System and method for performing charge and discharge tests of battery devices using bidirectional power conversion
Patent Information
- Application Number
- KR1020250139383
- Authority / Receiving Office
- KR · KR
- Patent Type
- Patents
- Current Assignee / Owner
- Filing Date
- 2025-09-25
- Publication Date
- 2026-08-14
- Estimated Expiration
- 2045-09-25
Smart Images
Figure 112025109968004-PAT00003_ABST
Abstract
Description
Technology Field
[0001] Embodiments of the present disclosure relate to a system and method for performing charging and discharging tests of a battery device using bidirectional power conversion. Background Technology
[0002] Recently, as the scope of secondary battery usage has expanded to various industrial fields such as electric vehicles, energy storage systems (ESS), and portable electronic devices, the importance of quality evaluation and lifespan prediction for battery devices is gradually increasing. In particular, since battery safety and reliability are critical factors in application fields such as electric vehicles and ESS, a system that performs precise charging and discharging tests during the manufacturing and operation stages is essential.
[0003] Conventional battery testing systems primarily adopt a unidirectional power conversion-based structure that prioritizes supporting only one mode, either charging or discharging. This structure has limitations, such as causing unnecessary energy consumption during testing and preventing regenerative power from being efficiently returned to the external power grid. In particular, power consumption and heat generation issues are exacerbated during the testing of large-capacity battery devices, resulting in inefficiencies in terms of maintenance costs and energy efficiency.
[0004] Furthermore, existing battery testing systems had limitations in reflecting diverse charge and discharge profiles due to restricted test items and conditions. For instance, tests simulating actual operating environments—such as rapid charging, high or low temperatures, and repetitive high-output discharge conditions—were limited, resulting in discrepancies between actual usage and test conditions. Consequently, predictions of remaining battery life or assessments of potential performance degradation often differed from actual operational results.
[0005] In particular, testing of high-capacity battery packs or medium-to-large cells requires the operation of high currents exceeding tens of amperes. Consequently, there is a demand for test equipment capable of stably controlling bidirectional energy flow while minimizing power loss and heat generation during this process. This requirement is leading to the development of test equipment based on bidirectional power conversion that incorporates power regeneration functions, moving beyond simple charging and discharging devices. The bidirectional power conversion method offers the advantage of significantly improving energy efficiency by not only supplying external power to the battery during charging but also regenerating and recycling energy generated from the battery back into the external power grid during discharge.
[0006] However, even in test devices utilizing conventional bidirectional power conversion technology, miniaturization, high-precision current control, and high-speed data acquisition and analysis capabilities are not fully realized, resulting in limitations in their application in actual process or R&D environments. For example, some systems have low sampling rates that fail to capture degradation signals, such as minute voltage fluctuations or changes in internal resistance, in a timely manner, while others suffer from insufficient control precision, leading to significant data deviations during repeated tests.
[0007] In addition, the control unit of existing test devices was often limited to simple measurement data collection and display functions, which had limitations in providing sufficient data interpretation capabilities.
[0008] Accordingly, a system and method for performing charge and discharge tests of a battery device using bidirectional power conversion are required. The problem to be solved
[0009] Embodiments of the present disclosure may provide a system and method for performing charging and discharging tests of a battery device using bidirectional power conversion.
[0010] The technical problems to be solved in the embodiments are not limited to those mentioned above, and other unmentioned technical problems may be considered by those skilled in the art from the various embodiments described below. means of solving the problem
[0011] A system for performing charging and discharging tests of a battery device according to one embodiment, wherein the system comprises: an isolation transformer that isolates and transforms AC power supplied from a power grid; an AC filter connected to the output terminal of the isolation transformer to filter AC power; a bidirectional power converter including a PWM rectifier connected to the AC filter to convert AC power into DC power during charging and a PWM inverter to convert DC power into AC power during discharging; a bidirectional DC converter connected to the DC terminal of the bidirectional power converter to step down or step up DC power and perform bidirectional power conversion for charging or discharging; a battery device to be tested connected to the output terminal of the bidirectional DC converter; a measurement unit that determines measurement information including values related to voltage, current, temperature, internal resistance, and battery status for the battery device to be tested; and a control unit that controls the bidirectional power converter and the bidirectional DC converter based on the measurement information transmitted from the measurement unit to perform charging tests, discharging tests, and power regeneration, and determines diagnostic information for the battery device to be tested, wherein the control unit performs charging tests and discharging tests according to test conditions set for the battery device to be tested, and the charging During the performance of the test and the discharge test, the satisfaction of a test termination condition or an abnormality detection condition included in the test condition is determined based on the measurement information, and if at least one of the test termination condition or the abnormality detection condition is satisfied, the test is terminated by controlling the bidirectional power converter and the bidirectional DC converter, and the measurement information and the diagnostic information are output to a user interface on a display connected to the control unit, and the test termination condition includes a threshold value for cumulative charging capacity, a threshold value for cumulative discharge capacity, a threshold value for the number of cycles, and a threshold value for total test time.The above abnormal detection conditions may include an overvoltage threshold, an overcurrent threshold, an overtemperature threshold, and an internal resistance threshold. Effects of the invention
[0012] According to the embodiments, the system includes a bidirectional power converter and a bidirectional DC converter, and since it can control both the charging and discharging of a battery device, it can improve energy efficiency compared to a unidirectional-based test device. In particular, because the power generated during discharge can be regenerated and recycled into an external power grid, it minimizes unnecessary energy waste during the testing process and provides the effect of reducing power costs even in large-scale battery pack testing.
[0013] According to the embodiments, the system can ensure the stability of the test by automatically determining test termination conditions and abnormal detection conditions. For example, if conditions such as overvoltage, overcurrent, or temperature imbalance are detected, the control unit can immediately block the power conversion operation to protect the battery device and the test device.
[0014] According to the embodiments, the system can generate diagnostic information based on measurement data after the test is completed, and in embodiments where an artificial intelligence-based data interpretation module is applied during this process, the system can automatically classify the types of battery abnormalities and degradation patterns. Accordingly, a precise diagnostic report can be provided without user intervention.
[0015] According to the embodiments, the system can visualize and display the test progress status, measurement information, and diagnostic information in real time through a display device connected to a test control unit. This provides the advantage that a user or operator can intuitively monitor the entire test process and respond immediately.
[0016] According to the embodiments, the system can be easily applied to various battery device testing environments by incorporating a modular structure and a lightweight design. In particular, it can be utilized not only in large-scale production lines but also in research and development environments and as field diagnostic equipment, thereby providing the effect of a wide range of applications.
[0017] The effects obtainable from the embodiments are not limited to those mentioned above, and other unmentioned effects can be clearly derived and understood by a person skilled in the art based on the detailed description below. Brief explanation of the drawing
[0018] The accompanying drawings, included as part of the detailed description to aid in understanding the embodiments, provide various embodiments and explain the technical features of the various embodiments together with the detailed description. FIG. 1 is a schematic diagram of a system for performing charging and discharging tests of a battery device using bidirectional power conversion according to one embodiment. FIG. 2 is a diagram showing the configuration of a system that performs charging and discharging tests of a battery device using bidirectional power conversion according to one embodiment. FIG. 3 is a flowchart of a method for performing charging and discharging tests of a battery device using bidirectional power conversion according to one embodiment. FIG. 4 is an example of an artificial intelligence model used in a system that performs charging and discharging tests of a battery device using bidirectional power conversion according to one embodiment. FIG. 5 is a block diagram showing the configuration of a server according to one embodiment. Specific details for implementing the invention
[0019] The following embodiments are combinations of the components and features of the embodiments in a predetermined form. Each component or feature may be considered optional unless otherwise explicitly stated. Each component or feature may be implemented in a form not combined with other components or features. Additionally, various embodiments may be constructed by combining some components and / or features. The order of operations described in various embodiments may be changed. Some components or features of one embodiment may be included in another embodiment, or may be replaced with corresponding components or features of another embodiment.
[0020] In the description of the drawings, procedures or steps that could obscure the essence of the various embodiments are not described, nor are procedures or steps that can be understood by a person with ordinary knowledge in the relevant technical field described.
[0021] Throughout the specification, when a part is described as "comprising" or "including" a component, this means that, unless specifically stated otherwise, it does not exclude other components but may include additional components. Furthermore, terms such as "...part," "...unit," and "module" as used in the specification refer to a unit that performs at least one function or operation, and this may be implemented in hardware, software, or a combination of hardware and software. Additionally, "one (a or an)," "one," "the," and similar related terms may be used in the context describing various embodiments (particularly in the context of the following claims) in both singular and plural forms, unless otherwise indicated in the specification or clearly contradicted by the context.
[0022] Hereinafter, embodiments according to various examples will be described in detail with reference to the accompanying drawings. The detailed description disclosed below, together with the accompanying drawings, is intended to describe exemplary embodiments of various examples and is not intended to represent the only embodiment.
[0023] In addition, specific terms used in various embodiments are provided to aid in understanding the various embodiments, and the use of such specific terms may be modified in other forms within the scope of not departing from the technical concept of the various embodiments.
[0024] FIG. 1 is a schematic diagram of a system for performing charging and discharging tests of a battery device using bidirectional power conversion according to one embodiment. The embodiment of FIG. 1 may be combined with various embodiments of the present disclosure.
[0025] Referring to FIG. 1, a system (100) (hereinafter, system (100)) that performs charging and discharging tests of a battery device using bidirectional power conversion can perform charging tests and discharging tests according to set test conditions for the battery device under test. During a charging test, the system (100) can convert AC power into DC power to supply power to the battery device under test, and during a discharging test, convert DC power emitted from the battery device under test into AC power to regenerate it to the power grid. At this time, the system (100) can insulate and transform the AC power to block noise and leakage current in the power grid and to ensure insulation safety of the test circuit. The system (100) can maintain the quality of the test environment by optimizing current THD (Total Harmonic Distortion) and EMI (Electromagnetic Interference) characteristics by removing high-frequency components from the transformed power and minimizing distortion of voltage and current waveforms. Additionally, the system (100) can collect measurement information in real time during the performance of charging and discharging tests to determine whether a test termination condition or an abnormality detection condition is satisfied. For example, if at least one of the test termination condition or the abnormality detection condition is satisfied, the system (100) can immediately terminate the test and block the power conversion path of the relevant channel.
[0026] The system (100) can maintain a stable power supply or safely stop the test state through the power converter and control logic even if a momentary power outage occurs during the test. In addition, the system (100) can perform an energy recovery algorithm that returns the power generated during the discharge test to the power grid or recycles it for charging other channels.
[0027] The system (100) can improve overall power conversion efficiency by using a multi-channel (e.g., 24 channels) parallel operation method to operate multiple test channels simultaneously and evenly distributing the load for each channel. The system (100) can ensure linearity and accuracy of the data by measuring the charging current, discharging current, voltage, temperature, and internal resistance of each channel with high precision.
[0028] The system (100) can output measurement information and diagnostic results in real time to a GUI-based user interface, allowing the operator to intuitively monitor the progress of the test and, if necessary, change test conditions or stop the test. In addition, the system (100) can utilize a battery management system (BMS) function to store and manage cell-level status information, pack-level status information, protection operation history, etc.
[0029] The system (100) can stably control currents of several hundred amperes generated during the charging and discharging of a large-capacity battery device through a high-current switch driving circuit. Through this, the system (100) can perform performance evaluation, life testing, and quality verification of the large-capacity battery device with high efficiency and high precision.
[0030] In this way, the system (100) can provide a test environment suitable for performance evaluation, life testing, and quality control of a battery device under test by integrating high-efficiency bidirectional power conversion, improvement of current THD / EMI characteristics, response to momentary power outages, energy regeneration, multi-channel parallel testing, precision control and measurement, and a user-friendly interface.
[0031] FIG. 2 is a diagram showing the configuration of a system for performing charging and discharging tests of a battery device using bidirectional power conversion according to one embodiment. The embodiment of FIG. 2 can be combined with various embodiments of the present disclosure.
[0032] Referring to FIG. 2, a system (100) for performing charging and discharging tests of a battery device using bidirectional power conversion may include an isolation transformer (110), an AC (Alternating Current) filter (120), a bidirectional power conversion unit (130), a bidirectional DC (Direct Current) converter (140), a battery device to be tested (150), an internal energy storage unit (160), an auxiliary DC converter (170), a measurement unit (180), and a control unit (190).
[0033] The isolation transformer (110) is a device that isolates and transforms AC power supplied from the power grid and outputs it. By electrically isolating the primary and secondary windings, safety between the system (100) and the power grid can be ensured. The isolation transformer (110) can block leakage current that may occur in the system (100) and prevent surge voltage or noise from the power grid from entering the DC converter. Additionally, the isolation transformer (110) can supply power within the input voltage range required by the bidirectional power converter (130) by adjusting the transformation ratio.
[0034] The AC filter (120) is a device connected to the output terminal of the isolation transformer (110) to filter AC power, and can suppress harmonic components and reduce distortion of voltage and current waveforms. The AC filter (120) can improve the input quality of the bidirectional power converter (130) by reducing the total harmonic distortion (THD) of the current and minimizing electromagnetic interference (EMI). The AC filter (120) can also correct the response to instantaneous voltage fluctuations in the power grid, thereby supporting the system to operate stably even in situations of instantaneous power outages or voltage drops.
[0035] The bidirectional power converter (130) may include a Pulse Width Modulation (PWM) rectifier connected to an AC filter (120) that converts AC power into DC power during a charging test and a PWM inverter that converts DC power into AC power during a discharging test. The bidirectional power converter (130) may be configured based on a bridge switching element. The bidirectional power converter (130) may be operated by a control unit (190).
[0036] For example, the bidirectional power converter (130) can track the frequency and phase of the power grid voltage waveform in real time through a phase-locked loop (PLL) during PWM rectifier operation and generate a target current waveform, i.e., a current reference value, based on the detected phase information. The current reference value is set to be in phase with the power grid voltage, enabling operation close to a unity power factor (1.0 Power Factor). For example, the control unit (190) can generate a PWM signal to track the target current waveform by comparing the generated current reference value with the actual current value detected by the measurement unit (180), calculating the error, and correcting the error through a current control loop. In this process, the external voltage loop determines the magnitude of the current reference value so that the DC link voltage maintains a preset reference voltage, and the internal current loop controls the instantaneous current response by adjusting the PWM duty applied to the switching element.
[0037] For example, the bidirectional power converter (130) can continuously monitor the DC voltage during PWM inverter operation to maintain a reference voltage and generate a target current waveform to be injected into the power grid. At this time, the injected current is separated into an active power component and a reactive power component and controlled; the active power component transmits energy emitted from the battery device (150) under test to the power grid, and the reactive power component can be used to adjust the power factor on the power grid side. At this time, the control unit (190) can contribute to voltage stabilization of the power grid or load characteristic correction by changing the reactive power reference value to adjust the power factor in a leading or lagging direction.
[0038] For example, the bidirectional power converter (130) can prevent surge current by performing a soft start function when the system (100) starts up and gradually increasing the DC side voltage. In addition, the bidirectional power converter (130) can limit surge current that is momentarily introduced when connecting to a power grid or when there is a load change, and can protect the circuit by immediately shutting off the corresponding switching element when the DC side or AC side voltage deviates from a preset overvoltage / undervoltage standard, or when an overcurrent is detected on both the DC and AC sides. Such protection operations are performed at a fast response speed of microseconds or milliseconds, thereby preventing abnormal situations from spreading throughout the system.
[0039] For example, a voltage sensor may be installed at the DC terminal of the bidirectional power converter (130). The voltage sensor is installed at the DC terminal of the bidirectional power converter (130) and can measure the voltage value across the DC terminal in real time. By transmitting the measured voltage value to the control unit (190), the voltage sensor can be used as a control standard for the charging and discharging operations of the auxiliary DC converter (170).
[0040] The bidirectional DC converter (140) is connected to the DC terminal of the bidirectional power converter (130) to boost or buck the DC power and can perform charging and discharging of the battery device (150) under test in both directions. The bidirectional DC converter (140) may be configured with at least one of a synchronous Buck / Boost-based phase-shifted full-bridge or interleaved multi-phase topology. The phase-shifted full-bridge method minimizes switching losses in high-voltage and high-power conversion and can easily implement a transformer isolation structure. The interleaved multi-phase method operates multiple switching phases with a phase difference from each other to reduce current ripple, reduce the capacity of the output filter, and equalize component heat generation.
[0041] For example, the control unit (190) can execute at least one of a plurality of test modes according to preset test conditions for the bidirectional DC converter (140). For example, the control unit (190) can execute at least one test mode for the bidirectional DC converter (140) among constant current (CC), constant voltage (CV), constant power (CP), constant resistance (CR), open circuit voltage (OCV) maintenance, and rest (REST) modes. The constant current mode can evaluate the current characteristics of the battery by maintaining a constant current during charging or discharging. The constant voltage mode (CV) can evaluate the end-of-charge or load characteristics of the battery by maintaining a constant voltage. The constant power mode (CP) can evaluate the thermal behavior and power characteristics of the battery by maintaining a constant power value. The constant resistance mode (CR) can evaluate the discharge characteristics of the battery by simulating conditions identical to the load resistance. The open circuit voltage maintenance mode (OCV) can measure the voltage recovery characteristics of the battery while the load is cut off. Rest mode (REST) stops charging and discharging and maintains a standby state, allowing for the analysis of voltage stabilization and the effects of internal chemical reactions.
[0042] For example, the control unit (190) can limit the rising and falling slopes (slew rate) of the charging and discharging currents by controlling the switching duty, phase, and frequency for the bidirectional DC converter (140). The control unit (190) can acquire voltage, current, and temperature for the bidirectional DC converter (140) at a sampling period of 10 ms or less. At this time, the measurements can be corrected to satisfy a minimum resolution of 1 mV / 1 mA and ±0.05% FS precision.
[0043] The battery device (150) under test may be a battery pack or battery module connected to the output terminal of a bidirectional DC converter (140) to be subject to charging and discharging tests. The battery device (150) under test conditions set under the control of the control unit (190) may be charged and discharged, and when a test termination condition or an abnormality detection condition is satisfied, the power conversion path of the corresponding channel may be blocked.
[0044] The built-in energy storage unit (160) is an auxiliary power supply unit that performs power exchange with the DC side of the bidirectional power converter (130) through an auxiliary DC converter (170), and may be composed of a supercapacitor or an auxiliary battery. The built-in energy storage unit (160) can store regenerative power generated during testing or supply power to the DC side in the event of a momentary power outage or power shortage.
[0045] An auxiliary DC converter (170) is connected between the DC terminal of the bidirectional power converter (130) and the built-in energy storage unit (160), and can control the charging or discharging of the built-in energy storage unit (160) according to the DC terminal voltage level. For example, the control unit (190) monitors the voltage value of the DC terminal of the bidirectional power converter (130) in real time through a voltage sensor, and when the voltage value of the DC terminal of the bidirectional power converter (130) is greater than or equal to a first reference voltage value, it can control the auxiliary DC converter (170) to charge the built-in energy storage unit (160) to a set current value. At this time, if the voltage value of the DC terminal of the bidirectional power converter (130) decreases to a second reference voltage value or lower while charging the built-in energy storage unit (160), the control unit (190) can stop charging the built-in energy storage unit (160). For example, when the voltage value of the DC terminal of the bidirectional power converter (130) is less than or equal to the third reference voltage value, the control unit (190) can control the auxiliary DC converter (170) to supply power from the built-in energy storage unit (160) to the DC terminal of the bidirectional power converter (130). At this time, the second reference voltage value may be set to be greater than the third reference voltage value and smaller than the first reference voltage value.
[0046] At this time, the first reference voltage value can be set to a voltage value that can start charging the built-in energy storage unit (160). The first reference voltage value can be set higher than the constant voltage reference value of the DC section to ensure that there is sufficient spare power in the DC section, for example, if the constant voltage reference value of the DC section is 750 V, the first reference voltage value can be set to approximately 760 V. The second reference voltage value can be set to a voltage value that stops charging the built-in energy storage unit (160). The second reference voltage value is set to ensure voltage stability of the main test circuit when the DC section voltage drops during charging operation, and can be set to a value smaller than the first reference voltage value and larger than the third reference voltage value. For example, if the constant voltage reference value of the DC section is 750 V, the first reference voltage value is 760 V, and the third reference voltage value is 745 V, the second reference voltage value can be set to approximately 755 V. The third reference voltage value can be set to a voltage value that initiates a discharge supplying power from the built-in energy storage unit (160) to the DC section. The third reference voltage value is set to restore the DC section voltage and replenish power to the main test circuit when the DC section voltage drops below that value, and, for example, can be set to about 745 V when the DC section constant voltage reference value is 750 V.
[0047] Through this, by including a control structure that responds immediately to DC voltage fluctuations, energy can be recovered to prevent overvoltage risks in the event of an excessive voltage rise during charging and discharging tests, and the stability of the test environment can be maintained by immediately supplying auxiliary power in the event of a voltage drop. In addition, since auxiliary power is supplied to respond to instantaneous load fluctuations by utilizing an internal energy storage unit, the need to draw additional power from the power grid is reduced, thereby lowering dependence on the power grid and simultaneously alleviating the burden on the power grid.
[0048] The measurement unit (180) can determine measurement information including values related to voltage, current, temperature, internal resistance, and battery status for the battery device (150) under test. For example, when measuring voltage, the measurement unit (180) can measure cell unit voltage, module unit voltage, and pack unit voltage separately. For example, when measuring current, the measurement unit (180) can calculate charging current, discharging current, cumulative charging capacity (Ah), and cumulative discharging capacity. For example, when measuring temperature, the measurement unit (180) can perform measurements for multiple locations, such as battery cell surface temperature, module internal temperature, and cooling channel temperature. For example, the measurement unit (180) can calculate the AC impedance and DC internal resistance of each cell as internal resistance. For example, values related to battery status may include State of Charge (hereinafter SOC), State of Power (hereinafter SOP), voltage deviation between cells, temperature imbalance index, cumulative charging capacity, and cumulative discharging capacity. SOC may be a value calculated as a percentage of the rated capacity of the amount of charge currently stored in the battery device (150) under test. SOP may represent the maximum power or current that can be instantaneously output considering the current environmental conditions (temperature and SOC). The inter-cell voltage deviation represents the voltage difference between cells connected in series, and the temperature imbalance index may represent the maximum and minimum difference between values measured by a plurality of temperature sensors pre-placed in the battery device under test.
[0049] For example, the measurement information may include at least one of cell unit voltage, module unit voltage, or pack unit voltage, at least one of charging current and discharging current, cell surface temperature, module internal temperature, cooling channel temperature, AC impedance of each cell, DC internal resistance, SOC, SOP, voltage deviation between cells, temperature imbalance index, cumulative charging capacity, and cumulative discharging capacity.
[0050] The measurement unit (180) has a measurement performance with a minimum resolution of 1mV / 1mA and a precision of ±0.05% FS, and can collect data related to voltage, current, temperature, internal resistance, and battery status at a sampling period of 10ms or less and transmit it to the control unit (190). At this time, the measurement information can be collected in time intervals according to a preset sampling period and transmitted to the control unit (190).
[0051] The control unit (190) controls the bidirectional power converter (130), the bidirectional DC converter (140), and the auxiliary DC converter (170) based on measurement information received from the measurement unit (180) to perform a charging test, a discharging test, and power regeneration, and can determine diagnostic information for the battery device (150) to be tested. The control unit (190) controls the test according to test conditions (discharge end voltage, charge end current, cumulative charging capacity, cumulative discharging capacity, etc.) and can terminate the test if at least one of overvoltage, overcurrent, overtemperature, or an over-internal resistance condition is detected. In addition, the control unit (190) outputs measurement information and diagnostic information to a GUI-based user interface so that an operator can monitor the test status in real time and change or stop the conditions.
[0052] The control unit (190) may be connected to a display. For example, the display may be electrically connected to the control unit (190) and configured to visually display measurement information and diagnostic information transmitted from the control unit (190). For example, the display may be pre-mounted integrally with the system (100). Alternatively, for example, it may be placed outside the system (100) in the form of an external device. The display may include at least one of an LCD, OLED, or E-ink panel and a control circuit that drives the display device. According to one embodiment, the display may include a capacitive or ultrasonic touch sensor to detect touch input from an operator and, if necessary, include a pressure sensor to measure the magnitude of the force generated upon touch, thereby providing a pressure-based input function.
[0053] For example, the display can be controlled to display the real-time operating status of the battery device (150) under test by configuration item. Items displayed on the display may include cell unit, module unit, or pack unit voltage, charging current, discharging current, cell surface temperature, module internal temperature, cooling channel temperature, AC impedance, DC internal resistance, state of charge (SOC), state of output available (SOP), cumulative charging capacity, and cumulative discharging capacity. Additionally, the remaining lifespan, potential for performance degradation, probability of anomaly occurrence, and related warning messages calculated by the control unit (190) using an artificial intelligence model may also be displayed on the display.
[0054] The display is configured based on a Graphic User Interface (GUI) and can intuitively display the test status using time-segment graphs, gauges, icons, and color changes. For example, an operator can monitor the test status in real time through the display. For instance, the display can provide an operation interface that allows inputting commands to change test conditions, pause the test, or terminate the test. Additionally, the display may include time-series graphs of measurement information, comparative indicators of the current status against test conditions, and a diagnostic results screen indicating the time of anomaly occurrence and its cause, thereby enabling the operator to intuitively verify the test process and results.
[0055] For example, the control unit (190) may include a processor, memory, and a communication module. The processor may execute software to control at least one other component (e.g., a hardware or software component) of a device connected to the processor and may perform various data processing or operations. According to one embodiment, as at least part of the data processing or operations, the processor may store commands or data received from other components (e.g., a battery or a communication module) in volatile memory, process the commands or data stored in volatile memory, and store the resulting data in non-volatile memory. According to one embodiment, the processor may include a main processor (e.g., a central processing unit or an application processor) or an auxiliary processor (e.g., a graphics processing unit, a neural processing unit (NPU), an image signal processor, a sensor hub processor, or a communication processor) that can operate independently or together with it.
[0056] For example, if a main processor and an auxiliary processor are included, the auxiliary processor may be configured to use less power than the main processor or to be specialized for a designated function. The auxiliary processor may be implemented separately from the main processor or as part thereof. The auxiliary processor may control at least some of the functions or states associated with at least one of the components of the system (100), for example, on behalf of the main processor while the main processor is in an inactive (e.g., sleep) state, or together with the main processor while the main processor is in an active (e.g., application execution) state. According to one embodiment, the auxiliary processor may be implemented as part of another functionally related component. According to one embodiment, the auxiliary processor may include a hardware structure specialized for processing an artificial intelligence model.
[0057] For example, the processor may use an artificial intelligence model. The artificial intelligence model may be generated through machine learning. Such learning may be performed, for example, on the device itself where the artificial intelligence model is executed (e.g., the control unit (190)), or through a separate server. The learning algorithm may include, for example, supervised learning, unsupervised learning, semi-supervised learning, or reinforcement learning, but is not limited to the examples described above. The artificial intelligence model may include a plurality of artificial neural network layers. The artificial neural network may be a deep neural network (DNN), a convolutional neural network (CNN), a recurrent neural network (RNN), a restricted Boltzmann machine (RBM), a deep belief network (DBN), a bidirectional recurrent deep neural network (BRDNN), deep Q-networks, or a combination of one or more of the above, but is not limited to the examples described above. Artificial intelligence models may include software structures, either additionally or as a substitute, in addition to hardware structures.
[0058] Memory may store various data used by at least one component of the system (100). The data may include, for example, input data or output data for software and related commands. Memory may include volatile memory or non-volatile memory. A program may be stored in memory as software and may include, for example, an operating system, middleware, or an application.
[0059] The communication module may support the establishment of a direct (e.g., wired) communication channel or a wireless communication channel between an external device or server and the system (100), and the performance of communication through the established communication channel. The communication module may include one or more communication processors that operate independently of a processor (e.g., an application processor) and support direct (e.g., wired) communication or wireless communication. According to one embodiment, the communication module may include a wireless communication module (e.g., a cellular communication module, a short-range wireless communication module, or a GNSS (global navigation satellite system) communication module) or a wired communication module (e.g., a LAN (local area network) communication module, or a power line communication module). Among these communication modules, the corresponding communication modules can communicate with an external device through a first network (e.g., a short-range communication network such as Bluetooth, WiFi (wireless fidelity) direct, or IrDA (infrared data association)) or a second network (e.g., a legacy cellular network, a 5G network, a next-generation communication network, the Internet, or a long-range communication network such as a computer network (e.g., LAN or WAN). These various types of communication modules may be integrated into a single component (e.g., a single chip) or implemented as multiple separate components (e.g., multiple chips).
[0060] The wireless communication module can support 5G networks following 4G networks and next-generation communication technologies, such as new radio access technology (NR access technology). NR access technology can support high-speed transmission of high-capacity data (enhanced mobile broadband (eMBB)), minimization of terminal power and connection of multiple terminals (massive machine type communications (mMTC)), or high reliability and low latency (ultra-reliable and low-latency communications (URLLC)). The wireless communication module can support high-frequency bands (e.g., mmWave band) to achieve high data transmission rates, for example. The wireless communication module can support various technologies to secure performance in high-frequency bands, such as beamforming, massive MIMO (multiple-input and multiple-output), full-dimensional MIMO (FD-MIMO), array antenna, analog beamforming, or large-scale antenna. The wireless communication module can support various requirements specified in external electronic devices or network systems. According to one embodiment, the wireless communication module can support a Peak data rate for eMBB realization (e.g., 20 Gbps or higher), loss coverage for mMTC realization (e.g., 164 dB or lower), or U-plane latency for URLLC realization (e.g., downlink (DL) and uplink (UL) each 0.5 ms or lower, or round trip 1 ms or lower).
[0061] At least some of the components of the system (100) may be connected to each other via a communication method between peripheral devices (e.g., bus, GPIO (general purpose input and output), SPI (serial peripheral interface), or MIPI (mobile industry processor interface)) and may exchange signals (e.g., commands or data) with each other.
[0062] According to one embodiment, commands or data may be transmitted or received between the system (100) and an external device through a server connected to a second network. According to one embodiment, all or part of the operations performed in the system (100) may be performed by one or more external devices. For example, when the system (100) needs to perform a function or service automatically or in response to a request from a user or another device, the system (100) may request one or more external devices to perform at least part of the function or service instead of performing the function or service itself or additionally. One or more external devices that receive the request may perform at least part of the requested function or service, or additional functions or services related to the request, and transmit the result of the execution to the system (100). The system (100) may provide the result as is or additionally processed as at least part of the response to the request. For this purpose, for example, cloud computing, distributed computing, mobile edge computing (MEC), or client-server computing technology may be used. The system (100) may provide ultra-low latency services, for example, by using distributed computing or mobile edge computing. In another embodiment, the external device may include an Internet of Things (IoT) device. The server may be an intelligent server using machine learning and / or neural networks. According to one embodiment, the external device or server may be included within a second network.
[0063] The server is connected to the system (100) and can provide services to the connected system (100). In terms of hardware, the server may have the same configuration as a conventional web server or service server. However, in terms of software, it may include program modules that perform various functions and are implemented through various languages such as C, C++, Java, Python, Golang, and Kotlin. Furthermore, the server generally refers to a computer system that is connected to an unspecified number of clients and / or other servers through an open computer network such as the Internet, receives requests for task execution from clients or other servers, and derives and provides the results of the tasks, as well as computer software (server programs) installed for this purpose. In addition, the server should be understood as a broad concept that includes, in addition to the aforementioned server programs, a series of application programs running on the server and, in some cases, various databases (DB: Database, hereinafter referred to as "DB") built internally or externally. Therefore, the server classifies data, stores it in a database, and manages it; this database can be implemented either internally or externally to the server. Furthermore, the server can be implemented on general-purpose server hardware using various server programs provided for operating systems such as Windows, Linux, UNIX, and Macintosh. Representative examples include IIS (Internet Information Server) used in Windows environments, and CERN, NCSA, APPACH, and TOMCAT used in UNIX environments, which can be used to implement web services. Additionally, the server can be integrated with user authentication systems for the service or authentication systems related to the service.
[0064] The first network and the second network refer to a connection structure capable of exchanging information between each node, such as terminals and servers, or a network connecting a server and a system (100). The first network and the second network include, but are not limited to, the Internet, LAN (Local Area Network), Wireless LAN (Wireless Local Area Network), WAN (Wide Area Network), PAN (Personal Area Network), 3G, 4G, LTE, 5G, Wi-Fi, etc. The first network and the second network may be closed first network and second networks such as LAN or WAN, but it is preferable that they be open networks such as the Internet. The Internet refers to a global open computer network structure comprising a first network and a second network that provides protocols such as TCP / IP, TCP, and UDP (User Datagram Protocol), as well as various services existing at the upper layers, namely HTTP (HyperText Transfer Protocol), Telnet, FTP (File Transfer Protocol), DNS (Domain Name System), SMTP (Simple Mail Transfer Protocol), SNMP (Simple Network Management Protocol), NFS (Network File Service), and NIS (Network Information Service).
[0065] A database may have a general data structure implemented in the storage space (hard disk or memory) of a computer system using a database management program (DBMS). A database may have a data storage form that allows for the free retrieval (extraction), deletion, editing, and addition of data. A database may be implemented to suit the purpose of an embodiment of the present disclosure using a relational database management system (RDBMS) such as Oracle, Informix, Sybase, and DB2, an object-oriented database management system (OODBMS) such as Gemston, Orion, and O2, and an XML native database such as Excelon, Tamino, and Sekaiju, and may have appropriate fields or elements to achieve its functions.
[0066] For example, the system (100) may support one or more designated protocols through an interface that can be used to connect directly or wirelessly with an external device. According to one embodiment, the interface may include, for example, a high definition multimedia interface (HDMI), a universal serial bus (USB) interface, an SD card interface, or an audio interface. For example, the TVWS integrated terminal (200) may include a connector that can be physically connected to an external device through a connection terminal. According to one embodiment, the connection terminal may include, for example, an HDMI connector, a USB connector, an SD card connector, or an audio connector (e.g., a headphone connector).
[0067] For example, the control unit (190) may use a quality prediction model and an XAI model that utilize a neural network. The quality prediction model may be a deep learning model that receives time-series data obtained during the test process as input and diagnoses the quality status of the battery device under test by performing data preprocessing, feature extraction, and multipath prediction operations. The XAI model may be an artificial intelligence model that quantitatively calculates the influence contributed by each time point and each item of the input data to the output result of the quality prediction model and provides the result as explanatory basis information.
[0068] At this time, at least one of the quality prediction model or XAI model may be operated by the control unit (190). For example, at least one of the quality prediction model or XAI model may be operated by a server that is pre-connected to the control unit (190). In this case, the control unit (190) may operate at least one of the quality prediction model or XAI model through communication with the server.
[0069] FIG. 3 is a flowchart illustrating a method for performing charging and discharging tests of a battery device using bidirectional power conversion according to one embodiment. The embodiment of FIG. 3 can be combined with various embodiments of the present disclosure.
[0070] Referring to FIG. 3, in step S310, the control unit can obtain test conditions set for the battery device under test.
[0071] The control unit can select and acquire a test condition that matches the battery device under test from among a plurality of pre-stored test conditions corresponding to the model information of the battery device. For example, test conditions may be pre-stored for each model of the battery device.
[0072] Test conditions may include a charging sequence, a discharging sequence, test termination conditions, and anomaly detection conditions.
[0073] For example, the charging sequence may include, for each of the plurality of charging sections, an operating mode (any one of CC, CV, CP, CR, OCV, REST), a set value related to the charging current, a charging end voltage value, a charging end current value, and a duration of each charging section.
[0074] For example, the discharge sequence may include, for each of the plurality of discharge sections, an operating mode, a set value related to the discharge current, a discharge termination voltage value, a discharge termination current value, and a duration of each discharge section.
[0075] For example, the test termination conditions may include a threshold for cumulative charge capacity, a threshold for cumulative discharge capacity, a threshold for the number of cycles, and a threshold for total test time.
[0076] For example, abnormal detection conditions may include an overvoltage threshold, an overcurrent threshold, an overtemperature threshold, and an internal resistance threshold.
[0077] For example, the control unit can map acquired test conditions to each channel and set up an initial test environment based on the mapping results. During the process of setting up the initial test environment, the control unit checks whether the sensor values of the measurement unit and the protection circuit are within the normal range, and if no abnormalities are detected, it can switch to a state ready to start the test.
[0078] In step S320, the control unit can execute a charging operation based on a charging sequence set for the battery device under test.
[0079] The control unit can control the charging sequence by dividing it into multiple charging sections. For example, the control unit can apply a charging current by controlling a bidirectional power converter and a bidirectional DC converter according to the operating mode for each charging section, set values related to duration and charging current, and values for the charging end voltage and charging end current. That is, during the charging operation, the control unit can dynamically adjust voltage and current parameters according to each operating mode. For example, the control unit can apply a constant current in CC mode, and then switch to CV mode when the battery voltage reaches the charging end voltage to gradually reduce the charging current, and terminate the charging operation when the current decreases below the charging end current value.
[0080] Additionally, for example, the control unit may apply an interleaving technique in a multi-channel parallel driving environment. For instance, the control unit may assign the same switching cycle to each channel, but set the switching start times between channels differently. For instance, when four channels are connected in parallel, the control unit may set the switching start times of the second, third, and fourth channels with a 90-degree phase interval relative to the switching start time of the first channel. When six channels are connected in parallel, the switching start times of each channel may be set with a 60-degree phase interval. If an overcurrent is detected in a specific channel, the control unit may block that channel and redistribute the switching phase intervals of the remaining channels to maintain the interleaving state. Accordingly, in a multi-channel parallel driving environment, the charging current is temporally dispersed, and the link current is synthesized into a waveform in which the outputs of each channel are mutually superimposed. Link current refers to a composite current formed by the superposition of currents output by each channel through switching operations on the time axis when multiple parallel-connected channels are connected to a common DC link. By applying an interleaving technique to distribute the switching start times of each channel differently, the control unit can control the link current to form a composite waveform in which the current pulses of individual channels are uniformly distributed.
[0081] Through this operation, the control unit can control the continuous generation of switching signals for each channel while maintaining a phase-dispersed state so that the current waveforms complement each other on the time axis. The control unit can also receive voltage and current values for each channel in real time from the measurement unit and dynamically correct the switching period and phase interval to set the operation of all channels to be maintained in a synchronized state. By dispersing the switching timing of each channel, the ripple of the link current can be reduced, and the total harmonic distortion (THD) flowing into the power grid can be decreased. Therefore, power quality during the charging process can be improved, and the stability of the test device can be ensured.
[0082] In addition, the control unit periodically collects voltage, current, temperature, and internal resistance values from the measurement unit during the charging process to determine whether an overvoltage, overcurrent, overtemperature, or excessive internal resistance condition corresponding to an abnormal detection condition occurs, and if such condition occurs, the charging operation can be stopped.
[0083] In step S330, the control unit can execute a discharge operation based on a discharge sequence set for the battery device under test.
[0084] The control unit can control the discharge sequence by dividing it into multiple discharge sections. For example, the control unit can apply a discharge current by controlling the bidirectional power converter and the bidirectional DC converter according to the operating mode for each discharge section, set values related to duration and discharge current, discharge termination voltage value, and discharge termination current value. That is, during the discharge operation, the control unit can dynamically adjust voltage and current parameters according to each operating mode. For example, the control unit can apply a constant current in CC mode and terminate the discharge operation for that section when the battery voltage reaches the discharge termination voltage. Additionally, the control unit can control the discharge current to change dynamically according to changes in battery voltage by applying a load based on a set resistance value in CR mode, or it can perform the discharge operation by applying a constant power load in CP mode. The termination of discharge for each section can be determined based on when the accumulated discharge capacity reaches a set value or when the duration defined in the test conditions has elapsed.
[0085] Additionally, for example, the control unit may apply an interleaving technique in a multi-channel parallel driving environment. For instance, the control unit may assign the same switching cycle to each channel, but set the switching start times between channels differently. For instance, when four channels are connected in parallel, the control unit may set the switching start times of the second, third, and fourth channels with a 90-degree phase interval relative to the switching start time of the first channel. When six channels are connected in parallel, the switching start times of each channel may be set with a 60-degree phase interval. If an overcurrent is detected in a specific channel, the control unit may block that channel and redistribute the switching phase intervals of the remaining channels to maintain the interleaving state. Accordingly, in a multi-channel parallel driving environment, the discharge current is temporally dispersed, and the link current is synthesized into a waveform in which the outputs of each channel are mutually superimposed.
[0086] Through this operation, the control unit can control the continuous generation of switching signals for each channel while maintaining a phase-dispersed state so that the current waveforms complement each other on the time axis. The control unit can also receive voltage and current values for each channel in real time from the measurement unit and dynamically correct the switching period and phase interval to set the operation of all channels to be maintained in a synchronized state. By dispersing the switching timing of each channel, the ripple of the link current can be reduced, and the total harmonic distortion (THD) flowing into the power grid can be decreased. Therefore, power quality during the charging process can be improved, and the stability of the test device can be ensured.
[0087] In addition, the control unit periodically collects voltage, current, temperature, and internal resistance values from the measurement unit during the discharge process to determine whether an overvoltage, overcurrent, overtemperature, or internal resistance exceeding condition corresponding to an abnormal detection condition occurs, and if such condition occurs, the discharge operation can be stopped.
[0088] In step S340, the control unit can determine whether the test termination condition or the abnormality detection condition is satisfied based on measurement information for the battery device under test.
[0089] The control unit can independently determine whether the test termination condition and the anomaly detection condition are satisfied, respectively, based on measurement information collected in real time from the measurement unit. For example, the control unit can determine whether the test termination condition or the anomaly detection condition is satisfied based on values related to voltage, current, temperature, internal resistance, and battery status included in the measurement information.
[0090] For example, the control unit can compare the current values of the cumulative charge capacity, cumulative discharge capacity, number of cycles, and total test time with corresponding threshold values. The control unit can independently determine whether the cumulative charge capacity has reached the cumulative charge capacity threshold, whether the cumulative discharge capacity has reached the cumulative discharge capacity threshold, whether the cumulative number of cycles has reached the number of cycles threshold, and whether the elapsed total test time has reached the total test time threshold. In this case, if any of the threshold values included in the test termination condition reaches the corresponding threshold value, the control unit can determine that the test termination condition has been satisfied.
[0091] For example, the control unit can determine whether an abnormality detection condition is satisfied based on measurement information. In this case, for example, the control unit can correct the overvoltage threshold, overcurrent threshold, overtemperature threshold, and internal resistance threshold based on SOC, SOP, inter-cell voltage deviation, and temperature imbalance index, and determine whether an overvoltage state, overcurrent state, overtemperature state, and internal resistance exceeding state occur based on each of the corrected threshold values.
[0092] For example, the control unit can correct the overcurrent threshold value based on the charging state and the output capability state. The control unit can determine the corrected overcurrent threshold value by calculating the ratio of the current charging state value (SOC) to the reference charging state value (SOC_ref) and the ratio of the current output capability state value (SOP) to the reference output capability state value (SOP_ref), respectively, and multiplying the correction coefficient obtained by multiplying the two ratios by the original overcurrent threshold value. Accordingly, as the SOC decreases or the SOP decreases, the correction coefficient becomes less than 1, and as a result, the corrected overcurrent threshold value is reduced so that even a small current overload can be determined as an overcurrent.
[0093] For example, the control unit can correct the overvoltage threshold value based on the charge state and the voltage deviation between cells. The control unit can determine the corrected overvoltage threshold value by calculating a reduction factor by multiplying a first weighting factor by the ratio of the current charge state value (SOC) to the maximum charge state value (SOC_max) and subtracting it from the original overvoltage threshold value, and simultaneously calculating an additional reduction factor by multiplying a second weighting factor by the ratio of the voltage deviation value between cells (ΔV) to the reference voltage deviation value (ΔV_ref) and applying it in the same way. Here, the first weighting factor and the second weighting factor can each be set to a value greater than 0 and less than or equal to 1. At this time, they can be set so that the sum of the two factors is 1. Accordingly, as the SOC approaches the end of charging or the voltage deviation between cells expands, the corrected overvoltage threshold value is gradually lowered so that it can be determined as being greater than a small voltage rise.
[0094] For example, the control unit can correct the internal resistance threshold value based on the output capability state. The control unit calculates a correction factor by dividing the current output capability state value (SOP) by the reference output capability state value (SOP_ref), and determines the corrected internal resistance threshold value by multiplying this by the original internal resistance threshold value (R_th). Accordingly, as the output capability state deteriorates, the correction factor becomes smaller, and the corrected internal resistance threshold value is reduced so that even a small increase in internal resistance can be determined as greater than that.
[0095] For example, the control unit can determine a corrected overtemperature threshold by calculating a reduction factor by multiplying the ratio obtained by dividing the temperature imbalance index by the reference temperature imbalance index by a third weighting factor, and then subtracting this from the original overtemperature threshold. Here, the reference temperature imbalance index can be set as a reference value representing a normal imbalance range. For example, the third weighting factor can be set to a value greater than 0 and less than or equal to 1. The influence of the reduction factor may vary depending on the value set for the third weighting factor. If the third weighting factor is set to a small value such as 0.1, the effect of an increase in the temperature imbalance index on the reduction of the overtemperature threshold is limited, so the correction effect is applied gradually. Conversely, if the third weighting factor is set to a large value such as 0.9, the overtemperature threshold is reduced significantly even at the same temperature imbalance index, so the correction effect is applied sensitively. Accordingly, as the temperature difference between cells increases, the corrected overtemperature threshold decreases, and localized heating in a specific cell may be determined to be abnormal early, even if the average temperature is within the normal range.
[0096] Additionally, for example, when multiple test channels are operated in parallel, the control unit can independently determine whether test termination conditions or abnormality detection conditions are satisfied for each channel. The control unit can individually record whether the test termination conditions and abnormality detection conditions are satisfied at the channel level. The control unit aggregates the results for each channel to perform an overall judgment on the status of the entire parallel operation, and, if necessary, can decide to stop testing only specific channels or terminate the test for the entire system. The determined results are displayed on the user interface along with diagnostic information for each channel, allowing the user to simultaneously check the status of individual channels and the status of the entire system.
[0097] For example, the control unit can periodically aggregate recorded judgment results to perform comprehensive judgments categorized by channel, group, and system units. For instance, at the channel level, if a test termination condition or an anomaly detection condition is satisfied in a specific channel, the control unit can disconnect that channel from the charging and discharging circuits and stop the test. At this time, other channels remain unaffected and continue to operate, ensuring that the termination of some channels does not directly affect the continuity of overall system operation. At the group level, the control unit identifies channels sharing the same power module or cooling module as a single group and can control the entire group to terminate the test collectively if a test termination condition occurs in multiple channels within the group. This prevents risks that may arise in environments sharing common resources from spreading to adjacent channels. At the system level, the control unit can comprehensively analyze the aggregated results of all channels. For instance, if the same type of anomaly occurs simultaneously in multiple channels, the control unit can control the immediate termination of tests for all channels to ensure the safety of the entire system. Furthermore, if a certain percentage or more of the total channels are terminated due to an anomaly detection condition, it orders the termination of the entire system test rather than a partial shutdown. In addition, even if an abnormal signal occurs in a common device such as the power supply or cooling unit, all channels can be stopped simultaneously to prevent potential risks in advance.
[0098] For example, the control unit may generate a control signal to terminate the test when at least one of the test termination condition or the abnormality detection condition is satisfied. The control unit may transmit the generated control signal to the bidirectional power converter and the bidirectional DC converter to immediately stop the test operation. At this time, the control unit may display the cause of the test termination and the measurement information at that point in time on the user interface via a display.
[0099] In step S350, if at least one of the test termination condition or the abnormal detection condition is satisfied, the control unit may terminate the test by controlling the bidirectional power converter and the bidirectional DC converter.
[0100] For example, the control unit can determine a charging or discharging state and gradually reduce power delivery by stepwise decreasing the output current indication value corresponding to the operating mode of the state at regular time intervals according to a predefined damping slope. In this case, the charging current is controlled to converge to zero in the charging state, and the load-side current is stepwise reduced in the discharging state to prevent instability caused by sudden power cutoff. For example, after the current damping operation is performed, the control unit can adjust the gate signal of the bidirectional DC converter to stepwise reduce the DC link voltage to below a preset stable reference voltage value. For example, it can be determined that the stable reference has been reached when the DC link voltage drops to 5 percent or less of the rated voltage. Once the DC link voltage is maintained below the stable reference voltage value, the control unit can completely shut off the bidirectional DC converter to remove the residual voltage. Subsequently, the control unit can individually check the termination conditions for each test channel; if the condition is met only in a specific channel, it can first shut off the output relay of that channel, and if the condition is met in all channels, it can shut off all relays simultaneously.
[0101] In step S360, the control unit can determine diagnostic information for the battery device under test based on measurement information for the battery device under test.
[0102] For example, the control unit can analyze various items such as voltage, current, temperature, internal resistance, and rate of change of capacity recorded during the charging and discharging sequences to calculate judgment results regarding the remaining lifespan, potential for performance degradation, and occurrence of abnormalities of the battery device under test. This judgment can be performed based on preset reference values, threshold ranges, and empirical correlations, and if a specific item deviates from the reference value, diagnostic information including an abnormality signal for that item can be generated.
[0103] According to one embodiment, the control unit can determine diagnostic information for a battery device under test based on measurement information for the battery device under test using an artificial intelligence model. The artificial intelligence model is a quality prediction model using a neural network, and can diagnose the quality status of the battery device under test by receiving time series data acquired during the test process as input and performing data preprocessing, feature extraction, and multipath prediction operations.
[0104] For example, the control unit can generate an input data set by arranging measurement information in chronological order through a quality prediction model and performing unit conversion and scale normalization. In this case, the chronological arrangement can be consistently performed based on the sampling interval. Normalization converts values into numerical values based on a predefined reference range, thereby enabling all items to be calculated at the same scale.
[0105] For example, the control unit can quantify the time-series and item-series features of the input data set through a quality prediction model and convert them into a sequence of feature vectors for prediction operations.
[0106] For example, the control unit can determine quality information including the remaining lifespan, potential for performance degradation, and probability of anomaly occurrence of the battery device under test through a quality prediction model based on a feature vector sequence.
[0107] For example, in the process of determining quality information, the control unit can determine the contribution by time point and by item by integrating the change in predicted values along the interpolation path between the reference input data set and the actual input data set.
[0108] For example, the control unit can determine the time intervals and items for which the contribution by time and item is greater than or equal to a preset threshold as supporting information.
[0109] For example, diagnostic information may include quality information and evidence information.
[0110] For example, the remaining life can be calculated as the predicted usable period based on the cumulative number of charge and discharge cycles, temperature change patterns, internal resistance growth rate, and capacity reduction rate.
[0111] For example, the likelihood of performance degradation can be calculated based on the probability that the capacity retention rate will decrease below a critical ratio within a preset prediction period and the probability that the internal resistance will rise above a critical resistance.
[0112] For example, the probability of anomaly occurrence can be calculated based on the vector distance between the actual data and the normal operation data distribution and the prediction error distribution.
[0113] In step S370, the control unit can output measurement information and diagnostic information for the battery device under test to a user interface on a display connected to the control unit.
[0114] For example, the control unit determines measurement and diagnostic information in the form of data panels separated by item, and converts each item into visual elements such as time-axis graphs, gauge displays, and status icons to display them intuitively on the display. For instance, voltage, current, and temperature values can be displayed as waveform graphs over time, while SOC and SOP values can be output in the form of gauges indicating the current status. Additionally, the remaining lifespan can be represented as a bar chart along with the estimated number of usable cycles, and the occurrence of an anomaly can be indicated via color-coded status icons. When a user selects a specific item, the control unit provides detailed data logs and diagnostic grounds for that item in a pop-up format, thereby enabling the user to comprehensively and in detail verify test results through the user interface.
[0115] FIG. 4 is an example of an artificial intelligence model used in a system that performs charging and discharging tests of a battery device using bidirectional power conversion according to one embodiment. The embodiment of FIG. 4 can be combined with various embodiments of the present disclosure.
[0116] Referring to FIG. 4, the artificial intelligence model used in a system that performs charging and discharging tests of a battery device using bidirectional power conversion is a multipath-based deep learning model that inputs measurement information obtained during the charging and discharging test process of the battery device under test as time-series measurement data to calculate remaining lifespan, potential for performance degradation, and probability of anomaly occurrence, and can be referred to as a quality prediction model (400).
[0117] The quality prediction model (400) can be implemented as a hybrid neural network structure combining a recurrent neural network for processing time series data and a convolution-based feature extraction unit, and can produce prediction values for each purpose in parallel through an output unit including a multilayer perceptron.
[0118] For example, the quality prediction model (400) may include an input preprocessing layer (410), a feature extraction layer (420), a recurrent neural network layer (430), an integrated representation layer (440), and a multipath output layer (450).
[0119] The input preprocessing layer (410) can generate an input data set by arranging the measurement information in chronological order and performing unit conversion and scale normalization.
[0120] For example, the input preprocessing layer (410) can sort the time series input tensor in chronological order, normalize the unit for each channel, generate a missing mask for items where missing values occur, and apply linear interpolation to generate a corrected time series as an input data set. In this case, a channel refers to different measurement items collected in parallel at the same time, and can be defined as physically separated measurement item units such as voltage, current, temperature, internal resistance, charge state, outputable state, cumulative charge capacity, cumulative discharge capacity, voltage deviation between cells, and temperature imbalance index.
[0121] Specifically, for example, the input preprocessing layer (410) can arrange measurement information in chronological order according to sampling points. In this case, if the data is collected discontinuously, the input preprocessing layer (410) can reconstruct a sequence structure arranged by point according to the collection cycle. The input preprocessing layer (410) can convert each item of the measurement information according to a preset reference unit system. In this case, the input preprocessing layer (410) can apply channel-specific min-max normalization or standardization. For example, voltage can be converted to a reference voltage unit, current can be scaled to a reference current range, and temperature can be converted and then normalized according to the absolute temperature system. If data does not exist at a specific point in time or a specific channel in the measurement information, the input preprocessing layer (410) can determine the corresponding location as missing and generate a binary mask tensor indicating whether the location is missing. Based on the generated missing mask, the input preprocessing layer (410) can calculate a correction value based on linear interpolation for the missing location. At this time, the input preprocessing layer (410) may refer to the value at the time immediately before and the value at the time immediately after the missing location to calculate the correction value for the missing location. At this time, the input preprocessing layer (410) may calculate the relative time interval between the missing location and two reference time points and weight the value at the time immediately before and the value at the time immediately after according to the ratio of the interval. The correction value calculated as a result of the weighted average may be determined as the value at the time point corresponding to the missing location. The input preprocessing layer (410) may generate continuous time series data with the missing values corrected by inserting the correction value calculated in this way into the missing location of the original time series. The input preprocessing layer (410) may generate an input data set by integrating the time series data, which has been aligned, normalized, and corrected, with the corresponding missing mask. The input data set may be generated in the form of a multidimensional time series tensor.
[0122] The feature extraction layer (420) can quantify the time-by-time and item-by-item features of the input data set and convert them into a sequence of feature vectors for prediction operations.
[0123] For example, the feature extraction layer (420) can extract short-term patterns such as local voltage change rates, current change rates, temperature deviations, and internal resistance increase rates by applying a one-dimensional convolutional neural network to a normalized time series tensor. Subsequently, the feature extraction layer (420) can perform linear embedding operations to convert the input time series into a feature vector sequence, which is a common-dimensional feature representation.
[0124] Specifically, the feature extraction layer (420) can extract features regarding the rate of change of voltage, current, temperature, and internal resistance occurring in local intervals by applying a one-dimensional convolution operation to each channel of the input data set. The one-dimensional convolution operation moves along a sliding window of a preset length and can produce a feature map for each channel regarding the change pattern in the local time interval. At this time, the feature extraction layer (420) can generate a normalized feature map reflecting a non-linear pattern by sequentially applying an activation function and a normalization operation to the output of the one-dimensional convolution operation. The feature extraction layer (420) can generate a feature vector sequence by performing a linear embedding operation on the normalized feature map for each channel, converting it into a vector space of the same dimension, and arranging the converted vectors in chronological order. That is, the feature extraction layer (420) can generate a feature vector sequence that can be used for prediction operations through a convolution-based time interval pattern extraction and an embedding-based dimension integration process.
[0125] The recurrent neural network layer (430) may be a layer in which a gate-based recurrent structure and an expansion convolution-based time series structure are stacked in parallel. For example, the recurrent neural network layer (430) may receive a sequence of feature vectors as input and produce an integrated time series representation vector that simultaneously reflects short-term events and long-term trends.
[0126] For example, the recurrent neural network layer (430) can process a sequence of feature vectors in parallel paths using a bidirectional LSTM (long-short term memory) block and a TCN (temporal Convolutional Network) block.
[0127] An LSTM block is a recurrent neural network that utilizes a gate structure and may include an input gate, a forget gate, and an output gate, and may reflect rapid event changes such as charging and discharging. In particular, a bidirectional LSTM block may have a bidirectional structure in which two paths, a forward path and a reverse path, are placed in parallel, and by reflecting the flow of time from the past to the future in the forward path and the flow from the future to the past in the reverse path, a first time series representation vector can be produced that simultaneously reflects contextual information across the entire time series for a feature vector sequence.
[0128] The TCN block is a convolution-based network designed to process time-series data and may include multilayer causal convolution and dilated convolution. Causal convolution can maintain the causal structure of the time series by constraining the output time point to depend solely on that time point and past input values. Dilated convolution can reflect long-term trends and cumulative patterns by forming a wide temporal coverage area through the expansion of the sampling interval of the sliding window. Through such a multilayer convolutional structure, the TCN block can produce a second time-series representation vector that captures cumulative degradation trends while preserving long-term dependencies.
[0129] Subsequently, the recurrent neural network layer (430) can perform a merge operation by aligning the first time series representation vector of the bidirectional LSTM block and the second time series representation vector of the TCN block at the same time point. The merged output can be composed of an integrated time series representation vector that simultaneously reflects short-term events and long-term trends, and can be provided as an input to a subsequent integrated representation layer (440).
[0130] Specifically, the recurrent neural network layer (430) can decompose the feature vector sequence into time units and transmit it simultaneously to the LSTM path and the TCN path. In the LSTM path, the bidirectional LSTM block can perform a recurrent operation by combining the input vector of each time unit with the previous hidden state, adjust the degree of reflection of the hidden state according to gate control, and combine the outputs of the forward and backward paths to produce a first time series representation vector. In the TCN path, the TCN block can produce a second time series representation vector by applying causal convolution and dilation convolution to the feature vector sequence. The recurrent neural network layer (430) can generate an integrated time series representation vector by aligning the first time series representation vector and the second time series representation vector into the same time unit and performing a merge operation.
[0131] The integrated representation layer (440) may be a layer that receives the integrated time-series representation vector produced by the recurrent neural network layer (430) as input and generates a global representation vector by reflecting mutual correction between features and importance by time point. For example, the integrated representation layer (440) may include a cross-feature layer and an attention-based pooling layer.
[0132] The cross-feature layer calculates an association degree reflecting the correlation between each feature item of the integrated time-series representation vector through a cross-feature attention operation, and can generate a mutually corrected feature sequence based on this association degree. Subsequently, the attention-based pooling layer performs a pooling operation on the feature sequence that reflects the contribution of each time point, thereby generating a global representation vector by emphasizing information at high-importance time points within the entire time series and mitigating the influence of low-importance time points.
[0133] Specifically, the integrated representation layer (440) can pass the integrated time series representation vector to the cross-feature attention layer. The cross-feature attention layer can compose the integrated time series representation vector into a query vector, a key vector, and a value vector, and can calculate a similarity matrix between features through an inner product operation between the query vector and the key vector. The calculated similarity matrix is converted into a weight matrix through softmax normalization, and the weight matrix is applied to the value vector, thereby producing a mutually corrected feature sequence for each time point and item.
[0134] An attention-based pooling layer can calculate scalar contribution scores for time-series feature vectors included in a feature sequence. The contribution scores can be calculated by a set of trainable parameters, and the contribution scores calculated for all time points can be transformed through softmax normalization so that their sum is 1. The attention-based pooling layer can adjust the reflection ratio by multiplying the normalized contribution scores by each time-series feature vector. Feature vectors at time points with high contribution scores can be emphasized, while feature vectors at time points with low contribution scores can be softened. The attention-based pooling layer can generate a global representation vector by combining all time-series feature vectors with reflected contribution scores.
[0135] The multipath output layer (450) can calculate the remaining lifespan, potential for performance degradation, and probability of anomaly occurrence through parallel paths separated by prediction purpose using a global representation vector as input.
[0136] For example, the multipath output layer (450) can duplicate a single global representation vector identically and transmit it to three paths in parallel. Each path is configured to be optimized for a different objective function, and can ultimately independently calculate the remaining lifespan, potential for performance degradation, and probability of anomaly occurrence.
[0137] The first output layer can predict the remaining usable period during which the battery can maintain normal performance as the remaining lifespan. The first output layer can extract degradation-related features—such as the number of charge and discharge cycles, temperature change patterns, internal resistance growth rate, and capacity reduction rate—from the input global feature vector and convert them into a regression path that undergoes fully connected computation and non-linear activation. Through this process, the first output layer can calculate the remaining lifespan in the form of a real value. The first output layer can utilize the loss for learning by calculating the difference between the predicted remaining lifespan and the actual remaining lifespan and taking the average. The first output layer adjusts parameters in the direction that reduces the value of the calculated loss function, and the value of the loss function can be used to update regression coefficients through a gradient descent-based optimization algorithm. By repeatedly performing this process, the first output layer can be trained to calculate the remaining lifespan with increasing precision.
[0138] The second output layer can calculate the probability that the battery's performance will degrade below a threshold within a preset prediction period as the potential for performance degradation. The second output layer can extract the rate of change of capacity retention rate and the rate of increase of internal resistance—features associated with performance degradation—from the global representation vector and process them using a logistic regression-based path. By applying a sigmoid function, the second output layer can output the potential for performance degradation as a probability value between 0 and 1. The second output layer can calculate the difference between the actual potential for performance degradation and the predicted potential for performance degradation using a log-based loss function and utilize this for training. The second output layer adjusts parameters in the direction where the calculated loss function value decreases; in this case, the value of the loss function can be used to update the logistic regression coefficients through a gradient descent-based optimization algorithm. By repeatedly performing this process, the second output layer can be trained to calculate the potential for performance degradation with increasing precision.
[0139] The third output layer can establish a reference distribution of global representation vectors based on a pre-stored normal driving data set and calculate the probability of anomalies by quantitatively evaluating how much the input global representation vectors deviate from this reference distribution. The third output layer can separately accumulate the pre-stored normal driving data set during the training process and establish a reference distribution of global representation vectors from the normal driving data set. For example, the reference distribution may include a normal mean vector and a normal covariance matrix. The normal mean vector may be an arithmetic mean vector obtained by summing all global representation vectors included in the normal driving data set by the same dimension and dividing by the number of vectors. The normal covariance matrix may be a matrix calculated by reflecting the correlation between the variance and dimension of the global representation vectors included in the normal driving data set.
[0140] For example, the third output layer can calculate the Mahalanobis distance by normalizing the difference between the input global representation vector and the normal mean vector by the normal covariance matrix. The Mahalanobis distance can be a numerical value indicating how far the input global representation vector is from the center of the normal data distribution. For example, the third output layer can calculate the Mahalanobis distance by calculating a deviation vector by subtracting the value for the component of the same dimension of the normal mean vector from the input global representation vector, determining a first value by multiplying the deviation vector by the inverse of the normal covariance matrix, determining a second value by taking the inner product of the first value and the deviation vector, and applying the square root of the second value. For example, the Mahalanobis distance It can be determined as follows. Here, x is the global representation vector, μ is the normal mean vector, and (a) T represents the transpose of a inside the parentheses, and ∑ can be the normal covariance matrix.
[0141] Additionally, the third output layer can calculate the cosine distance based on the directional similarity between the input global representation vector and the normal mean vector. The cosine distance is a value that indicates how similar the input global representation vector is to the directionality of the normal data distribution, based on the angle formed by the two vectors. For example, the third output layer can calculate the unit vector by dividing the input global representation vector and the normal mean vector by their respective magnitudes, determine the cosine similarity by summing the products of the components of the same dimension of the two unit vectors, and calculate the cosine distance by subtracting the cosine similarity from 1. A cosine distance of 0 indicates the exact same direction, a value of 1 indicates perpendicularity, and a value close to 2 indicates opposite directions.
[0142] The third output layer can input a combination of Mahalanobis distance and cosine distance into a logistic regression operation and convert the result into a probability value between 0 and 1 to generate anomaly occurrence probability.
[0143] The third output layer can utilize the difference between the calculated anomaly probability and the actual label value for training by calculating it using a binary cross-entropy-based loss function. The third output layer adjusts parameters in a direction that reduces the value of the calculated loss function, and the value of the loss function can be used to update logistic regression coefficients through a gradient descent-based optimization algorithm. By repeatedly performing this process, the third output layer can be trained to calculate probability values that distinguish between normal and anomaly data with increasing precision.
[0144] Additionally, the XAI model can determine supporting information by determining time-series and item-series contributions using the actual input data set and the reference input data set of the quality prediction model. The reference input data set can be constructed by calculating representative values per channel from normal operation data and extending the vector of said representative values into a time series of the same length as the actual input. For example, if the current is close to zero and the internal resistance has a stable average value during the normal operation period, these values can be set as representative values per channel to generate the reference input.
[0145] The XAI model can establish an interpolation path between the reference input data set and the actual input data set. An interpolation path is a procedure that generates intermediate sequences that gradually change from the reference input to the actual input at regular intervals, and prepares the response of the quality prediction model to be observed at each interval. For example, the XAI model can generate a total of 32 intermediate sequences from the reference input to the actual input by evenly dividing the entire interpolation path into 32 steps based on the reference input data set and the actual input data set.
[0146] The XAI model can determine the contribution by time point and item by integrating the rate of change of predicted values along the interpolation path between the reference input data set and the actual input data set. Specifically, the XAI model calculates the rate of change of the input relative to the output of the quality prediction model at each segmentation point of the interpolation path and generates a representative rate of change map by integrating this rate of change across all segmentation points. Subsequently, the XAI model can calculate the final contribution by multiplying the generated rate of change map by the difference between the reference input and the actual input. In this way, the XAI model can quantitatively reveal the extent to which specific time points and items contribute to the prediction results by cumulatively reflecting changes in output values along the interpolation path. Specifically, the XAI model can calculate the rate of change for the output and input of the quality prediction model for each intermediate sequence of the interpolation path. The calculation of the rate of change can be performed using automatic differentiation techniques. That is, through automatic differentiation techniques, the XAI model can numerically express how sensitively the output value responds for each time point and each channel. For example, if a large rate of change is calculated at a specific point in time of an internal resistance channel, it can be interpreted to mean that that point in time and the channel are sensitive to the target output.
[0147] The XAI model generates a representative change rate map by averaging the change rates calculated across the entire path, and then calculates the raw contribution by time point and item by multiplying the difference between the actual input data set and the reference input data set by the representative change rate. In this case, the difference between the actual input data set and the reference input data set represents the result of subtracting the reference input data value from the actual input data value at each time point and each channel. Since the multiplication is applied individually to each time point and each channel, it can indicate which channel at which time point contributed to the change in the output value. For example, if the internal resistance value increased relative to the reference in a specific section during a test and the change rate averaged in a positive direction, the contribution of that section can be calculated as a positive value, indicating that it contributed to the increase in the output value.
[0148] XAI models can perform stabilization and normalization on raw contributions. Stabilization is a procedure designed to mitigate sharp oscillations through smoothing, such as time-axis moving averages, while normalization can be a procedure to reduce the difficulty of comparison caused by differences in the magnitude of values across channels. This process can be performed while preserving sign information. For example, after applying a moving average over five time points, comparability can be enhanced by scaling based on the maximum absolute value of each channel.
[0149] The XAI model can identify meaningful continuous intervals based on normalized contributions. A continuous interval refers to a time range where the absolute value of the contribution remains above a preset threshold, and it can be configured to satisfy both minimum duration and minimum average contribution conditions. Additionally, to verify whether the input value has actually changed sufficiently relative to the reference, it can check whether the average input change amount within that interval exceeds a preset minimum magnitude. For example, in an internal resistance channel, if the contribution remains high for a certain period while the average input change amount is simultaneously sufficiently large in the positive direction, that interval can be determined as a valid basis interval.
[0150] The XAI model can determine the determined interval as supporting information. For the determined interval, fields such as the target output item, channel identifier, interval start time, interval end time, interval average contribution, interval maximum contribution, interval average input variation, influence code, and output reliability can be structured and recorded. Reliability can be calculated based on the variance of output values between interpolation segments or the consistency of recalculation. For example, regarding the anomaly occurrence probability output, if a specific time interval of the internal resistance channel simultaneously demonstrates a high average contribution and a sufficient input variation, that interval can be registered as supporting information.
[0151] Additionally, the XAI model can verify the degree of agreement between the total sum of the calculated contributions and the change in the model output. This is a procedure to check whether the sum of contributions numerically matches the actual output change, and it verifies whether it falls within a predefined tolerance range. If it exceeds the tolerance, recalculation can be performed by increasing the number of interpolation steps or adjusting the stabilization settings.
[0152] Additionally, the XAI model can record confirmed evidence information in the test log and provide visualization data to the user interface. The visualization can be displayed in the form of a contribution attribution map along the time axis, and can be configured to highlight selected evidence intervals so that users can intuitively verify them. For example, key intervals of the internal resistance channel can be indicated by color intensity, and when a corresponding interval is selected, the average contribution, input change amount, influence sign, and confidence value can be displayed together.
[0153] FIG. 5 is a block showing the configuration of a server according to one embodiment. The embodiment of FIG. 5 can be combined with various embodiments of the present disclosure.
[0154] As illustrated in FIG. 5, the server (500) may include a processor (510), a communication unit (520), and a memory (530). However, not all components illustrated in FIG. 5 are essential components of the server (500). The server (500) may be implemented with more components than those illustrated in FIG. 5, or with fewer components than those illustrated in FIG. 5. For example, according to some embodiments, the server (500) may further include a user input interface (not shown), an output unit (not shown), etc., in addition to the processor (510), the communication unit (520), and the memory (530).
[0155] The processor (510) typically controls the overall operation of the server (500). The processor (510) may have one or more processors to control other components included in the server (500). For example, the processor (510) may control the communication unit (520) and the memory (530) overall by executing programs stored in the memory (530). Additionally, the processor (510) may perform the functions of the server (500) described in FIGS. 1 to 4 by executing programs stored in the memory (530).
[0156] The communication unit (520) may include one or more components that enable the server (500) to communicate with another device (not shown) and the server (not shown). The other device (not shown) may be a computing device such as the server (500) or a sensing device, but is not limited thereto. The communication unit (520) may receive user input from another electronic device or receive data stored in an external device from an external device via a network.
[0157] The memory (530) can store a program for processing and controlling the processor (510). For example, the memory (530) can store information input to the server or information received from another device via a network. Additionally, the memory (530) can store data generated by the processor (510). The memory (530) can also store information input to the server (500) or output from the server (500).
[0158] The memory (530) may include at least one type of storage medium among flash memory type, hard disk type, multimedia card micro type, card type memory (e.g., SD or XD memory, etc.), RAM (Random Access Memory), SRAM (Static Random Access Memory), ROM (Read-Only Memory), EEPROM (Electrically Erasable Programmable Read-Only Memory), PROM (Programmable Read-Only Memory), magnetic memory, magnetic disk, and optical disk.
[0159] The embodiments described above may be implemented as hardware components, software components, and / or combinations of hardware and software components. For example, the devices, methods, and components described in the embodiments may be implemented using one or more general-purpose or special-purpose computers, such as, for example, a processor, a controller, an arithmetic logic unit (ALU), a digital signal processor, a microcomputer, a field programmable gate array (FPGA), a programmable logic unit (PLU), a microprocessor, or any other device capable of executing and responding to instructions. The processing unit may execute an operating system (OS) and one or more software applications executed on said operating system. Additionally, the processing unit may access, store, manipulate, process, and generate data in response to the execution of the software. For ease of understanding, the processing unit may be described as being used as a single unit, but those skilled in the art will understand that the processing unit may include multiple processing elements and / or multiple types of processing elements. For example, the processing unit may include multiple processors or one processor and one controller. Additionally, other processing configurations, such as parallel processors, are also possible.
[0160] Software may include computer programs, code, instructions, or a combination of one or more of these, and may configure a processing unit to operate as desired or command the processing unit independently or collectively. Software and / or data may be permanently or temporarily embodied in any type of machine, component, physical device, virtual equipment, computer storage medium or device, or transmitted signal wave so as to be interpreted by the processing unit or to provide instructions or data to the processing unit. Software may be distributed over networked computer systems and may be stored or executed in a distributed manner. Software and data may be stored on one or more computer-readable recording media.
[0161] The method according to the embodiment may be implemented in the form of program instructions that can be executed through various computer means and recorded on a computer-readable medium. The computer-readable medium may include program instructions, data files, data structures, etc., either alone or in combination. The program instructions recorded on the medium may be those specifically designed and configured for the embodiment, or they may be those known and available to those skilled in the art of computer software. Examples of computer-readable recording media include magnetic media such as hard disks, floppy disks, and magnetic tapes; optical recording media such as CD-ROMs and DVDs; magneto-optical media such as floptical disks; and hardware devices specifically configured to store and execute program instructions, such as ROM, RAM, and flash memory. Examples of program instructions include machine code, such as that generated by a compiler, as well as high-level language code that can be executed by a computer using an interpreter, etc. The hardware devices described above may be configured to operate as one or more software modules to perform the operation of the embodiment, and vice versa.
[0162] Although the embodiments have been described above with reference to the limited drawings, those skilled in the art can apply various technical modifications and variations based on the above. For example, suitable results may be achieved even if the described techniques are performed in a different order than described, and / or if the components of the described system, structure, device, circuit, etc. are combined or assembled in a form different from described, or replaced or substituted by other components or equivalents.
[0163] Therefore, other implementations, other embodiments, and equivalents to the claims also fall within the scope of the claims set forth below.
Claims
Claim 1 A system for performing charging and discharging tests of a battery device comprises: an isolation transformer that isolates and transforms AC power supplied from a power grid; an AC filter connected to the output terminal of the isolation transformer to filter AC power; a bidirectional power converter including a Pulse Width Modulation (PWM) rectifier connected to the AC filter to convert AC power into DC power during charging and a PWM inverter to convert DC power into AC power during discharging; a bidirectional DC converter connected to the DC terminal of the bidirectional power converter to step down or step up DC power and perform power conversion for charging or discharging in both directions; a battery device to be tested connected to the output terminal of the bidirectional DC converter; and a measurement unit that determines measurement information including values related to voltage, current, temperature, internal resistance, and battery status for the battery device to be tested. The apparatus includes a control unit that controls the bidirectional power converter and the bidirectional DC converter based on measurement information transmitted from the measurement unit to perform a charging test, a discharging test, and power regeneration, and determines diagnostic information for the battery device under test. The control unit performs a charging test and a discharging test according to test conditions set for the battery device under test, and during the performance of the charging test and the discharging test, determines whether a test termination condition or an abnormality detection condition included in the test conditions is satisfied based on the measurement information. If at least one of the test termination condition or the abnormality detection condition is satisfied, the control unit controls the bidirectional power converter and the bidirectional DC converter to terminate the test, and outputs the measurement information and the diagnostic information to a user interface on a display connected to the control unit. The test termination condition includes a threshold value for cumulative charging capacity, a threshold value for cumulative discharging capacity, a threshold value for the number of cycles, and a threshold value for total test time. The abnormality detection condition includes an overvoltage threshold value,A system comprising an overcurrent threshold, an overtemperature threshold, and an internal resistance threshold, a voltage sensor installed at the DC terminal of the bidirectional power converter, an internal energy storage unit connected to the DC terminal of the bidirectional power converter, and an auxiliary DC converter that boosts or buckes the power between the internal energy storage unit and the DC terminal of the bidirectional power converter, wherein the control unit receives the voltage value of the DC terminal in real time from the voltage sensor while performing the charging test and the discharging test, and if the voltage value of the DC terminal is greater than or equal to a first reference voltage value, controls the auxiliary DC converter to perform charging with a current value set for the internal energy storage unit, stops the charging if the voltage value of the DC terminal decreases to a second reference voltage value or less during the charging, and if the voltage value of the DC terminal is less than or equal to a third reference voltage value, controls the auxiliary DC converter to supply power from the internal energy storage unit to the DC terminal, wherein the second reference voltage value is set to be greater than the third reference voltage value and smaller than the first reference voltage value. Claim 2 delete Claim 3 In claim 1, the control unit generates an input data set by arranging the measurement information in chronological order and performing unit conversion and scale normalization through a quality prediction model utilizing a neural network; quantifies the time-by-time and item-by-item features of the input data set through the quality prediction model and converts them into a feature vector sequence for prediction calculation; determines quality information including the remaining lifespan, potential for performance degradation, and probability of anomaly occurrence of the battery device under test through the quality prediction model based on the feature vector sequence; determines the contribution by time-by-time and item-by-item by integrating the change in predicted values along an interpolation path between a reference input data set and an actual input data set during the process of determining the quality information; determines the time interval and item where the contribution by time-by-time and item-by-item is greater than or equal to a preset threshold value as basis information; the diagnostic information includes the quality information and the basis information; the remaining lifespan is calculated as a predicted usable period based on the cumulative number of charge and discharge cycles, temperature change patterns, internal resistance increase rate, and capacity decrease rate; the potential for performance degradation is calculated based on the conversion rate of the capacity retention rate and the increase rate of internal resistance within a preset prediction period; and the probability of anomaly occurrence is based on pre-stored normal operation data A system produced through comparison with a set.
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