Method for diagnosing the operation of an active air flow regulation system

Abstract

The present invention relates to an active air flow regulation system and a method for diagnosing the operation of an active air flow regulation system for a vehicle, comprising: an obstructing mechanical member; - a mechatronic system provided with a housing containing a magnet motor driven by a control circuit and coupled to a movement transformation for moving the obstructing mechanical member; - electronic communication lines for exchanging information with the DCU (8) of the vehicle or receiving commands from the DCU of the vehicle, which is characterised by comprising a series of steps consisting in: - storing, during an operating cycle of the active air flow regulation system, a numerical sequence S of data in a memory, the data being acquired by the control circuit inside the mechatronic system; - applying an algorithmic statistical analysis model to the numerical sequence of data stored in the memory in order to determine a singularity; - identifying a sporadic error associated with the singularity in order to provide information on the state of the active air flow regulation system.

Description

Method for diagnosing the operation of an active air flow regulation system Field of invention

[0001] The present invention relates to the field of active airflow regulation of a vehicle, and in particular the modification of the coefficient of penetration into the air of a vehicle, by using motorized spoilers or fins, the thermal control of the vehicle, in particular of the engine, of a braking system and more generally any component actively regulated in temperature, in particular of a motor vehicle with an internal combustion, electric or hybrid engine by active airflow regulation systems driving an obstructing member, for example motorized air flaps of the grille or curtains. This obstructing member is intended to modify the quantity of air passing through the vehicle's radiator and the engine compartment and to adjust the airflow in order to manage the efficiency of the vehicle's engine and of the various main aggregates (thermal engine, electric traction motor and its inverter, gearbox, battery pack, etc.)) Engine temperature management has indeed become, due to the evolution of anti-pollution standards, a major area of ​​development in engine design. Engine cooling is now optimized to best control the temperature of the different areas of the engine while limiting its energy consumption.

[0002] The thermal management circuit's function is to maintain the various internal components of an engine (cylinders, cylinder head, etc.) and, where applicable, the peripheral elements (the turbocharger for example) at their ideal operating temperature.

[0003] If the engine temperature is too high, the gases inside the cylinder are hotter: there is therefore less gas in the cylinder and the risks of self-ignition (and knocking) are higher. On the other hand, the components deform more, there is a significant risk of breakage.

[0004] If the engine is, on the other hand, cold, there is generally greater friction between the piston and the cylinder liner and combustion is incomplete, which generates significantly higher emissions of polluting gases.

[0005] On many newer vehicles, the grille fins in front of the radiator are hinged and motorized to control the airflow through the radiator. This is because when the vehicle is traveling at high speeds and low loads, a significant airflow is not necessarily necessary to properly cool the engine.

[0006] Additionally, the air flowing through the radiator disrupts the main flow around the vehicle and therefore generates aerodynamic drag. Therefore, it may be advantageous to block the air intakes located upstream of the radiator with shutters. In some cases, the shutters only have two positions (fully open or closed).

[0007] Unfortunately, the CO2 emission reduction benefits of these active shutter grilles disappear if a shutter remains stuck in the open or closed position, or in an intermediate position, or if it suffers damage, for example during an impact or if one or more shutters are missing. The correct functioning of an active shutter grille has an impact on the vehicle's pollutant emissions, so it is of great importance, even legally mandatory, to ensure correct operation of the air shutter device and to inform the driver of any deterioration in the consumption and pollution of his vehicle, and this every time he drives, encouraging him to go to the garage quickly to restore the legal level of emissions of the latter. For this purpose, solutions have been provided for checking the generic functionality of the air shutter arrangement by means of an on-board diagnostic system.For electric vehicles, thermal control of the engine and batteries is also an important issue, resulting in the same needs, as well as aerodynamic efficiency: a defective active airflow regulation system could penalize the vehicle's air penetration and therefore negatively impact the vehicle's autonomy. State of the art

[0008] Various solutions are known in the state of the art to meet this objective of detecting a malfunction of the active shutters of a grille.

[0009] Patent DE102018108162 describes a method for controlling an actuator and an actuator in which a DC motor drives a system for moving a first body relative to a second body, the first body sliding along a contact surface of the second body. The principle of this solution is based on current analysis to evaluate the behavior of the actuator by providing areas with modified mechanical resistance on the contact surface between the roller shutter and the guide rails.

[0010] This solution has several drawbacks:

[0011] Firstly, it requires providing a particular reference zone on the regulation system, which is essential for detecting an anomaly according to this prior art solution. The detection of the anomaly is strictly limited to detecting the anomaly at the level of this reference zone, and in no case allows the detection of anomalies in general in the mechanical and aeraulic transmission chain. This reference zone can also become worn, which then makes the exploitation of the signals ineffective.

[0012] Secondly, it is limited to mechatronic systems implementing DC motors, and cannot be implemented for systems using stepper motors; the use of torque (or DC current) measurement requires closed-loop control of the motor, which is at odds with stepper motor control, making prior art solutions unusable or difficult in this context.

[0013] Thirdly, the analyzed signal is the current, image of the torque, transmitted to the computer. However, the update frequency of this information is of the order of 10 µs while the capacity to transmit on multiplexed protocols of the LIN type is of the order of 100 ms, which leads to an incompatibility, the bandwidth being insufficient to transmit the necessary information. The transmission of all the information acquisitions is not adapted to the communication network with very limited bandwidth intended for the vehicle control electronics.

[0014] Patent US2020300156 describes another system for detecting an anomaly of an engine cooling water recirculation system comprising: a grille shutter capable of adjusting a flow of circulating air coming from outside a vehicle to the environment of an engine body, implementing four learned neural networks are stored, which are obtained by using at least five parameters consisting of an engine cooling water temperature at the time of engine start, an amount of air admitted into the engine, an amount of fuel injected into the engine,an outside temperature, air temperature, and vehicle speed as input parameters of the neural networks and using a measured value of the engine cooling water temperature as training data to learn weights for four states including a state where the grille shutter is closed and air blown by the blower does not flow through the air conditioning use heater, a state where the grille shutter is open and air blown by the fan does not flow through the air conditioning use heater, a state where the grille shutter is closed and air blown by the blower flows through the air conditioning use heater,and a state in which the grille shutter is open and air blown by the blower circulates through the air conditioning use heater. The engine cooling water temperature is estimated from among said five parameters using any one of the learned neural networks corresponding to a current state of the grille shutter and the circulation state of air blown by the blower in the air conditioning use heater from among the four learned neural networks. An abnormality of the engine cooling water recirculation system is detected based on an estimated value of the engine cooling water temperature.,

[0015] The disadvantage of this solution is that it provides information relating to the prediction of the cooling water temperature, which does not allow an anomaly in the kinematic and aeraulic chain of the sealing system to be characterized.

[0016] Moreover, this solution requires the deployment of four neural networks, and therefore involves extremely complex processing.

[0017] Patent DE10201201469 describes an air flap device also having an adjustment drive, which is connected to the at least one air flap in a movement and so as to transmit force in order to adjust the at least one air flap between its predetermined operating positions, the air flap device also having a control device coupled to the adjustment drive in terms of signal transmission, a control device connected to the control device, a storage device coupled in terms of signal transmission and a data acquisition device which captures the operating data of the air flap device memory device, in which a difference in the operating positions representing a reference operating data set is stored in the memory device.

[0018] US213338870 discloses another system solution and method for diagnosing a mechanical fault affecting an automotive Active Grille Shutter "AGS" system. The method includes receiving at least one mechanical fault status signal (410), such signal indicating that a particular mechanical fault condition has been detected in the AGS system. The method also includes receiving a temperature signal (418) indicating a temperature at a location proximate to the grille shutter of the AGS system, and determining whether to perform an extended diagnosis, at least based on the mechanical fault status signal and the temperature signal.

[0019] Disadvantages of the prior art

[0020] The solutions of the prior art do not make it possible to reliably characterize an anomaly in the state of the mechanical and airflow chain of the closure, without any mechanical modification of this mechanical and airflow chain, for example the deformation of a shutter or the partial breakage of the air intake system.

[0021] Solution provided by the invention

[0022] In order to address these drawbacks and to provide a diagnostic function for the complete system without modifying the obstructing member, to characterize an airflow malfunction, by using the data internal to the mechatronic system mobilizing the obstructing member or the data generated by the obstructing member where appropriate. To this end, the present invention relates, in its most general sense, to a method for diagnosing the operation of an active airflow regulation system for a vehicle having the characteristics set out in claim 1 as well as a system having the characteristics set out in the system claim.

[0023] The method comprises:an obstructing mechanical member,a mechatronic system, provided with a housing containing a magnet motor driven by a control circuit and coupled to a motion transformation making it possible to move the obstructing mechanical member,electronic communication lines for exchanging information with the DCU of the vehicle or receiving commands from the DCU of the vehicle,said method comprises a series of steps consisting of:recording, during an operating cycle of the active air flow regulation system, a digital sequence S of data in a memory , said data being acquired by said control circuit internal to the mechatronic system, apply to said digital sequence of data recorded in the memory an algorithmic model of statistical analysis to determine a singularity, discriminate a sporadic error associated with said singularity to deliver information on the state of the active air flow regulation system.

[0024] In a variant, said step of discriminating the sporadic error is carried out by the control circuit, said control circuit transmitting to the DCU information representative of said discriminated sporadic error.

[0025] In another variant, said sequence of digital data recorded in the memory is subject to: pre-processing with a first processing frequency by said control circuit and post-processing by said DCU / ECU of the vehicle with a second processing frequency lower than said first processing frequency making it possible to complete the analysis and to discriminate a sporadic error, the set of data pre-processed by said control circuit being transmitted to said DCU / ECU via the control lines.

[0026] In a variant, said digital sequence of data S recorded in memory comprises: data of a first type and data of at least one second type different from the first type.

[0027] In another variant, said method comprises as additional steps: consolidation of the sporadic error following an instruction transmitted by the DCU of the vehicle giving rise to a consolidated diagnostic state, transmission of the consolidated diagnostic state to the DCU of the vehicle.

[0028] For the purposes of this patent, “ECU” means an “electronic control unit” controlling a specific function of the vehicle.

[0029] However, driving safety, improved comfort and infotainment functions lead to unsustainable system complexity – up to hundreds of ECUs per vehicle.

[0030] It is also known to use domain controllers (DCUs) with the aim of centralization via this DCU - domain controller, rather than the use of multiple ECUs. The rise of the automotive domain controller is particularly driven by vehicle automation, which requires the fusion of data from different sensors, high-speed processing, and must meet the most stringent safety requirements. It must also offer the possibility of continuous development of new features and functionalities throughout the vehicle's life, through Over The Air (OTA) updates.

[0031] The invention will mention an ECU or DCU indifferently, the nature of the controller depending on the choice of computer architecture of the vehicle on which the invention is implemented.

[0032] Furthermore, the statistical analysis algorithm can be equipped with a supervised, unsupervised or reinforcement learning sequence.

[0033] Alternatively, the statistical analysis algorithm can be equipped with a clustering selection method.

[0034] In one variant, the obstructing mechanical organ is devoid of elements dedicated to diagnosis external to said mechatronic system.

[0035] In a permitted variation, the digital data relates to the operation of the magnet motor of the mechatronic system.

[0036] In an anticipated alternative, the digital data is a measure of the motor phase current.

[0037] In this case, the said digital data can be a measure of the rotor load angle of the magnet motor.

[0038] In a variant, the algorithmic model of statistical analysis for determining a singularity is provided with a comparison between the sampled digital data sequence recorded and a reference data card.

[0039] In a second variant, the algorithmic model of statistical analysis for determining a singularity is provided with a detection of a singular point in the sampled digital data sequence recorded.

[0040] In a third variant, the algorithmic model of statistical analysis to determine a singularity consists of submitting the sampled digital data sequence recorded to a model obtained by training a neural network from learning data corresponding to the nominal operation of the obstructing mechanical organ.

[0041] In all cases, a reference sampled digital data sequence can be recorded for each mechatronic system at the end of the assembly line.

[0042] In this case, the statistical analysis algorithmic model can compare the sampled digital data sequence with the reference sampled digital data sequence in order to detect a behavioral drift that could give rise to degradation of the mechatronic system or that could validate normal wear behavior of the system.

[0043] In one variant, the sampled digital data sequence recorded is derived from digital data depending on the mechanical load in the active grid system.

[0044] In this case, the identification of a singularity in the sampled digital data sequence recorded can be permitted with knowledge of: the masses or inertias to be moved in the active grid system, and / or the mechanical friction in said system, and / or the air load exerted on said system, and / or the temperature of the system.

[0045] For the purposes of this patent, the term "singularity" shall be understood to mean a point or a subset of points of the recorded data sequence that differs from a reference sequence corresponding to nominal operation, by comparison with one or more pre-recorded nominal data sequences, or by difference between the signature of the data sequence and the signature of a nominal data sequence acquired during a preliminary learning phase, obtained by the same signature calculation function, or a singularity in the sense of Gaussian statistics or a point or a set of points that differs from the projection of the median curve formed by the preceding points significantly from the average of the fluctuations of the recorded points with respect to said median curve, significantly being understood as "exceeding by at least 20% the amplitude of the fluctuations in normal or nominal operation.

[0046] In particular, we distinguish between the data for which the signature (torque, current, position, etc.) is sought, and the information recorded in the internal memory of the mechatronic system, and in particular in the volatile or non-volatile memory of the controller, for example a temperature.

[0047] According to a variant, said algorithmic model of statistical analysis for determining a singularity consists of submitting the sampled digital data sequence recorded to a model obtained by training a neural network from learning data corresponding to the nominal operation of the obstructing mechanical organ.

[0048] According to a particular embodiment, a reference sampled digital data sequence is recorded for each mechatronic system at the end of the assembly line.

[0049] Advantageously, said statistical analysis algorithmic model is capable of comparing the sampled digital data sequence with the reference sampled digital data sequence so as to detect a behavioral drift.

[0050] Alternatively, the sampled digital data sequence recorded is derived from digital data depending on the mechanical load in the active grid system.

[0051] According to particular embodiments, identifying a singularity in the sampled digital data sequence recorded is permitted with knowledge of: the masses or inertias to be moved in the active grid system, and / or the mechanical friction in said system, and / or the air load exerted on said system.

[0052] The invention also relates to an electromechanical system for active air flow regulation (1) for a vehicle, comprising:an obstructing mechanical member,a mechatronic system, provided with a housing containing a magnet motor (10) driven by a control circuit and coupled to a motion transformation making it possible to move the obstructing mechanical member,electronic communication lines for exchanging information with the DCU of the vehicle or receiving commands from the DCU of the vehicle,characterized in that said method comprises a microcontroller or a microprocessor executing a computer program recorded in its read-only memory, controlling a series of steps consisting in:recording, during an operating cycle of the active air flow regulation system, a sequence of sampled digital data in a memory , where the sampled digital data sequence consists of a set of digital data, , acquired by said control circuit internal to the mechatronic system (3), apply to said sequence of sampled digital data recorded in the memory an algorithmic model of statistical analysis to determine a singularity, discriminate by the control circuit and transmit to the DCU, a sporadic error associated with the singularity, being information on the state of the active air flow regulation system (1).Detailed description of non-limiting examples of embodiment

[0053] The present invention will be better understood upon reading the description of various non-limiting variant embodiments which follows, referring to the appended drawings where:

[0054] represents a schematic view of an active air flow regulation system controlled by a stepper-type motor according to the invention,

[0055] represents a schematic view of an alternative embodiment of an active air flow regulation system controlled by a BLDC type motor called a sensor according to the invention,

[0056] represents a schematic view of an alternative embodiment of an active air flow regulation system controlled by a sensorless BLDC type motor according to the invention,

[0057] represents an example of a diagnostic algorithm for fault identification dedicated to a shutter system,

[0058] represents an example of a learning algorithm for recognizing failures of the algorithm presented in,

[0059] represents an exploded view of an active air control system with shutters,

[0060] represents a perspective view of an active air curtain regulation system,

[0061] ,

[0062] ,

[0063] ,

[0064] ,

[0065] Figures 8 to 12 illustrate different examples of malfunctions related to the system represented in,

[0066] ,

[0067] ,

[0068] ,

[0069] Figures 13 to 16 illustrate different examples of malfunctions related to the system represented in,

[0070]

[0071]

[0072]

[0073] Figure 17 (17a, 17b, 17c, 17d) shows the Fresnel diagram of a three-phase motor allowing the motor load angle to be visualized,

[0074] represents a method of acquiring a series of data, for the identification of a singularity, based on a measurement of the power consumed by the engine,

[0075] illustrates an example of a supervised learning algorithm that can serve as a decision-making basis for the algorithm presented in.

[0076] General principle of an active air flow regulation system

[0077] The figure represents a general view of an active air flow regulation system (1). This regulation system is composed of an obstructing mechanical member (2) which is positioned on the grille of the automobile and is capable of obstructing the arrival of the air flow on the radiator, and comprises a mechatronic system (3) controlling the positioning of the obstructing mechanical member (2), such as flaps or a roller, allowing the modulation of the air flow received by the radiator, depending on the state of said obstructing mechanical member (2).

[0078] The aim of the invention is to automate the detection and characterization of an anomaly in the mechanical and / or aeraulic elements for regulating the air flow, in particular the disappearance of a flap, or the modification of the geometry of a flap resulting for example from an impact, even if these anomalies do not modify the general kinematics of the mechatronic air flow regulation system. Of course, the invention also makes it possible to detect anomalies in the mechatronic air flow regulation system, for example a blockage or seizure.

[0079] The mechatronic system (3) comprises a movement transmission chain (20) driven by a polyphase synchronous motor with permanent magnets, hereinafter called a magnet motor (10), controlled by an electronic control circuit (30), for example a three-phase motor as illustrated by the.

[0080] The invention consists in exploiting the signals available on the electronic control circuit (30) of the mechatronic system (3), without requiring the addition of one or more additional sensors on the obstructing mechanical member (2) of the active air flow regulation system (1), so that the mechatronic system (3) according to the invention makes it possible to provide diagnostic information when it is integrated into an active air flow regulation system (1) initially lacking this functionality. However, when additional sensors are already implemented in the active air flow regulation system (1), for example on the obstructing mechanical member (2), the invention can also exploit the signals from these sensors provided that they are processed by the electronic control circuit (30).

[0081] The case presented illustrates a configuration in which the magnet motor (10) has step-by-step control. The mechatronic system (3) is provided with: an electrical connector (6) collecting the electrical power supply (5) coming from the vehicle's DC electrical network, electrical communication lines (7) composed of commands coming from the vehicle's domain control unit or DCU (8) and information generated by the mechatronic system (3) itself and intended for said DCU (8) of the vehicle, an electrical protection and filtering module (9) making it possible to comply with the applicable electrical and electromagnetic standards, a control circuit (11) provided with memories, such as a microcontroller, generating the vehicle communication, the control functions and the control of the magnet motor (10) by association with a transistor control module, of the magnet motor (10) generating the desired displacement and torque,of a polyphase inverter (12), preferably two-phase or three-phase, composed of MOSFET type transistors supplying the magnet motor (10) with electrical energy, of a movement transmission chain (20), for reducing or transforming the movement of the rotor of the magnet motor (10), the input stage of which is driven by the rotor of the magnet motor (10) and the output stage (21) of which is coupled to the obstructing mechanical member (2).,

[0082] It is important to note that the control circuit (11), the transistor control module and the polyphase inverter (12) can be integrated into a specific component (13) known to those skilled in the art by the acronym SOC (System On Chip). Preferably, the data sequence is generated by the combination of at least two pieces of information of different types, these types being able to come from, without being limiting, the following elements: one (or more) vibration sensors (13) measuring vibration levels of a mobile sub-assembly taking into account the context of the active air flow regulation system (1), one (or more) temperature sensors (14) measuring the internal temperature of the mechatronic system (3) and making it possible to deduce the local temperature of a region of the active air flow regulation system (1),one (or more) position sensor (15) continuously measuring the position of the rotor of the magnet motor (10) or of a moving element in the mechatronic system (3) and making it possible to reconstruct the load angle of the magnet motor (10), one (or more) electrical sensor (16) measuring one or more electrical quantities, such as voltages or currents, at the level of the polyphase inverter (12) and which, when combined, make it possible to reconstruct the load angle of the magnet motor (10).,

[0083] Combining information from several types of sensors can be advantageous when there is a strong economic pressure on the components integrated in the actuator, for example very basic control electronics or low sensitivity sensors, which do not allow extracting a singularity from information coming from a single type of sensor. This singularity can nevertheless be isolated by the cooperation of weak signals, it is then identified in a sequence of sampled data resulting from the aggregation of data coming from sources of different types. However, this embodiment is not limiting because when the economic pressure disappears, it can be considered to use a more complex system and the sporadic error can be extracted from a sequence of data presenting information of a single type.

[0084] In the example of the, a typical scenario can be described as follows. The control circuit (11) receives a movement instruction from the DCU (8), it then generates the appropriate control sequence of the switches of the polyphase inverter (12) so as to generate said movement. The movement instruction from the DCU (8) can be an opening or closing movement, complete or partial, or an absolute position of the stroke to be reached. The invention consists in acquiring a data sequence by the control circuit (11), recording it in the memory, analyzing it using digital processing capable of detecting a singularity and transmitting to the DCU (8) a sporadic error associated with this singularity via the communication lines (7).

[0085] The data sequence can be acquired for any movement, or episodically according to an instruction from the DCU (8) or the control circuit (11).

[0086] The control circuit (11) is capable of extracting a singularity from this recorded data sequence using a more or less complex statistical processing depending on the functional requirements, linked to the types of malfunctions sought and the desired sensitivity. For example, a fault linked to a sudden deterioration, such as the breakage of a shutter blocking the travel of the obstructing mechanical member (2), has a signature that is much easier to highlight than a progressive aging of the actuator leading to a slight degradation of performance close to the required minimum. This singularity can therefore be the symptom of a wide variety of malfunctions and requires the application of the appropriate processing algorithm to generate a sporadic error resulting from this error in order to transmit it to the DCU (8).Sporadic error means an error code intelligible to the DCU (8) and capable of signaling a wide variety of malfunctions encountered by the actuator, such as the loss of one of the moving elements, the separation of one of the moving elements from the movement transmission chain, the loss of several of the elements of the obstructing mechanical member (2), or even breakage of a part of the movement transmission chain (20), but not limited to these errors.

[0087] As shown, the mechatronic system (3) can integrate various internal sensors to generate the signals used by the control circuit (11). These sensors can be of various types and, without limitation, can be a vibration sensor (13), a temperature sensor (14), a pressure sensor, or even an analog or digital probe measuring the magnetic angle of the rotor. The invention can also judiciously take advantage of the combination of several signals to generate the integrity diagnosis of the active air flow regulation system.

[0088] This is an alternative embodiment of an active air flow regulation system (1) according to the invention. This embodiment differs from that presented in that the control of the magnet motor (11) is of the BLDCsensored type, that is to say that the magnet motor (11) is provided with direct control or FOC in closed loop with direct measurement of the position of its rotor. For this purpose, one or more sensors (15) are provided to inform the microcontroller of the exact position of the rotor of the permanent magnet motor (11), itself generating the desired displacement and torque.

[0089] The present invention is an alternative embodiment of an active air flow regulation system (1) according to the invention. This embodiment differs from that presented in that the control of the magnet motor (11) is of the BLDCsensorless type, that is to say that the position information of the rotor of the magnet motor (11) no longer comes from the position sensors (15) but is estimated from the electrical quantities measured at the terminals of the supply lines of the phases of the magnet motor (11), for example using a measuring device (17) for the phase currents and / or voltages, this measuring device (17) being able to be provided with one or more voltage and / or current sensors.

[0090] Of course, these embodiments of the mechatronic system are not limiting of the invention and the person skilled in the art could easily imagine other alternatives for the production and control of the magnet motor, as for the content of the sensors internal to the mechatronic system used to detect a malfunction.

[0091] Through multiple exemplary embodiments, different types of malfunctions are illustrated, different means for detecting these malfunctions are also presented, as are multiple solutions for extracting a singularity from the information provided by the detection means. Finally, several algorithmic implementations for converting the detected singularity into a tangible error signal are provided. The examples described are provided for illustrative purposes only and are in no way limiting of the invention, the person skilled in the art being able to use part or all of these examples and replace a missing brick with a solution known in the art.

[0092] Detailed description of a variant of a malfunction detection algorithm

[0093] An example of an algorithm (200) describing the diagnosis of fault identification for an active air flow regulation system (1), the obstructing mechanical member of which is a set of flaps, is represented by the flowchart given in. Following the perfect completion of a single or cyclic learning phase (100), described in, this algorithm can be triggered upon receipt of a request from the DCU (8), or be automatically initiated upon the movement of the actuator by the DCU (8).

[0094] An optional step (120) upstream of this algorithm consists of obtaining authorization from the DCU (8) to carry out a diagnosis of the state of the system in the event of diagnosis on demand, failing which, the diagnosis will be activated permanently during any movement carried out by the mechatronic system.

[0095] The first step (210) consists of initiating a movement phase of the obstructing mechanical organ (2) generated by the mechatronic system (3). During this movement phase, step (211) consists of acquiring a series of data by the control circuit (11). The data recording can take place on part or all of the movement of the obstructing member and ends in all cases at step (212), triggered by the end of said movement. The microcontroller then proceeds to an analysis of the data series recorded using a dedicated algorithm to detect a singularity (213), this algorithm being similar to a machine learning or deep learning model. This is followed by a step (214) of classifying the information resulting from the analysis of the data series , giving rise to 4 possible outcomes: No singularity was detected, the system was therefore classified as a “nominal system”. This is followed by a step (220) during which a good functioning code is generated by the mechatronic system (3) for the DCU (8), this code being associated, during a step (221), with the transmission of a confidence rate of the classification carried out previously (214) to the DCU (8). A singularity corresponding to a degradation equivalent to the malfunction of a single active shutter was detected, the system was therefore classified punctually as a “system with a degraded shutter”.This is followed by a step (230) during which a sporadic error code is generated by the mechatronic system (3) for the DCU (8), this code being associated during step (232) with the transmission of a confidence rate of the classification carried out previously, during step (214), as well as with the transmission of the environmental context, during a step (233), in which the diagnosis, steps (210) to (214), was carried out. A singularity corresponding to a degradation equivalent to the malfunction of several active shutters was detected, the system was therefore classified punctually as a “system with significant degradation”.This is followed by a step (231) during which a sporadic error code is generated by the mechatronic system (3) for the DCU (8), this code being associated during step (232) with the transmission of a confidence rate of the classification carried out previously, during step (214), as well as with the transmission of the environmental context, during step (233), in which the diagnosis, steps (210) to (214), was carried out. The classification resulting from the analysis of the data series, carried out in step (214), did not allow convergence towards information with a sufficient confidence rate to be able to disseminate it to the DCU (8).This is followed by a step (240) during which a code corresponding to the state of the “unavailability” of the diagnostic classification is generated and transferred by the mechatronic system (3) to the DCU (8), this code being associated during step (241) with the transmission to the DCU (8) of the environmental context in which the diagnosis, steps (210) to (214), was executed and could not converge.

[0096] Step (250), common to all classes providing tangible information, directs, depending on the confidence level generated during the previous steps, towards a step (260) if the confidence level is greater than or equal to a pre-established threshold, for example 99.99%. During step (260), a consolidated error code or a consolidated good operation code is generated and transferred by the mechatronic system (3) to the DCU (8), which can be used to reliably supply the diagnostic strategy at vehicle level (called OBD). Alternatively, if the confidence level is less than the pre-established threshold, step (250) is followed by a step (270) during which a recommendation for a strategy for consolidating the sporadic error, essentially composed of one or more partial or complete movements of the actuator, will be proposed by the mechatronic system to the DCU.

[0097] During step (300) and in the case where step (241) has been previously executed, the synthesis of an update of the analysis of the series of recorded data (therefore of the dedicated algorithm or of the model) is by the mechatronic system (3) in order to be able to classify in the future the series of data which did not allow the current statistical analysis to converge.

[0098] During step (300) and in the case where step (270) has been previously executed, the synthesis of an update of the analysis of the series of recorded data (therefore of the dedicated algorithm or of the model) is initiated by the mechatronic system (3) in order to be able to classify in the future the series of data with a better level of confidence compared to that which has just been calculated.

[0099] Steps (260) and (300) then lead to step (280) corresponding to the end of the algorithm for identifying malfunctions.

[0100] Note that the consolidation strategy can be processed by the algorithm for the identification of malfunctions presented here or be an alternative algorithm, potentially specific to the consolidation code transferred to the DCU (8), proposed by the system or imposed by the DCU (8). The DCU (8) can preferentially have the responsibility of the strategy to adopt to consolidate the information (i.e. the sporadic error code or the good functioning code).

[0101] The algorithm for malfunction identification described here is given for illustrative purposes only and should in no way be considered as limiting the invention. A person skilled in the art can easily imagine more or less similar alternatives meeting the essential technical need, namely informing the DCU (8) of a divergence from the nominal operation using an error code.

[0102] A sequence (300) describing, by way of example, the backpropagation applied to a learning model is represented by the flowchart shown in. If in step (301) it is revealed that the previous analysis of the data sequence has diverged instead of converging towards a correct classification of the system among the predetermined options, given in, a step (302) of internal algorithmic analysis is executed by the control circuit (11) (or by the DCU (8) of the vehicle) in order to propose an update of the model (i.e. of the algorithm) used to carry out said analysis of the data sequence.During step (303) if an update could be proposed allowing this data series to be classified, while guaranteeing the same performance levels on the classifications of the previous samples, then an official update request can be sent to the vehicle DCU during a step (307), then in the event of agreement, be recorded and activated in the MCU during a step (308). If however no update solution could be highlighted during steps (302) and (303), then the environmental context in which the diagnosis was carried out is recorded, during a step (304), in non-volatile memory of the control circuit (11) (or of the vehicle DCU (8)) to allow the limitations of the current solution to be identified a posteriori, this step ending the sequence.

[0103] If during step (301) it is revealed that the previous analysis of the data sequence generated a classification with a confidence level of less than 99.99%, an internal algorithmic analysis loop is initiated by the control circuit (11) (or by the DCU (8) of the vehicle), in a step (305), in order to propose an update of the model (i.e. of the algorithm) used to carry out said analysis of the data sequence. During step (306), if an update could be proposed allowing the classification to be generated with a confidence level greater than 99.99% while guaranteeing the same confidence levels on the classifications of the previous samples, then an official update request can be sent to the vehicle DCU (8), during a step (307), then in the event of agreement, be recorded and activated in the control circuit (11) during a step (308).If no update solution could be proposed during step (306), the sequence is terminated.

[0104] Different types of malfunctions

[0105] The present invention represents an exemplary embodiment of an active air flow regulation system (1) whose obstructing mechanical member (2) consists of a set of flaps. This active air flow regulation system is positioned on the grille of the automobile, in front of the radiator, and comprises a mechatronic system (3) driving an axle (4) passing through the output toothed wheel of said mechatronic system (3). This axle (4) controls the positioning of the right flaps (23 to 25) and the left flaps (26 to 28) via a transmission (19, 29).

[0106] The present invention represents an alternative example of an active air flow regulation system (1) whose obstructing mechanical member (2) consists of a frame (31) which can be concealed by a curtain (32) which can be rolled around an axis (4) passing through an output toothed wheel of the mechatronic system (3).

[0107] Figures 8 to 16 illustrate in a non-limiting manner different types of malfunctions related to the active air flow regulation system (1), Figures 8 to 12 illustrating malfunctions for an obstructing mechanical member (2) provided with shutters, as presented in, and Figures 13 to 16 illustrating malfunctions for an obstructing mechanical member (2) provided with a curtain, as presented in. These figures represent systems with active grilles whose shutter rotation axes are positioned horizontally, but the invention also extends to any other orientation of the shutter rotation axes.

[0108] Corresponds to a situation where one of the central flaps is missing, following an impact or a break in its pivot axis. It then leaves room for a permanent opening (34) allowing an air flow to pass through, regardless of the orientation controlled by the actuator. However, the missing flap (24) will result in a modification of the forces exerted on the mechatronic system (3) during the change of orientation, and the signature of these forces can be detected by processing the electrical signals directly measured on the power supply and control ports of the mechatronic system (3). In the example described by, a central flap is missing, but the invention is not limited to this scenario and different signatures can be observed when an end flap is missing or even when several flaps are missing, the signature can also vary depending on the combination of missing flaps.

[0109] Figures 9 to 11 correspond to another malfunction situation on the active air flow regulation system by flaps, where all the flaps (26, 27, 28) are present, but one (or more) of them is no longer driven by the movement transmission kinematics (36). This can for example come from the breakage of a connecting rod (37), as shown in the, or from the breakage of a drive lug (38) of the flap as shown in, or even the breakage of the transmission shaft (39) visible in the.

[0110] This malfunction causes a change in the forces exerted on the mechatronic system (3) when changing orientation. The signature of these forces can be detected by processing the electrical signals directly measured on the power and control ports of the mechatronic system (3).

[0111] This corresponds to another malfunction situation, where a shutter (24) is no longer driven and disrupts the opening or closing of the adjacent shutters (23, 25). This behavior can occur when the drive shaft of a shutter breaks and is accompanied by a displacement in the travel of the other shutters. This type of breakage can result in a one-off overtorque necessary to unhinder the shutters in contact, or by a strong overtorque due to a complete blockage of the system in a position located outside the functional stops. Note that the seizure of the drive shaft of a shutter or of all the shutters can also lead to a one-off or general increase in the torque to be provided on the opening or closing travel.

[0112] Figures 13 to 15 correspond to another malfunction situation on the active air flow regulation system by curtain, where an element in the movement transmission kinematics is faulty. This can for example come from the breakage of the transmission axis (4) as illustrated in, from the breakage of a drive cable (33) as illustrated in, or from the tear (40) of the curtain (32) as shown in.

[0113] This results in a change in the forces exerted on the mechatronic system (3) when changing orientation. The signature of these forces is detected by processing the electrical signals directly measured on the power and control ports of the mechatronic system (3).

[0114] This corresponds to another malfunction situation, where the frame (31) of the curtain (32) is deformed or even broken. This type of breakage (35) can result in a one-off overtorque required to move the curtain, or a high overtorque due to a complete blockage of the system. Note that the seizure of the drive shaft of a shutter or of all the shutters can also lead to a one-off or general increase in the torque to be provided on the opening or closing stroke by the mechatronic system (3).

[0115] Detailed description of a first variant of measurement of a singularity

[0116] According to one of the embodiments, the detection of operating singularities is based on the sampling and analysis of the load angle.

[0117] The load angle corresponds to the magnetic angular offset of the rotor relative to the angular position canceling the magnetic torque between the rotor and the stator. A common way to represent this is the use of a Fresnel diagram, as illustrated in Figure 17 by sub-figures a, b, c and d, in which the magnetic states of the rotor and the stator are represented by vectors. In this figure, representing a three-phase machine, the vectors U, V, W are the magnetic states corresponding to the supply of each of the phases of the motor with the same voltage.

[0118] In this representation, the load angle (52) corresponds to the angle between the stator vector (50) and the rotor vector (51) of the magnetic field. The resulting torque at the rotor generated by the electrical supply at the stator varies from zero torque when this angle is equal to 0°, as represented in, to a maximum when the vectors are at 90°, as represented in. The torque, represented by the torque vector (53) is directly proportional to the sine of the load angle (52) and to the supply current. Figures 17b and 17c represent intermediate situations where the load angle is between 0° and 90° so as to visualize the evolution of the torque vector between these values.

[0119] Assuming a movement without any load at the rotor, the load angle (52) is equal to 0° and the stator and rotor vectors are collinear. The actual angular position of the rotor is identical to the command position.

[0120] When a force is applied to the rotor, for example by a braking torque, or a load or a driving torque, the load angle (52) increases and is then no longer equal to 0°. When this load angle (52) exceeds 90°, the torque exerted decreases and can cause a loss of synchronism between the rotor and the stator field, called rotor stall.

[0121] Thus, the measurement of the load angle (52) requires the measurement of the rotor angular position.

[0122] In its most general sense, the invention is compatible with all types of synchronous motor control, but this solution is more specifically dedicated to motors controlled in step-by-step mode. Various solutions then exist for measuring the position of the rotor, and the use of dedicated sensors is preferred, said sensor being able to be an analog magneto-sensitive probe, which nevertheless has the disadvantage of a modest resolution which can show its limits when one seeks to measure very small variations in the load angle (52). Indeed, in the context of actuators with high mechanical reduction, such as those used in the invention, the reduction chain reduces, by a factor equal to the reduction, the perceptible effects on the drive motor of a variation in load experienced by the member to be driven.Also, still because of this reduction, the rotation speed of the rotor is much greater than that of the driven organ, by a factor equal to the reduction. The calculation of the rotor angle at each step, or at each micro-step, therefore requires a very significant and therefore expensive computing resource, often incompatible with the target price of these applications. Thus, instead of measuring the load angle by comparing the stator angle to the absolute rotor angle, reconstructed from two signals of an analog probe in quadrature, it is proposed to drastically reduce the number of measurements of the load angle during a complete rotation of the rotor to improve the angular resolution.

[0123] A first solution consists of always using an analog probe measuring the field of the sensor magnet, but by freeing oneself from the reconstruction of the absolute rotor angle, a source of inaccuracies and consumer of computing resources. We then wish to measure only the zero crossings of said probe, which are those with the best precision because these are where the field variations are the most significant. To correctly identify this zero crossing, it is necessary to measure and record the maximum value and the minimum value of the probe for each magnetic period, the zero crossing then being reconstructed as the average value between the maximum and minimum values ​​of the previous step.This makes it possible to provide insensitivity to variations in the amplitude of the magnetization, either slow and irreversible variations due to aging, or reversible and shorter-term variations due, for example, to temperature variations. Measuring the load angle therefore consists of: triggering a counter,. , steps, or micro-steps, stop the counter when a zero crossing, , of the analog probe is measured, preferably using the formula , And being respectively the maximum and minimum values ​​of the turn ,save for the tour the maximum and minimum values ​​measured by the analog probe, calculate the load angle thanks to the formula , being the number of steps or microsteps over a mechanical period.

[0124] A second solution consists of replacing the analog probe with a digital probe. This solution slightly degrades the accuracy obtained because it does not allow the value of the transition threshold to be adjusted for each magnetic period according to the measurements made during the previous period, but presents both a financial saving and a reduction in the necessary computing resources. Indeed, by integrating a digital probe, it is possible to use a microcontroller without an analog input and it is no longer necessary to perform the reconstruction, by calculation, of the rotor angle. The method for measuring the load angle is very similar to the previous solution and consists of: starting a step counter, or micro-steps, when a zero crossing of the digital probe is measured stopping the counter when a zero crossing of the digital probe is measured calculating the load angle thanks to the formula .

[0125] For both solutions, the measurement of the charge angle over an electrical period, , perhaps averaged according to the formula ,

[0126] And being respectively the number of steps counted for triggering the counter at a predetermined rotor angle

[0127] To obtain better accuracy, the movement can be analyzed over a large number of electrical periods, or even one or more revolutions of the reducer output shaft, it is then considered to use a sliding average of the load angle on samples, such as a weighted moving average, when we want to give more weight to the most recently measured samples:

[0128]

[0129] being the average measurement of the load angle taken for the electric tower and being the measurement of the load angle made for the electric tower. It should be noted that good results are obtained with a number of samples between 50 and 100. Of course, other types of moving averages can be considered depending on the precision required and the memory and computing resources available.

[0130] Detailed description of a second variant of measuring a singularity

[0131] According to one of the embodiment variants, the detection of operating singularities is based on the sampling and analysis of the electrical signal measured at the terminals of an RC filter in series with the power supply of the mechatronic system (3), also called an actuator, comprising a polyphase motor coupled with a so-called self-commutated control, known by those skilled in the art as "BLDC control" and carried by the electronic module integrated into the mechatronic system (3).

[0132] The simplified electrical diagram of a three-phase driver according to the invention, using six transistors Q1 to Q6, Q1 and Q4 (respectively Q2 and Q5, Q3 and Q6) controlling the current flowing through phase C (respectively B and A). The resistor (60) is a sampling resistor which is used to measure the sum of the currents flowing through each phase of the magnet motor (10), via the RC filter (63), composed of a resistor (61) and a capacitor (62). The output of the RC filter (63) will be acquired in the form of voltage using the analog / digital converter and will be transformed and preprocessed continuously into information by a microcontroller for example.

[0133] Concise description of alternatives for measuring a singularity

[0134] Another parameter that could be interesting to measure to detect a fault is the number of steps taken by a motor controlled in stepper mode to move from one stop position to another. This type of quantity is particularly useful for detecting a blocking fault, as the effective travel can be significantly reduced. A detailed example is also proposed to identify a missing shutter using this parameter.

[0135] The measurement of a singularity is not limited to quantities functional to the electric motor, such as the measurement of the current or the measurement of the load angle as presented previously, but can just as easily come from a dedicated sensor but internal to the actuator. We can in particular think of a pressure or temperature sensor whose variations would be proportional to the incoming air flow. It would therefore be entirely possible to develop different scenarios to make the measurement of these sensors sensitive to the failures mentioned above.

[0136] Description of an example of a singularity identification algorithm

[0137] A means of detecting singularity, for example compatible with the current measurement presented in the description of the, is to identify in the recorded data series, a value exceeding the expected value beyond several standard deviations. This is described here using the formalism developed for current measurement, but can quite easily be used for other types of measured data. Singularity detection is carried out by digitally processing the evolution of a physical value consisting of the sum of the current I of the N phases of the polyphase motor, measured in the resistor (60). The current has a stable value during nominal operation in synchronous mode, regular and excluding symptomatic behavior of the rotor of the geared motor, but this value evolves towards a different and more irregular value when the geared motor is subjected to an abnormal or faulty active air flow regulation system (1).

[0138] The processing of the variation of the current I measured in the resistor (60) is sampled, using an analog / digital converter of a microcontroller for example, and the standard deviation σ of the amplitude values ​​of the current measured at the terminals of the resistor (60) on N samples is calculated on a sliding or fixed time window which is less than or equal to the duration of the movement associated with the acquisition of the information.

[0139] The characterization of the operating mode and the detection of failures is carried out by analyzing the variation of this standard deviation σ possibly supplemented by the analysis of the evolution of the median value on N samples of the current measured at the terminals of R1:A stable standard deviation σ below a threshold value measured in nominal operation, corresponds to operation without failureAn increasing standard deviation exceeding the threshold value and / or a significant increase in the median current measured at the terminals of R1 corresponds to a seizure of one or more flaps and / or their drive mechanism and therefore a drift of the fluidic controlAn increasing standard deviation exceeding the threshold value and / or a significant decrease in the median current measured at the terminals of R1 corresponds to a loss of one or more flaps, to a blockage in the open position, to a partial destruction of one or more flaps, and therefore a drift of the fluidic control.For example, the value N. 2 * σ 2 is compared with the value of the total current I of the phases. This digital processing makes it possible to discriminate between areas of regular operation and areas of abnormal operation, and to set a standard deviation threshold E for the values ​​N 2 * σ 2 from which the microcontroller, or ASIC, decides that the geared motor is driving a faulty gate mechanism. Without limitation, the use of a combination of several other types of statistical analysis algorithmic models is possible. Description of a variant of a singularity identification algorithm

[0140] In an alternative embodiment, the identification of the singularity is done using a learning algorithm, this algorithm being able to be trained to discriminate the type of error encountered, such as a missing flap or a broken flap blocking the system, but also being able to discriminate the location of the fault, for example a missing flap located at one end or in the middle.

[0141] In the context where the member to be driven is made up of a set of shutters, the fault detection algorithm can benefit from a constitutive modification of the member to be driven to improve detection. It is thus envisaged to provide a signature specific to each shutter that can be reliably discriminated by the algorithm. This can be a binary code, as done in patent application US9810138B2, with the addition of a point resistive element for each shutter, this resistive element being detectable in the form of a point overtorque to be generated by the actuator, the point resistive elements being judiciously placed so that each induces an overtorque at a different time in the opening or closing stroke. The absence of a shutter is then signified by the absence of a friction peak during a complete movement of the member to be driven.It is worth noting that such a signature can be measured by a very simple algorithm detecting, for example, a peak of current consumed by the actuator exceeding a certain threshold at predetermined positions to approve the presence of each shutter.

[0142] In the context where the organ to be driven can be a set of shutters or a curtain, the signature can be more tenuous and benefit from the power of deep learning by providing each shutter of the actuator with a specific surface area, a specific mass, or even a specific profile. This can lead to the measurement of a very specific signature, for example linked to the inertia of the system, to its experienced air pressure, or even by obtaining a vibration specific to each shutter.

[0143] Description of other variants of singularity identification algorithm

[0144] It should be noted that there are a multitude of possible variants of singularity identification algorithms compatible with the invention, each with its advantages and disadvantages. For example, some are more robust, but must benefit from a certain actuation sequence of the member to be driven to measure a defect, others must necessarily be carried out while stationary, or others necessarily in motion, still others require the addition of external data such as vehicle speed, wind speed or even temperature, so that the chosen solution or all of the chosen solutions is defined by the specifications. In a non-limiting manner, some examples of identification algorithms are provided below.

[0145] According to a variant of the invention, the singularity identification algorithm is based on a measurement of the variation of the hydrodynamic torque. When a shutter is missing or the curtain is torn, the pressure exerted on the entire blackout device decreases, which results in a reduction in the torque to be provided by the actuator to perform an opening or closing movement. The hydrodynamic torque, , which can be expressed as a function of the actuator stroke, , according to the formula:

[0146] ,

[0147] Or is a coefficient linked to the geometry of the occulting device and to the inlet air flow, being the maximum opening area and the opening surface for the race , this surface expressing itself differently in the context of a healthy or broken actuator.

[0148] The algorithm can then directly exploit a measurement of torque, current, or load angle, as a sequence of sampled digital data, so as to identify a singularity or can, to increase its precision, acquire this data throughout the stroke of the actuator, so as to identify a singularity on the work, , provided by the actuator during the movement between And , according to the formula:

[0149]

[0150] The measured work can then be simply compared to a reference curve corresponding to a healthy actuator. It should be noted that this method consumes very little computing resources but is highly dependent on the wind speed and the vehicle. However, if the available computing power allows for the implementation of a more intelligent algorithm, it is possible to perform several specific movements of the actuator to estimate the relative speed of the air incident on the occulting device.

[0151] According to another variant of the invention, when the occulting system is a set of shutters, the singularity identification algorithm can be based on a measurement of the stiffness of the system.

[0152] According to another variant of the invention, the singularity identification algorithm can be based on a measurement of the inertia of the system. If the AGS actuator is controlled so that the angular speed of the actuator rotor increases linearly between two instants, then the acceleration of the rotor is constant. By performing an opening cycle with the vehicle stationary, a fault, such as a missing shutter, can be detected by measuring a variation in inertia during this cycle compared to the reference value recorded in the system. This variation in inertia could alternatively be measured from a reading of the torque, the current or even the load angle, these examples not being limiting of the invention.

[0153] Detailed description of a learning algorithm variant

[0154] According to one of the embodiment variants, the algorithm describing the malfunction detection diagnosis requires an initial supervised learning phase fed with a determined number of cycles, the system being in a nominal (or functional) mode only. An example of an algorithm of said supervised learning is represented by the flowchart of the.

[0155] We note the number of learning cycles X necessary to provide a result, during the entire duration of use of the classification system of the sampled acquisition of the data series of interest, with the optimal confidence rate. The confidence rate is a variable that increases as a function of X, it shows rapid growth as a function of X and then only changes slightly beyond a certain value of X. It is therefore possible to obtain a confidence rate close to 100%, 100% meaning total certainty and 0% meaning total uncertainty, with a limited number of learning cycles, for example 10 or 20 depending on the sensitivity of the algorithm and for a desired high confidence level greater than 99%.

[0156] The learning algorithm (100) is triggered each time the actuator is moved by the control circuit (11), which we call step (110), followed by a step (111) of verifying the perfect completion of the initial learning phase, this being able to be done by reading a memory register. If the learning phase is already successfully completed, then the learning algorithm ends and triggers the failure detection algorithm (200).

[0157] If, on the other hand, the learning phase is not deemed to be completed, the “non-operational” state of the malfunction detection system is generated by the mechatronic system (3) for the DCU (8) in a step (112). Then the movement is initiated by the mechatronic system (3). Step (113) of the learning algorithm consists of a sampled acquisition of the data of interest by the control circuit (11) and recording this data in the internal memory of the mechatronic system (3). Step (113) ends when the end of the movement of the actuator is detected. The learning algorithm then triggers, in step (114), the statistical analysis of the data recorded for this learning cycle during step (113).The following step (115) consists of a verification by the control circuit (11) of the correct conformity of the movement carried out by the actuator and the correct environmental conditions (i.e. the correct context) leading to the decision of whether or not to keep the data of the current cycle as reference data for the learning algorithm. In the positive case, these data are recorded as reference data in a memory register during a final step (116). The recording of the reference data preferably includes contextual data of the recorded cycle, such as for example information on temperature, vehicle speed, air pressure at the flaps, etc. which may come from external sensors, associated information then being able to be generated by the mechatronic system (3) to the DCU (8).If the verification concludes that the data is inadequate, the latter is not recorded, the cycle is therefore not considered a reference cycle and information can be generated by the mechatronic system (3) to inform the DCU (8).

[0158] Implementation mode where data processing is shared between the computer and the DCU (8)

[0159] In the preceding description, it is generally planned to carry out all of the algorithmic processing via the control circuit (11) only, which then transmits information characterizing the nature of the malfunction deduced on the air chain, with or without mechanical anomaly.

[0160] However, it is also possible to distribute the algorithmic processing between the control circuit (11) and the vehicle DCU (8), which generally has a significantly higher computing capacity and which also allows other vehicle data to be optionally taken into account in order to achieve a more advanced integration of the vehicle diagnosis. This implementation must, however, take into account the fact that the transmission BUS between a device and the vehicle DCU has a limited bandwidth and does not allow the raw data from the sensors to be transmitted directly to the vehicle DCU.The implementation in "distributed" mode of the invention then consists of carrying out on the control circuit (11) high-frequency pre-processing of the raw data, to calculate pre-processed data sets which are then transmitted, with a periodicity compatible with the bandwidth of the communication lines (7), for example according to a LIN protocol, from the control circuit (11) to the DCU (8) of the vehicle, the latter using these data sets transmitted at low frequency to carry out post-processing at a second frequency, lower than the first frequency, to enable it to complete the analysis, and discriminate a sporadic error which then enables the DCU (8) to characterize the aeraulic and / or mechanical failure of the air flow regulation system.

Claims

A method for diagnosing the operation of an active airflow control system (1) for a vehicle, comprising: a mechanical obstruction (2), a mechatronic system (3) equipped with a housing containing a magnet motor (10) driven by a control circuit (11) and coupled to a motion transformation for moving the mechanical obstruction (2), and electronic communication lines (7) for exchanging information with the vehicle's DCU (8) or receiving commands from the vehicle's DCU (8), characterized in that said method comprises a series of steps consisting of: recording, during an operating cycle of the active airflow control system (1), a digital sequence S of data in a memory , said data being acquired by said control circuit (11) internal to the mechatronic system (3), apply to said digital data sequence stored in memory an algorithmic model of statistical analysis to determine a singularity, discriminate a sporadic error associated with said singularity to deliver information on the state of the active airflow regulation system (1). Method for diagnosing the operation of an active airflow control system (1) according to the preceding claim characterized in that said sporadic error discrimination step is carried out by the control circuit (11), said control circuit (11) transmitting to the DCU information representative of said discriminated sporadic error. Method for diagnosing the operation of an active airflow control system (1) according to claim 1 characterized in that said sequence of digital data recorded in the memory is subjected to: pre-processing with a first processing frequency by said control circuit (11) and post-processing by said DCU / ECU (8) of the vehicle with a second processing frequency lower than said first processing frequency allowing to complete the analysis, and to discriminate a sporadic error, the set of data pre-processed by said control circuit (11) being transmitted to said DCU / ECU (8) via the control lines (7). Method for diagnosing the operation of an active airflow control system (1) according to any one of the preceding claims characterized in that said digital data sequence S stored in memory comprises: data of a first type and data of at least a second type different from the first type. Method for diagnosing the operation of an active airflow control system (1) according to any one of the preceding claims characterized in that said method comprises as additional steps: consolidation of the sporadic error following an instruction transmitted by the DCU (8) of the vehicle giving rise to a consolidated diagnostic state, transmission of the consolidated diagnostic state to the DCU (8) of the vehicle. Method for diagnosing the operation of an active airflow control system (1) according to claim 1 or 5 characterized in that one of said data is a measurement of the motor phase current. Method for diagnosing the operation of an active airflow control system (1) according to claim 1 or 5 characterized in that one of said data is a measurement of the load angle of the rotor of the magnet motor (10). Method for diagnosing the operation of an active airflow control system (1) according to claim 1 characterized in that the statistical analysis algorithm is provided with a learning sequence. Method for diagnosing the operation of an active airflow control system (1) according to claim 1 characterized in that the statistical analysis algorithm is provided with a grouping selection method. Method for diagnosing the operation of an active airflow regulation system (1) according to claim 1 characterized in that the obstructing mechanical part (2) is devoid of elements, dedicated to diagnosis, external to said mechatronic system (3). Method for diagnosing the operation of an active airflow control system (1) according to claim 1 or 5 characterized in that one of said data relates to the operation of the magnet motor (10) of the mechatronic system (3). Method for diagnosing the operation of an active airflow control system (1) according to claim 1 characterized in that said algorithmic statistical analysis model for determining a singularity is provided with a comparison between the sampled numerical data sequence recorded and a reference data card . Method for diagnosing the operation of an active airflow control system (1) according to claim 1 characterized in that said algorithmic statistical analysis model for determining a singularity is provided with a detection of at least one singular point in the sampled numerical data sequence recorded. Method for diagnosing the operation of an active airflow control system (1) according to claim 1 characterized in that said algorithmic statistical analysis model for determining a singularity consists of submitting the sampled numerical data sequence recorded to a model obtained by training a neural network from learning data corresponding to the expected functioning of the obstructing mechanical organ (2). A method for diagnosing the operation of an active airflow control system (1) according to any one of the preceding claims, characterized in that a reference sampled digital data sequence is recorded for each mechatronic system (3) at the end of the assembly line of the system (1), or of the system (1) on the vehicle. A method for diagnosing the operation of an active airflow control system (1) according to the preceding claim, characterized in that said statistical analysis algorithm model is capable of comparing the sampled numerical data sequence with the reference sampled numerical data sequence in order to detect a behavioral drift that could lead to a degradation of the mechatronic system or that could validate a normal wear behavior of the system. Method for diagnosing the operation of an active airflow control system (1) according to claim 1, characterized in that the sampled digital data sequence recorded is derived from a numerical data function of the mechanical load in the active grid system. Method for diagnosing the operation of an active airflow control system (1) according to the preceding claim, characterized in that the identification of a singularity in the sampled digital data sequence recorded is permitted with knowledge of: the masses or inertias to be moved in the active grid system, and / or the mechanical frictions in said system, and / or the aerodynamic load exerted on said system, and / or the temperature of the system. Electromechanical system for active airflow regulation (1) for vehicle, comprising: a mechanical obstructing element (2), a mechatronic system (3), equipped with a housing containing a magnet motor (10) controlled by a control circuit (11) and coupled to a motion transformation enabling the mechanical obstructing element (2) to be moved, electronic communication lines (7) for exchanging information with the vehicle's DCU (8) or receiving commands from the vehicle's DCU (8), characterized in that said method comprises a microcontroller or a microprocessor executing a computer program stored in its read-only memory, controlling a series of steps corresponding to at least one of the preceding claims.

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