Method for operating a motor vehicle and motor vehicle

Abstract

This invention relates to a method for operating a motor vehicle and a motor vehicle designed for this purpose. Here, a map with location-discriminately recorded high-load events is provided. High-load events expected to be relevant during the current operation of the motor vehicle are determined based on the current location and / or route of the motor vehicle. Before the motor vehicle has reached the corresponding event location of the relevant high-load event, at least one vehicle component subjected to above-average loads during the relevant high-load event is thermally pre-adjusted by automatically controlling at least one device of the motor vehicle accordingly.

Description

Technical Field

[0001] The present invention relates to a method for operating a motor vehicle and a motor vehicle configured to implement or participate in the method. Background Technology

[0002] Currently, motor vehicles are complex technological structures with numerous components, each possessing complex characteristics and behaviors. For example, different components are known to exhibit temperature-dependent behaviors or properties. Therefore, optimized temperature control of vehicle components can, on the one hand, improve the operation of motor vehicles; on the other hand, such temperature control is not a routine task. Given that today's motor vehicle technology is far removed from purely mechanical processes and considering the growing expectations for sustainability, further improvements and optimizations in motor vehicle operation are anticipated.

[0003] As one approach, DE102016102618A1 describes a method and apparatus for predictive vehicle pre-adjustment. Here, a planned ignition switch on-time is selected from a schedule of possible vehicle start-up times, said planned ignition switch on-time being based on the minimum probability of occurrence of observed vehicle use. If the current vehicle-related temperature indicates that vehicle pre-adjustment is correct, the vehicle is pre-adjusted until a preset pre-adjustment setting is reached. Therefore, the vehicle can be placed in a driving-ready state before it is started, improving the driving experience.

[0004] A similar approach is known from DE102018111259A1, which describes pre-conditioning for hybrid electric vehicles. Here, in response to a pre-conditioning signal indicating the vehicle's start-up time, the battery's state of charge and an external power signal are monitored. Using the vehicle's thermal management system, the temperature of the vehicle's battery or cabin should be pre-conditioned before the vehicle's start-up time. This is done based on the corresponding conditioning profile, the state of charge, and the external power signal. Therefore, the power availability of the battery and external power source is considered to adjust the desired state for starting the vehicle. Summary of the Invention

[0005] The objective of this invention is to achieve particularly efficient and economical operation of motor vehicles. According to the invention, this objective is achieved by a method for operating a motor vehicle according to the invention and a motor vehicle according to the invention. Advantageous embodiments and further improvements of the invention are given in the specification and drawings.

[0006] The method according to the invention is used for operating motor vehicles. In one step of the method, a map is provided in which high-load events are recorded at location resolution, the high-load events causing above-average loads on at least one vehicle component. Here, the map, in the present sense, can be particularly a digital dataset that gives the high-load events and includes the event locations of the high-load events, i.e., the locations or coordinates of the locations where the high-load events occur. Furthermore, the high-load events can be characterized or described in the dataset, i.e., in the map, and thus, for example, the corresponding type of high-load event can be given. Here, the map can contain or include data about traffic routes, as in a conventional road map. In the sense of the invention, the map can also be, for example, a layer for digital road maps and / or reference a pre-given, particularly world-fixed coordinate system, so that the data given or included in the map can be overlaid with conventional digital road maps.

[0007] Providing a map may, for example, mean or include transferring or transmitting a map—completely, partially, or locally—to a data processing or control device designed for implementing the method, particularly a data processing or control device for a motor vehicle. In the sense of this invention, providing a map may also, for example, mean or include retrieving or loading a map from a data storage device and / or retrieving or loading a map into a data storage device, particularly the data storage device of the motor vehicle's data processing or control device. As will be explained in more detail below, the map may be stored in a data storage device external to the vehicle and managed there, for example, by means of an external server device, i.e., a cloud server, backend, computing center, etc.

[0008] High-load events recorded on the map can specifically indicate the corresponding thermal load of the at least one vehicle component, but also, for example, illustrate above-average power calls or power demands of the vehicle component, or by the vehicle component. Here, the vehicle component may be part of a motor vehicle or other vehicle to be operated according to the method, as will be explained in more detail below.

[0009] Therefore, in the sense of this invention, a high-load event is a load or load peak that is located when a vehicle passes through the corresponding event location or event area of ​​the corresponding high-load event, that is, a load or load peak that occurs in a spatially and / or temporally limited manner.

[0010] High-load events can occur, i.e., be detected or measured, in the past, particularly in the operation of the motor vehicle and / or one or more other motor vehicles. Energy demand forecasts for the operation of the motor vehicle or said motor vehicle can also be created based on provided maps or provided map data, or an energy demand map can be provided that gives location-specific (absolute or relative) predicted and / or measured energy demands for operating the motor vehicle. The predicted energy demands can be simulated or estimated, for example, using a pre-given model and map data, such as location-specific road types and / or corresponding gradients. The map can, for example, be divided into various road segments or sections, giving corresponding energy demands for said road segments or sections. Energy demands can also be recorded in the map as continuous curves of change, for example, in the form of so-called heat maps. Thus, the energy demand at each location can be given in the map. Based on this, high-load events can then be identified, for example, using a pre-given threshold for the energy demand, particularly the highest pre-given value of the energy demand within a spatial range. Therefore, it is possible to identify or locate high-load events at each location where energy demand reaches or exceeds a pre-defined threshold for energy demand, based on a map or map data. The provided map, in which high-load events are recorded, may correspond to or be generated from an energy demand map.

[0011] In another method step according to the invention, the current position and / or route of the motor vehicle is determined during its operation. This can be done, for example, by means of activated destination guidance and / or automatically with the support of a satellite-assisted navigation system or location determination system. The determination of the current position or the route currently traveled or followed by the motor vehicle can be performed after the start of operation, i.e., during the driving operation of the motor vehicle. In particular, it can be performed once at the start of driving and / or continuously or regularly during driving, i.e., repeatedly during the current operation of the motor vehicle. This also applies to the remaining method steps according to the invention.

[0012] In another method step according to the invention, at least one high-load event is determined from the high-load events recorded in the map based on the determined current location and / or route of the motor vehicle, said at least one high-load event being expected to be relevant to the motor vehicle or to the motor vehicle's operation during the current operation. A high-load event can be categorized as relevant to the motor vehicle's current operation when the motor vehicle is expected, i.e., with a pre-given minimum probability, to arrive at the corresponding event location during the current operation. However, other criteria may exist, which can be automatically evaluated to determine relevance or one or more relevant high-load events. Such criteria may, for example, relate to the characteristics of the motor vehicle or its current or expected state and / or more similar aspects, as will be further elaborated below. Thus, in other words, a high-load event can be categorized as relevant to the motor vehicle's current operation or to the motor vehicle during the current operation if said high-load event is expected, i.e., particularly with a pre-given minimum probability, and at least as long as no corresponding countermeasures are taken or implemented, to occur during the motor vehicle's current operation.

[0013] Here, current operation may refer to or include, for example, the current driving time or the running time until the next shutdown or until reaching a pre-given navigation destination, and / or more similar times.

[0014] In another method step according to the invention, before the vehicle has arrived at the corresponding event location of the relevant high-load event, at least one device that automatically drives the vehicle automatically pre-adjusts the vehicle components that are subjected to above-average loads in the determined relevant at least one high-load event. In other words, the at least one vehicle component is automatically prepared for or in response to the high-load event as the vehicle approaches the corresponding event location. This allows the vehicle components to be adjusted or pre-adjusted, particularly temperature-controlled, upon arrival at the event location, so as to pass through the high-load event or corresponding event location particularly efficiently and / or economically.

[0015] High-load events can be of different types and involve different components, such as exceptionally high driving or power loads or requirements, exceptionally high charging loads, exceptionally high climatic loads caused by environmental conditions, and / or more similar events. Thus, climatic loads can, for example, mean exceptionally high thermal loads due to corresponding extreme external temperatures or, for example, direct sunlight. High-load events can also be caused by user demands for air conditioning or temperature control within the vehicle's interior space, for example. Accordingly, vehicle components subjected to loads exceeding the average level can be, for example, the vehicle's traction battery, transmission, drive system, braking system, air conditioning system, pump, charging system, and / or more similar components. At least some of these components may have their own temperature control or air conditioning system, which can then be automatically driven for thermal pre-conditioning of the corresponding device. Correspondingly, the device controlled for thermal pre-conditioning may correspond to a vehicle component or a part of a vehicle component.

[0016] However, the controlled device can also differ from the vehicle component to be thermally pre-conditioned. The device could, for example, be an electric power consumer in the vehicle. By correspondingly controlling the device or the electric power consumer, its power demand can be reduced, thereby placing less load on the vehicle's battery, the vehicle's generator, and / or power supply lines (via which the electric power consumer is powered). Consequently, these vehicle components, or other vehicle components arranged in close proximity to the device, can experience less heat loss power, which can effectively induce or contribute to regulating a specific temperature of the vehicle component, i.e., thermal pre-conditioning of the vehicle component. Similarly, at least one vehicle component can be heated for thermal pre-conditioning by specifically increasing the power demand or energy consumption of a device in the vehicle.

[0017] Therefore, the present invention enables predictive thermal regulation of vehicle components, thereby allowing for more accurate and reliable regulation of optimized operating temperatures of vehicle components during vehicle operation than to date, or avoiding or at least reducing excessive heat loads compared to conventionally operated vehicles, or achieving pre-defined temperature thresholds for safe operation. This reduces corresponding adverse effects or consequences, such as temperature-related wear, temperature-related noise emissions, or limited power availability, and, where necessary, improves driving comfort. This can be advantageously achieved through predictive control according to the invention, resulting in reduced power or power consumption compared to conventionally operated vehicles. Consequently, overall energy-efficient and economical operation of the vehicle is advantageously achieved. For example, cooling can be predictably implemented over extended periods, thereby allowing for the use, for example, reduced ventilation equipment speeds, to achieve optimized operating temperatures of the corresponding vehicle components at the event location or for corresponding high-load events. This advantageously avoids, for example, the sudden activation of ventilation equipment at maximum power and correspondingly maximum volume, as sometimes observed currently.

[0018] Particularly advantageously, this invention enables thermal pre-conditioning not only when destination guidance of the vehicle is activated but also when destination guidance is not activated, thus providing thermal pre-conditioning consistently or continuously during vehicle operation. This allows for advantageous adjustments to the design of, for example, the vehicle's temperature control or air conditioning system. In particular, smaller or lower-power designs are possible because fewer sudden power demands are present or occur through predictive control, thus avoiding demand peaks or power peaks for temperature control or air conditioning. This advantageously saves both manufacturing resources and weight, ultimately leading to more efficient and economical operation of the vehicle.

[0019] For high-load events recorded on a map, the corresponding probability of these high-load events occurring during the current operation of the vehicle can be determined. Therefore, if multiple corresponding event locations exist, for example, along the current route or within a pre-given surrounding area of ​​the vehicle's current location, control or thermal pre-regulation can be implemented based on the high-load event with the highest probability. Particularly preferably, one or more probability thresholds can be pre-given. Once the probability of one of the high-load events occurring reaches or exceeds a pre-given probability threshold, thermal pre-regulation or corresponding control of the at least one device can be automatically initiated. Particularly preferably, different probability thresholds can be pre-given for different control measures or interventions. This can be tiered, for example, based on the corresponding energy requirements of different control measures. Thus, a lower-energy-density control measure can be initiated when a first probability threshold is reached, while a second, higher-energy-density control measure is initiated only when a higher second probability threshold is reached. In this way, when there is uncertainty regarding whether or when a determined high-load event occurs during the current operation of the vehicle, or which high-load event occurs during the current operation of the vehicle, a favorable trade-off can be achieved between the time for thermal pre-regulation and the energy or power requirements for controlling the at least one device.

[0020] A map containing recorded high-load events can be provided in advance, i.e., the map is provided, detected, or invoked as input data for implementing the method according to the invention. The generation of the map, i.e., the detection of location resolution of high-load events, and the aggregation, i.e., collection or concentration of high-load events in the map, can also be part of the method according to the invention, i.e., implemented in another method step of the method according to the invention, and particularly implemented automatically.

[0021] If necessary, as part of the method according to the invention, the map can also be automatically updated, for example, by means of data detected or recorded by the vehicle during its operation, particularly in the area of ​​the corresponding event location. Therefore, particularly preferably, the load or temperature of the at least one vehicle component can be monitored as the vehicle thermally pre-conditioned through the corresponding event location. Then, based on the corresponding temperature data or monitoring data recorded therein, a check of the control or the measures implemented for thermal pre-conditioning can be automatically performed to determine whether the control or the measures have yielded a predetermined result. This predetermined result or target may, for example, be or include the vehicle component temperature not reaching a predetermined temperature threshold or moving within a predetermined temperature range during a high-load event. Then, if necessary, preferably, the strategies or measures used or taken for controlling the device or for thermally pre-conditioning the vehicle component for the corresponding high-load event can also be automatically adjusted. In this way, vehicle control can be iteratively optimized specifically for individual high-load events at determined event locations.

[0022] The corresponding strategies, tips, or supplementary instructions that may be used or considered when controlling the at least one device for thermal preconditioning of the at least one vehicle component can also be recorded in a map or a corresponding dataset. Thus, this data or information can advantageously be used not only for motor vehicles but also for the thermal preconditioning of vehicle components of other vehicles.

[0023] In an advantageous further improvement of the invention, the map is generated using fleet data, which represents high-load events detected by multiple fleet vehicles during their respective operations. In other words, the corresponding data of different vehicles in the respective fleets are thus aggregated, i.e., integrated into a common dataset, i.e., a map. Consequently, the map can advantageously achieve sufficient coverage or database for the available applications in a particularly simple, fast, and low-cost manner. In particular, the fleet vehicles can therefore be private customer vehicles used in their regular operation, thus advantageously avoiding the need for additional vehicles to create the map, i.e., to store or provide such additional vehicles specifically for this application purpose, and on the other hand, the detected high-load events are advantageously detected under normal usage conditions that are also expected for motor vehicles.

[0024] Here, fleet data may preferably also include vehicle-specific characteristics or current status data of the vehicles in the fleet during and / or before the corresponding high-load event. For example, fleet data may include corresponding speeds, accelerations and / or decelerations, air conditioning settings, state of charge, battery status, current total energy consumption, routes traveled up to the event location of the corresponding high-load event, timestamps or time-varying curves of these and / or other data, and more. Based on this fleet data, pre-given assessments or calculations can then be performed as necessary, for example, to determine corresponding probabilities, averages, limits, turning probabilities, the correlation between one or more parameters or states, and / or, for example, the time of day or year, or weather or environmental conditions, and / or more similar factors. These data or corresponding results may also be recorded on a map. This advantageously enables more accurate and reliable determination of high-load events relevant to the corresponding motor vehicle in its current operation, and enables particularly effective or efficient control of thermal pre-conditioning as necessary.

[0025] In an advantageous further improvement of the invention, as part of the fleet data, the event location is also detected: what percentage of the fleet vehicles have passed the corresponding event location without a high-load event occurring there. Based on this, a corresponding probability of occurrence or occurrence of the high-load event is assigned. This can be done, in particular, based on the driving history of the corresponding fleet vehicles before passing the corresponding event location. The driving history can, for example, provide information on which road segment or route, or after what duration of operation, or even after what driving or operating state, the corresponding event location was reached by the corresponding fleet vehicles. The determination and consideration of probabilities presented herein advantageously allows for the particularly flexible consideration of multiple potential high-load events detected along the route or in the surrounding environment of the vehicles, if necessary. Therefore, in this case, it is possible to automatically and correctly respond, at least generally, to the corresponding operation of the vehicles.

[0026] In another advantageous embodiment of the invention, routes traveled by at least one vehicle are detected for generating the map. This can be implemented, for example, for the aforementioned fleet of vehicles. Here, route detection can be performed continuously or persistently, particularly automatically, regardless of any high-load events to date or anticipated. Based on the detection of high-load events by the corresponding vehicles, feature values ​​are assigned in the map to at least one segment of the detected route that was traveled by the vehicle before the occurrence of the corresponding high-load event. Here, the feature values ​​indicate that the corresponding segment leads to the event location of the high-load event. Here, in particular, the feature values ​​can be determined or assigned in relation to distance, i.e., the distance from the corresponding segment to the corresponding event location. Thus, correspondingly, segments farther from the event location can be assigned smaller feature values ​​than segments closer to or including the event location. These feature values ​​are then used as a basis for determining the at least one high-load event expected to be relevant to the vehicle during its operation. For this purpose, it is possible to determine, in particular, which segment the vehicle is moving on or on, and which segment or segments connect to it, especially along the direction of travel of the vehicle. In this sense, a segment of a route may correspond, for example, to a road section between two intersections, forks, exits, etc.

[0027] The characteristic values ​​assigned here can be absolute values, i.e., numbers. The characteristic value can be incremented by 1 for a specific segment, for example, if the vehicle has already passed through that segment before a high-load event is detected during vehicle operation. The characteristic value can also be a relative value. For example, the characteristic value can give the proportion of vehicles that have experienced or detected a high-load event when or after passing through the corresponding segment. The characteristic value can also give the probability that a high-load event will occur during the current operation when or after a vehicle or motor vehicle passes through the segment. This can preferably be determined using relevant fleet data, thereby advantageously enabling the characteristic value to be determined particularly accurately and reliably.

[0028] If a motor vehicle is operating, for example, on a defined section where a characteristic value has been assigned, the characteristic value can be used as a measure or probability of the occurrence of a detected high-load event, particularly a high-load event detected along the section or a typical route including the section, particularly the occurrence of the corresponding most recent high-load event in the current operation of the motor vehicle.

[0029] Maps similar to so-called heatmaps can be generated using eigenvalues ​​or by assigning eigenvalues ​​by segment. This allows for the advantageous existence of a corresponding current probability or metric at each location of the vehicle, enabling the prediction of high-load events during the vehicle's current operation. Therefore, this can be advantageously implemented particularly simply and with particularly low computational cost and with particular reliability, because, for example, it is neither necessary to activate destination guidance nor to predict the vehicle's most probable path (MPP) during its operation.

[0030] In an advantageous further improvement of the invention, when a segment is part of a route leading to different event locations, a separate characteristic value is assigned to each segment for each corresponding high-load event. In other words, a segment can thus be assigned multiple characteristic values, which give a measure or probability of the occurrence of different high-load events detected at different event locations on or around the segment. This advantageously enables particularly accurate and reliable determination of high-load events relevant to the motor vehicle's operation on the corresponding segment. It also advantageously enables optimized control of the at least one device for thermal pre-conditioning. If, for example, two different characteristic values ​​are given for a segment for different high-load events, which have different requirements for thermal pre-conditioning, control or pre-conditioning, for example, balancing these two requirements, can be implemented or prepared based on the characteristic values ​​or corresponding probabilities of the different high-load events. This can be implemented at least when the two probabilities or characteristic values ​​differ by at most a predetermined value and / or when the two probabilities or characteristic values ​​are below a predetermined threshold. Therefore, the system can react quickly and flexibly to how the vehicle develops during its current operation, based on which section the vehicle will subsequently pass through and the characteristic values ​​or probabilities of different high-load events. This allows control or thermal pre-regulation to be adapted to the more likely high-load events or those with higher characteristic values. Consequently, even when multiple locations of detected high-load events exist in the vehicle's current surroundings or along its current route, highly efficient and economical operation of the vehicle can be advantageously achieved.

[0031] Alternatively, all corresponding high-load events can individually increase a unique characteristic value of the corresponding section leading to the location of the corresponding event. It can also be specified that, for example, a high-load event typically requiring vehicle components will decrease the corresponding characteristic value by 1, while a high-load event typically requiring heating of vehicle components may increase the characteristic value by 1. Therefore, the characteristic value of the section can be positive or negative, thereby allowing for the particularly simple and cost-effective determination of what type of control or measures are expected or will be needed. It can also be specified that the characteristic value is determined based on the typical time or power requirements for the intensity or thermal pre-conditioning of the corresponding high-load event. Particularly strong high-load events, i.e., those placing particularly high demands on thermal pre-conditioning, can be represented or considered correspondingly by a larger characteristic value of the section or a larger change in the characteristic value, i.e., an increase or decrease. In other words, this allows the corresponding characteristic value to not only directly encode and represent the number of spatially adjacent or high-load events in the surrounding environment of the corresponding section, but also to directly encode and represent the type or characteristics of said high-load events. This also enables particularly simple and low-cost responses, for example, with a particularly small amount of data to be transmitted or processed during the operation of the vehicle, and with particularly accurate and reliable responses.

[0032] In another advantageous embodiment of the invention, when automatic destination guidance is not activated during the operation of the vehicle, a corresponding probability is determined for high-load events located within a pre-given surrounding area of ​​the vehicle's corresponding current location, indicating the probability that the vehicle will experience such high-load events during its current operation. In the sense of the invention, automatic destination guidance is based on or assisted by a device, such as a navigation device or system of the vehicle. Thermal pre-adjustment or corresponding control of at least one device of the vehicle is then implemented based on the high-load event with the highest probability. Therefore, in this case, navigation to a pre-given destination is not activated by the vehicle via a navigation system, and thus there is no known route along which the vehicle is guided. Therefore, instead of considering high-load events existing along such routes, the corresponding event locations in the vehicle's surrounding environment are currently determined or considered. Here, all high-load events located in the pre-given surrounding environment of the vehicle can be attributed to being relevant to the vehicle's corresponding operation. This can be done under at least one or more other criteria, as mentioned elsewhere here. The probability of a high-load event can be recorded in a map, as described, or determined or calculated individually for the corresponding motor vehicle, the corresponding driver of the motor vehicle, the corresponding position of the motor vehicle, the corresponding operation or operating state of the motor vehicle, and / or more similar factors, according to pre-given rules. For example, it can be considered whether the determined high-load event is located on a road that has been driven too much or too little, or on a road previously traveled by the corresponding motor vehicle, or whether the determined high-load event is, for example, located in the direction of travel of the motor vehicle or against the direction of travel of the motor vehicle, and / or more similar factors. By considering the probability, it is advantageous to at least generally average and, particularly reliably, respond correctly in many situations and / or motor vehicles, thus implementing control of the at least one device or thermal pre-conditioning of the at least one vehicle component in response to high-load events that actually occur during the corresponding operation of the motor vehicle.

[0033] In another advantageous embodiment of the invention, the map is managed via a central server device located outside the vehicle. This could be, for example, the server device mentioned elsewhere. Furthermore, vehicle-specific data of the corresponding motor vehicle is considered when determining the at least one event expected to be relevant, and this data is not transmitted to the central server device. This vehicle-specific data can specifically provide or relate to the current state of charge, current operating mode, current component temperature, and / or technical equipment of the corresponding motor vehicle. Operating mode can, for example, indicate whether sport or economy mode is currently activated, which or more driver assistance systems (e.g., speed controller or automatic distance control) are being used, and / or whether the motor vehicle is guided in purely manual, assisted, partially autonomous, or fully autonomous operation, and / or more similarly. In other words, data managed or provided outside the vehicle, particularly maps, can therefore be combined or fused with local data, i.e., data that exists only in or is known within the corresponding motor vehicle. This allows for particularly accurate and reliable determination of which or which high-load events are actually relevant to the corresponding motor vehicle. Therefore, for example, a high-load event marked on the map could occur or be anticipated only when the vehicle is manually driven in Sport mode to the corresponding event location, rather than when the vehicle autonomously or partially autonomously guides itself to the corresponding event location in Economy mode. In this way, thermal pre-conditioning can be optimized for vehicle personalization, i.e., thermal pre-conditioning can be implemented only when needed or according to the actual needs of the vehicle. This can advantageously lead to or contribute to further improvements in the efficiency and more economical operation of the vehicle.

[0034] In another advantageous embodiment of the invention, the at least one event expected to be relevant in the actual operation of the motor vehicle and / or the probability of the event occurring or becoming relevant is determined based on the driver-personalized characteristics of the motor vehicle's driver. These driver-personalized characteristics, i.e., the corresponding driver data, can in particular provide or relate to driver type and / or automatically learned driver behavior. As driver type, it is possible to distinguish, for example, a sporty or dynamic driver versus a mild or average driver and / or a less dynamic or slow or restrained driver. In other words, a driver model can therefore be considered, preferably one that can be automatically learned, i.e., one that can be automatically formed, and particularly dynamically matched, based on the driver's behavior, driving style, and / or habits during the operation of the motor vehicle by the corresponding driver. By considering such a driver model, i.e., the corresponding characteristics of driver personalization, it is advantageously possible to determine, with particular accuracy and reliability, whether the identified high-load event is expected to be relevant for the corresponding motor vehicle with the corresponding driver combination. Therefore, further improved, personalized, and optimized operation of the motor vehicle can be advantageously achieved. As described, the driver-personalized characteristics are preferably managed locally by the motor vehicle management system or within the motor vehicle, i.e., the driver-personalized characteristics are not transmitted to a central server device outside the vehicle. This allows for the consideration of driver personalization without compromising driver privacy, i.e., with particularly simple compliance with relevant data protection regulations. Simultaneously, the map can advantageously be used in a single version for multiple vehicles, thereby minimizing associated administrative costs.

[0035] It is also advantageous to detect the aforementioned vehicle-specific and / or driver-specific data or characteristics for use in generating a map along with the high-load event or the event location of the high-load event, and to record or label it on the map. Thus, the map can advantageously characterize the high-load event in particularly detailed terms, thereby allowing for the particularly accurate and reliable determination of the corresponding correlation of the high-load event based on the map. For example, to determine the relevant high-load event, the vehicle-specific and / or driver-specific data or characteristics associated with the high-load event can be compared with the vehicle-specific and / or driver-specific data or characteristics of the motor vehicle. This advantageously saves computational costs on the motor vehicle side.

[0036] In another advantageous embodiment of the invention, the high-load events are classified according to the corresponding occurrence of the high-load event and / or the operating state, particularly speed and / or load, existing prior to the corresponding occurrence of the high-load event, during which the high-load event has been detected. Control measures to be implemented for thermal pre-conditioning are then pre-defined for each assigned class or classification. These control measures may also be recorded in a map, i.e., the control measures are part of the corresponding map data. Thus, during the operation of the motor vehicle, the pre-defined control measures for the corresponding at least one high-load event classified as relevant are automatically implemented for thermal pre-conditioning. Alternatively, the current operating state of the motor vehicle can be compared with the corresponding class or classification to determine or verify the correlation of the corresponding high-load events. Therefore, different types or classes of high-load events can be identified here by means of the operating state. Thus, high-load events of the same class or classification can appear in groups in corresponding charts or characteristic curve families. Different high-load events of the same class may, for example, result in above-average thermal loads on the same vehicle components and / or appear with similar time-varying curves, profiles, and / or peaks. Correspondingly, the same control measures can be configured for a corresponding type of high-load event. This allows for advantageously consistent and predictable control of the at least one device or thermal pre-conditioning of the at least one vehicle component, further reducing the costs associated with determining appropriate control measures in the vehicle. In particular, when generating maps based on fleet data, optimized control measures can be advantageously identified in this way, and then, correspondingly, automatically implemented by each vehicle applying the method without further costs. For example, it can be identified that high-load events consistently or typically cause above-average thermal loads on certain vehicle components in or within a defined operating state, and / or that the heat absorption, heat dissipation, or heat conduction capacity of these components is a bottleneck for temperature regulation of other vehicle components or for particularly efficient or economical operation of the vehicle. This can particularly relate to vehicle components for which no temperature sensor is provided, such as axles, axles, or bearings.

[0037] Another aspect of the invention is a motor vehicle having a positioning device for determining the current location and / or route of the motor vehicle. Furthermore, the motor vehicle according to the invention has at least one data interface for acquiring event data, the event data location-discriminately indicating high-load events that have previously caused at least one vehicle component to experience above-average loads, particularly thermal loads. Furthermore, the motor vehicle according to the invention has a control device connected to the data interface for controlling at least one device of the motor vehicle for thermal pre-conditioning of at least one vehicle component, particularly a correspondingly above-average load vehicle component. Here, the motor vehicle according to the invention is designed to implement, particularly automatically implement, at least one variation or embodiment of the method according to the invention. Therefore, the motor vehicle according to the invention can particularly be a motor vehicle mentioned in conjunction with the method according to the invention. Accordingly, the motor vehicle according to the invention can have some or all of the characteristics and / or components or parts described in conjunction with the method according to the invention. The control device can particularly have a computer-readable data memory and a processor device connected to the data memory. Preferably, a computer program executable by the processor device can then be stored on the data memory, the computer program encoding or representing method steps or measures or corresponding control instructions according to the method. Therefore, execution of the computer program by the processor device can realize or cause the automatic implementation of the corresponding method. Here, event data can be detected by the vehicle's own devices or components via an interface. Event data, especially the corresponding map or corresponding map data, can also be acquired or received by devices outside the vehicle, particularly by the aforementioned central server device outside the vehicle, via a data interface.

[0038] The data storage and / or control device (combined with a data interface if necessary) can be a separate aspect of the invention.

[0039] Other features of the invention can be derived from the accompanying drawings and description. The features and combinations thereof mentioned above in the specification, as well as those mentioned below in the description of the drawings and / or shown separately in the drawings, can be used not only in the corresponding given combinations, but also in other combinations or individually, without departing from the scope of the invention. Attached Figure Description

[0040] In the diagram:

[0041] Figure 1 A schematic overview diagram illustrating a method for operating a motor vehicle is shown;

[0042] Figure 2 A schematic overview diagram illustrating the map generation used for the method is shown.

[0043] Figure 3 A schematic overview diagram illustrating a first variant of the method is shown;

[0044] Figure 4 A schematic overview diagram illustrating a second variation of the method is shown.

[0045] Figure 5 A schematic overview diagram illustrating a third variation of the method is shown. Detailed Implementation

[0046] In the accompanying drawings, identical and functionally identical elements are given the same reference numerals.

[0047] Figure 1 An exemplary method diagram is shown, which is used to illustrate the thermal preconditioning for operating motor vehicle 44 (see...). Figures 3 to 5 The method involves first collecting and testing input data 12. Input data 12 may include, for example, traditional geometric or geographical map data 14. Map data 14 may provide a road network 34 (see...). Figure 2 That is, for example, the direction of a road or traffic route, as well as, for example, the slope, section, road type, location and type of point of interest (Pol), charging station, gas station and / or more similar things.

[0048] Input data 12 may also include fleet data 16, which is obtained by means of vehicle fleet 36 (see Figure 2 The fleet data 16 can, for example, provide the location-resolved operating status of the vehicles in the vehicle fleet 36, and high-load events 42 detected during the operation of said vehicles (see...). Figures 2 to 5 Acceleration, deceleration, air conditioning settings during charging, energy consumption, turning probability, state of charge or fuel tank level, corresponding driver habits or behaviors, payment systems used for charging or refueling, and / or more similar items.

[0049] Input data 12 may also include vehicle data 18 for the motor vehicle 44 to be operated or its driver. Vehicle data 18 may include, for example, destination input for a navigation system, the current route 48 or location of the motor vehicle 44, the charging behavior of the motor vehicle, the destination or route learned for the motor vehicle 44 or its driver, equipment with a driving assistance system and / or the use or status of said equipment and / or more of the like.

[0050] Then, event determination 20 is performed based on input data 12, in which a high-load event 42 related to the current operation of motor vehicle 44 is determined. For this purpose, in particular, a map can be generated first based on map data 14 and fleet data 16, in which the high-load event 42 and, if necessary, other data belonging to or characterizing the high-load event are recorded.

[0051] Then, with the help of vehicle data 18 and the map, high-load events 42 that are expected to be relevant to a specific motor vehicle 44 can be identified or selected from all the high-load events 42 recorded therein.

[0052] This map-based determination of at least one high-load event 42 relevant to a single motor vehicle 44 can be understood as event radar. Therefore, by means of the event radar, starting from the current position of the motor vehicle 44, similar to conventional radar (however map-based or data-based), the high-load event 42 is determined or detected intuitively by scanning the map from the position of the motor vehicle 44 or along the current route 48 of the motor vehicle.

[0053] Based on the at least one high-load event 42 identified as relevant, particularly the most recent or expected high-load event 42, a strategy 22 is determined, i.e., judged or selected for controlling at least one device 24 of the motor vehicle 44 or for at least one vehicle component 26 of the motor vehicle 44 for thermal preconditioning.

[0054] Before the vehicle 44 reaches the event location of the corresponding high-load event 42, i.e., during the approach of the vehicle to the event location, the corresponding device 24 for predictive temperature adjustment, i.e., thermal pre-conditioning, is controlled or driven according to strategy 22. Thus, the vehicle assembly 26 predictively adjusts its temperature for the corresponding high-load event 42 during the operation of the vehicle 44, so that once the vehicle 44 reaches the event location of the corresponding high-load event 42, the vehicle assembly has an optimized or adapted temperature. The vehicle assembly 26 may be, for example, or include the drive unit 28 of the vehicle 44, the high-voltage system 30, and / or the interior space 32.

[0055] If the relevant event location is, for example, on a long and steep slope, and the ambient temperature or the current component temperature of the vehicle 44 there is higher than a predetermined threshold, then the drive unit 28 and / or high-voltage system 30 can be predictably cooled, for example, to avoid or delay overheating or degradation of the drive unit and / or high-voltage system while driving over the slope. If the high-load event 42 includes, for example, a prolonged period of vehicle idling under strong sunlight, then the interior space 32 can be predictably cooled, for example, to avoid or delay reaching the upper comfort temperature threshold in the interior space 32 during vehicle idling, thereby ensuring that the ventilation or air conditioning system of the vehicle 44 does not need to be activated and / or activated at reduced power during vehicle idling, for example, only later, to guarantee occupant comfort. And if the relevant high-load event 42 determines, for example, a rapid charge of the traction battery of the vehicle 44, then the traction battery can be slowly heated, for example, to enable particularly efficient rapid charging.

[0056] Figure 2 A schematic overview diagram illustrating the generation of the map is shown. For this purpose, traffic network 34 is traversed by vehicles in convoy 36, indicated here by a first convoy of vehicles 38 and a second convoy of vehicles 40. Exemplarily, a high-load event 42 occurs at a defined location in road network 34 during the movement of the first convoy of vehicles 38. This is then transmitted by the first convoy of vehicles 38 to a server 46 located outside the vehicles shown schematically here, which centralizes the corresponding data for all vehicles in convoy 36. Here, it is also possible to detect, for example, when the second convoy of vehicles 40 passes the location of the high-load event 42 without experiencing the high-load event 42 itself.

[0057] Figure 3 A schematic overview diagram illustrating a first variation of the method is shown. Here, the current route 48 of the vehicle 44 is known. Route 48 can be determined, for example, by a navigation system, or it can be a driving path learned for the vehicle 44 or its driver, or an automatically determined most probable driving path. Route 48 is compared with a map in which multiple high-load events 42 are recorded to determine which of the high-load events 42 exist along the route 48 of the vehicle 44 and are therefore at least potentially or expected to be related to the vehicle.

[0058] Figure 4A schematic overview diagram illustrating a second variation of the method is shown. Here, the current route 48 of vehicle 44 is unknown. Instead, the current position of vehicle 44 is determined and then, by comparison with a map, high-load events 42 are identified, which are located within a pre-defined surrounding environment 50 around the current position of vehicle 44. High-load events 42 located within the surrounding environment 50 are then considered relevant, while high-load events 42 located outside the environment 50 can be disregarded. Here, during vehicle movement, the surrounding environment 50 can be guided along with the current position of vehicle 44, so that different high-load events 42 can be located within and outside the surrounding environment 50 over time.

[0059] Figure 5 A schematic overview diagram illustrating a third variation of the method is shown. Here, road network 34 is also traversed, for example, by vehicles 38 of the first convoy. Here, road network 34 is divided into multiple segments 52. Here, a high-load event 42 is also detected by the vehicles 38 of the first convoy on a specific segment 52. Then, a correspondingly increased feature value is assigned to the segment 52 that has been traversed by the vehicles 38 of the first convoy until the event location of the high-load event 42 is reached. This can be implemented, for example, by a server 46. Therefore, the segments 52 shown here, exemplarily in dashed lines, that have not been traversed by the vehicles 38 of the first convoy, are not assigned a corresponding feature value, or for that segment 52, the feature value as a response to the high-load event 42 remains unchanged. In this way, feature values ​​or probabilities, i.e., the degree to which the high-load event 42 occurs when or after traversing the corresponding segment, are progressively learned for each segment 52.

[0060] When the motor vehicle 44 is running on the road network 34, it can then be determined at each location of the motor vehicle 44 which segment of the segment 52 is currently moving on and what characteristic value or probability is assigned to the segment 52. Then, with the aid of the characteristic value or probability, it can be determined which high-load event 42 is likely to be relevant to the motor vehicle 44 with what probability.

[0061] The described variations of the method are merely exemplary. Additionally or alternatively, other variations or implementations of the described method or based on the ideas of the method may be feasible.

[0062] Regardless of specific variations of the method, vehicle-specific and / or driver-specific data or characteristics of the vehicle fleet 36 and / or motor vehicle 44 can be considered separately as described. If, for example, charging of the traction battery is identified as a high-load event 42, then to determine whether this high-load event 42 is relevant to the corresponding motor vehicle 44, the following can be considered: how much range the motor vehicle 44 currently has, with what remaining range the driver of the motor vehicle 44 typically (if necessary, depending on the type of road traveled or the available charging power) drives to the charging point, whether the motor vehicle 44's assistance system has already output a corresponding charging stop recommendation, and how far away the motor vehicle 44 is from its current destination, etc.

[0063] In summary, the described examples demonstrate how thermal preconditioning can be advantageously implemented based on event radar, i.e., based on predictably determined relevant high-load events 42, in order to achieve particularly efficient and economical vehicle operation.

[0064] List of reference numerals

[0065] 10 Method Diagrams

[0066] 12 Input Data

[0067] 14 Map Data

[0068] 16 Team Data

[0069] 18 vehicle data

[0070] 20 events confirmed

[0071] 22 Strategy

[0072] 24 devices

[0073] 26 vehicle components

[0074] 28 drive units

[0075] 30 High Voltage System

[0076] 32 interior space

[0077] 34 road network

[0078] 36-vehicle convoy

[0079] Vehicles of the First Team of 38

[0080] 40 Second Team Vehicles

[0081] 42 High-load events

[0082] 44 motor vehicles

[0083] 46 servers

[0084] Route 48

[0085] 50 Surrounding Environment

[0086] 52 sections

Claims

1. A method (10) for operating a motor vehicle (44), the method comprising the following steps: A map is provided in which high-load events (42) are recorded at location-specific intervals, said high-load events having caused above-average loads on at least one vehicle component (26). Determine the current location and / or route (48) of the motor vehicle (44) during its operation. Based on the determined current location and / or route (48), at least one high-load event is identified from the high-load events (42) recorded on the map, said at least one high-load event being expected to be relevant to the vehicle (44) during the current operation, and Before the motor vehicle (44) has reached the corresponding event location of the relevant high-load event (42), the vehicle components (26) subjected to above-average loads in the relevant at least one high-load event (42) are thermally pre-regulated by at least one device (24) of the motor vehicle (44) through corresponding automatic control. For high-load events recorded in the map (42), determine the corresponding probability of the high-load event occurring during the current operation of the motor vehicle, wherein, Multiple different probability thresholds are given in advance. When a first probability threshold is reached, a control measure with lower energy density to be implemented for thermal preconditioning is initiated. In contrast, a second control measure with higher energy density is initiated only when a higher second probability threshold is reached.

2. The method (10) according to claim 1, characterized in that, The map is generated using fleet data (14), which provides information on high-load events (42) detected by multiple fleet vehicles (36, 38, 40) during their respective operations.

3. The method (10) according to claim 2, characterized in that, As part of the fleet data (14), the event location of the high-load event (42) is also detected: what percentage of the fleet vehicles (36, 38, 40) have passed the corresponding event location without the occurrence of the high-load event (42), and based on this, a corresponding probability of the occurrence of the high-load event (42) is assigned.

4. The method (10) according to claim 3, characterized in that, The probability of a high-load event (42) is assigned based on the driving history of the vehicles in the fleet before they pass the corresponding event location.

5. The method (10) according to any one of claims 1 to 4, characterized in that, In order to generate the map, routes traveled by at least one vehicle are detected. Based on the detection of the high-load event (42) by the vehicle, feature values ​​are assigned in the map to at least one segment (52) of the route that the vehicle has driven before the occurrence of the corresponding high-load event (42), the feature values ​​indicating that the corresponding segment (52) leads to the event location of the high-load event (42), and the feature values ​​are used as the basis for determining the at least one high-load event (42) expected to be relevant to the vehicle (44) during the operation of the motor vehicle (44).

6. The method (10) according to claim 5, characterized in that, The eigenvalues ​​are distance-related eigenvalues.

7. The method (10) according to claim 5, characterized in that, In cases where a segment (52) is part of a route leading to different event locations, a separate characteristic value is assigned to the segment (52) for each corresponding high-load event (42).

8. The method (10) according to any one of claims 1 to 4, characterized in that, In the absence of automatic destination guidance being activated during the operation of the motor vehicle (44), the probability of the high load event (42) being experienced by the motor vehicle (44) during the current operation of the motor vehicle (44) is determined for a high load event (42) located in a pre-given surrounding range around the corresponding current position of the motor vehicle (44), and thermal preconditioning is performed based on the high load event (42) with the highest probability.

9. The method (10) according to any one of claims 1 to 4, characterized in that, The map is managed by a central server device (46) located outside the vehicle and also takes into account vehicle-specific data of the motor vehicle (44) when determining the at least one event that is expected to be relevant, the data of which is not transmitted to the central server device (46).

10. The method (10) according to claim 9, characterized in that, The vehicle-specific data includes the current state of charge, current operating mode, current component temperature, and / or technical equipment of the motor vehicle (44).

11. The method (10) according to any one of claims 1 to 4, characterized in that, The probability of the at least one high-load event (42) being expected to be relevant and / or the high-load event becoming relevant is determined based on the driver-personal characteristics of the driver of the motor vehicle (44).

12. The method (10) according to claim 11, characterized in that, The personalized characteristics of the driver are driver type and / or automatically learned driver behavior.

13. The method (10) according to any one of claims 1 to 4, characterized in that, The high-load event (42) is classified according to the vehicle's corresponding occurrence of the high-load event and / or the operating state existing prior to the corresponding occurrence of the high-load event, during which the high-load event (42) has been detected in the operation of the vehicle, wherein for each classification assigned herein, control measures to be implemented for thermal pre-conditioning are pre-defined, and During the operation of the motor vehicle (44), control measures are automatically implemented for at least one high-load event (42) that is classified as relevant, in order to perform thermal pre-conditioning.

14. The method (10) according to claim 13, characterized in that, The operating state of the vehicle is the vehicle's speed and / or load.

15. A motor vehicle (44) having: a positioning device for determining the current location and / or route (48) of the motor vehicle (44); a data interface for acquiring event data, the event data location-discriminately providing high-load events (42), the high-load events having previously caused at least one vehicle component (26) to have above-average loads; and a control device connected to the data interface, the control device for controlling at least one device (24) of the motor vehicle (44) for thermally pre-conditioning at least one vehicle component (26) of the motor vehicle (44), wherein, The motor vehicle is designed to carry out the method (10) according to any one of claims 1 to 14.

16. The motor vehicle (44) according to claim 15, characterized in that, The motor vehicle is designed to automatically implement the method (10) according to any one of claims 1 to 14.

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