Closure element actuation identification

Abstract

The invention relates to a method for detecting an actuation of a closing element (10) of a motor vehicle. The method comprises at least one step of detecting an actuation force curve acting on the closing element (10), in particular on a handle element (12) of the closing element (10). The method also has a step of identifying (22) an actuation event as a function of the detected actuation force profile. The invention also relates to a system for detecting an actuation of a closing element (10) of a motor vehicle.

Description

Technical Field

[0001] The invention relates to a method for detecting the actuation of a closure element of a motor vehicle. The invention also relates to a system for detecting the actuation of a closure element of a motor vehicle. Background Art

[0002] In conventional motor vehicle doors, these doors are released and opened by pulling a door handle. The door, which is mechanically connected to a lock, is released by pulling a pivoted, movable door handle. The user can then use physical force to pull the door open.

[0003] The doors of motor vehicles can also be unlocked automatically and / or opened by an electric motor. However, this also requires some form of actuation, such as pulling or touching the door handle. The electric adjustment and release of the door means that no mechanical connection is required between the door handle and the lock. Likewise, the door handle itself can be immovable, since it is no longer necessary to mechanically transmit forces and movements to the door lock. It can be important that the actuation of the door is reliably detected so that the door is not adjusted undesirably.

[0004] DE 10 2021 112 324 A1 describes a system for detecting inputs and controlling downstream devices, which detects time-varying input signals in the form of movements. The user's movements are detected by a sensor device and the movements are evaluated or analyzed and interpreted. The system can be used to open the rear lid of a car. Summary of the invention

[0005] A first aspect of the invention relates to a method for identifying the actuation of a closing element of a motor vehicle. The actuation of the closing element can be a manipulation corresponding to the adjustment desire of the user. For example, the actuation can be pulling the closing element or pressing the closing element, in particular in a predetermined position. The closing element can be designed as a door or a rear cover, for example. The closing element can also be designed as a window, a folding roof or a sliding roof of a motor vehicle. The closing element can be adjusted, for example, between an open position and a closed position, in which an access opening to the motor vehicle is at least partially opened, and in which the access opening is blocked. The closing element can, for example, be rotatably mounted on one side of the motor vehicle body. The closing element can be designed to be automatically adjusted. For example, the rear cover of the motor vehicle can be pivoted between its closed position and its open position by a motor vehicle. The closing element can also unlock and / or release the lock here. For example, the adjustment can be performed in response to the actuation of the detected closing element. The closing element can have an adjustment motor. For example, the rear cover can be adjusted using a spindle drive. The motor vehicle can, for example, be designed as a bus or a truck.

[0006] The method has a step of detecting an actuation force profile acting on the closing element. The actuation force may be an external force acting on the closing element. For example, the actuation force may be caused by a user. The course of the actuation force may be an actuation force at at least two different time points. For example, the actuation force may be detected continuously as an analog signal. However, the actuation force may also be detected at discrete intervals, for example using a measurement frequency of a sensor, to detect the actuation force profile. One or more force sensors may be provided for detection. For example, the force sensor may be designed as a strain gauge. The deformation of a rigid and / or immovable part of the closing element may then be used to determine the force acting thereon.

[0007] The detection can be permanently activated or triggered by an activation signal. For example, the detection can be activated when the wireless key of the motor vehicle approaches below a minimum distance and / or when the motor vehicle is in an unlocked state. The actuation force curve can have the direction of the force. For example, the direction of the force can be detected as a vector. For example, only a tensile force and a compressive force can be distinguished, for example, by the sign of the actuation force. This allows different reactions to occur depending on the direction of the force. However, for the actuation force curve, only the absolute magnitude of the actuation force can be detected.

[0008] The actuation forces can also be filtered to detect the actuation force curve. For example, the raw signal from each force sensor can be filtered with a low-pass filter. This allows interference signals to be reduced.

[0009] For example, the actuation force curve acting on the handle element of the closing element can be detected. The handle element can be designed as, for example, a door handle or another actuation element (for example, a logo of a car manufacturer). For example, the handle element can be designed as a rigid component. The handle element can be arranged immovably or movably on the remaining closing element. For example, only the actuation force curve acting on the handle element can be detected. The corresponding sensor can be arranged on the handle element or in the handle element. Therefore, forces that are not intended for actuation, such as forces pressing on the door surface at a certain distance from the handle element, can basically be ignored.

[0010] The method further comprises a step of identifying an actuation event based on the detected actuation force curve. The actuation event may correspond to an actual actuation expectation. This enables the distinction of other forces acting on the closing element, such as forces due to wind, weather, animals and / or the cleaning of the motor vehicle. For example, the actuation event may be that a user pulls a door handle in a specific way. On the other hand, pulling a door handle by washing fabric in a wash lane is not an actuation event. For example, unnecessary door openings may be avoided.

[0011] For example, an actuation event can be identified by pattern comparison, comparison with a threshold value, comparison of values ​​derived from an actuation force curve, and / or by a trained neural network. The neural network can be pre-trained using synthetic or experimentally generated training data. The corresponding comparison data (e.g., pattern and / or threshold value) can be specific to the closing element. For example, the actuation force curve corresponding to the actuation event can depend on the shape, material, and / or other characteristics of the closing element and / or handle element. The identification of the actuation event can distinguish forces acting on the closing element due to reasons other than actuation. The method can also include identifying non-actuation events, such as driving through a lane wash.

[0012] In response to the recognition of the actuation event, the closure element may be opened. Opening may include unlocking and / or adjusting the closure element in the direction of the open position. The actuation event may also be responded to by automatically closing the closure element, for example when the closure element is in the open position. Closing may involve adjusting the closure element in the direction of the closed position and / or involve locking.

[0013] Different actuation events may also be identified. For example, a first actuation event may result in the closure element opening, while a second actuation event may result in the closure element closing. Different actuation events may be distinguished, for example, based on respective modes and / or directions of forces. For example, when a door handle is pressed as an actuation event when the door is in a closed position, the door may be locked, while when a door handle is pulled as an actuation event when the door is in a closed position, the door may be opened.

[0014] In another embodiment of the method, it can be provided that the method has a step of detecting the temperature of the handle element. The temperature of the handle element can be detected using an integrated temperature sensor in the control device. The control device can be arranged in the handle element, on the handle element or adjacent to the handle element. The detection step can be performed once or multiple times, for example at periodic intervals. The temperature can then be detected, for example, in the form of a temperature curve as a function of time. The control device can, for example, be an evaluation device and, for example, be arranged to perform some or all steps of the method for identifying the actuation of the closing element. The temperature sensor of the control device can be a temperature sensor implemented in the control device. For example, the control device can have a microcontroller, and the temperature sensor can be a temperature sensor integrated on a circuit board of the microcontroller.

[0015] Alternatively or additionally, a dedicated temperature sensor can be provided, for example, in and / or on the handle element, which is designed to detect the temperature of the handle element. There can also be a plurality of temperature sensors, of which, for example, at least one is integrated in the control device and at least one is arranged on or in the handle element. With the temperature sensor on or in the handle element, the temperature can be detected at a position close to the handle element, for example on a handle element made of plastic. With the temperature sensor of the control device, the temperature of a metal part, which can be arranged on it, for example, closer to the handle element itself, can be detected. Due to the different thermal conductivity of plastic and metal, the slight time lag in detecting the temperature of the handle element can be reduced.

[0016] At least one of the detection of the actuation force curve and the identification of the actuation event is performed based on the detected temperature of the handle element. For example, one of the detection of the actuation force curve and the identification of the actuation event can be accurately performed based on the detected temperature of the handle element. For example, one of the detection of the actuation force curve and the identification of the actuation event can be accurately performed independently of the detected temperature of the handle element. Alternatively, the detection of the actuation force curve and the identification of the actuation event can be performed based on the detected temperature of the handle element. For example, when detecting the force, different deformation behaviors of the handle element can be taken into account.

[0017] The sensor (e.g. a force sensor) for detecting the actuation force curve can be parameterized depending on the temperature of the handle element detected. Thus, an actuation force curve detected by the same sensor at a first temperature can be different from an actuation force curve detected by the sensor at a second temperature different from the first temperature, although the same force can act on the sensor in both cases. However, in both cases, different forces can act on the handle element and the temperature dependency when detecting the actuation force curve can be compensated by the dependency of the detection of the actuation force curve on the temperature of the handle element. For example, for different temperatures, the corresponding temperature curve for the correct identification of the actuation force curve can be stored, for example, in a memory of the sensor (e.g. a force sensor) and / or in a control device, which can, for example, be configured to identify an actuation event.

[0018] For example, the criteria for identifying an actuation event can be modified depending on the temperature of the detected handle element. For example, at least one criterion for identifying an actuation event can be modified depending on the temperature of the detected handle element. For example, at least one of the threshold values, patterns, comparison data and / or other values ​​derived from the actuation force curve can be changed. Alternatively or additionally, the neural network can be trained accordingly to take into account the temperature of the detected handle element as a criterion and therefore as an input variable. This means that, for example, an actuation event for a specific actuation force curve can be identified at a first temperature, while an actuation event for the same actuation force curve cannot be identified at a second temperature other than the first temperature. Therefore, the identification of an actuation event can depend on the temperature of the handle element.

[0019] For example, when detecting the actuation force curve and / or when identifying an actuation event, the stiffness that varies with the temperature of the handle element (e.g., the plastic housing of the handle element) can be compensated. The temperature curve of the handle element stored for this compensation can depict the dependence of the actuation force curve on certain temperatures. This dependence can be, for example, linear, quadratic or generally polynomial or even exponential or logarithmic.

[0020] In another embodiment of the method, at least one of detecting the actuation force curve and identifying the actuation event can be performed based on the life of the handle element (e.g., the plastic cover of the handle element).Alternatively or additionally, at least one of the detection of the actuation force curve and the identification of the actuation event can be performed based on the life of the closing element.

[0021] For example, a time update of the temperature curve can be performed. Thus, aging of the closing element and / or the handle element can be compensated. For example, when detecting the actuation force on the handle element, a plastic handle as a handle element that hardens over time can cause a change in the actuation force curve due to changes in material stiffness and / or sensor degradation.

[0022] For example, the criteria for identifying an actuation event can be modified according to the life of the handle element and / or the closing element. For example, at least one criterion for identifying an actuation event can be modified according to the life of the closing element and / or the handle element. For example, at least one of the threshold values, patterns, comparison data and / or other values ​​derived from the actuation force curve can be changed. Alternatively or additionally, the neural network can be trained accordingly to consider the life of the handle element and / or the closing element as a criterion and therefore as an input variable. This means, for example, that an actuation event of a specific actuation force curve can be identified under a first life, while an actuation event of the same actuation force curve cannot be identified under a second life other than the first life. Therefore, the identification of an actuation event can depend on the life of the closing element and / or the handle element.

[0023] The method may further comprise the step of determining the lifespan of the closure element and / or the handle element. For example, the step of determining the lifespan may comprise the step of detecting the lifespan. For example, the lifespan may be determined by starting a timer, for example when the method is performed for the first time. This makes it easy to determine the lifespan of the closure element and / or the handle element. For example, the timer may be reset when a new closure element and / or handle element is installed.

[0024] In another embodiment of the method, it can be provided that detecting the actuation force curve comprises detecting the first actuation force curve using a first sensor, and detecting the second actuation force curve using a second sensor. The first sensor and the second sensor can be spatially spaced from each other. The first sensor and the second sensor can independently detect the first actuation force curve and the second actuation force curve. The first sensor and the second sensor can be a first force sensor and a second force sensor. The actuation force curve can have a first actuation force curve and a second actuation force curve, or consist of the first actuation force curve and the second actuation force curve. The actuation force curve can alternatively have only one of the first actuation force curve and the second actuation force curve, or consist of only one of the first actuation force curve and the second actuation force curve.

[0025] This means that, for example, redundancy can be created in the event of a failure or absence of the first actuation force curve or the second actuation force curve, for example due to a failure of the first sensor or the second sensor. For example, it can be distinguished whether there is an error in the first actuation force curve or the second actuation force curve, or whether there is a real effect, such as an actuation expectation, in the actuation force curve. The detection of the actuation force curve can be performed as a separate evaluation and then performed based on only one of the first actuation force curve and the second actuation force curve. Alternatively, it is also possible to determine the average value of the first actuation force curve and the second actuation force curve as the actuation force curve. For example, the actuation force curve can be determined based on the first actuation force curve and the second actuation force curve.

[0026] In another embodiment of the method, it can be provided that the method has a step of determining the application point of the actuation force and / or the direction of the actuation force based on the detected first actuation force curve and the second actuation force curve. For example, at least one of the application point and the direction of the actuation force can be determined by comparing the first actuation force curve and the second actuation force curve. For example, vectorization of the actuation force curve can be performed in this way. For example, a relative force curve can be determined by comparing the first actuation force curve and the second actuation force curve, and the direction of the actuation force acting on the handle element can be reflected. For this purpose, three, four or more sensors can also be provided, which detect the corresponding actuation force curves.

[0027] The first sensor and the second sensor can be arranged differently. Therefore, in a first embodiment, the first sensor can be arranged on the upper part of the handle element, and the second sensor can be arranged on the lower part of the handle element. For example, the direction of the actuation force can be easily determined in the vertical direction. Alternatively, the first sensor can be arranged on the left side of the handle element and the second sensor can be arranged on the right side of the handle element. This means that, for example, the actuation force in the horizontal direction can be easily determined. The application point can also be easily determined vertically and / or horizontally. For example, the first sensor can be arranged in the upper left corner of the handle element, and the second sensor can be arranged in the lower right corner of the handle element. For example, the application point can be determined both vertically and horizontally. The direction of the actuation force can also be determined, for example, both vertically and horizontally. If there are more than two sensors, some or all combinations of the arrangements discussed previously can also be provided.

[0028] In one embodiment, when both the first actuation force curve and the second actuation force curve can be detected, at least one of the application point and the direction of the actuation force can be determined at least in time based on the detected first actuation force curve and the second actuation force curve. If at other times, for example, the first actuation force curve or the second actuation force curve cannot be correctly detected due to a failure of the first sensor or the second sensor, the determination of the application point and the determination of the direction of the actuation force can be omitted. Then, only the absolute value of the actuation force can be determined without, for example, determining the application point and / or direction of the actuation force.

[0029] In another embodiment of the method, it can be provided that the identification of the actuation event includes comparing the first actuation force curve and the second actuation force curve. For example, if the actuation event is identified in both the first actuation force curve and the second actuation force curve, the actuation event at a specific time point can be identified. Alternatively or additionally, a specific actuation event can be identified by comparing the first actuation force curve and the second actuation force curve. For example, it can be identified that the handle element is being pulled or pushed by the user with a specific force, with a specific application point and / or with a specific actuation force direction. When comparing the first actuation force curve and the second actuation force curve, the entire force curve can be compared. Alternatively or additionally, a single criterion of the first actuation force curve and the second actuation force curve (e.g., the level or slope of the actuation force curve) can be compared with each other. Then, the identification of the actuation event can be performed based on comparing the entire first actuation force curve and the second actuation force curve, and alternatively or additionally comparing the individual criteria of the first actuation force curve and the second actuation force curve.

[0030] In another embodiment of the method, it can be provided that after a specific time period of the actuation event is not identified, the detection of the actuation force curve is performed at a reduced sampling rate. The specific time period can be one or more seconds, minutes, hours, days or months. For example, a single actuation event may not be identified within a specific time period. For example, an actuation event can be identified at a first time point. Then, the actuation force curve can be detected at a first sampling rate. The first sampling rate can be, for example, 20, 50 or 100ms. After a specific time period (for example, a week), the sampling rate can be reduced, for example, to a sampling rate of once per second or once every 10 seconds. The actuation force curve can be detected using such a second sampling rate that is less than the first sampling rate, for example, until an actuation event is identified again. If an actuation event is identified, the second sampling rate can be changed back to the first sampling rate, and therefore, for example, the sampling rate can be increased.

[0031] Thus, a power saving mode can be implemented by the method, in which the actuation force profile is detected at a reduced sampling rate when the motor vehicle has been parked for a long time, for example. On the one hand, it can be ensured that the user's desire to actuate the closing element is recognized quickly enough, while at the same time, the detection of the actuation force profile does not have to be performed too frequently at certain times, so that power can be saved. In addition, the reduced sampling rate can also reduce the probability of erroneous recognition of an actuation event, because, for example, an object falling on the handle element will only apply force to the handle element once.

[0032] For example, the time period for reducing the sampling rate can also be determined based on the battery charge level. Alternatively, the time period for reducing the sampling rate can be fixed. There may be one or more specific time periods. Therefore, one or more sampling rates can be assigned to one or more specific time periods. For example, a specific sampling rate can be assigned to a specific time period, and different sampling rates can be assigned to different time periods. For example, if the time period becomes longer, the sampling rate can be further reduced. Alternatively or additionally, the time period can be determined not only based on the battery charge state, but also based on temperature (such as the ambient temperature of the motor vehicle) and life (such as the life of a battery that can power a control device that performs the method).

[0033] In another embodiment of the method, it can be provided that the step of identifying an actuation event includes calculating an actuation probability. The actuation probability can be calculated, for example, as a percentage, where 100% determines actuation and 0% determines non-actuation. The actuation probability can be calculated by fusing multiple probabilities. For example, multiple data such as the absolute size of the maximum actuation force and its duration can be compared to a threshold. The distance to the threshold can correspond to a probability. For example, these probabilities can be multiplied to calculate the actuation probability. When the calculated actuation probability is greater than a first threshold, an actuation event can be identified. The first threshold can be fixed, for example 90%. By using the actuation probability, it can be avoided that many actuation events are not identified and the closing element does not react. This means that user acceptance may be high.

[0034] The first threshold value may also be determined based on the detected actuation force curve. For example, if there may be many interference signals and vibrations contained in the actuation force curve, the first threshold value may be higher. This means that in potentially harmful situations, such as a wash lane and / or other strong external force influences, the probability of falsely identifying an actuation event may be reduced. Similarly, another non-actuation event, such as driving through a wash lane, may be identified based on the detected actuation force curve, and the first threshold value may be increased in the case of a non-actuation event. As a result, the user may need to actuate the door more forcefully or particularly noticeably so that the door still opens.

[0035] In another embodiment of the method, it can be provided that if the calculated actuation probability is greater than a second threshold, a wake-up signal is generated. The second threshold may be less than the first threshold. The wake-up signal may be, for example, a trigger signal. The wake-up signal may be transmitted, for example, via a CAN data bus of a motor vehicle. The wake-up signal may be used to wake up an ECU and / or a closing element of a motor vehicle. The wake-up signal may be used to activate a power supply to an actuator, to start authentication of an access authorization, and / or to initiate further measures to prepare for the adjustment of the closing element. With the aid of the wake-up signal, once an actuation event is identified, the closing element may be unlocked and / or moved to its open position, for example, almost instantaneously or even immediately. Thus, for example, a door opening is prepared using a low actuation probability, even if the low actuation probability is not yet sufficient to cause the actual door opening. This allows an increased reaction speed without increasing the risk of an undesired adjustment. For example, when the wake-up signal is generated, no actuation event is identified. The second threshold may be fixed or variable like the first threshold. In addition, the second threshold may also have a fixed predetermined difference with the first threshold variably determined as described above.

[0036] In another embodiment of the method, it can be provided that the actuation force curve has a level, a slope and a standard deviation as attributes. For example, the actuation force curve can have a level, a slope and / or a standard deviation at one or more time points (e.g., at each time point). For example, the actuation force curve within a time range can have a level, a slope and / or a standard deviation. For example, the level can be the absolute value of the actuation force at a certain time point. The slope can be the first-order derivative of the actuation force curve at a certain time point. The slope can be the average slope of the actuation force within a certain time range. The standard deviation can be relative to multiple values ​​of the actuation force in the time domain and the average value of the actuation force in the time domain. Therefore, the standard deviation can define the distribution of multiple values ​​of the actuation force within the time range around the average value of the actuation force within the time range. The standard deviation can be a measure of signal noise.

[0037] In addition, calculating the probability of actuation may include calculating the individual probability of actuation. The individual probability of actuation can only be calculated according to one of the attributes respectively. For example, the individual probability of actuation can be calculated for the level. In addition, the individual probability of actuation can be calculated for the slope and the standard deviation respectively. For example, by the functional relationship between the level, the slope or the standard deviation and the probability, the individual probability of actuation can be calculated for one of the level, the slope and the standard deviation respectively. For example, the calculation can be performed using a properly trained neural network. This means that the calculation of the individual probability can correspond particularly accurately to the actual probability. Alternatively or additionally, the calculation can be performed using a lookup table. The calculation can then be performed very quickly and almost no calculation effort is required. These lookup tables may have been previously determined based on a large amount of test data. In addition, the neural network may also have been trained with test data.

[0038] The actuation force curve can be plotted as a function of time, whereby force can be plotted relative to time. The calculated individual probabilities can also be plotted on the same time scale. For example, individual probabilities for level, slope, and standard deviation can be plotted for each individual time point. Individual probabilities can be calculated for a period of time of the actuation force curve, such as a specific time window before the last detection of the actuation force.

[0039] The actuation probability may be calculated based on at least one of the calculated individual probabilities. For example, the actuation probability may be calculated based on all calculated individual probabilities. For example, the actuation probability may be calculated for any point in time or time range for which the actuation force may also be used as an actuation force curve. For example, the actuation probability at a specific time point may be calculated based on and according to the individual probabilities at the specific time point. For example, the actuation probability and the overall actuation probability may be calculated by averaging or multiplying the individual probabilities. Alternatively, the actuation probability or the overall actuation probability may be calculated by integrating the individual probabilities. This means that by calculating the individual probabilities, the actuation probability may be easily calculated and requires very little computational effort.

[0040] In another embodiment of the method, it can be provided that the same value is calculated for the individual probabilities in each region of the corresponding attribute. The functional relationship of mapping the attribute value to the individual probability of the attribute can remain unchanged within a range, for example, neither rising nor falling. Different values ​​of the attribute can be mapped to the same individual probability value. There may be one or more such regions. For example, there may be a first region of the attribute, wherein the value of the region is mapped to a first individual probability. There may be a second region with the same attribute that is different from the first region, whereby the value of the second region can be mapped to a second individual probability. The first and second individual probabilities can be different or the same at this point.

[0041] At least one (e.g., exactly one, multiple, or all) attribute may have at least one region in which the same value is calculated for the individual probabilities. For example, if the standard deviation is very small, the individual probability of the standard deviation may be 100%, e.g., up to a maximum threshold of the standard deviation. If the standard deviation is below the maximum threshold, the individual probability may be 100%, and if the standard deviation is equal to or above the maximum threshold, the individual probability may be less than 100%. For example, the individual probabilities may then decrease as the standard deviation increases, e.g., linearly or quadratically.

[0042] In addition, if the level has exceeded a certain threshold, the individual probability of the level can be, for example, 100%. If the level is below a certain threshold, the individual probability of the level can have a first value. If the level is equal to or greater than the certain threshold but less than another certain threshold, the individual probability can have a second value, which can be greater than the first value. As the level increases, the individual probability of the level can, for example, increase, for example, step by step.

[0043] If the slope is greater than a certain threshold and less than another certain threshold, the individual probability of the slope can be, for example, 100%. If the slope is outside this range, the individual probability of the slope may be less than 100%. For example, in a certain area or zone of the slope, the individual probability of the slope can be, for example, 100%. Outside this area or zone, the individual probability can be smaller, for example, 50% or 0%. Outside this area or zone, the individual probability can change with the change of the slope, for example, linearly or quadratically. If the slope of the actuation force curve (i.e., the change of the actuation force curve over time) is located in this area or zone, that is, the user applies an actuation force that changes over time to the handle element, then the individual probability of actuation based on the slope may be very high.

[0044] In another embodiment of the method, provision may be made that the actuation probability is calculated based on only one or two of the calculated individual probabilities. For example, if at least one of the individual probabilities exceeds a certain threshold, the actuation probability may be calculated based on only one or two of the calculated individual probabilities. For example, if the level exceeds a certain individual probability, the actuation probability may be determined based on the individual probability of the level alone. Alternatively or additionally, depending on a specific value of the attribute (e.g., depending on a specific level), the actuation probability may be calculated based on the calculated individual probabilities alone.

[0045] Furthermore, the identification of the actuation event can be verified by those individual probabilities that are not used to calculate the actuation probability. Thus, for example, the actuation probability can be calculated only based on the calculated individual probabilities of the levels. Furthermore, a step of verifying the identification can be performed, wherein the verification step is performed based on the individual probabilities of the slope and the standard deviation. For example, a rapid opening of the closing element is performed only based on the calculated individual probabilities of the slope, because the actuation event can be quickly identified and verified only by the individual probabilities of the levels.

[0046] In some cases, the level can also be used as a unique individual probability for calculating the actuation probability and thus identifying the actuation event. Energy saving functions can therefore also be implemented, since calculating the actuation probability can be performed more easily and requires fewer calculation steps. This can also be done faster than calculating the actuation probability based on all calculated individual probabilities and having to calculate all individual probabilities. In addition, the actuation force curve may also not be continuous and may be represented by a discontinuous function. For example, the slope may then not be determined for all time points or regions of the actuation force curve. It may then be advantageous if the actuation probability is determined only based on the individual probabilities calculated for the level and / or standard deviation. This means that the method can be performed even if the detected actuation force curve cannot be represented by a continuous function.

[0047] In another embodiment of the method, it can be provided that the identification of the actuation event is performed according to the standard deviation. For example, the criteria for identifying the actuation event can be modified. For example, at least one criterion for identifying the actuation event can be modified according to the standard deviation. For example, at least one of the threshold values, the mode, the comparison data and / or other values ​​derived from the actuation force curve can be changed. Alternatively or additionally, the neural network can be trained accordingly to take the standard deviation into account as a criterion and therefore as an input variable. This means that, for example, an actuation event can be identified at a first standard deviation, while an actuation event cannot be identified at a second standard deviation in addition to the first standard deviation.

[0048] For example, the criteria for identifying an actuation event can be changed by changing the threshold. For example, if the standard deviation increases, the threshold for identifying an actuation event can be increased. For example, if the actuation force curve fluctuates more strongly in a certain area, an actuation event can only be identified at a larger level. If the standard deviation is even larger, for example, if it is identified that the motor vehicle is in a lane wash, the method for identifying an actuation event can also be ended or closed. Alternatively or additionally, when the standard deviation increases, the area or zone of at least one property can also be reduced. For example, if the standard deviation increases from a first time point to a later second time point, the area of ​​the attribute (e.g., level) with a relatively high value (e.g., 100%) of the individual probability can be reduced. This means that a smaller value of the corresponding attribute (e.g., a smaller value of the level) can fall into this area, and a higher individual probability value (here 100%) can be output for a smaller value of this level. Therefore, as the standard deviation increases, the area or zone can be reduced. Alternatively or additionally, as the standard deviation increases, only the individual probability of the level or slope is still determined, and the actuation probability is determined only based on the calculated individual probability.

[0049] In another embodiment of the method, it can be provided that if the standard deviation is higher than the third threshold value, the calculation of the actuation probability is performed only according to the individual probability about the level or slope. For example, the actuation probability can be calculated according to the individual probability about the level or according to the individual probability about the slope. For example, the actuation probability can be calculated according to at least two (for example, two or exactly two about two levels or two slopes at different times) individual probabilities. Therefore, for example, the actuation probability can be calculated according to exactly two individual probabilities of two levels at different times or time points. Alternatively, the actuation probability can be calculated according to two individual probabilities of two slopes at different times or time points. The actuation probability can be calculated according to more than two individual probabilities of multiple values ​​of the attribute at different times. If the standard deviation is particularly large, it may be advantageous, for example, if only the level or slope is used instead of two values ​​to calculate the actuation probability by the individual probability of the corresponding attribute.

[0050] In another embodiment of the method, it can be provided that an actuation event is identified when the slope is below a fourth threshold and the level is above a fifth threshold for a certain minimum duration. Thus, for example, an actuation can be identified when the user actuates the handle element with a certain force, wherein the force no longer varies over time as defined above the fourth threshold, and at the same time, the magnitude of the force (i.e. the level for a certain minimum duration) is above the fifth threshold, i.e., for example, the user pulls or pushes the handle element with a certain minimum force.

[0051] A specific actuation event can be identified based on the sign of the slope. Thus, for example, it can be identified that the user is pulling or pushing. Thus, for example, only rising edges or falling edges in the actuation force curve can be of interest.

[0052] In another embodiment of the method, it can be provided that the method has a step of detecting the speed of the motor vehicle. The speed of the motor vehicle can be detected by means of a speed sensor. For example, if the detected speed is greater than a threshold speed, the recognition of the actuation event can be deactivated. For example, the threshold speed can be 3, 4 or 5 km / h. For example, an actuation event can only be recognized when the vehicle moves at a maximum threshold speed. For example, it can be ensured that an actuation event is not recognized if the vehicle moves at a speed exceeding the threshold speed. In this way, for example, incorrect recognition and thus, for example, incorrect opening of a closing element can be prevented while driving.

[0053] In another embodiment of the method, it can be provided that if at least one parameter of the detected actuation force curve is greater than a bridging threshold, an opening signal is generated, regardless of whether an actuation event is identified. The parameter can be, for example, the maximum pulling force during the detection. Alternatively or additionally, an opening signal can be generated if, for example, a pulling force acting as an actuation of the closing element exceeds a minimum pulling force for a minimum time period. If multiple parameters are considered, a bridging threshold can be assigned to each parameter. For example, in order to generate an opening signal, all, some or only one parameter must exceed its configured bridging threshold. The parameter can be, for example, a force or a derivative of a force. For example, a parameter can be a characteristic force curve, and each bridging threshold can represent a characteristic force curve. The bridging threshold or each bridging threshold can be a limit at which the closing element is always open, optionally under the condition of access authorization, regardless of whether an actuation event is identified. This means that access to the motor vehicle is always possible in an emergency. For example, an opening signal can also be generated when a non-actuation event (such as driving through a wash lane) is identified. The opening signal, for example, causes the unlocking of the closing element and / or adjusts the closing element to an open position.

[0054] The corresponding bridging threshold may be fixedly predefined or determined according to the detected actuation force profile. For example, in case of a disturbing signal such as vibration, the bridging threshold may be increased. It is also possible to identify non-actuation events according to the detected actuation force profile, such as driving through a lane wash, and the bridging threshold may be increased in case of non-actuation events.

[0055] In another embodiment of the method, it can be provided that the method has a step of determining the first derivative of the detected actuation force curve. The first derivative of the detected actuation force curve can be the gradient or slope of the actuation force curve. The identification of the actuation event can be carried out according to the specific first derivative of the detected actuation force curve. By considering the first derivative of the detected actuation force curve, it can be more accurately identified whether the closing element is actually actuated by the user or whether there are other forces acting on the closing element, and it should not be reacted to by adjusting the closed element. In order to identify the actuation event, the first derivative of the actuation force curve detected at only one time point or the curve of the first derivative of the actuation force curve can be used. For example, the maximum height of the first derivative of the actuation force curve detected can be determined only and compared with a threshold value. An upper threshold and a lower threshold can also be provided. For example, if the first derivative of the actuation force curve detected is within or outside the range defined by these thresholds, the actuation event can be identified. Alternatively or additionally, the form of the first derivative of the actuation force curve can be considered and can be compared with the characteristic curve for actuation of the first derivative of the actuation force curve.

[0056] In another embodiment of the method, it can be provided that the method has a step of determining the second-order derivative of the detected actuation force curve. The identification of the actuation event can be carried out according to the specific second-order derivative of the detected actuation force curve. For example, the second-order derivative can be considered in place of the first-order derivative or in addition to the first-order derivative. The second-order derivative can indicate whether the slope of the detected actuation force curve is reduced or increased. By considering the second-order derivative of the detected actuation force curve, it can be more accurately identified whether the closing element is actually actuated by the user or whether there are other forces acting on the closing element, and it should not be reacted to by adjusting the closed element. In order to identify the actuation event, the second-order derivative of the actuation force curve detected at only one time point or the curve of the second-order derivative of the actuation force curve can be used. For example, the maximum height of the second-order derivative of the actuation force curve detected can be determined only and compared with a threshold value. Alternatively or additionally, the form of the second-order derivative of the actuation force curve can be considered and can be compared with the characteristic curve for actuation of the second-order derivative of the actuation force curve.

[0057] In another embodiment of the method, it can be provided that the method has a step of determining an interference signal based on the detected actuation force curve. The interference signal can, for example, be a part of the detected actuation force curve that is not caused by the force applied by the user to the closing element. The interference signal can be, for example, vibration or other periodic forces. The interference signal can be caused by, for example, wind and / or weather. The interference signal may be caused by a passing vehicle. For example, the interference signal can be identified based on the periodicity of the interference signal and / or the corresponding derivative of the detected actuation force curve. The interference signal can also be compared with a known interference signal, for example from a wash lane, by pattern comparison.

[0058] In another embodiment of the method, it can be provided that the identification of an actuation event is performed based on a special interference signal. For this purpose, for example, the interference signal can be calculated based on the detected actuation force curve. Only after this calculation can the actuation event be identified using the actuation force curve corrected in this way. This makes the identification of an actuation event more reliable. Based on the interference signal, for example, non-actuation events can also be identified.

[0059] In another embodiment of the method, it can be provided that at least one threshold value is determined according to a specific interference signal. For example, all, some or only one of the above threshold values ​​can be set or changed according to a specific interference signal. In this way, for example in the case of a strong interference signal, the actuation probability can be increased, thereby identifying an actuation event and opening the closing element.

[0060] In another embodiment of the method, it can be provided that the force detection offset is taken into account when detecting the actuation force curve. The detection can also be corrected thereby. For example, due to residual stresses in the closing element, temperature changes and / or other influencing variables, the detected force may have an offset that is not caused by the actuation. These influencing variables can, for example, be detected and used to determine the offset. For example, the temperature of the force sensor and / or the closing element can be detected, and the force detection offset can be determined based on the detected temperature. For example, the handle element cannot be deformed back completely after actuation, and a strain gauge as a force sensor can permanently detect forces that are not caused by the actuation. By taking this situation into account, the actuation event can be identified more reliably. The force detection offset can be used to calibrate the detection, for example to a zero point.

[0061] In another embodiment of the method, it can be provided that the force detection offset is determined according to the detected actuation force curve. This means that no additional sensor detection offset is required. For example, the force detection offset can be determined as the average value of the detected actuation force curve. If the change of the detected actuation force curve is greater than the change threshold, the determination of the force detection offset can be suspended. For example, the average value of the force detected in a previous time period (e.g., 10 seconds) and / or since the last suspension determination can be determined as the force offset. The average value can be determined within a floating time period. It can also be averaged overall since the motor vehicle was started or parked for the last time, optionally excluding the suspension time range caused by exceeding the change threshold. The determination of the force detection offset can be suspended for a predetermined time period and / or until the change of the detected actuation force curve is less than the change threshold. After the change of the force curve is less than or equal to the change threshold, the determination of the force detection offset can continue to be suspended for a predetermined time period. The change can be a first-order derivative. For example, if the gradient is greater than the maximum gradient, the determination of the force detection offset can be suspended. However, the change can also be the absolute magnitude of the force difference at a predetermined time interval, which must be greater than the change threshold in order to suspend the determination of the force detection offset. By suspending the determination of the force detection offset, temporary external influences, such as actual actuation or disturbances, for example from a wash lane, can be ignored in order to calibrate the zero value of the force detection.

[0062] In another embodiment of the method, it can be provided that the force detection offset can be taken into account by analog compensation. For example, the force detection offset can be taken into account before the detected actuation force curve is amplified. For example, the absolute value of the output signal of the force sensor can be changed by analog compensation. The control device that is configured to be able to identify the actuation event can also be configured to, for example, control the analog compensation. For example, the control device can control a digital analog converter via a digital output, and analog compensation can be performed by the digital analog converter. Multiple force sensors can be controlled by multiple different digital outputs of the control device via multiple digital analog converters, and the digital analog converter can be designed separately from the control device or can be integrated in the control device. A variety of different analog compensations can be performed here, for example, one analog compensation for each force sensor. Analog compensation (especially before amplification) can improve the resolution of the detected actuation force curve. Therefore, the recognition of the actuation event can be improved.

[0063] A second aspect relates to a system for detecting the actuation of a closing element of a motor vehicle. The system can be designed to perform the method according to the first aspect. The corresponding advantages and further features can be found in the description of the first aspect, wherein the design of the first aspect also forms the design of the second aspect, and vice versa.

[0064] The system has a force detection device, which is designed to detect an actuation force curve acting on the closing element, in particular on a handle element of the closing element. The system has an evaluation device, which is designed to identify an actuation event based on the detected actuation force curve. The system can also have a closing element and / or a handle element. The handle element can be immovably fastened to the remaining closing element and / or can be designed as a rigid component. The system can also have an actuator for the closing element. The system can have a control device, which is designed to control the closing element based on the identified actuation event, for example to adjust the closing element between its closed position and open position.

[0065] In another embodiment of the method, it can be provided that the force detection device has a strain gauge as a force sensor. The force detection device can also have a plurality of strain gauges. As a result, different deformation directions and force directions can be detected and / or distinguished particularly well. The force sensor can be arranged, for example, in or on the closing element. For example, the strain gauge can be bonded to the handle element or integrated in the handle element.

[0066] In another embodiment of the system, it can be provided that the force detection device has a strain gauge as a sensor (eg, force sensor). The features, advantages and explanations described above with respect to the embodiment of the method with respect to the strain gauge as a force sensor also apply to the embodiment with respect to the system.

[0067] The strain gauge can be designed as a Wheatstone measuring bridge. The Wheatstone measuring bridge can be a quarter bridge, a half bridge or a full bridge. For example, the Wheatstone measuring bridge can have four resistors. For example, the four resistors can be connected together to form a closed loop or a square. The supply voltage can be applied to a diagonal of the square. The voltage measuring device can be connected to the other diagonal of the square.

[0068] In another embodiment of the system, the force detection device can have a first force sensor and a second force sensor. The force detection device can have an additional force sensor. The first force sensor and the second force sensor can be arranged independently of each other to detect the actuation force curve. For example, the system has two strain gauges as two separate and independent Wheatstone measuring bridges, for example designed as a half bridge or a full bridge.

[0069] In another embodiment of the system, the system can have a first digital-to-analog converter for the first force sensor and a second digital-to-analog converter for the second force sensor. The two digital-to-analog converters can be implemented on one circuit board or on different circuit boards. The two digital-to-analog converters can be implemented on the circuit board together with the evaluation device or separately on another circuit board that is the same as the evaluation device. Both digital-to-analog converters can be controlled via the digital output of the evaluation device. The first digital-to-analog converter can be controlled via the first digital output, and the second digital-to-analog converter, for example, which is independent of the first digital-to-analog converter, can be controlled via the second digital output in addition to the first digital output.

[0070] The digital-to-analog converter can be configured to simulate a compensation force detection offset for the actuation force curve. For example, each digital output of the evaluation device can present three states: 0, power supply voltage or high resistance. Each digital-to-analog converter can be connected to the evaluation device via four digital outputs. Each digital-to-analog converter can have, for example, four bits, such as four different resistors. Each bit can be connected to the digital output of the evaluation device. 81 states can thus be achieved. Therefore, the resolution can be 81, instead of 16, and the digital output has two states, namely 0 and power supply voltage. The specific behavior of the digital-to-analog converter can be achieved by a specific selection of the resistance of the bits of the digital-to-analog converter, such as certain factors between the resistances of the bits. For example, the resistances can differ from each other by a factor of 3. The first resistance can be three times larger than the second resistance. The third resistance can be three times larger than the second resistance. This means that the almost linear behavior of the digital-to-analog converter can be achieved by a linear resolution between 81 states. The different behaviors of the digital-to-analog converter can be achieved by other factors between the individual resistances.

[0071] For n force sensors and, for example, n strain gauges, n digital-analog converters can be used. This means that the force detection offset of each force sensor can be easily taken into account by modularity using the evaluation device. A hardware adaptation of the evaluation device (e.g., by means of a plurality of digital-analog converters provided in the hardware of the evaluation device) is not necessary, but rather digital outputs or pins that are usually present in such evaluation devices can be used. The digital-analog converters can then be used modularly and, depending on the number of digital outputs, any number of digital-analog converters can be used.

[0072] Further embodiments and designs of the present disclosure can be found in the following subject list:

[0073] 1. A method for detecting an actuation of a closing element 10 of a motor vehicle, the method comprising at least the following steps:

[0074] - detecting 20 an actuation force profile acting on said closure element 10 , in particular on the handle element 12 of the closure element 10 ; and

[0075] - Identifying 22 an actuation event based on the detected actuation force curve.

[0076] 2. Based on the method of Topic 1,

[0077] Therein, the method comprises the step of detecting 18 the temperature of the handle element 12 with an integrated temperature sensor of the control device, and

[0078] Therein, at least one of the detection 20 of the actuation force curve and the recognition 22 of the actuation event is performed as a function of the detected temperature of the handle element 12 .

[0079] 3. A method according to any one of the preceding subjects,

[0080] Therein, at least one of the detection 20 of the actuation force curve and the identification 22 of the actuation event is performed depending on the life of the handle element (12).

[0081] 4. A method according to any one of the preceding subjects,

[0082] Therein, the detection 20 of the actuation force curve comprises detecting 20a a first actuation force curve by means of a first sensor 13a and detecting 20b a second actuation force curve by means of a second sensor 13b, wherein the first and second sensors 13a, 13b are spatially spaced apart from each other.

[0083] 5. Based on the method of Topic 4,

[0084] Therein, the method has the step of determining 23 a point of application of the actuation force and / or determining 24 a direction of the actuation force based on the detected first actuation force curve and the second actuation force curve.

[0085] 6. Based on the method of topic 4 or 5,

[0086] Therein, identifying 22 an actuation event comprises comparing 25 a first actuation force profile and a second actuation force profile.

[0087] 7. A method according to any one of the preceding subjects,

[0088] Therein, the detection 20 of the actuation force curve is performed at a reduced sampling rate after a certain period of time in which no actuation event has been identified.

[0089] 8. A method according to any one of the preceding subjects,

[0090] The step 22 of identifying an actuation event comprises calculating an actuation probability,

[0091] When the calculated actuation probability is greater than a first threshold, an actuation event is identified.

[0092] In particular, the first threshold is determined according to a detected actuation force curve.

[0093] 9. Based on the method of Topic 8,

[0094] When the calculated actuation probability is greater than a second threshold, a wake-up signal is generated.

[0095] In particular, the second threshold is smaller than the first threshold.

[0096] 10. Based on the method of topic 8 or 9,

[0097] The actuation force curve 90 has a level 92, a slope 94 and a standard deviation 96 as attributes,

[0098] Therein, calculating the probability of actuation 100 involves calculating individual probabilities of actuation 102, 104, 106,

[0099] Therein, the individual probabilities 102, 104, 106 of actuation are respectively calculated depending on only one of the properties, and

[0100] Therein, the actuation probability 100 is calculated based on at least one of the calculated individual probabilities 102 , 104 , 106 .

[0101] 11. According to the method of topic 10,

[0102] Therein, the same value is calculated for the individual probabilities 102 , 104 , 106 in each region of the corresponding attribute.

[0103] 12. The method according to any one of subject 10 or 11,

[0104] Therein, the actuation probability 100 is calculated based on only one or two of the calculated individual probabilities 102 , 104 , 106 .

[0105] 13. The method according to any one of items 10 to 12,

[0106] Therein, the identification 22 of the actuation event is performed based on the standard deviation.

[0107] 14. The method according to any one of items 10 to 13,

[0108] Therein, if the standard deviation is higher than the third threshold, the calculation of the actuation probability is performed only based on the individual probabilities regarding the level or slope (for example, based on two individual probabilities regarding two levels or two slopes at different times).

[0109] 15. The method according to any one of items 10 to 14,

[0110] Therein, an actuation event is identified when the slope is below a fourth threshold and the level is above a fifth threshold for a certain minimum duration.

[0111] 16. A method according to any one of the preceding subjects,

[0112] Therein, the method has the step of detecting 26 the speed of the motor vehicle and, if the detected speed is greater than a threshold speed, deactivating the detection 22 of an actuation event.

[0113] 17. A method according to any one of the preceding subjects,

[0114] wherein an opening signal is generated when at least one parameter of the detected actuation force curve is greater than a bridging threshold,

[0115] Regardless of whether the actuation event is recognized.

[0116] 18. A method according to any one of the preceding subjects,

[0117] Therein, the method has the step of determining 38 a first derivative of the detected actuation force curve, and

[0118] The identification of an actuation event is performed 22 based on a specific first-order derivative of the detected actuation force curve.

[0119] 19. A method according to any one of the preceding subjects,

[0120] wherein the method comprises the step of determining a second derivative of the detected actuation force curve, and

[0121] The identification of an actuation event is performed based on the specific second-order derivative of the detected actuation force curve.

[0122] 20. A method according to any one of the preceding subjects,

[0123] The method comprises the step of determining a disturbance signal based on a detected actuation force curve.

[0124] 21. According to the method of topic 20,

[0125] Wherein, the actuation event is identified according to the specific interference signal; 22

[0126] and / or

[0127] Wherein, at least one threshold is determined according to the specific interference signal.

[0128] 22. A method according to any one of the preceding subjects,

[0129] Therein, when detecting the actuation force curve, the force detection offset is taken into account.

[0130] 23. A method according to any one of the preceding subjects,

[0131] wherein the force detection offset is determined according to the detected actuation force curve,

[0132] Therein, if the change of the detected actuation force curve is greater than a change threshold, the determination of the force detection offset is suspended.

[0133] 24. A method according to any one of the preceding subjects,

[0134] In this case, the force detection offset is taken into account by means of analog compensation.

[0135] 25. System for detecting the actuation of a closing element 10 of a motor vehicle, in particular wherein the system is designed to carry out a method according to any one of the preceding subjects,

[0136] The system has

[0137] a force detection device designed to detect an actuation force profile acting on the closure element 10, in particular on the handle element 12 of the closure element 10, and

[0138] An evaluation device is designed to identify an actuation event based on the detected actuation force curve.

[0139] 26. Based on the system of topic 25,

[0140] In this case, the force detection device has a strain gauge as a force sensor.

[0141] 27. Based on the system of topic 26,

[0142] The strain gauge is designed as a Wheatstone measuring bridge 72 .

[0143] 28. A system according to any one of subjects 25 to 27,

[0144] The force detection device comprises a first force sensor and a second force sensor, and the first force sensor and the second force sensor are independently arranged to detect an actuation force curve.

[0145] 29. Based on the system of topic 28,

[0146] The system has a first digital-to-analog converter 78a for a first force sensor and a second digital-to-analog converter 78b for a second force sensor, wherein both digital-to-analog converters 78a, 78b are controlled via a digital output 80 of the evaluation device 86, and wherein the digital-to-analog converters 78a, 78b are configured to simulate a force detection offset that compensates for an actuation force curve. BRIEF DESCRIPTION OF THE DRAWINGS

[0147] Figure 1a A closing element of a motor vehicle designed as a door is shown in a schematic perspective illustration.

[0148] Figure 1b The arrangement of a plurality of sensors in a handle element is schematically shown.

[0149] Figure 2 Schematically shows the method for identifying Figure 1a and Figure 1b Method of actuation of a closing element.

[0150] Figure 3 Schematically shows that when the identification is based on Figure 1a and Figure 1b Evaluation of data during actuation of the closing element.

[0151] Figure 4 The diagram shows force curves detected in a parked motor vehicle.

[0152] Figure 5 A graph of the force profile detected in a lane wash is shown.

[0153] Figure 6 The diagram shows the force curve detected during the actual actuation of the closing element.

[0154] Figure 7 The actuation force curve is schematically shown.

[0155] Figures 8a to 8c The calculation of individual probabilities is schematically shown.

[0156] Fig. 9 The calculation of the actuation probability based on the individual probabilities is schematically illustrated.

[0157] Fig.10 A simulated compensation of the force detection offset is schematically shown. DETAILED DESCRIPTION

[0158] Figure 1aA closing element of a motor vehicle designed as a door 10 is shown in a schematic perspective view. The door 10 can be adjusted automatically by an actuator between an open position, in which the access opening to the interior of the motor vehicle is released, and a closed position, in which the access opening is blocked. The door 10 has a rigid handle element 12. When the handle element 12 is actuated by a user, for example when the handle element 12 is pulled, the door 10 should be opened. For this purpose, the door lock is released and the adjustment of the door 10 is controlled by the actuator in the direction of the open position. However, if the handle element 12 is acted on in another way, for example by a cleaning brush and a cleaning cloth in a wash lane, the door 10 should remain in its closed position.

[0159] In the handle element 12, as Figure 1b As schematically shown, in one embodiment, the first sensor 13a and the second sensor 13b are arranged at a distance from each other, rather than just one sensor. Figure 1b The cross section of the handle element 12 and the top view of the handle element 12 are schematically shown, just as the user standing in front of the handle element 12 and the closing element 10 will see the elements in the transparent handle element 12. The first sensor 13a is arranged at the upper left and the second sensor 13b is arranged at the lower right. In an alternative embodiment, the two sensors 13a, 13b can also be arranged on the same horizontal line and / or vertical line. Other spatial arrangements of the two sensors 13a, 13b relative to each other are also possible, for example the first sensor 13a is arranged at the lower left and the second sensor 13b is arranged at the upper right.

[0160] Figure 2 A method for detecting an actuation of a door 10 of a motor vehicle is shown. In a first step 20, an actuation force profile acting on a handle element 12 is detected. For this purpose, at least one strain gauge is integrated in the handle element 12 as a force sensor of a force detection device. When the handle element 12 is deformed, for example by pulling the handle element 12, a measurement signal corresponding to the force acting on the handle element 12 is generated. In step 22, an actuation event is detected using an evaluation device based on the detected actuation force profile.

[0161] In addition, if Figure 2As shown, according to an embodiment, the method optionally has a step 18 of detecting the temperature of the handle element 12. The evaluation device as a control device has an integrated temperature sensor for detecting step 18. In one embodiment, a step 20 of detecting the actuation force curve is performed as a function of the detected temperature of the handle element 12. In an alternative embodiment, a step 22 of identifying an actuation event is performed as a function of the detected temperature of the handle element 12. In another embodiment, the step 20 of detecting the actuation force curve and the step 22 of identifying the actuation event are performed as a function of the detected temperature of the handle element 12. For example, when detecting the actuation force curve of step 20, different temperature curves can lead to different detected force curves, with which, for example, the temperature dependency of the stiffness of the handle element 12 can be compensated, and thus the actuation force curve can be accurately detected.

[0162] In addition, if Figure 2 As shown, the method optionally has a step 16 of determining the life of at least one of the closing element 10 and the handle element 12. The life is determined using a timer implemented on the evaluation device. At least one of the steps 20 of detecting the actuation force curve and 22 of identifying the actuation event is performed depending on the specific life of the closing element 10 and / or the handle element 12. The step 18 of detecting the temperature and the step 16 of determining the life can be performed independently of each other.

[0163] In the embodiment with two sensors 13a, 13b, the detection step 20 of the actuation force profile comprises a detection step 20a of a first actuation force profile using the first sensor 13a and a detection step 20b of a second actuation force profile using the second sensor 13b.

[0164] Furthermore, in the step 22 of identifying an actuation event, the method may optionally have a step 23 of determining a point of application of the actuation force based on the detected first and second actuation force curves. Furthermore, the identification 22 of the actuation event may include a step 24 of determining a direction of the actuation force based on the detected first and second actuation force curves. Figure 1b As shown in the example in , two sensors 13a, 13b are arranged offset from each other in the vertical and horizontal directions. This can determine the application point of the actuation force applied by the user to the handle element 12 and the direction of the actuation force. For example, the user's actuation force can be vectorially resolved, that is, the direction of the actuation force can be determined. Therefore, it is not only possible to determine whether the user is pushing or pulling, but also to determine in which area of ​​the handle element 12 the user applies the force and in which direction the force is applied.

[0165] The step 22 of identifying an actuation event further comprises the step 25 of comparing the first and second actuation force curves. For example, the first actuation force curve and the second actuation force curve may be compared with each other at certain time points or regions. Based on the comparison, an actuation event may then be identified. For example, if it is identified that both the first actuation force curve and the second actuation force curve have specific values ​​within a specific time range, an actuation event may be identified. However, if, for example, only one of the first actuation force curve and the second actuation force curve has a specific value, then, for example, an actuation event may not be identified.

[0166] Figure 2 An optional feedback from the step 22 of identifying an actuation event to the step 20 of detecting an actuation force curve according to the illustrated embodiment is also schematically shown. If no actuation event is identified within a certain period of time, the step 20 of detecting an actuation force curve is performed at a reduced sampling rate. Thus, the step 20 of detecting an actuation force curve can first be performed at a first sampling rate. If no actuation event is identified within a certain period of time (approximately one week) because the motor vehicle is parked and the user does not want to open the door, the evaluation device can operate the detection step 20 in a reduced sampling rate based on the identification step 22. With such a reduced second sampling rate, which is less than the first sampling rate, the step 20 of detecting an actuation force curve can continue to be performed (for example, until an actuation event is identified again). Thereafter, for example, the step 20 of detecting an actuation force curve can be performed again using the first sampling rate.

[0167] Furthermore, the method has a step 26 of detecting the speed of the motor vehicle. In one embodiment, if the detected speed is greater than a threshold speed, for example 3, 4 or 5 km / h, the step 22 of identifying the actuation event is deactivated. It can thus be ensured that if the motor vehicle moves too fast, no actuation event is identified.

[0168] Figure 3 The data evaluation is schematically shown. In step 30, the strain gauge supplies an unprocessed sensor signal. In step 32, this sensor signal is filtered with a low-pass filter in order to filter out high-frequency force changes. High-frequency force changes do not correspond to actuations by the user here, but can be caused, for example, by electronic components on a circuit board. In step 34, the detected actuation force curve is shifted by a force detection offset. Residual stresses remaining in the handle element 12 and displacements caused by aging and temperature are thereby corrected. This will refer to Figure 6 Further explanation: In step 36, the magnitude of the detected actuation force is determined, here it is the maximum value of the force during actuation. In step 38, the first derivative of the detected actuation force curve is determined, here it is the slope of the force during actuation.

[0169] In step 40, the actual analysis of the data generated from the detected actuation force curve is performed so that possible actuation events can be identified based on the detected actuation force curve. For this purpose, for example, the magnitude of the detected actuation force is compared with a minimum force as a threshold. Likewise, for example, the first-order derivative of the detected actuation force is compared with another threshold. Depending on the degree of excess, each variable is assigned an actuation probability here. Other thresholds can also be provided. For example, if the maximum force is exceeded, the calculated actuation probability is lower. The maximum force can, for example, be a force that the user can no longer normally generate, because an average person in a normal posture is too weak for this. In addition, the curve shape of the actuation force curve can also be compared with a known shape. Individual parameters are used to identify actuation events during the analysis, for which they were previously stored in step 42. These parameters are, for example, application-specific, customer-specific and vehicle-specific.

[0170] All calculated actuation probabilities of the previously described comparisons are multiplied with one another here in order to calculate an overall actuation probability. If the calculated actuation probability is greater than a first threshold value, an actuation event is recognized in step 44 and an actuation signal is generated. This actuation signal can control the adjustment of the door 10 to the open position. Optionally, a wake-up signal is generated in advance in step 44 if the calculated actuation probability is greater than a second threshold value, wherein the second threshold value is less than the first threshold value. This means that the door can be prepared for opening even if an actuation event has not yet been recognized with sufficient certainty. Therefore, when an actuation event is recognized, the door 10 can be opened with a particularly short delay.

[0171] Figure 4 The force curve detected on the handle element 12 in a parked vehicle is shown. For example, due to other road users passing by, the handle element 12 is subject to slight vibrations and thus deformations. In the case of such low forces, no actuation event is detected. Figure 5 The force curve on the handle element 12 detected when driving through a wash lane is shown. As can be seen in region 50, the handle element 12 is subjected to stronger vibrations here, which are also substantially continuous, unlike other road users driving by. In region 52, the washing brush contacts the handle element 12, whereby the handle element 12 is subjected to large forces and thus deforms. However, due to the analysis and consideration of the above-mentioned data and threshold values, no actuation event is detected. Thus, an undesired automatic opening of the door 10 in a wash lane is avoided.

[0172] Figure 6 The diagram shows the detected actuation force curve when the door 10 or the handle element 12 is actually actuated by the user. Due to the shape of the force curve, the actuation event can be reliably identified in the analysis. Figure 5A comparison of the force curves of the handle element 12 and the force curves of the handle element 12 shows that, for example, the actuation event in the region 54, here a pull on the handle element 12, has a characteristic force curve with a clearly defined force peak as a maximum and a sharp, almost linear increase. On the other hand, a movement on the handle element 12 with the cleaning brush leads to a more irregular force curve with a number of successive local maxima in the region 52.

[0173] Figure 6 There is a first curve 56, which is an unprocessed detected actuation force curve. It can be seen that the force sensor permanently detects an actuation force, which is never zero. In the example shown, this zero point offset is caused by sensor aging and / or external temperature changes. Therefore, the detected unprocessed actuation force curve is offset by the force detection offset, thereby generating a second curve 58 as a compensated detection end-side actuation force curve. The force detection offset corresponds to the floating average value of the first curve 56. If the change of the detected actuation force curve is greater than the change threshold, the determination of the force detection offset or the floating average value is suspended here. In the example shown, this is the case in area 54. The duration of the pause is shown by a further characteristic curve 60. The pause occurs when the change threshold is exceeded and ends after a fixed predetermined time period after falling below the change threshold. Therefore, as Figure 6 It can be seen in FIG. 1 that the determined pause of the force detection deflection varies in length and depends on the detected actuation force curve.

[0174] In the region 62 of the first curve 56, it can also be seen that after the handle element 12 is actuated, residual stress is retained in the handle element 12. Therefore, even when the handle element 12 is not actuated, a higher force than other actuation events is detected, and the force is mechanically caused, rather than caused by aging and temperature fluctuations. This results in a larger force detection offset in the region 62, so that in the corresponding region 64, the second curve 58 or the compensated actuation force curve corresponds to zero again. The force in this region 64 can correspond to zero again. A step can also be identified at the end of the determination pause of the force detection offset. During further subsequent actuation, the residual stress in the handle element 12 is released again, and as a result, the region 66 of the unprocessed, detected actuation force curve returns to the usual force value caused by aging and temperature when the handle element 12 is not actuated. This results in the previous usual force detection offset being generated again in the region 66, so that in the corresponding region 68, the second curve 58 or the compensated actuation force curve corresponds to zero again. The force in the region 68 can correspond to zero. A phase can also be seen at the end of the pause in determining the force recognition offset, but here in the opposite direction. Residual stresses in the handle element 12 can also be compensated in order to increase the reliability of the recognition of the corresponding actuation event.

[0175] Figure 7An actuation force curve is schematically shown. Shown is an actuation force curve 90 according to a particular embodiment, wherein Figure 7 The actuation force curve 90 shown plots the actuation force on the vertical axis relative to the time on the horizontal axis. A specific level 92, a specific gradient 94 and a specific standard deviation 96 of the actuation force curve 90 are also shown. The standard deviation 96 here corresponds to the dispersion of the individual measurement points of the actuation force curve 90. The level 92 is determined relative to the actuation force at a specific time point and can be determined for different time points. The slope 94 is determined relative to two specific time points and is determined here, for example, as the average slope 94 of the actuation force curve 90 between the two time points. Alternatively, the slope 94 can also be determined as the slope at a certain point, i.e., the first derivative of the actuation force curve at a specific time point. The standard deviation 96 is determined for a specific value of the actuation force curve within a specific time range. The slope 94 and the standard deviation 96 at different time points in the actuation force curve can also be determined.

[0176] Calculating the probability of actuation involves calculating the individual probabilities of actuation. The individual probabilities of actuation are calculated separately and according to one of the attributes. In this way, in each case and depending on the respective attributes, an individual probability of level 92, another individual probability of slope 94 and another individual probability of standard deviation 96 are calculated.

[0177] Figures 8a to 8c The calculation of individual probabilities is schematically shown. Figure 8a The calculation of the individual probabilities for level 92 is shown schematically. Figure 8b The calculation of the individual probabilities of the slope 94 is schematically shown, and Figure 8c The calculation of individual probabilities for a standard deviation of 96 is schematically shown. Figure 8a In , individual probabilities are plotted against the force, Figure 8b is plotted against the force per time (i.e., the slope of the force) and in Figure 8c , plotted against the standard deviation. It can be seen that specific individual probabilities are assigned specific values. For example, Figure 8a As can be seen in , with increasing force, that is, with increasing level 92, the individual probability of level 92 also increases. Regarding the slope 94, in Figure 8b As can be seen in , the highest individual probabilities are achieved in the middle range of the slope. Figure 8c As can be seen in Figure 1, as the standard deviation increases, the individual probability of a standard deviation of 96 becomes smaller. Within a certain range of level, slope, and standard deviation values, the value of the individual probability is the largest, while outside these ranges, the value of the individual probability is not the largest. Figure 8a , 8b8c also show that the same value is calculated for the individual probabilities in the region of the corresponding attribute. This means that the same individual probability value can be calculated for different levels, slopes or standard deviations.

[0178] Based on at least one of the calculated individual probabilities, the actuation probability is calculated. For example, the actuation probability can be calculated based on only one or two of the calculated individual probabilities. Alternatively, the actuation probability is calculated based on all three calculated individual probabilities. Furthermore, in alternative embodiments not shown in detail herein, additional individual probabilities not further described herein can be calculated and used to calculate the actuation probability.

[0179] Fig. 9 The calculation of the actuation probability or also the overall actuation probability based on the calculated individual probabilities is schematically shown. Fig. 9 , the probability is shown on the vertical axis and the time is shown on the horizontal axis. The individual probabilities 100, 102, 104, 106 are aligned with each other in time and arranged one above the other. The actuation probability 100 is shown at the top. In addition, the individual probabilities 102 of the level 92, the individual probabilities 104 of the slope 94 and the individual probabilities 106 of the standard deviation 96 are arranged one above the other and below the actuation probability 100. As described above, the individual probabilities 102, 104, 106 are calculated, especially for multiple time points. Now, the actuation probability 100 is calculated based on these individual probabilities 102, 104, 106 by multiplication. Alternatively, the actuation probability 100 can also be calculated based on the individual probabilities 102, 104, 106 using integration, averaging or summing or other operations. For each time point, the actuation probability is calculated based on the value of the individual probability. This is done within a time range so as to obtain a curve of the actuation probability 100.

[0180] For different use cases, the actuation force curve has different values ​​of attribute levels, slopes, and standard deviations. For example, in the use case of normal opening by the user, a high level and a not too steep slope will appear, while when the user taps the handle element 12, a medium to high level and a steep slope will appear. Other use cases have different attributes. Different attributes and their values ​​can be used to accurately identify actuation events.

[0181] The identification step 22 can also be performed not only based on the individual probabilities, but also based on the standard deviation itself. For example, if the standard deviation is above the third threshold, the calculation of the actuation probability can be performed specifically based on the individual probabilities for the level or the slope. For example, the calculation can also be performed based on multiple individual probabilities for only one attribute. The actuation probability can be calculated based on more than two individual probabilities for the level or more than two individual probabilities for the slope. More than two individual probabilities for the same attribute may be calculated at different points in time.

[0182] When the slope is lower than the fourth threshold and the level is higher than the fifth threshold for a certain minimum duration, an actuation event can also be identified. Therefore, it can be accurately identified that the user is pulling or pushing the handle element 12 with a certain force. An actuation event can be accurately identified.

[0183] Fig.10 The analog compensation of the force detection offset is schematically shown. A Wheatstone measuring bridge 72 is schematically shown, which has resistors R1 to R4 and forms a force sensor for detecting the actuation force curve. The resistors R1 to R4 all have the same resistance, for example 1200 ohms. The Wheatstone measuring bridge 72 is part of the analog signal conditioning 70a. The second Wheatstone measuring bridge of another analog signal conditioning 70b is not shown in detail, which forms an optional second force sensor for detecting the actuation force curve. Therefore, the system forms one or two strain gauges to identify the actuation, wherein each strain gauge is designed as a force sensor as a Wheatstone measuring bridge 72 as schematically shown here. The two force sensors are designed to detect the actuation force curve independently of each other. Here, the potential VDD is applied to the Wheatstone measuring bridge 72 for power supply. In addition, the potentials SG-, SG+ are shown, which are used to detect the 20 actuation force curve.

[0184] also, Fig.10 Schematically shown in FIG. 8 is a microcontroller 86 which can form an evaluation device for at least partially executing the steps of the method. The microcontroller 86 has an amplifier 82 and an analog-digital converter 84. The analog signal conditioning 70 a is electrically connected to the microcontroller 86. Furthermore, as part of the analog signal conditioning 70 a, a filter 74 is shown, via which the Wheatstone measuring bridge 72 is connected to the amplifier 82. Here, the time curve of the potential SG-, SG+ is filtered via the filter 74 and amplified by the amplifier 82. The analog signal is then converted into a digital signal by the analog-digital converter 84 to obtain a digital signal that is related to the actuation force curve.

[0185] In addition, the microcontroller 86 has a digital output 80. The digital output 80 is designed as a three-state output, a so-called three-state output 80. At each output of the digital output 80, 0 V, the supply voltage of the microcontroller 86 or a high impedance can be applied. The system also has digital-to-analog converters 78a and 78b. The analog signal conditioning 70a, 70b is connected to the microcontroller 86 via such digital-to-analog converters 78a, 78b.

[0186] The digital-to-analog converter 78a has 4 resistors R5 to R8, which can be referred to as bits. The resistors R5 to R8 are spaced 3 times apart from each other by their resistance. For example, the resistance of resistor R5 is 17400 ohms, the resistance of resistor R6 is 52300 ohms, the resistance of resistor R7 is 158000 ohms, and the resistance of resistor R8 is 470000 ohms. Using a three-state output, 81 states can be represented by the digital-to-analog converter 78a. The factor 3 between resistors R5 to R8 allows the formation of an almost linear resolution as the output of the digital-to-analog converter 78a. The output of the digital-to-analog converter 78a can be applied to the filtered value of the actuation force curve via the electrical connection 76. This allows the detected actuation force curve to be moved, for example, by a force detection offset. Here, the shift occurs in a purely analog manner by adding the voltage present at the digital-to-analog converter 78a to the output voltage as the measurement signal of the force sensor.

[0187] Two digital-analog converters 78a, 78b are provided for simulating a compensation force detection offset for the actuation force curve. The two different digital-analog converters 78a, 78b can be controlled differently via digital outputs 80 in order to compensate for the respective individual force detection offsets of the respective individual Wheatstone measuring bridges 72 and strain gauges.

[0188] A further resistor R9 is shown, via which an actuation force curve can be amplified, for example for additional amplification of the amplifier 82 .

[0189] like Figure 3 As shown, the step 23 of determining the point of application of the actuation force, the step 24 of determining the direction of the actuation force and the step 25 of comparing the first and second actuation force curves can be performed as part of a data analysis step 40. The analysis step 40 can be performed in accordance with the step 16 of determining the life of the closing element 10 and / or the handle element 12. The analysis step 40 can be performed in accordance with the step 18 of detecting the temperature. The analysis step 40 can be performed in accordance with the step 26 of detecting the speed. Alternatively or additionally, the identification step 44 can be performed in accordance with at least one of the determination steps 16, 23, 24, the comparison step 25 and the identification steps 18, 26. The calculation of probabilities, such as one, a plurality of or all individual probabilities and / or actuation probabilities can be performed in at least one of the analysis step 40 and the identification step 44.

[0190] Reference numerals list

[0191] 10 Closing elements / door

[0192] 12 handle elements

[0193] 16 Steps: Determine the life of the closing element / handle element

[0194] Step 18: Check the temperature

[0195] Step 20: Detect the actuation force curve

[0196] Step 20a: Detecting the first actuation force curve

[0197] Step 20b: Detecting the second actuation force curve

[0198] 22 Steps: Identify the actuation event

[0199] Step 23: Determine the point of application of the actuation force

[0200] 24 Steps: Determine the direction of the actuation force

[0201] 25 Step: Comparison of the first and second actuation force curves

[0202] 26 Steps: Testing Speed

[0203] Step 30: Provide unprocessed sensor signals

[0204] Step 32: Filter the sensor signal using a low pass filter

[0205] 34 Steps: Moving the Actuation Force Curve

[0206] Step 36: Determine the actuation force

[0207] 38 Steps: Pushing leads to power curves

[0208] 40 Steps: Analyze Data

[0209] Step 42: Storing comparison parameters

[0210] Step 44: Identify an actuation event and generate an actuation signal

[0211] Area 50: Background vibration cleaning facilities

[0212] Area 52: Contact cleaning brush with handle element

[0213] Area 54: Actuation by pulling the handle element

[0214] 56 First curve / non-deflected actuation force curve

[0215] 58 Second curve / offset actuation force curve

[0216] 60 Pause duration characteristic curve

[0217] 62 Area: Retained residual stress

[0218] 64 corresponding area

[0219] 66 area: common force values

[0220] 68 corresponding area

[0221] 70a, b Analog signal conditioning

[0222] 72 Wheatstone measuring bridge

[0223] 74 Filter

[0224] 76 Electrical connection

[0225] 78a, b Digital to Analog Converter

[0226] 80 Digital outputs

[0227] 82 Amplifier

[0228] 84 Analog-to-digital converter

[0229] 86 Microcontroller

[0230] 90 Actuation force curve

[0231] 92 Levels

[0232] 94 Slope

[0233] 96 standard deviation

[0234] 100 Actuation probability

[0235] 102 Individual Probability Level

[0236] 104 Individual Probability Slope

[0237] 106 Individual probability standard deviation

[0238] SG-, SG+ potential

[0239] R1-R9 resistors

[0240] VDD potential

Claims

1. A method for detecting the actuation of a closing element (10) of a motor vehicle, the method comprising at least the following steps: detecting (20) an actuation force profile acting on the closing element (10), in particular a handle element (12) of the closing element (10); and Based on the detected actuation force profile, an actuation event is identified (22).

2. The method according to claim 1, in, The method comprises the steps of detecting (18) the temperature of the handle element (12) by means of an integrated temperature sensor of the control device, and Therein, at least one of a detection (20) of an actuation force curve and a recognition (22) of an actuation event is performed based on the detected temperature of the handle element (12).

3. The method according to any one of the preceding claims, in, The detection (20) of the actuation force profile comprises detecting (20a) a first actuation force profile using a first sensor (13a) and detecting (20b) a second actuation force profile using a second sensor (13b), wherein the first sensor (13a) and the second sensor (13b) are spatially spaced apart from each other.

4. The method according to claim 3, in, The method comprises the steps of determining (23) a point of application of the actuation force and / or determining (24) a direction of the actuation force based on the detected first actuation force curve and the second actuation force curve.

5. The method according to any one of the preceding claims, in, After a certain period of time in which no actuation events have been identified, the detection of the actuation force curve is performed at a reduced sampling rate (20).

6. The method according to any one of the preceding claims, in, The step (22) of identifying an actuation event comprises calculating an actuation probability, When the calculated actuation probability is greater than a first threshold, an actuation event is identified. In particular, the first threshold is determined according to a detected actuation force curve.

7. The method according to claim 6, in, The actuation force curve (90) has as attributes a level (92), a slope (94) and a standard deviation (96), wherein calculating the probability of actuation (100) involves calculating individual probabilities of actuation (102, 104, 106), wherein the individual probabilities of actuation are calculated (102, 104, 106) respectively depending on only one of the properties, and Therein, the actuation probability (100) is calculated based on at least one of the calculated individual probabilities (102, 104, 106).

8. The method according to any one of the preceding claims, in, The method has the step of identifying (26) the speed of the motor vehicle and, if the detected speed is greater than a threshold speed, deactivating the identification of an actuation event (22).

9. The method according to any one of the preceding claims, in, When detecting the actuation force curve, the force detection offset is taken into account.

10. The method according to any one of the preceding claims, in, The force detection offset is determined based on the detected actuation force curve, If the detected change in the actuation force curve is greater than a change threshold, the determination of the force detection offset is suspended.

11. The method according to any one of the preceding claims, in, The force detection offset is taken into account by means of simulation compensation.

12. System for detecting the actuation of a closing element (10) of a motor vehicle, in particular wherein: The system is designed to perform the method according to any one of the preceding claims, The system has a force detection device, designed to detect an actuation force profile acting on the closing element (10), in particular on a handle element (12) of the closing element (10), and The evaluation device is designed to identify an actuation event based on the detected actuation force profile.

13. The system according to claim 12, in, The force detection device has a strain gauge as a force sensor, which is designed as a Wheatstone measuring bridge (72).

14. A system according to any one of claims 12 or 13, in, The force detection device has a first force sensor and a second force sensor, and the first force sensor and the second force sensor are provided independently of each other to detect an actuation force curve.

15. The system according to claim 14, in, The system comprises a first digital-to-analog converter (78a) for a first force sensor and a second digital-to-analog converter (78b) for a second force sensor, wherein both digital-to-analog converters (78a, 78b) are controlled via a digital output (80) of the evaluation device (86), and wherein the digital-to-analog converters (78a, 78b) are configured to simulate a force detection offset that compensates for an actuation force curve.

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