25 can be operated with different actuator values 26, so that different values y1, y2 and y3 are obtained for the braking facility 19. For example, a first braking force y1 is obtained in the case of a first actuator value z1, a second braking force y2 in the case of a second actuator value z2 and a third braking force y3 in the case of a third actuator value z3.

[0060] Figure 4 It is shown that the coolant pump 28 can operate with different pump operating values of a third operating parameter 30 (third degree of freedom). The third operating parameter 30 can be, for example, the delivery volume Q (= the volume flow of the coolant which can be delivered by the coolant pump 28). The delivery volume can be assumed, purely by way of example, to be a first value Q1, a second value Q2 and a third value Q3. Different magnitudes of the delivery volume Q1, Q2 and Q3 of the coolant pump 28 result in different magnitudes of the power P of the coolant pump 28. pump This can be described by other longitudinal models 14 of the powertrain 7 of the motor vehicle 1 described below.

[0061] In addition, the coolant pump 28 can also have an actuator 31 (for example a motor) which operates or drives the coolant pump 28. The second actuator 31 can be operated with different actuator values 32 (for example with different rotational speeds n1, n2, n3 for driving the coolant pump 28), so that different pump operating values Q1, Q2 and Q3 are obtained for the coolant pump 28. For example, a first delivery volume Q1 is obtained at a first rotational speed n1 (first actuator value n1), a second delivery volume Q2 at a second rotational speed n2 and a third delivery volume Q3 at a third rotational speed n3.

[0062] A computer program product 11 can be stored on the memory unit 4. The computer program product 11 can be executed on the processor unit 3, for which purpose the processor unit 3 and the memory unit 4 are communicatively connected to one another via a communication interface 5. When the computer program product 11 is executed on the processor unit 3, it instructs the processor unit 3 to implement the functions shown in connection with the figures or to carry out method steps.

[0063] The computer program product 11 includes the MPC algorithm 13. The MPC algorithm 13 in turn includes the longitudinal dynamics model 14 of the powertrain 7 of the motor vehicle 1. In addition, the MPC algorithm 13 also includes the cost function 15 to be minimized. The first term c pump Illustrates the power of the coolant pump 28. The first term c pump Represents the parameterizable cost regarding the power of the coolant pump 28. The second term c of the cost function 15 efficiency Represents the total losses of the motor vehicle 1, which in the illustrated embodiment is related to the efficiency of the first component 18 and the second component 19. The cost function 15 to be minimized can be mathematically expressed as follows:

[0064] min(c pump +c efficieny +c time +c comfort )

[0065] Here:

[0066] c pump The parameterizable cost regarding the power of the coolant pump,

[0067] c efficiency The parameterizable cost regarding efficiency,

[0068] c time The parameterizable cost regarding travel time, and

[0069] c comfort The parameterizable cost regarding comfort.

[0070] The detection unit 6 can measure the current state parameters of the motor vehicle 1, record the corresponding data and send it to the MPC algorithm 13. In addition, in particular, the road segment data in the electronic map within the forward-looking range or the prediction range (for example, 400 m) of the motor vehicle 1 can be periodically updated or upgraded. These road segment data can include, for example, slope information, curve information and information about speed limits. In addition, the curve curvature can be converted into the speed limit of the motor vehicle 1 through the maximum allowable lateral acceleration. In addition, the motor vehicle can also be oriented with the help of the detection unit 6, especially by accurately positioning on the electronic map through the signal generated by the GNSS sensor 12. In addition, the detection unit for detecting the external environment of the motor vehicle 1 can include, for example, a radar sensor, a camera system and / or a lidar sensor. The processor unit 3 can access the information of these elements through the communication interface 5, for example. These information can be incorporated into the longitudinal model 14 of the motor vehicle 1, especially as constraints or auxiliary conditions.

[0071] The processor unit 3 executes the MPC algorithm 13 and thereby determines an optimal speed profile based on the longitudinal model 14, taking into account in particular the route topology, traffic, and other environmental information (as described above). It is now possible to plan the speed profile in combination with the new degrees of freedom. In this sense, the strategy no longer only plans the speed profile but also adjusts the power of the coolant pump 28 along this speed profile. This is achieved by the processor unit 3 executing the MPC algorithm 13 and thereby determining a pump operating value profile that matches the optimal speed profile.

[0072] Thus, for each road point within the prediction horizon, an optimized speed value for the motor vehicle 1 and an optimized pump operating value for the pump 28 are assigned simultaneously. In the simplified example according to Figure 4 the processor unit 3 can combine the possible speed values with the possible three delivery volumes Q1, Q2, Q3 of the coolant pump 28 for each road point within the prediction horizon and select the combination that minimizes the first term c Pump of the cost function 15 and the cost function 15 as a whole. In this way, an integrated optimization of the various degrees of freedom is carried out, which in particular results in a more efficient driving behavior overall. It should be noted here that the optimization of the individual degrees of freedom (e.g., the speed of the motor vehicle 1 and the delivery volume of the coolant pump 28) is carried out simultaneously.

[0073] The processor unit 3 can send the corresponding value pairs (value 1: optimized speed of vehicle 1; value 2.1: optimized delivery volume of the coolant pump 28; alternative value 2.2: optimized rotational speed of the motor 30 of the coolant pump 28) to the target generator 33, which can be integrated as a software module into the MPC algorithm 13. Alternatively, the target generator 33 can also be included, for example, as a software module in the driver assistance system 16, as Figure 1 shown. Based on this value pair, in particular, the speed of the motor vehicle 1 can be set to value 1 and the delivery volume of the coolant pump 28 can be set to value 2.1 with the aid of the target generator 33. In addition, the processor unit 3 can also adjust the third actuator 30 to value 2.2, thereby generating value 2.1 for the coolant pump 28.

[0074] In the illustrated embodiment, the longitudinal dynamics model 14 includes a loss model 27 of the motor vehicle 1. The loss model 27 describes the operating behavior of the efficiency-related components 18, 19 with respect to their efficiency or with respect to their losses. Thereby, the total losses of the motor vehicle 1 are obtained. The processor unit 3 executes the MPC algorithm 13 and thereby predicts the behavior of the motor vehicle 1 based on the longitudinal dynamics model 14, wherein the cost function 15 is minimized.

[0075] The total loss of the motor vehicle 1 depends on the combination of operating values. The combination of operating values includes a first value of a first operating parameter and a second value of a second operating parameter. In the simplified embodiment shown, there are six possible combinations of operating values. The first combination of operating values includes a first height h1 of the chassis 21 and a first braking force y1 of the braking facility 19. In the case of the first combination of operating values, the first total loss of the motor vehicle 1 is obtained. The second combination of operating values includes a first height h1 of the chassis 21 and a second braking force y2 of the braking facility 19. In the case of the second combination of operating values, the second total loss of the motor vehicle 1 is obtained. The third combination of operating values includes a first height h1 of the chassis 21 and a third braking force y3 of the braking facility 19. In the case of the third combination of operating values, the third total loss of the motor vehicle 1 is obtained. The fourth combination of operating values includes a second height h2 of the chassis 21 and a first braking force y1 of the braking facility 19. In the case of the fourth combination of operating values, the fourth total loss of the motor vehicle 1 is obtained. The fifth combination of operating values includes a second height h2 of the chassis 21 and a third braking force y3 of the braking facility 19. In the case of the fifth combination of operating values, the fifth total loss of the motor vehicle 1 is obtained. The sixth combination of operating values includes a third height h3 of the chassis 21 and a third braking force y3 of the braking facility 19. In the case of the sixth combination of operating values, the sixth total loss of the motor vehicle 1 is obtained.

[0076] The processor unit 3 can determine the six combinations of operating values according to the loss model 14 by executing the MPC algorithm 13. Here, the processor unit 3 can compare the total losses obtained in the six different combinations of operating values with each other. Here, the processor unit 3 can for example confirm that the third combination of operating values (h1; y3) results in the smallest total loss of the motor vehicle 1. The processor unit 3 can select the third combination of operating values and send the corresponding values to the target generator 33 for example. Based on the determined combination of operating values (h1; y3), the first component 18 can be set to the first value h1 of the first operating parameter 20 and the second component 19 can be set to the third value y3 of the second operating parameter 24, in particular by means of the target generator 33. In addition, the processor unit 3 can also set the first actuator 22 to the first actuator value x1, so that the first value h1 of the first operating parameter 20 occurs for the first component 18. In a similar way, the processor unit 3 can set the second actuator 25 to the third actuator value z3, so that the third value y3 of the second operating parameter 24 occurs for the second component 19.

[0077] For the points calculated in the forward-looking range, the optimal speed and the optimal torque of the electric machine 8 can also be obtained as an optimized output of the MPC algorithm 13. For this purpose, the processor unit 3 can determine the input parameters of the electric machine 8, so that the optimal speed and the optimal torque occur. The processor unit 3 can control the electric machine 8 based on the determined input parameters. However, this can also be done by the driver assistance system 16, in particular by means of its target generator 33.

[0078] The longitudinal dynamics model 14 of the motor vehicle 1 can be represented mathematically as follows:

[0079]

[0080] Here,

[0081] v is the speed of the motor vehicle;

[0082] F trac is the traction force, which is applied to the wheels of the motor vehicle by a motor or a brake and is affected, for example, by the different braking forces y1, y2, and y3 of the braking facility 19;

[0083] F r is the rolling resistance, which is the deformation effect when the tire rolls and depends on the wheel load (the normal force between the wheel and the road) and thus on the inclination angle of the road;

[0084] F gr is the slope resistance, which represents the longitudinal component of the gravitational force acting on the motor vehicle during uphill or downhill driving and depends on the slope of the lane;

[0085] F d is the air resistance of the motor vehicle and is affected, for example, by the different heights h1, h2, and h3 of the chassis 21;

[0086] P pump is the power of the cooling pump and is affected, for example, by the different delivery volumes Q1, Q2, and Q3 of the cooling pump 28, and

[0087] m eq is the equivalent mass of the motor vehicle; the equivalent mass particularly includes the inertia of the rotating components (motor, transmission drive shaft, wheels) of the powertrain, and these rotating components are subjected to the acceleration of the motor vehicle.

[0088] By converting from time to distance and using a coordinate transformation to eliminate the square term of the speed in the air resistance, we obtain:

[0089]

[0090] In order to solve this problem quickly and simply by the MPC algorithm 13, the dynamic equation of the longitudinal dynamics model 14 can be linearized by representing the speed by a coordinate transformation from the kinetic energy de kin Thus, for calculating the air resistance F dThe squared terms are replaced by linear terms. At the same time, the longitudinal dynamics model 14 of the motor vehicle 1 is no longer described as time-dependent as usual, but as distance-dependent. This is well-suited to the optimization problem because the forward-looking information for the electric range is distance-based.

[0091] In addition to the kinetic energy, there are two other state variables that can also be described linearly and distance-dependently in the context of a simple optimization problem. On the one hand, the electrical energy consumption of the powertrain 7 is often described in the form of a characteristic curve family that depends on the torque and the motor speed. In the illustrated embodiment, the motor vehicle 1 has a fixed transmission ratio between the electric motor 8 and the road on which the motor vehicle 1 moves. Thereby, the speed of the electric motor 8 can be directly converted into the speed of the motor vehicle 1 or also into the kinetic energy of the motor vehicle 1. In addition, the electrical power of the electric motor 8 can be converted into the energy consumption per meter by dividing by the corresponding speed. Thereby, the characteristic curve of the electric motor 8 is obtained in the form of Figure 5 as can be seen. In order to be able to use this characteristic curve family for optimization, it is linearly approximated: Energy per meter (Energy perMeter ) ≥ a i *e kin +b i *F trac for all i.

[0092] Specifically, the cost function 15 to be minimized can be represented mathematically as follows:

[0093]

[0094] Herein:

[0095] c pump Parametrizable cost regarding the cooling pump power,

[0096] w Bat Weighting factor for the battery energy consumption

[0097] E Bat Battery energy consumption

[0098] S Distance

[0099] S E-1 Distance at the time step before the end of the prediction range

[0100] F A Driving force provided by the electric motor, which is constantly converted by the transmission and applied to the wheels of the motor vehicle

[0101] W Tem Weighting factor for the torque gradient

[0102] W TemStart Weighting factor for the torque mutation

[0103] The time required for vehicle T to travel the entire predicted distance within the prediction range

[0104] w Time The weighting factor for time T

[0105] S E The distance at the end of the range

[0106] w Slack The weighting factor for the slack variable

[0107] Var Slack The slack variable.

[0108] The cost function 15 only has linear terms and square terms. Thus, the entire problem has the form of quadratic optimization with linear auxiliary conditions, and a convex problem that can be solved well and quickly is obtained.

[0109] In addition to the above-parametrizable cost c of the cooling pump 28 pump and the efficiency cost of components 19, 20, the cost function 15 also includes the electrical energy E weighted by the first weighting factor w Bat and predicted according to the longitudinal dynamics model Bat As an additional term, this electrical energy is provided by the battery 9 of the powertrain 7 to drive the electric motor 8 within the prediction range. The battery 9 and the electric motor 8 can be referred to as the efficiency-related components of the motor vehicle 1, such as the above-mentioned system 18 for horizontal adjustment and the above-mentioned braking facility 19. Correspondingly, the electrical energy E weighted by the first weighting factor w Bat and predicted according to the longitudinal dynamics model Bat can also be incorporated into the term c efficiency .

[0110] The cost function 15 includes, as an additional term, the required driving time T for the motor vehicle 1 to travel the predicted distance, weighted by the second weighting factor W Time and predicted according to the longitudinal dynamics model 14. This results in that, depending on the choice of the weighting factor, a low speed is not always evaluated as optimal and there is no longer a problem that the resulting speed always lies at the lower limit of the permitted speed. The energy consumption and the driving time can be evaluated and weighted respectively at the end of the prediction range. These terms are only valid for the last point of the prediction range.

[0111] An excessive torque gradient within the range is disadvantageous. Therefore, the torque gradient has been penalized in the cost function 15, that is, by the term being penalized. The square of the deviation of the driving force per meter is weighted by the weighting factor W Tem and minimized within the cost function. Instead of the driving force F per meter A the torque M provided by the electric motor 8 can also be used EMand weighted with the weighting factor W Tem to obtain an alternative term Due to the constant transmission ratio of the transmission 10, the driving force and the torque are directly proportional to each other.

[0112] To ensure a comfortable drive, another term for penalizing torque jumps is introduced into the cost function 15, namely, w TemStart ·(F A (s1) - F A (s0)) 2 . The alternative driving force F A Here, the torque M provided by the electric motor 8 can also be used EM to obtain an alternative term w TemStart ·(M EM (s1) - M EM (s0)) 2 . For the first point within the prediction range, the deviation from the last set torque is evaluated negatively and weighted with the weighting factor W TemStart to ensure a seamless and jerk-free transition when switching between the old and new trajectories.

[0113] The speed limit is a hard limit for the optimization and must not be exceeded. A slight overshoot of the speed limit is always permitted in real-world situations and is normal especially when transitioning from one speed zone to a second. In a dynamic environment where the speed limit transitions from one calculation cycle to the next, it may occur that no valid solution for the cluster of speed curves can be found in the case of a completely hard limit. To increase the stability of the calculation algorithm, a constraint ("soft constraint") can be introduced into the cost function 15. Here, a relaxation variable Var Slack weighted with the weighting factor W Slack becomes effective within a predefined narrow range. Solutions that are very close to the speed limit, i.e., whose speed trajectories maintain a certain distance from the hard limit, are evaluated worse.

[0114] Reference Signs

[0115] h1 The first value of the first operating parameter

[0116] h2 The second value of the first operating parameter

[0117] h3 The third value of the first operating parameter

[0118] n1 The first actuator value of the third actuator

[0119] n2 The second actuator value of the third actuator

[0120] n3 The third actuator value of the third actuator

[0121] Q1 First pump operation value

[0122] Q2 Second pump operation value

[0123] Q3 Third pump operation value

[0124] x1 First actuator value of the first actuator

[0125] x2 First actuator value of the first actuator

[0126] x3 First actuator value of the first actuator

[0127] y1 First value of the second operating parameter

[0128] y2 Second value of the second operating parameter

[0129] y3 Third value of the second operating parameter

[0130] z1 First actuator value of the second actuator

[0131] z2 First actuator value of the second actuator

[0132] z3 First actuator value of the second actuator

[0133] 1 Vehicle

[0134] 2 System

[0135] 3 Processor unit

[0136] 4 Memory unit

[0137] 5 Communication interface

[0138] 6 Detection unit

[0139] 7 Powertrain

[0140] 8 Electric motor

[0141] 9 Battery

[0142] 10 Transmission

[0143] 11 Computer program product

[0144] 12 GNSS sensor

[0145] 13 MPC algorithm

[0146] 14 Longitudinal dynamics model

[0147] 15 Cost function

[0148] 16 Driver assistance system

[0149] 17 Internal combustion engine

[0150] 18 System for horizontal adjustment

[0151] 19 Braking facility

[0152] 20 First operating parameter

[0153] 21 Chassis

[0154] 22 First actuator

[0155] 23 Actuator value of the first actuator

[0156] 24 Second operating parameter

[0157] 25 Second actuator

[0158] 26 Actuator value of the second actuator

[0159] 27 Loss model

[0160] 28 Cooling pump

[0161] 29 Cooling circuit

[0162] 30 Third operating parameter

[0163] 31 Third actuator

[0164] 32 Actuator value of the third actuator

[0165] 33 Target generator

[0166] 34 Power electronics

[0167] 35 Heat exchanger

Claims

1. A processor unit (3) for model-based predictive adjustment of a motor vehicle (1), wherein, The motor vehicle (1) includes a coolant pump (28) that can operate with different pump operating values (Q1, Q2, Q3) of at least one operating parameter (30). - The processor unit (3) is configured to execute a model predictive control (MPC) algorithm (13) for adjusting the motor vehicle (1), where the MPC algorithm (13) includes a longitudinal dynamics model (14) of the motor vehicle (1) and a cost function (15) to be minimized. The cost function (15) includes multiple terms, and the first term represents the power of the coolant pump (28), and - The processor unit (3) is configured to - obtain the speed trajectory of the motor vehicle (1) within the prediction range - depending on the longitudinal dynamics model (14) and simultaneously - obtain the pump operating value trajectory within the prediction range, so as to minimize the first term of the cost function (15).

2. The processor unit (3) according to claim 1, wherein, The coolant pump (28) is arranged in the powertrain (7) of the motor vehicle (1) and is configured to convey coolant through a coolant circuit (29), where the coolant circuit (29) cools at least one of the following components of the powertrain (7), namely, - an electric motor (8) - a transmission (10), - power electronics (34), - a battery (9).

3. The processor unit (3) according to claim 1 or 2, wherein, The coolant pump is configured to convey coolant through a coolant circuit, where the coolant circuit cools the interior space of the motor vehicle (1).

4. The processor unit (3) according to any one of claims 1-2, wherein, The coolant pump is configured to convey coolant through a coolant circuit, where the coolant circuit cools the on-board charger of the motor vehicle (1).

5. The processor unit (3) according to any one of claims 1-2, wherein, The coolant pump is a vane pump in the transmission of the motor vehicle (1) and is configured to convey coolant through a coolant circuit, where the coolant circuit cools the components of the transmission.

6. The processor unit (3) according to any one of claims 1-2, where - The motor vehicle (1) has a first component (18) in addition to the coolant pump (28), and the first component can operate with different values (h1, h2, h3) of a first operating parameter (20). - The motor vehicle (1) has a second component (19) in addition to the coolant pump (28), and the second component can operate with different values (y1, y2, y3) of a second operating parameter (24). - The longitudinal dynamics model (14) includes a loss model (27) of the motor vehicle (1). - The loss model (27) describes the total losses of the motor vehicle (1). - The cost function (15) includes a second term representing the total losses of the motor vehicle (1). - The total losses depend on an operating value combination that includes one value from different values (h1, h2, h3) of the first operating parameter (20) and one value from different values (y1; y2; y3) of the second operating parameter (24), and - The processor unit (3) is configured to determine, by executing the MPC algorithm (13), the combination of operating values that minimizes the second term of the cost function (15) depending on the loss model (14).

7. The processor unit (3) according to any one of claims 6, wherein - The second term includes electrical energy weighted by a first weighting factor and predicted according to the longitudinal dynamics model (14), and the electrical energy is provided by the battery (9) of the powertrain (7) of the motor vehicle (1) within the prediction range to drive the electric machine (8) of the powertrain (7), - The cost function (15) includes the travel time weighted by a second weighting factor and predicted according to the longitudinal dynamics model (14) as a third term, and the motor vehicle (1) requires this travel time to cover the entire predicted distance within the prediction range, and - The processor unit (3) is configured to determine, by executing the MPC algorithm (13), the input parameters for the electric machine (8) depending on the second term and depending on the third term, so as to minimize the cost function (15).

8. Motor vehicle (3), said motor vehicle comprising a processor unit (3), a driver assistance system (16), a powertrain (7) and a coolant pump (18) according to any one of the preceding claims, said coolant pump being able to operate with different pump operating values (Q1, Q2, Q3) of at least one operating parameter (30), wherein, The driver assistance system (16) is configured to - access the speed trajectory of the motor vehicle (1) determined by the processor unit (3) within the prediction range, - access the pump operating value trajectory determined by the processor unit (3) within the prediction range, - control the powertrain (7) of the motor vehicle (1) based on the speed trajectory of the motor vehicle (1), and - control the coolant pump (28) based on the pump operating value trajectory.

9. A method for model-based predictive adjustment of a motor vehicle (1), wherein, The motor vehicle (1) includes a coolant pump (28) that can operate with different pump operating values (Q1, Q2, Q3) of at least one operating parameter (30), and the method includes the steps of - executing an MPC algorithm (13) for model predictive adjustment of the motor vehicle (1), wherein the MPC algorithm (13) includes the longitudinal dynamics model (14) of the motor vehicle (1) and a cost function (15) to be minimized, and wherein the cost function (15) includes a plurality of terms, and the first term thereof represents the power of the coolant pump (28), - determining, by executing the MPC algorithm (13), the speed trajectory of the motor vehicle (1) within the prediction range depending on the longitudinal dynamics model (14), and - determining, by executing the MPC algorithm (13), the pump operating value trajectory within the prediction range so as to minimize the first term of the cost function (15), wherein the determination of the speed trajectory and the pump operating value trajectory is performed simultaneously.

10. A computer program product (11) for model-based predictive adjustment of a motor vehicle (1), wherein, The motor vehicle (1) includes a coolant pump (28) that can operate with different pump operating values (Q1, Q2, Q3) of at least one operating parameter (30), and when the computer program product (11) is executed on the processor unit (3), the computer program product instructs the processor unit (3), -Execute the MPC algorithm (13) for model-based predictive adjustment of a motor vehicle (1), wherein the MPC algorithm (13) includes a longitudinal dynamics model (14) of the motor vehicle (1) and a cost function (15) to be minimized, wherein the cost function (15) includes a plurality of terms, and the first of which represents the power of the coolant pump (28), and -By executing the MPC algorithm (13) -Obtain the motor vehicle (1) depending on the longitudinal dynamics model (14) The speed trajectory within the prediction range, and at the same time -Obtain the pump operation value trajectory within the prediction range so as to minimize the first term of the cost function (15).

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