Device and method for supporting failure response in hydrogen fuel cell based mobility

Abstract

A device and method for supporting fault response in a hydrogen fuel cell-based mobility are disclosed. The fault response support device includes a collection unit that collects status information related to the mobility and status information related to a communication network when the hydrogen fuel cell-based mobility is turned on; a diagnosis unit that applies the collected status information to a diagnosis model that has undergone fault diagnosis learning to diagnose whether there is an abnormality in the mobility, and if it is diagnosed that there is an abnormality in the mobility, sets a risk for the cause of the diagnosed abnormality; and a drive control unit that controls the operation of the mobility based on the set risk.

Description

Technology Field

[0001] The present invention relates to a fault response support device, and more specifically, to a fault response support device and method for hydrogen fuel cell-based mobility that diagnoses the cause of an abnormality in hydrogen fuel cell-based mobility and supports the implementation of optimal response measures corresponding to the diagnosed cause of the abnormality. Background Technology

[0002] Generally, a hydrogen fuel cell is a power generation device that utilizes the reverse reaction of electrolysis to produce electricity and heat by supplying hydrogen extracted from sources such as petroleum gas as fuel and reacting it with oxygen from the air. Compared to turbine power generation methods using fossil fuels, hydrogen fuel cells are an eco-friendly energy source with higher energy efficiency and lower greenhouse gas emissions, and they are currently being used as a power source for transportation mobility.

[0003] Meanwhile, compared to conventional fossil fuel-based mobility, safety issues such as fire and explosion are becoming prominent in hydrogen fuel cell-based mobility; consequently, research on fault diagnosis and response measures to prevent safety accidents in advance is continuously underway. Prior art literature

[0004] Published Patent Application No. 10-2020-0042568 (April 24, 2020) The problem to be solved

[0005] The problem that the present invention aims to solve is to provide a fault response support device and method for a hydrogen fuel cell-based mobility that diagnoses abnormalities in the hydrogen fuel cell-based mobility from various angles using artificial intelligence, and controls the operation of the mobility so that optimized response measures are taken according to the diagnosed cause of the abnormality when an abnormality occurs in the diagnosis results. means of solving the problem

[0006] To solve the above problem, the fault response support device according to the present invention includes a collection unit that collects status information related to the mobility and status information related to the communication network when the ignition of the hydrogen fuel cell-based mobility is turned on; a diagnosis unit that applies the collected status information to a diagnosis model in which fault diagnosis-related learning has been performed to diagnose whether there is an abnormality in the mobility, and if it is diagnosed that there is an abnormality in the mobility, sets a risk regarding the cause of the diagnosed abnormality; and a drive control unit that controls the operation of the mobility based on the set risk.

[0007] In addition, the above-mentioned collection unit is characterized by collecting at least one state information among hydrogen leakage, stack voltage, stack current, stack temperature, stack hydrogen flow rate, stack vibration, power pack hydrogen pressure, power pack temperature, power pack hydrogen flow rate, power pack current, battery voltage, battery current, battery temperature, battery charging speed, battery electrolyte leakage, mobility body coolant temperature, mobility body oil pressure, mobility body hydraulic pressure, mobility body hydraulic temperature, mobility body pump status, mobility internal communication / electronic control status, supercapacitor temperature, supercapacitor capacitance, supercapacitor self-discharge, supercapacitor internal resistance, supercapacitor voltage, external communication signal strength, external communication speed, external communication latency, external communication packet loss rate, and external communication reconnection frequency.

[0008] In addition, the diagnostic unit is characterized by classifying the state of the mobility using the diagnostic model into normal, hydrogen leakage, stack abnormality, abnormality of at least one of the power pack and battery, communication abnormality, pump abnormality, and supercapacitor abnormality, and diagnosing the cause of the abnormality.

[0009] In addition, the diagnostic unit is characterized by setting the hydrogen leakage as a serious risk, setting the stack abnormality, at least one of the power pack and battery abnormality, and the pump abnormality as a significant risk, and setting the communication abnormality and the supercapacitor abnormality as minor risks.

[0010] In addition, the drive control unit is characterized by performing control to turn off the ignition of the mobility when the risk is set to a serious risk, performing control to stop the operation of the abnormality-related component when the risk is set to a grave risk, and performing control to output an abnormality-related warning message when the risk is set to a minor risk.

[0011] In addition, the diagnostic unit is characterized by setting the risk with the highest risk among the multiple abnormal causes as the representative risk of the diagnosed result if the diagnosed result includes multiple abnormal causes.

[0012] In addition, the diagnostic unit is characterized by performing transfer learning of the diagnostic model in real-time or at preset intervals by linking with an external cloud server that derives an answer relatively closer to the correct answer than the diagnostic model.

[0013] In addition, the diagnostic unit is characterized by providing diagnostic information diagnosed by the diagnostic model to the cloud server, and performing the transfer learning using diagnostic information derived from the cloud server based on the provided diagnostic information.

[0014] A fault response support method performed by a fault response support device that supports response measures for a fault in a hydrogen fuel cell-based mobility according to the present invention comprises: a step of collecting status information related to the mobility and status information related to a communication network when the mobility is turned on; a step of diagnosing whether there is an abnormality in the mobility by applying the collected status information to a diagnostic model that has undergone learning related to fault diagnosis; a step of setting a risk for the cause of the diagnosed abnormality when it is diagnosed that there is an abnormality in the mobility; and a step of controlling the operation of the mobility based on the set risk. Effects of the invention

[0015] According to an embodiment of the present invention, an accurate diagnosis of abnormalities in hydrogen fuel cell-based mobility can be performed by using artificial intelligence to subdivide them into hydrogen leakage, stack abnormalities, power pack / battery abnormalities, communication abnormalities, pump abnormalities, and supercapacitor abnormalities.

[0016] In addition, the operation of the mobility can be controlled by setting risks based on diagnosed abnormal causes so that optimal response measures appropriate to those risks are taken. Brief explanation of the drawing

[0017] FIG. 1 is a configuration diagram for explaining a fault response support system according to an embodiment of the present invention. FIG. 2 is a block diagram illustrating a fault response support device according to an embodiment of the present invention. FIG. 3 is a block diagram for explaining a control unit according to an embodiment of the present invention. FIG. 4 is a diagram illustrating the structure of a diagnostic model according to an embodiment of the present invention. FIG. 5 is a diagram illustrating transfer learning of a diagnostic model according to an embodiment of the present invention. FIG. 6 is a diagram illustrating drive control according to risk according to an embodiment of the present invention. FIG. 7 is a flowchart illustrating a fault response support method according to an embodiment of the present invention. FIG. 8 is a block diagram illustrating a computing device according to an embodiment of the present invention. Specific details for implementing the invention

[0018] Embodiments of the present invention are described below with reference to the attached drawings so that those skilled in the art can easily implement them. However, the present invention may be embodied in various different forms and is not limited to the embodiments described herein. Furthermore, in order to clearly explain the present invention in the drawings, parts unrelated to the explanation have been omitted, and similar parts throughout the specification are denoted by similar reference numerals.

[0019] In this specification and drawings (hereinafter referred to as the 'this specification'), redundant descriptions of identical components are omitted.

[0020] Furthermore, when a component is described in this specification as being 'connected' or 'connected' to another component, it should be understood that it may be directly connected to or connected to the other component, or that there may be other components in between. On the other hand, when a component is described in this specification as being 'directly connected' or 'directly connected' to another component, it should be understood that there are no other components in between.

[0021] Furthermore, the terms used in this specification are used merely to describe specific embodiments and are not intended to limit the invention.

[0022] Additionally, in this specification, singular expressions may include plural expressions unless the context clearly indicates otherwise.

[0023] Furthermore, in this specification, terms such as 'comprising' or 'having' are intended merely to indicate the existence of the features, numbers, steps, actions, components, parts, or combinations thereof described in the specification, and should be understood as not excluding in advance the existence or addition of one or more other features, numbers, steps, actions, components, parts, or combinations thereof.

[0024] Additionally, in this specification, the term "and / or" includes a combination of the plurality of described items or any of the plurality of described items. In this specification, "A or B" may include "A," "B," or "both A and B."

[0025] In addition, detailed descriptions of known functions and configurations that may obscure the essence of the invention will be omitted in this specification.

[0027] FIG. 1 is a configuration diagram for explaining a fault response support system according to an embodiment of the present invention.

[0028] Referring to FIG. 1, the fault response support system (400) uses artificial intelligence to diagnose whether there is an abnormality in the hydrogen fuel cell-based mobility (M) from various angles, and if an abnormality occurs in the diagnosis results, controls the operation of the mobility so that an optimized response measure is taken according to the cause of the diagnosed abnormality. The fault response support system (400) includes a fault response support device (100), a cloud server (200), and a user terminal (300).

[0029] A fault response support device (100) is installed in the mobility (M) and controls the operation according to the cause of the fault of the mobility (M). Here, the mobility (M) is a means of transportation driven by a hydrogen fuel cell. That is, the mobility (M) may have the same overall structure as a conventional fossil fuel-based mobility, except that the engine block is replaced by a power pack (not shown). The power pack may be a power supply unit that integrates a fuel cell stack, an air supply device (air compressor, humidifier, etc.), a hydrogen supply module, a thermal management system, and a power conversion device (DC / DC converter, inverter) into a single module.

[0030] The fault response support device (100) can diagnose faults using a diagnostic model to which Artificial Intelligence (AI) technology is applied. The diagnostic model is a model that has completed learning to diagnose faults based on the status information of the mobility (M). When the fault response support device (100) diagnoses that there is an abnormality in the mobility (M), it sets a risk for the cause of the diagnosed abnormality and can automatically control the operation of the mobility (M) based on the set risk. Through this, the fault response support device (100) accurately diagnoses accidents caused by the fault and helps the driver to respond quickly and optimally. In addition, the fault response support device (100) generates monitoring information including the status information of the mobility (M), diagnostic information, and operation information. The fault response support device (100) can output the monitoring information or transmit it to a user terminal (300).

[0031] The cloud server (200) communicates with the fault response support device (100). The cloud server (200) may be a cloud that derives the optimal answer for fault diagnosis and drive control of the hydrogen fuel cell-based mobility (M), and its performance may derive an answer that is relatively closer to the correct answer than the diagnostic model of the fault response support device (100). That is, the cloud server (200) may include at least one server computer and be equipped with high-performance computing capabilities. Preferably, the cloud server (200) includes an artificial intelligence model linked to the diagnostic model of the fault response support device (100), and may perform transfer learning in real time or at preset intervals using the said artificial intelligence model. Here, the artificial intelligence model may be a model with higher diagnostic accuracy than the diagnostic model of the fault response support device (100).

[0032] The user terminal (300) is a terminal used by the user and performs communication with the fault response support device (100). The user terminal (300) receives monitoring information from the fault response support device (100) and outputs the received monitoring information. Through this, the user terminal (300) supports the user in checking information related to mobility (M) in real time from a remote location. That is, the user terminal (300) helps the user perform a rapid accident response by allowing the user to remotely check information regarding the occurrence of an abnormality in the mobility (M) and measures taken therein.

[0033] Meanwhile, the fault response support system (400) supports communication between the fault response support device (100), the cloud server (200), and the user terminal (300) by establishing a communication network (450). The communication network (450) may be composed of a backbone network and a subscriber network. The backbone network may be composed of one or more integrated networks among an X.25 network, a Frame Relay network, an ATM network, an MPLS (Multi-Protocol Label Switching) network, and a GMPLS (Generalized Multi-Protocol Label Switching) network. The subscriber network may be FTTH (Fiber To The Home), ADSL (Asymmetric Digital Subscriber Line), cable network, Zigbee, Bluetooth, Wireless LAN (IEEE 802.11b, IEEE 802.11a, IEEE 802.11g, IEEE 802.11n), Wireless Hart (ISO / IEC 62591-1), ISA 100.11a (ISO / IEC 62734), CoAP (Constrained Application Protocol), MQTT (Message Queuing Telemetry Transport), WIBro (Wireless Broadband), WiMAX, 3G, HSDPA (High Speed ​​Downlink Packet Access), 4G, 5G, and 6G, etc. In some embodiments, the communication network (450) may be an internet network and a mobile communication network. Additionally, the communication network (450) may include any other widely known or future-developed wireless or wired communication methods.

[0035] FIG. 2 is a block diagram illustrating a fault response support device according to an embodiment of the present invention.

[0036] Referring to FIGS. 1 and 2, the fault response support device (100) includes a communication unit (10), a sensor unit (20), a control unit (30), an output unit (40), and a storage unit (50).

[0037] The communication unit (10) performs communication with the cloud server (200), the user terminal (300), and the mobility (M). The communication unit (10) transmits diagnostic information to the cloud server (200) and receives diagnostic information derived from the cloud server (200). Additionally, the communication unit (10) transmits monitoring information related to the mobility (M) to the user terminal (300) and transmits a driving control signal of the mobility (M) to the ECU (Electronic Control Unit) (not shown) of the mobility (M).

[0038] The sensor unit (20) measures status information related to the mobility (M) and may include a hydrogen detection sensor, a current sensor, a voltage sensor, a temperature sensor, a pressure sensor, etc. Specifically, the sensor unit (20) measures hydrogen leakage of the mobility (M). The sensor unit (20) measures voltage, temperature, hydrogen flow rate, vibration, etc. related to the stack (not shown) of the mobility (M). The sensor unit (20) measures hydrogen pressure, temperature, hydrogen flow rate, current, etc. related to the power pack of the mobility (M). The sensor unit (20) measures voltage, current, temperature, charging speed, etc. related to the battery (not shown) of the mobility (M). The sensor unit (20) measures coolant temperature, oil pressure, hydraulic pressure, hydraulic temperature, pump status, etc. related to the vehicle body of the mobility (M). The sensor unit (20) measures temperature, capacitance, internal resistance, voltage, etc. related to the super capacitor (not shown) of the mobility (M). The sensor unit (20) transmits the measured state information to the control unit (30).

[0039] The control unit (30) performs overall control of the fault response support device (100). When the mobility (M) is turned on, the control unit (30) collects status information related to the mobility (M) and status information related to the communication network (450). The control unit (30) applies the collected status information to a diagnostic model that has undergone fault diagnosis training to diagnose whether there is an abnormality in the mobility (M). Here, the diagnostic model is a model that incorporates artificial intelligence technology and can be implemented as an Artificial Neural Network (ANN) or a Multilayer Perceptron (MLP), but is not limited thereto. If the control unit (30) diagnoses that there is no abnormality in the mobility (M), it maintains the current operation without any additional operation. Additionally, if the control unit (30) diagnoses that there is an abnormality in the mobility (M), it sets the risk for the diagnosed cause of the abnormality. The control unit (30) controls the operation to be optimized for the current state of the mobility (M) based on the set risk. That is, the control unit (30) generates a drive control signal that controls the operation of the mobility (M) and can control the generated drive control signal to be transmitted to the ECU of the mobility (M). The control unit (40) generates monitoring information including the state information, diagnostic information, and drive information of the mobility (M). The control unit (40) can control the monitoring information to be output or transmitted to a user terminal (300).

[0040] The output unit (40) outputs monitoring information generated from the control unit (40). The output unit (50) is installed on a dashboard provided inside the mobility (M) and can visually output the information (e.g., display, head-up, etc.). In addition, the output unit (40) can audibly output an alarm message (e.g., alarm sound, notification sound, etc.).

[0041] The storage unit (50) stores a program or algorithm for operating the fault response support device (100). The storage unit (50) stores information collected, diagnosed, set, and determined during the process of managing mobility (M) related faults. For example, the storage unit (50) may store status information, diagnostic information, setting information, driving control information, monitoring information, etc. The storage unit (50) may include a flash memory type, a hard disk type, a multimedia card micro type, a card type memory (e.g., SD or XD memory, etc.), RAM (Random Access Memory), SRAM (Static Random Access Memory), ROM (Read-Only Memory), EEPROM (Electrically Erasable Programmable Read-Only Memory), PROM (Programmable Read-Only Memory), magnetic memory, a magnetic disk, and an optical disk, etc.

[0043] FIG. 3 is a block diagram for explaining a control unit according to an embodiment of the present invention, FIG. 4 is a diagram for explaining the structure of a diagnostic model according to an embodiment of the present invention, FIG. 5 is a diagram for explaining transfer learning of a diagnostic model according to an embodiment of the present invention, and FIG. 6 is a diagram for explaining driving control according to risk according to an embodiment of the present invention.

[0044] Referring to FIGS. 1 to 6, the control unit (30) includes a collection unit (31), a diagnosis unit (32), and a drive control unit (33).

[0045] The collection unit (31) collects status information related to the mobility (M) and status information related to the communication network (450) when the mobility (M) is turned on. The collection unit (31) collects the status of the stack, power pack, battery, body, and super capacitor of the mobility (M). For example, the collection unit (31) collects at least one of hydrogen leakage, voltage of the stack, temperature of the stack, hydrogen flow rate of the stack, vibration of the stack, hydrogen pressure of the power pack, temperature of the power pack, hydrogen flow rate of the power pack, current of the power pack, voltage of the battery, current of the battery, temperature of the battery, charging speed of the battery, coolant temperature of the mobility (M) vehicle body, oil pressure of the mobility (M) vehicle body, hydraulic pressure of the mobility (M) vehicle body, hydraulic temperature of the mobility (M) vehicle body, pump status of the mobility (M) vehicle body, internal communication (e.g., CAN communication) of the mobility (M), electronic control (e.g., ECU) status, temperature of the super capacitor, capacitance of the super capacitor, internal resistance of the super capacitor, and voltage of the super capacitor. In addition, the collection unit (31) collects the status of communication related to the communication network (450). That is, the collection unit (31) collects information related to the status of communication performed with an external device. For example, the collection unit (31) collects at least one of the signal strength of the external communication, the speed of the external communication, the delay time of the external communication, the packet loss rate of the external communication, and the reconnection frequency of the external communication.

[0046] The diagnostic unit (32) applies the collected status information to a diagnostic model (DM) that has undergone fault-related learning to diagnose whether there is an abnormality in the mobility (M). That is, the diagnostic unit (32) uses the diagnostic model (DM) to classify the state of the mobility (M) into hydrogen leakage (C1), stack abnormality (C2), abnormality of at least one of the power pack and battery (C3), communication abnormality (C4), pump abnormality (C5), supercapacitor abnormality (C6), and normal (C7) to diagnose whether there is an abnormality. Preferably, the diagnostic unit (32) can use the diagnostic model (DM) to perform an optimal diagnosis close to the correct answer for the situation occurring in the actual mobility (M).

[0047] The diagnostic model (DM) may be a multilayer perceptron and includes an input layer (IL), a plurality of hidden layers (HL, HL1 to HLk), and an output layer (OL). Each of the plurality of layers (IL, HL, OL) includes a plurality of nodes. For example, the input layer (IL) may include n input nodes (i1 to in), and the output layer (OL) may include 7 output nodes (o1 to o7). Additionally, among the hidden layers (HL), the first hidden layer (HL1) may include a number of nodes (h11 to h1a), the second hidden layer (HL2) may include b number of nodes (h21 to h2b), and the k-th hidden layer (HLK) may include c number of nodes (hk1 to hkc).

[0048] All multiple nodes in each layer have operations. In particular, multiple nodes in different layers are connected by weighted channels. Here, the result of an operation by a single node becomes the input to the next layer node to which the weight is applied.

[0049] In this way, a node corresponding to a layer of the diagnostic model (DM) receives a value with weights applied to the input from a node of the previous layer, sums it to apply an activation function, and transmits the result as an input to the next layer. Accordingly, when state information is input to the input layer (IL) of the diagnostic model (DM), the diagnostic model (DM) performs multiple operations with weights applied from multiple layers (IL, HL, OL) to the state information and outputs a diagnostic result, which is a predicted diagnostic value. At this time, the diagnostic result may be classified and output as hydrogen leakage (C1), stack abnormality (C2), abnormality of at least one of the power pack and battery (C3), communication abnormality (C4), pump abnormality (C5), supercapacitor abnormality (C6), and normal (C7).

[0050] Here, the diagnostic model (DM) can diagnose a hydrogen leak (C1) by using status information related to a drop in pressure of the hydrogen tank (not shown) of the mobility (M), a hydrogen tank of the mobility (M), an input / output line, a power pack, etc. The diagnostic model (DM) can diagnose a stack abnormality (C2) by using at least one of status information related to a drop in the preset voltage of the stack, a current amount lower than the amount of current applied to the stack, a rise in the temperature of the stack, a decrease in the hydrogen flow rate of the stack, analysis by spectroscopy, and vibration analysis regarding impact on the stack. The diagnostic model (DM) can diagnose an abnormality (C3) of at least one of the power pack and the battery by using at least one of status information related to a drop in hydrogen pressure of the power pack, a rise in the temperature of the power pack, a decrease in the hydrogen flow rate of the power pack, an overcurrent / undercurrent of the power pack, an overvoltage / undervoltage of the battery, an overcurrent / undercurrent of the battery, a rise in the temperature of the battery, a decrease in the charging speed of the battery, and electrolyte leakage of the battery. The diagnostic model (DM) can diagnose a communication abnormality (C4) by using at least one of the status information related to a decrease in the signal strength of external communication, a decrease in the speed of external communication, an increase in the latency of external communication, an increase in the packet loss rate of external communication, and an increase in the frequency of reconnection of external communication. The diagnostic model (DM) can diagnose a pump abnormality (C5) indicating an abnormal hydraulic fluid pressure by using at least one of the status information related to an increase in the coolant temperature of the mobility body, an increase in the oil pressure of the mobility body, an increase in the hydraulic pressure of the mobility body, an increase in the hydraulic temperature of the mobility body, the pump status of the mobility body, and the internal communication / electronic control status of the mobility. The diagnostic model (DM) can diagnose a supercapacitor abnormality (C6) by using at least one of the following status information: a rise in temperature of the supercapacitor (fast charging / discharging, cell failure), a decrease in capacitance of the supercapacitor (decrease in stored charge), an increase in internal resistance of the supercapacitor (cell degradation, contact failure), an increase in self-discharge of the supercapacitor (insulation breakdown, internal short circuit), and an overvoltage / undervoltage of the supercapacitor (balancing circuit abnormality).Finally, the diagnostic module (DM) can diagnose it as normal (C7) if it determines that there are no abnormalities in the collected status information.

[0051] Here, the diagnostic unit (32) can perform transfer learning of the diagnostic model (DM) in real time or at preset intervals by linking with an external cloud server (200) that derives an answer that is relatively closer to the correct answer than the diagnostic model (DM).

[0052] For example, transfer learning can be performed through the following process. The diagnostic unit (32) performs a fault diagnosis of the mobility (M) based on the diagnostic model (DM). The diagnostic unit (32) transmits the diagnostic information, which is the result of the diagnosis, to the cloud server (200). The cloud server (200) derives a diagnosis of the fault based on the received information. At this time, the cloud server (200) can derive an answer that is relatively closer to the correct answer than the diagnostic model (DM), and thus can predict more accurate fault-related diagnostic information than the diagnostic model (DM). The cloud server (200) transmits the derived diagnostic information to the diagnostic model (DM). The diagnostic model (DM) performs transfer learning based on the information received from the cloud server (200), and can further improve the accuracy related to the diagnosis through transfer learning. That is, when the diagnostic model (DM) performs a fault diagnosis in the future, it can perform the fault diagnosis using the transferred-learned diagnostic model.

[0053] When the diagnostic unit (32) diagnoses that there is an abnormality in the mobility (M), it sets the risk for the cause of the diagnosed abnormality. Here, the risk is an indicator that indicates the possibility of danger to the safety of the driver operating the mobility (M) due to the abnormality, and may be an indicator that considers the possibility of explosion, fire, etc.

[0054] In detail, the diagnostic unit (32) may set hydrogen leakage (C1) as a serious risk (D1), a stack abnormality (C2), an abnormality of at least one of the power pack and battery (C3), and a pump abnormality (C5) as a significant risk (D2), and set communication abnormality (C4) and supercapacitor abnormality (C6) as minor risks (D3). In this case, the risk may be in the order of serious risk (D1), significant risk (D2), and minor risk (D3), with the likelihood of safety risk being higher. Additionally, if the diagnostic result includes multiple causes of abnormality, the diagnostic unit (32) sets the risk with the highest risk among the multiple causes of abnormality as the representative risk of the diagnostic result. For example, if the diagnostic result includes a stack abnormality (C2) and a communication abnormality (C4), the diagnostic unit (32) may set the significant risk (D2) related to the stack abnormality (C2), which has a higher risk, as the representative risk.

[0055] The drive control unit (33) controls the operation of the mobility (M) based on the set risk level. If the risk level of the mobility (M) is set to a serious risk, the drive control unit (33) performs control to turn off the engine of the mobility (M). In the case of a serious risk (D1), the drive control unit (33) determines that there is a possibility of explosion within a short period of time, changes the engine of the mobility (M) to an off state, and controls the output of a warning message through emergency power. In the case of a serious risk (D2) of the mobility (M), the drive control unit (33) determines that there is no possibility of explosion within a short period of time, but there is a possibility of fire or explosion if a certain period of time is maintained, and controls the operation of the abnormal component while maintaining the engine of the mobility (M), and controls the output of a warning message through emergency power. For example, if the cause of failure among the serious risks (D2), the drive control unit (33) may stop the operation related to the stack and maintain the operation of the remaining components. The drive control unit (43) determines that if the risk of the mobility (M) is minor (D3), there is no problem with driving the mobility (M), but if the fault persists, there is a possibility of fire or explosion, and controls the operation so that a warning message is displayed while the mobility (M) is running. Through this, the driver can check the warning message and take measures to resolve the issue. That is, the drive control unit (43) can control the operation so that optimal countermeasures are performed while ensuring the safety of the user (e.g., driver, passenger) riding the mobility (M) through the control described above.

[0056] Additionally, the drive control unit (33) generates monitoring information including status information, diagnostic information, and drive information of the mobility (M). The drive control unit (33) can control the monitoring information to be output or transmitted to a user terminal (300). Here, the monitoring information may include messages delivered to the user, such as warning messages and notification messages.

[0058] FIG. 7 is a flowchart illustrating a fault response support method according to an embodiment of the present invention.

[0059] Referring to FIGS. 1 and 7, the fault response support method can accurately diagnose abnormalities in a hydrogen fuel cell-based mobility (M) by using artificial intelligence to subdivide them into hydrogen leakage, stack abnormalities, power pack / battery abnormalities, communication abnormalities, pump abnormalities, and supercapacitor abnormalities. In addition, the fault response support method can control the operation of the mobility (M) so that optimal response measures are taken according to the risk level based on the diagnosed cause of the abnormality.

[0060] In step S101, the hydrogen fuel cell-based mobility (M) is turned on. The mobility (M) can be turned on by a user.

[0061] In step S103, the fault response support device (100) collects information related to mobility (M). The fault response support device (100) collects status information related to mobility (M) and status information related to the communication network (450). The status information related to mobility (M) may include information related to hydrogen leakage, information related to stack abnormalities, information related to power pack / battery abnormalities, information related to the operating fluid pressure (pump abnormality) of mobility (M), information related to supercapacitor abnormalities, etc., and the status information related to the communication network (450) may include information related to the communication status between external devices, etc.

[0062] In step S105, the fault response support device (100) diagnoses an abnormality in the mobility (M). The fault response support device (100) determines the abnormality in the mobility (M) using a diagnostic model learned in relation to fault diagnosis. The fault response support device (100) can diagnose the cause of the abnormality by classifying the state of the mobility (M) using the diagnostic model into normal, hydrogen leakage, stack abnormality, abnormality of at least one of the power pack and battery, communication abnormality, pump abnormality, and supercapacitor abnormality.

[0063] In step S107, the fault response support device (100) determines whether the mobility (M) is diagnosed to have an abnormality. If the fault response support device (100) is diagnosed to have an abnormality in the mobility (M), it performs step S109, and if it is diagnosed to have no abnormality in the mobility (M), it performs step S103 again.

[0064] In step S109, the fault response support device (100) sets the risks of the mobility (M). The fault response support device (100) may set hydrogen leakage as a serious risk, a stack malfunction, a malfunction of at least one of the power pack and battery, and a pump malfunction as a major risk, and a communication malfunction and a supercapacitor malfunction as minor risks.

[0065] In step S111, the fault response support device (100) determines whether the risk of mobility (M) is set to a serious risk. If the risk of mobility (M) is set to a serious risk, the fault response support device (100) performs step S113, and if it is not set to a serious risk, performs step S115.

[0066] In step S113, the fault response support device (100) turns off the engine of the mobility (M). If the risk is severe, the fault response support device (100) determines that there is a possibility of explosion within a short period of time, changes the engine of the mobility (M) to the off state, and outputs a warning message through emergency power.

[0067] In step S115, the fault response support device (100) determines whether the risk of mobility (M) is set to a critical risk. If the risk of mobility (M) is set to a critical risk, the fault response support device (100) performs step S117, and if it is not set to a critical risk (set to a minor risk), it performs step S119.

[0068] In step S117, the fault response support device (100) stops the abnormal component of the mobility (M). If the risk is critical, the fault response support device (100) determines that there is no possibility of explosion within a short period of time, but there is a possibility of fire or explosion if a certain period of time is maintained, and stops the operation of the abnormal component while keeping the mobility (M) running, and outputs a warning message through emergency power.

[0069] In step S119, the fault response support device (100) outputs a warning message regarding an abnormality of the mobility (M). If the risk is minor, the fault response support device (100) determines that there is no problem with the driving of the mobility (M), but if the fault persists, there is a possibility of fire or explosion, and outputs the warning message while keeping the mobility (M) running.

[0071] FIG. 8 is a block diagram illustrating a computing device according to an embodiment of the present invention.

[0072] Referring to FIG. 8, the computing device (TN100) may be a device described in the present specification (e.g., a fault response support device, a cloud server, a user terminal, etc.).

[0073] The computing device (TN100) may include at least one processor (TN110), a transceiver (TN120), and a memory (TN130). Additionally, the computing device (TN100) may further include a storage device (TN140), an input interface device (TN150), an output interface device (TN160), etc. The components included in the computing device (TN100) may be connected by a bus (TN170) to communicate with each other.

[0074] The processor (TN110) can execute a program command stored in at least one of the memory (TN130) and the storage device (TN140). The processor (TN110) may refer to a central processing unit (CPU), a graphics processing unit (GPU), or a dedicated processor on which methods according to embodiments of the present invention are performed. The processor (TN110) may be configured to implement the procedures, functions, and methods described in relation to embodiments of the present invention. The processor (TN110) can control each component of the computing device (TN100).

[0075] Each of the memory (TN130) and the storage device (TN140) can store various information related to the operation of the processor (TN110). Each of the memory (TN130) and the storage device (TN140) may be composed of at least one of a volatile storage medium and a non-volatile storage medium. For example, the memory (TN130) may be composed of at least one of read-only memory (ROM) and random access memory (RAM).

[0076] The transmitting and receiving device (TN120) can transmit or receive wired or wireless signals. The transmitting and receiving device (TN120) can be connected to a network to perform communication.

[0078] Meanwhile, embodiments of the present invention are not limited to being implemented only through the apparatus and / or methods described so far, but may also be implemented through a program that realizes a function corresponding to the configuration of the embodiments of the present invention or a recording medium on which such program is recorded, and such implementation can be easily achieved by a person skilled in the art to which the present invention belongs based on the description of the embodiments described above.

[0080] Although embodiments of the present invention have been described in detail above, the scope of the present invention is not limited thereto, and various modifications and improvements made by a person skilled in the art using the basic concept of the present invention as defined in the following claims also fall within the scope of the present invention. Explanation of the symbols

[0081] 10: Communications Department 20: Sensor section 30: Control unit 31: Collection Department 32: Diagnostic Department 33: Drive control unit 40: Output section 50: Storage section 100: Fault Response Support Device 200: Cloud Server 300: User terminal 400: Fault Response Support System 450: Communication network

Claims

Claim 1 A fault response support device comprising: a collection unit that collects status information related to the mobility and status information related to the communication network when the ignition of a hydrogen fuel cell-based mobility is turned on; a diagnosis unit that applies the collected status information to a diagnosis model in which fault diagnosis-related learning has been performed to diagnose whether there is an abnormality in the mobility, and if it is diagnosed that there is an abnormality in the mobility, sets a risk for the cause of the diagnosed abnormality; and a drive control unit that controls the operation of the mobility based on the set risk; wherein the diagnosis unit classifies the state of the mobility into normal, hydrogen leakage, stack abnormality, abnormality of at least one of the power pack and battery, communication abnormality, pump abnormality, and supercapacitor abnormality using the diagnosis model to diagnose the cause of the abnormality, and sets the hydrogen leakage as a serious risk, the stack abnormality, the abnormality of at least one of the power pack and battery, and the pump abnormality as a major risk, and sets the communication abnormality and the supercapacitor abnormality as minor risks. Claim 2 A fault response support device according to claim 1, wherein the collection unit collects at least one state information among hydrogen leakage, voltage of a stack, current of a stack, temperature of a stack, hydrogen flow rate of a stack, vibration of a stack, hydrogen pressure of a power pack, temperature of a power pack, hydrogen flow rate of a power pack, current of a power pack, voltage of a battery, current of a battery, temperature of a battery, charging speed of a battery, electrolyte leakage of a battery, coolant temperature of a mobility vehicle body, oil pressure of a mobility vehicle body, hydraulic pressure of a mobility vehicle body, hydraulic temperature of a mobility vehicle body, pump status of a mobility vehicle body, internal communication / electronic control status of a mobility vehicle, temperature of a supercapacitor, capacitance of a supercapacitor, self-discharge of a supercapacitor, internal resistance of a supercapacitor, voltage of a supercapacitor, signal strength of an external communication, speed of an external communication, latency of an external communication, packet loss rate of an external communication, and reconnection frequency of an external communication. Claim 3 delete Claim 4 delete Claim 5 A fault response support device according to claim 1, wherein the drive control unit performs control to turn off the ignition of the mobility when the risk is set to a serious risk, performs control to stop the operation of the abnormality-related component when the risk is set to a grave risk, and performs control to output an abnormality-related warning message when the risk is set to a minor risk. Claim 6 A fault response support device according to claim 1, wherein the diagnostic unit sets the risk with the highest risk among the multiple causes of abnormality as the representative risk of the diagnosed result if the diagnosed result includes multiple causes of abnormality. Claim 7 A fault response support device according to claim 1, wherein the diagnostic unit performs transfer learning of the diagnostic model in real-time or at preset intervals by linking with an external cloud server that derives an answer relatively closer to the correct answer than the diagnostic model. Claim 8 A fault response support device according to claim 7, wherein the diagnostic unit provides diagnostic information diagnosed by the diagnostic model to the cloud server, and performs the transfer learning using diagnostic information derived from the cloud server based on the provided diagnostic information. Claim 9 A fault response support method performed by a fault response support device that supports response measures for a failure in a hydrogen fuel cell-based mobility, comprising: a step of collecting status information related to the mobility and status information related to a communication network when the mobility is turned on; a step of diagnosing whether the mobility is abnormal by applying the collected status information to a diagnostic model that has undergone learning related to fault diagnosis; a step of setting a risk for the cause of the diagnosed abnormality when the mobility is diagnosed as abnormal; and a step of controlling the operation of the mobility based on the set risk; wherein the diagnosing step classifies the state of the mobility into normal, hydrogen leakage, stack abnormality, abnormality of at least one of the power pack and battery, communication abnormality, pump abnormality, and supercapacitor abnormality using the diagnostic model to diagnose the cause of the abnormality, and the setting step is characterized by setting the hydrogen leakage as a serious risk, the stack abnormality, the abnormality of at least one of the power pack and battery, and the pump abnormality as a major risk, and the communication abnormality and the supercapacitor abnormality as minor risks.

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