METHOD FOR DETERMINING A WEAR INDEX OF A VEHICLE AND VEHICLE - Patent application

Abstract

The present invention relates to a method for determining a wear index of a vehicle (1), in which the vehicle (1) continuously detects operational data using a monitoring unit over its service life. The method according to the present invention is characterized in that a portion of the operational data dependent on driving behavior and a portion of the operational data dependent on vehicle function settings are aggregated into a usage vector (NV), a portion of the operational data representative of the vehicle's environmental conditions is aggregated into an environment vector (UV), and the vehicle (1) transmits the usage vector (NV) and the environment vector (UV) to a central computing unit (3) external to the vehicle. The central computing unit (3) accesses a wear database (4) containing correlations between data that can be aggregated into the usage vector (NV) and the environment vector (UV) and wear characteristics of vehicle components, matches the usage vector (NV) and the environment vector (UV) with the contents of the wear database (4), determines a wear vector (AV) taking into account, in particular, relationship information (R) from a relationship information memory (8), and calculates the wear index taking into account the vector elements of the wear vector (AV).

Description

[Technical Field]

[0001] The present invention relates to a method for determining a wear indicator of a vehicle as defined in more detail in the preamble of claim 1, as well as to a corresponding vehicle for carrying out said method. [Background technology]

[0002] Vehicles, such as passenger cars, are everyday items that wear out over time, especially with frequent use. For example, it may be necessary to replenish washer fluid, replace oil or clean air filters, or replace worn components of the braking system, such as worn brake pads or corroded brake discs. Therefore, to prevent vehicles from breaking down and becoming stranded, vehicle maintenance is performed at fixed, predetermined intervals, such as every 30,000 kilometers or once a year. However, because not all vehicles wear out at the same rate, the predetermined, fixed, predetermined maintenance intervals may be too early or too late. Typically, maintenance intervals are set with a sufficient margin of error so that no problems arise during normal vehicle use. However, this often results in unnecessary inspections or replacements of vehicle components, even though they are not required. There is also a risk that certain vehicle components may wear out more rapidly than expected, necessitating earlier maintenance. Therefore, there is a need for methods and means for better estimating when vehicle maintenance should be performed.

[0003] From WO 2022 / 012837 a method for predicting maintenance times is known.

[0004] Furthermore, EP 2 674 343 A1 discloses a method and an apparatus for determining the driving behavior of a vehicle driver. During vehicle use, parameters of the driver of each vehicle are collected, which can describe the driving behavior. For this purpose, longitudinal and / or lateral acceleration acting on the vehicle are used as parameters. The determined characteristics of the driving behavior can be output in the vehicle in order to encourage the driver to drive more safely.

[0005] Furthermore, in everyday life, people tend to treat their own possessions differently than other people's possessions. Vehicle owners typically take good care of their vehicles, so that component wear is relatively slow, whereas this is not a major consideration when using, for example, a rental or leased car. This is because, among other reasons, wear on the rental car is not a significant concern to the vehicle driver, who is only using the rental car temporarily and does not have to directly incur maintenance costs.

[0006] Furthermore, German Patent Application No. 102016221086 discloses a method for determining wear characteristics of a vehicle. The vehicle detects data representative of its use, which are evaluated by a server to determine wear characteristics. If there is a risk of vehicle failure due to excessive wear of vehicle components, a warning message is output to the user in a timely manner.

[0007] Furthermore, German Patent Application No. 102014213522 discloses a device and a method for determining the load profile of a vehicle. Here, too, a server derives corresponding wear parameters depending on operating parameters collected from the vehicle. Among the operating parameters that can be taken into account are driving time, speed, longitudinal and / or lateral acceleration, engine speed, and the respective standard deviations of the respective parameters. Summary of the Invention [Problem to be solved by the invention]

[0008] The present invention is based on the object of providing an improved method for determining the wear index of a vehicle, which makes it possible to determine very accurately the point in time when maintenance of a particular vehicle component would be beneficial. [Means for solving the problem]

[0009] According to the invention, this object is achieved by a method for determining a wear indicator of a vehicle with the features of claim 1 and a vehicle for carrying out this method with the features of claim 8. Advantageous embodiments and developments emerge from the claims dependent on this claim.

[0010] A common method for determining a wear indicator of a vehicle is for the vehicle to continuously detect operational data over its lifetime using a monitoring unit, at least a portion of which is stored over the life of the vehicle and processed by a computing unit, and a portion of which is dependent on the driving behavior of the vehicle operator (the person driving the vehicle). - a portion of the operation data that depends on the driving behavior and a portion of the operation data that depends on the setting of the vehicle function are aggregated into a usage vector, and data generated by each monitoring unit is assigned to each vector element of the usage vector; A portion of the operational data representing the environmental conditions of the vehicle is aggregated into an environmental vector, and data generated by each monitoring unit is assigned to each vector element of the environmental vector; the vehicle transmits the use vector and the environmental vector to a central computing unit external to the vehicle, and the central computing unit accesses a wear database containing a relationship between data that can be aggregated into the use vector and data that can be aggregated into the environmental vector and wear characteristics of vehicle components; the central computing unit matches the use vector and the environmental vector with the contents of a wear database to derive a wear vector therefrom, each vector element of the wear vector representing the degree of wear of an individual vehicle component; the central calculation unit calculates the wear index taking into account the vector elements of the wear vector; the central computing unit replaces at least one vector element of the usage vector with a corresponding emulated vector element, and determines an alternative wear vector and an alternative wear indicator based on this, and transmits them to the vehicle; the central computing unit transmits the wear indicators and the wear vectors to the vehicle; - the wear indicator and the alternative wear indicator and / or the value of at least one vector element of the wear vector and the alternative wear vector are output to the vehicle operator in the vehicle. It has been further developed in this respect.

[0011] The wear index is information that indicates the degree of wear on a vehicle. Depending on the vehicle type and model, various reference values ​​can be stored against which the wear index is compared. A wear index that is above or below these reference values ​​can be interpreted as an indication for performing maintenance. Individual vector elements of the wear vector represent the degree of wear on individual vehicle components. For example, the wear vector can have entries for spark plugs, parts of the exhaust system, brake discs, brake pads, V-belts, etc., as vector elements, each of which is associated with a numerical value. This numerical value can take a value between 0 and 10, for example. The values ​​of the wear vector are all 0 for a new vehicle and increase with the amount of wear. Similarly, the wear index can be normalized to a range, for example, between 0 and 1, 0 and 10, or 0 and 100.

[0012] Furthermore, individual reference values ​​can be stored for the various vector elements, so that the central computing unit can compare these vector element-specific reference values ​​with the individual vector elements and determine that maintenance must be performed if the individual vector elements of the wear vector exceed the corresponding reference value. Thus, the method according to the present invention can very accurately estimate the time when the corresponding maintenance must be performed.

[0013] In this case, comprehensive influencing factors for vehicle wear or vehicle component wear are taken into account. Influencing factors include the driving behavior of the vehicle driver. If the vehicle driver's driving style is sportier, a stronger acceleration profile is applied, which leads to faster tire and brake pad wear, faster clogging of the oil particle filter, and wear of mechanical parts. Furthermore, the settings of vehicle functions made by the vehicle driver affect the wear of vehicle components. For example, if the vehicle driver sets a sporty shift profile, the automatic transmission shifts to a higher RPM, which similarly leads to faster wear of the corresponding vehicle components. If the vehicle driver prefers a stronger fan setting and a lower interior temperature, the vehicle's air conditioning system will also be subjected to a stronger load and will similarly wear faster. As a result, for example, the clean air filter will need to be replaced sooner, the air conditioning system's cooling medium will need to be replaced, or the air conditioning system's ducts will need to be inspected for leaks. This information is compiled into a usage vector.

[0014] To detect operational data, vehicles use monitoring units. These monitoring units include sensors such as acceleration sensors, temperature sensors, wheel speed sensors, mass flow sensors, and environmental sensors such as cameras, laser scanners, radar sensors, and ultrasonic sensor systems. Furthermore, the monitoring units can be used to read out information processed by the control unit, particularly configuration parameters. For this purpose, the monitoring units can be permanently integrated into a corresponding computing unit or can read out information transmitted via a data bus, such as a CAN bus. The monitoring units can detect, for example, the opening degree of a power window regulator, the adjusted seat position, the set fan level of an air conditioning system, the on-state status of a lighting system, etc. For example, the operating time of individual light sources in the vehicle interior can be tracked, and a timely replacement of the corresponding light source can be suggested when the light source's lifespan is about to expire.

[0015] Furthermore, the vector elements of the wear vector or the wear indicator itself can be multiplied by a cost factor, which allows for an estimate of the cost of performing maintenance. In other words, it allows for the extent to which the value of the vehicle is reduced due to wear. This cost factor can be determined arbitrarily. For example, for a brake disc, a cost factor of 0 can be used if the vector elements of the wear vector indicate a low value that does not yet require replacement. If the brake disc wears out so much that the vector elements exceed their respective reference values, the cost factor for the brake disc can increase from 0 to the actual current part value, which would require payment for, for example, a factory replacement.

[0016] In this case, the central computing unit can track the wear vectors or wear indicators of each vehicle in the fleet individually. For this purpose, a corresponding database can be stored in the central computing unit. If different people use the same vehicle, such as in the case of leased or rented cars, each wear event resulting from the individual use of the vehicle by different people is taken into account. The central computing unit can be, for example, a cloud server, also referred to as a backend. The central computing unit can be operated, for example, by the vehicle manufacturer. Any wireless technology can be used for data exchange between the vehicle and the central computing unit, such as mobile radio, Wi-Fi, Bluetooth, ZigBee, etc. For this purpose, the vehicle can have a wireless communication interface, for example, formed by a telematics unit.

[0017] The environmental vector can be used to track a vehicle's environmental conditions over its lifetime. For example, it can track whether the vehicle is frequently used in winter and / or in adverse weather conditions such as cold, rain, or snow. This provides an additional indicator of increased wear due to moisture-induced corrosion, particularly corrosion exacerbated by deicing agents. For example, if a vehicle is exposed to frequent temperature changes, such as being used in a climate where it is very warm during the day and very cold at night, or being used in the cold during winter but parked in a warm garage, these temperature changes can also adversely affect the lifetime of individual vehicle components. Thermal expansion, for example, can cause gaps to widen or liquid viscosities to deviate from their target range sooner. A vehicle can determine environmental conditions not only by itself using sensors but also from external sources. For example, the vehicle can receive information wirelessly from traffic or weather services. If the vehicle determines that it is frequently driven in dusty or pollen-heavy conditions, this can affect the clogging of the vehicle's air filter. If a vehicle is frequently driven in heavy or stationary traffic, such as during rush hour, the start / stop process will be performed more frequently by the engine automatic shutdown device.

[0018] The wear database specifies rules for how the characteristics of the wear vector's value should be tracked over the service life, depending on the information contained in the usage vector and the environmental vector. For example, if there are many start-stop processes, heavy braking, or if the vehicle is frequently parked in the hot summer sun, the wear database specifies how each vector element of the wear vector should be increased depending on these characteristics. The rules may be fixed and predefined, or may be implemented by machine learning methods. That is, approximation methods, heuristic equations, and machine learning models can be used to match the usage vector and the environmental vector with the contents of the wear database.

[0019] The vehicles of the fleet transmit their operational data over their lifetime to a central computing unit, which keeps track of exactly when each maintenance work on each individual vehicle is performed and whether it was necessary or if it was performed too late. Additionally, the costs incurred are also collected and stored for reference. Taking this information into account, the central computing unit processes the wear database to define newer rules for how entries in the usage vector and environmental vector affect the wear vector. In addition to defining new rules, it can also adapt existing rules.

[0020] Furthermore, there are various possibilities on how to determine the wear index from the wear vector: for example, it is possible to add all vector elements, to evaluate the wear vector, to determine the average value of all vector elements, or to calculate a normalized wear vector.

[0021] The wear vectors and wear indicators are stored and managed in a central computing unit, which can thus track the wear for each vehicle in the fleet and determine, for example, a workshop appointment for carrying out maintenance work in a timely manner, where the corresponding workshop appointment can also be automatically booked by the central computing unit.

[0022] The method assumes the following: the central computing unit transmits the wear indicators and the wear vectors to the vehicle; The wear indicator and / or the value of at least one vector element of the wear vector in the vehicle is output to the vehicle driver.

[0023] The wear index and / or the value of at least one vector element of the wear vector can be output in the vehicle to inform the vehicle driver of the degree of wear of the vehicle. This can be used to make the vehicle driver aware that his or her usage behavior affects the wear of the vehicle. For example, the vehicle driver can be very clearly shown the impact of his or her driving behavior, vehicle function settings, and usage behavior on wear. This can encourage the vehicle driver to drive more conservatively to slow down wear on the vehicle and minimize maintenance costs. Similarly, environmental conditions can be taken into consideration. For example, if a vehicle driver frequently parks his or her vehicle in the hot sun in the summer, this may accelerate the aging of the vehicle's paint. Therefore, information can be output to the vehicle driver to park his or her vehicle in the shade more frequently in the summer. Therefore, in addition to outputting the value of the wear index or the value of the vector element of the wear vector in the vehicle, complementary instructions that can slow down wear on the vehicle can also be output. Output of information in the vehicle can be performed via existing means. Thus, for example, visual information output can be realized via a display device such as an instrument cluster, a head unit, a head-up display, etc. Acoustic information output can be realized via a speaker.

[0024] Furthermore, vehicle usage behavior can be tracked live by the vehicle in use. At the moment when the vehicle driver performs an action that causes excessive wear on the vehicle, for example, by particularly strong acceleration or braking, information can be output at that moment to warn the vehicle driver of this unfavorable usage behavior. Here, tactile information output, such as vibration of the vehicle steering wheel, is also considered.

[0025] Preferably, for example, the vehicle's infotainment system has a setting option that allows the vehicle operator to set how much information about the wear indicators or wear vectors they want, where the vehicle operator can also input, for example, whether the information should be output live or not.

[0026] The wear indicators or wear vectors of each vehicle can also be provided to third parties, for example by accessing them via a central computing unit. The third parties can be, for example, suppliers of the vehicle manufacturer or service providers such as insurance companies. The third parties can process the retrieved information and use it to adapt their business models.

[0027] Furthermore, according to the present invention, the central computing unit replaces at least one vector element of the usage vector with a corresponding emulated vector element, determines an alternative wear vector and an alternative wear index based on this, and transmits them to the vehicle. This allows for outputting suggestions to the vehicle driver on how the vehicle driver can adapt his or her vehicle usage behavior to reduce wear. For example, the longitudinal acceleration of the vehicle over time may be considered as a vector element. An acceleration profile including a relatively small acceleration amplitude, i.e., an acceleration amplitude corresponding to a more conservative driving style, is provided as the emulated vector element. The central computing unit considers this "emulated" usage vector to calculate the alternative wear vector and the alternative wear index. Therefore, the alternative wear vector or the alternative wear index will have a lower value, representing less vehicle wear. This particularly illustrates to the vehicle driver how he or she can advantageously change his or her usage behavior to slow down vehicle wear and also reduce costs.

[0028] According to an advantageous embodiment of the method, the vehicle identifies the vehicle driver, assigns the vehicle driver a user profile specific to that person, and transmits the user profile to a central computing unit, whereby a wear indicator is associated with the user profile. The vehicle or the central computing unit collects a plurality of relevant information, one relevant information representing the contractual conditions under which the vehicle driver uses the vehicle. The central computing unit then determines a relevant-information-specific wear indicator for each new relevant information. By assigning individual user profiles to individual vehicle drivers, not only can the central computing unit track the wear characteristics of individual vehicles, but also take into account the influence of specific people.

[0029] Of particular importance here is "relationships," i.e., the relationship between a vehicle and a vehicle driver. As noted above, users often engage in behaviors that result in faster wear on rental or leased vehicles compared to their own vehicles. To account for this, relationship information includes entries for, for example, their own property, rental cars owned by others, and leased cars owned by others.

[0030] If the same person changes vehicles, the person can be assigned a different wear index for each vehicle. For this purpose, the relationship information can be defined as a vector, a matrix, or an n-th order tensor. Each time another vehicle is used for the first time, an entry in the form of relationship information is associated with the corresponding vector, matrix, or tensor for this user profile. Over time, more information about the user is aggregated, describing how the user uses different vehicles. The relationship information can be used to track the user's usage history for different vehicles. For example, a vector, matrix, or tensor can represent that the same user has already driven three different owned vehicles and ten rental vehicles. Then, a relationship-specific wear index is determined for each of these vehicles. This allows for identifying patterns, such as a "low" wear index for owned vehicles and a "high" wear index for rental vehicles.

[0031] One embodiment of the relationship information as a tensor, e.g., a second-order tensor (matrix), allows for unambiguous assignment of information. That is, the first column of the relationship information matrix can represent the relationship between a vehicle and a user, i.e., whether the vehicle is owned or rented. The second column can store a driving style assessment for this vehicle based on a usage vector. The third column can store a relationship-specific wear index. A new row is added to this matrix each time a new vehicle is used for the first time or when the contract terms change, e.g., when a person takes over driving a leased car.

[0032] Human identification is achieved using conventional methods such as logging in with a user account and password, scanning a biometric feature, identifying an identification token on, for example, a carried vehicle key, reading key parameters from a smartphone, etc.

[0033] The association of the wear indicator with the user profile may be performed in the vehicle or by a central computing unit.

[0034] Manual input by the vehicle driver is usually required so that the vehicle can collect the relevant information. For this purpose, the vehicle can actively request input from the vehicle driver via, for example, a vehicle-integrated human-machine interface (HMI), such as a touch-sensitive display, or via a mobile terminal device directly or indirectly connected to the vehicle. A corresponding application can then be executed on the mobile terminal device, through which the vehicle driver can input the relevant data. The mobile terminal device can then be connected to the Internet via mobile radio and further to a central computing unit. It is also conceivable that the vehicle driver can log in to a user account on the central computing unit using a unique user profile on a desktop or tablet computer at home and enter the relevant information via an input screen in an Internet browser.

[0035] The relation-specific wear indicators can be determined in the same way as the wear indicators: for this purpose, various approximation methods, heuristic equations or machine learning models can be used, each operating on the basis of fixed rules.

[0036] Multiple relationship-specific wear indicators can be output simultaneously, e.g., in direct comparison, to the same user in the vehicle, making the user aware that he or she has different usage behaviors depending on the vehicle, i.e., many people are unaware that they drive their rental cars differently than their personal vehicles.

[0037] Preferably, the central computing unit considers the relationship information as another influencing parameter for determining the characteristics of each relationship-specific wear indicator. As already mentioned, the relationship-specific wear indicator can generally be determined in the same way as the wear indicator. For this purpose, the characteristics of the usage vector and the environmental vector are compared with the wear database by the central computing unit. Typically, since an individual person behaves differently in vehicles of different relationships, i.e., in owned vehicles compared to rental vehicles, each relationship-specific wear indicator will have different characteristics in different relationships simply due to the differences in each entry of the respective usage vector. However, the relationship information itself can also be used as another influencing factor that affects the result of the relationship-specific wear indicator.

[0038] The relationship information can also be considered as a kind of weighting factor. For example, for owned vehicles, the relationship-specific wear indicator can be multiplied by a relatively low value, such as 0.5. In contrast, for rented or leased cars, it can be multiplied by a relatively high value, such as 1.5 or 3. This allows the characteristics of the relationship-specific wear indicator to be adapted very quickly and easily.

[0039] In this case, the already calculated wear indicators do not necessarily have to be weighted, but the individual elements of the underlying relationship information specific wear vector may be weighted.

[0040] According to a further advantageous embodiment of the method, the central computing unit determines for each wear indicator a confidence value depending on the completeness of the relationships stored in the use vector, the environmental vector, and / or the wear database, the confidence value representing a high probability of accuracy in the case of a relatively complete use vector, the environmental vector, and / or the wear database during the determination of the respective wear indicator, and a low probability of accuracy in the case of a relatively incomplete use vector, the environmental vector, and / or the wear database. If the vehicle has only been used for a short time, there is currently little information available to form the use vector and the environmental vector. Therefore, a relatively low confidence value is determined in this case. This confidence value can be output in the vehicle, so that the respective vehicle user is informed that a wear indicator has been determined, but that its information value is low. Similarly, certain relationships may be missing from the wear database.

[0041] On the other hand, if the vehicle has been used by a human for a long time, more information exists in the usage vector and the environment vector, and therefore a higher confidence value is determined, thereby informing the vehicle driver that the information value of the wear indicator is already relatively high.

[0042] In a further advantageous embodiment of the method according to the invention, the central computing unit determines relationship-specific alternative wear vectors and alternative wear indicators and transmits these to the vehicles. For each relationship, i.e. for each relationship between a user and various vehicles, an individual alternative wear vector and alternative wear indicator can be determined taking into account the relationship information.

[0043] According to a further advantageous embodiment of the method according to the invention, the vehicle tracks the progress of at least one wear indicator, wear vector element, confidence value and / or averaged wear indicator, wear vector element and / or confidence value over a time interval, compares each tracked value with a respective predetermined threshold value and outputs an instruction to the vehicle to perform vehicle maintenance earlier if the tracked value is above the threshold value, or outputs an instruction later if the tracked value is below the threshold value. Thus, not only the central computing unit but also the vehicle itself can inform or output recommendations to the vehicle driver about when maintenance should be performed.

[0044] This allows for a reduction in the time margin or wear reserve. Due to the increased precision of the exact time at which maintenance should be performed, maintenance appointments can be shifted forward or backward in time depending on the vehicle component. This increases user comfort, as vehicle components can be avoided from being replaced unnecessarily or replaced in a timely manner. In this case, for the wear indicator, each vector element of the wear vector can be set, and for the confidence value or a value correspondingly averaged therefrom, individual thresholds can be set.

[0045] For example, an inspection may need to be performed once a year or every 15,000 kilometers. In this case, a visual indication that an inspection is required can be displayed on the vehicle, for example, when the vehicle is started, 30 days before the end of the year or 1,000 kilometers before reaching 15,000 kilometers. This information output can be omitted or performed earlier, taking into account the wear vector or wear indicator. This information can be supplemented by an explanation of which vehicle components must be maintained during the inspection or maintenance period, such as replacing brake pads on the front axle, triggered by the vector elements of the wear vector exceeding the respective threshold values.

[0046] In a further advantageous embodiment of the method according to the present invention, the tracked values ​​of the wear indicator, the wear vector element, and / or the confidence value, or the characteristic quantities averaged therefrom, are compared by the vehicle with respective predetermined thresholds. Based on this, the vehicle reduces the maximum available drive force and / or modifies the drive control behavior of at least one driver assistance system to a more conservative behavior if the tracked values ​​exceed the thresholds. In other words, if the vehicle detects a usage behavior that leads to increased wear, it can adapt the usability so that the aforementioned conditions that lead to increased wear no longer occur in the vehicle's usage characteristic map. In this case, reducing the maximum drive force is the simplest step. However, the vehicle can also adapt the behavior of individual driver assistance systems, thereby providing even more means for promoting conservative driving behavior. For example, adaptive cruise control can increase the minimum distance to the vehicle ahead, thereby ensuring a longer braking distance in the event of heavy braking. This means that the vehicle does not have to brake as hard in emergency braking, thereby reducing braking wear. The shift behavior of an automatic transmission can also be modified. For example, if the vehicle operator activates a sport mode, the RPM threshold for shift changes stored in the sport mode may be lowered, such that gears are changed earlier in the sport mode, but not as early as in the normal operating mode.

[0047] The time interval can be any length, but a relatively long time interval, such as several weeks or months, is preferably used, since this allows for a sporty driving style for a short period of time, i.e., user comfort is ensured and sufficient driving force is available even in dangerous situations, for example, for quick overtaking.

[0048] According to the present invention, a vehicle is provided with an internal computing unit, a plurality of monitoring units, and an interface for wireless data communication, the computing unit, the monitoring units, and the interface for wireless data communication being configured to implement the steps of the above-mentioned method to be performed by the vehicle. The vehicle may be any vehicle, such as a car, truck, transporter, bus, etc. Thus, the vehicle can determine the degree of wear of individual components and plan maintenance intervals or appointments based on the degree of wear, so that the vehicle is brought into the workshop exactly when it is actually needed. Preferably, the vehicle can be designed to adapt its usability, so that driving situations that cause increased wear are avoided or at least reduced in number.

[0049] Further advantageous embodiments of the method according to the invention for determining a wear indicator of a vehicle will also become apparent from the exemplary embodiments which are explained in more detail below with reference to the drawings. [Brief explanation of the drawings]

[0050] [Figure 1] 1 is a schematic plan view of a vehicle according to the present invention; [Figure 2] FIG. 2 is a schematic diagram of data processed by a computing unit. [Figure 3] 1 is a flow chart of a method according to the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0051] FIG. 1 shows a vehicle 1 according to the present invention, which includes a computing unit 2, a control device 5, sensors 6, and a telecommunication unit 7. The vehicle 1 or the computing unit 2 continuously detects operational data over the life of the vehicle 1. For this purpose, sensor data generated by the sensors 6 is evaluated and data is retrieved from the control device 5. The computing unit 2 aggregates part of the operational data that depends on the driving behavior and part of the operational data that depends on the settings of the vehicle functions into a usage vector NV, as shown in FIG. 2. Furthermore, the computing unit 2 aggregates part of the operational data that depends on the environmental conditions of the vehicle 1 into an environment vector UV, also shown in FIG. 2. Using the telecommunication unit 7, the computing unit 2 transmits the usage vector NV and the environment vector UV to a central computing unit 3, for example a cloud server.

[0052] The central computing unit 3 has access to a wear database 4. As shown, the wear database 4 may be integrated into the central computing unit 3. However, the wear database 4 may also be implemented externally to the central computing unit 3 and connected to communicate with the central computing unit 3. The wear database 4 stores relationships between data that can be aggregated into a usage vector NV and an environment vector UV, and the wear characteristics of vehicle components. The central computing unit 3 matches the usage vector NV and the environment vector UV received from the vehicle 1 with the contents of the wear database 4 and generates a wear vector AV, also shown in the figure, therefrom. Each vector element of the wear vector AV represents the degree of wear of an individual vehicle component.

[0053] Various methods can be used to calculate the vector elements of the wear vector AV, such as approximation methods, the use of heuristic functions, or the use of machine learning models. The central computing unit 3 then calculates a wear index from the wear vector AV, which represents an assessment of the overall degree of wear of the vehicle 1. The central computing unit 3 further comprises a relationship information memory 8, also as shown in FIG. 2, in which relationship information R is stored. For this purpose, each driver of the vehicle 1 can be assigned a unique user profile in which relationship information R is stored. The relationship information R is the relationship between the user and the vehicle, for example whether the vehicle 1 is owned by the vehicle driver or is leased or rented by the person.

[0054] The wear index or wear vector AV determined by the central computing unit 3 can be sent back to the vehicle 1 and optionally transmitted to a third party 9. The third party 9 can be, for example, a supplier of the vehicle manufacturer or a service provider such as an insurance provider.

[0055] Figure 2 shows the data processed by the computation unit 2 and the central computation unit 3. The computation unit 2 comprises a usage data collection module 10.1 for aggregating a usage vector NV and an environment data collection module 10.2 for aggregating environment data to generate an environment vector UV.

[0056] The use vector NV is a set of distinct vector elements v1, v2, v m For example, vector element v1, which may represent a battery consumption class, may be assumed to be a characteristic of 1 in the range of 1 to 10, representing very low consumption. Vector element v2, for example, may represent the consumption of an air conditioner and may be assumed to be a characteristic of 8, representing high consumption for vehicles that frequently have their fan set to maximum and are therefore highly cooled. Another optional vector element may represent speed averages, for example, an average speed of 5 within a city and an average speed of 4 outside a city.

[0057] The environment vector UV is the vector elements u1, u2, un For example, vector element u1 represents the average temperature around the vehicle, and can assume a value of 3 in the range of 1 to 10. This represents the normal range of the area where vehicle 1 is staying. Vector element u2 may represent, for example, traffic volume, and can assume a value of 3 when traffic volume is low.

[0058] The wear database 4 includes parameters K that represent the relationship of each characteristic of the usage vector NV and the environment vector UV to the degree of wear of the vehicle components. These parameters K represent, for example, tire wear, battery aging, spark plug condition, etc. Similarly, cost factors may be included that may be multiplied by a wear factor to predict the costs incurred when maintaining the vehicle components. Thus, the corresponding costs for each vector element of the wear vector AV can be determined individually or aggregated for wear indicators.

[0059] The relationship information memory 8 stores relationship information R associated with a user profile. For example, the relationship information R may be stored as a matrix as shown in FIG. 2. For example, R1, R2, and R q are stored in the first column of the matrix. Then, for each piece of relationship information R, the wear indices AI1, AI2, and AI q are calculated and stored in the second column. The central calculation unit 3 can calculate further characteristic quantities that can be derived from the usage vector NV, such as driving style evaluations FB1, FB2, FB q can be stored in the third column. Each characteristic quantity contained in the matrix can then be taken into account to determine the wear vector AV or the wear index.

[0060] 2 exemplarily shows the formulas taken into account by the central computing unit 3. In the first case, the decision is made based only on the use vector NV and the environment vector UV, without taking into account the relationship information R. Here, the index "m" corresponds to the number of vector elements of the use vector NV, "n" corresponds to the corresponding number of vector elements of the environment vector UV, and "l" corresponds to the respective relationship stored in the wear database 4.

[0061] Case 2 illustrates the consideration of relational information R. Variable r xy represents each matrix element of the matrix containing the relationship information R. Iteration may be performed over all matrix elements or only over selected ones, for example, over all column elements in the first column. The variable "x" may correspond to the number of rows in the matrix, and "r_xy" represents the individual relationship information R1, R2, R q It may correspond to the items.

[0062] In the equations shown in Figure 2, the first and second cases are merely examples. In general, different approximation or heuristic equations can be used. Also, black-box models can be used as machine learning techniques, for example by using artificial neural networks.

[0063] Furthermore, the central computing unit 3 can determine a confidence value P depending on how much data is present in the usage vector NV, the environment vector UV or the relationship contained in the wear database 4. This represents the probability that the determined wear indicator is actually of informative value. To determine the confidence value P, for example, the reciprocal can be formed from the sum of the missing data. It is also possible to form a confidence value specific to the relationship information.

[0064] The characteristic quantities calculated by the central computing unit 3 can then be output to a vehicle occupant 12 and / or a third party 9 as suggested by arrow 11 .

[0065] 3 shows the sequence of the method according to the invention. The method starts in method step 301. In method step 302, a vehicle driver uses a vehicle with which he has a particular relationship, such as his own vehicle, a rental car, or the vehicle of a friend or acquaintance. In method step 303, the computing unit 2 or the central computing unit 3 checks whether a specific user profile should be assigned to the vehicle driver and, if so, whether such a user profile already exists.

[0066] If no profile is used or no profile exists yet, operational data during use of the vehicle 1 is detected and a corresponding use vector NV and environment vector UV are generated in method step 304. Complementarily, relationship information R can be collected.

[0067] In method step 305, the data generated in this way is transmitted to the central computing unit 3 and stored there. The vehicles 1 of the fleet continuously transmit corresponding data in method step 306, which can also transmit maintenance costs as well as wear events identified during maintenance. This allows the wear database 4 to be continuously processed in method step 307. In method step 308, the existing data of the respective vehicle 1 is made available for further calculations, taking into account previously determined information, for example previously calculated relationship-specific wear indicators if necessary.

[0068] In method step 309, the central computing unit 3 reads the use vector NV, the environment vector UV, and the above-mentioned relationship information R, and, if necessary, also reads further matrix elements of the matrix shown in FIG. 2. In method step 310, the central computing unit 3 accesses historical operating data of the vehicle 1, i.e., previously existing operating data. In method step 311, the central computing unit 3 calculates a wear vector and a wear index, and, if necessary, calculates a confidence value P. Furthermore, in method step 312, the central computing unit 3 additionally calculates an alternative wear vector or an alternative wear index. For this purpose, at least one vector element of the use vector NV used in calculating the wear vector is replaced by an emulated value.

[0069] In method step 313, the data calculated in this way is continuously stored in the central computing unit 3. This allows the user profile of the vehicle driver to be processed over time. For this purpose, the individual vector or matrix elements can be defined as time-dependent characteristic quantities. Each vector or matrix element therefore represents a time-dependent function.

[0070] In method step 314, the calculated result is sent back to the vehicle 1. Subsequently, in method step 315, the respective vehicle 1 or computing unit 2 checks whether a critical threshold has been exceeded. If the critical threshold of at least one vector element of the wear vector or the threshold of the wear indicator exceeds a predefined value, this means that a vehicle component is exhibiting excessive wear events. Based on this, in method step 316, corresponding information can be output acoustically and / or visually in the vehicle 1. Additionally or alternatively, in method step 317, vehicle functions can be adapted, for example, by reducing the maximum available drive force or increasing the minimum permissible distance to the vehicle ahead in the case of adaptive cruise control.

[0071] In method step 318, it is checked whether vehicle 1 is currently in use. If vehicle 1 is in use, a periodic recalculation is carried out on the basis of the current values ​​in method step 319. On the other hand, if vehicle 1 is not in use, the method ends in method step 320. [Prior art documents] [Patent documents]

[0072] [Patent Document 1] International Publication No. 2022 / 012837 [Patent Document 2] European Patent Application Publication No. 2674343 [Patent Document 3] German Patent Application Publication No. 102016221086 [Patent Document 4] German Patent Application Publication No. 102014213522

Claims

1. A method for determining a wear indicator of a vehicle (1), the vehicle (1) continuously detecting operational data over the life of the vehicle (1) using a monitoring unit, at least a part of the operational data being stored over the life of the vehicle (1) and processed by a computing unit (2, 3), and a part of the operational data being dependent on the driving behavior of a vehicle driver, The part of the operational data that depends on the driving behavior and the part of the operational data that depends on the settings of the vehicle functions are aggregated into a usage vector (NV), and each vector element (v １ , v ２ , v ｍ ) are assigned the data generated by each monitoring unit, A part of the operational data representing the environmental conditions of the vehicle (1) is collected into an environmental vector (UV), and each vector element (u １ , u ２ , u ｎ ) are assigned the data generated by each monitoring unit, - the vehicle (1) transmits the use vector (NV) and the environment vector (UV) to a central computing unit (3) external to the vehicle, and the central computing unit (3) accesses a wear database (4) containing correlations between data that can be collected in the use vector (NV) and data that can be collected in the environment vector (UV) and wear characteristics of vehicle components; - said central computing unit (3) matches said use vector (NV) and said environment vector (UV) with the contents of said wear database (4) to derive therefrom a wear vector (AV), each vector element of said wear vector (AV) representing the degree of wear of an individual vehicle component; - said central calculation unit (3) calculates said wear indicator taking into account said vector elements of said wear vector (AV); said central calculation unit (3) calculates at least one vector element (v) of said usage vector (NV); １ , v ２ , v ｍ ) with the corresponding emulated vector elements, and based on this, determine an alternative wear vector and an alternative wear index, and transmit the alternative wear vector and the alternative wear index to the vehicle (1); - the central computing unit (3) transmits the wear indicators and the wear vector (AV) to the vehicle (1); - the wear indicator and the alternative wear indicator and / or the value of at least one vector element of the wear vector (AV) and the alternative wear vector are output to the vehicle driver in the vehicle (1); A method characterized by:

2. - the vehicle (1) identifies the vehicle driver and assigns him / her a person-specific user profile and transmits said user profile to the central computing unit (3), said wear indicators being associated with said user profile; The vehicle (1) or the central computing unit (3) stores a number of related information (R １ , R ２ , R ｑ ) and one relationship information (R) indicates under what contract conditions the vehicle driver uses the vehicle (1); - the central computing unit (3) calculates for each new relation (R) a relation-specific wear index (AI) １ , A.I. ２ , A.I. ｑ ) 2. The method of claim 1.

3. The central computing unit (3) converts the relationship information (R) into a wear index (AI) specific to each relationship information. １ , A.I. ２ , A.I. ｑ ) as another influential parameter for determining the characteristics of 3. The method according to claim 2.

4. The central computing unit (3) determines for each wear indicator a confidence value (P) depending on the completeness of the relationship stored in the usage vector (NV), the environment vector (UV) and / or the wear database (4), the confidence value (P) representing a high probability of accuracy in the case of a relatively complete usage vector (NV), environment vector (UV) and / or wear database (4) during the determination of each wear indicator, and a low probability of accuracy in the case of a relatively incomplete usage vector (NV), environment vector (UV) and / or wear database (4). The method according to any one of claims 1 to 3, characterized in that

5. The central computing unit (3) determines a relationship-specific alternative wear vector and an alternative wear index, and transmits the relationship-specific alternative wear vector and the alternative wear index to the vehicle (1). The method according to any one of claims 2 to 4, characterized in that

6. The vehicle (1) tracks the progress of at least one wear index, wear vector element, confidence value (P), and / or averaged wear index, wear vector element, and / or confidence value over a time interval, compares each tracked value with a respective predetermined threshold, and outputs instructions to the vehicle (1) to perform vehicle maintenance at an earlier time if the tracked value exceeds the threshold, and outputs the instructions at a later time if the tracked value is below the threshold. The method according to any one of claims 1 to 5, characterized in that

7. The vehicle (1) tracks the progress of at least one wear index, wear vector element, confidence value (P), and / or averaged wear index, wear vector element, and / or confidence value over a time interval, compares each tracked value with a respective predetermined threshold, and reduces the maximum available drive force and / or changes the drive control behavior of at least one driving assistance system to a conservative behavior if the tracked value exceeds the threshold. The method according to any one of claims 1 to 6, characterized in that

8. A vehicle (1) comprising an internal computing unit (2), a plurality of monitoring units, and an interface for wireless data communication, The computing unit (2), the monitoring unit and the interface for wireless data communication are configured to carry out the steps of the method according to any one of claims 1 to 7 to be executed by the vehicle. A vehicle (1).

Images