Vehicle air conditioner monitoring device and method

Abstract

The present invention relates to a next-generation vehicle air conditioning monitoring device and method, and more specifically, to a system capable of monitoring and calibrating the air conditioning system of a vehicle, particularly an eco-friendly vehicle, in real time. More specifically, the system comprises high-performance data collection and analysis software, a matching toolkit module, and a sensor module. The real-time data collection and analysis software is implemented as a Windows-based application and transmits and receives data via USB or WiFi. The matching toolkit module provides equipment setting and data inquiry functions via an LCD screen and implements the UDS protocol via a CAN interface. The sensor module supports up to 32 various sensors and communicates with the main module via RS-485. Additionally, the system status can be checked and controlled remotely via a mobile application. The system is equipped with a fast data sampling cycle of less than 1 second and the ability to simultaneously process up to 150 variables, thereby enabling the next-generation vehicle air conditioning monitoring device and method to measure, analyze, and optimize the performance of the air conditioning system in real time. According to the present invention, real-time monitoring of the vehicle air conditioning system is possible due to a fast data sampling cycle of less than 1 second, thereby enabling immediate response to problematic situations.

Description

Technology Field

[0001] The present invention relates to a next-generation vehicle air conditioning monitoring device and method, and more specifically, to a system capable of monitoring and calibrating the air conditioning system of a vehicle, particularly an eco-friendly vehicle, in real time. More specifically, the system comprises high-performance data collection and analysis software, a matching toolkit module, and a sensor module. The real-time data collection and analysis software is implemented as a Windows-based application and transmits and receives data via USB or WiFi. The matching toolkit module provides equipment setting and data inquiry functions via an LCD screen and implements the UDS protocol via a CAN interface. The sensor module supports up to 32 various sensors and communicates with the main module via RS-485. Additionally, the system status can be checked and controlled remotely via a mobile application. The system is equipped with a fast data sampling cycle of less than 1 second and the ability to simultaneously process up to 150 variables, thereby enabling the next-generation vehicle air conditioning monitoring device and method to measure, analyze, and optimize the performance of the air conditioning system in real time. Background Technology

[0002] Generally, vehicle air conditioning systems are divided into ventilation systems and heating and cooling systems. Here, the ventilation system is a device that maintains comfortable air inside the vehicle by replacing contaminated air with fresh outside air or by circulating the air while removing pollutants through filters, while the heating and cooling system is a device that selectively maintains the temperature inside the vehicle.

[0003] Vehicle air conditioning systems are generally composed of an air filter, a blower, an evaporator, and air vents, but the air filter, blower, evaporator, and air vents have the disadvantage of being easily contaminated by condensation on their surfaces.

[0004] In particular, when fine dust or mold is generated, bacteria proliferate, which leads to problems such as reduced efficiency of the air conditioning system and poor air quality inside the vehicle, such as blockage of the exhaust passage or the generation of bad odors. Therefore, an air conditioning system management system is required that can effectively prevent the vehicle air conditioning system from becoming contaminated.

[0005] Meanwhile, conventional vehicle air conditioning monitoring and calibration systems have the following problems.

[0006] First, real-time monitoring is difficult due to the slow speed of data collection and processing.

[0007] In other words, the existing system updates the current state of the air conditioning unit, and there is an interval of about 2 seconds or more.

[0008] Second, it is difficult to analyze all elements of a complex HVAC system simultaneously because the number of variables that can be processed at the same time is limited.

[0009] Third, it is difficult to apply to various vehicle models because it does not support standardized protocols.

[0010] Fourth, the lack of remote monitoring and control functions reduces work efficiency at the site.

[0011] Fifth, the lack of automatic calibration capabilities through data analysis requires a significant amount of time and effort to optimize the HVAC system.

[0012] Sixth, in particular, there is a lack of energy-efficient air conditioning control functions that take into account the characteristics of eco-friendly vehicles.

[0013] Therefore, the present invention is proposed to solve the problems of the conventional technology as described above. Prior art literature

[0014] (Prior Art 1) Korean Patent Publication No. 10-2024-0041040 The problem to be solved

[0015] Therefore, the present invention has been devised to resolve the aforementioned conventional problems,

[0016] The objective of the present invention is to enable real-time monitoring by realizing a fast data sampling cycle of less than 1 second.

[0017] Another objective of the present invention is to implement a high-performance system capable of processing up to 150 variables simultaneously.

[0018] Another objective of the present invention is to provide a system applicable to various vehicle models by supporting the standard UDS protocol.

[0019] Another objective of the present invention is to provide remote monitoring and control functions through a mobile application.

[0020] Another objective of the present invention is to automate the air conditioning system optimization process through an automatic calibration function based on data analysis.

[0021] Another objective of the present invention is to provide an energy-efficient air conditioning control algorithm that takes into account the characteristics of eco-friendly vehicles. means of solving the problem

[0022] In order to achieve the problem that the present invention aims to solve,

[0023] A next-generation vehicle air conditioning monitoring device according to one embodiment of the present invention is,

[0024] A sensor unit (100) comprising a plurality of sensors that collect data at various points of a vehicle air conditioning system, and

[0025] A matching toolkit unit (200) that processes data collected from the sensor unit, communicates with the vehicle ECU through a CAN interface, and provides equipment setting and data inquiry functions through an LCD screen, and

[0026] A real-time data collection and analysis software unit (300) that receives, analyzes, and visualizes data from the above alignment toolkit unit and executes an automatic calibration algorithm, and

[0027] A mobile application unit (400) that communicates with the aforementioned alignment toolkit unit via WiFi to check and control the system status remotely, and

[0028] The problem of the present invention is solved by including an energy efficiency optimization algorithm unit (500) that simultaneously optimizes battery consumption and interior comfort by considering the characteristics of an eco-friendly vehicle. Effects of the invention

[0029] The next-generation vehicle air conditioning monitoring device and method of the present invention provides the following remarkable effects.

[0030] First, due to the fast data sampling cycle of less than 1 second, real-time monitoring of the vehicle air conditioning system becomes possible, enabling immediate response to problematic situations.

[0031] Second, it can process up to 150 variables simultaneously, providing the effect of comprehensively analyzing all elements of a complex air conditioning system.

[0032] Third, support for the standard UDS protocol enables application to various vehicle models, significantly enhancing the system's universality and scalability.

[0033] Fourth, remote monitoring and control through mobile applications increases work efficiency and provides the effect of reducing time and costs.

[0034] Fifth, the automatic calibration function through data analysis automates the HVAC system optimization process, providing the effect of significantly shortening the development period.

[0035] Sixth, energy-efficient climate control tailored to the characteristics of eco-friendly vehicles extends the vehicle's driving range and enhances user satisfaction. Brief explanation of the drawing

[0036] FIG. 1 is an overall configuration diagram of a next-generation vehicle air conditioning monitoring device according to an embodiment of the present invention. FIG. 2 is a conceptual diagram of the configuration of a sensor unit (100) of a next-generation vehicle air conditioning monitoring device according to an embodiment of the present invention. FIG. 3 is a graph showing the relationship between the data collection period (sampling rate) and measurement accuracy of a sensor of a next-generation vehicle air conditioning monitoring device according to an embodiment of the present invention. FIG. 4 is a conceptual diagram of the configuration of a matching toolkit section (200) of a next-generation vehicle air conditioning monitoring device according to an embodiment of the present invention. Figure 5 is a graph showing the data processing performance of the matching toolkit section of a next-generation vehicle air conditioning monitoring device according to an embodiment of the present invention. FIG. 6 is a conceptual diagram of the configuration of a real-time data collection and analysis software unit (300) of a next-generation vehicle air conditioning monitoring device according to an embodiment of the present invention. FIG. 7 is a graph showing the change in indoor temperature before and after automatic calibration of a next-generation vehicle air conditioning monitoring device according to an embodiment of the present invention. FIG. 8 is a conceptual diagram of the configuration of a mobile application unit (400) of a next-generation vehicle air conditioning monitoring device according to an embodiment of the present invention. FIG. 9 is a graph showing the response time according to the distance between the mobile application unit and the matching toolkit unit of a next-generation vehicle air conditioning monitoring device according to an embodiment of the present invention. FIG. 10 is a conceptual diagram of the configuration of the energy efficiency optimization algorithm unit (500) of a next-generation vehicle air conditioning monitoring device according to an embodiment of the present invention. FIG. 11 is a graph showing the relationship between energy consumption and indoor comfort of a next-generation vehicle air conditioning monitoring device according to an embodiment of the present invention. Specific details for implementing the invention

[0037] The following description merely illustrates the principles of the present invention. Therefore, those skilled in the art may invent various devices that embody the principles of the present invention and are included within the concept and scope of the present invention, even though they are not explicitly described or illustrated in this specification.

[0038] Furthermore, all conditional terms and embodiments listed in this specification are, in principle, explicitly intended only for the purpose of enabling an understanding of the concept of the invention and should be understood not as being limited to the embodiments and conditions specifically listed as such.

[0039] A next-generation vehicle air conditioning monitoring device according to one embodiment of the present invention is,

[0040] A sensor unit (100) comprising a plurality of sensors that collect data at various points of a vehicle air conditioning system, and

[0041] A matching toolkit unit (200) that processes data collected from the sensor unit, communicates with the vehicle ECU through a CAN interface, and provides equipment setting and data inquiry functions through an LCD screen, and

[0042] A real-time data collection and analysis software unit (300) that receives, analyzes, and visualizes data from the above alignment toolkit unit and executes an automatic calibration algorithm, and

[0043] A mobile application unit (400) that communicates with the aforementioned alignment toolkit unit via WiFi to check and control the system status remotely, and

[0044] It is characterized by being configured to include an energy efficiency optimization algorithm unit (500) that simultaneously optimizes battery consumption and interior comfort by taking into account the characteristics of an eco-friendly vehicle.

[0045] In addition, the monitoring method of the next-generation vehicle air conditioning monitoring device is,

[0046] A step in which the sensor unit (100) collects data at various points of the vehicle air conditioning system;

[0047] A step in which the alignment toolkit unit (200) processes data collected from the sensor unit, communicates with the vehicle ECU through a CAN interface, and provides equipment setting and data inquiry functions through an LCD screen;

[0048] A step in which the real-time data collection and analysis software unit (300) receives data from the alignment toolkit unit, analyzes and visualizes it, and executes an automatic calibration algorithm;

[0049] A step in which the mobile application unit (400) communicates with the matching toolkit unit via WiFi to check and control the system status remotely;

[0050] The energy efficiency optimization algorithm unit (500) is characterized by including a step of simultaneously optimizing battery consumption and interior comfort by considering the characteristics of an eco-friendly vehicle.

[0051] Hereinafter, the next-generation vehicle air conditioning monitoring device and method according to the present invention will be described in detail through embodiments.

[0052] FIG. 1 is an overall configuration diagram of a next-generation vehicle air conditioning monitoring device according to an embodiment of the present invention.

[0053] As illustrated in FIG. 1, a next-generation vehicle air conditioning monitoring device according to one embodiment of the present invention is,

[0054] A sensor unit (100) comprising a plurality of sensors that collect data at various points of a vehicle air conditioning system, and

[0055] A matching toolkit unit (200) that processes data collected from the sensor unit, communicates with the vehicle ECU through a CAN interface, and provides equipment setting and data inquiry functions through an LCD screen, and

[0056] A real-time data collection and analysis software unit (300) that receives, analyzes, and visualizes data from the above alignment toolkit unit and executes an automatic calibration algorithm, and

[0057] A mobile application unit (400) that communicates with the aforementioned alignment toolkit unit via WiFi to check and control the system status remotely, and

[0058] It is characterized by being configured to include an energy efficiency optimization algorithm unit (500) that simultaneously optimizes battery consumption and interior comfort by taking into account the characteristics of an eco-friendly vehicle.

[0059] Before explaining in detail, I will briefly explain the functions of the aforementioned components.

[0060] The sensor unit (100) can support up to 32 different sensor types, can communicate with a main module using RS-485, and has a high durability design.

[0061] The above-mentioned matching toolkit section (200) provides equipment setting and data inquiry functions through an LCD screen, PC communication functions through WiFi and USB interfaces, data storage functions for more than 2 days through built-in memory, and UDS protocol implementation through a CAN interface.

[0062] The above real-time data collection and analysis software unit (300) applies a Windows-based application, provides data transmission and reception functions via USB or WiFi, includes a database that stores vehicle information, air conditioning information, sensor signals, variable information, etc., provides real-time data visualization and analysis functions, and is configured to include an automatic calibration algorithm.

[0063] The above mobile application unit (400) provides real-time monitoring and simple control functions and performs communication with the main module via WiFi.

[0064] The above energy efficiency optimization algorithm unit (500) provides air conditioning control logic that takes into account the characteristics of an eco-friendly vehicle, and provides an optimization algorithm that simultaneously takes into account battery consumption and indoor comfort.

[0065] The main components of the system of the present invention will be described in more detail below with reference to the drawings.

[0066] The sensor unit (100) includes a plurality of sensors that collect data at various points of the vehicle air conditioning system.

[0067] In addition, as illustrated in FIG. 2, the sensor unit (100) is,

[0068] A plurality of sensors (110) that measure one or more of temperature, humidity, pressure, flow rate, and CO2 concentration;

[0069] A data collector (120) that collects data from the plurality of sensors above;

[0070] It is characterized by being configured to include a communication module (130) that transmits data collected from the above data collector to the above alignment toolkit unit (200) via RS-485 communication.

[0071] The above sensor type refers to various sensors such as temperature, humidity, pressure, flow rate, and CO2 concentration, and is characterized by being able to simultaneously support up to 32 sensors.

[0072] In addition, the data collector (120) collects data from a plurality of sensors, and the data sampling period is set so that data can be collected at least every 0.1 seconds.

[0073] In addition, the communication module (130) transmits the data collected from the data collector to the matching toolkit unit (200) via RS-485 communication.

[0074] At this time, each of the above sensors is characterized by having IP67 waterproof / dustproof performance to maintain durability.

[0075] In addition, the data processing process of the sensor unit (100) can be expressed by the following formula.

[0076] S = {s1, s2, ..., s32} / / Sensor set

[0077] D(t) = {d1(t), d2(t), ..., d32(t)} / / Sensor data set at time t

[0078] Here, S represents the set of sensors, and D(t) represents the set of sensor data at time t.

[0079] In addition, Figure 3 is a graph showing the relationship between the data acquisition period (sampling rate) of the sensor and the measurement accuracy, where the X-axis represents the sampling rate (Hz) and the Y-axis represents the accuracy (%).

[0080] The graph above shows that accuracy improves as the sampling rate increases, and in particular, the accuracy at 10Hz is highlighted with a red dot.

[0081] The above graph may help determine the appropriate sampling rate of the sensor unit.

[0082] And, the above-mentioned matching toolkit unit (200) processes data collected from the sensor unit, communicates with the vehicle ECU through a CAN interface, and performs the function of providing equipment setting and data inquiry functions through an LCD screen.

[0083] In order to perform the above function, as shown in FIG. 4, the alignment toolkit part (200) is,

[0084] A data receiving unit (210) that receives data from the above sensor unit via RS-485;

[0085] A data processing unit (220) that processes received data;

[0086] A storage unit (230) for storing processed data;

[0087] A display unit (240) that displays processed data;

[0088] It is characterized by being configured to include a communication unit (250) that communicates with a vehicle ECU via a CAN interface and communicates with an external device via WiFi or USB.

[0089] Specifically, the data receiving unit (210) receives data from the sensor unit via RS-485, and the data processing unit (220) processes the received data, for example, processing 150 variables simultaneously per second.

[0090] In addition, the storage unit (230) is configured to store data for more than 2 days through built-in memory.

[0091] In addition, the display unit (240) displays processed data, for example, by providing a user interface through a 5-inch touchscreen LCD.

[0092] In addition, the communication unit (250) communicates with the vehicle ECU via a CAN interface and communicates with an external device via WiFi or USB.

[0093] For example, the communication interface supports WiFi, USB, and CAN, and the Unified Diagnostic Services (UDS) protocol is implemented through the CAN interface.

[0094] At this time, the above data processing process can be expressed by the following formula.

[0095] P(t) = f(D(t), E(t))

[0096] Here, P(t) represents processed data at time t, D(t) represents sensor data, E(t) represents data from the ECU, and f represents the data processing function.

[0097] In addition, Figure 5 is a graph showing the data processing performance of the alignment toolkit, where the x-axis represents the number of variables processed and the y-axis represents the processing time (milliseconds).

[0098] At this point, it can be seen that the processing time increases linearly as the number of variables increases.

[0099] Through the graph above, it was confirmed that the alignment toolkit module maintained a processing time of within 1.25ms even when processing 150 variables.

[0100] And, the real-time data collection and analysis software unit (300) receives data from the alignment toolkit unit, analyzes and visualizes it, and executes an automatic calibration algorithm.

[0101] In order to perform the above function, as illustrated in FIG. 6, the real-time data collection and analysis software unit (300) is,

[0102] A data preprocessing module (310) that filters data received from the alignment toolkit and removes outliers;

[0103] A data analysis module (320) that analyzes the above-mentioned preprocessed data to calculate performance indicators of the air conditioning system;

[0104] A visualization module (330) that displays the analysis results in the form of a graph and a dashboard;

[0105] It is characterized by being configured to include an automatic calibration module (340) that automatically adjusts the parameters of the air conditioning system based on the analysis results.

[0106] Specifically, the data preprocessing module (310) receives 100 data points per second from the matching toolkit and performs noise removal and outlier detection as data preprocessing.

[0107] For example, if temperature data falls outside the range of -50℃ to 150℃, it is considered an outlier.

[0108] The above data analysis module (320) analyzes the preprocessed data and performs time series analysis, pattern recognition, etc.

[0109] For example, the rate of change in indoor temperature is calculated using the following formula.

[0110] dT / dt = (T2 - T1) / (t2 - t1)

[0111] Here, T is temperature and t is time.

[0112] The above visualization module (330) visualizes data through real-time graphs, dashboards, etc.

[0113] The above automatic calibration module (340) automatically adjusts the air conditioning system parameters based on the analysis results.

[0114] For example, if the indoor temperature is 2°C or higher than the target value, the cooling output is increased by 10%.

[0115] In addition, Figure 7 is a graph showing the change in indoor temperature before and after automatic calibration, where the X-axis represents time (minutes) and the Y-axis represents temperature (°C).

[0116] In this example, the red line shows the temperature change before calibration, and the blue line shows the temperature change after calibration; the calibration point is indicated by an arrow on the graph to clearly show when the system performed automatic adjustment.

[0117] The effectiveness of the automatic calibration system could be visually confirmed through the graph above.

[0118] And, the mobile application unit (400) communicates with the matching toolkit unit via WiFi to perform the function of checking and controlling the system status remotely.

[0119] In order to perform the above function, as illustrated in FIG. 8, the mobile application unit (400) is,

[0120] A user interface module (410) that receives input from the user and displays processed data;

[0121] A data processing module (420) that processes received data and interprets user requests;

[0122] It includes a communication module (430) that transmits and receives data to and from the matching toolkit unit via WiFi, and

[0123] The above communication module is characterized by implementing secure communication using AES-256 encryption.

[0124] Specifically, the mobile application unit (400) updates the key variables of the air conditioning system at a 1-second interval as a real-time monitoring unit.

[0125] In addition, as a remote control, the settings of the air conditioning system can be changed from a distance of up to 100m, and as for the notification function, the user is immediately notified via push notification in the event of an abnormal situation.

[0126] In addition, as a data visualization, the data of the last 24 hours is displayed as a graph.

[0127] In addition, for user authentication, secure communication is implemented using AES-256 encryption.

[0128] In addition, Figure 9 is a graph showing the response time according to the distance between the mobile application unit and the alignment toolkit unit, where the X-axis represents the distance (meters) and the Y-axis represents the response time (milliseconds).

[0129] As can be seen in the graph above, the response time increases linearly as the distance increases.

[0130] The basic response time at 0m is 50ms, and the response time increases by approximately 5ms for every 10m increase in distance, and the response time is maintained within 100ms even at a distance of 100m, demonstrating that near-real-time monitoring and control are possible even at a long distance.

[0131] In addition, the data transmission process can be expressed by the following formula.

[0132] T = D * R / B

[0133] Here, T is the transmission time (seconds), D is the data size (bits), R is the transmission distance (meters), and B is the bandwidth (bps).

[0134] The above energy efficiency optimization algorithm unit (500) performs the function of simultaneously optimizing battery consumption and indoor comfort by taking into account the characteristics of an eco-friendly vehicle.

[0135] In order to perform the above function, as illustrated in FIG. 10, the energy efficiency optimization algorithm unit (500) is,

[0136] A data collection module (510) that collects indoor temperature, humidity, outside temperature, and battery status data from the vehicle's sensors;

[0137] A situation analysis module (520) that analyzes the current driving situation, user settings, and estimated driving distance;

[0138] An energy consumption prediction module (530) that calculates the expected energy consumption based on the current settings;

[0139] A comfort evaluation module (540) that quantifies the comfort level of the current indoor environment;

[0140] An optimization calculation module (550) that calculates an optimal setting considering the balance between energy consumption and comfort;

[0141] It is characterized by being configured to include a control command generation module (560) that generates an air conditioning system control command based on calculated optimal settings.

[0142] Specifically, the data collection module (510) performs the role of collecting data related to the vehicle's internal and external environment.

[0143] For example, sensors inside the vehicle collect data such as indoor temperature and humidity, outdoor temperature, and battery status in real time, and continuously update data such as indoor temperature of 22℃, indoor humidity of 50%, outdoor temperature of 30℃, and battery level of 80%.

[0144] The above data serves as important foundational data for subsequent analysis and control.

[0145] The above situation analysis module (520) evaluates the requirements of the air conditioning system by analyzing the current vehicle operation status and user settings based on the collected data.

[0146] For example, if it is determined that a vehicle is traveling on a highway and the outside temperature is high, potentially causing the interior to heat up, the analysis predicts that the cooling demand of the air conditioning system will increase.

[0147] In addition, when the user sets the air conditioning system to 'Eco Mode', it becomes possible to analyze whether the cooling intensity needs to be adjusted to minimize battery consumption.

[0148] The above energy consumption prediction module (530) predicts the expected energy consumption based on the operating mode of the air conditioning system currently set.

[0149] For example, assuming the air conditioning system operates at maximum output while maintaining the indoor temperature at 20℃ and the outside temperature is 30℃ or higher, it can be predicted that about 5% of the battery power will be consumed in one hour.

[0150] If the driver changes the settings, the resulting change in energy consumption is immediately recalculated.

[0151] The above comfort evaluation module (540) is a module that quantifies and evaluates the comfort of the indoor environment, and comprehensively considers factors such as temperature, humidity, and air quality.

[0152] For example, if the indoor temperature is 24℃ and the humidity is 50%, this condition can be considered comfortable for most users.

[0153] However, if the outside temperature rises rapidly or the humidity rises above 70%, the comfort score decreases, and based on this, it can be determined that the air conditioning system needs to be adjusted.

[0154] The above optimization calculation module (550) calculates an optimal setting that takes into account the balance between energy consumption and comfort.

[0155] For example, to reduce energy consumption while maintaining comfort, it can be calculated that setting the air conditioning system to 22°C and keeping the fan speed low is optimal.

[0156] The above settings adjust the system in a way that minimizes battery consumption while increasing user comfort.

[0157] The above control command generation module (560) generates control commands to be actually applied to the air conditioning system based on the results derived from the optimization calculation module.

[0158] For example, a command is generated to set the indoor temperature to 22℃ and maintain the fan speed at a medium level, and this is transmitted to the vehicle air conditioning system.

[0159] The above command is transmitted to the ECU of the air conditioning system via the CAN bus, and the operation of the air conditioning system is adjusted in real time.

[0160] If the situation changes and new optimal conditions are required, this module generates new commands to readjust the system.

[0161] As mentioned above, by having each module operate in conjunction, the vehicle climate control system maintains an optimal state in accordance with the real-time changing environment, thereby increasing energy efficiency while ensuring user comfort.

[0162] Meanwhile, the objective function considering the balance between energy consumption (E) and comfort (C) can be expressed as follows.

[0163] F = w1 * E + w2 * (1 - C)

[0164] Here, w1 and w2 are weights, and the goal is to minimize F.

[0165] In addition, Figure 11 is a graph showing the relationship between energy consumption and indoor comfort, where the X-axis represents energy consumption (%) and the Y-axis represents indoor comfort (%).

[0166] In this example, the blue curve shows how indoor comfort changes as energy consumption increases, while the red dot represents the optimal point, which is the point where the balance between energy consumption and comfort is best achieved.

[0167] Through the graph above, it was possible to visually confirm how the energy efficiency optimization algorithm finds a balance between energy consumption and indoor comfort.

[0168] According to the present invention, real-time monitoring of the vehicle air conditioning system is possible due to a fast data sampling cycle of less than 1 second, thereby enabling immediate response to problematic situations.

[0169] In addition, it can process up to 150 variables simultaneously, providing the effect of comprehensively analyzing all elements of a complex air conditioning system.

[0170] Those skilled in the art to which the present invention pertains will understand that the present invention, as described above, may be implemented in other specific forms without altering the technical concept or essential features of the invention. Therefore, the embodiments described above should be understood as illustrative in all respects and not restrictive.

[0171] The scope of the present invention is defined by the claims set forth below rather than by the detailed description above, and all modifications or variations derived from the meaning and scope of the claims and equivalent concepts thereof should be interpreted as being included within the scope of the present invention. Explanation of the symbols

[0172] 100 : Sensor section 200 : Alignment Toolkit Section 300: Real-time Data Collection and Analysis Software Division 400 : Mobile Application Division 500 : Energy Efficiency Optimization Algorithm Department

Claims

Claim 1 A vehicle air conditioning monitoring device comprises: a sensor unit (100) including a plurality of sensors that collect driving state data related to temperature, humidity, pressure, or air quality at various points of the vehicle air conditioning system; a matching toolkit unit (200) that processes driving state data collected from the sensor unit, communicates with a vehicle ECU via a CAN interface, and provides equipment setting and driving state data inquiry functions via an LCD screen; a real-time data collection and analysis software unit (300) that receives driving state data transmitted from the matching toolkit unit (200), analyzes and visualizes it in real time, and executes an automatic calibration algorithm to evaluate the driving state of the vehicle air conditioning system; a mobile application unit (400) that links with the matching toolkit unit (200) via WiFi communication to provide monitoring information for checking the driving state of the vehicle air conditioning system remotely; and an energy efficiency optimization algorithm unit (500) that comprehensively analyzes battery consumption characteristics and indoor comfort based on the driving state data, taking into account the driving environment of an eco-friendly vehicle, wherein the sensor unit (100), the matching toolkit unit (200), and the real-time data A vehicle air conditioner monitoring device characterized by the collection and analysis software unit (300), mobile application unit (400), and energy efficiency optimization algorithm unit (500) performing information processing to diagnose and monitor the operating status of the vehicle air conditioning system.

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