Setting method for component cooling system of vehicle

Abstract

A method of setting a component cooling system for a component of a vehicle includes generating a drive cycle plan for a remaining drive cycle; and calculating an instantaneous cooling requirement based on the drive cycle plan and the maximum temperature; and generating a control signal by means of which a continuous setting of the component cooling system is carried out during the remaining driving cycle as a function of the instantaneous cooling requirement; thereby minimizing the total cooling work during the remaining driving cycle; and a computing unit configured to perform the method.

Description

Technical Field

[0001] The invention relates to a method for setting a cooling system of a vehicle, a vehicle control unit designed to implement the setting method, a calculation unit for implementing the setting method, and a vehicle. Background Art

[0002] Due to their ease of operation and low implementation complexity, rule-based methods for operating cooling systems of vehicle components in road vehicles are currently preferred. Rule-based methods make decisions on the settings of the cooling system based on the current state of the vehicle. Optimization of the settings is usually not performed.

[0003] A possible rule-based method that can be used in a PHEV vehicle with an internal combustion engine during operation in urban traffic is described by the following rule:

[0004] 1. If there is energy in the battery, the battery energy is only used until it is exhausted. Then, the internal combustion engine is used.

[0005] 2. If the vehicle is driven through a city, battery energy is utilized.

[0006] This rule-based approach only considers the instantaneous state of the vehicle during the trip, not the future state.

[0007] Dynamic programming (DP) provides an alternative to rule-based methods. DP is based on the optimization principle proposed by Richard Bellman in the 1950s, according to which the best solution can be found by dividing the problem into multiple sub-problems or intervals. The boundary condition for applying DP is that the driving cycle or trip must be known in advance, which brings the necessity of speed prediction.

[0008] Control strategies for setting or regulating various vehicle parameters based on optimization principles are known from the prior art under laboratory conditions, but do not relate to component cooling systems, in particular in electric vehicles.

[0009] The paper “Modellbasierte Optimalsteuerung imEnergiemanagement des Kraftfahrzeugs (Model-based Optimal Control in Energy Management of Motor Vehicles)” by Christian Appelt describes a model-based optimal control of a motor vehicle, in particular a vehicle operated with an internal combustion engine or a hybrid vehicle, which allows optimal control of temperature development during an exemplary journey.

[0010] Here, global optimization is used. The parameters to be optimized include minimization of time consumption and energy consumption by selecting the vehicle speed, minimization of fuel consumption by operating the heat storage system in the thermal management of the motor and transmission, and minimization of fuel consumption by operating the hybrid powertrain.

[0011] Michael Back's Paper "Antriebsregelung zum EnergieoptimalenBetrieb von Hybridfahrzeugen" (predictive drive control for energy-optimal operation of a hybrid vehicle) describes an optimized control of a hybrid vehicle based on local optimization strategies. However, a prediction of the entire driving cycle is also not used here.

[0012] Roland Reinhard Schmid's paper "Optimierungsbasiertes vorausschauendesEnergiemanagement von Plug-in-Hybridfahrzeugen (Optimization-based Forward-looking Energy Management of Plug-in Hybrid Vehicles)" describes an optimization-based operating strategy for a plug-in hybrid vehicle (PHEV) that sets the degrees of freedom for load point shifting and engine start / stop decisions in a fuel-optimized manner based on forecast information. Information about the road ahead is taken into account.

[0013] Torben Fischer's paper The patent application "Model predictive control for innovative thermal management systems for battery electric vehicles" relates to the development of an integrated, energy-efficient thermal management system based on a reversible heat pump. A nonlinear model predictive control (NMPC) is described. The control method described uses a mathematical model of the system and optimizes control variables within a defined time frame based on current measurement data. Summary of the invention

[0014] The object of the present disclosure is to provide a method for setting an active component cooling system, which method provides optimized active cooling of a component, thereby at least partially overcoming the disadvantages of rule-based methods and local optimization methods.

[0015] By making software decisions, the application of DP can improve vehicle performance without the necessity of structural or mechanical changes or installation of new components.

[0016] This object is achieved by a method for setting a component cooling system for a component of a vehicle, a vehicle control unit designed to carry out the setting method, a computing unit, a vehicle control unit and a vehicle.

[0017] Other advantageous designs of the present disclosure are derived from the following description of the technical solutions of the present invention and preferred embodiments of the present disclosure. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Embodiments of the present disclosure will now be described exemplarily and with reference to the accompanying drawings, in which:

[0019] Figure 1 A flowchart showing an embodiment of a method according to the present disclosure; and

[0020] Figure 2 A diagram showing a route segment plan, a drive cycle plan, and a cooling plan; and

[0021] Figure 3A a diagram showing an example of performing back propagation; and

[0022] Figure 3B a diagram showing a second example of performing back propagation; and

[0023] Figure 4 a diagram showing a linear temperature increase as an optimization objective; and

[0024] Figure 5A A diagram showing a first embodiment of drive cycle planning; and

[0025] Figure 5B A diagram showing a second embodiment of drive cycle planning; and

[0026] Fig. 6A a graph showing predicted component power requirements; and

[0027] Figure 6B a graphical representation showing predicted component torque requirements; and

[0028] Fig. 7A shows a comparison of trips performed using the method according to the present disclosure and using a conventional method; and

[0029] Figure 7B shows a comparison of cooling powers set when using the method according to the present disclosure and when using a conventional method; and

[0030] Figure 8 schematically illustrates one embodiment of a computing unit; and

[0031] Fig. 9 A second specific embodiment of a computing unit or a vehicle control unit is schematically shown. DETAILED DESCRIPTION

[0032] A setting method for a component cooling system for a vehicle component according to the present disclosure includes: generating a driving cycle plan for a remaining driving cycle; and calculating an instantaneous cooling requirement based on the driving cycle plan and a maximum temperature; and generating a control signal, by means of which continuous setting of the component cooling system is performed according to the instantaneous cooling requirement during the remaining driving cycle; thereby, the total cooling work during the remaining driving cycle is minimized.

[0033] A component cooling system is a system or device that provides active cooling for a component. According to the present disclosure, such a system may be a conventional system that is commonly used in a vehicle. For example, the component cooling system may be a heat pump. Here, the component cooling system may include its own control unit or manipulation unit, which may receive control signals from a control unit or computing unit of the vehicle. The control unit or manipulation unit of the component cooling system may also be the same as the control unit or computing unit of the vehicle.

[0034] A component may be any component or group of components of a vehicle, such as a motor, in particular an electric motor (which may also be referred to as an electric machine), a running gear, a transmission, a battery, a transformer or other components typically installed in the drive train of a vehicle. The transient cooling requirement is the cooling or cooling setting that causes the component cooling system to actively cool the component. The transient cooling setting is calculated based on the driving cycle plan and the maximum temperature.

[0035] The instantaneous cooling requirement is calculated based on the driving cycle plan and the maximum temperature by means of regression optimization. In this context, a determination of the operating point can be provided. The determination of the operating point includes a prediction of the power of the component to be expected when carrying out the driving cycle plan. The operating point is determined based on known operating models of the vehicle components. The determination of the operating point can also include a prediction of the thermal power released by the component, so that the temperature of the component can be predicted in conjunction with active cooling by the component cooling system.

[0036] The regression optimization, which can also be referred to as dynamic programming (DP), is extended backward in time so that boundary conditions are met that lead to the optimal setting of the component cooling system. In this case, the boundary condition can be that the temperature of the component does not exceed the derating temperature, which can be 63° C., for example. If the temperature of the component exceeds the derating temperature at a certain point in time despite the use of the strongest possible cooling, this increase is propagated back, that is, it is included in the calculation of the instantaneous cooling requirement at an earlier point in time. Thus, for example, reaching the derating temperature can be avoided by cooling more strongly at an earlier point in time.

[0037] In order that regression optimization can be performed, the actual temperature of the component at any point in time during the journey must be known or predictable. Therefore, it is necessary to predict the temperature of the component. By using DP or regression optimization, future events can be known in advance and incorporated into the vehicle's operating strategy. The driving cycle planning does not have to include the entire driving cycle, but may only include the remaining driving cycle, which starts at the point where the vehicle is when performing the method according to the present disclosure. However, the remaining driving cycle may include the entire driving cycle. The travel speed may be a speed that may be the travel speed of the vehicle or the forward speed of the vehicle. The travel speed associated with the road segment parameters may be referred to as speed planning or speed prediction.

[0038] There are embodiments in which the setting method further comprises a cooling plan; wherein the cooling plan comprises a plurality of instantaneous cooling requirements associated with the road segment parameters; wherein respective ones of the instantaneous cooling requirements are associated with respective ones of the road segment parameters; and the control signal is generated based on the cooling plan. By incorporating the prediction of the entire remaining driving cycle, a plan of cooling requirements can be created, which can be referred to as a cooling plan.

[0039] By using the prediction of the entire remaining driving cycle, the cooling work can be minimized globally (ie, taking into account the prediction of the remaining driving cycle, which can be the entire driving cycle). In this case, the total cooling work is the energy used for active cooling during the journey.

[0040] There are embodiments in which the drive cycle plan includes a value for a travel speed associated with a road segment parameter; and / or

[0041] The driving cycle plan includes a value of a road slope associated with a road segment parameter; wherein the road segment parameter includes a value of a traversed road segment or a value of an elapsed driving time.

[0042] The drive cycle plan includes as a driving parameter a prediction of an expected speed (which may also be referred to as a driving speed) at which the vehicle will proceed as expected during a drive cycle (which may be a trip and starts with leaving a departure location and ends with reaching a destination location). The drive cycle plan may also include as a parameter a prediction of a slope (which may be a road gradient) that the vehicle must overcome during the trip. The drive cycle plan may also include further parameters. The parameters of the drive cycle may be associated with the road segment parameters.

[0043] There are embodiments in which the drive cycle planning is also generated based on contextual parameters, and the contextual parameters include at least one of a user profile, traffic density, time of day, season, trip goal, common driving behavior, traffic guidance, or traffic information. For example, by applying an algorithm suitable for this, by means of machine learning or by applying an artificial neural network, it can be identified whether the user usually drives from a specific departure point to a specific destination point at a specific time of day. When using such known algorithms, the time of day and the departure point can be regarded as contextual parameters by way of example.

[0044] There are embodiments in which the driving cycle planning is also generated based on the route planning, wherein the route planning is generated based on the context parameters. The route planning includes a route segment, which can also be referred to as a road or a route route, through which the vehicle passes under the control of the user or under the autonomous control of the automatic system from the location of the vehicle to the driving destination or the target location. The location of the vehicle can be the starting point of the trip or a location on the road between the starting point and the driving destination (which can also be referred to as the target point).

[0045] Other possibilities different from the ones mentioned are also known to the person skilled in the art, namely to determine the route planning and / or the destination location by context parameters. Other parameters are also known to the person skilled in the art, which can be used as context parameters in the sense of the present disclosure. For example, such other parameters can include the day of the week, the weather, the season, the identity of the user or the identity of the user's companions. In addition, the context parameters can also include a user profile, which contains personal-related data and / or a list of the user's destination locations and / or a schedule and / or information about the user's customary driving behavior, etc.

[0046] The temperature prediction mentioned above can be performed based on route planning. For example, route planning can be performed based on the expected driving target by the user's input. The expected driving target is an example of a possible context parameter. The route planning can be a road in a street map here. The road in the street map can be provided as an electronic file so that further processing is achieved by a data processing method. The route planning can also be performed by means of a method not implemented by a computing unit. For this reason, the user can input the expected driving target into a navigation device suitable for this, such as a navigation computer of the vehicle or a corresponding peripheral device (such as a separate navigation computer, a mobile phone or a smart phone), and then the navigation device recommends a travel route by means of a method used by the navigation computer. In addition, the creation of the route planning can be performed online, in the cloud or in a computing center. The route planning created in this way can then be transmitted to the computing unit by an appropriate method or an appropriate device. By incorporating speed and / or road slope into the driving cycle planning, the thermal power released during the execution of the trip can be calculated, so that a more accurate calculation of the instantaneous cooling requirement can occur. Please note that the context parameters are not limited to the parameters mentioned here.

[0047] It should be noted that the optimal solution depends on a criterion or objective, which can also be a contextual parameter. The objective can be to minimize emissions (or fuel consumption), maintain battery charge, use only a certain amount of charge, etc. Based on the objective setting, the control unit determines the boundary conditions that are taken into account when calculating the optimal solution.

[0048] There are embodiments in which the setting of the component cooling system according to the instantaneous cooling requirement leads to a predictable linear rise in the temperature of the component. A predictable linear rise is a linear rise that occurs according to the modeling of the vehicle when the driving cycle planning is executed without errors. Error-free execution is an execution that should be expected when no other external influences affect the execution of the trip, apart from those considered in the driving cycle planning. For example, other external influences can be unforeseen events, such as inconsistent driving behavior of the user, but also traffic accidents, construction sites, etc.

[0049] There are embodiments in which a component cooling system causes a measurable linear rise in the temperature of a component according to the setting of an instantaneous cooling requirement. The task of the instantaneous cooling requirement may be to cool the component in such a way that a linear rise in the temperature of the component is predicted or measured during operation. Although the setting of the component cooling system is intended to be a setting for a predictable linear rise, a non-linear temperature rise may also be measured during operation. In addition to a linear rise, embodiments with other relationships may also be envisaged. For example, according to the present disclosure, a quadratic or exponential rise may be provided. The prediction of a linear rise in the temperature of a component may make the calculation of a target temperature change efficient.

[0050] There are embodiments in which the maximum temperature is determined based on the maximum temperature tolerance of the component; or the maximum temperature is determined based on the optimal temperature of the component; or the maximum temperature is determined based on the desired target temperature of the component. The maximum temperature may also be the maximum temperature tolerance of the component or the optimal temperature of the component, the desired target temperature of the component.

[0051] For example, the maximum temperature may be the maximum temperature of the component desired by the user or the manufacturer of the vehicle or component; the maximum temperature may also be the optimal temperature or the optimal or maximum operating temperature of the component. The maximum temperature may also be a derated temperature, wherein the derated temperature is a temperature above which the component may be damaged due to the level of the temperature. As described above, the derated temperature may be, for example, 63°C.

[0052] There are embodiments in which the calculation of the transient cooling requirement is also based on the instantaneous temperature of the component and / or on the change in the instantaneous temperature of the component and / or on situational parameters. By calculating the transient cooling requirement based on the instantaneous temperature and / or on the change in the instantaneous temperature of the component and / or on situational parameters, it can be avoided that the maximum temperature is exceeded due to unforeseen events. There are embodiments in which the calculation of the transient cooling requirement includes a progressive optimization. The progressive optimization aims to reach the maximum temperature, if possible, just at the end of the planned driving cycle. As a result, the cooling work for executing the driving cycle as a trip can be further minimized.

[0053] There are embodiments in which the component is a battery of a vehicle; or the component is a motor of a vehicle; or the component is a transmission of a vehicle. In addition to the applications described herein, the method according to the present disclosure can be used for other applications. For example, the temperature of a component can be set by optimization so that it has an optimal temperature for subsequent applications when it reaches the target location at the end of the journey. For example, the temperature of a vehicle battery as a component can be set at the end of the journey so that it has an optimal temperature as a maximum temperature for a fast charging process. By optimizing along the method according to the present disclosure and selecting the maximum temperature accordingly, an extended service life of the component can also be achieved.

[0054] Furthermore, by setting along the method according to the present disclosure, a predictive eBoost can be achieved. Predictive eBoost can be achieved by keeping the temperature of the relevant components (such as the battery, electric motor, power electronics or DCDC converter) below the derated temperature. When the vehicle requires the full power of the battery and provides it to the electric motor, heat exchange may occur in the traditional setting method, by which the components are heated above the derated temperature and thus damaged. By applying a proactive cooling strategy based on predicting the temperature characteristics according to the method according to the present disclosure, the components are kept within a safe temperature range.

[0055] Furthermore, the method according to the present disclosure can be used for further applications. For example, the method can be used for the optimization of component heating systems. The method can be used for the problem of power distribution of different drive trains of a hybrid vehicle. In this respect, it is decided at any point in time which percentage the internal combustion engine is used, while the remainder of the driving power is generated by the electric drive train. The method can be used to improve customer comfort by optimizing the air conditioning system.

[0056] There is an embodiment in which a vehicle control unit is provided which is configured to perform the method according to the present disclosure and which is further configured to set a component cooling system of the vehicle based on a control signal.

[0057] The method according to the present disclosure can be implemented by a computing unit of the vehicle (which is, for example, an onboard computer of the vehicle) or by a peripheral device (which is not a fixed component of the vehicle). For example, the method can be implemented on a mobile phone, a personal computer (PC) or a mobile navigation device.

[0058] There are embodiments in which a computing unit is provided, wherein the computing unit is designed to transmit control signals to a control unit of the vehicle.

[0059] The method can also be performed in a computing center or server device. In this case, the control signal can be transmitted to the control unit of the vehicle by means of an electronic data transmission standard such as Bluetooth (registered trademark) or a radio signal. The situation parameters can also be determined by the control unit of the vehicle and transmitted to a peripheral device (which is not a fixed component of the vehicle).

[0060] There is an embodiment in which a vehicle control unit is provided, which is configured to receive a control signal generated by means of a method according to the present disclosure and to set a component cooling system of the vehicle based on the control signal. In this case, the control signal can be transmitted to the control unit of the vehicle by means of an electronic data transmission standard such as Bluetooth (registered trademark) or a radio signal. The situational parameters can also be determined by the control unit of the vehicle and transmitted to a peripheral device that is not a fixed component of the vehicle. By arranging for the execution of the method according to the present disclosure in a computing unit that is not part of the vehicle, the computing resources of the vehicle can be taken care of.

[0061] There are embodiments in which a vehicle is provided which includes a computing unit according to the disclosure and / or a vehicle control unit according to the disclosure.

[0062] By using a computing unit according to the present disclosure or a vehicle control unit according to the present disclosure, the cooling work for cooling components can be minimized, thereby saving electrical energy and / or fuel. By saving electrical energy and / or fuel, pollutant emissions can be reduced and the mileage of the vehicle between charging processes or refueling can be increased.

[0063] The vehicle may be an electric vehicle. Here, the electric vehicle may be a vehicle with a hybrid drive system that uses a plurality of machine types for propulsion, as long as one of the machine types is an electric machine. Since in an electric vehicle, the electrical energy used to operate the component cooling system is not provided for operating the drive machine, the effect of minimizing the cooling work can be particularly large.

[0064] In the following, embodiments are described with reference to the accompanying drawings.

[0065] according to Figure 1 According to one embodiment, the setting method according to the present disclosure comprises: creating a road segment plan 10 in a first step S1; calculating a speed prediction, which may be referred to as a driving cycle plan 20 in a second step S2; performing a regression optimization in a third step S3; determining in a fourth step S4 whether a maximum temperature T is reached at a target point of the remaining driving cycle. max ; calculating the progressive optimization in a fifth step S5; and completing the optimization in a sixth step S6, which produces a cooling plan 30 including the instantaneous cooling requirements.

[0066] The third step S3 in turn comprises determining the target temperature change S30, a regression step S31, checking the fulfillment of the boundary conditions in step S32 and calculating the final temperature T in step S33. f .

[0067] The regression step S31 comprises determining an operating point S311 , determining a target temperature change in step S312 (step S312 may be different from step S30 ), determining an expected temperature change S313 , forming a difference S314 and checking for fulfillment of boundary conditions S315 .

[0068] As mentioned above, the creation of the route plan 10 in step S1 can be performed based on the context parameters. Please note that as further described below, the context parameters including other parameters are discussed, and the prediction of the speed value is also discussed.

[0069] From the route plan 10 created in step S1 , a driving cycle plan 20 is determined in step S2 . Figure 2The driving cycle plan 20 shown together with the route segment plan 10 in comprises a prediction of the instantaneous speed 21 of the vehicle associated with the route segment parameters 22. Please note that the instantaneous speed 21 is also referred to as speed value 21 or trip speed 21. The instantaneous speed 21 is here the speed at which the vehicle moves in the expected situation described by the situation parameters. The driving cycle plan 20 may also include a value for the road gradient. The route segment parameter 22 may be a route segment traversed. The route segment parameter 22 may also be the time elapsed since leaving the starting point of the trip. In particular, in the sense of a speed prediction, a respective one of a plurality of values ​​of the instantaneous speed 21 may be associated with a respective one of an equally large number of values ​​of the route segment parameter 22 (see Figure 2 ). Here, it can be assumed that the value of the segment parameter 22 is monotonically increasing. Therefore, each value of the segment parameter 22 is defined at a point or time in the execution of the segment plan 10 and the driving cycle plan 20. As shown in the figure, the driving cycle plan 20 is generated by the segment plan 10.

[0070] From the driving cycle planning 20, by means of steps S3 to S5 (hereinafter referred to as Figure 1 31 ) generates a cooling plan 30, which includes an instantaneous cooling requirement 31. In the following, such points or time points in the execution of the route plan 10 and the driving cycle plan 20 may also be referred to as samples C. The number of samples C, the time length or the route length can be determined by measuring intervals, in which, for example, the actual speed during the journey is measured.

[0071] Anticipatory, Figure 2 It is shown that iterations are performed in a temporally backward-pointing order according to a method according to the present disclosure.

[0072] Back to Figure 1 , the measurement intervals may, for example, have the same length as the increments described below. For example, the initial temperature T0 may be 20°C, and the maximum temperature T max (which may be the de-rating temperature) may be 50° C. In this case, the measurement interval may be 1 second. In a 300 second driving cycle planning, the target temperature change may be, for example, 0.1° C. / second.

[0073] It is conceivable that the following embodiments according to the present disclosure are implemented in which the speed values ​​21 called up successively are presented in the form of a list or an array, wherein the list index of the speed value 21 (which may be the index of the sample C) is used as the segment parameter 22. For example, in the sense of the driving cycle planning 20, an instantaneous speed 21 of 30 km / h may be associated with a 500 m traversed segment as the segment parameter 22. The speed value 21 may be associated with the increment of the traversed segment here. For example, a driving cycle planning 20 for the segment planning 10 (which includes a 30 km trip) may include 30,000 individual speed values ​​21 and 30,000 individual segment parameter values ​​22, wherein each speed value 21 is assigned a segment value, which is spaced at intervals of 1 m of the traversed segment. The traversed segment of the trip is determined individually for each trip and any value that makes sense for the trip may be adopted. According to the present disclosure, other increments may be set, such as 1 cm or less, 10 m or 100 m or more.

[0074] Thus, the first sample C of the drive cycle plan 20 (which may, for example, have the index value 0 and thus refer to Figure 2 The sample C0 may be referred to as representing the point or time at which the driving cycle plan 20 starts, and the last sample CN represents the end of the driving cycle plan. For example, if the driving cycle plan contains 30,000 speed values ​​and 30,000 road segment parameter values, the last sample has an index value of 30,000. Generally, the driving cycle plan contains n speed values ​​and n road segment parameter values, so the last sample has an index of N. Therefore, in the example described here, the value of the index N is 1 less than the total number of samples C.

[0075] Instead of the route segments passed as route parameters 22, the route plan 10 can also be associated with increments of time. For example, the route plan 10 can include a travel time with a time value of 3600 seconds, wherein 3600 speed values ​​21 are associated with a respective one of the time values. Embodiments are also conceivable in which the time values ​​are converted into route values ​​by means of the speed values ​​21, or the route values ​​are converted into time values ​​by means of the speed values ​​21.

[0076] Back to Figure 1 The speed value 21 required for the driving cycle planning 20 is determined based on the above-mentioned situational parameters. Therefore, the situational parameters also contain traffic information of the section included in the section planning 10. The traffic information here includes information about traffic guidance in the section included in the section planning 10. The information about traffic guidance may include the maximum allowed vehicle speed, the number and position of traffic lights or other traffic guidance equipment, the number and position of railway crossings, etc. In addition, the traffic information may include information about the instantaneous or expected traffic flow and / or the instantaneous or expected traffic density.

[0077] The driving cycle plan 20 can be generated based on an algorithm suitable for this purpose, machine learning or an artificial neural network suitable for this purpose. The prediction of the instantaneous speed 21, the result of which can be the driving cycle plan 20, can also be carried out by means of a method that is not implemented by the computing unit. For example, a navigation device suitable for this purpose, such as a navigation computer of the vehicle or a corresponding peripheral device (such as a separate navigation computer, mobile phone or smartphone) then generates a speed prediction by means of a method used by the navigation computer. In addition, the prediction of the speed can be carried out online, in the cloud or in a computing center. The speed prediction generated in this way can then be transmitted to the computing unit by a suitable method or a suitable device.

[0078] In step S3, which includes a plurality of sub-steps, a temporary cooling plan is generated. A detailed description of the method steps included in step S3 is given below. The temporary cooling plan includes a plurality of values ​​for a temporary transient cooling requirement 32. In this case, each value of a route parameter 22 included in the driving cycle plan 20 is assigned a value for the temporary transient cooling requirement 32, so that after or upon reaching the destination (i.e., before or at the end of the driving cycle plan 20), a maximum temperature T max In this paper, the maximum temperature T max is the maximum temperature of the vehicle component to be cooled which is cooled by the component cooling system.

[0079] For example, the maximum temperature may be the maximum temperature of the component desired by the user or the manufacturer of the vehicle or component, the maximum temperature may also be the optimal temperature or the optimal or maximum operating temperature of the component. The maximum temperature may also be a derating temperature, wherein the derating temperature is a temperature above which the component is damaged due to the temperature level.

[0080] In step S4, it is checked that the maximum temperature T in the sample CN max does not occur at index n (ie at the end of the drive cycle planning 20). Since the optimization performed in step S3 prevents T max occurs before the end of the driving cycle planning 20, so the temperature T max It occurs either at the end of the driving cycle plan 20 or after the end of the driving cycle plan 20. If the temperature T max does not occur at the end of the driving cycle plan 20 (i.e., before the end of the driving cycle plan 20), Figure 1 In the example, if the option "Yes" is shown after step S4, then step S4 is followed by a progressive optimization step S5. In the progressive optimization step, a change of the temporary cooling plan or a change of the temporary instantaneous cooling requirement may occur.

[0081] If the maximum temperature T max Occurs at the end of the drive cycle planning 20, Figure 1 If the option “No” is shown after step S4 , the progressive optimization step S5 is unnecessary and can be skipped.

[0082] If T max If it does not occur at the end of the driving cycle planning 20 (i.e., before the end of the driving cycle planning 20), this means that the temporary cooling planning resulting from step S3 contains an excessively high transient cooling requirement 31. In the progressive optimization step S5, an optimization is performed that leads to a reduction in the temporary transient cooling requirement. The execution of the progressive optimization is further described below. The temporary transient cooling requirements that constitute the temporary cooling planning calculated in this way are output in the completion step S6. In the completion step S6, the temporary cooling planning including the temporary transient cooling requirement is transferred to the cooling planning 30 including the transient cooling requirement 31. The temporary cooling planning including the temporary transient cooling requirement is transferred to the cooling planning 30 including the transient cooling requirement 31 in such a way that the cooling planning 30 including the transient cooling requirement 31 is brought to the same state or the same form as the temporary cooling planning at the point of execution of step S6, which includes the temporary transient cooling requirement 32. As a result, the total cooling work used to execute the driving cycle planning 20 is minimized.

[0083] For example, the progressive optimization step S5 may use the following equation (1). Herein, the value of the final temperature is contemplated to be set to the value of the temperature of the last sample CN.

[0084] The instantaneous cooling requirement 31 may be a value, an electronic signal, a command or an indication, which causes the component cooling system to provide a specific cooling power and thus cools the component according to the cooling requirement 31. The cooling requirement 31 may also be a value, an electronic signal, a command or an indication, which causes the component cooling system to provide a specific cooling power and thus cools the component according to the cooling requirement 31. In particular, the instantaneous cooling requirement 31 may be a value of the cooling power, according to which the setting of the component cooling system is selected. For example, a component cooling system with a maximum cooling power of 1000 Watt may be cooled according to different cooling power settings. For example, settings may be made according to six cooling power settings, which represent percentage values ​​of the maximum cooling power of 1000 Watt:

[0085] Cooling power setting = [40, 50, 60, 70, 80, 90].

[0086] For example, if the instantaneous cooling requirement 31 of sample C is 400 Watts, a cooling power setting of "40" (corresponding to 40% of 1000 Watts) may be selected. Here, it may be provided that if the instantaneous cooling requirement 31 of sample C is between two suitable cooling power settings, the higher of the two cooling power settings is selected. For example, an instantaneous cooling requirement 31 of 756 Watts may result in the selection of a cooling power setting of "80" (corresponding to 80% of 1000 Watts). Other higher or lower values ​​of the maximum cooling power and other or other numbers of cooling power settings are foreseen in accordance with the present disclosure.

[0087] If, as checked in step S4, T max If it occurs at the end of the driving cycle planning 20 (i.e. before the end of the driving cycle planning 20), this means that the total cooling power for executing the driving cycle planning 20 has been minimized without executing the progressive optimization step S5. In this case, the provisional cooling planning obtained by step S3 is transferred to the cooling planning 30 in the completion step S6 after step S4.

[0088] The cooling plan 30 is used by a control unit of the vehicle in order to set the component cooling system. The control unit can be identical to or different from the computing unit which performs the method or parts of the method according to steps S1, S2, S3, S4, S5 and S6. Furthermore, the control unit can be part of the vehicle without the computing unit being part of the vehicle. In embodiments in which the computing unit and the control unit are different from one another, the cooling plan 30 is transmitted from the computing unit to the control unit by means of a suitable data transmission method and / or a suitable data transmission device.

[0089] According to the present disclosure, the method including steps S1, S2, S3, S4, S5 and S6 can be executed only once at the beginning of the trip (in which case the driving cycle plan 20 includes the entire driving cycle as the remaining driving cycle), and can be repeatedly executed during the trip (in which case the driving cycle plan 20 includes the driving cycle from the point where the method is executed accordingly as the remaining driving cycle).

[0090] Back to Figure 1 The regression optimization step S3 in the embodiment includes a series of sub-steps. In step S30, the target temperature change is calculated, which can be obtained by using the variable or parameter ΔT 目标 Indicates. Target temperature change ΔT 目标 is the temperature variation that is expected in each sample when setting the component cooling system according to the cooling plan 30 (including the transient cooling requirement 31) in order to achieve optimal cooling according to the present disclosure. However, as described below with reference to Figure 2 As mentioned above, ΔT 目标can have different values ​​in different samples depending on the cooling plan. Therefore, ΔT 目标 It can be instantaneous ΔT 目标 , which is the expected ΔT in the corresponding instantaneous sample C 目标 Therefore, the instantaneous ΔT 目标 is associated with the corresponding sample. It should be noted that if the check performed in step S32 described further below requires the execution of step S33 described further below (step S33 is followed by a repetition of the execution of step S30), step S30 can be repeated. If the execution of step S30 is followed by step S2, the target temperature change ΔT is selected in this way. 目标 (In the following: ΔT 目标 ), so that in each sample C ΔT 目标 The same, and the temperature rises linearly from the initial temperature T0 to the maximum temperature T max Here, the initial temperature T0 can be the ambient temperature or the instantaneous temperature of the component at the beginning of the trip. The initial temperature T0 can also be the instantaneous temperature of the component. For example, ΔT 目标 Calculated according to the following equation (1):

[0091] ΔT 目标 =(T max -T0-(T f -T max )) / (N) (1)

[0092] Here, the variable T max , T0 and N have the above meanings. The variable T f represents the final temperature. If step S30 is performed after step S2 (i.e., without repetition), the variable T f With maximum temperature T f If step S30 is performed after step S33, then T f With the value of the expected maximum temperature described below. However, according to the present disclosure, a method different from the method shown in equation (1) for calculating the temperature based on T max , T0 and N to calculate ΔT 目标 Other methods of are possible. For example, a quadratic equation may be set as the distribution.

[0093] Step S30 is followed by a regression step S31, which in turn may consist of a series of sub-steps, which are further described in detail below. The result of the regression step S31 is a temporary cooling plan, which contains a plurality of temporary transient cooling requirements 32, wherein each temporary transient cooling requirement 32 (corresponding to the transient cooling requirement 31) is associated with a sample. Based on the temporary transient cooling requirements 32 calculated in the regression step S31, the expected maximum temperature of the component can be calculated. It should be noted that the expected maximum temperature is related to the maximum temperature Tmax The expected maximum temperature is here the maximum temperature that is expected to occur during operation of the vehicle when cooling is performed according to the temporary cooling plan. For example, the instantaneous ΔT of all samples C can be 目标 Add them to each other and to T0 to calculate the expected maximum temperature.

[0094] In the above checking step S32, it is checked whether the expected maximum temperature exceeds the maximum temperature T max Here, the expected maximum temperature does not exceed the maximum temperature T max The requirement should be considered as a boundary condition for regression optimization. If the expected maximum temperature does not exceed the maximum temperature T max , then after checking step S32, in step S33 the final temperature T f After step S33, step S30 is repeated, wherein the final temperature has the value of the expected maximum temperature determined in step S32. Steps S30, S31, S32 and S33 are repeated until the expected maximum temperature is lower than the maximum temperature T max Once the expected maximum temperature is lower than the maximum temperature T max , then continue as described above after step S4.

[0095] In a specific example, the maximum temperature T max There may be a derated temperature, which has, for example, a value of 63° C. For example, the expected maximum temperature may be 65° C. In this case, the final temperature T is set to 65° C. in step S33 , and the optimization is repeated in step S30 .

[0096] Back to Figure 1 , which includes a series of sub-steps S311, S312, S313, S314 and S315, which can be repeatedly performed. Each repeated implementation of steps S311, S312, S313, S314 and S315 can be called an iterative step. Here, by repeatedly implementing steps S311, S312, S313, S314 and S315, iteration is performed through samples C in the reverse order of samples C. As described above, the last sample in time associated with the end of the driving cycle plan 20 has an index value N, and the first sample associated with the beginning of the driving cycle plan 20 has an index value 0. As described above, each sample C has a speed that is the predicted speed of the point of the driving cycle plan 20 associated with the corresponding sample C.

[0097] Returning to the above-mentioned step S30, step S31 is followed by step S31, which starts with step S311. Steps S311, S312, S313, S314 and S315 are implemented here according to the corresponding instantaneous sample C and the speed value and / or route parameter associated with the instantaneous sample C. When or before the first implementation of step S311, the running index is set to the index value of the last sample C, that is, N. When or before each repetition of step S311 (expectedly followed by step S315), the running index is reduced by one. Therefore, in each repetition, steps S311, S312, S313, S314 and S315 are implemented according to the corresponding instantaneous sample C, wherein the corresponding instantaneous sample C has an running index that is one less than the corresponding previously implemented instantaneous sample C. In this way, it is iterated through the samples C of the driving cycle plan 20 in a time-backward order.

[0098] In step S311, the operating point of the component is determined based on the context parameters and the speed prediction of the current sample C. The determination of the operating point may further include a prediction of the power generated by the component necessary to achieve the predicted speed or overcome the expected slope based on the component parameters. Furthermore, the determination of the operating point may include a prediction of the thermal power generated by the component.

[0099] In step S312 following step S311, the instantaneous ΔT is calculated again. 目标 The instantaneous ΔT in step S312 目标 The calculation of ΔT in step S30 目标 The calculation of is different. In step S312, by Δ Due (Δ Due The calculation of is further described below, and Δ Due Calculated in the previous iteration step) plus the ΔT calculated in step S30 目标 To calculate the instantaneous ΔT 目标 Based on the instantaneous ΔT calculated in step S312 目标 To calculate the temporary transient cooling requirement 32, which is added to the temporary cooling plan.

[0100] Based on the operating point determined in step S311 and the temporary transient cooling requirement 32, the expected transient temperature change ΔT is calculated in step S313. 样本 (This expected instantaneous temperature change can also be called instantaneous ΔT 样本 ), the expected instantaneous temperature change is the temperature change expected in the instantaneous sample C when the driving cycle planning 20 is executed according to the temporary cooling plan. Here, the instantaneous ΔT 样本The calculation can be based on the power requirement of the component, which can be calculated based on the driving cycle plan 20 and the operating point of the component. The method for calculating the operating point is based on a physical model of the component. Such models are known to those skilled in the art.

[0101] In step S314, based on the instantaneous ΔT 目标 and instantaneous ΔT 样本 Calculation parameter Δ Due , which is added to the instantaneous ΔT in the next iteration step in step S312 目标 Here, Δ Due It can be instantaneous ΔT 目标 and instantaneous ΔT 样本 difference.

[0102] In step S315, it is checked whether the running index of the instantaneous sample C is equal to 0, i.e., whether the iteration pointing backward in time has reached the beginning of the driving cycle plan 20. For example, if the running index of the instantaneous sample is equal to 0 or less than 0, step S315 is followed by the above-mentioned step S32. If the running index of the instantaneous sample is not equal to 0, i.e., the iteration in time has not reached the beginning of the driving cycle plan 20, step S315 is followed by a repetition of step S311, wherein, as described above, the iteration is performed pointing backward in time.

[0103] Figure 3A and Figure 3B shows the execution as in Figure 1 The effect of one embodiment of the regression step S31 is shown in . As described above, after the speed prediction in step S2 , an iteration is performed in a temporally backward manner through the total number of samples C in the drive cycle plan 20 . Figure 3A and Figure 3B Five samples C from a driving cycle plan 20 are shown in each case, which are numbered by way of example with index values ​​0 to 4. Index value 0 represents the first sample C in time of the driving cycle plan 20. Index value 4 represents the last sample C in time of the driving cycle plan 20. According to the markings, the bars represent the instantaneous ΔT of the respective sample C. 目标 and instantaneous ΔT 样本 As mentioned above, the task of the transient cooling requirement 31 is to cool the component to achieve the corresponding transient ΔT 目标 , which in turn leads to the desired linear rise in component temperature. The regression optimization aims to find the instantaneous ΔT in each sample C. 目标 The value and instantaneous ΔT 样本 The values ​​are the same.

[0104] Start at Figure 3AIn the example shown in FIG. 4 , sample C4 is iterated through all samples C pointing backwards in time. In each sample C, the corresponding instant ΔT 目标 Is it greater than, less than, or equal to the calculated instantaneous ΔT? 样本 This check is obtained, for example, by checking the instantaneous Δ Due (not shown) (which is, for example, the instantaneous ΔT 目标 and instantaneous ΔT 样本 The difference between ) is greater than, less than, or equal to 0. This check corresponds to Figure 1 Steps S312, S313 and S314 in .

[0105] If the instantaneous ΔT is determined during the backward-directed iteration 目标 Less than instantaneous ΔT 样本 (For example, in Figure 3A As shown in sample 1), the instantaneous Δ Due propagates back to sample C0. If the instantaneous ΔT 目标 Less than instantaneous ΔT 样本 , this means that the component in sample C0 is not cooled sufficiently strongly, so the instantaneous ΔT in sample C0 目标 For example, "back propagation" is performed by converting the calculated instantaneous Δ Due Added to the instantaneous ΔT of sample C0 目标 Here, it should be noted that Figure 3A The Δ calculated in sample 1 Due has a negative value. The effect of the return propagation is to substantially reduce Figure 3A The instantaneous ΔT of sample C0 in 目标 , so that sufficient cooling can be provided in sample C1. The instantaneous ΔT thus calculated is 目标 Introduced into temporary cooling planning.

[0106] If, on the other hand, the instantaneous ΔT is determined in an iterative process directed backward in time 目标 Greater than instantaneous ΔT 样本 (For example, in Figure 3B As shown in the sample C1 in FIG, the instantaneous Δ Due propagates back to sample C0. If the instantaneous ΔT 目标 Greater than instantaneous ΔT 样本 , this means that the components in sample C0 are cooled too strongly and the components in sample C1 are not heated sufficiently to ensure linear heating of the components during the remaining driving cycle. In addition, the instantaneous ΔT in sample C0 目标 Too big. Figure 3B The calculated Δ Due has a positive value. The effect of the return propagation is fully increased here Figure 3B The instantaneous ΔT of sample C0 in 目标 , so that it can be Figure 3B The instantaneous ΔT calculated in this way is 目标 Introduced into temporary cooling planning. Here, it should be noted that the instantaneous ΔT 目标 The smaller the instantaneous ΔT is, the higher the quality of the instantaneous cooling requirement 31 based thereon is. 目标 The larger , the lower in quality the transient cooling requirement 31 based thereon.

[0107] In this way, an optimization of future conditions predicted by speed prediction can be considered since errors and inconsistencies are propagated back to previous samples C.

[0108] Figure 4 A schematic diagram according to the present disclosure showing the temperature rise over time produced and expected by optimization. As described above, the variable T max It represents the maximum temperature, the maximum temperature of the component desired by the user or the manufacturer of the vehicle or component, the optimal temperature or the best or highest operating temperature of the component. As mentioned above, the maximum temperature can also be a derated temperature, wherein the derated temperature is the temperature above which the component is damaged due to the level of temperature.

[0109] As further described above, the initial temperature T0 may be the ambient temperature or the instantaneous temperature of the component at the beginning of the stroke. The initial temperature T0 may also be the instantaneous temperature of the component.

[0110] As described, the optimization aims to make the instantaneous temperature of the component (through Figure 4 The y-axis shows the relationship between the Figure 4 The temperature of the component at the beginning of the driving cycle planning 20 (which may be present at the beginning of the journey, but may also be present at the time of carrying out the method according to the present disclosure) is the initial temperature T0. The temperature of the component at the end of the driving cycle planning 20 (which is present at the end of the journey) is represented here, for example, by T max express.

[0111] The method is thus able to select a cooling setting that ensures a linear temperature rise.The method is thus also able to take into account foreseeable future events of the driving cycle by propagating future errors back to previous samples C of the trip.

[0112] Figure 5A and Figure 5BA schematic route prediction is shown as part of the driving cycle planning 20, as it may be generated, for example, in step S2. Here, the time that has elapsed since the start of the driving cycle planning 20 is used as the route parameter. However, there are also embodiments in which the route that has been traveled is used as the route parameter. Figure 5A The predicted speed is shown. Figure 5B The predicted slope is shown. Figure 5A and Figure 5B The road segment predictions shown in should be considered merely as explanatory examples and not limiting.

[0113] Fig. 6A and Figure 6B Schematic diagrams of possible predictions of the power requirements of a component, which is here for example an electric motor, are respectively shown. However, according to the present disclosure, simulation predictions for other components than electric motors, such as batteries or transmissions, can be envisaged. For example, Fig. 6A The data points in are instantaneous samples (e.g., Figure 1 The predicted power requirement (y-axis) with time as a segment parameter (x-axis) is generated in step S311 in FIG. Figure 6B The data points in are instantaneous samples C (such as Figure 1 The power requirement may also be a power requirement associated with the corresponding component. For example, if the component is a battery, the power requirement may be a current requirement of the battery. Fig. 6A and Figure 6B The power requirements shown in should be considered as illustrative examples only and not limiting.

[0114] Fig. 7A and Figure 7B An exemplary comparison of a setting method according to the present disclosure and a conventional setting method with the aid of cooling of an electric motor is shown. Fig. 7A The possible temperature development of a component of a vehicle that is cooled according to the method according to the present disclosure (optimized temperature guidance) and the possible temperature development of a component of a vehicle that is cooled according to a conventional method (conventional temperature guidance) are shown. Figure 7B The possible cooling power that can be applied according to the method according to the present disclosure (optimized temperature guidance) and the possible cooling power that can be applied according to the conventional method (conventional temperature guidance) are shown. Fig. 7A and Figure 7B The temperature developments and cooling powers shown in are to be regarded merely as illustrative examples and not restrictive.

[0115] exist Fig. 7AIt can be seen that according to the present disclosure, an optimization aimed at a linear rise in temperature during a trip does not necessarily have to result in an actual linear rise in temperature during a trip with a vehicle controlled according to the present disclosure, but nevertheless minimizes the total cooling work.

[0116] In order to achieve a linear temperature rise, cooling is performed at a higher power according to the method according to the present disclosure, for example, during the first 1000 seconds. After, for example, 1000 seconds, the cooling settings are selected according to the method according to the present disclosure in such a way that cooling is achieved with the highest efficiency for the corresponding predicted speed and slope. At the end of the journey, the components cooled according to the method according to the present disclosure reach a higher temperature than the components cooled according to the conventional method. Since the journey has ended at this point in time, the components cooled according to the method according to the present disclosure can be cooled passively in a stationary state, thereby saving energy that would otherwise be used for active cooling. For example, the maximum temperature of the components cooled according to the conventional method (maximum temperature and the above-mentioned maximum temperature T max different) is 55.8°C, and the maximum temperature of the component cooled according to the method according to the present disclosure is 66.1°C.

[0117] For example, the average cooling setting of the components cooled according to the conventional method is 80.9%, and the average cooling setting of the components cooled according to the method according to the present disclosure is 59.6%. Thus, energy savings can be achieved compared to the conventional method, which in this example is 24.51%.

[0118] Figure 8A schematic diagram of an embodiment of a computing unit 100 is shown, which is configured to perform a method according to the present disclosure. The computing unit 100 includes, for example, a CPU 101, a data memory 102, a random access memory (RAM) 103, a communication unit 120, and a user interface 112. The user interface 112 may include, for example, a display, a speaker, or an input device, such as a touch display, a voice recognition device, a keyboard, etc. The CPU 101 is configured so that it can perform the method steps according to the present disclosure based on the situation parameters and / or the route planning 10 received via the communication unit 120 or the user interface 112. The computing unit includes a data memory 102, in which data can be stored and read out by the CPU 101 by means of a hard disk. For example, the data can be situation parameters, route planning 10, driving cycle planning 20, instantaneous speed 21, route parameters 22, temporary cooling planning, temporary instantaneous cooling requirements 32, cooling planning 30, instantaneous cooling requirements 31 and / or intermediate values ​​or intermediate results generated when calculating the mentioned parameters. The communication unit 120 can receive data, signals or instructions from an external source. The external source may be, for example, an electronic device such as a remote control, a smartphone or a user terminal, a computing center, a control unit of a vehicle, etc. It may also be provided that information is transmitted to the aforementioned external device by means of the communication unit 120. For example, data stored in the data memory 102 or generated by the CPU 101 may be transmitted to the aforementioned external device.

[0119] The data transmission via the communication unit 120 can take place by means of corresponding signal transmission standards such as Bluetooth (registered trademark), WLAN or a cable connection, etc., and the communication unit 120 is designed accordingly for this purpose.

[0120] In particular, but not necessarily, a data transmission standard for vehicle communication may be provided. Examples of available standards are ETC, SAFESPOT, PReVENT, COMeSafety, NoW, IVI, DSRC, WAVE, VANET, IoV, 802.11p, ITS-G5, V2X, Bluetooth, etc.

[0121] The computing unit 100 is included in or provided in other electronic devices or components. The computing unit 100 may utilize existing components of a device, such as the CPU 101 , the storage unit 102 , the RAM 103 , the user interface 112 or the communication unit 120 .

[0122] Fig. 9A schematic diagram of a second embodiment of a computing unit 200 is shown, which is configured to perform a method according to the present disclosure. The computing unit 200 includes, for example, a CPU 201, a data memory 202, a random access memory (RAM) 203, a communication unit 220, and a user interface 212. The user interface 212 may include, for example, a display, a speaker, or an input device, such as a touch display, a voice recognition device, a keyboard, etc. The CPU 201 is configured so that it can perform the method steps according to the present disclosure based on the situation parameters and / or the route planning 10 received via the communication unit 220 or the user interface 212. The computing unit includes a data memory 202, in which data can be stored and read out by the CPU 201 by means of, for example, a hard disk. For example, the data can be situation parameters, route planning 10, driving cycle planning 20, instantaneous speed 21, route parameters 22, temporary cooling planning, temporary instantaneous cooling requirements 32, cooling planning 30, instantaneous cooling requirements 31 and / or intermediate values ​​or intermediate results generated when calculating the mentioned parameters. The communication unit 220 can receive data, signals or instructions from an external source. The external source may be, for example, an electronic device such as a remote control, a smartphone or a user terminal, a computing center, a control unit of a vehicle, etc. It may also be provided that information is transmitted to the aforementioned external device by means of the communication unit 220. For example, data stored in the data memory 202 or generated by the CPU 201 may be transmitted to the aforementioned external device.

[0123] The data transmission via the communication unit 220 can take place by means of corresponding signal transmission standards such as Bluetooth (registered trademark), WLAN or a cable connection, etc., and the communication unit 220 is designed accordingly for this purpose.

[0124] In particular, but not necessarily, a data transmission standard for vehicle communication can be set. Examples of available standards are ETC, SAFESPOT, PReVENT, COMeSafety, NoW, IVI, DSRC, WAVE, VANET, IoV, 802.11p, ITS-G5, V2X, Bluetooth, etc. The computing unit 200 also includes an interface 204 for manipulating the component cooling system, by means of which the setting of the component cooling system according to the present disclosure is implemented. The interface 204 is any device that allows the setting, adjustment or control of the component cooling system. In addition, the interface 204 is any device that allows data transmission to the component cooling system if the component cooling system is set for this purpose. If the computing unit 200 is a control unit of a vehicle, a component interface 205 can be set, by means of which the setting, adjustment or control of the components to be cooled by the component cooling system is implemented.

[0125] It can be provided that the setting of the component cooling system is realized only by means of the computing unit 200, and the calculation of the instantaneous cooling requirement 31 or the cooling plan 30, the driving cycle plan 20 or the creation of the route plan 10 is not performed by the computing unit 200. Instead, it can be provided that the computing unit obtains the instantaneous cooling requirement 31, the cooling plan 30, the driving cycle plan 20 or the route plan 10 from an external source according to the above description by means of the communication unit 220.

[0126] The computing unit 200 is included or provided in other electronic devices or components. The computing unit 200 can utilize existing components of the device, such as CPU 201, storage unit 202, RAM 203, user interface 212, communication unit 220, interface 204 or component interface 205.

[0127] The method steps described above should be understood as exemplary rather than limiting for the method according to the present disclosure. For those skilled in the art, alternative methods or altered orders of executing parts of the method may be apparent. For example, Figure 1 Step S312 and step S313 in can be implemented in the reverse order. Figure 1 Intermediate steps may be implemented between the method steps disclosed in the present disclosure, which may also be arranged according to the present disclosure.

[0128] The method according to the present disclosure and the device according to the present disclosure may in particular include the following:

[0129] (1) A method for setting a component cooling system for a component of a vehicle, comprising generating a driving cycle plan 20 for a remaining driving cycle; and based on the driving cycle plan 20 and a maximum temperature T max calculating the instantaneous cooling requirement 31; and generating a control signal by means of which a continuous setting of the component cooling system is performed during the remaining driving cycle according to the instantaneous cooling requirement 31; so that the total cooling work during the remaining driving cycle is minimal.

[0130] (2) According to the setting method described in (1), it also includes a cooling plan 30; wherein the cooling plan 30 includes multiple instantaneous cooling requirements 31 associated with the road section parameters 22; wherein a corresponding one of the instantaneous cooling requirements 31 is associated with a corresponding one of the road section parameters 22; and a control signal is generated based on the cooling plan 30.

[0131] (3) A setting method according to any one of (1) or (2), wherein the driving cycle plan 20 includes a value of the travel speed 21 associated with the road segment parameter 22; and / or

[0132] The driving cycle plan 20 includes a value of a road segment gradient associated with a road segment parameter 22 ; wherein the road segment parameter 21 includes a value of a road segment passed or a value of an elapsed driving time.

[0133] (4) A setting method according to any one of (1) to (3), wherein the driving cycle plan 20 is also generated based on situational parameters; and the situational parameters include at least one of user profile, traffic density, time of day, season, trip goal, common driving behavior, traffic guidance or traffic information.

[0134] (5) The setting method according to (4), wherein the driving cycle plan 20 is generated based on the road segment plan 10, wherein the road segment plan 10 is generated based on the situational parameters.

[0135] (6) The setting method according to any one of (1) to (5), wherein the setting of the component cooling system according to the transient cooling request 31 results in a predictable linear rise in the temperature of the component.

[0136] (7) The setting method according to any one of (1) to (6), wherein the setting of the component cooling system according to the transient cooling request 31 results in a measurable linear increase in the temperature of the component.

[0137] (8) The setting method according to any one of (1) to (7), wherein the maximum temperature T max Determined based on the maximum temperature tolerance of the component; or the maximum temperature T max Determined based on the optimum temperature of the component; or the maximum temperature T max Determined based on the desired target temperature of the component.

[0138] (9) The setting method according to any one of (1) to (8), wherein the calculation of the transient cooling requirement 31 is also performed based on the transient temperature of the component and / or on the change in the transient temperature of the component and / or on the situational parameters.

[0139] (10) The setting method according to any one of (1) to (9), wherein the calculation of the transient cooling requirement 31 includes asymptotic optimization.

[0140] (11) The setting method according to any one of (1) to (10), wherein the component is a battery of the vehicle; or the component is a motor of the vehicle; or the component is a transmission of the vehicle.

[0141] (12) A vehicle control unit 100, 200 configured to execute the method according to any one of (1) to (11), and further configured to set a component cooling system of the vehicle based on the control signal.

[0142] (13) A computing unit 100, 200 configured to execute the method according to any one of (1) to (11), wherein the computing unit is further configured to transmit a control signal to a control unit 200 of the vehicle.

[0143] (14) A vehicle control unit 200 configured to receive a control signal generated by the method according to any one of (1) to (11), and to set a component cooling system of the vehicle based on the control signal.

[0144] (15) A vehicle comprising the vehicle control unit 100, 200 according to any one of (12) or (14).

[0145] Reference numerals list

[0146] 1-9 Subject to change

[0147] 10 Road section planning

[0148] 11-19 Subject to change

[0149] 20 Driving cycle planning

[0150] 21 Instantaneous speed

[0151] 22 Section Parameters

[0152] 23-29 May be subject to change

[0153] 30 Cooling Planning

[0154] 31 Instantaneous cooling requirements

[0155] 32 Temporary instant cooling requirements

[0156] 33-99 Subject to change

[0157] 100 computing units

[0158] 101CPU

[0159] 102 Memory

[0160] 103RAM

[0161] 104-111 Subject to change

[0162] 112 Communication Unit

[0163] 120 User Interface

[0164] 200 computing units

[0165] 201CPU

[0166] 202 Memory

[0167] 203RAM

[0168] 204 Interface

[0169] 205 Component Interface

[0170] 206-211 Subject to change

[0171] 212 Communication Unit

[0172] 220 User Interface

[0173] S1-S6 Method Steps

[0174] S7-S30 may change

[0175] S31-S33 Method Steps

[0176] S34-S310 Subject to change

[0177] S311-S315 Method Steps

[0178] C sample.

Claims

1. A method for setting a component cooling system for a component of a vehicle, comprising: generating a driving cycle plan (20) for the remaining driving cycle; as well as Based on the driving cycle planning (20) and the maximum temperature (T max ) Calculate the instantaneous cooling requirement (31); as well as A control signal is generated, by means of which a continuous setting of the component cooling system is performed during the remaining driving cycle according to the instantaneous cooling requirement (31); so that the total cooling work during the remaining driving cycle is minimal.

2. The setting method according to claim 1, further comprising: Cooling plan (30); wherein, The cooling plan (30) includes a plurality of instantaneous cooling requirements (31) associated with the road segment parameters (22); wherein: A respective one of the transient cooling requirements (31) is associated with a respective one of the road segment parameters (22); and The control signal is generated based on the cooling plan (30).

3. The setting method according to any one of claims 1 or 2, wherein: The driving cycle plan (20) includes a value of a travel speed (21) associated with a route segment parameter (22); and / or The driving cycle plan (20) includes a value of a road segment slope associated with a road segment parameter (22); wherein, The road section parameter (21) includes a value of a road section passed or a value of an elapsed travel time.

4. The setting method according to any one of claims 1 to 3, wherein The driving cycle plan (20) is also generated based on situational parameters; and The contextual parameters include at least one of a user profile, traffic density, time of day, season, trip goal, common driving behavior, traffic guidance, or traffic information.

5. The setting method according to claim 4, wherein The driving cycle plan (20) is generated based on the route segment plan (10), wherein: The route plan (10) is generated based on the situation parameters.

6. The setting method according to any one of claims 1 to 5, wherein: The setting of the component cooling system according to the transient cooling requirement (31) results in a predictable linear rise in the temperature of the component.

7. The setting method according to any one of claims 1 to 6, wherein: The setting of the component cooling system in accordance with the instantaneous cooling requirement (31) results in a measurable linear rise in the temperature of the component.

8. The setting method according to any one of claims 1 to 7, wherein: The maximum temperature (T max ) is determined based on a maximum temperature tolerance of the component; or The maximum temperature (T max ) is determined based on an optimum temperature of the component; or The maximum temperature (T max ) is determined based on a desired target temperature of the component.

9. The setting method according to any one of claims 1 to 8, wherein: The calculation of the transient cooling requirement (31) is also performed based on the transient temperature of the component and / or on the change in the transient temperature of the component and / or on the situational parameters.

10. The setting method according to any one of claims 1 to 9, wherein: The calculation of the transient cooling requirement (31) involves a progressive optimization.

11. The setting method according to any one of claims 1 to 10, wherein: the component is a battery of the vehicle; or The component is a motor of the vehicle; or The component is a transmission of the vehicle.

12. A vehicle control unit (100; 200) configured to execute the method according to any one of claims 1 to 11, and further configured to set a component cooling system of the vehicle based on the control signal.

13. A computing unit (100; 200) configured to execute the method according to any one of claims 1 to 11, wherein: The computing unit is designed to transmit the control signal to a control unit (200) of the vehicle.

14. A vehicle control unit (200), further configured to: A control signal generated by means of the method according to any one of claims 1 to 11 is received, and a cooling system component of the vehicle is set based on the control signal.

15. A vehicle comprising A vehicle control unit (100; 200) according to any one of claims 12 or 14.

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