Battery pack, and apparatus and method for monitoring heat distribution

Abstract

The present disclosure relates to a battery pack, and an apparatus and a method for monitoring heat distribution. The technical problem to be solved is to present a mechanism that accurately measures three-dimensional heat distribution inside a battery in real time and enables immediate action to be taken on the basis of such data. To this end, the present disclosure provides a configuration that enables detection of a temperature imbalance or a hotspot and prediction of heat distribution in a battery pack through a terahertz holographic imaging module and a 3D visualization module.

Description

Battery pack and heat distribution monitoring device and method

[0001] The present disclosure relates to a battery pack and a thermal distribution monitoring device and method.

[0002] xEVs, such as electric vehicles (EVs) and hybrid vehicles (HEVs), are playing a key role in the eco-friendly automotive market. One of the most critical factors determining the performance and safety of these xEVs is the battery pack.

[0003] A battery pack applied to electric vehicles, etc., may include multiple battery packs, each comprising a battery module and a slave Battery Management System (SMS) that manages the battery modules. Additionally, the battery system may further include a master Battery Management System (Master BMS) that communicates with the vehicle system and manages multiple battery packs.

[0004] The efficiency and lifespan of a battery pack are highly sensitive to temperature changes, and the optimal operating range is generally between 20°C and 40°C. Therefore, temperature management technology is an essential element for maintaining the performance and safety of the battery pack.

[0005] Currently, most xEV battery systems monitor temperature using thermistor-based temperature sensors. This method has the drawback of being unable to accurately detect uneven heat distribution or hot spots (i.e., areas where heat is locally concentrated) within the battery pack, as it can only measure temperature at specific points. Additionally, measurement technologies such as thermal imaging cameras can only detect surface temperatures, failing to provide information on the heat distribution within the battery. Furthermore, existing techniques like impedance spectroscopy suffer from limitations in accuracy and spatial resolution because they estimate temperature indirectly based on the battery's internal electrical characteristics.

[0006] The information described above disclosed in the background technology of this invention is intended only to enhance understanding of the background of the present invention and may therefore include information that does not constitute prior art.

[0007] The objective of the present invention is to provide a battery pack and a heat distribution monitoring device and method that accurately measure the three-dimensional heat distribution inside a battery in real time and enable immediate action to be taken based on such data.

[0008] However, the technical problems that the present invention aims to solve are not limited to those described above, and other unmentioned problems can be clearly understood by those skilled in the art from the description of the invention below.

[0009] A thermal distribution monitoring device for a battery pack according to an embodiment of the present invention for solving the above technical problem comprises: a terahertz holographic imaging module that measures the three-dimensional thermal distribution inside the battery pack in a non-contact manner using terahertz waves; a 3D visualization module that visualizes and displays the thermal distribution data inside the battery pack in three dimensions in real time; and a main control module that integrates the terahertz holographic imaging module and the 3D visualization module to detect temperature imbalance or hot spots and predict the thermal distribution of the battery pack based on a deep learning model.

[0010] In the present invention, the 3D visualization module is characterized by processing acquired hologram data using ASM (Angular Spectrum Method), generating a real-time 3D heat distribution map through GPU (Graphic Processing Unit) acceleration, reconstructing the heat distribution into a 3D volume form through a ray casting algorithm, and providing a WebGL (Web Graphics Library)-based user interface.

[0011] In the present invention, the terahertz holographic imaging module is characterized by including a quantum cascade laser (QCL) to generate terahertz waves.

[0012] In the present invention, the terahertz holographic imaging module is characterized by including a microbolometer detector or a Schottky barrier diode array to detect phase and amplitude information of terahertz waves transmitted through a battery pack.

[0013] In the present invention, the terahertz holographic imaging module is characterized by performing spatial frequency separation in an off-axis configuration.

[0014] In the present invention, the terahertz holographic imaging module is characterized by comprising: a terahertz source unit that generates terahertz waves and irradiates the inside of a battery pack; a terahertz detector unit that detects terahertz waves passing through or reflected from the battery pack and collects data; and an optical unit that controls the path of the terahertz waves and generates an interference pattern to support thermal distribution imaging processing.

[0015] In the present invention, the optical unit is characterized by including an off-axis parabolic mirror, a silicon lens for adjusting and converting terahertz waves into a desired shape, and a beam splitter for separating and combining a measurement beam and a reference beam.

[0016] In the present invention, the terahertz holographic imaging module further comprises a data acquisition and preprocessing unit capable of collecting terahertz signals and preprocessing them to improve data quality for analysis; wherein the data acquisition and preprocessing unit converts an analog signal into a digital signal through a high-speed ADC (Analog-to-Digital Converter), removes unnecessary data to improve signal quality based on a noise removal filter, restores phase information based on interference data using a phase restoration algorithm, and processes and refines data for accurate thermal distribution analysis.

[0017] In the present invention, the terahertz holographic imaging module further comprises a reference beam generator that generates a reference beam to generate an interference pattern of terahertz waves; wherein the reference beam generator generates a reference beam to enable the calculation of a heat distribution inside a battery by comparison with a measurement beam, stably maintains the phase of the reference beam through a phase stabilization device, performs synchronization with a terahertz source unit through a synchronization control circuit, and precisely analyzes heat distribution data through the formation of a terahertz interference pattern.

[0018] The present invention further includes a vehicle integrated interface module capable of exchanging and linking data in real time between a thermal distribution monitoring device of a battery pack, a battery management system (BMS) of a vehicle, and an Electronic Control Unit (ECU); wherein the vehicle integrated interface module can be integrated with major systems within the vehicle using CAN FD (Controller Area Network Flexible Data-rate) and Automotive Ethernet, is compatible with OBD-II interfaces and complies with ISO 26262 functional safety standards, can be linked with designated external systems or compatible with vehicle diagnostic systems, and is implemented to optimize battery performance and control an intelligent cooling control module through data exchange.

[0019] The present invention further comprises an intelligent cooling control module capable of performing a dynamic cooling strategy for battery thermal management; wherein the intelligent cooling control module can implement a local precision cooling strategy and a predictive cooling strategy based on temperature data using a Model Predictive Control (MPC) algorithm, performs thermal management by controlling a cooling device including a cooling fan and a pump, and includes a PWM-controlled pump, a fan, and a solenoid valve array as cooling actuators.

[0020] The present invention further comprises a data security module for encrypting and ensuring the integrity of data generated and exchanged by the thermal distribution monitoring device of the battery pack; wherein the data security module is characterized by enhancing the security of the data through end-to-end encryption, protecting the system from external attacks using a hardware security module, recording the history of data changes using blockchain technology, and verifying the integrity of the thermal distribution data.

[0021] A method for monitoring the thermal distribution of a battery pack according to another embodiment of the present invention is characterized in that a terahertz holographic imaging module of a thermal distribution monitoring device comprises the steps of: acquiring internal data of a battery pack; performing preprocessing of the acquired data; performing hologram reconstruction using the preprocessed data; mapping the thermal distribution data to a temperature using the absorption coefficient in a specific frequency band of the reconstructed hologram; visualizing a 3D thermal distribution after mapping the thermal distribution data to a temperature; performing postprocessing and data analysis after visualizing the 3D thermal distribution; and performing data compression and transmission or performing a vehicle system integration process after performing postprocessing and data analysis.

[0022] In the present invention, in the step of performing preprocessing of the acquired data, the terahertz holographic imaging module is characterized by including a noise removal process, a phase unwrapping process, and a reference beam correction process as processes for the preprocessing.

[0023] In the present invention, in the step of performing hologram reconstruction using the preprocessed data, the terahertz holographic imaging module is characterized by including, as a process for the reconstruction, a spectrum reconstruction process and a multi-frequency synthesis process.

[0024] In the present invention, in the step of mapping the heat distribution data to temperature, the terahertz holographic imaging module is characterized by including, as a process of mapping the heat distribution data to temperature, a process of extracting absorption coefficients, a process of applying temperature-absorption coefficient relationships, and a process of applying a machine learning-based correction algorithm.

[0025] In the present invention, in the step of visualizing the 3D heat distribution, the terahertz holographic imaging module is characterized by including, as a process of visualizing the 3D heat distribution, a process of projecting a ray from each pixel through a ray casting algorithm, calculating color and opacity at a sampling point, determining a final pixel value through front / back composite, mapping temperature to color and opacity using a transfer function, and an isotherm generation process.

[0026] In the present invention, in the step of performing the post-processing and data analysis, the terahertz holographic imaging module is characterized by including a hotspot detection process, a temporal change analysis process, and a pattern recognition and anomaly detection process as a process for performing the post-processing and data analysis.

[0027] In the present invention, in the step of performing data compression and transmission or performing a vehicle system integration process, the terahertz holographic imaging module is characterized by including a data compression process and an encryption and transmission process, and also includes a process of transmitting data to an intelligent cooling control module to apply a local cooling strategy in real time.

[0028] A battery pack according to another embodiment of the present invention is characterized by comprising: a terahertz holographic imaging module that measures a three-dimensional heat distribution inside the battery pack in a non-contact manner using terahertz waves; a 3D visualization module that visualizes and displays heat distribution data inside the battery pack in three dimensions in real time; and a main control module that integrates the terahertz holographic imaging module and the 3D visualization module to detect temperature imbalance or hot spots and predict heat distribution of the battery pack based on a deep learning model.

[0029] According to the present invention, by utilizing terahertz waves and holographic technology, the 3D thermal distribution inside a battery pack can be accurately measured at the sub-millimeter level, thereby enabling the early detection of temperature imbalances or hot spots and preventing problems in advance.

[0030] According to the present invention, by detecting rapid temperature changes in real time and responding quickly to hot spots, serious accidents such as battery fires or explosions can be prevented, thereby improving the overall safety and reliability of electric vehicles.

[0031] According to the present invention, by optimizing charging and discharging strategies based on thermal distribution data inside the battery to maximize energy efficiency, the degradation of the battery cell is prevented, and the battery life is extended in the long term.

[0032] According to the present invention, by utilizing high-resolution heat distribution data, local and dynamic cooling control is possible, thereby reducing energy consumption and improving the efficiency of the cooling system compared to conventional uniform cooling methods.

[0033] According to the present invention, by using terahertz waves to accurately measure the temperature and condition of a battery pack without altering or damaging the internal structure of the battery pack, the degree of freedom in battery design is increased, and the problem of additional thermal path generation caused by the installation of existing sensors can be prevented.

[0034] However, the effects obtainable through the present invention are not limited to those described above, and other unmentioned technical effects will be clearly understood by those skilled in the art from the description of the invention below.

[0035] The following drawings attached to this specification illustrate preferred embodiments of the present invention and serve to further enhance understanding of the technical concept of the present invention together with the detailed description of the invention provided below; therefore, the present invention should not be interpreted as being limited only to the matters described in such drawings.

[0036] FIG. 1 is an exemplary diagram showing the schematic configuration of a heat distribution monitoring device according to one embodiment of the present invention.

[0037] FIG. 2 is an example diagram schematically showing a more detailed configuration of the terahertz holographic imaging module in FIG. 1.

[0038] FIG. 3 is a flowchart illustrating a terahertz holographic imaging data processing process according to an embodiment of the present invention.

[0039] Figure 4 is an example showing raw data of terahertz waves transmitted through a battery pack collected by a 2D detector array in Figure 3.

[0040] Preferred embodiments of the present invention will be described in detail below with reference to the attached drawings. Prior to this, terms and words used in this specification and claims should not be interpreted as being limited to their ordinary or dictionary meanings. Instead, based on the principle that the inventor may appropriately define the concepts of terms to best describe his invention, they should be interpreted in a meaning and concept consistent with the technical spirit of the present invention. Therefore, it should be understood that the embodiments described in this specification and the configurations illustrated in the drawings are merely some of the most preferred embodiments of the present invention and do not represent all of the technical spirit of the present invention; thus, various equivalents and modifications that can replace them may exist at the time of filing this application. Furthermore, as used in this specification, "comprise" or "include" and / or "comprising" or "including" specify the presence of the mentioned features, numbers, steps, actions, parts, elements, and / or groups thereof, and do not exclude the presence or addition of one or more other features, numbers, actions, parts, elements, and / or groups. In addition, when describing embodiments of the present invention, "may" and "may be" may include "one or more embodiments of the present invention."

[0041] Additionally, to aid in understanding the invention, the attached drawings are not drawn to actual scale, and the dimensions of some components may be exaggerated. Furthermore, the same reference numerals may be assigned to identical components in different embodiments.

[0042] The statement that two subjects of comparison are 'identical' means that they are 'substantially identical.' Therefore, substantial identity may include deviations considered low in the industry, for example, deviations within 5%. Additionally, the statement that a parameter is uniform in a given area may mean that it is uniform from an average perspective.

[0043] Although terms such as "first," "second," etc., are used to describe various components, it goes without saying that these components are not limited by these terms. These terms are used merely to distinguish one component from another, and unless specifically stated otherwise, the first component may also be the second component.

[0044] Throughout the specification, unless specifically stated otherwise, each component may be singular or plural.

[0045] The fact that any configuration is placed on the "upper (or lower)" of a component or on the "upper (or lower)" of a component may mean not only that any configuration is placed in contact with the upper (or lower) surface of said component, but also that another configuration may be interposed between said component and any configuration placed on (or below) said component.

[0046] Furthermore, where it is stated that one component is "connected," "coupled," or "connected" to another component, it should be understood that while said components may be directly connected or connected to each other, another component may be "interposed" between each component, or that each component may be "connected," "coupled," or "connected" through another component. Additionally, when it is stated that a part is electrically coupled with another part, this includes not only cases where they are directly connected but also cases where they are connected with another component in between.

[0047] Throughout the specification, "A and / or B" means A, B, or A and B unless specifically stated otherwise. That is, "and / or" includes any combination or any combination of the enumerated items. "C to D" means C or more and D or less, unless specifically stated otherwise.

[0048]

[0049] A battery pack includes at least one battery module and a pack housing having a receiving space formed therein for accommodating at least one battery module.

[0050] The battery module may comprise a plurality of battery cells and a module housing. The battery cells may be accommodated inside the module housing in a stacked form. The battery cells may be equipped with a positive lead and a negative lead. Depending on the battery shape, circular, prismatic, or pouch-type battery cells may be used.

[0051] In the above battery pack, a single stacked cell stack may constitute a single module instead of the battery module. The cell stack may be accommodated in a receiving space of the pack housing or in a receiving space partitioned by a frame, bulkhead, etc.

[0052] The battery cell mentioned above generates a large amount of heat during charging and discharging. The generated heat accumulates in the battery cell and accelerates its degradation. Therefore, the battery pack further includes a cooling member to suppress battery cell degradation. The cooling member is provided at the bottom of the housing space where the battery cell is located, but is not limited thereto and may also be provided at the top or side depending on the battery pack.

[0053] Each of the above battery cells can discharge exhaust gas from inside the battery cell to the outside of the battery cell, which is generated under abnormal operating conditions also known as thermal runaway or thermal event. The battery pack or the battery module may be equipped with an exhaust port for exhaust gas discharge to prevent damage to the battery pack or module caused by the exhaust gas.

[0054] A battery pack may include a battery and a Battery Management System (BMS) for managing the battery. The Battery Management System (BMS) may include a detection device, a balancing device, and a control device. A battery module may include a plurality of cells connected to each other in series or in parallel. Battery modules may be connected to each other in series or in parallel.

[0055] The detection device can detect the state of the battery (voltage, current, temperature, etc.) and detect state information indicating the state of the battery. The detection device can detect the voltage of each cell or each battery module constituting the battery. The detection device may also detect the current flowing through each battery module constituting the battery module or battery pack. The detection device may also detect the cell and / or module and / or ambient temperature at at least one point of the battery.

[0056] The balancing device can perform balancing operations on battery modules and / or cells constituting the battery. The control device can receive status information (voltage, current, temperature, etc.) of the battery module from the detection device. Based on the status information received from the detection device, the control device can monitor and calculate the status of the battery module (voltage, current, temperature, State of Charge (SOC), State of Health (SOH), etc.). In addition, based on the results of the status monitoring, the control device may perform control functions (e.g., temperature control, balancing control, charge / discharge control, etc.) and protection functions (e.g., over-discharge, over-charge, over-current protection, short circuit, fire extinguishing functions, etc.). Furthermore, the control device may perform wired or wireless communication functions with external devices of the battery pack (e.g., a higher-level controller, a vehicle, a charger, or a PCS, etc.).

[0057] The control device may also control the charging and discharging operations and protection operations of the battery. To this end, the control device may include a charging and discharging control unit, a balancing control unit, and a protection unit.

[0058] A battery management system is a system that monitors battery status and performs diagnostic, control, communication, and protection functions. It can calculate charge / discharge status, calculate battery life or state of health (SOH), cut off battery power (relay control) when necessary, control thermal management (cooling, heating, etc.), perform high-voltage interlock functions, and detect or calculate insulation and short-circuit status.

[0059] A relay can be a mechanical contactor that is turned on and off by the magnetic force of a coil, or a semiconductor switch such as a MOSFET (Metal Oxide Semiconductor Field Effect Transistor).

[0060] Relay control is a function that cuts off the power supply from the battery in the event of a problem with the vehicle and battery system, and can be configured with one or more relays and pre-charge relays at the positive and negative terminals, respectively.

[0061] Since there is a risk of inrush current occurring in the high-voltage capacitor on the inverter input side when the battery load is connected, pre-charge control may be equipped with a function to operate the pre-charge relay before connecting the main relay when the vehicle starts to prevent the inflow of inrush current and to connect it to the pre-charge resistor.

[0062] A high-voltage interlock is a circuit that uses small signals to detect whether all high-voltage components in the entire automotive system are connected, and it can be equipped with the function of forcibly opening a relay if an open circuit occurs at any point on the entire loop.

[0063]

[0064] The terahertz waves described in the following embodiments are electromagnetic waves with a frequency range of 0.1 to 10 THz, and have penetrability (i.e., a characteristic useful for internal structure analysis as they can penetrate many non-metallic materials), safety (i.e., a characteristic safe for living organisms as non-ionizing radiation unlike X-rays), high resolution (i.e., a characteristic providing high spatial resolution at the sub-millimeter level due to their short wavelength), and spectral characteristics (i.e., a characteristic useful for material identification as they provide the intrinsic spectral characteristics of various materials).

[0065] In addition, in this embodiment, holography is a technology that records and reproduces both amplitude and phase information of light, and has characteristics including interference (i.e., recording the interference pattern of the object wave and the reference wave), diffraction (i.e., reproducing a three-dimensional image using the diffraction of light caused by the recorded interference pattern), and information density (i.e., high information density capable of storing three-dimensional information in a two-dimensional medium).

[0066] In this embodiment, terahertz holography is a technology that combines the characteristics of terahertz waves with the principles of holography, and has phase sensitivity (i.e., a characteristic that can achieve high and deep resolution by measuring the phase change rate of terahertz waves), broadband characteristics (i.e., a characteristic that can implement multi-frequency holography utilizing a wide frequency band of terahertz waves), transmission mode (i.e., a mode that can analyze the internal structure using terahertz waves transmitted through an object), and reflection mode (i.e., a mode that can analyze the surface structure using terahertz waves reflected from the surface of an object).

[0067] The reason (or principle) why terahertz holographic thermal imaging technology can be applied to an xEV battery system in this embodiment is explained below.

[0068] First, by utilizing the penetration characteristics of terahertz waves, the interior of a battery pack can be inspected non-destructively, and the internal structure of the battery module, excluding the metal case, can be directly observed, enabling precise diagnosis without physical damage. This overcomes the limitations of existing inspection methods and can significantly improve battery management efficiency.

[0069] In addition, 3D imaging technology using terahertz holography can precisely measure the heat distribution inside the battery pack with sub-millimeter resolution, which allows for the accurate identification of hot spots inside the battery and the visualization of complex heat distribution to effectively identify the cause of the problem.

[0070] In addition, by utilizing the fast response characteristics of terahertz waves, changes in heat distribution inside the battery pack can be tracked in real time, and rapid temperature changes can be detected with millisecond-level time resolution, thereby preventing safety issues such as battery overheating or thermal runaway in advance.

[0071] In addition, terahertz spectroscopy can be used to analyze the material properties of battery components and monitor changes in electrolyte state or electrode material to track the degradation process of the battery, thereby contributing to the optimization of battery life and performance.

[0072] FIG. 1 is an exemplary diagram showing the schematic configuration of a heat distribution monitoring device according to one embodiment of the present invention.

[0073] Referring to FIG. 1, the heat distribution monitoring device according to the present embodiment may include a terahertz holographic imaging module (100), a 3D visualization module (200), a vehicle integrated interface module (300), an intelligent cooling control module (400), a main control module (500), and a data security module (600).

[0074] The terahertz holographic imaging module (100) generates terahertz waves and can measure the three-dimensional heat distribution inside the battery pack in a non-contact manner.

[0075] The terahertz holographic imaging module (100) can generate terahertz waves with a quantum cascade laser (QCL) to penetrate the battery pack.

[0076] The terahertz holographic imaging module (100) can detect phase and amplitude information of terahertz waves transmitted through the battery pack using a microbolometer detector. Here, the microbolometer is a non-cooled thermal sensing sensor that detects non-visible light, such as infrared and terahertz waves, and converts it into an electrical signal.

[0077] Based on holographic data obtained through the terahertz holographic imaging module (100), a hot spot (i.e., an area where heat is locally concentrated) or a temperature imbalance inside the battery can be detected.

[0078] For example, holographic data may have a reference beam-object beam angle of 2° and a frame rate of 60Hz.

[0079] The terahertz holographic imaging module (100) can detect the interference pattern of the transmitted terahertz waves and the reference beam with a microbolometer array.

[0080] Here, the reference beam is a reference ray used in interference technology, which is a ray (beam) used to combine with a signal beam reflected or scattered from an object to form an interference pattern.

[0081] The terahertz holographic imaging module (100) can realize spatial frequency separation in an off-axis configuration.

[0082] Here, an off-axis configuration refers to a configuration in which optical elements (e.g., mirrors, lenses, interferometers, etc.) are placed slightly offset from the optical axis in the path of electromagnetic waves, such as terahertz waves or light. Additionally, spatial frequency separation refers to the process of extracting necessary frequency components (i.e., useful information) from a signal and removing noise or unnecessary components (i.e., interference elements).

[0083] The 3D visualization module (200) can visualize the heat distribution data inside the battery in three dimensions in real time and present it in a way that is easy for the user to understand.

[0084] The 3D visualization module (200) can process the acquired hologram data using the Angular Spectrum Method (ASM) and can perform real-time 3D heat distribution map generation through GPU (Graphic Processing Unit) acceleration.

[0085] Here, ASM (Angular Spectrum Method) is an algorithm used to calculate and simulate propagating waves, and is primarily used in the fields of optics, acoustics, and electromagnetics to efficiently process wave propagation, or in imaging and reconstruction techniques (e.g., holography, terahertz imaging).

[0086] The 3D visualization module (200) can reconstruct the heat distribution into a 3D volume form through a ray casting algorithm.

[0087] Here, the Ray Casting algorithm is a technique that works by projecting virtual rays into a 3D data space, analyzing the data where the rays meet, and mapping it to pixels on a screen. It is an algorithm that generates an image by projecting 3D data (e.g., volume data) onto a 2D screen and can be used particularly for 3D volume rendering or real-time visualization.

[0088] The 3D visualization module (200) can implement a ray casting algorithm optimized with a user-defined shader.

[0089] Here, a user-defined shader refers to code programmed to meet user requirements for specific tasks (e.g., lighting, color calculation, transparency processing, etc.) performed by the GPU (Graphics Processing Unit) in the graphics processing pipeline.

[0090] The 3D visualization module (200) can ensure cross-platform compatibility of visualization data by providing a WebGL (Web Graphics Library) based user interface.

[0091] Here, a WebGL-based user interface refers to a user interface that renders 3D graphics and visualization content in real time in a browser using WebGL (Web Graphics Library) technology. WebGL operates based on the HTML5 Canvas element and can implement high-performance graphics on a web browser without additional software or plugins.

[0092] The 3D visualization module (200) allows for intuitive monitoring of the temperature status by reflecting real-time changing data.

[0093] The 3D visualization module (200) can be implemented with rendering performance of 60fps or more (based on 1080p resolution).

[0094] The 3D visualization module (200) may include data analysis functions such as real-time hotspot detection and tracking, thermal pattern time series analysis, battery cell degradation prediction model, and cooling system efficiency analysis.

[0095] The 3D visualization module (200) may include visualization functions such as 3D volume rendering (updated in real time), 2D cross-section view (custom plane), augmented reality (AR) based maintenance support view, and display of summary information in the form of a dashboard.

[0096] The 3D visualization module (200) may include user interface functions such as a touchscreen-based intuitive GUI (Graphic User Interface), voice command support (optimized for in-vehicle use), and a web interface for remote monitoring.

[0097] The vehicle integrated interface module (300) can exchange and link data in real time between the system and the vehicle's battery management system (BMS) and ECU (Electronic Control Unit).

[0098] The vehicle integrated interface module (300) enables integration with key systems within the vehicle using CAN FD and Automotive Ethernet.

[0099] Here, CAN FD (Controller Area Network Flexible Data-rate) is an upgraded version of CAN (Controller Area Network), a network standard for data communication between Electronic Control Units (ECUs) of a vehicle, with significantly improved speed and data processing capabilities compared to the existing CAN protocol.

[0100] Automotive Ethernet is an Ethernet technology that supports high-speed data communication within a vehicle. Optimized for automotive use, it offers high speed, low latency, and cost-effectiveness, and can meet the complex network requirements of a vehicle.

[0101] The vehicle integrated interface module (300) can be OBD-II interface compatible and comply with ISO 26262 functional safety standards.

[0102] The vehicle integrated interface module (300) can be linked to an external system.

[0103] For example, it can perform cloud-based data storage and analysis (edge ​​computing priority), real-time data sharing with vehicle manufacturer diagnostic systems, and remote monitoring functions via smartphone apps.

[0104] The vehicle integrated interface module (300) can control battery performance optimization and intelligent cooling control module (400) through data exchange.

[0105] The vehicle integrated interface module (300) can be compatible with the vehicle diagnostic system.

[0106] The intelligent cooling control module (400) can perform a dynamic cooling strategy for battery thermal management.

[0107] The intelligent cooling control module (400) can implement a local precision cooling strategy based on temperature data and a predictive cooling strategy using an MPC (Model Predictive Control) algorithm (e.g., prediction horizon 5 seconds, control cycle 100 ms).

[0108] Here, the prediction horizon refers to a time window or range for predicting future states in a prediction model.

[0109] The intelligent cooling control module (400) can perform efficient heat management by controlling cooling devices such as cooling fans and pumps.

[0110] The intelligent cooling control module (400) may include a PWM-controlled pump and fan and a solenoid valve array as cooling actuators.

[0111] The intelligent cooling control module (400) can maintain the battery temperature within an optimal range, reduce energy consumption, and maximize performance.

[0112] The main control module (500) can manage the overall functions of the components (100 to 400, 600).

[0113] The main control module (500) is responsible for data processing and analysis based on an artificial intelligence processor and can perform data communication and computation in real time.

[0114] The main control module (500) can process large amounts of data in parallel by utilizing a high-performance main processor SoC (e.g., NVIDIA Drive AGX) and an auxiliary processor FPGA (Field-Programmable Gate Array). In addition, it can be implemented including memory (e.g., 32GB LPDDR5 + 128GB NVMe SSD) and a dedicated GPU (e.g., based on NVIDIA Ampere architecture).

[0115] The main control module (500) may include a real-time operating system (e.g., QNX Neutrino RTOS), a deep learning framework (e.g., TensorRT-optimized TensorFlow), an image processing library (e.g., OpenCV + custom CUDA kernel), and a database (e.g., time-series optimized influxDB).

[0116] For example, the main control module (500) may use a custom angle spectrum method (GPU acceleration) as a hologram reconstruction algorithm, a parallelized Goldstein algorithm as a phase unwrapping algorithm, a machine learning-based correction algorithm (e.g., XGBoost model) as a thermal distribution mapping algorithm, and an 'autoencoder + LSTM combined model' as an anomaly detection algorithm.

[0117] The main control module (500) can use an artificial intelligence-based data analysis algorithm. The deep learning model may be a CNN+LSTM combined structure, and can perform hot spot detection through a YOLOV5 variant model and can also perform heat flow prediction using a physical information-based Graph Neural Network.

[0118] The main control module (500) can use a CNN+LSTM algorithm (or deep learning model) to predict temperature imbalance or hot spot detection and heat distribution of the battery (or battery pack).

[0119] Here, the CNN+LSTM algorithm is a deep learning model designed to process spatial and temporal data simultaneously by combining a Convolutional Neural Network (CNN) and a Long Short-Term Memory Network (LSTM). By leveraging the strengths of each, this CNN+LSTM algorithm can deliver excellent performance in analyzing complex patterns and predicting future states.

[0120] The main control module (500) can transmit the data processing results to the vehicle integrated interface module (300) and the intelligent cooling control module (400).

[0121] The data security module (600) can encrypt data generated and exchanged in the device according to the present embodiment and ensure integrity.

[0122] The data security module (600) can enhance the security of the data through AES-256-based end-to-end encryption (e.g., AES-256-GCM, ECDSA P-384 signature).

[0123] The data security module (600) can protect the system from external attacks by using a hardware security module (HSM, e.g., CC EAL 5+ certification, physical non-intrusive security).

[0124] The data security module (600) can use blockchain technology to record data change history and verify integrity.

[0125] Meanwhile, although not illustrated in the drawings, the present embodiment may further include a power management system capable of dynamic voltage scaling (e.g., 0.6V to 1.2V, 10mV step) and power caching (e.g., wake-up time of 50ns or less), thereby enabling low-power operation, real-time power consumption optimization through dynamic voltage scaling, and power cutoff of circuit blocks in an idle state.

[0126] Meanwhile, in this embodiment, the functions of each component (100 to 600) are described as being performed separately, but depending on the embodiment, each component (100 to 600) may be optionally integrated. For example, the terahertz holographic imaging module (100) and the main control module (500) may be integrated, and the terahertz holographic imaging module (100), the main control module (500), and the 3D visualization module (200) may be integrated.

[0127] In addition, each component (100 to 600) of the heat distribution monitoring device according to the present embodiment can be integrated not only functionally but also in hardware. Through this, maintenance can be facilitated by a modular design, a liquid cooling system can be applied (heat management based on high-performance computation), and an EMI (Electromagnetic Interference) and EMC (Electromagnetic Compatibility) shielding design (suitable for automotive environments) can be implemented. Furthermore, the packaging of the heat distribution monitoring device according to the present embodiment can, for example, implement a compact overall system size (e.g., 25cm*20cm*10cm), a weight of less than 5kg, an IP67 waterproof / dustproof design, and an operating temperature range of -40℃ to 85℃.

[0128] In addition, the heat distribution monitoring device according to the present embodiment can perform power management within a specified range for input voltage, maximum power consumption, and standby mode power consumption, and can incorporate an overvoltage / overcurrent protection circuit.

[0129] In addition, the heat distribution monitoring device according to the present embodiment can manage spatial resolution, temperature resolution, measurement range, frame rate, system response time, and data processing capacity, etc., as performance indicators within a specified range.

[0130] FIG. 2 is an example diagram schematically showing a more detailed configuration of the terahertz holographic imaging module (100) in FIG. 1.

[0131] Referring to FIG. 2, the terahertz holographic imaging module (100) may include a terahertz source unit (110), a terahertz detector unit (120), an optical unit (130), a data acquisition and preprocessing unit (140), a reference beam generator (150), and a temperature correction unit (160).

[0132] The terahertz source unit (110) can generate terahertz waves to inspect the inside of the battery pack. The generated terahertz waves pass through or reflect the battery pack and can be used to collect thermal distribution data.

[0133] The terahertz source section (110) can generate terahertz waves in the frequency band of 0.5 to 2 THz using a quantum cascade laser (QCL) array as the main source, and can use a photomixer-based continuous wave terahertz generator as the auxiliary source.

[0134] For example, a quantum cascade laser (QCL) array can be a 1x4 array, each variable from 0.5 to 2 THz, with an output greater than 10 mW (continuous wave mode), a beam quality of M^2 < 1.2 (approaching a Gaussian beam), and a frequency stability less than 1 MHz / hour.

[0135] The terahertz source unit (110) can maintain the stability of the terahertz wave signal using an oscillator.

[0136] The terahertz source unit (110) can adjust the direction and intensity of the terahertz waves using a beam control device. At this time, the beam control technology can use a MEMS (Micro Electro Mechanical Systems) based beam steering system and can perform electronic beam forming using a phase array antenna.

[0137] The terahertz source unit (110) can provide a signal source necessary for non-destructively analyzing the internal structure of the battery pack.

[0138] The terahertz detector (120) can collect data by detecting terahertz waves that pass through or are reflected from the battery pack.

[0139] The terahertz detector (120) can provide information related to heat distribution and abnormal conditions (e.g., hot spots).

[0140] For example, the specifications of the terahertz detector (120) are: pixel count: 1024 * 768 (approx. 780,000 pixels), pixel size: 17 μm * 17 μm, frame rate: >60Hz, noise equivalent output (NEP): < 1 , dynamic range: can be >70dB.

[0141] The terahertz detector (120) can accurately measure terahertz signals based on a high-resolution sensing sensor such as a microbolometer array as a main detector, and can use a Schottky barrier diode array as an auxiliary detector.

[0142] For example, the specifications of a microbolometer are 1024*768 pixels, 17μm pitch, NEP<1 It could be.

[0143] The terahertz detector (120) can improve data quality by amplifying a weak signal through a signal amplifier.

[0144] The terahertz detector (120) can perform sensor cooling for high-sensitivity measurement using a cooling device.

[0145] The terahertz detector (120) can detect and analyze thermal data inside the battery at high resolution.

[0146] The optical unit (130) can control the path of the terahertz waves and generate an interference pattern to support thermal distribution imaging processing.

[0147] The optical unit (130) can reflect and focus terahertz waves through an off-axis parabolic mirror.

[0148] For example, the optical unit (130) may include a 4-inch off-axis parabolic mirror (minimizing chromatic aberration), a terahertz lens array (high refractive index silicon lenses), and a beam splitter (for holographic interferometer configuration).

[0149] The optical unit (130) can adjust the terahertz waves through a silicon lens to convert them into a desired shape.

[0150] The optical unit (130) can separate and combine the measurement beam and the reference beam through a beam splitter.

[0151] The optical unit (130) can support the formation of an interference pattern by controlling the focus and direction of the terahertz waves.

[0152] For example, the optical unit (130) can be designed with a Fourier optical configuration (easy spatial frequency filtering), a 4f system-based imaging structure, and a resolution close to the diffraction limit: <100μm@1THz, and the optical alignment system can be implemented with a piezo-driven micro-alignment stage and a real-time wavefront sensing and correction system.

[0153] The data collection and preprocessing unit (140) can collect terahertz signals and preprocess them to improve data quality so that they can be analyzed.

[0154] The data collection and preprocessing unit (140) can convert an analog signal into a digital signal through a high-speed ADC (Analog-to-Digital Converter).

[0155] For example, a high-speed ADC can utilize an on-chip analog-to-digital converter (14-bit ADC).

[0156] The data collection and preprocessing unit (140) can remove unnecessary data to improve signal quality based on a noise removal filter.

[0157] The data collection and preprocessing unit (140) can restore phase information based on interference data using a phase retrieval algorithm.

[0158] The data collection and preprocessing unit (140) can process and refine the data for accurate heat distribution analysis.

[0159] The reference beam generator (150) can generate a reference beam to generate an interference pattern of terahertz waves.

[0160] The reference beam generator (150) can calculate the heat distribution inside the battery by comparing it with the measurement beam.

[0161] The reference beam generator (150) can stably maintain the phase of the reference beam through a phase stabilization device.

[0162] The reference beam generator (150) can perform synchronization with the terahertz source unit (110) through a synchronization control circuit.

[0163] The reference beam generator (150) can precisely analyze heat distribution data by forming a terahertz interference pattern.

[0164] The temperature correction unit (160) converts collected data based on terahertz signals into temperature and can correct the precision.

[0165] The temperature correction unit (160) can store data including the temperature-terahertz wave absorption coefficient relationship through a correction database.

[0166] The temperature correction unit (160) can calculate an accurate temperature value based on the measured data using a real-time correction algorithm.

[0167] The temperature correction unit (160) can periodically correct the precision of the sensor and data processing through a device calibration device.

[0168] The temperature correction unit (160) can increase the accuracy of the battery heat distribution data and provide reliable results.

[0169] The operation process of the terahertz holographic imaging module (100) is described in detail.

[0170] The terahertz source unit (110) irradiates the generated terahertz waves onto the battery pack, the terahertz detector unit (120) detects the terahertz signal transmitted or reflected from the battery, the optical unit (130) adjusts the focus of the signal and forms an interference pattern, the data collection and preprocessing unit (140) collects data to improve quality, the reference beam generator (150) generates an interference pattern to analyze the data, and the temperature correction unit (160) corrects the data to finally provide an accurate heat distribution.

[0171] FIG. 3 is a flowchart illustrating a terahertz holographic imaging data processing process according to an embodiment of the present invention.

[0172] Referring to FIG. 3, a terahertz holographic imaging module (100) can acquire internal data of a battery pack (S101).

[0173] For example, terahertz waves can be generated from a terahertz source unit (110) and irradiated into the battery pack. At this time, the terahertz waves can pass through or reflect into the battery pack in a non-contact manner and provide a signal containing thermal distribution information. A terahertz detector unit (120) can detect the terahertz signal that has been passed through or reflected into the battery pack. This terahertz signal contains information about the temperature and thermal distribution inside the battery pack and may include amplitude and phase data.

[0174] The transmitted terahertz waves can be collected by a 2D detector array, and the raw data collected therefrom may be a complex matrix containing amplitude and phase information as shown in FIG. 4. FIG. 4 is an example diagram showing the raw data of terahertz waves transmitted through a battery pack collected by a 2D detector array in FIG. 3.

[0175] The data generated at this stage can be stored in the form of complex numbers and can be used as basic data for analyzing the detailed thermal distribution inside the battery.

[0176] The terahertz holographic imaging module (100) can perform preprocessing of the acquired data (S102).

[0177] For example, the terahertz holographic imaging module (100) can decompose the signal into multiple scales using wavelet transform as a noise removal process, perform threshold-based noise removal at each scale, and restore the refined signal through inverse transform. Additionally, as a phase unwrapping process, it can convert phase information folded in 2π units into continuous values, and apply Goldstein's Branch Cut algorithm to calculate the phase residue, generate a branch cut, and perform phase integration along the path. Additionally, as a reference beam correction process, it can perform system error correction by comparing with a separately measured reference beam and make the 'corrected signal = measured signal / reference signal' through complex division operations.

[0178] The terahertz holographic imaging module (100) can perform hologram reconstruction using preprocessed data (S103).

[0179] For example, the terahertz holographic imaging module (100) can perform a 2D Fourier transform, apply a propagation function, and perform an inverse 2D Fourier transform as a spectrum reconstruction process by converting to the frequency domain using a Fourier transform and applying an Angular Spectrum Method for each frequency. In addition, as a multi-frequency synthesis process, the images reconstructed at each frequency can be synthesized by assigning weights, and the weights can be determined in proportion to the signal-to-noise ratio (SNR) of each frequency.

[0180] The terahertz holographic imaging module (100) can map thermal distribution data to temperature by utilizing the absorption coefficient in a specific frequency band of the reconstructed hologram (S104).

[0181] For example, the terahertz holographic imaging module (100) applies the Beer-Lambert law as an absorption coefficient extraction process. It can be calculated. Here, α: absorption coefficient, d: sample thickness, I: transmission intensity, : Refers to the incident intensity, and the absorption coefficient α can be calculated through log transformation. Furthermore, as part of the process of applying the temperature-absorption coefficient relationship, a pre-established lookup table can be used, the magnitude can be estimated through interpolation, or a polynomial fitting model considering non-linear relationships can be applied. Alternatively, to map thermal distribution data to temperature, the relationship between terahertz wave absorption rate and temperature (e.g., ) can be utilized. Here, α: absorption coefficient, , : Refers to material property constants. In addition, machine learning-based correction algorithms (e.g., support vector regression) can be applied to map heat distribution data to temperature.

[0182] The terahertz holographic imaging module (100) can visualize the 3D heat distribution after mapping the heat distribution data to the temperature (S105).

[0183] For example, the terahertz holographic imaging module (100) can project a ray from each pixel through a ray casting algorithm, calculate color and opacity at a sampling point, determine the final pixel value through front / back synthesis, and map the temperature to color and opacity using a transfer function. Additionally, as an isotherm generation process, isotherms highlighting a specific temperature range can be generated using a Marching Cubes algorithm, volume data can be divided into cubes, the temperature value of each cube vertex can be evaluated, the isotherm location can be determined through linear interpolation, and a triangle mesh can be generated.

[0184] The Marching Cubes algorithm is an algorithm that generates surfaces by extracting contours from 3D data. It is mainly used in medical imaging (CT, MRI), 3D visualization, and heat distribution analysis, and is useful for visualizing 3D spatial data by converting it into a triangular mesh.

[0185] The terahertz holographic imaging module (100) can visualize the 3D heat distribution and then perform post-processing and data analysis (S106).

[0186] For example, the terahertz holographic imaging module (100) can apply a local maximum algorithm as a hotspot detection process, define hotspots by considering temperature thresholds and spatial connectivity, and classify and prioritize hotspots according to importance. In addition, as a temporal change analysis process, it can track heat flow by applying an Optical Flow algorithm, and use a Kalman filter to remove noise and predict future heat distribution. In addition, as a pattern recognition and anomaly detection process, it can classify normal / abnormal heat patterns using a convolutional neural network (CNN), and determine an area with a large reconstruction error as an anomaly through anomaly detection using an autoencoder.

[0187] The terahertz holographic imaging module (100) can perform post-processing and data analysis, and then perform data compression and transmission (S107).

[0188] For example, the terahertz holographic imaging module (100) can perform data compression processes, such as reducing data size while retaining key information through wavelet-based lossy compression, selectively quantizing and encoding only important coefficients, and performing entropy encoding through Huffman coding or arithmetic coding. Additionally, as encryption and transmission processes, data encryption can be performed using the AES-256 algorithm, important information can be transmitted first and detailed information subsequently through segmented transmission, and an error detection and correction code (Reed-Solomon code) can be used to maintain data integrity during transmission.

[0189] The terahertz holographic imaging module (100) can perform post-processing and data analysis, and then perform the vehicle system integration process (S108).

[0190] For example, the terahertz holographic imaging module (100) can share analyzed data with the vehicle's battery management system (BMS) and electronic control unit (ECU) through CAN FD and Automotive Ethernet communication processes, and can optimize battery charging / discharging and temperature control strategies through integration with the BMS. Additionally, by transmitting data to the intelligent cooling control module (400), local cooling strategies can be applied in real time, and efficiency can be improved by dynamically adjusting fan speed, coolant flow, etc. based on heat distribution data.

[0191] As described above, this embodiment combines the transmittance of terahertz waves with holographic technology to measure the 3D thermal distribution of the entire battery pack with sub-millimeter resolution, thereby enabling precise identification of thermal distribution at the battery cell level. This allows for the early detection and response to temperature imbalances and heat concentration phenomena, which has the effect of extending the battery's lifespan and improving safety.

[0192] In addition, this embodiment measures 3D heat distribution in real time at a frame rate of 60Hz or higher and performs immediate data analysis through a high-performance signal processing unit (e.g., main control module), thereby enabling the immediate detection of rapid temperature changes in milliseconds and localized overheating (hot spots), which can prevent serious accidents such as battery fires or explosions.

[0193] In addition, this embodiment enables accurate temperature measurement without changing the internal structure of the battery pack through non-contact measurement using terahertz waves, thereby increasing the design freedom of the battery pack and preventing the creation of additional thermal paths due to sensor installation, thus enabling more accurate thermal management.

[0194] In addition, this embodiment utilizes terahertz spectroscopic characteristics to simultaneously measure not only temperature but also the state of charge (SOC) and state of health (SOH) of the battery cell, thereby enabling the comprehensive condition of the battery to be determined with a single system, allowing for more accurate battery management and lifespan prediction.

[0195] In addition, this embodiment combines high-resolution 3D thermal data with an AI-based analysis algorithm to precisely analyze the state of the battery and predict future performance, thereby dynamically adjusting charging, discharging, and cooling strategies to maintain optimal performance during the battery's life cycle, which can improve battery efficiency and lifespan.

[0196] In addition, this embodiment enables local and dynamic cooling control based on precise 3D heat distribution data, thereby significantly improving the efficiency of the cooling system, reducing energy consumption, and improving the overall performance of the battery.

[0197] In addition, this embodiment enables early detection of even minute anomalies through high-resolution 3D thermal data and AI-based pattern recognition, thereby preventing battery failure or safety issues in advance and improving the overall safety and reliability of the xEV.

[0198] As used herein, the term “part” may include a unit implemented in hardware, software, or firmware, and may be used interchangeably with terms such as logic, logic block, component, or circuit. The “part” may be a component formed integrally, or a minimum unit of said component or a part thereof that performs one or more functions. For example, according to one embodiment, the “part” may be implemented in the form of an Application-Specific Integrated Circuit (ASIC).

[0199] The implementations described herein may be implemented, for example, as methods or processes, devices, software programs, data streams, or signals. Even if discussed only in the context of a single form of implementation (e.g., discussed only as a method), the implementation of the discussed features may also be implemented in other forms (e.g., devices or programs). Devices may be implemented in appropriate hardware, software, and firmware, etc. Methods may be implemented in devices such as processors, which generally refer to processing devices including, for example, computers, microprocessors, integrated circuits, or programmable logic devices. Processors also include communication devices such as computers, cell phones, portable / personal digital assistants ("PDAs"), and other devices that facilitate the communication of information between end-users.

[0200] Although the present invention has been described above by limited embodiments and drawings, the present invention is not limited thereto, and it is obvious that various modifications and variations are possible within the scope of the technical spirit of the present invention and the equivalent scope of the claims described below by those skilled in the art to which the present invention belongs.

Claims

1. A terahertz holographic imaging module that measures the three-dimensional heat distribution inside a battery pack in a non-contact manner using terahertz waves; A 3D visualization module that visualizes and displays thermal distribution data inside the battery pack in real-time in three dimensions; and A battery pack heat distribution monitoring device characterized by including a main control module that integrates the terahertz holographic imaging module and the 3D visualization module to predict temperature imbalance or hot spot detection and heat distribution of the battery pack based on a deep learning model.

2. In Paragraph 1, The above 3D visualization module is, The acquired hologram data is processed using the Angular Spectrum Method (ASM), and Generates real-time 3D heat distribution maps through GPU (Graphic Processing Unit) acceleration, and Reconstructing the heat distribution into a 3D volume form using a ray casting algorithm, and A thermal distribution monitoring device for a battery pack characterized by providing a WebGL (Web Graphics Library)-based user interface.

3. In Paragraph 1, The above terahertz holographic imaging module is, A thermal distribution monitoring device for a battery pack characterized by including a quantum cascade laser (QCL) to generate terahertz waves.

4. In Paragraph 1, The above terahertz holographic imaging module is, A thermal distribution monitoring device for a battery pack characterized by including a microbolometer detector or a Schottky barrier diode array to detect phase and amplitude information of terahertz waves transmitted through the battery pack.

5. In Paragraph 1, The above terahertz holographic imaging module is, A thermal distribution monitoring device for a battery pack characterized by performing spatial frequency separation in an off-axis configuration.

6. In Paragraph 1, The above terahertz holographic imaging module is, A terahertz source unit that generates terahertz waves to irradiate the inside of a battery pack; A terahertz detector that collects data by detecting terahertz waves passing through or reflected from a battery pack; and A thermal distribution monitoring device for a battery pack, characterized by including an optical unit that controls the path of terahertz waves and generates an interference pattern to support thermal distribution imaging processing.

7. In Paragraph 6, The above optical unit is, A thermal distribution monitoring device for a battery pack, characterized by including an off-axis parabolic mirror, a silicon lens for adjusting terahertz waves to convert them into a desired shape, and a beam splitter for separating and combining a measurement beam and a reference beam.

8. In Paragraph 6, The above terahertz holographic imaging module is, It further includes a data collection and preprocessing unit capable of collecting terahertz signals and preprocessing them to improve data quality so that they can be analyzed; The above data collection and preprocessing unit is, A thermal distribution monitoring device for a battery pack characterized by converting an analog signal into a digital signal through a high-speed ADC (Analog-to-Digital Converter), removing unnecessary data to improve signal quality based on a noise removal filter, restoring phase information based on interference data using a phase restoration algorithm, and processing and refining data for accurate thermal distribution analysis.

9. In Paragraph 6, The above terahertz holographic imaging module is, It further includes a reference beam generator that generates a reference beam to generate an interference pattern of terahertz waves, and The above reference beam generator is, A thermal distribution monitoring device for a battery pack characterized by generating a reference beam to calculate the thermal distribution inside the battery by comparison with a measurement beam, stably maintaining the phase of the reference beam through a phase stabilization device, performing synchronization with a terahertz source unit through a synchronization control circuit, and precisely analyzing thermal distribution data through the formation of a terahertz interference pattern.

10. In Paragraph 1, A vehicle integrated interface module capable of exchanging and linking data in real time between a battery pack thermal distribution monitoring device, a vehicle's battery management system (BMS), and an Electronic Control Unit (ECU); further comprising The above vehicle integrated interface module is, A thermal distribution monitoring device for a battery pack, characterized by being able to integrate with key in-vehicle systems using CAN FD (Controller Area Network Flexible Data-rate) and Automotive Ethernet, being compatible with OBD-II interfaces and complying with ISO 26262 functional safety standards, being able to interlock with designated external systems or be compatible with vehicle diagnostic systems, and being implemented to optimize battery performance and control an intelligent cooling control module through data exchange.

11. In Paragraph 1, It further includes an intelligent cooling control module capable of performing a dynamic cooling strategy for battery thermal management, and The above intelligent cooling control module is, A thermal distribution monitoring device for a battery pack characterized by being able to implement a local precision cooling strategy and a predictive cooling strategy based on temperature data using an MPC (Model Predictive Control) algorithm, performing thermal management by controlling a cooling device including a cooling fan and a pump, and including a PWM-controlled pump, a fan, and a solenoid valve array as cooling actuators.

12. In Paragraph 1, It further includes a data security module for encrypting and ensuring integrity of data generated and exchanged by the thermal distribution monitoring device of the battery pack; The above data security module is, A thermal distribution monitoring device for a battery pack characterized by enhancing data security through end-to-end encryption, protecting the system from external attacks using a hardware security module, recording data change history using blockchain technology, and verifying the integrity of thermal distribution data.

13. The terahertz holographic imaging module of the heat distribution monitoring device, Step of acquiring internal data of the battery pack; A step of performing preprocessing on acquired data; A step of performing hologram reconstruction using preprocessed data; A step of mapping thermal distribution data to temperature by utilizing the absorption coefficient in a specific frequency band of the reconstructed hologram; A step of visualizing the 3D heat distribution after mapping the heat distribution data to temperature; A step of performing post-processing and data analysis after visualizing the 3D heat distribution; and A method for monitoring the thermal distribution of a battery pack, characterized by including the step of performing post-processing and data analysis, followed by performing data compression and transmission or performing a vehicle system integration process.

14. In Paragraph 13, In the step of performing preprocessing of the acquired data above, The above terahertz holographic imaging module is, A method for monitoring the thermal distribution of a battery pack, characterized by including a noise removal process, a phase unwrapping process, and a reference beam correction process as processes for the above-mentioned preprocessing.

15. In Paragraph 13, In the step of performing hologram reconstruction using the above-mentioned preprocessed data, The above terahertz holographic imaging module is, A method for monitoring the thermal distribution of a battery pack, characterized by including, as a process for the above-mentioned reconstruction, a spectrum reconstruction process and a multi-frequency synthesis process.

16. In Paragraph 13, In the step of mapping the above heat distribution data to temperature, The above terahertz holographic imaging module is, A method for monitoring the heat distribution of a battery pack, characterized by including, as a process of mapping the above-mentioned heat distribution data to temperature, a process of extracting absorption coefficients, a process of applying temperature-absorption coefficient relationships, and a process of applying a machine learning-based correction algorithm.

17. In Paragraph 13, In the step of visualizing the above 3D heat distribution, The above terahertz holographic imaging module is, A method for monitoring the heat distribution of a battery pack, characterized by including, as a process for visualizing the above 3D heat distribution, a process of projecting a ray from each pixel through a ray casting algorithm, calculating color and opacity at a sampling point, determining the final pixel value through front / back composite, mapping the temperature to color and opacity using a transfer function, and a process for generating isotherms.

18. In Paragraph 13, In the step of performing the above post-processing and data analysis, The above terahertz holographic imaging module is, A method for monitoring the heat distribution of a battery pack, characterized by including a hotspot detection process, a temporal change analysis process, and a pattern recognition and anomaly detection process as a process for performing the above-mentioned post-processing and data analysis.

19. In Paragraph 13, In the step of performing the above data compression and transmission or performing the vehicle system integration process, The above terahertz holographic imaging module is, It includes data compression processes, and encryption and transmission processes, and also A method for monitoring the heat distribution of a battery pack, characterized by including a process of transmitting data to an intelligent cooling control module to apply a local cooling strategy in real time.

20. As a battery pack, Terahertz holographic imaging module that measures the three-dimensional heat distribution inside a battery pack in a non-contact manner using terahertz waves; A 3D visualization module that visualizes and displays thermal distribution data inside the battery pack in real-time in three dimensions; and A battery pack characterized by including a main control module that integrates the terahertz holographic imaging module and the 3D visualization module to predict temperature imbalance or hot spot detection and heat distribution of the battery pack based on a deep learning model.

Images