Method and device for monitoring the thermal ageing of a component of a vehicle

Abstract

The invention relates to a method (300) for monitoring the thermal ageing of a component (110) of a vehicle (100). The method (300) comprises determining (301), on the basis of a test vehicle (100), a set of measurement datasets (230) for temperature conditions in which the component (110) has in each case a temperature (212) that exceeds a predefined temperature threshold value; wherein each individual measurement dataset (230) comprises measurement values for N measurement variables (202), where N > 1. The method (300) also comprises determining (302), on the basis of the set of measurement datasets (230), at least one rule for the presence of a temperature condition; wherein the rule includes a combination of value ranges (240) for the N measurement variables (202). The method (300) also comprises providing (303) the rule for monitoring the thermal ageing of the component (110) in a standard vehicle (100).

Description

[0001] Method and device for monitoring the thermal aging of a component of a vehicle

[0002] The invention relates to a method and a corresponding device designed to monitor the thermal aging of a component of a motor vehicle.

[0003] A (motor) vehicle has a multitude of different components, each of which can be exposed to temperatures during vehicle operation that can lead to thermal aging of the respective component. The temperature of a vehicle component may not be directly measured, so an operating situation that leads to thermal aging of the component may not be directly detectable.

[0004] This document deals with the technical task of being able to monitor the thermal aging of a vehicle component in an efficient and precise manner, even without directly recording the temperature of the vehicle component.

[0005] The object is achieved by each of the independent claims. Advantageous embodiments are described, inter alia, in the dependent claims. It is pointed out that additional features of a patent claim dependent on an independent patent claim can form a separate invention independent of the combination of all features of the independent patent claim, without the features of the independent patent claim or only in combination with a subset of the features of the independent patent claim, which invention can be made the subject of an independent claim, a divisional application or a subsequent application. This applies equally to technical teachings described in the description, which can form an invention independent of the features of the independent patent claims. According to one aspect, a method for monitoring the thermal aging of a component of a (motor) vehicle is described.Example components are: an electronic component, a control unit, an electrical machine, an inverter and / or an elastomer bearing (e.g. an assembly bearing or a chassis bearing) of the vehicle.

[0006] The method comprises determining, using a test vehicle, a set of measurement data sets for temperature situations in which the component has a temperature that is greater than a predefined temperature threshold. The individual measurement data sets each comprise measured values ​​for N measured variables, with N>1 (in particular N>5 or N>10). The test vehicle typically comprises corresponding sensors to record measured values ​​for the N measured variables. Furthermore, the test vehicle preferably has a temperature sensor for directly recording measured values ​​of the temperature of the component. The temperature threshold can be such that periods in which the temperature of the component is greater than the temperature threshold are relevant for thermal aging, and / or that periods in which the temperature of the component is equal to or less than the temperature threshold are not relevant for thermal aging.

[0007] Furthermore, the method comprises determining, based on the set of measurement data sets, at least one rule for the presence of a temperature situation. The rule can comprise a combination of value ranges for the N measured variables. In particular, the rule can specify a value range for each of the N measured variables. The value range for a measured variable can be limited in each case by a minimum value of measured values ​​of the measured variable and / or by a maximum value of measured values ​​of the measured variable. The rule can specify that • a temperature situation exists at a measurement time if the measured values ​​of all N measured variables are each within the respective value range for the respective measured variable; and / or

[0008] • there is no temperature situation at a measurement time if the measured value of at least one measured variable lies outside the value range for this measured variable.

[0009] The rule can thus make it possible to detect a temperature situation that is relevant for the thermal aging of the component even without directly measuring the temperature of the component.

[0010] The method further comprises providing and / or using the rule for monitoring the thermal aging of the component in a standard vehicle. The standard vehicle typically includes sensors for acquiring measured values ​​for the N measured variables. On the other hand, the standard vehicle typically does not include a temperature sensor for acquiring measured values ​​of the temperature of the component and / or does not include means for directly measuring the temperature of the component.

[0011] Using the rule, one or more periods during the operation of the standard vehicle can be identified in which a temperature situation exists for the component of the standard vehicle. Furthermore, an estimate of the thermal aging of the component of the standard vehicle can be determined based on the one or more identified periods.

[0012] By determining and providing one or more rules for detecting temperature situations of a component of a vehicle, particularly efficient and reliable thermal monitoring of the vehicle component can be achieved. The method can further comprise determining, on the basis of the set of measurement data sets and using a cluster algorithm, in particular using the DBSCAN algorithm, M clusters of measurement data sets, with M>1. Value ranges for each of the N measured variables can then be determined for each of the M clusters on the basis of the measurement data sets of the respective cluster. Based on the value ranges for the N measured variables of the M clusters, M rules for the presence of a temperature situation can then be determined in a precise manner.

[0013] Within the scope of the method, measured values ​​of a plurality of measured variables and measured values ​​of the component's temperature can be determined using the test vehicle and during a measurement period. In particular, measured values ​​of the plurality of measured variables and a measured value of the component's temperature can be determined for each individual measurement point in a sequence of measurement points.

[0014] A correlation analysis can then be performed between the measured values ​​of the multitude of measured variables and the corresponding measured values ​​of the component's temperature (for each corresponding measurement time point). A set of N measured variables, reduced from the multitude of measured variables, can be determined. The correlation analysis can be performed using a Spearman correlation.

[0015] Thus, within the framework of a correlation analysis, the N measured variables that have a significant influence on the component's temperature can be identified. The other measured variables can be ignored when determining a rule for the presence of a temperature situation. This allows the thermal aging of the component to be monitored particularly efficiently. Thus, based on the test vehicle, for each individual measurement point in a sequence of measurement points,

[0016] • a measurement data set is determined which includes one measurement value for each of the individual measurement variables in the set of N measurement variables; and

[0017] • a measured value of the temperature of the component is determined.

[0018] Furthermore, the measurement data sets can be identified for which the measured temperature of the component is greater than the temperature threshold (and which were thus recorded at a time when the component's temperature situation existed). The set of measurement data sets used to determine the rule for monitoring the component's thermal aging can then be determined based on the identified measurement data sets. This enables particularly precise thermal monitoring of the component's thermal aging.

[0019] According to another aspect, a software (SW) program is described. The SW program can be configured to be executed on a processor and thereby to carry out the method described in this document.

[0020] According to a further aspect, a storage medium is described. The storage medium can comprise a software program configured to be executed on a processor and thereby to carry out the method described in this document.

[0021] According to a further aspect, a device for monitoring the thermal aging of a component of a (motor) vehicle is described. The device is configured to use a test vehicle to determine a set of measurement data sets for temperature situations in which the component has a temperature greater than a predefined temperature threshold. The individual measurement data sets each comprise measured values ​​for N measured variables, where N>1.

[0022] The device is further configured to determine at least one rule for the presence of a temperature situation based on the set of measurement data sets, wherein the rule particularly comprises a combination of value ranges for the N measured variables. The device can further be configured to provide and / or use the rule for monitoring the thermal aging of the component in a standard vehicle.

[0023] According to a further aspect, a (road) motor vehicle (in particular a passenger car or a truck or a bus or a motorcycle) is described which comprises the device described in this document.

[0024] It should be noted that the methods, devices, and systems described in this document can be used both alone and in combination with other methods, devices, and systems described in this document. Furthermore, any aspects of the methods, devices, and systems described in this document can be combined in a variety of ways. In particular, the features of the claims can be combined in a variety of ways. Furthermore, features listed in parentheses are to be understood as optional features.

[0025] The invention will be described in more detail below using exemplary embodiments.

[0026] Figure 1 shows an exemplary vehicle component;

[0027] Figure 2a the determination of a set of relevant measured variables;

[0028] Figure 2b the selection of relevant measurement data sets;

[0029] Figure 2c shows the clustering of measurement data sets to determine one or more rules for the presence of a temperature situation (relevant for thermal aging); and Figure 3 shows a flowchart of an exemplary method for determining a rule for monitoring the thermal aging of a vehicle component.

[0030] As stated at the beginning, this document deals with the efficient and more precise monitoring of thermal aging of a vehicle component. In this context, Fig. 1 shows an exemplary vehicle 100 with an exemplary component 110 that is to be monitored during operation of the vehicle 100. Example components include an elastomer bearing (e.g., an assembly mount or a chassis mount), a converter, a control unit, a battery, etc.

[0031] A vehicle component 110 may heat up during operation of the vehicle 100, e.g., due to heat generated within the component 110 itself and / or due to heat from the spatial environment of the component 110. The component 110 may not have a temperature sensor to directly detect the temperature of the component 110.

[0032] A vehicle 100 typically has a plurality of sensors 102, wherein the individual sensors 102 are each configured to record measured values ​​for a specific measured variable. Examples of measured variables are

[0033] • the temperature at the location of the respective sensor 102;

[0034] • the pressure at the location of the respective sensor 102;

[0035] • the acceleration of the vehicle 100; and / or

[0036] • the inclination of the vehicle 100.

[0037] During operation of the vehicle 100, measured values ​​for the corresponding plurality of measured variables can be acquired using the plurality of sensors 102. The amount of data acquired is typically too large to be permanently stored in a data storage device (of a standard vehicle) and / or to be sent via a (wireless) communication connection (from a standard vehicle) to a vehicle-external unit (e.g., to a backend server). Alternatively, a data storage device can be installed in a dedicated test vehicle, which is configured to store the measured values ​​of the plurality of measured variables for a specific measurement period.

[0038] A dedicated temperature sensor 103 can be installed in the test vehicle 100, which is configured to record measured values ​​related to the temperature of the component 110. This temperature sensor 103 is typically installed only in a test vehicle 100, but not in a standard vehicle 100. During the measurement period, measured values ​​for the temperature of the component 110 can be recorded using the temperature sensor 103.

[0039] Fig. 2 shows an exemplary analysis unit 200 which is configured

[0040] • the measured values ​​of the plurality 221 of measured variables 202 of the corresponding plurality of sensors 102 recorded for the measuring period; and

[0041] • to analyze the measured values ​​of the temperature 212 of the component 110 recorded for the measurement period, in particular to identify a set 222 of measured variables 202 (reduced compared to the plurality 221 of measured variables 202) that have a (significant) influence on the temperature 212 of the component 110. For this purpose, a correlation analysis can be performed (e.g., using the Spearman correlation).

[0042] By determining a (reduced) set 222 of relevant measured variables 202, the amount of data that must be stored, transmitted and / or evaluated during the operation of a standard vehicle 100 can be reduced.

[0043] The individual measured values ​​of the set 222 of measured variables 202 and the measured values ​​of the temperature 212 of the component 110 are each assigned to a measuring time point from the measuring period. In other words, for the individual measuring times of the sequence of measuring times of the measuring period,

[0044] • Measured values ​​for the quantity 222 of measured quantities 202; and

[0045] • a measured value for the temperature 212 of the component 110.

[0046] These measured values ​​can be summarized for each individual measurement time point into a measurement data set 230, as shown by way of example in Fig. 2b. Thus, for each individual measurement time point of the sequence of measurement times, a measurement data set 230 can be provided, which specifies a measured value (recorded at the respective measurement time point) for the set 222 of measured variables 202, and which specifies a measured value (recorded at the respective measurement time point) for the temperature 212 of the component 110.

[0047] A situation relevant to the thermal aging of component 110 typically exists (possibly only) when the temperature 212 of component 110 is greater than a certain temperature threshold. Situations in which the temperature 212 of component 110 is equal to or less than the temperature threshold may be ignored when determining the thermal aging of component 110.

[0048] Therefore, from the total set of measurement data sets 230 (for the sequence of measurement times), those measurement data sets 230 can be selected for which the measured value of the temperature 212 of the component 110 is greater than the temperature threshold. Thus, a subset of measurement data sets 230 can be provided (as shown by way of example in Fig. 2b), wherein the individual measurement data sets 230 of the subset of measurement data sets 230 each represent a situation that is relevant to the thermal aging of the component 110. Such situations (i.e., situations in which the temperature 212 of the component 110 is greater than the temperature threshold) are referred to in this document as temperature situations. The subset of measurement data sets 230 can be analyzed to identify one or more rules that each indicate that a temperature situation exists. These one or more rules can, for example,determined using a clustering method, as shown by way of example in Fig. 2c. The individual measurement data sets 230 each comprise an N-dimensional measurement vector with the measured values ​​for N measured variables 202 (where the set 222 of measured variables 202 has exactly N different measured variables 202). The number N of relevant measured variables 202 can be, for example, 2 or more, or 5 or more, or 10 or more. In the example shown in Fig. 2c, the case N=2 is considered.

[0049] The measurement vectors of the individual measurement data sets 230 can each be viewed as points in an N-dimensional space. These points can be combined into a specific number of clusters 250 using a cluster algorithm (e.g., DBSCAN). The measured values ​​of the individual measured variables 202 of a cluster 250 each lie within a specific value range 240, starting from a specific minimum value 241 up to a specific maximum value 242. For a cluster 250 of measurement data sets 230, a value range 240 can thus be specified for the N different measured variables 202, into which the measured values ​​of the respective measured variable 202 of the measurement data sets 230 of this cluster 250 fall.

[0050] For example, M different clusters 250 can be determined (e.g. M>1, or M>2, or M>5). For each of the M clusters 250, a combination of N value ranges 240 for the corresponding N measured variables 202 can be determined. The combination of N value ranges 240 of a cluster 250 can be regarded as a rule that indicates that a temperature situation exists. In other words, if it is detected during operation of a (standard) vehicle 100 that the measured values ​​of the N measured variables 202 fall within the corresponding N value ranges 240 of a previously determined rule, it can be assumed that a temperature situation exists. The combination of N value ranges 240 of a cluster 250 can thus be used as an indicator of the existence of a temperature situation.

[0051] Thus, using the procedure M described in this document, different indicators or rules for the existence of a temperature situation can be determined, whereby the individual indicators or rules each comprise a combination of N value ranges 240 for N relevant measured variables 202.

[0052] During operation of a (standard) vehicle 100, the M indicators or rules can be used to detect temperature situations and / or to store measured values ​​for the N relevant measured variables 202 in a data storage device and / or transmit them to a backend server. This allows for efficient and reliable monitoring of the thermal aging of a vehicle component 110.

[0053] This allows for rule-based acquisition of measurement data whenever a relatively high thermal load exists for a component 110. For this purpose, one or more rules can be defined that allow for the detection of as many relevant temperature situations as possible while simultaneously capturing as little non-relevant data as possible in order to save computing resources.

[0054] One or more rules can be derived from measurement data, including temperature measurements of the relevant component 110, which initiate the acquisition of time-resolved vehicle signals (i.e., measurement variables 202). To determine the one or more rules, a correlation analysis can be performed to select relevant vehicle signals (i.e., measurement variables 202). Furthermore, the acquired measurement data sets 230 can be filtered for measurement data sets 230 that contain relevant temperatures. Furthermore, clustering can be used to identify groups 250 of similar operating conditions. The individual clusters or groups 250 can be analyzed to define the one or more rules. If necessary, the individual rules can be further simplified.

[0055] Vehicle signals (i.e., measured variables 202) can be identified that exhibit a linear relationship with the component temperature 212. Spearman correlation can be applied for this purpose. The extracted vehicle signals (i.e., measured variables 202) describe the operating state of the vehicle 100. The measured values ​​of the measured variables 202 at a measurement time point define a measurement data set 230 for this measurement time point.

[0056] The measurement data sets 230 can then be filtered for measurement data sets 230 with relevant temperatures. Relevant temperatures are greater than a certain temperature threshold, whereby the temperature threshold typically depends on the behavior of the component 110 with regard to its thermal aging. The filtered data sets 230 thus represent all operating states of the vehicle 100 that lead to relevant temperatures.

[0057] Since not all combinations of operating conditions lead to relevant temperatures, clustering (e.g. the DB SCAN algorithm) can be used to detect the groups 250 of combinations that lead to relevant temperatures. For each cluster 250, a rule can be derived by defining a condition for each vehicle signal (i.e. for each measured variable 202) by a minimum value 241 and a maximum value 242 (i.e. by a value range 240) of the respective measured variable 202. The rule for a cluster 250 is composed of the individual conditions (i.e. value ranges 240) for the individual measured variables 202. Finally, individual maximum values ​​242 can be removed if a higher value than the maximum value 242 is also likely to lead to a relevant temperature, as is to be expected, for example, with the outside temperature as measured variable 202.

[0058] The defined rules can be used to collect measurement data during the operation of a vehicle 100 only when relevant temperatures are present. The rule-based collected measurement data can be used to efficiently and accurately determine a prediction of the thermal aging of a component 110.

[0059] Fig. 3 shows a flowchart of a (possibly computer-implemented) method 300 for monitoring the thermal aging of a component 110 of a (motor) vehicle 100. The method 300 can be executed by a computing unit and / or by a device.

[0060] The method 300 includes determining 301, using a test vehicle 100, a set of measurement data sets 230 for temperature situations in which the component 110 each has a temperature 212 that is greater than a predefined temperature threshold, wherein the individual measurement data sets 230 each comprise measured values ​​for N measured variables 202, with N>1. Example measured variables 202 are: a temperature, a pressure, an acceleration, an inclination, a yaw rate, a force, a speed, etc.

[0061] The method 300 further comprises determining 302, based on the set of measurement data sets 230, at least one rule for the presence of a temperature situation, wherein the rule comprises a combination of value ranges 240 for the N measured variables 202. The rule can be determined using a cluster algorithm for forming one or more clusters 250 of the measurement data sets 230. Furthermore, the method 300 comprises providing and / or using 303 the rule for monitoring the thermal aging of the component 110 in a standard vehicle 100 (which typically does not have a sensor 103 for detecting the temperature 212 of the component 110). In this way, the thermal aging of the component 110 of a vehicle 100 can be monitored efficiently and reliably.

[0062] The present invention is not limited to the embodiments shown. In particular, it should be noted that the description and figures are intended only to illustrate the principle of the proposed methods, devices, and systems by way of example.

Claims

Claims 1) Method (300) for monitoring the thermal aging of a component (110) of a vehicle (100); the method (300) comprising - Determining (301), using a test vehicle (100), a set of measurement data sets (230) for temperature situations in which the component (110) has a temperature (212) that is greater than a predefined temperature threshold; wherein the individual measurement data sets (230) each comprise measured values ​​for N measured variables (202), with N>1; - determining (302), on the basis of the set of measurement data sets (230), at least one rule for the presence of a temperature situation; wherein the rule comprises a combination of value ranges (240) for the N measured variables (202); and - Providing (303) the rule for monitoring the thermal aging of the component (110) in a standard vehicle (100). 2) Method (300) according to claim 1, wherein the method (300) comprises - determining, on the basis of the set of measurement data sets (230) and using a cluster algorithm, in particular using a DBSCAN algorithm, M clusters (250) of measurement data sets (230), with M>1; - determining, for each of the M clusters (250), value ranges (240) for each of the N measured variables (202) on the basis of the measurement data sets (230) of the respective cluster (250); and - Determine, on the basis of the value ranges (240) for the N measured variables (202) of the M clusters (250), M rules for the existence of a temperature situation. 3) Method (300) according to one of the preceding claims, wherein the method (300) comprises - Determining, using the test vehicle (100) and during a measurement period, measured values ​​of a plurality (221) of measured variables (202) and measured values ​​of the temperature (212) of the component (110); and - Determining a set (222) of N measured variables (202) that is reduced compared to the plurality (221) of measured variables (202) based on a correlation analysis between the measured values ​​of the plurality (221) of measured variables and the measured values ​​of the temperature (212) of the component (110); wherein the correlation analysis comprises, in particular, the use of a Spearman correlation. 4) Method (300) according to one of the preceding claims, wherein the method (300) comprises - Determine, using the test vehicle (100), for each individual measurement point in time of a sequence of measurement points, - a measurement data set (230) comprising a measurement value for each of the individual measurement variables (202) of the set (222) of N measurement variables (202); and - a measured value of the temperature (212) of the component (110); and - identifying the measurement data sets (230) for which the measured value of the temperature (212) of the component (110) is greater than the temperature threshold; and - Determining the set of measurement data sets (230) used to determine the rule for monitoring the thermal aging of the component (110) on the basis of the identified measurement data sets (230). 5) Method (300) according to one of the preceding claims, wherein - the rule specifies a range of values ​​(240) for each of the N measured variables (202); - the value range (240) for a measured variable (202) is limited in particular by a minimum value (241) of measured values ​​of the measured variable (202) and by a maximum value (242) of measured values ​​of the measured variable (202); and - the rule states that - at a measurement time, a temperature situation exists if the measured values ​​of all N measured variables (202) are each within the respective value range (240) for the respective measured variable (202); and / or - there is no temperature situation at a measuring time if the measured value of at least one measured variable (202) lies outside the value range (240) for this measured variable (202). 6) Method (300) according to one of the preceding claims, wherein - the test vehicle (100) comprises a temperature sensor (103) for detecting measured values ​​of the temperature (212) of the component (110); and - the standard vehicle (100) does not comprise a temperature sensor (103) for detecting measured values ​​of the temperature (212) of the component (110), and / or does not comprise any means for directly measuring the temperature (212) of the component (110). 7) Method (300) according to one of the preceding claims, wherein the test vehicle (100) and the standard vehicle (100) have sensors (102) to record measured values ​​for the N measured variables (202). 8) Method (300) according to one of the preceding claims, wherein the method (300) comprises - Identifying, based on the rule, one or more periods during the operation of the standard vehicle (100) in which a Temperature situation of the component (110) of the standard vehicle (100); and - Determining an estimated value of the thermal aging of the component (110) of the standard vehicle (100) based on the one or more identified time periods. 9) Device for monitoring the thermal aging of a component (110) of a vehicle (100); the device being designed - using a test vehicle (100), to determine a set of measurement data sets (230) for temperature situations in which the component (110) has a temperature (212) that is greater than a predefined temperature threshold; wherein the individual measurement data sets (230) each comprise measured values ​​for N measured variables (202), with N>1; - to determine at least one rule for the presence of a temperature situation based on the set of measurement data sets (230); wherein the rule comprises a combination of value ranges (240) for the N measured variables (202); and - to provide the rule for monitoring the thermal ageing of the component (110) in a standard vehicle (100).

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