Method for diagnosing the operation of an active airflow control system.

Abstract

The present invention relates to an active airflow control system and a method for diagnosing the operation of an active airflow control system (1) for a vehicle, comprising: a mechanical obstructing element (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 enabling the movement of the mechanical obstructing element (2), 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 sequence of sampled digital data in a memory, where the sequence of sampled digital data consists of a set of digital data,acquired by said control circuit (11) internal to the mechatronic system (3), apply to said sampled digital data sequence stored in memory an algorithmic statistical analysis model to determine a singularity, discriminate by the control circuit (11) and transmit to the DCU, a sporadic error code associated with the singularity, said sporadic error code being information on the state of the active airflow regulation system (1). Figure from the abstract: Figure 1,

Description

Title of the invention: Method for diagnosing the operation of an active airflow regulation system. Scope of the invention

[0001] The present invention relates to the field of thermal control of the engine of an internal combustion, electric, or hybrid motor vehicle by means of active airflow regulation systems driving an obstructing device, for example, motorized air flaps in the grille or curtains. This obstructing device is designed to modify the amount of air passing through the vehicle's radiator and engine compartment and to adjust the airflow in order to manage the efficiency of the vehicle's engine and the various main components (internal combustion 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 has the function of maintaining 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: therefore, there is less gas in the cylinder and the risks of auto-ignition (and knocking) are higher. Furthermore, as the components deform more, there is a significant risk of breakage.

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

[0005] On many modern vehicles, the grille fins located in front of the radiator are articulated and motorized to control the airflow through the radiator. Indeed, when the vehicle is traveling at high speed and under low load, a large airflow is not necessarily essential for proper engine cooling.

[0006] Furthermore, the air flowing through the radiator disrupts the main airflow around the vehicle and therefore generates aerodynamic drag. It can therefore be advantageous to block the air intakes located upstream of the radiator using flaps. In some cases, the flaps have only two positions (fully open or closed).

[0007] Unfortunately, the CO2 emission reduction benefits of these active flap grilles are lost if a flap remains stuck in the open or closed position, or in an intermediate position, or if it is damaged, for example, in an impact, or if one or more flaps are missing. The proper functioning of an active flap grille impacts the vehicle's pollutant emissions; therefore, it is of great importance, even legally required, to ensure the correct operation of the air flap system and to inform the driver of any deterioration in fuel consumption and emissions at each drive, prompting them to take the vehicle to a garage as soon as possible to restore it to the legal emission level. Solutions have been developed for this purpose to verify the generic functionality of the air flap arrangement using an on-board diagnostic system.For electric vehicles, thermal management of the motor and batteries is also a significant challenge, resulting in the same requirements, as well as aerodynamic efficiency: a defective active airflow regulation system can penalize the vehicle's air penetration and therefore negatively impact the vehicle's range. State of the art

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

[0009] US patent 10710450 describes, for example, a grille with active air flaps for a motor vehicle comprising a housing having an air passage opening, at least two air flaps movably mounted on the housing, and a force- and motion-transmitting drive coupled to the two air flaps. Sensors are associated with the air flaps so that a position outside the closed and open position of the air flaps can be detected by the sensors formed by switches.

[0010] US patent 10953740B2 describes a grille with movable air flaps for modifying an effective flow area. A Hall effect sensor and a magnetizable material cooperate such that the Hall effect sensor, which detects a magnetic field emanating from the sensing section, emits a first detection signal when a flap is in a reference position and a different signal when at least one air flap is not in the prescribed position.

[0011] Such sensors require a location to house them in the grille, must be powered and increase the cost of assembly as well as the vulnerability to failures because they are exposed to strong stresses (wind, rain, small impacts with gravel...) and also increase the cost of the air control system.

[0012] US patent 9810138 describes another solution providing diagnostics without requiring a separate sensor for each shutter. This solution is based on the provision, for at least one shutter, of a resistive element associated with said shutter, designed to modify, or increase, the torque required to actuate the shutter. A diagnostic device acquires at least one power supply parameter as information concerning the state of the arrangement of the closing elements.

[0013] A similar solution is described in patent application DE102018108162A1 proposing a method for controlling an actuation device with a first body and a second body, the second body having a contact surface and the contact surface having a zone of change of mechanical resistance at a defined position. This method comprises the following steps: - to control the actuator to move the first body relative to the second body, thereby causing the second body to slide along its contact surface. - detect a variation of a motor parameter of the drive actuator which is dependent on an operating parameter, such as motor current or torque, and based on the motor parameter, knowing that the first body is located on the part of the contact surface with a modified mechanical resistance.

[0014] This second family of solutions is also not entirely satisfactory because it involves the presence of a disruptive component, creating a known resistance whose presence must be regularly checked. This creates several problems. This disruptive resistance will modify, at least marginally, the operation of the active flaps and impair the operation of the grille. This resistance is also likely to change over time due to wear, which can alter the reference resistance and disrupt detection. This family of solutions involves performing complete opening or closing movements of the system, reducing the system's lifespan and introducing unwanted complete movements that may be incompatible with the control strategies desired for new electric vehicle platforms.This family of solutions involves transmitting all information regarding the state of the closure element arrangement over a communication network with very limited bandwidth (typically the maximum bandwidth of the LIN bus is less than or equal to 19200 bits per second) to the vehicle's control electronics (DCU for Domain Control Unit) so that it can process all the data and diagnose the system's state. This bandwidth incompatibility results in slow data storage and transmission, delaying diagnosis, or transmitting only part of this information (called undersampling), degrading its reliability and accuracy. portionly.

[0015] German patent application DE 102018201469Al describes an air damper device, in which the reference operating data set represents the adjustment path of at least one air damper between its predetermined operating positions, with a diagnostic command set further stored in the memory device, the execution of which by the control device causes a diagnostic adjustment of at least one air damper between its predetermined operating positions.

[0016] The control device is designed to control the adjustment drive at least once for a diagnostic adjustment of at least one air damper based on the occurrence of a predetermined event according to the diagnostic command set. The control device is further designed to compare the operating data (operating data set) recorded by the data acquisition device during the diagnostic adjustment and representing the adjustment path of the diagnostic adjustment with the reference operating data set and, based on the result of the comparison, to generate a diagnostic data set and transmit it via a signal transmission line to a data processing device.

[0017] This third solution is also not entirely satisfactory because it requires the shutter to execute a cycle dedicated to diagnosis, and requires additional sensors to detect the stop situation or a reference position. Disadvantages of prior art

[0018] Prior art solutions all have the disadvantage of involving a disruption of the normal operation of the active flap system, by the addition of additional sensors, mechanical modifications of the system, imposing a movement cycle often complete specific to the diagnosis, involving the transmission of all information acquisitions concerning the state of the arrangement of the closing elements on the communication network with very limited bandwidth to the vehicle control electronics in order to allow it to process the whole and diagnose the state of the system and the need for complete opening or closing movements to allow a complete diagnosis of all the moving elements composing said system.

[0019] Moreover, solutions involving the use of torque (or DC current) measurement require closed-loop motor control, which is incompatible with stepper motor control, making prior art solutions unusable or difficult in this context. Solution provided by the invention

[0020] In order to overcome these drawbacks and to enable a diagnostic function for the entire system without modifying the obstructing component, by exploiting the internal data of the mechatronic system activating the obstructing component or the data generated by the obstructing component, as appropriate. To this end, the present invention relates, in its most general sense, to a method for diagnosing the operation of an active airflow control system for a vehicle, comprising: • an obstructing mechanical component, • a mechatronic system, equipped with a housing containing a magnet motor driven by a control circuit and coupled to a motion transformation allowing the obstructioning mechanical component to be moved. • electronic communication lines for exchanging information with the vehicle's DCU or receiving commands from the vehicle's DCU, characterized in that said method comprises a series of steps consisting of: • to record, during an operating cycle of the active airflow control system, a sequence of sampled digital data 5 in a memory, where the sequence of sampled digital data consists of a set of digital data, if, acquired by said control circuit internal to the mechatronic system, • apply, to said sampled numerical data sequence S stored in memory, an algorithmic statistical analysis model to determine a singularity, • discriminate by the control circuit and transmit to the DCU, a sporadic error code associated with the singularity, said sporadic error code being information on the state of the active airflow regulation system (1).

[0021] A "sporadic fault code" is a numerical parameter corresponding to a type of intermittent anomaly or control failure, which is recorded in the vehicle's DCU memory and is automatically deleted when the failure no longer occurs after a certain number of drive cycles, unlike a consolidated fault code which remains saved in memory and will interfere with a diagnostic Trouble Code DTC.

[0022] In one variant, when the sporadic error code returned does not correspond to a nominal system state, the diagnostic procedure for the operation of an active airflow control system includes the following additional steps: • the consolidation of the sporadic error following an instruction transmitted by the vehicle's DCU, resulting in a consolidated diagnostic state, • the transmission of the consolidated diagnostic status to the vehicle's DCU.

[0023] In addition, the statistical analysis algorithm can be equipped with a learning sequence.

[0024] Alternatively, the statistical analysis algorithm may be equipped with a grouping selection method.

[0025] In one variant, the obstructing mechanical organ is devoid of elements dedicated to the external diagnosis of the mechatronic system.

[0026] In a permitted variation, the numerical data sr relates to the operation of the magnet motor of the mechatronic system.

[0027] In an anticipated alternative, the numerical data si is a measure of the motor phase current.

[0028] In this case, said numerical data may be a measurement of the load angle of the rotor of the magnet motor.

[0029] In one variant, the algorithmic statistical analysis model for determining a singularity is equipped with a comparison between the recorded sampled numerical data sequence 5 and a reference data map.

[0030] In a second variant, the algorithmic statistical analysis model for determining a singularity is equipped with a detection of a singular point in the recorded sampled numerical data sequence S.

[0031] In a third variant, the algorithmic statistical analysis model for determining a singularity consists of submitting the sampled numerical data sequence S recorded to a model obtained by training a neural network from training data corresponding to the nominal operation of the obstructing mechanical organ.

[0032] In all cases, a reference sampled digital data sequence Sr can be recorded for each mechatronic system at the end of the assembly line.

[0033] In this case, the statistical analysis algorithmic model can compare 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.

[0034] In one variant, the recorded sampled digital data sequence 5 is derived from a digital data function of the mechanical load in the active grid system.

[0035] In this case, the identification of a singularity in the recorded sampled digital data sequence 5 can be permitted by knowing: • the masses or inertials 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.

[0036] For the purposes of this patent, "singularity" shall mean a point or subset of points in 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 a Gaussian statistic, or a point or 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 regime.

[0037] In particular, a distinction is made between the data for which the signature (torque, current, position ...) 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.

[0038] According to one variant, said algorithmic statistical analysis model for determining a singularity consists of submitting the sampled numerical data sequence 5 recorded to a model obtained by training a neural network from training data corresponding to the nominal operation of the obstructing mechanical organ.

[0039] According to a particular embodiment, a reference sampled digital data sequence is recorded for each mechatronic system at the end of the assembly line.

[0040] Advantageously, said statistical analysis algorithmic model is capable of comparing the sampled numerical data sequence with the reference sampled numerical data sequence in order to detect behavioral drift.

[0041] According to one variant, the recorded sampled digital data sequence 5 is derived from a digital data function of the mechanical load in the active grid system.

[0042] According to particular embodiments, the identification of a singularity in the recorded sampled numerical data sequence 5 is permitted with knowledge: • masses or inertias to be moved within the active grid system, • and / or mechanical friction within said system, • and / or the aerodynamic load exerted on said system.

[0043] The invention also relates to an electromechanical system for active airflow regulation (1) for a vehicle, comprising: - an obstructing mechanical component, - a mechatronic system, equipped with a housing containing a magnet motor (10) controlled by a control circuit and coupled to a motion transformation allowing the movement of the obstructing mechanical part, - Electronic communication lines for exchanging information with the vehicle's DCU or receiving commands from the vehicle's DCU, characterized in that said method comprises a microcontroller or a micro A processor executing a computer program stored in its read-only memory, commanding a series of steps consisting of: - to record, during an operating cycle of the active airflow regulation system, a sequence of sampled digital data S in a memory, where the sequence of sampled digital data consists of a set of digital data, if, acquired by said control circuit internal to the mechatronic system (3), - to apply, to the said sampled numerical data sequence 5 stored in memory, an algorithmic model of statistical analysis to determine a singularity, - discriminate by the control circuit and transmit to the DCU, a sporadic error code associated with the singularity, said sporadic error code being information on the state of the active airflow regulation system (1). Detailed description of non-exhaustive examples of implementation

[0044] The present invention will be better understood upon reading the following description of various non-limiting embodiments, with reference to the accompanying drawings where:

[0045] [Fig.1] [Fig.1] represents a schematic view of an active airflow control system driven by a stepper motor according to the invention,

[0046] [Fig.2] [Fig.2] represents a schematic view of an alternative embodiment of a active airflow regulation system controlled by a BLDC type motor, also known as sensored, according to the invention,

[0047] [Fig.3] [Fig.3] represents a schematic view of an alternative embodiment of a active airflow control system driven by a BLDC type motor, also known as sensorless, according to the invention,

[0048] [Fig.4] [Fig.4] represents an example of a diagnostic algorithm for fault identification dedicated to a damper system,

[0049] [Fig. 5] [Fig. 5] represents an example of a learning algorithm for the recognition of emergence of failures in the algorithm presented in [Fig.4],

[0050] [Fig.6] [Fig.6] shows an exploded view of an active air control system with flaps,

[0051] [Fig.7] [Fig.7] represents a perspective view of an active air control system with a curtain,

[0052] [Fig.8]

[0053] [Fig.9]

[0054] [Fig. 10]

[0055] [Fig. 11]

[0056] [Fig. 12] Figures 8 to 12 illustrate different examples of malfunctions related to the system represented in [Fig. 6],

[0057] [Fig. 13]

[0058] [Fig. 14]

[0059] [Fig. 15]

[0060] [Fig. 16] Figures 13 to 16 illustrate various examples of related malfunctions to the system represented in [Fig.7],

[0061] [Fig. 17a]

[0062] [Fig. 17b]

[0063] [Fig. 17c]

[0064] [Fig. 17d] Figure 17 (17a, 17b, 17c, 17d) represents the Fresnel diagram of a three-phase motor allowing visualization of the motor's load angle,

[0065] [Fig. 18] [Fig. 18] represents a method for acquiring a series of data, for the identification of a singularity, based on a measurement of the power consumed by the motor,

[0066] [Fig. 19] [Fig. 19] illustrates an example of a supervised learning algorithm that can serve as a decision basis for the algorithm presented in [Fig.4].

[0067] General principle of an active airflow control system

[0068] Fig. 1 represents a general view of an active airflow control system (1). This control system consists of a mechanical obstructing element (2) which is positioned on the grille of the automobile and is capable of obstructing the arrival of the airflow on the radiator, and includes a mechatronic system (3) controlling the positioning of the mechanical obstructing element (2), such as flaps or a roller, allowing the modulation of the airflow received by the radiator, depending on the state of said mechanical obstructing element (2).

[0069] The mechatronic system (3) includes a motion transmission chain (20) driven by a permanent magnet polyphase synchronous motor, hereafter referred to as a magnet motor (10), controlled by an electronic control circuit (30), for example a three-phase motor as illustrated by [Fig.1].

[0070] The invention consists of 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 element (2) of the active airflow control system (1), so that the mechatronic system (3) according to the invention can provide diagnostic information when integrated into an active airflow control system (1) initially lacking this functionality. However, when additional sensors are already implemented in the active airflow control system (1), for example on the obstructing mechanical element (2), the invention can also exploit the signals from these sensors provided that they are processed by the electronic control circuit (30).

[0071] The case presented in [Fig. 1] illustrates a configuration in which the magnet motor (10) has stepper-type control. The mechatronic system (3) is equipped with: • an electrical connector (6) collecting the electrical power (5) from the vehicle's continuous electrical network, • electrical communication lines (7) consisting of commands from the vehicle's domain control unit or DCU (8) and information generated by the mechatronic system (3) itself and destined for said vehicle's DCU (8), • an electrical protection and filtering module(9) enabling compliance with applicable electrical and electromagnetic standards, • a control circuit (11) equipped with memory, such as a microcontroller, generating vehicle communication, control functions and driving the magnetic motor (10) by association with a transistor control module, • a magnetic motor (10) generating the desired displacement and torque, • a polyphase inverter (12), preferably two-phase or three-phase, composed of MOSFET-type transistors supplying electrical energy to the magnetic motor (10), • of a motion transmission chain (20), for reducing or transforming the motion 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 of which (21) is coupled to the obstructing mechanical element (2).

[0072] It is important to note that the control circuit (11), the transistor driver module, and the polyphase inverter (12) can be integrated into a specific component (13) known to those skilled in the art as a System On Chip (SOC). Preferably, but not exclusively, the data sequence is generated by the following elements, taken alone or in combination: • one (or more) vibration sensor (13) measuring vibration levels of a moving subassembly taking into account the context of the active airflow control system (1), • one (or more) temperature sensor (14) measuring the internal temperature of the mechatronic system (3) and allowing the local temperature of a region of the active airflow control system (1) to be deduced, • 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 allowing the load angle of the magnet motor (10) to be reconstructed, • 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 recombined, allow the load angle of the magnet motor (10) to be reconstructed. • In the example of [Fig. 1], a typical scenario can be described as follows. The control circuit (11) receives a movement instruction from the DCU (8), and then generates the appropriate drive sequence for the switches of the polyphase inverter (12) to generate said movement. The movement instruction from the DCU (8) can be a full or partial opening or closing movement, or an absolute position of the stroke to be achieved. The invention consists of acquiring a data sequence by the control circuit (11), storing it in memory, analyzing it using digital processing capable of detecting a singularity, and transmitting to the DCU (8) a sporadic error code associated with this singularity via the communication lines (7).

[0073] The data sequence can be acquired for any movement, or episodically according to an instruction from the DCU (8) or the control circuit (11).

[0074] The control circuit (11) is capable of extracting a singularity from this recorded data sequence using statistical processing of varying complexity, depending on the functional requirements related to the types of malfunctions sought and the desired sensitivity. For example, a fault linked to sudden deterioration, such as the breakage of a flap blocking the movement of the obstructing mechanical component (2), has a signature that is much easier to identify than gradual aging of the actuator leading to a slight degradation of performance close to the minimum required. 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 code from this error in order to transmit it to the DCU (8). A sporadic error code is understood to be an in Readable by 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 parts, the detachment of one of the moving parts from the motion transmission chain, the loss of several parts of the obstructing mechanical component (2), or even breakage of a part of the motion transmission chain (20), but not limited to these errors. Of course, the error code can also indicate that the system is functioning correctly or that a diagnosis is inconclusive.

[0075] As shown in [Fig. 1], 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 include a vibration sensor (13), a temperature sensor (14), a pressure sensor, or an analog or digital probe measuring the rotor's magnetic angle. The invention can also judiciously take advantage of combining several signals to generate an integrity diagnosis of the active airflow control system.

[0076] Figure 2 presents an alternative embodiment of an active airflow control system (1) according to the invention. This embodiment differs from that presented in Figure 1 in that the control of the permanent magnet motor (11) is of the sensored BLDC type, that is to say, the permanent magnet motor (11) is equipped with direct or closed-loop FOC control with direct measurement of the position of its rotor. To this end, one or more sensors (15) are provided to inform the microcontroller of the exact position of the rotor of the permanent magnet motor (11), which itself generates the desired displacement and torque.

[0077] Figure 3 shows an alternative embodiment of an active airflow control system (1) according to the invention. This embodiment differs from that shown in Figure 2 in that the control of the magnet motor (11) is of the BLDC sensorless 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 power supply lines of the phases of the magnet motor (11), for example by means of a measuring device (17) of the phase currents and / or voltages, this measuring device (17) being able to be equipped with one or more voltage and / or current sensors.

[0078] Of course, these embodiments of the mechatronic system are not limiting to the invention and a person skilled in the art could easily imagine other alternatives for the construction and control of the magnet motor, as well as for the content of the internal sensors in the mechatronic system used to detect a malfunction.

[0079] Through multiple implementation examples, different types of malfunctions are illustrated, as well as different means of detecting these malfunctions. are also presented, along with multiple solutions for extracting a singularity from the information provided by the detection methods. Finally, several algorithmic implementations for converting the detected singularity into a tangible error code are provided. The examples described are for illustrative purposes only and are in no way limiting to the invention; those skilled in the art may use some or all of these examples and replace any missing elements with a solution known from the art.

[0080] Detailed description of a variant of a fault detection algorithm

[0081] An example of an algorithm (200) describing the fault identification diagnosis for an active airflow control system (1), whose obstructing mechanical component is a set of flaps, is represented by the flowchart given in [Fig.4]. Following the successful completion of a single or cyclic learning phase (100), described in [Fig.5], this algorithm can be triggered upon receipt of a request from the DCU (8), or be automatically initiated when the actuator is set in motion by the DCU (8).

[0082] An optional step (120) upstream of this algorithm consists of obtaining authorization from the DCU (8) to perform a diagnosis of the state of the system in case of diagnosis on demand, otherwise the diagnosis will be activated permanently during any movement carried out by the mechatronic system.

[0083] The first step (210) consists of initiating a movement phase of the obstructing mechanical element (2) generated by the mechatronic system (3). During this movement phase, step (211) consists of acquiring a series of data s by the control circuit (11). Data recording may take place over part or all of the movement of the obstructing element and in all cases ends at step (212), triggered by the end of said movement. The microcontroller then performs an analysis of the recorded data series s 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 s, giving rise to 4 possible outcomes: a. No singularity was detected, so the system was classified as a "nominal system". This is followed by a step (220) during which a proper 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 level of the classification previously carried out (214) to the DCU (8). b. A singularity corresponding to a degradation equivalent to the malfunction of a single active flap was detected; therefore, the system was classified punctually as "system with a degraded flap". 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 level 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 executed. c. A singularity corresponding to a degradation equivalent to the malfunction of several active flaps was detected; therefore, the system was temporarily classified 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 is associated, during step (232), with the transmission of a confidence level for the classification previously performed in step (214), as well as with the transmission of the environmental context in step (233), in which the diagnostic, steps (210) to (214), was executed. d. The classification resulting from the analysis of the data series, carried out in step (214), did not converge to information with a sufficient level of confidence to be disseminated to the DCU (8). A subsequent step (240) is then performed in which a code corresponding to the "unavailability" state of the diagnostic classification is generated and transferred by the mechatronic system (3) to the DCU (8). This code is associated, in step (241), with the transmission to the DCU (8) of the environmental context in which the diagnostic, steps (210) to (214), was performed and failed to converge.

[0084] Step (250), common to all classes providing tangible information, directs the process, based on the confidence level generated in the preceding steps, to 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 fault code or a consolidated functional code is generated and transferred by the mechatronic system (3) to the DCU (8), which can then be used to reliably support the vehicle-level diagnostic strategy (known as OBD). Alternatively, if the confidence level is less than the pre-established threshold, step (250) is followed by step (270) in which the mechatronic system proposes a strategy for consolidating the sporadic error, consisting essentially of one or more partial or complete movements of the actuator, to the DCU.

[0085] During step (300) and in the case where step (241) has been previously executed, the synthesis of an update of the analysis of the recorded data series (therefore of the dedicated algorithm or model) is by the mechatronic system (3) in order to be able to classify in the future the data series which has not allowed the current statistical analysis to converge.

[0086] During step (300) and in the case where step (270) has been previously executed, the synthesis of an update of the analysis of the recorded data series (therefore of the dedicated algorithm or model) is initiated by the mechatronic system (3) in order to be able to classify in the future the data series with a better level of confidence compared to that which has just been calculated.

[0087] Steps (260) and (300) then lead to step (280) corresponding to the end of the algorithm for identifying malfunctions.

[0088] Note that the consolidation strategy can be handled by the malfunction identification algorithm 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) may preferentially be responsible for the strategy to adopt for consolidating the information (i.e., the sporadic error or correct operation code).

[0089] The fault identification algorithm described herein is given for illustrative purposes only and should in no way be considered as limiting the invention. Those skilled in the art can easily imagine more or less similar alternatives that meet the essential technical need, namely informing the DCU (8) of a deviation from nominal operation using an error code.

[0090] A sequence (300) describing, by way of example, backpropagation applied to a learning model is represented by the logic diagram shown in [Fig. 5]. If during step (301) it is revealed that the previous analysis of the data set has diverged instead of converging towards a correct classification of the system among the predetermined options, given in [Fig. 4], an internal algorithmic analysis step (302) is executed by the control circuit (11) (or by the vehicle's DCU (8)) in order to propose an update of the model (i.e., the algorithm) used to perform said analysis of the data set.During step (303), if an update can be proposed that allows this data set to be classified while ensuring the same performance levels as the classifications of previous samples, then an official update request can be sent to the vehicle's DCU during step (307), and if agreed, recorded and activated in the MCU during step (308). However, if no update solution can be identified during steps (302) and (303), then the environmental context in which the diagnostic was performed is recorded, during step (304), in the non-volatile memory of the control circuit (11) (or the vehicle's DCU (8)) to allow for subsequent identification. the limitations of the current solution, this step concluding the sequence.

[0091] If, during step (301), it is revealed that the previous analysis of the dataset generated a classification with a confidence level below 99.99%, an internal algorithmic analysis loop is initiated by the control circuit (11) (or by the vehicle's DCU (8)) in step (305) to propose an update to the model (i.e., the algorithm) used to perform said analysis of the dataset. During step (306), if an update can be proposed that generates the classification with a confidence level above 99.99% while maintaining the same confidence levels for the classifications of the previous samples, then an official update request can be sent to the vehicle's DCU (8) in step (307), and, if agreed, recorded and activated in the control circuit (11) in step (308).If no update solution could be proposed during step (306), the sequence is complete. Different types of malfunctions

[0092] Figure 6 represents an example of an implementation of an active regulation system airflow (1) whose mechanically obstructing element (2) consists of a set of flaps. This active airflow control system is positioned on the car's grille, in front of the radiator, and includes a mechatronic system (3) driving a shaft (4) passing through the output gear of said mechatronic system (3). This shaft (4) controls the positioning of the right flaps (23 to 25) and the left flaps (26 to 28) via a transmission (19, 29).

[0093] Figure 7 represents an alternative example of the implementation of a control system active airflow (1) whose obstructing mechanical element (2) consists of a frame (31) that can be covered by a curtain (32) that can be rolled up around an axis (4) passing through a toothed output wheel of the mechatronic system (3).

[0094] Figures 8 to 16 illustrate, in a non-limiting manner, various types of malfunctions related to the active airflow control system (1), Figures 8 to 12 illustrating malfunctions for an obstructing mechanical element (2) equipped with flaps, as shown in [Fig. 6], and Figures 13 to 16 illustrating malfunctions for an obstructing mechanical element (2) equipped with a curtain, as shown in [Fig. 7]. These figures represent systems with active grilles whose flap rotation axes are positioned horizontally, but the invention also extends to any other orientation of the flap rotation axes.

[0095] Figure 8 corresponds to a situation where one of the central flaps is missing, at the following an impact or breakage of its pivot axis. It then leaves a permanent opening (34) allowing airflow, 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 efforts can be detected by processing the electrical signals directly measured on the power and control ports of the mechatronic system (3). In the example described in [Fig. 8], a central flap is missing, but the invention is not limited to this case and different signatures can be observed when an end flap is missing or even when several flaps are missing, the signature also being able to vary depending on the combination of missing flaps.

[0096] Figures 9 to 11 correspond to another malfunction situation on the active airflow control system by flaps, where all the flaps (26, 27, 28) are present, but one (or more) of them is no longer driven by the motion transmission kinematics (36). This can, for example, result from the breakage of a connecting rod (37), as shown in [Fig. 9], or from the breakage of a drive lug (38) of the flap as shown in [Fig. 10], or even from the breakage of the transmission shaft (39) visible in [Fig. 11].

[0097] This malfunction causes a change in the forces exerted on the mechatronic system (3) during the change of 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).

[0098] Figure 12 illustrates another malfunction situation, where a flap (24) is no longer driven and disrupts the opening or closing of adjacent flaps (23, 25). This behavior can occur when the drive shaft of a flap breaks and is accompanied by a displacement in the stroke of the other flaps. This type of breakage can result in a temporary overtorque required to free the contacting flaps, or in a large overtorque due to a complete blockage of the system in a position outside the functional limits. It should be noted that seizing of the drive shaft of a flap or of all the flaps can also lead to a temporary or general increase in the torque required during the opening or closing stroke.

[0099] Figures 13 to 15 correspond to another malfunction situation on the active airflow control system by curtain, where an element in the motion transmission kinematics is faulty. This can, for example, result from the breakage of the transmission shaft (4) as illustrated in [Fig. 13], from the breakage of a drive cable (33) as illustrated in [Fig. 14], or from the tearing (40) of the curtain (32) as shown in [Fig. 15].

[0100] This results in a change in the forces exerted on the mechatronic system (3) during the change of 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).

[0101] Figure 16 corresponds to another malfunction situation, where the frame (31) The curtain (32) is deformed or even broken. This type of breakage (35) can result in a localized overtorque required to move the curtain, or in a significant overtorque due to a complete blockage of the system. It should be noted that seizing of the drive shaft of a shutter or all the shutters can also lead to a localized or general increase in the torque required during the opening or closing stroke by the mechatronic system (3).

[0102] Detailed description of a first variant of a singularity measurement

[0103] According to one of the embodiment variants, the detection of operating singularities is based on sampling and analysis of the load angle.

[0104] The load angle corresponds to the magnetic angular displacement of the rotor relative to the angular position that cancels the magnetic torque between the rotor and the stator. A common way to represent this is by using a Fresnel diagram, as illustrated in Figure 17 by subfigures a, b, c, and d, in which the magnetic states of the rotor and stator are represented by vectors. In this figure, representing a three-phase machine, the vectors U, V, and W are the magnetic states corresponding to supplying each of the motor phases with the same voltage.

[0105] 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 when this angle is equal to 0°, as shown in [Fig. 17a], to a maximum when the vectors are at 90°, as shown in [Fig. 17d]. The torque, represented by the torque vector (53), is directly proportional to the sine of the load angle (52) and the supply current. Figures 17b and 17c represent intermediate situations where the load angle is between 0° and 90° in order to visualize the evolution of the torque vector between these values.

[0106] Assuming no load on the rotor, the load angle (52) is 0° and the stator and rotor vectors are collinear. The actual angular position of the rotor is identical to the control position.

[0107] When a force is applied to the rotor, for example by a braking torque, or a load or a drive torque, the load angle (52) increases and is no longer equal to 0°. When this load angle (52) exceeds 90°, the applied torque decreases and can lead to a loss of synchronism between the rotor and the stator field, called rotor stall.

[0108] Thus, measuring the load angle (52) requires measuring the rotor angular position. In its broadest sense, the invention is compatible with all types of synchronous motor control, but this solution is more specifically dedicated to stepper motors. Various solutions exist for measuring the rotor position, and the use of dedicated sensors is The preferred sensor is an analog magnetosensitive probe, which nevertheless has the drawback of a modest resolution that can prove limiting when trying 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 effects perceptible on the drive motor of a load variation experienced by the driven component. Also, again due to this reduction, the rotor's rotational speed is much greater than that of the driven component, by a factor equal to the reduction. Calculating the rotor angle at each step, or at each microstep, therefore requires a very significant and thus 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 from an analog probe in quadrature, it is proposed to drastically reduce the number of load angle measurements during a complete rotor rotation to improve angular resolution.

[0109] One solution is to always use an analog probe measuring the field of the sensing magnet, but without reconstructing the absolute rotor angle, which is a source of inaccuracies and a consumer of computing resources. We then wish to measure only the zero crossings of said probe, which offer the best accuracy because they are where the field variations are greatest. To correctly identify this zero crossing, it is necessary to measure and record the maximum and minimum values ​​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 allows for insensitivity to variations in magnetization amplitude, whether slow and irreversible variations due to aging, or reversible and shorter-term variations due, for example, to temperature variations. The measurement of the load angle therefore consists of: • trigger a counter, steps, or microsteps when the stator angle is 90 degrees or 270 degrees, • stop the counter when a zero crossing, Ze, of the analog probe is measured, preferably using the formula = v^Vm, Vm and Vm being respectively the maximum and minimum values ​​of revolution n - 1, • record for the turn the maximum and minimum values ​​measured by the analog probe, • calculate the load angle LA using the formula LA — ^-x 360- N s being the number of steps or micro-steps over a mechanical period.

[0110] 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 transition threshold value to be adjusted for each magnetic period based on measurements taken during the previous period, but it offers both financial savings and a reduction in the computing resources required. Indeed, By integrating a digital probe, it is possible to use a microcontroller without an analog input, and it is no longer necessary to reconstruct the rotor angle by calculation. The method for measuring the load angle is extremely similar to the previous solution and consists of: • trigger a step counter, or microstep counter, when the stator angle is 90 degrees or 270 degrees, • Stop the counter when a zero crossing of the digital probe is measured, i.e., a rotor angle of 90 electrical degrees or 270 electrical degrees, • Calculate the load angle LA using the formula 4 = 4 x 360

[0111] For both of these solutions, the measurement of the charging angle over one electrical period, u, perhaps averaged according to the formula x 35g, n^g(i and zL.2TObeing respectively the number of steps counted for a counter trigger at a rotor angle of 90 degrees or 270 degrees electrical.

[0112] To obtain better accuracy, the movement can be analyzed over a large number of electrical periods, or even one or more revolutions of the output shaft of the reducer; it is then envisaged to use a moving average of the load angle over N samples, such as a weighted moving average, when it is desired to give more weight to the most recently measured samples:

[0114] lf(x) being the average measurement of the charging angle taken for the xth electric turn and LA being the measurement of the charging angle taken for the electric turn. It should be noted that good results are obtained with a number of samples N between 50 and 100. Of course, other types of moving averages can be considered depending on the required precision and the available memory and computing resources.

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

[0116] According to one embodiment, the detection of operating singularities is based on sampling and analyzing the electrical signal measured across an RC filter in series with the power supply of the mechatronic system (3), also called an actuator, comprising a polyphase motor coupled with so-called automatic control switched, known to those in the trade as "BLDC control" and carried by the electronic module integrated into the mechatronic system (3).

[0117] Figure 18 shows 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) driving the current through phase C (respectively B and A). The resistor (60) is a sampling resistor used to measure the sum of the currents through each phase of the motor with magnets (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 as a voltage using the analog-to-digital converter and will be continuously transformed and preprocessed into information by a microcontroller, for example.

[0118] Concise description of measurement alternatives for a singularity

[0119] Another parameter that could be useful to measure for detecting a fault is the number of steps taken by a stepper motor to move from one end position to the other. This type of measurement is particularly useful for identifying a blocking fault, as the effective travel can be significantly reduced. A detailed example is also provided to identify a missing shutter using this parameter.

[0120] The measurement of a singularity is not limited to functional quantities of the electric motor, such as current measurement or load angle measurement as previously described, but can just as easily be derived from a dedicated sensor internal to the actuator. One could, for example, consider a pressure or temperature sensor whose variations would be proportional to the incoming airflow. It would therefore be entirely feasible to develop different scenarios to make the measurement from these sensors sensitive to the aforementioned failures.

[0121] Description of an example singularity identification algorithm

[0122] A means of singularity detection, for example compatible with the current measurement presented in the description of [Fig. 18], is to identify, within the recorded data series, a value exceeding the expected value by several standard deviations. This is described here using the formalism developed for current measurement, but can equally well be used for other types of measured data. Singularity detection is performed by the numerical processing of 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, which is regular and free from symptomatic behavior of the geared motor rotor, but this value evolves towards a different and more irregular value when the geared motor is subjected to an abnormal or faulty active airflow control system (1).

[0123] 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 o of the amplitude values ​​of the current measured across the resistor (60) over N samples is calculated over 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.

[0124] The characterization of the operating mode and the detection of faults is carried out by analyzing the variation of this standard deviation, possibly supplemented by analyzing the evolution of the median value over N samples of the current measured across the terminals of RI: - A stable standard deviation 0, below a threshold value measured during nominal operation, corresponds to fault-free operation - A standard deviation exceeding the threshold value and / or a significant increase in the median current measured at the RI terminals corresponds to a seizing of one or more flaps and / or their drive mechanism, and therefore a drift in the fluidic control - An increasing standard deviation exceeding the threshold value and / or a significant decrease in the median current measured at the terminals of RI corresponds to a loss of one or more flaps, a blockage in the open position, a partial destruction of one or more flaps, and therefore a drift in the fluidic control. - As an example, the value N2 * o2 is compared with the total phase current I. This digital processing makes it possible to distinguish between areas of normal operation and areas of abnormal operation, and to set a standard deviation threshold E for the N2 * o2 values, above which the microcontroller, or ASIC, determines that the geared motor is driving a faulty gate mechanism. The use of a combination of several other types of statistical analysis algorithmic models is also possible, though this is not an exhaustive list. Description of a variant of a singularity identification algorithm

[0125] In one 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 able to discriminate the location of the defect, for example a missing flap located at one end or in the middle.

[0126] In cases where the driven element consists of a set of flaps, the fault detection algorithm can benefit from a constitutive modification of the driven element to improve detection. It is thus envisaged to provide a

[0127]

[0128]

[0129]

[0130]

[0131]

[0132] A unique signature for each flap can be reliably discriminated by the algorithm. This can be a binary code, as described in US patent application 9810138B2, with the addition of a point resistive element for each flap. This resistive element is detectable as a point overtorque generated by the actuator. The point resistive elements are strategically placed so that each induces an overtorque at a different point during the opening or closing stroke. The absence of a flap is then indicated by the absence of a friction peak during a complete movement of the driven component. It is worth noting that such a signature can be measured by a very simple algorithm that detects, for example, a current peak consumed by the actuator exceeding a certain threshold at predetermined positions to confirm the presence of each flap. In cases where the actuator is a set of flaps or a curtain, the signature can be more subtle and leverage the power of deep learning by assigning each flap a specific surface area, mass, or profile. This can lead to the measurement of a highly specific signature, for example, related to the system's inertia, the aerodynamic pressure it experiences, or by obtaining a vibration specific to each flap. Description of other variants of the singularity identification algorithm It should be noted that there are numerous possible variants of singularity identification algorithms compatible with the invention, each with its own advantages and disadvantages. For example, some are more robust but require a specific actuation sequence for the driven component to measure a defect; others must be performed while stationary, or while in motion; still others require the addition of external data such as vehicle speed, wind speed, or temperature. Therefore, the chosen solution or set of solutions is defined by the specifications. A few examples of identification algorithms are provided below, without limitation. According to one embodiment of the invention, the singularity identification algorithm is based on a measurement of the variation in hydrodynamic torque. When a shutter is missing or the curtain is torn, the pressure exerted on the entire shading device decreases, resulting in a reduction of the torque required from the actuator to perform an opening or closing movement. The hydrodynamic torque, T, can be expressed as a function of the actuator stroke, x, according to the formula: where is a coefficient related to the geometry of the shading device and the incoming airflow, Atnax being the maximum opening area and A(at) the opening area for the stroke %, this surface expresses itself differently in the context of a healthy or broken actuator.

[0133] 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 accuracy, acquire this data throughout the stroke of the actuator, so as to identify a singularity on the work supplied by the actuator during the movement between xo and xi, according to the formula:

[0134] / \ p Wlx{> x^l^r^xjdx

[0135] 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 few computing resources but is highly dependent on the wind speed and the vehicle speed. Nevertheless, 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 shading device.

[0136] According to another variant of the invention, when the obscuring system is a set of shutters, the singularity identification algorithm can be based on a measurement of the stiffness of the system.

[0137] According to another embodiment of the invention, the singularity identification algorithm can be based on a measurement of the system's inertia. If the AGS actuator is driven such that the angular velocity of the actuator's rotor increases linearly between two instants, then the rotor's acceleration is constant. By performing an opening cycle with the vehicle stationary, a fault, such as a missing flap, can be detected by measuring a change in inertia during this cycle relative to the reference value recorded in the system. This change in inertia could alternatively be measured from a reading of the torque, current, or load angle; these examples are not limiting to the invention.

[0138] Detailed description of a variant of a learning algorithm

[0139] According to one embodiment, the algorithm describing the fault detection diagnosis requires an initial supervised learning phase fed with a predetermined number of cycles, the system being in nominal (or functional) mode only. An example of said supervised learning algorithm is represented by the logic diagram in [Fig. 19].

[0140] We denote the number of training cycles X required to provide a result, over the entire duration of use of the classification system for the sampled acquisition of the data series s of interest, with the confidence level optimal. The confidence rate is a variable that increases with X; it grows rapidly with X and then changes only 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%.

[0141] The learning algorithm (100) is triggered each time the actuator is moved by the control circuit (11), which we call step (110). This is followed by a step (111) to verify the successful completion of the initial learning phase, which can be done by reading a memory register. If the learning phase has already been successfully completed, then the learning algorithm terminates and triggers the fault detection algorithm (200).

[0142] If, on the other hand, the learning phase is not considered complete, the "non-operational" state of the fault detection system is generated by the mechatronic system (3) for the DCU (8) in 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 actuator's movement 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 next step (115) involves the control circuit (11) verifying that the actuator's movement is correct and that the environmental conditions (i.e., the context) are suitable. This leads to a decision on whether or not to retain the current cycle data as reference data for the learning algorithm. If retained, this data is recorded as reference data in a memory register during a final step (116). The reference data recording preferentially includes contextual data from the recorded cycle, such as temperature, vehicle speed, air pressure at the flaps, etc., which may originate from external sensors. Associated information can then be generated by the mechatronic system (3) and sent to the DCU (8).In the event that the verification concludes that the data is inadequate, the data 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).

Claims

Demands

1. A method for diagnosing the operation of an active airflow control system (1) for a vehicle, comprising: - an obstructing mechanical component (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 allowing the movement of the obstructing mechanical part (2), - 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 process comprises a series of steps consisting of: - to record, during an operating cycle of the active airflow control system (1), a sequence of sampled digital data 5 in a memory, where the sequence of sampled digital data consists of a set of digital data, if, acquired by said control circuit (11) internal to the mechatronic system (3), - to apply, to the said sampled numerical data sequence S stored in memory, an algorithmic statistical analysis model to determine a singularity, - discriminate by the control circuit (11) and transmit to the DCU, a sporadic error code associated with the singularity, said sporadic error code being information on the state of the active airflow regulation system (1).

2. A method for diagnosing the operation of an active airflow control system (1) according to the preceding claim, characterized in that when the sporadic error code returned does not correspond to a nominal state of the system, said method comprises the following additional steps: a. consolidation of the sporadic error following an instruction transmitted by the vehicle's DCU (8) resulting in a consolidated diagnostic state, b. transmission of the consolidated diagnostic state to the vehicle's DCU (8).

3. 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.

4. 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.

5. 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 the diagnosis, external to said mechatronic system (3).

6. Method for diagnosing the operation of an active airflow control system (1) according to claim 1 characterized in that said numerical data s> is related to the operation of the magnet motor (10) of the mechatronic system (3).

7. Method for diagnosing the operation of an active airflow control system (1) according to the preceding claim characterized in that the numerical data si is a measurement of the motor phase current.

8. Method for diagnosing the operation of an active airflow control system (1) according to claim 6 characterized in that said numerical data is a measurement of the load angle of the rotor of the magnet motor (10).

9. 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 recorded sampled numerical data sequence 5 and a reference data map.

10. Method for diagnosing the operation of a control system active airflow (1) according to claim 1 characterized in that said algorithmic statistical analysis model for determining a singularity is provided with a detection of a singular point in the recorded sampled numerical data sequence S.

11. Method for diagnosing the operation of an active airflow control system (1) according to claim 1 characterized in that said algorithmic model for statistical analysis to determine a singularity consists of submitting the sampled numerical data sequence S recorded to a model obtained by training a neural network from training data corresponding to the nominal operation of the obstructing mechanical part (2).

12. Method for diagnosing the operation of an active airflow control system (1) according to any one of the preceding claims characterized in that a sampled reference digital data sequence Sr is recorded for each mechatronic system (3) at the end of the assembly line of the system (1) of the system (1) on the vehicle.

13. Method for diagnosing the operation of an active airflow control system (1) according to the preceding claim characterized in that said statistical analysis algorithmic model is capable of comparing the sampled numerical data sequence with the reference sampled numerical data sequence so as to detect a behavioral drift that may lead to degradation of the mechatronic system or that may validate normal wear behavior of the system.

14. Method for diagnosing the operation of an active airflow control system (1) according to claim 1, characterized in that the recorded sampled digital data sequence S is derived from a digital data function of the mechanical load in an active grid system.

15. A 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 recorded sampled digital data sequence 5 is permitted with knowledge of: • the masses or inertials to be moved in an active grid system, • and / or the mechanical frictions in said system, • and / or the aerodynamic load exerted on said system.

16. Electromechanical active airflow control system (1) for vehicles, comprising: - an obstructing mechanical component (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 allowing the movement of the obstructing mechanical part (2), - 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 microprocessor executing a computer program stored in its read-only memory, controlling a series of steps consisting of: - to record, during an operating cycle of the active airflow regulation system (1), a sequence of sampled digital data 5 in a memory, where the sequence of sampled digital data consists of a set of digital data, s>, acquired by said control circuit (11) internal to the mechatronic system (3), - apply to said sequence of sampled 3” digital data stored in memory an algorithmic statistical analysis model to determine a singularity, - discriminate by the control circuit (11) and transmit to the DCU, a sporadic error code associated with the singularity, said sporadic error code being information on the state of the active airflow regulation system (1).

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