WO2023249277A1 - Electric vehicle diagnosis and prediction system - Google Patents

Electric vehicle diagnosis and prediction system Download PDF

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Publication number
WO2023249277A1
WO2023249277A1 PCT/KR2023/007260 KR2023007260W WO2023249277A1 WO 2023249277 A1 WO2023249277 A1 WO 2023249277A1 KR 2023007260 W KR2023007260 W KR 2023007260W WO 2023249277 A1 WO2023249277 A1 WO 2023249277A1
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Prior art keywords
electric vehicle
component
graph
vehicle monitoring
sensing
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PCT/KR2023/007260
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French (fr)
Korean (ko)
Inventor
김윤희
하창수
오준석
황진상
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(주)부품디비
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Priority to CN202380009358.8A priority Critical patent/CN117616438A/en
Publication of WO2023249277A1 publication Critical patent/WO2023249277A1/en

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    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60L—PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
    • B60L3/00—Electric devices on electrically-propelled vehicles for safety purposes; Monitoring operating variables, e.g. speed, deceleration or energy consumption
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60L—PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
    • B60L58/00—Methods or circuit arrangements for monitoring or controlling batteries or fuel cells, specially adapted for electric vehicles
    • B60L58/10—Methods or circuit arrangements for monitoring or controlling batteries or fuel cells, specially adapted for electric vehicles for monitoring or controlling batteries
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N20/00—Machine learning
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N20/00—Machine learning
    • G06N20/20—Ensemble learning
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/08—Learning methods
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N7/00—Computing arrangements based on specific mathematical models
    • G06N7/01—Probabilistic graphical models, e.g. probabilistic networks
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00—Systems or methods specially adapted for specific business sectors, e.g. utilities or tourism
    • G06Q50/10—Services

Definitions

  • the present invention relates to an electric vehicle diagnosis and prognosis system, and more specifically, to an electric vehicle diagnosis and prognosis system that can predict failure of an electric vehicle by learning time series data of electric vehicle parts.
  • a typical electric vehicle charger flows current from the charger to the battery in order to charge the battery in the electric vehicle.
  • current may accidentally flow in the reverse direction from the battery to the charger.
  • a reverse current prevention diode is generally provided in the charger.
  • An electric vehicle charger uses a power conversion device that receives alternating current power and converts it into direct current to charge the battery inside the electric vehicle.
  • Power conversion devices may be manufactured and used as a single large capacity device, but may also be used by connecting multiple small capacity power conversion devices in parallel.
  • the small capacity power conversion device described above is called a power conversion module or charging module.
  • a reverse current prevention diode In the case of a general charging module, a reverse current prevention diode is built into the charging module. If an electric vehicle charger is used for a long time, the reverse current prevention diode may fail. Diodes are semiconductors, so when they fail, they usually become short-circuited. If the reverse current prevention diode fails and becomes short-circuited, reverse current may flow from the vehicle battery to the charger in the early stages of charging.
  • the reverse current charges the large capacitance installed on the output side of the charger (right in front of the reverse current prevention diode), it instantaneously becomes a very large surge-type current.
  • a separate current sensor is attached to the output side of the charger, and when a large surge current flows, the control circuit detects this and blocks the circuit.
  • Korean Patent Publication No. 10-2019-0065906 relates to a method and device for detecting failure of an electric vehicle charger. More specifically, when a reverse current in the form of a surge current flows from the battery to the charger during the electric vehicle charging process, the battery and surrounding circuits may fail. Therefore, it is disclosed that it is possible to detect the presence or absence of a reverse current prevention diode failure in order to prevent reverse current occurring during the charging process of an electric vehicle.
  • Korean Patent No. 10-2471911 relates to an intelligent electrical fire prediction monitoring and control system and an electrical fire prediction monitoring and control method using the same. More specifically, it is a sensor that detects electrical signals generated from a power system unit including a plurality of power facilities. Detects various types of current signals through an electrical sensor, monitors them in real time in the signal processing unit, generates and stores various types of converted pattern data, analyzes change trends, and detects leakage current, which is the resistance component of electrical equipment technical standards. It is disclosed that through real-time monitoring, abnormal signs such as leakage current and overcurrent, which are the causes of accidents, can be detected and the cause of the defect can be diagnosed.
  • Korean Patent No. 10-2456499 relates to an integrated information system for electric vehicle maintenance, and more specifically, to store and manage knowledge information related to electric vehicles, knowledge information for electric vehicle maintenance, and data for configuration management of electric vehicles.
  • information for maintenance of electric vehicles can be managed by integrating all information according to users, electric vehicle handling companies, and mechanics.
  • Korean Patent No. 10-2043050 relates to an electric vehicle charger failure determination system. More specifically, if a reverse current in the form of a surge current flows from the battery to the charger during the electric vehicle charging process, the battery and surrounding circuits may fail, so the electric vehicle charging Provides a failure detection method and device for an electric vehicle charger that detects the presence or absence of a failure of the reverse current prevention diode to prevent reverse current occurring in the process.
  • Korean Patent Publication No. 10-2021-0041724 relates to a failure determination system for an electric vehicle charger. More specifically, it relates to a device and method for predicting failure of an electric vehicle charger by using artificial intelligence technology to input the surrounding environment of the electric vehicle charger.
  • the method of operating an electronic device for predicting a failure of an electric vehicle charger includes acquiring sensor data measured by a sensor provided in a first electric vehicle charger, and obtaining regional information indicating the area where the first electric vehicle charger is located. An operation of acquiring weather information at the time when the sensor data of the area was measured, an artificial neural network using the sensor data, the area information, and the weather information as input variables, and the operating state of the electric vehicle charger as an output variable.
  • the purpose of the present invention is to provide an electric vehicle diagnosis and prognosis system that can predict failure of an electric vehicle by learning time series data of electric vehicle parts.
  • the purpose of the present invention is to provide an electric vehicle diagnosis and prognosis system that analyzes sensing data for each electric vehicle component to diagnose the condition of each component and prevents the occurrence of failure.
  • An electric vehicle diagnosis and prognosis system receives sensing information for each part from a plurality of sensors formed on each part of the electric vehicle, an electric vehicle monitoring device that receives and provides sensing values for each component, and an electric vehicle monitoring device that receives and provides sensing values for each component from the electric vehicle monitoring device.
  • An electric vehicle monitoring server that uses the received sensing value for each part to check the status of the part and provides the verification result, and upon receiving the verification result from the electric vehicle monitoring server, a user terminal that performs inspection of the electric vehicle according to the verification result.
  • the electric vehicle monitoring device may receive sensing values for each component measured by a sensor, display them as a first graph, divide the first graph into specific units, compress it, and provide it.
  • the electric vehicle monitoring server expresses the sensing value for each component as a third graph, analyzes the third graph, determines that the state of the component is normal if the sensing value falls within the normal range, and displays the third graph. If the sensing value is outside the normal range through analysis, the condition of the component can be determined to be abnormal.
  • the electric vehicle monitoring server expresses the sensing value for each component as a third graph, determines the performance of the component by analyzing the third graph, and then determines the state of the component according to the matching scenario.
  • an electric vehicle diagnosis and prognosis method for achieving this purpose includes the steps of an electric vehicle monitoring device receiving sensing information for each component from a plurality of sensors formed on each component of the electric vehicle and providing the component-specific sensing information to an electric vehicle monitoring server, wherein the electric vehicle monitoring server Checking the status of the part using the sensing value for each part received from the electric vehicle monitoring device and providing it to the user terminal; and when the user terminal receives the confirmation result from the electric vehicle monitoring server, inspecting the electric vehicle according to the confirmation result. May include execution steps.
  • the step of the electric vehicle monitoring device receiving component-specific sensing information from a plurality of sensors formed on each component of the electric vehicle and providing the component-specific sensing information to the electric vehicle monitoring server includes the electric vehicle monitoring device receiving component-specific sensing information measured by the sensor. It may include receiving a value, displaying it as a first graph, dividing the first graph into specific units, compressing it, and providing it to an electric vehicle monitoring server.
  • the step of the electric vehicle monitoring server checking the status of the component using the sensing value for each component received from the electric vehicle monitoring device and providing the component to the user terminal is the step of the electric vehicle monitoring server sending the sensing value for each component to a third Express it in a graph and analyze the third graph to determine that the part is in a normal state if the sensing value is within the normal range. If the sensing value is outside the normal range by analyzing the third graph, the part is judged to be in an abnormal state. It may include steps.
  • the step of the electric vehicle monitoring server checking the status of the component using the sensing value for each component received from the electric vehicle monitoring device and providing the status to the user terminal represents the sensing value for each component in a third graph, After determining the performance of the part by analyzing the third graph, the state of the part can be determined according to the matching scenario.
  • the electric vehicle diagnosis and prognosis system to achieve this purpose includes an electric vehicle monitoring device that receives sensing information for each component from a plurality of sensors formed on each component of the electric vehicle, and receives and provides sensing values for each component, the electric vehicle monitoring device The user checks the status of the part using the sensing value for each part received from the user, and upon receiving the confirmation result from the metaverse server and the electric vehicle monitoring server that provides the confirmation result, performs inspection of the electric vehicle according to the confirmation result. Includes terminal.
  • the electric vehicle monitoring device may receive sensing values for each component measured by a sensor, display them as a first graph, divide the first graph into specific units, compress it, and provide it.
  • the metaverse server expresses the sensing value for each component as a third graph, analyzes the third graph, determines that the state of the component is normal if the sensing value falls within the normal range, and displays the third graph. If the sensing value is outside the normal range through analysis, the condition of the component can be determined to be abnormal.
  • the metaverse server expresses the sensing value for each component as a third graph, determines the performance of the component by analyzing the third graph, and then determines the state of the component according to the matching scenario.
  • FIG. 1 is a network configuration diagram for explaining an electric vehicle diagnosis and prognosis system according to an embodiment of the present invention.
  • Figure 2 is a network configuration diagram for explaining an electric vehicle diagnosis and prognosis system according to another embodiment of the present invention.
  • Figure 3 is a flowchart illustrating an embodiment of the electric vehicle diagnosis and prognosis method according to the present invention.
  • Figure 4 is a flowchart illustrating an embodiment of the electric vehicle diagnosis and prognosis method according to the present invention.
  • Figure 5 is a diagram for explaining the internal structure of an electric vehicle monitoring device according to an embodiment of the present invention.
  • parts may include a battery, motor, OBC (On Board Charger), LDC (Low Voltage DC-DC Converter), AAF (Acrive Air Flap), and GPS.
  • OBC On Board Charger
  • LDC Low Voltage DC-DC Converter
  • AAF Automatic Air Flap
  • FIG. 1 is a network configuration diagram for explaining an electric vehicle diagnosis and prognosis system according to an embodiment of the present invention.
  • the electric vehicle diagnosis and prognosis system includes an electric vehicle monitoring device 100, an electric vehicle monitoring server 200, a user terminal 300, and a plurality of sensors 400_1 to 400_N.
  • the electric vehicle monitoring device 100 includes a plurality of sensors (400_1 to 400_N), and the plurality of sensors (400_1 to 400_N) are formed on each part of the electric vehicle 500 to generate sensing information and then transmit the sensing information to the electric vehicle monitoring server.
  • the plurality of sensors (400_1 to 400_N) may be implemented as a current sensor, pressure sensor, temperature sensor, vibration sensor, noise sensor, current sensor, etc.
  • the electric vehicle monitoring device 100 receives sensing information for each component from a plurality of sensors 400_1 to 400_N formed on each component of the electric vehicle 500, and provides the sensing information for each component to the electric vehicle monitoring server 200. At this time, the electric vehicle monitoring device 100 measures the sensing value for each component received from each component of the electric vehicle 500 and displays it in a first graph.
  • the electric vehicle monitoring device 100 receives the sensing value for each component measured by the sensor and displays it in a first graph. At this time, the shape displayed in the first graph is different depending on the state of the component.
  • the electric vehicle monitoring device 100 provides the sensing value for each part to the electric vehicle monitoring device 100 each time it receives the sensing value for each part, traffic increases and costs increase. Therefore, the sensing value for each part received from the sensor continuously The compressed sensing value for each component is provided to the electric vehicle monitoring server 200.
  • the electric vehicle monitoring device 100 divides the first graph into specific units and groups them to create a plurality of groups.
  • the electric vehicle monitoring device 100 divides the first graph into specific units and groups them to create a plurality of groups.
  • the electric vehicle monitoring device 100 analyzes the first graph and, if a waveform exists, divides and groups the graph into a period unit to create a plurality of groups.
  • the electric vehicle monitoring device 100 compresses the sensing value of each group of a plurality of groups.
  • the electric vehicle monitoring device 100 divides the first graph into specific units and groups them to create a plurality of groups, and calculates an average value by averaging the sensing values of the group for each of the plurality of groups. At this time, the electric vehicle monitoring device 100 may divide the sensing values in the first graph into specific number units to create a plurality of groups.
  • the electric vehicle monitoring device 100 compresses the sensing values of each of the plurality of groups and displays them as a second graph, then extracts a specific sensing value from the second graph corresponding to each group and sends it to the electric vehicle monitoring server 200. to provide.
  • the electric vehicle monitoring device 100 averages the sensing values of each of a plurality of groups and displays the calculated average value in a second graph at a location corresponding to the group. At this time, the second graph averages the specific number of sensing values in the group for each group and then displays the sensing value corresponding to the average value.
  • the electric vehicle monitoring device 100 averages the sensing values of each of the plurality of groups and displays them as a second graph, then analyzes the second graph to extract the largest slope value and displays the average of the sensing values of the corresponding group for each of the plurality of groups. ) is provided.
  • the electric vehicle monitoring device 100 calculates a slope difference value by comparing the slope values extracted from each group, merges the groups according to the slope difference value, and extracts only one slope value to provide the electric vehicle monitoring server 200 ) is provided.
  • the electric vehicle monitoring device 100 calculates a slope difference value by comparing the slope values extracted from each of the first group and the second group, and if the slope difference value is less than a certain value, the first group and the second group After merging the groups, the larger slope value of the largest slope value extracted from the first group and the largest slope value extracted from the second group is provided to the electric vehicle monitoring server 200.
  • the electric vehicle monitoring device 100 checks the number of currently merged groups before merging the first group and the second group, and if the number of merged groups is more than a certain number, the electric vehicle monitoring device 100 does not perform merging, but does not perform merging. If the number of groups is less than a certain number, merge is performed.
  • the electric vehicle monitoring device 100 calculates a slope difference value by comparing the slope values extracted from each of the first group and the second group, and if the slope difference value is greater than a certain value, the first group and the second group Without merging the groups, the largest slope value of each of the first group and the second group is provided to the electric vehicle monitoring server 200.
  • the electric vehicle monitoring server 200 can check the status of the corresponding component only by the slope value. It is possible.
  • the electric vehicle monitoring server 200 uses the sensing value for each component received from the electric vehicle monitoring device 100 to check the status of the component.
  • the electric vehicle monitoring server 200 analyzes the third graph to determine whether the sensing value falls within the normal range and determines the state of the corresponding component according to the determination result.
  • the electric vehicle monitoring server 200 may analyze the third graph and determine that the state of the component is normal if the sensing value falls within the normal range.
  • the electric vehicle monitoring server 200 may analyze the third graph and determine that the state of the component is abnormal if the sensing value is outside the normal range.
  • the electric vehicle monitoring server 200 analyzes the third graph and, when the sensing value is outside the normal range, compares the sensing value immediately before deviating from the normal range and the sensing value immediately after deviating from the normal range to sense the difference. If the value is greater than a certain value, it is determined that the state of the corresponding part has suddenly changed to an abnormal state, and a notification message is provided to the administrator terminal.
  • the electric vehicle monitoring server 200 determines that the state of the component is abnormal when the section from when the sensing value deviates from the normal range until the sensing value returns to the normal range occurs repeatedly.
  • Figure 2 is a network configuration diagram for explaining an electric vehicle diagnosis and prognosis system according to another embodiment of the present invention.
  • the electric vehicle monitoring device 100 is formed on each part of an actual electric vehicle, generates a sensing value, and then provides the sensing value to the metaverse server 500.
  • the plurality of sensors (400_1 to 400_N) may be implemented as a temperature sensor, vibration sensor, noise sensor, current sensor, etc.
  • the electric vehicle monitoring device 100 receives sensing information for each component from a plurality of sensors (400_1 to 400_N) formed on each component of the electric vehicle, and provides sensing information for each component to the metaverse server 500. At this time, the electric vehicle monitoring device 100 measures the sensing value for each component received from each component of the electric vehicle and displays it in the first graph.
  • the electric vehicle monitoring device 100 receives the sensing value for each component measured by the sensor and displays it in a first graph. At this time, the shape displayed in the first graph is different depending on the state of the component.
  • the electric vehicle monitoring device 100 provides the sensing value for each part to the metaverse server 500 each time it receives the sensing value for each part, the traffic increases and the cost increases. Therefore, the sensing value for each part received from the sensor continuously It is compressed and the compressed sensing value for each component is provided to the metaverse server 500.
  • the electric vehicle monitoring device 100 divides the first graph into specific units and groups them to create a plurality of groups.
  • the electric vehicle monitoring device 100 divides the first graph into specific units and groups them to create a plurality of groups.
  • the electric vehicle monitoring device 100 analyzes the first graph and, if a waveform exists, divides and groups the graph into a period unit to create a plurality of groups.
  • the electric vehicle monitoring device 100 compresses the sensing value of each group of a plurality of groups.
  • the electric vehicle monitoring device 100 compresses the sensing values of each of the plurality of groups and displays them as a second graph, then extracts a specific sensing value from the second graph corresponding to each group and sends it to the electric vehicle monitoring server 200. to provide.
  • the electric vehicle monitoring device 100 calculates a slope difference value by comparing the slope values extracted from each group, merges the groups according to the slope difference value, extracts only one slope value, and sends the metaverse server 500 ) is provided.
  • the electric vehicle monitoring device 100 calculates a slope difference value by comparing the slope values extracted from each of the first group and the second group, and if the slope difference value is less than a certain value, the first group and the second group After merging the groups, the larger slope value of the largest slope value extracted from the first group and the largest slope value extracted from the second group is provided to the metaverse server 500.
  • the electric vehicle monitoring device 100 calculates a slope difference value by comparing the slope values extracted from each of the first group and the second group, and if the slope difference value is greater than a certain value, the first group and the second group Without merging the groups, the largest slope value of each of the first group and the second group is provided to the metaverse server 500.
  • the metaverse server 500 can check the status of the corresponding part only by the slope value. It is possible.
  • the metaverse server 500 is a server that creates a metaverse space to provide to the user, then creates a car model based on the car owned by the user, places it in the metaverse space, and manages it.
  • the metaverse server 500 allows facility icons and component icons to be placed on the virtual electric vehicle in the metaverse space. Accordingly, the metaverse server 500 can simulate the virtual electric vehicle after placing part icons on the virtual electric car.
  • the metaverse server 500 uses the sensing values for each component received from the electric vehicle monitoring device 100 to check the status of the component.
  • the metaverse server 500 when the metaverse server 500 receives the sensing value for each part from the electric vehicle monitoring server 200, it expresses the sensing value for each part in a third graph and analyzes the third graph to determine the state of the part. At this time, the third graph displays the sensing value with the largest slope among the sensing values for each component received from the corresponding component monitoring device ( ).
  • the metaverse server 500 analyzes the third graph to determine whether the sensing value is within the normal range, determines the state of the corresponding part according to the judgment result, and then returns the state of the corresponding part of the electric vehicle to the determined state. Change to to simulate a virtual electric car.
  • the metaverse server 500 analyzes the third graph, determines that the state of the part is normal if the sensing value falls within the normal range, and sets the performance of the part to its original performance to simulate a virtual electric vehicle. do.
  • the metaverse server 500 analyzes the third graph, determines that the state of the part is abnormal if the sensing value is outside the normal range, and sets the performance of the part to the sensed value to create a virtual electric vehicle. Simulate.
  • the metaverse server 500 analyzes the third graph and, when the sensing value is outside the normal range, compares the sensing value immediately before deviating from the normal range and the sensing value immediately after deviating from the normal range to sense the difference. If the value is greater than a certain value, it is determined that the state of the corresponding part has suddenly changed to an abnormal state, and a notification message is provided to the administrator terminal.
  • the metaverse server 500 simulates a virtual electric vehicle based on a scenario according to the performance of each part of the electric vehicle.
  • the metaverse server 500 when the metaverse server 500 receives the sensing value for each part from the electric vehicle monitoring server 200, it expresses the sensing value for each part in a third graph and determines the performance of the part by analyzing the third graph. Then, the virtual electric vehicle is simulated according to the matched scenario.
  • the metaverse server 500 analyzes the third graph to determine the performance of the part, and if there is a scenario set to the same performance as that of the corresponding part among the scenarios, it simulates the electric vehicle according to the scenario.
  • the metaverse server 500 displays the temperature value, vibration value, noise value, and current value of the parts of the electric vehicle on the metaverse space 210 through graphs, including a temperature value graph, a vibration value graph, and a noise value graph.
  • graphs including a temperature value graph, a vibration value graph, and a noise value graph.
  • the metaverse server 500 extracts information when the threshold line is exceeded using the parts monitoring data stored in the parts monitoring database to generate a monitoring pattern, and uses the monitoring pattern to predict the pattern after a certain point in time. Diagnose whether the equipment is malfunctioning.
  • the metaverse server 200 extracts a part when it exceeds the threshold line, and if the number of parts is two or more, checks whether the two parts are related, and if so, the part is at the critical point at the same time. Depending on the number of times the line has been exceeded, the presence or absence of a malfunction of the relevant equipment is diagnosed.
  • item associations are stored in advance as temperature, current, vibration, and noise.
  • FIG. 3 is a flowchart illustrating an embodiment of the electric vehicle diagnosis and prognosis method according to the present invention.
  • An embodiment of FIG. 3 relates to an embodiment that can provide sensing values for each component sensed by an electric vehicle monitoring device to an electric vehicle monitoring server.
  • the electric vehicle monitoring device 100 receives sensing information for each component from a plurality of sensors 400_1 to 400_N formed on each component of the electric vehicle 500 (step S310).
  • the electric vehicle monitoring device 100 divides the first graph into specific units and groups the sensing information for each component to create a plurality of groups (step S320).
  • the electric vehicle monitoring device 100 analyzes the first graph and creates a plurality of groups (step S330).
  • the electric vehicle monitoring device 100 compresses the sensing value of each of the plurality of groups (step S340).
  • step S340 the electric vehicle monitoring device 100 divides the first graph into specific units and groups them to create a plurality of groups, and averages the sensing values of the group for each of the plurality of groups to obtain an average value. Calculate At this time, the electric vehicle monitoring device 100 may divide the sensing values in the first graph into specific number units to create a plurality of groups.
  • the electric vehicle monitoring device 100 compresses the sensing values of each of the plurality of groups and displays them as a second graph, then extracts a specific sensing value from the second graph corresponding to each group and sends it to the electric vehicle monitoring server 200. Provided (step S350).
  • step S350 the electric vehicle monitoring device 100 averages the sensing values of each of the plurality of groups and displays the calculated average value in a second graph at a position corresponding to the group.
  • the second graph averages the specific number of sensing values in the group for each group and then displays the sensing value corresponding to the average value.
  • the electric vehicle monitoring device 100 averages the sensing values of each of the plurality of groups and displays them as a second graph, then analyzes the second graph to extract the largest slope value and displays the average of the sensing values of the corresponding group for each of the plurality of groups. ) is provided.
  • the electric vehicle monitoring device 100 calculates a slope difference value by comparing the slope values extracted from each group, merges the groups according to the slope difference value, and extracts only one slope value to provide the electric vehicle monitoring server 200 ) is provided.
  • the electric vehicle monitoring device 100 calculates a slope difference value by comparing the slope values extracted from each of the first group and the second group, and if the slope difference value is less than a certain value, the first group and the second group After merging the groups, the larger slope value of the largest slope value extracted from the first group and the largest slope value extracted from the second group is provided to the electric vehicle monitoring server 200.
  • the electric vehicle monitoring device 100 checks the number of currently merged groups before merging the first group and the second group, and if the number of merged groups is more than a certain number, the electric vehicle monitoring device 100 does not perform merging, but does not perform merging. If the number of groups is less than a certain number, merge is performed.
  • the electric vehicle monitoring device 100 calculates a slope difference value by comparing the slope values extracted from each of the first group and the second group, and if the slope difference value is greater than a certain value, the first group and the second group are not merged. Instead, the largest slope value of each of the first group and the second group is provided to the electric vehicle monitoring server 200.
  • the electric vehicle monitoring server 200 can check the status of the corresponding component only by the slope value. It is possible.
  • Figure 4 is a flowchart illustrating an embodiment of the electric vehicle diagnosis and prognosis method according to the present invention. 4 relates to an embodiment in which an electric vehicle monitoring server can determine the status of a component by receiving sensing values for each component from an electric vehicle monitoring device.
  • the electric vehicle monitoring server 200 when the electric vehicle monitoring server 200 receives the sensing value for each part from the electric vehicle monitoring server 200 (step S410), it expresses the sensing value for each part in a third graph and analyzes the third graph. Determine the condition of the part (step S420). At this time, the third graph displays the sensing value with the largest slope among the sensing values for each component received from the electric vehicle monitoring device 100.
  • the electric vehicle monitoring server 200 analyzes the third graph to determine whether the sensing value is within the normal range (step S430) and determines the status of the corresponding component according to the determination result (step S440).
  • Figure 5 is a diagram for explaining the internal structure of an electric vehicle monitoring device according to an embodiment of the present invention.
  • the electric vehicle monitoring device 100 is formed on each component of an actual electric vehicle and generates sensing values.
  • the plurality of sensors (400_1 to 400_N) may be implemented as a temperature sensor, vibration sensor, noise sensor, current sensor, etc.
  • a plurality of sensors (400_1 to 400_N) are formed in the parts of the electric vehicle, and the parts include battery, motor, OBC (On Board Charger), LDC (Low Voltage DC-DC Converter), AAF (Acrive Air Flap), and GPS. may include.
  • the electric vehicle monitoring device 100 receives sensing information for each component from a plurality of sensors 400_1 to 400_N formed on each component of the electric vehicle 500, and provides the sensing information for each component to the electric vehicle monitoring server 200. At this time, the electric vehicle monitoring device 100 measures the sensing value for each component received from each component of the electric vehicle 500 and displays it in a first graph.
  • the electric vehicle monitoring device 100 receives the sensing value for each component measured by the sensor and displays it in a first graph. At this time, the shape displayed in the first graph is different depending on the state of the component.
  • the electric vehicle monitoring device 100 provides the sensing value for each part to the electric vehicle monitoring device 100 each time it receives the sensing value for each part, traffic increases and costs increase. Therefore, the sensing value for each part received from the sensor continuously The compressed sensing value for each component is provided to the electric vehicle monitoring server 200.

Abstract

An electric vehicle diagnosis and prediction system according to one embodiment of the present invention comprises: an electric vehicle monitoring device for receiving component-specific sensing information from a plurality of sensors formed in each component of an electric vehicle, and receiving and providing a component-specific sensing value; an electric vehicle monitoring server which confirms the state of the components by using the component-specific sensing value received from the electric vehicle monitoring device, and which provides the confirmation result; and a user terminal for inspecting the electric vehicle according to the confirmation result when the confirmation result is received from the electric vehicle monitoring server.

Description

전기차 진단 및 예지 시스템Electric vehicle diagnosis and prognosis system
본 발명은 전기차 진단 및 예지 시스템에 관한 것으로, 보다 구체적으로 전기차 부품의 시계열 데이터를 학습하여 전기차의 고장을 예측할 수 있도록 하는 전기차 진단 및 예지 시스템에 관한 것이다. The present invention relates to an electric vehicle diagnosis and prognosis system, and more specifically, to an electric vehicle diagnosis and prognosis system that can predict failure of an electric vehicle by learning time series data of electric vehicle parts.
일반적인 전기차 충전기는 전기차 내의 배터리를 충전하기 위하여, 충전기로부터 배터리로 전류를 흘려준다. 충전기로부터 배터리로 전류를 흘려줄 때, 잘못하여 배터리로부터 충전기로 역으로 전류가 흐르게 된다. 전기차의 충전 과정에서 역전류가 흐르는 것을 방지하기 위해 일반적으로 역전류 방지 다이오드가 충전기에 구비된다.A typical electric vehicle charger flows current from the charger to the battery in order to charge the battery in the electric vehicle. When current flows from the charger to the battery, current may accidentally flow in the reverse direction from the battery to the charger. To prevent reverse current from flowing during the charging process of an electric vehicle, a reverse current prevention diode is generally provided in the charger.
전기차 충전기는 전기차 내부의 배터리를 충전하기 위하여 교류 전력을 인가받아서 직류로 변환해 주는 전력변 환장치를 사용한다. 전력변환장치는 큰 용량 한 개로 제작하여 사용하는 경우도 있지만, 복수 개의 소용량 전력변환장치를 병렬 연결하여 사용하기도 한다. 전술한, 소용량의 전력변환장치를 전력변환모듈 또는 충전모듈이라칭한다.An electric vehicle charger uses a power conversion device that receives alternating current power and converts it into direct current to charge the battery inside the electric vehicle. Power conversion devices may be manufactured and used as a single large capacity device, but may also be used by connecting multiple small capacity power conversion devices in parallel. The small capacity power conversion device described above is called a power conversion module or charging module.
일반적인 충전모듈의 경우는 역전류 방지 다이오드를 충전모듈에 내장시켜 사용한다. 전기차 충전기를 오랫동안 사용하게 되면 역전류 방지 다이오드가 고장이 날 수 있는데, 다이오드는 반도체로서 고장시 대개 단락 상태가 된다. 역전류 방지 다이오드가 고장나서 단락 상태가 되면, 충전시 초기에 차량 배터리로부터 충전기로 역전류가 흐를 수 있다In the case of a general charging module, a reverse current prevention diode is built into the charging module. If an electric vehicle charger is used for a long time, the reverse current prevention diode may fail. Diodes are semiconductors, so when they fail, they usually become short-circuited. If the reverse current prevention diode fails and becomes short-circuited, reverse current may flow from the vehicle battery to the charger in the early stages of charging.
역전류는 충전기의 출력측(역전류 방지 다이오드의 바로 앞단)에 설치된 대용량의 콘덴서를 충전하기 때문에 순간적으로 매우 큰 서지(Surge) 형태의 전류가 된다. 일반적으로 전기차의 배터리를 역전류로부터 보호하기 위한 방법은 다음과 같다. 충전기의 출력측에 별도의 전류 센서를 부착하여 역으로 큰 서지 전류가 흐르면 이것을 제어회로에서 검출하여 회로를 차단하는 방식이다.Because the reverse current charges the large capacitance installed on the output side of the charger (right in front of the reverse current prevention diode), it instantaneously becomes a very large surge-type current. In general, methods to protect the battery of an electric vehicle from reverse current are as follows. A separate current sensor is attached to the output side of the charger, and when a large surge current flows, the control circuit detects this and blocks the circuit.
한국공개특허 제10-2019-0065906호는 전기차 충전기의 고장 검출 방법 및 장치에 관한 것으로, 보다 구체적으로 전기차 충전 과정에서 배터리로부터 충전기로 서지 전류 형태의 역전류가 흐르는 경우, 배터리 및 주변 회로가 고장날 수 있으므로, 전기차 충전 과정에서 발생하는 역전류를 방지하기 위해 역전류 방지 다이오드의 고장 유무를 검출할 수 있다는 내용이 개시되어 있다.Korean Patent Publication No. 10-2019-0065906 relates to a method and device for detecting failure of an electric vehicle charger. More specifically, when a reverse current in the form of a surge current flows from the battery to the charger during the electric vehicle charging process, the battery and surrounding circuits may fail. Therefore, it is disclosed that it is possible to detect the presence or absence of a reverse current prevention diode failure in order to prevent reverse current occurring during the charging process of an electric vehicle.
한국등록특허 제10-2471911호는 지능형 전기화재 예측 감시제어시스템 및 이를 이용한 전기화재 예측 감시제어방법에 관한 것으로, 보다 구체적으로 복수의 전력설비를 포함하는 전력계통부로부터 발생되는 전기적인 신호를 검출센서부인 전기센서를 통하여 다양한 형태의 전류신호로 검출하고, 신호처리부에서 실시간으로 모니터링하여 다양한 형태의 변환된 패턴 데이터를 생성하여 저장하고 변화추이를 분석하며, 전기설비 기술기준의 저항성분인 누설전류를 실시간으로 모니터링하여 사고의 원인인 누설전류, 과전류 등 이상 징후를 포착하여 결함원인을 진단할 수 있다는 내용이 개시되어 있다. Korean Patent No. 10-2471911 relates to an intelligent electrical fire prediction monitoring and control system and an electrical fire prediction monitoring and control method using the same. More specifically, it is a sensor that detects electrical signals generated from a power system unit including a plurality of power facilities. Detects various types of current signals through an electrical sensor, monitors them in real time in the signal processing unit, generates and stores various types of converted pattern data, analyzes change trends, and detects leakage current, which is the resistance component of electrical equipment technical standards. It is disclosed that through real-time monitoring, abnormal signs such as leakage current and overcurrent, which are the causes of accidents, can be detected and the cause of the defect can be diagnosed.
한국등록특허 제10-2456499호는 전기차 유지보수 대상 통합정보 시스템에 관한 것으로, 보다 구체적으로 전기차 관련 지식정보, 전기차 유지보수를 위한 지식정보 및 전기차의 형상관리를 위한 데이터를 저장 및 관리할 수 있도록 구현되되, 전기차의 유지보수를 위한 정보를 사용자, 전기차 취급 기업 및 정비사에 따른 정보를 모두 통합하여 관리할 수 있다는 내용이 개시되어 있다. Korean Patent No. 10-2456499 relates to an integrated information system for electric vehicle maintenance, and more specifically, to store and manage knowledge information related to electric vehicles, knowledge information for electric vehicle maintenance, and data for configuration management of electric vehicles. However, it is disclosed that information for maintenance of electric vehicles can be managed by integrating all information according to users, electric vehicle handling companies, and mechanics.
한국등록특허 제10-2043050호는 전기차충전기 고장 판단 시스템에 관한 것으로, 보다 구체적으로 전기차 충전 과정에서 배터리로부터 충전기로 서지 전류 형태의 역전류가 흐르는 경우, 배터리 및 주변 회로가 고장날 수 있으므로, 전기차 충전 과정에서 발생하는 역전류를 방지하기 위해 역전류 방지 다이오드의 고장 유무를 검출하도록 하는 전기차 충전기의 고장 검출 방법 및 장치를 제공한다Korean Patent No. 10-2043050 relates to an electric vehicle charger failure determination system. More specifically, if a reverse current in the form of a surge current flows from the battery to the charger during the electric vehicle charging process, the battery and surrounding circuits may fail, so the electric vehicle charging Provides a failure detection method and device for an electric vehicle charger that detects the presence or absence of a failure of the reverse current prevention diode to prevent reverse current occurring in the process.
한국공개특허 제10-2021-0041724호는 전기차충전기 고장판단 시스템에 관한 것으로, 보다 구체적으로 인공 지능 기술을 이용하여 전기차 충전기의 주변 환경을 입력으로 하여 전기차 충전기의 고장을 예측하는 장치 및 방법에 관한 것으로, 전기차 충전기의 고장을 예측하는 전자 장치의 동작 방법은 제1 전기차 충전기에 구비된 센서에 의해 측정된 센서 데이터를 획득하는 동작, 상기 제1 전기차 충전기가 위치하는 지역을 나타내는 지역 정보를 획득하는 동작, 상기 지역의 상기 센서 데이터가 측정된 시점의 날씨 정보를 획득하는 동작, 상기 센서 데이터, 상기 지역 정보 및 상기 날씨 정보를 입력변수로 하고, 전기차 충전기의 동작 상태를 출력변수로 하는 인공 신경망에 기초한 고장 예측 모델을 생성하는 동작, 과거에 수집된 상기 센서 데이터, 상기 지역 정보 및 상기 날씨 정보에 기초한 학습 데이터를 생성하는 동작, 상기 학습 데이터에 기초하여 상기 고장 예측 모델을 학습시키는 동작, 획득한 상기 센서 데이터, 상기 지역 정보 및 상기 날씨 정보에 기초하여 입력 데이터를 생성하는 동작, 생성된 입력 데이터를 학습된 상기 고장 예측 모델에 입력하여 상기 제1 전기차 충전기의 동작 상태에 대한 결과를 획득하는 동작 및 상기 결과에 기초하여 상기 제1 전기차 충전기의 고장 가능성을 예측하는 동작을 포함할 수 있으며, 전기차 충전기에 대한 고장 발생 가능성을 예측하고, 가능성이 있는 전기차 충전기에 대해 사전 점검을 수행하도록 함으로써 전기차 충전기의 고장 발생을 사전에 예방할 수 있다는 내용이 개시되어 있다. Korean Patent Publication No. 10-2021-0041724 relates to a failure determination system for an electric vehicle charger. More specifically, it relates to a device and method for predicting failure of an electric vehicle charger by using artificial intelligence technology to input the surrounding environment of the electric vehicle charger. In other words, the method of operating an electronic device for predicting a failure of an electric vehicle charger includes acquiring sensor data measured by a sensor provided in a first electric vehicle charger, and obtaining regional information indicating the area where the first electric vehicle charger is located. An operation of acquiring weather information at the time when the sensor data of the area was measured, an artificial neural network using the sensor data, the area information, and the weather information as input variables, and the operating state of the electric vehicle charger as an output variable. An operation of generating a failure prediction model based on the sensor data collected in the past, an operation of generating learning data based on the regional information and the weather information, an operation of training the failure prediction model based on the learning data, An operation of generating input data based on the sensor data, the local information, and the weather information, and an operation of obtaining a result of the operating state of the first electric vehicle charger by inputting the generated input data into the learned failure prediction model. And an operation of predicting the possibility of a failure of the first electric vehicle charger based on the results, predicting the possibility of a failure of the electric vehicle charger, and performing a preliminary inspection on the electric vehicle charger with a possibility of the electric vehicle charger. It is disclosed that the occurrence of failure can be prevented in advance.
본 발명은 전기차 부품의 시계열 데이터를 학습하여 전기차의 고장을 예측할 수 있도록 하는 전기차 진단 및 예지 시스템을 제공하는 것을 목적으로 한다.The purpose of the present invention is to provide an electric vehicle diagnosis and prognosis system that can predict failure of an electric vehicle by learning time series data of electric vehicle parts.
또한, 본 발명은 전기차 부품 별 센싱 데이터를 분석하여 부품 별 상태를 진단하여 고장 발생을 억제할 수 있도록 예지하는 전기차 진단 및 예지 시스템을 제공하는 것을 목적으로 한다.In addition, the purpose of the present invention is to provide an electric vehicle diagnosis and prognosis system that analyzes sensing data for each electric vehicle component to diagnose the condition of each component and prevents the occurrence of failure.
본 발명의 목적들은 이상에서 언급한 목적으로 제한되지 않으며, 언급되지 않은 본 발명의 다른 목적 및 장점들은 하기의 설명에 의해서 이해될 수 있고, 본 발명의 실시예에 의해 보다 분명하게 이해될 것이다. 또한, 본 발명의 목적 및 장점들은 특허 청구 범위에 나타낸 수단 및 그 조합에 의해 실현될 수 있음을 쉽게 알 수 있을 것이다.The objects of the present invention are not limited to the objects mentioned above, and other objects and advantages of the present invention that are not mentioned can be understood by the following description and will be more clearly understood by the examples of the present invention. Additionally, it will be readily apparent that the objects and advantages of the present invention can be realized by the means and combinations thereof indicated in the patent claims.
이러한 목적을 달성하기 위한 전기차 진단 및 예지 시스템은 전기차의 부품 각각에 형성되어 있는 복수의 센서로부터 부품 별 센싱 정보를 수신하고, 부품 별 센싱 값을 수신하여 제공하는 전기차 모니터링 장치, 상기 전기차 모니터링 장치로부터 수신된 부품 별 센싱 값을 이용하여 부품의 상태를 확인하고, 상기 확인 결과를 제공하는 전기차 모니터링 서버 및 상기 전기차 모니터링 서버로부터 확인 결과를 수신하면 상기 확인 결과에 따라 상기 전기차의 점검을 실행하는 사용자 단말을 포함한다. An electric vehicle diagnosis and prognosis system to achieve this purpose receives sensing information for each part from a plurality of sensors formed on each part of the electric vehicle, an electric vehicle monitoring device that receives and provides sensing values for each component, and an electric vehicle monitoring device that receives and provides sensing values for each component from the electric vehicle monitoring device. An electric vehicle monitoring server that uses the received sensing value for each part to check the status of the part and provides the verification result, and upon receiving the verification result from the electric vehicle monitoring server, a user terminal that performs inspection of the electric vehicle according to the verification result. Includes.
일 실시예에서, 상기 전기차 모니터링 장치는 센서에 의해 측정된 부품 별 센싱 값을 수신하여 제1 그래프로 표시하고, 제1 그래프를 특정 단위로 나눈 후 압축하여 제공할 수 있다.In one embodiment, the electric vehicle monitoring device may receive sensing values for each component measured by a sensor, display them as a first graph, divide the first graph into specific units, compress it, and provide it.
일 실시예에서, 상기 전기차 모니터링 서버는 상기 부품 별 센싱 값을 제3 그래프로 표현하고 제3 그래프를 분석하여 센싱 값이 정상 범위에 해당하면 부품의 상태를 정상 상태라고 판단하고, 제3 그래프를 분석하여 센싱 값이 정상 범위를 벗어나면 부품의 상태를 비정상 상태라고 판단할 수 있다.In one embodiment, the electric vehicle monitoring server expresses the sensing value for each component as a third graph, analyzes the third graph, determines that the state of the component is normal if the sensing value falls within the normal range, and displays the third graph. If the sensing value is outside the normal range through analysis, the condition of the component can be determined to be abnormal.
일 실시예에서, 상기 전기차 모니터링 서버는 상기 부품 별 센싱 값을 제3 그래프로 표현하며, 상기 제3 그래프를 분석하여 부품의 성능을 결정한 후 매칭되는 시나리오에 따라 상기 부품의 상태를 결정할 수 있다.In one embodiment, the electric vehicle monitoring server expresses the sensing value for each component as a third graph, determines the performance of the component by analyzing the third graph, and then determines the state of the component according to the matching scenario.
또한 이러한 목적을 달성하기 위한 전기차 진단 및 예지 방법은 전기차 모니터링 장치가 전기차의 부품 각각에 형성되어 있는 복수의 센서로부터 부품 별 센싱 정보를 수신하여 전기차 모니터링 서버에 제공하는 단계, 상기 전기차 모니터링 서버가 상기 전기차 모니터링 장치로부터 수신된 부품 별 센싱 값을 이용하여 부품의 상태를 확인하여 사용자 단말에 제공하는 단계 및 상기 사용자 단말이 상기 전기차 모니터링 서버로부터 확인 결과를 수신하면 상기 확인 결과에 따라 상기 전기차의 점검을 실행하는 단계를 포함할 수 있다.In addition, an electric vehicle diagnosis and prognosis method for achieving this purpose includes the steps of an electric vehicle monitoring device receiving sensing information for each component from a plurality of sensors formed on each component of the electric vehicle and providing the component-specific sensing information to an electric vehicle monitoring server, wherein the electric vehicle monitoring server Checking the status of the part using the sensing value for each part received from the electric vehicle monitoring device and providing it to the user terminal; and when the user terminal receives the confirmation result from the electric vehicle monitoring server, inspecting the electric vehicle according to the confirmation result. May include execution steps.
일 실시예에서, 상기 전기차 모니터링 장치가 전기차의 부품 각각에 형성되어 있는 복수의 센서로부터 부품 별 센싱 정보를 수신하여 전기차 모니터링 서버에 제공하는 단계는 상기 전기차 모니터링 장치가 센서에 의해 측정된 부품 별 센싱 값을 수신하여 제1 그래프로 표시하고, 상기 제1 그래프를 특정 단위로 나눈 후 압축하여 전기차 모니터링 서버에 제공하는 단계를 포함할 수 있다.In one embodiment, the step of the electric vehicle monitoring device receiving component-specific sensing information from a plurality of sensors formed on each component of the electric vehicle and providing the component-specific sensing information to the electric vehicle monitoring server includes the electric vehicle monitoring device receiving component-specific sensing information measured by the sensor. It may include receiving a value, displaying it as a first graph, dividing the first graph into specific units, compressing it, and providing it to an electric vehicle monitoring server.
일 실시예에서, 상기 전기차 모니터링 서버가 상기 전기차 모니터링 장치로부터 수신된 부품 별 센싱 값을 이용하여 부품의 상태를 확인하여 사용자 단말에 제공하는 단계는 상기 전기차 모니터링 서버가 상기 부품 별 센싱 값을 제3 그래프로 표현하고 제3 그래프를 분석하여 센싱 값이 정상 범위에 해당하면 부품의 상태를 정상 상태라고 판단하고, 제3 그래프를 분석하여 센싱 값이 정상 범위를 벗어나면 부품의 상태를 비정상 상태라고 판단하는 단계를 포함할 수 있다.In one embodiment, the step of the electric vehicle monitoring server checking the status of the component using the sensing value for each component received from the electric vehicle monitoring device and providing the component to the user terminal is the step of the electric vehicle monitoring server sending the sensing value for each component to a third Express it in a graph and analyze the third graph to determine that the part is in a normal state if the sensing value is within the normal range. If the sensing value is outside the normal range by analyzing the third graph, the part is judged to be in an abnormal state. It may include steps.
일 실시예에서, 상기 전기차 모니터링 서버가 상기 전기차 모니터링 장치로부터 수신된 부품 별 센싱 값을 이용하여 부품의 상태를 확인하여 사용자 단말에 제공하는 단계는 상기 부품 별 센싱 값을 제3 그래프로 표현하며, 상기 제3 그래프를 분석하여 부품의 성능을 결정한 후 매칭되는 시나리오에 따라 상기 부품의 상태를 결정할 수 있다.In one embodiment, the step of the electric vehicle monitoring server checking the status of the component using the sensing value for each component received from the electric vehicle monitoring device and providing the status to the user terminal represents the sensing value for each component in a third graph, After determining the performance of the part by analyzing the third graph, the state of the part can be determined according to the matching scenario.
또한 이러한 목적을 달성하기 위한 전기차 진단 및 예지 시스템은 전기차의 부품 각각에 형성되어 있는 복수의 센서로부터 부품 별 센싱 정보를 수신하고, 부품 별 센싱 값을 수신하여 제공하는 전기차 모니터링 장치, 상기 전기차 모니터링 장치로부터 수신된 부품 별 센싱 값을 이용하여 부품의 상태를 확인하고, 상기 확인 결과를 제공하는 메타버스 서버 및 상기 전기차 모니터링 서버로부터 확인 결과를 수신하면 상기 확인 결과에 따라 상기 전기차의 점검을 실행하는 사용자 단말을 포함한다.In addition, the electric vehicle diagnosis and prognosis system to achieve this purpose includes an electric vehicle monitoring device that receives sensing information for each component from a plurality of sensors formed on each component of the electric vehicle, and receives and provides sensing values for each component, the electric vehicle monitoring device The user checks the status of the part using the sensing value for each part received from the user, and upon receiving the confirmation result from the metaverse server and the electric vehicle monitoring server that provides the confirmation result, performs inspection of the electric vehicle according to the confirmation result. Includes terminal.
일 실시예에서, 상기 전기차 모니터링 장치는 센서에 의해 측정된 부품 별 센싱 값을 수신하여 제1 그래프로 표시하고, 제1 그래프를 특정 단위로 나눈 후 압축하여 제공할 수 있다.In one embodiment, the electric vehicle monitoring device may receive sensing values for each component measured by a sensor, display them as a first graph, divide the first graph into specific units, compress it, and provide it.
일 실시예에서, 상기 메타버스 서버는 상기 부품 별 센싱 값을 제3 그래프로 표현하고 제3 그래프를 분석하여 센싱 값이 정상 범위에 해당하면 부품의 상태를 정상 상태라고 판단하고, 제3 그래프를 분석하여 센싱 값이 정상 범위를 벗어나면 부품의 상태를 비정상 상태라고 판단할 수 있다.In one embodiment, the metaverse server expresses the sensing value for each component as a third graph, analyzes the third graph, determines that the state of the component is normal if the sensing value falls within the normal range, and displays the third graph. If the sensing value is outside the normal range through analysis, the condition of the component can be determined to be abnormal.
일 실시예에서, 상기 메타버스 서버는 상기 부품 별 센싱 값을 제3 그래프로 표현하며, 상기 제3 그래프를 분석하여 부품의 성능을 결정한 후 매칭되는 시나리오에 따라 상기 부품의 상태를 결정할 수 있다.In one embodiment, the metaverse server expresses the sensing value for each component as a third graph, determines the performance of the component by analyzing the third graph, and then determines the state of the component according to the matching scenario.
전술한 바와 같은 본 발명에 의하면, 전기차 부품의 시계열 데이터를 학습하여 전기차의 고장을 예측할 수 있다는 장점이 있다.According to the present invention as described above, there is an advantage that failure of an electric vehicle can be predicted by learning time series data of electric vehicle parts.
또한 본 발명에 의하면, 전기차 부품 별 센싱 데이터를 분석하여 부품 별 상태를 진단하여 고장 발생을 억제할 수 있도록 예지할 수 있다는 장점이 있다.In addition, according to the present invention, there is an advantage that it is possible to diagnose the condition of each part by analyzing the sensing data for each electric vehicle part and predict the occurrence of a failure to suppress the occurrence.
도 1은 본 발명의 일 실시예에 따른 전기차 진단 및 예지 시스템을 설명하기 위한 네트워크 구성도이다.1 is a network configuration diagram for explaining an electric vehicle diagnosis and prognosis system according to an embodiment of the present invention.
도 2는 본 발명의 다른 일 실시예에 따른 전기차 진단 및 예지 시스템을 설명하기 위한 네트워크 구성도이다.Figure 2 is a network configuration diagram for explaining an electric vehicle diagnosis and prognosis system according to another embodiment of the present invention.
도 3은 본 발명에 따른 전기차 진단 및 예지 방법의 일 실시예를 설명하기 위한 흐름도이다. Figure 3 is a flowchart illustrating an embodiment of the electric vehicle diagnosis and prognosis method according to the present invention.
도 4는 본 발명에 따른 전기차 진단 및 예지 방법의 일 실시예를 설명하기 위한 흐름도이다.Figure 4 is a flowchart illustrating an embodiment of the electric vehicle diagnosis and prognosis method according to the present invention.
도 5는 본 발명의 일 실시예에 따른 전기차 모니터링 장치의 내부 구조를 설명하기 위한 도면이다. Figure 5 is a diagram for explaining the internal structure of an electric vehicle monitoring device according to an embodiment of the present invention.
전술한 목적, 특징 및 장점은 첨부된 도면을 참조하여 상세하게 후술되며, 이에 따라 본 발명이 속하는 기술분야에서 통상의 지식을 가진 자가 본 발명의 기술적 사상을 용이하게 실시할 수 있을 것이다. 본 발명을 설명함에 있어서 본 발명과 관련된 공지 기술에 대한 구체적인 설명이 본 발명의 요지를 불필요하게 흐릴 수 있다고 판단되는 경우에는 상세한 설명을 생략한다. 이하, 첨부된 도면을 참조하여 본 발명에 따른 바람직한 실시예를 상세히 설명하기로 한다. 도면에서 동일한 참조부호는 동일 또는 유사한 구성요소를 가리키는 것으로 사용된다.The above-mentioned objects, features, and advantages will be described in detail later with reference to the attached drawings, so that those skilled in the art will be able to easily implement the technical idea of the present invention. In describing the present invention, if it is determined that a detailed description of known technologies related to the present invention may unnecessarily obscure the gist of the present invention, the detailed description will be omitted. Hereinafter, preferred embodiments according to the present invention will be described in detail with reference to the attached drawings. In the drawings, identical reference numerals are used to indicate identical or similar components.
본 명세서에서 사용된 용어 중 “부품”은 배터리, 모터, OBC(On Board Charger), LDC(Low Voltage DC-DC Converter), AAF(Acrive Air Flap) 및 GPS를 포함할 수 있다. Among the terms used in this specification, “parts” may include a battery, motor, OBC (On Board Charger), LDC (Low Voltage DC-DC Converter), AAF (Acrive Air Flap), and GPS.
도 1은 본 발명의 일 실시예에 따른 전기차 진단 및 예지 시스템을 설명하기 위한 네트워크 구성도이다.1 is a network configuration diagram for explaining an electric vehicle diagnosis and prognosis system according to an embodiment of the present invention.
도 1을 참조하면, 전기차 진단 및 예지 시스템은 전기차 모니터링 장치(100), 전기차 모니터링 서버(200), 사용자 단말(300) 및 복수의 센서(400_1~400_N)를 포함한다. Referring to FIG. 1, the electric vehicle diagnosis and prognosis system includes an electric vehicle monitoring device 100, an electric vehicle monitoring server 200, a user terminal 300, and a plurality of sensors 400_1 to 400_N.
전기차 모니터링 장치(100)는 복수의 센서(400_1~400_N)를 포함하며, 복수의 센서(400_1~400_N)는 전기차(500)의 부품 각각에 형성되어 센싱 정보를 생성한 후 센싱 정보를 전기차 모니터링 서버(200)에 제공한다. 이때, 복수의 센서(400_1~400_N)는 전류 센서, 압력 센서, 온도 센서, 진동 센서, 소음 센서, 전류 센서 등으로 구현될 수 있다. The electric vehicle monitoring device 100 includes a plurality of sensors (400_1 to 400_N), and the plurality of sensors (400_1 to 400_N) are formed on each part of the electric vehicle 500 to generate sensing information and then transmit the sensing information to the electric vehicle monitoring server. Provided at (200). At this time, the plurality of sensors (400_1 to 400_N) may be implemented as a current sensor, pressure sensor, temperature sensor, vibration sensor, noise sensor, current sensor, etc.
전기차 모니터링 장치(100)는 전기차(500)의 부품 각각에 형성되어 있는 복수의 센서(400_1~400_N)로부터 부품 별 센싱 정보를 수신하고, 부품 별 센싱 정보를 전기차 모니터링 서버(200)에 제공한다. 이때, 전기차 모니터링 장치(100)는 전기차(500)의 부품 각각으로부터 수신된 부품 별 센싱 값을 측정하여 제1 그래프로 표시한다. The electric vehicle monitoring device 100 receives sensing information for each component from a plurality of sensors 400_1 to 400_N formed on each component of the electric vehicle 500, and provides the sensing information for each component to the electric vehicle monitoring server 200. At this time, the electric vehicle monitoring device 100 measures the sensing value for each component received from each component of the electric vehicle 500 and displays it in a first graph.
따라서, 전기차 모니터링 장치(100)는 센서에 의해 측정된 부품 별 센싱 값을 수신하여 제1 그래프로 표시한다. 이때, 부품의 상태에 따라 제1 그래프로 표시된 형상은 상이하다. Accordingly, the electric vehicle monitoring device 100 receives the sensing value for each component measured by the sensor and displays it in a first graph. At this time, the shape displayed in the first graph is different depending on the state of the component.
상기와 같이, 전기차 모니터링 장치(100)가 부품 별 센싱 값을 수신할 때마다 전기차 모니터링 장치(100)에 제공하는 경우 트래픽이 많아져 비용이 증가하기 때문에 연속적으로 센서로부터 수신된 부품 별 센싱 값을 압축하여 압축된 부품 별 센싱 값을 전기차 모니터링 서버(200)에 제공한다. As described above, if the electric vehicle monitoring device 100 provides the sensing value for each part to the electric vehicle monitoring device 100 each time it receives the sensing value for each part, traffic increases and costs increase. Therefore, the sensing value for each part received from the sensor continuously The compressed sensing value for each component is provided to the electric vehicle monitoring server 200.
이를 위해, 전기차 모니터링 장치(100)는 제1 그래프를 특정 단위로 나눈 후 그룹핑하여 복수의 그룹을 생성한다.To this end, the electric vehicle monitoring device 100 divides the first graph into specific units and groups them to create a plurality of groups.
일 실시예에서, 전기차 모니터링 장치(100)는 제1 그래프를 특정 단위로 나눈 후 그룹핑하여 복수의 그룹을 생성한다.In one embodiment, the electric vehicle monitoring device 100 divides the first graph into specific units and groups them to create a plurality of groups.
다른 일 실시예에서, 전기차 모니터링 장치(100)는 제1 그래프를 분석하여 파형이 존재하는 경우 주기 단위로 나누어 그룹핑하여 복수의 그룹을 생성한다.In another embodiment, the electric vehicle monitoring device 100 analyzes the first graph and, if a waveform exists, divides and groups the graph into a period unit to create a plurality of groups.
그 후, 전기차 모니터링 장치(100)는 복수의 그룹 각각에 대해서 해당 그룹의 센싱 값을 압축한다. Afterwards, the electric vehicle monitoring device 100 compresses the sensing value of each group of a plurality of groups.
일 실시예에서, 전기차 모니터링 장치(100)는 제1 그래프를 특정 단위로 나눈 후 그룹핑하여 복수의 그룹을 생성하고, 복수의 그룹 각각에 대해서 해당 그룹의 센싱 값을 평균화하여 평균값을 산출한다. 이때, 전기차 모니터링 장치(100)는 제1 그래프 중 센싱 값을 특정 개수 단위로 분할하여 복수의 그룹을 생성할 수 있다. In one embodiment, the electric vehicle monitoring device 100 divides the first graph into specific units and groups them to create a plurality of groups, and calculates an average value by averaging the sensing values of the group for each of the plurality of groups. At this time, the electric vehicle monitoring device 100 may divide the sensing values in the first graph into specific number units to create a plurality of groups.
그 후, 전기차 모니터링 장치(100)는 복수의 그룹 각각의 센싱 값을 압축하여 제2 그래프로 표시한 후, 그룹 각각에 해당하는 제2 그래프 중 특정 센싱 값을 추출하여 전기차 모니터링 서버(200)에 제공한다. Afterwards, the electric vehicle monitoring device 100 compresses the sensing values of each of the plurality of groups and displays them as a second graph, then extracts a specific sensing value from the second graph corresponding to each group and sends it to the electric vehicle monitoring server 200. to provide.
일 실시예에서, 전기차 모니터링 장치(100)는 복수의 그룹 각각의 센싱 값을 평균화하여 산출된 평균값을 그룹에 해당하는 위치에 제2 그래프로 표시한다. 이때, 제2 그래프는 각각의 그룹에 대해서 해당 그룹에 있는 특정 개수의 센싱 값을 평균화한 후 평균값에 해당하는 센싱 값을 표시한 것이다. In one embodiment, the electric vehicle monitoring device 100 averages the sensing values of each of a plurality of groups and displays the calculated average value in a second graph at a location corresponding to the group. At this time, the second graph averages the specific number of sensing values in the group for each group and then displays the sensing value corresponding to the average value.
상기와 같이, 전기차 모니터링 장치(100)는 복수의 그룹 각각에 대해서 해당 그룹의 센싱 값을 평균화하여 제2 그래프로 표시한 후 제2 그래프를 분석하여 가장 큰 기울기 값을 추출하여 전기차 모니터링 서버(200)에 제공한다. As described above, the electric vehicle monitoring device 100 averages the sensing values of each of the plurality of groups and displays them as a second graph, then analyzes the second graph to extract the largest slope value and displays the average of the sensing values of the corresponding group for each of the plurality of groups. ) is provided.
일 실시예에서, 전기차 모니터링 장치(100)는 그룹 각각에서 추출된 기울기 값을 비교하여 기울기 차이 값을 산출하고, 기울기 차이 값에 따라 그룹을 병합하여 하나의 기울기 값만을 추출하여 전기차 모니터링 서버(200)에 제공한다. In one embodiment, the electric vehicle monitoring device 100 calculates a slope difference value by comparing the slope values extracted from each group, merges the groups according to the slope difference value, and extracts only one slope value to provide the electric vehicle monitoring server 200 ) is provided.
상기의 실시예에서, 전기차 모니터링 장치(100)는 제1 그룹 및 제2 그룹 각각에서 추출된 기울기 값을 비교하여 기울기 차이 값을 산출하고, 기울기 차이 값이 특정 값 이하이면 제1 그룹 및 제2 그룹을 병합한 후 제1 그룹에서 추출된 가장 큰 기울기 값 및 제2 그룹에서 추출된 가장 큰 기울기 값 중 더 큰 기울기 값을 전기차 모니터링 서버(200)에 제공한다. In the above embodiment, the electric vehicle monitoring device 100 calculates a slope difference value by comparing the slope values extracted from each of the first group and the second group, and if the slope difference value is less than a certain value, the first group and the second group After merging the groups, the larger slope value of the largest slope value extracted from the first group and the largest slope value extracted from the second group is provided to the electric vehicle monitoring server 200.
상기와 같은 과정을 반복하여 실행하는 과정에서, 병합된 그룹의 개수가 특정 개수 이상인 경우 병합을 정지하게 된다. 따라서, 전기차 모니터링 장치(100)는 제1 그룹 및 제2 그룹을 병합을 실행하기 이전에 현재 병합된 그룹의 개수를 확인하고, 병합된 그룹의 개수가 특정 개수 이상이면 병합을 실행하지 않지만 병합된 그룹의 개수가 특정 개수 이하이면 병합을 실행한다. In the process of repeatedly executing the above process, if the number of merged groups exceeds a certain number, merging is stopped. Therefore, the electric vehicle monitoring device 100 checks the number of currently merged groups before merging the first group and the second group, and if the number of merged groups is more than a certain number, the electric vehicle monitoring device 100 does not perform merging, but does not perform merging. If the number of groups is less than a certain number, merge is performed.
상기의 실시예에서, 전기차 모니터링 장치(100)는 제1 그룹 및 제2 그룹 각각에서 추출된 기울기 값을 비교하여 기울기 차이 값을 산출하고, 기울기 차이 값이 특정 값 이상이면 제1 그룹 및 제2 그룹을 병합하지 않고 제1 그룹 및 제2 그룹 각각의 가장 큰 기울기 값을 전기차 모니터링 서버(200)에 제공한다. In the above embodiment, the electric vehicle monitoring device 100 calculates a slope difference value by comparing the slope values extracted from each of the first group and the second group, and if the slope difference value is greater than a certain value, the first group and the second group Without merging the groups, the largest slope value of each of the first group and the second group is provided to the electric vehicle monitoring server 200.
상기와 같이, 센싱 값 전체를 전기차 모니터링 서버(200)에 전송하지 않고 주기 별 가장 큰 기울기 값만을 전기차 모니터링 서버(200)에 전송하더라도 전기차 모니터링 서버(200)는 기울기 값만으로 해당 부품의 상태를 확인할 수 있는 것이다.As described above, even if the entire sensing value is not transmitted to the electric vehicle monitoring server 200 and only the largest slope value per cycle is transmitted to the electric vehicle monitoring server 200, the electric vehicle monitoring server 200 can check the status of the corresponding component only by the slope value. It is possible.
전기차 모니터링 서버(200)는 전기차 모니터링 장치(100)로부터 수신된 부품 별 센싱 값을 이용하여 부품의 상태를 확인한다. The electric vehicle monitoring server 200 uses the sensing value for each component received from the electric vehicle monitoring device 100 to check the status of the component.
먼저, 전기차 모니터링 서버(200)는 전기차 모니터링 서버(200)로부터 부품 별 센싱 값을 수신하면, 부품 별 센싱 값을 제3 그래프로 표현하며, 제3 그래프를 분석하여 부품의 상태를 판단한다. 이때, 제3 그래프는 해당 전기차 모니터링 서버(200)로부터 수신된 부품 별 센싱 값 중 기울기가 가장 큰 센싱 값을 표시한 것이다.First, when the electric vehicle monitoring server 200 receives the sensing value for each part from the electric vehicle monitoring server 200, it expresses the sensing value for each part in a third graph and analyzes the third graph to determine the state of the part. At this time, the third graph displays the sensing value with the largest slope among the sensing values for each component received from the electric vehicle monitoring server 200.
그 후, 전기차 모니터링 서버(200)는 제3 그래프를 분석하여 센싱 값이 정상 범위에 해당하는지 여부를 판단하고, 판단 결과에 따라 해당 부품의 상태를 판단한다. Thereafter, the electric vehicle monitoring server 200 analyzes the third graph to determine whether the sensing value falls within the normal range and determines the state of the corresponding component according to the determination result.
일 실시예에서, 전기차 모니터링 서버(200)는 제3 그래프를 분석하여 센싱 값이 정상 범위에 해당하면 부품의 상태를 정상 상태라고 판단할 수 있다.In one embodiment, the electric vehicle monitoring server 200 may analyze the third graph and determine that the state of the component is normal if the sensing value falls within the normal range.
다른 일 실시예에서, 전기차 모니터링 서버(200)는 제3 그래프를 분석하여 센싱 값이 정상 범위를 벗어나면 부품의 상태를 비정상 상태라고 판단할 수 있다. In another embodiment, the electric vehicle monitoring server 200 may analyze the third graph and determine that the state of the component is abnormal if the sensing value is outside the normal range.
상기의 실시예에서, 전기차 모니터링 서버(200)는 제3 그래프를 분석하여 센싱 값이 정상 범위를 벗어나면, 정상 범위를 벗어나기 직전의 센싱 값 및 정상 범위를 벗어난 직후의 센싱 값을 비교하여 차이 센싱 값이 특정 값 이상이면 해당 부품의 상태가 갑자기 비정상 상태로 변경되었다고 판단하여 관리자 단말에 알림 메시지를 제공한다. In the above embodiment, the electric vehicle monitoring server 200 analyzes the third graph and, when the sensing value is outside the normal range, compares the sensing value immediately before deviating from the normal range and the sensing value immediately after deviating from the normal range to sense the difference. If the value is greater than a certain value, it is determined that the state of the corresponding part has suddenly changed to an abnormal state, and a notification message is provided to the administrator terminal.
상기의 실시예에서, 전기차 모니터링 서버(200)는 센싱 값이 정상 범위를 벗어난 시점부터 센싱 값이 정상 범위에 되돌아 올때까지의 시간이 특정 시간 이하이면 부품의 상태를 정상 상태라고 판단하지만, 센싱 값이 정상 범위를 벗어난 시점부터 특정 시간 이내에 센싱 값이 정상 범위에 되돌아오지 않으면 부품의 상태를 비정상 상태라고 판단하여 판단한다. In the above embodiment, the electric vehicle monitoring server 200 determines that the state of the component is normal if the time from when the sensing value deviates from the normal range until the sensing value returns to the normal range is less than a certain time, but the sensing value If the sensing value does not return to the normal range within a certain time from the time it deviates from this normal range, the condition of the component is determined to be abnormal.
이때, 전기차 모니터링 서버(200)는 센싱 값이 정상 범위를 벗어난 시점부터 센싱 값이 정상 범위에 되돌아 올때까지의 구간이 반복적으로 발생되는 경우, 부품의 상태를 비정상 상태라고 판단한다. At this time, the electric vehicle monitoring server 200 determines that the state of the component is abnormal when the section from when the sensing value deviates from the normal range until the sensing value returns to the normal range occurs repeatedly.
도 2는 본 발명의 다른 일 실시예에 따른 전기차 진단 및 예지 시스템을 설명하기 위한 네트워크 구성도이다.Figure 2 is a network configuration diagram for explaining an electric vehicle diagnosis and prognosis system according to another embodiment of the present invention.
도 2를 참조하면, 전기차 진단 및 예지 시스템은 전기차 모니터링 장치(100), 사용자 단말(300), 복수의 센서(400_1~400_N) 및 메타버스 서버(500)를 포함한다. Referring to FIG. 2, the electric vehicle diagnosis and prognosis system includes an electric vehicle monitoring device 100, a user terminal 300, a plurality of sensors (400_1 to 400_N), and a metaverse server 500.
전기차 모니터링 장치(100)는 실제 전기차의 부품 각각에 형성되어 센싱 값을 생성한 후 센싱 값을 메타버스 서버(500)에 제공한다. 이때, 복수의 센서(400_1~400_N)는 온도 센서, 진동 센서, 소음 센서, 전류 센서 등으로 구현될 수 있다. The electric vehicle monitoring device 100 is formed on each part of an actual electric vehicle, generates a sensing value, and then provides the sensing value to the metaverse server 500. At this time, the plurality of sensors (400_1 to 400_N) may be implemented as a temperature sensor, vibration sensor, noise sensor, current sensor, etc.
전기차 모니터링 장치(100)는 복수의 센서(400_1~400_N)를 포함하며, 복수의 센서(400_1~400_N)는 전기차(500)의 부품 각각에 형성되어 센싱 정보를 생성한 후 센싱 정보를 메타버스 서버(500)에 제공한다. 이때, 복수의 센서(400_1~400_N)는 전류 센서, 압력 센서, 온도 센서, 진동 센서, 소음 센서, 전류 센서 등으로 구현될 수 있다. The electric vehicle monitoring device 100 includes a plurality of sensors (400_1 to 400_N), and the plurality of sensors (400_1 to 400_N) are formed on each part of the electric vehicle 500 to generate sensing information and then send the sensing information to the metaverse server. Provided at (500). At this time, the plurality of sensors (400_1 to 400_N) may be implemented as a current sensor, pressure sensor, temperature sensor, vibration sensor, noise sensor, current sensor, etc.
전기차 모니터링 장치(100)는 전기차의 부품 각각에 형성되어 있는 복수의 센서(400_1~400_N)로부터 부품 별 센싱 정보를 수신하고, 부품 별 센싱 정보를 메타버스 서버(500)에 제공한다. 이때, 전기차 모니터링 장치(100)는 전기차의 부품 각각으로부터 수신된 부품 별 센싱 값을 측정하여 제1 그래프로 표시한다. The electric vehicle monitoring device 100 receives sensing information for each component from a plurality of sensors (400_1 to 400_N) formed on each component of the electric vehicle, and provides sensing information for each component to the metaverse server 500. At this time, the electric vehicle monitoring device 100 measures the sensing value for each component received from each component of the electric vehicle and displays it in the first graph.
따라서, 전기차 모니터링 장치(100)는 센서에 의해 측정된 부품 별 센싱 값을 수신하여 제1 그래프로 표시한다. 이때, 부품의 상태에 따라 제1 그래프로 표시된 형상은 상이하다. Accordingly, the electric vehicle monitoring device 100 receives the sensing value for each component measured by the sensor and displays it in a first graph. At this time, the shape displayed in the first graph is different depending on the state of the component.
상기와 같이, 전기차 모니터링 장치(100)가 부품 별 센싱 값을 수신할 때마다 메타버스 서버(500)에 제공하는 경우 트래픽이 많아져 비용이 증가하기 때문에 연속적으로 센서로부터 수신된 부품 별 센싱 값을 압축하여 압축된 부품 별 센싱 값을 메타버스 서버(500)에 제공한다. As described above, if the electric vehicle monitoring device 100 provides the sensing value for each part to the metaverse server 500 each time it receives the sensing value for each part, the traffic increases and the cost increases. Therefore, the sensing value for each part received from the sensor continuously It is compressed and the compressed sensing value for each component is provided to the metaverse server 500.
이를 위해, 전기차 모니터링 장치(100)는 제1 그래프를 특정 단위로 나눈 후 그룹핑하여 복수의 그룹을 생성한다.To this end, the electric vehicle monitoring device 100 divides the first graph into specific units and groups them to create a plurality of groups.
일 실시예에서, 전기차 모니터링 장치(100)는 제1 그래프를 특정 단위로 나눈 후 그룹핑하여 복수의 그룹을 생성한다.In one embodiment, the electric vehicle monitoring device 100 divides the first graph into specific units and groups them to create a plurality of groups.
다른 일 실시예에서, 전기차 모니터링 장치(100)는 제1 그래프를 분석하여 파형이 존재하는 경우 주기 단위로 나누어 그룹핑하여 복수의 그룹을 생성한다.In another embodiment, the electric vehicle monitoring device 100 analyzes the first graph and, if a waveform exists, divides and groups the graph into a period unit to create a plurality of groups.
그 후, 전기차 모니터링 장치(100)는 복수의 그룹 각각에 대해서 해당 그룹의 센싱 값을 압축한다. Afterwards, the electric vehicle monitoring device 100 compresses the sensing value of each group of a plurality of groups.
일 실시예에서, 전기차 모니터링 장치(100)는 제1 그래프를 특정 단위로 나눈 후 그룹핑하여 복수의 그룹을 생성하고, 복수의 그룹 각각에 대해서 해당 그룹의 센싱 값을 평균화하여 평균값을 산출한다. 이때, 전기차 모니터링 장치(100)는 제1 그래프 중 센싱 값을 특정 개수 단위로 분할하여 복수의 그룹을 생성할 수 있다. In one embodiment, the electric vehicle monitoring device 100 divides the first graph into specific units and groups them to create a plurality of groups, and calculates an average value by averaging the sensing values of the group for each of the plurality of groups. At this time, the electric vehicle monitoring device 100 may divide the sensing values in the first graph into specific number units to create a plurality of groups.
그 후, 전기차 모니터링 장치(100)는 복수의 그룹 각각의 센싱 값을 압축하여 제2 그래프로 표시한 후, 그룹 각각에 해당하는 제2 그래프 중 특정 센싱 값을 추출하여 전기차 모니터링 서버(200)에 제공한다. Afterwards, the electric vehicle monitoring device 100 compresses the sensing values of each of the plurality of groups and displays them as a second graph, then extracts a specific sensing value from the second graph corresponding to each group and sends it to the electric vehicle monitoring server 200. to provide.
일 실시예에서, 전기차 모니터링 장치(100)는 복수의 그룹 각각의 센싱 값을 평균화하여 산출된 평균값을 그룹에 해당하는 위치에 제2 그래프로 표시한다. 이때, 제2 그래프는 각각의 그룹에 대해서 해당 그룹에 있는 특정 개수의 센싱 값을 평균화한 후 평균값에 해당하는 센싱 값을 표시한 것이다. In one embodiment, the electric vehicle monitoring device 100 averages the sensing values of each of a plurality of groups and displays the calculated average value in a second graph at a location corresponding to the group. At this time, the second graph averages the specific number of sensing values in the group for each group and then displays the sensing value corresponding to the average value.
상기와 같이, 전기차 모니터링 장치(100)는 복수의 그룹 각각에 대해서 해당 그룹의 센싱 값을 평균화하여 제2 그래프로 표시한 후 제2 그래프를 분석하여 가장 큰 기울기 값을 추출하여 메타버스 서버(500)에 제공한다. As described above, the electric vehicle monitoring device 100 averages the sensing values of the group for each of the plurality of groups and displays them as a second graph, then analyzes the second graph to extract the largest slope value and displays the average of the sensing values of the group in the metaverse server 500. ) is provided.
일 실시예에서, 전기차 모니터링 장치(100)는 그룹 각각에서 추출된 기울기 값을 비교하여 기울기 차이 값을 산출하고, 기울기 차이 값에 따라 그룹을 병합하여 하나의 기울기 값만을 추출하여 메타버스 서버(500)에 제공한다. In one embodiment, the electric vehicle monitoring device 100 calculates a slope difference value by comparing the slope values extracted from each group, merges the groups according to the slope difference value, extracts only one slope value, and sends the metaverse server 500 ) is provided.
상기의 실시예에서, 전기차 모니터링 장치(100)는 제1 그룹 및 제2 그룹 각각에서 추출된 기울기 값을 비교하여 기울기 차이 값을 산출하고, 기울기 차이 값이 특정 값 이하이면 제1 그룹 및 제2 그룹을 병합한 후 제1 그룹에서 추출된 가장 큰 기울기 값 및 제2 그룹에서 추출된 가장 큰 기울기 값 중 더 큰 기울기 값을 메타버스 서버(500)에 제공한다. In the above embodiment, the electric vehicle monitoring device 100 calculates a slope difference value by comparing the slope values extracted from each of the first group and the second group, and if the slope difference value is less than a certain value, the first group and the second group After merging the groups, the larger slope value of the largest slope value extracted from the first group and the largest slope value extracted from the second group is provided to the metaverse server 500.
상기와 같은 과정을 반복하여 실행하는 과정에서, 병합된 그룹의 개수가 특정 개수 이상인 경우 병합을 정지하게 된다. 따라서, 전기차 모니터링 장치(100)는 제1 그룹 및 제2 그룹을 병합을 실행하기 이전에 현재 병합된 그룹의 개수를 확인하고, 병합된 그룹의 개수가 특정 개수 이상이면 병합을 실행하지 않지만 병합된 그룹의 개수가 특정 개수 이하이면 병합을 실행한다. In the process of repeatedly executing the above process, if the number of merged groups exceeds a certain number, merging is stopped. Therefore, the electric vehicle monitoring device 100 checks the number of currently merged groups before merging the first group and the second group, and if the number of merged groups is more than a certain number, the electric vehicle monitoring device 100 does not perform merging, but does not perform merging. If the number of groups is less than a certain number, merge is performed.
상기의 실시예에서, 전기차 모니터링 장치(100)는 제1 그룹 및 제2 그룹 각각에서 추출된 기울기 값을 비교하여 기울기 차이 값을 산출하고, 기울기 차이 값이 특정 값 이상이면 제1 그룹 및 제2 그룹을 병합하지 않고 제1 그룹 및 제2 그룹 각각의 가장 큰 기울기 값을 메타버스 서버(500)에 제공한다. In the above embodiment, the electric vehicle monitoring device 100 calculates a slope difference value by comparing the slope values extracted from each of the first group and the second group, and if the slope difference value is greater than a certain value, the first group and the second group Without merging the groups, the largest slope value of each of the first group and the second group is provided to the metaverse server 500.
상기와 같이, 센싱 값 전체를 메타버스 서버(500)에 전송하지 않고 주기 별 가장 큰 기울기 값만을 메타버스 서버(500)에 전송하더라도 메타버스 서버(500)는 기울기 값만으로 해당 부품의 상태를 확인할 수 있는 것이다.As described above, even if the entire sensing value is not transmitted to the metaverse server 500 and only the largest slope value per cycle is transmitted to the metaverse server 500, the metaverse server 500 can check the status of the corresponding part only by the slope value. It is possible.
메타버스 서버(500)는 사용자에게 제공하기 위한 메타버스 공간을 생성한 후 사용자가 소유하는 자동차를 기초로 자동차 모델을 생성하여 메타버스 공간 상에 배치하여 관리하는 서버이다.The metaverse server 500 is a server that creates a metaverse space to provide to the user, then creates a car model based on the car owned by the user, places it in the metaverse space, and manages it.
메타버스 서버(500)는 메타버스 공간 상에서 전기차의 부품을 아이콘 형태로 제공하여 사용자가 전기차를 구동하거나 부품을 다른 부품으로 교체하여 실행해볼 수 있도록 한다. The metaverse server 500 provides electric vehicle parts in the form of icons in the metaverse space, allowing users to drive the electric car or replace parts with other parts.
이를 위해, 메타버스 서버(500)는 메타버스 공간 상에서 가상 전기차에 설비 아이콘 및 부품 아이콘을 배치할 수 있도록 한다. 따라서, 메타버스 서버(500)는 가상 전기차에 부품 아이콘을 배치한 후의 가상 전기차를 시뮬레이션 할 수 있다. To this end, the metaverse server 500 allows facility icons and component icons to be placed on the virtual electric vehicle in the metaverse space. Accordingly, the metaverse server 500 can simulate the virtual electric vehicle after placing part icons on the virtual electric car.
메타버스 서버(500)는 전기차 모니터링 장치(100)로부터 수신된 부품 별 센싱 값을 이용하여 부품의 상태를 확인한다. The metaverse server 500 uses the sensing values for each component received from the electric vehicle monitoring device 100 to check the status of the component.
먼저, 메타버스 서버(500)는 전기차 모니터링 서버(200)로부터 부품 별 센싱 값을 수신하면, 부품 별 센싱 값을 제3 그래프로 표현하며, 제3 그래프를 분석하여 부품의 상태를 판단한다. 이때, 제3 그래프는 해당 부품 모니터링 장치()로부터 수신된 부품 별 센싱 값 중 기울기가 가장 큰 센싱 값을 표시한 것이다.First, when the metaverse server 500 receives the sensing value for each part from the electric vehicle monitoring server 200, it expresses the sensing value for each part in a third graph and analyzes the third graph to determine the state of the part. At this time, the third graph displays the sensing value with the largest slope among the sensing values for each component received from the corresponding component monitoring device ( ).
그 후, 메타버스 서버(500)는 제3 그래프를 분석하여 센싱 값이 정상 범위에 해당하는지 여부를 판단하고, 판단 결과에 따라 해당 부품의 상태를 판단한 후 전기차의 해당 부품의 상태를 판단된 상태로 변경하여 가상 전기차를 시뮬레이션한다. Afterwards, the metaverse server 500 analyzes the third graph to determine whether the sensing value is within the normal range, determines the state of the corresponding part according to the judgment result, and then returns the state of the corresponding part of the electric vehicle to the determined state. Change to to simulate a virtual electric car.
일 실시예에서, 메타버스 서버(500)는 제3 그래프를 분석하여 센싱 값이 정상 범위에 해당하면 부품의 상태를 정상 상태라고 판단하여 해당 부품의 성능을 원래의 성능으로 설정하여 가상 전기차를 시뮬레이션한다. In one embodiment, the metaverse server 500 analyzes the third graph, determines that the state of the part is normal if the sensing value falls within the normal range, and sets the performance of the part to its original performance to simulate a virtual electric vehicle. do.
다른 일 실시예에서, 메타버스 서버(500)는 제3 그래프를 분석하여 센싱 값이 정상 범위를 벗어나면 부품의 상태를 비정상 상태라고 판단하고, 해당 부품의 성능을 센싱된 값으로 설정하여 가상 전기차를 시뮬레이션한다. In another embodiment, the metaverse server 500 analyzes the third graph, determines that the state of the part is abnormal if the sensing value is outside the normal range, and sets the performance of the part to the sensed value to create a virtual electric vehicle. Simulate.
상기의 실시예에서, 메타버스 서버(500)는 제3 그래프를 분석하여 센싱 값이 정상 범위를 벗어나면, 정상 범위를 벗어나기 직전의 센싱 값 및 정상 범위를 벗어난 직후의 센싱 값을 비교하여 차이 센싱 값이 특정 값 이상이면 해당 부품의 상태가 갑자기 비정상 상태로 변경되었다고 판단하여 관리자 단말에 알림 메시지를 제공한다. In the above embodiment, the metaverse server 500 analyzes the third graph and, when the sensing value is outside the normal range, compares the sensing value immediately before deviating from the normal range and the sensing value immediately after deviating from the normal range to sense the difference. If the value is greater than a certain value, it is determined that the state of the corresponding part has suddenly changed to an abnormal state, and a notification message is provided to the administrator terminal.
메타버스 서버(500)는 전기차의 부품 별 성능에 따른 시나리오를 기초로 가상 전기차를 시뮬레이션을 한다.The metaverse server 500 simulates a virtual electric vehicle based on a scenario according to the performance of each part of the electric vehicle.
일 실시예에서, 메타버스 서버(500)는 전기차 모니터링 서버(200)로부터 부품 별 센싱 값을 수신하면, 부품 별 센싱 값을 제3 그래프로 표현하며, 제3 그래프를 분석하여 부품의 성능을 결정한 후 매칭되는 시나리오에 따라 가상 전기차를 시뮬레이션을 한다.In one embodiment, when the metaverse server 500 receives the sensing value for each part from the electric vehicle monitoring server 200, it expresses the sensing value for each part in a third graph and determines the performance of the part by analyzing the third graph. Then, the virtual electric vehicle is simulated according to the matched scenario.
그 후, 메타버스 서버(500)는 제3 그래프를 분석하여 부품의 성능을 결정한 후 시나리오 중 해당 부품의 성능과 동일한 성능으로 설정된 시나리오가 존재하면 해당 시나리오에 따라 전기차를 시뮬레이션을 한다.Afterwards, the metaverse server 500 analyzes the third graph to determine the performance of the part, and if there is a scenario set to the same performance as that of the corresponding part among the scenarios, it simulates the electric vehicle according to the scenario.
또한, 메타버스 서버(500)는 메타버스 공간(210) 상에서 전기차의 부품의 온도 수치, 진동 수치, 소음 수치 및 전류 수치 각각을 그래프를 통해 표시하며, 온도 수치 그래프, 진동 수치 그래프, 소음 수치 그래프 및 전류 수치 그래프 각각이 미리 결정된 임계선을 초과하는 경우, 임계선을 초과한 항목, 초과했을 때의 시점 및 초과했을 때의 수치를 포함하는 부품 모니터링 데이터를 기록하여 부품 모니터링 데이터베이스에 저장한다. In addition, the metaverse server 500 displays the temperature value, vibration value, noise value, and current value of the parts of the electric vehicle on the metaverse space 210 through graphs, including a temperature value graph, a vibration value graph, and a noise value graph. When each of the and current numerical graphs exceeds a predetermined threshold line, component monitoring data including the item that exceeded the threshold line, the point in time when it was exceeded, and the value when it was exceeded are recorded and stored in the component monitoring database.
즉, 메타버스 서버(500)는 부품 모니터링 데이터베이스에 저장된 부품 모니터링 데이터를 이용하여 임계선을 초과했을 때의 정보를 추출하여 모니터링 패턴을 생성하고, 모니터링 패턴을 이용하여 특정 시점 이후의 패턴을 예측하여 해당 설비의 고장 유무를 진단한다. In other words, the metaverse server 500 extracts information when the threshold line is exceeded using the parts monitoring data stored in the parts monitoring database to generate a monitoring pattern, and uses the monitoring pattern to predict the pattern after a certain point in time. Diagnose whether the equipment is malfunctioning.
일 실시예에서, 메타버스 서버(200)는 임계선을 초과했을 때의 부품을 추출하고 부품의 개수가 두 개 이상인 경우 두 부품이 연관성이 있는지 여부를 확인하고 연관성이 있는 경우 해당 부품이 동시에 임계선을 초과했을 횟수에 따라 해당 설비의 고장 유무를 진단한다. 이를 위해 항목 연관성은 온도 및 전류 그리고 진동 및 소음으로 미리 저장되어 있다. In one embodiment, the metaverse server 200 extracts a part when it exceeds the threshold line, and if the number of parts is two or more, checks whether the two parts are related, and if so, the part is at the critical point at the same time. Depending on the number of times the line has been exceeded, the presence or absence of a malfunction of the relevant equipment is diagnosed. For this purpose, item associations are stored in advance as temperature, current, vibration, and noise.
도 3은 본 발명에 따른 전기차 진단 및 예지 방법의 일 실시예를 설명하기 위한 흐름도이다. 도 3의 일 실시예는 전기차 모니터링 장치에 의해 센싱된 부품 별 센싱 값을 전기차 모니터링 서버에 제공할 수 있는 일 실시예에 관한 것이다.Figure 3 is a flowchart illustrating an embodiment of the electric vehicle diagnosis and prognosis method according to the present invention. An embodiment of FIG. 3 relates to an embodiment that can provide sensing values for each component sensed by an electric vehicle monitoring device to an electric vehicle monitoring server.
도 3을 참조하면, 전기차 모니터링 장치(100)는 전기차(500)의 부품 각각에 형성되어 있는 복수의 센서(400_1~400_N)로부터 부품 별 센싱 정보를 수신한다(단계 S310).Referring to FIG. 3, the electric vehicle monitoring device 100 receives sensing information for each component from a plurality of sensors 400_1 to 400_N formed on each component of the electric vehicle 500 (step S310).
전기차 모니터링 장치(100)는 부품 별 센싱 정보를 제1 그래프를 특정 단위로 나눈 후 그룹핑하여 복수의 그룹을 생성한다(단계 S320).The electric vehicle monitoring device 100 divides the first graph into specific units and groups the sensing information for each component to create a plurality of groups (step S320).
전기차 모니터링 장치(100)는 제1 그래프를 분석하여 복수의 그룹을 생성한다(단계 S330).The electric vehicle monitoring device 100 analyzes the first graph and creates a plurality of groups (step S330).
그 후, 전기차 모니터링 장치(100)는 복수의 그룹 각각에 대해서 해당 그룹의 센싱 값을 압축한다(단계 S340). Afterwards, the electric vehicle monitoring device 100 compresses the sensing value of each of the plurality of groups (step S340).
단계 S340에 대한 일 실시예에서, 전기차 모니터링 장치(100)는 제1 그래프를 특정 단위로 나눈 후 그룹핑하여 복수의 그룹을 생성하고, 복수의 그룹 각각에 대해서 해당 그룹의 센싱 값을 평균화하여 평균값을 산출한다. 이때, 전기차 모니터링 장치(100)는 제1 그래프 중 센싱 값을 특정 개수 단위로 분할하여 복수의 그룹을 생성할 수 있다. In one embodiment of step S340, the electric vehicle monitoring device 100 divides the first graph into specific units and groups them to create a plurality of groups, and averages the sensing values of the group for each of the plurality of groups to obtain an average value. Calculate At this time, the electric vehicle monitoring device 100 may divide the sensing values in the first graph into specific number units to create a plurality of groups.
그 후, 전기차 모니터링 장치(100)는 복수의 그룹 각각의 센싱 값을 압축하여 제2 그래프로 표시한 후, 그룹 각각에 해당하는 제2 그래프 중 특정 센싱 값을 추출하여 전기차 모니터링 서버(200)에 제공한다(단계 S350). Afterwards, the electric vehicle monitoring device 100 compresses the sensing values of each of the plurality of groups and displays them as a second graph, then extracts a specific sensing value from the second graph corresponding to each group and sends it to the electric vehicle monitoring server 200. Provided (step S350).
단계 S350에 대한 일 실시예에서, 전기차 모니터링 장치(100)는 복수의 그룹 각각의 센싱 값을 평균화하여 산출된 평균값을 그룹에 해당하는 위치에 제2 그래프로 표시한다. 이때, 제2 그래프는 각각의 그룹에 대해서 해당 그룹에 있는 특정 개수의 센싱 값을 평균화한 후 평균값에 해당하는 센싱 값을 표시한 것이다. In one embodiment of step S350, the electric vehicle monitoring device 100 averages the sensing values of each of the plurality of groups and displays the calculated average value in a second graph at a position corresponding to the group. At this time, the second graph averages the specific number of sensing values in the group for each group and then displays the sensing value corresponding to the average value.
상기와 같이, 전기차 모니터링 장치(100)는 복수의 그룹 각각에 대해서 해당 그룹의 센싱 값을 평균화하여 제2 그래프로 표시한 후 제2 그래프를 분석하여 가장 큰 기울기 값을 추출하여 전기차 모니터링 서버(200)에 제공한다. As described above, the electric vehicle monitoring device 100 averages the sensing values of each of the plurality of groups and displays them as a second graph, then analyzes the second graph to extract the largest slope value and displays the average of the sensing values of the corresponding group for each of the plurality of groups. ) is provided.
일 실시예에서, 전기차 모니터링 장치(100)는 그룹 각각에서 추출된 기울기 값을 비교하여 기울기 차이 값을 산출하고, 기울기 차이 값에 따라 그룹을 병합하여 하나의 기울기 값만을 추출하여 전기차 모니터링 서버(200)에 제공한다. In one embodiment, the electric vehicle monitoring device 100 calculates a slope difference value by comparing the slope values extracted from each group, merges the groups according to the slope difference value, and extracts only one slope value to provide the electric vehicle monitoring server 200 ) is provided.
상기의 실시예에서, 전기차 모니터링 장치(100)는 제1 그룹 및 제2 그룹 각각에서 추출된 기울기 값을 비교하여 기울기 차이 값을 산출하고, 기울기 차이 값이 특정 값 이하이면 제1 그룹 및 제2 그룹을 병합한 후 제1 그룹에서 추출된 가장 큰 기울기 값 및 제2 그룹에서 추출된 가장 큰 기울기 값 중 더 큰 기울기 값을 전기차 모니터링 서버(200)에 제공한다. In the above embodiment, the electric vehicle monitoring device 100 calculates a slope difference value by comparing the slope values extracted from each of the first group and the second group, and if the slope difference value is less than a certain value, the first group and the second group After merging the groups, the larger slope value of the largest slope value extracted from the first group and the largest slope value extracted from the second group is provided to the electric vehicle monitoring server 200.
상기와 같은 과정을 반복하여 실행하는 과정에서, 병합된 그룹의 개수가 특정 개수 이상인 경우 병합을 정지하게 된다. 따라서, 전기차 모니터링 장치(100)는 제1 그룹 및 제2 그룹을 병합을 실행하기 이전에 현재 병합된 그룹의 개수를 확인하고, 병합된 그룹의 개수가 특정 개수 이상이면 병합을 실행하지 않지만 병합된 그룹의 개수가 특정 개수 이하이면 병합을 실행한다. In the process of repeatedly executing the above process, if the number of merged groups exceeds a certain number, merging is stopped. Therefore, the electric vehicle monitoring device 100 checks the number of currently merged groups before merging the first group and the second group, and if the number of merged groups is more than a certain number, the electric vehicle monitoring device 100 does not perform merging, but does not perform merging. If the number of groups is less than a certain number, merge is performed.
이때, 전기차 모니터링 장치(100)는 제1 그룹 및 제2 그룹 각각에서 추출된 기울기 값을 비교하여 기울기 차이 값을 산출하고, 기울기 차이 값이 특정 값 이상이면 제1 그룹 및 제2 그룹을 병합하지 않고 제1 그룹 및 제2 그룹 각각의 가장 큰 기울기 값을 전기차 모니터링 서버(200)에 제공한다. At this time, the electric vehicle monitoring device 100 calculates a slope difference value by comparing the slope values extracted from each of the first group and the second group, and if the slope difference value is greater than a certain value, the first group and the second group are not merged. Instead, the largest slope value of each of the first group and the second group is provided to the electric vehicle monitoring server 200.
상기와 같이, 센싱 값 전체를 전기차 모니터링 서버(200)에 전송하지 않고 주기 별 가장 큰 기울기 값만을 전기차 모니터링 서버(200)에 전송하더라도 전기차 모니터링 서버(200)는 기울기 값만으로 해당 부품의 상태를 확인할 수 있는 것이다.As described above, even if the entire sensing value is not transmitted to the electric vehicle monitoring server 200 and only the largest slope value per cycle is transmitted to the electric vehicle monitoring server 200, the electric vehicle monitoring server 200 can check the status of the corresponding component only by the slope value. It is possible.
도 4는 본 발명에 따른 전기차 진단 및 예지 방법의 일 실시예를 설명하기 위한 흐름도이다. 도 4의 일 실시예는 전기차 모니터링 서버가 전기차 모니터링 장치로부터 부품 별 센싱 값을 수신하여 부품의 상태를 판단할 수 있는 일 실시예에 관한 것이다.Figure 4 is a flowchart illustrating an embodiment of the electric vehicle diagnosis and prognosis method according to the present invention. 4 relates to an embodiment in which an electric vehicle monitoring server can determine the status of a component by receiving sensing values for each component from an electric vehicle monitoring device.
도 4를 참조하면, 전기차 모니터링 서버(200)는 전기차 모니터링 서버(200)로부터 부품 별 센싱 값을 수신하면(단계 S410), 부품 별 센싱 값을 제3 그래프로 표현하며, 제3 그래프를 분석하여 부품의 상태를 판단한다(단계 S420). 이때, 제3 그래프는 해당 전기차 모니터링 장치(100)로부터 수신된 부품 별 센싱 값 중 기울기가 가장 큰 센싱 값을 표시한 것이다.Referring to FIG. 4, when the electric vehicle monitoring server 200 receives the sensing value for each part from the electric vehicle monitoring server 200 (step S410), it expresses the sensing value for each part in a third graph and analyzes the third graph. Determine the condition of the part (step S420). At this time, the third graph displays the sensing value with the largest slope among the sensing values for each component received from the electric vehicle monitoring device 100.
전기차 모니터링 서버(200)는 제3 그래프를 분석하여 센싱 값이 정상 범위에 해당하는지 여부를 판단하고(단계 S430), 판단 결과에 따라 해당 부품의 상태를 판단한다(단계 S440). The electric vehicle monitoring server 200 analyzes the third graph to determine whether the sensing value is within the normal range (step S430) and determines the status of the corresponding component according to the determination result (step S440).
도 5는 본 발명의 일 실시예에 따른 전기차 모니터링 장치의 내부 구조를 설명하기 위한 도면이다.Figure 5 is a diagram for explaining the internal structure of an electric vehicle monitoring device according to an embodiment of the present invention.
도 5를 참조하면, 전기차 모니터링 장치(100)는 실제 전기차의 부품 각각에 형성되어 센싱 값을 생성한다. 이때, 복수의 센서(400_1~400_N)는 온도 센서, 진동 센서, 소음 센서, 전류 센서 등으로 구현될 수 있다. 이때, 복수의 센서(400_1~400_N)는 전기차의 부품에 형성되어 있으며, 부품은 배터리, 모터, OBC(On Board Charger), LDC(Low Voltage DC-DC Converter), AAF(Acrive Air Flap) 및 GPS를 포함할 수 있다. Referring to FIG. 5, the electric vehicle monitoring device 100 is formed on each component of an actual electric vehicle and generates sensing values. At this time, the plurality of sensors (400_1 to 400_N) may be implemented as a temperature sensor, vibration sensor, noise sensor, current sensor, etc. At this time, a plurality of sensors (400_1 to 400_N) are formed in the parts of the electric vehicle, and the parts include battery, motor, OBC (On Board Charger), LDC (Low Voltage DC-DC Converter), AAF (Acrive Air Flap), and GPS. may include.
전기차 모니터링 장치(100)는 전기차(500)의 부품 각각에 형성되어 있는 복수의 센서(400_1~400_N)로부터 부품 별 센싱 정보를 수신하고, 부품 별 센싱 정보를 전기차 모니터링 서버(200)에 제공한다. 이때, 전기차 모니터링 장치(100)는 전기차(500)의 부품 각각으로부터 수신된 부품 별 센싱 값을 측정하여 제1 그래프로 표시한다. The electric vehicle monitoring device 100 receives sensing information for each component from a plurality of sensors 400_1 to 400_N formed on each component of the electric vehicle 500, and provides the sensing information for each component to the electric vehicle monitoring server 200. At this time, the electric vehicle monitoring device 100 measures the sensing value for each component received from each component of the electric vehicle 500 and displays it in a first graph.
따라서, 전기차 모니터링 장치(100)는 센서에 의해 측정된 부품 별 센싱 값을 수신하여 제1 그래프로 표시한다. 이때, 부품의 상태에 따라 제1 그래프로 표시된 형상은 상이하다. Accordingly, the electric vehicle monitoring device 100 receives the sensing value for each component measured by the sensor and displays it in a first graph. At this time, the shape displayed in the first graph is different depending on the state of the component.
상기와 같이, 전기차 모니터링 장치(100)가 부품 별 센싱 값을 수신할 때마다 전기차 모니터링 장치(100)에 제공하는 경우 트래픽이 많아져 비용이 증가하기 때문에 연속적으로 센서로부터 수신된 부품 별 센싱 값을 압축하여 압축된 부품 별 센싱 값을 전기차 모니터링 서버(200)에 제공한다. As described above, if the electric vehicle monitoring device 100 provides the sensing value for each part to the electric vehicle monitoring device 100 each time it receives the sensing value for each part, traffic increases and costs increase. Therefore, the sensing value for each part received from the sensor continuously The compressed sensing value for each component is provided to the electric vehicle monitoring server 200.
한정된 실시예와 도면에 의해 설명되었으나, 본 발명은 상기의 실시예에 한정되는 것은 아니며, 이는 본 발명이 속하는 분야에서 통상의 지식을 가진 자라면 이러한 기재로부터 다양한 수정 및 변형이 가능하다. 따라서, 본 발명 사상은 아래에 기재된 특허청구범위에 의해서만 파악되어야 하고, 이의 균등 또는 등가적 변형 모두는 본 발명 사상의 범주에 속한다고 할 것이다.Although the present invention has been described with reference to limited embodiments and drawings, the present invention is not limited to the above embodiments, and various modifications and variations can be made by those skilled in the art from these descriptions. Accordingly, the spirit of the present invention should be understood only by the scope of the claims set forth below, and all equivalent or equivalent modifications thereof shall fall within the scope of the spirit of the present invention.

Claims (12)

  1. 전기차의 부품 각각에 형성되어 있는 복수의 센서로부터 부품 별 센싱 정보를 수신하고, 부품 별 센싱 값을 수신하여 제공하는 전기차 모니터링 장치;An electric vehicle monitoring device that receives sensing information for each component from a plurality of sensors formed on each component of the electric vehicle, and receives and provides sensing values for each component;
    상기 전기차 모니터링 장치로부터 수신된 부품 별 센싱 값을 이용하여 부품의 상태를 확인하고, 상기 확인 결과를 제공하는 전기차 모니터링 서버;an electric vehicle monitoring server that checks the status of parts using sensing values for each part received from the electric vehicle monitoring device and provides the confirmation result;
    상기 전기차 모니터링 서버로부터 확인 결과를 수신하면 상기 확인 결과에 따라 상기 전기차의 점검을 실행하는 사용자 단말을 포함하는 것을 특징으로 하는 Characterized in that it includes a user terminal that performs inspection of the electric vehicle according to the confirmation result upon receiving the confirmation result from the electric vehicle monitoring server.
    전기차 진단 및 예지 시스템.Electric vehicle diagnosis and prognosis system.
  2. 제1항에 있어서,According to paragraph 1,
    상기 전기차 모니터링 장치는The electric vehicle monitoring device is
    센서에 의해 측정된 부품 별 센싱 값을 수신하여 제1 그래프로 표시하고, 제1 그래프를 특정 단위로 나눈 후 압축하여 제공하는 것을 특징으로 하는 Characterized by receiving the sensing value for each component measured by the sensor, displaying it as a first graph, dividing the first graph into specific units, compressing it, and providing it.
    전기차 진단 및 예지 시스템.Electric vehicle diagnosis and prognosis system.
  3. 제1항에 있어서,According to paragraph 1,
    상기 전기차 모니터링 서버는The electric vehicle monitoring server is
    상기 부품 별 센싱 값을 제3 그래프로 표현하고 제3 그래프를 분석하여 센싱 값이 정상 범위에 해당하면 부품의 상태를 정상 상태라고 판단하고, 제3 그래프를 분석하여 센싱 값이 정상 범위를 벗어나면 부품의 상태를 비정상 상태라고 판단하는 것을 특징으로 하는 The sensing value for each component is expressed in a third graph, and the third graph is analyzed to determine that the component is in a normal state if the sensing value falls within the normal range. If the third graph is analyzed and the sensing value is outside the normal range, the component is judged to be in a normal state. Characterized by determining that the state of the part is abnormal
    전기차 진단 및 예지 시스템.Electric vehicle diagnosis and prognosis system.
  4. 제1항에 있어서,According to paragraph 1,
    상기 전기차 모니터링 서버는The electric vehicle monitoring server is
    상기 부품 별 센싱 값을 제3 그래프로 표현하며, 상기 제3 그래프를 분석하여 부품의 성능을 결정한 후 매칭되는 시나리오에 따라 상기 부품의 상태를 결정하는 것을 특징으로 하는 The sensing value for each part is expressed in a third graph, and the performance of the part is determined by analyzing the third graph, and then the state of the part is determined according to the matching scenario.
    전기차 진단 및 예지 시스템.Electric vehicle diagnosis and prognosis system.
  5. 전기차 모니터링 장치가 전기차의 부품 각각에 형성되어 있는 복수의 센서로부터 부품 별 센싱 정보를 수신하여 전기차 모니터링 서버에 제공하는 단계;An electric vehicle monitoring device receiving sensing information for each component from a plurality of sensors formed on each component of the electric vehicle and providing the component-specific sensing information to an electric vehicle monitoring server;
    상기 전기차 모니터링 서버가 상기 전기차 모니터링 장치로부터 수신된 부품 별 센싱 값을 이용하여 부품의 상태를 확인하여 사용자 단말에 제공하는 단계; 및The electric vehicle monitoring server checking the status of each component using the sensing value for each component received from the electric vehicle monitoring device and providing the information to the user terminal; and
    상기 사용자 단말이 상기 전기차 모니터링 서버로부터 확인 결과를 수신하면 상기 확인 결과에 따라 상기 전기차의 점검을 실행하는 단계를 포함하는 것을 특징으로 하는 When the user terminal receives a confirmation result from the electric vehicle monitoring server, performing an inspection of the electric vehicle according to the confirmation result.
    전기차 진단 및 예지 방법.Electric vehicle diagnosis and prognosis methods.
  6. 제5항에 있어서,According to clause 5,
    상기 전기차 모니터링 장치가 전기차의 부품 각각에 형성되어 있는 복수의 센서로부터 부품 별 센싱 정보를 수신하여 전기차 모니터링 서버에 제공하는 단계는The step of the electric vehicle monitoring device receiving sensing information for each component from a plurality of sensors formed on each component of the electric vehicle and providing it to the electric vehicle monitoring server.
    상기 전기차 모니터링 장치가 센서에 의해 측정된 부품 별 센싱 값을 수신하여 제1 그래프로 표시하고, 상기 제1 그래프를 특정 단위로 나눈 후 압축하여 전기차 모니터링 서버에 제공하는 단계를 포함하는 것을 특징으로 하는 The electric vehicle monitoring device receives the sensing value for each component measured by the sensor, displays it as a first graph, divides the first graph into specific units, compresses it, and provides it to the electric vehicle monitoring server.
    전기차 진단 및 예지 방법.Electric vehicle diagnosis and prognosis methods.
  7. 제5항에 있어서,According to clause 5,
    상기 전기차 모니터링 서버가 상기 전기차 모니터링 장치로부터 수신된 부품 별 센싱 값을 이용하여 부품의 상태를 확인하여 사용자 단말에 제공하는 단계는The step of the electric vehicle monitoring server checking the status of the component using the sensing value for each component received from the electric vehicle monitoring device and providing the status to the user terminal.
    상기 전기차 모니터링 서버가 상기 부품 별 센싱 값을 제3 그래프로 표현하고 제3 그래프를 분석하여 센싱 값이 정상 범위에 해당하면 부품의 상태를 정상 상태라고 판단하고, 제3 그래프를 분석하여 센싱 값이 정상 범위를 벗어나면 부품의 상태를 비정상 상태라고 판단하는 단계를 포함하는 것을 특징으로 하는 The electric vehicle monitoring server expresses the sensing value for each component in a third graph, analyzes the third graph, determines that the state of the component is normal if the sensing value falls within the normal range, and analyzes the third graph to determine that the sensing value is in a normal state. Characterized by including a step of determining that the state of the part is abnormal if it is outside the normal range.
    전기차 진단 및 예지 방법.Electric vehicle diagnosis and prognosis methods.
  8. 제5항에 있어서,According to clause 5,
    상기 전기차 모니터링 서버가 상기 전기차 모니터링 장치로부터 수신된 부품 별 센싱 값을 이용하여 부품의 상태를 확인하여 사용자 단말에 제공하는 단계는The step of the electric vehicle monitoring server checking the status of the component using the sensing value for each component received from the electric vehicle monitoring device and providing the status to the user terminal.
    상기 부품 별 센싱 값을 제3 그래프로 표현하며, 상기 제3 그래프를 분석하여 부품의 성능을 결정한 후 매칭되는 시나리오에 따라 상기 부품의 상태를 결정하는 것을 특징으로 하는 The sensing value for each part is expressed in a third graph, and the performance of the part is determined by analyzing the third graph, and then the state of the part is determined according to the matching scenario.
    전기차 진단 및 예지 방법.Electric vehicle diagnosis and prognosis methods.
  9. 전기차의 부품 각각에 형성되어 있는 복수의 센서로부터 부품 별 센싱 정보를 수신하고, 부품 별 센싱 값을 수신하여 제공하는 전기차 모니터링 장치;An electric vehicle monitoring device that receives sensing information for each component from a plurality of sensors formed on each component of the electric vehicle, and receives and provides sensing values for each component;
    상기 전기차 모니터링 장치로부터 수신된 부품 별 센싱 값을 이용하여 부품의 상태를 확인하고, 상기 확인 결과를 제공하는 메타버스 서버; 및a metaverse server that checks the status of parts using sensing values for each part received from the electric vehicle monitoring device and provides the confirmation result; and
    상기 전기차 모니터링 서버로부터 확인 결과를 수신하면 상기 확인 결과에 따라 상기 전기차의 점검을 실행하는 사용자 단말을 포함하는 것을 특징으로 하는 Characterized in that it includes a user terminal that performs inspection of the electric vehicle according to the confirmation result upon receiving the confirmation result from the electric vehicle monitoring server.
    전기차 진단 및 예지 시스템.Electric vehicle diagnosis and prognosis system.
  10. 제9항에 있어서,According to clause 9,
    상기 전기차 모니터링 장치는The electric vehicle monitoring device is
    센서에 의해 측정된 부품 별 센싱 값을 수신하여 제1 그래프로 표시하고, 제1 그래프를 특정 단위로 나눈 후 압축하여 제공하는 것을 특징으로 하는 Characterized by receiving the sensing value for each component measured by the sensor, displaying it as a first graph, dividing the first graph into specific units, compressing it, and providing it.
    전기차 진단 및 예지 시스템.Electric vehicle diagnosis and prognosis system.
  11. 제9항에 있어서,According to clause 9,
    상기 메타버스 서버는The metaverse server is
    상기 부품 별 센싱 값을 제3 그래프로 표현하고 제3 그래프를 분석하여 센싱 값이 정상 범위에 해당하면 부품의 상태를 정상 상태라고 판단하고, 제3 그래프를 분석하여 센싱 값이 정상 범위를 벗어나면 부품의 상태를 비정상 상태라고 판단하는 것을 특징으로 하는 The sensing value for each component is expressed in a third graph, and the third graph is analyzed to determine that the component is in a normal state if the sensing value falls within the normal range. If the third graph is analyzed and the sensing value is outside the normal range, the component is judged to be in a normal state. Characterized by determining that the state of the part is abnormal
    전기차 진단 및 예지 시스템.Electric vehicle diagnosis and prognosis system.
  12. 제9항에 있어서,According to clause 9,
    상기 메타버스 서버는The metaverse server is
    상기 부품 별 센싱 값을 제3 그래프로 표현하며, 상기 제3 그래프를 분석하여 부품의 성능을 결정한 후 매칭되는 시나리오에 따라 상기 부품의 상태를 결정하는 것을 특징으로 하는 The sensing value for each part is expressed in a third graph, and the performance of the part is determined by analyzing the third graph, and then the state of the part is determined according to the matching scenario.
    전기차 진단 및 예지 시스템.Electric vehicle diagnosis and prognosis system.
PCT/KR2023/007260 2022-06-20 2023-05-26 Electric vehicle diagnosis and prediction system WO2023249277A1 (en)

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