System and method for diagnosing internal resistance of lithium-ion batteries of kWh class or higher

Abstract

The present invention relates to an internal resistance diagnosis system for kWh-class or higher lithium-ion batteries, and more particularly to an internal resistance diagnosis system and method for diagnosing the electrical condition of kWh-class or higher lithium-ion batteries for ESS. The present invention includes an internal resistance measurement unit that measures the AC internal resistance of the battery in the frequency domain and sets a reference value for the internal resistance, a battery module state diagnosis unit that evaluates the state of a battery module using the reference value for internal resistance set by the internal resistance measurement unit and identifies an abnormal battery module by analyzing a deviation in the internal resistance value, a module replacement determination unit that determines whether to replace the battery module by performing a precise analysis using electrochemical impedance spectroscopy on battery modules classified as abnormal based on the internal resistance measurement results, and a control unit that monitors normal and abnormal battery modules using the internal resistance measurement results and battery module state diagnosis information. This makes it possible to diagnose the electrical condition of a kWh-class or higher lithium-ion battery for ESS by reflecting the structural characteristics of the kWh-class or higher lithium-ion battery, improve the reliability of internal resistance measurement through remote measurement, and ensure the protection of the user.

Description

[Technical Field]

[0001] The present invention relates to an internal resistance diagnostic system for a kWh or higher lithium ion battery, and more particularly to an internal resistance diagnostic system and method for a kWh or higher lithium ion battery for an ESS, for diagnosing the electrical condition of the battery. [Background technology]

[0002] Currently, diagnostic systems related to the use of lithium-ion battery-based ESS are being installed and operated with large capacities of over kWh through renewable energy linkages, frequency adjustment, and peak load reduction. Measuring internal resistance is one of the factors that can determine the condition of a lithium-ion battery. Methods for measuring internal resistance include the direct current (DCIR) method, the alternating current (ACIR) method, and electrochemical impedance spectroscopy (EIS). Each of the common internal resistance measurement methods has its own advantages and disadvantages. In particular, it is difficult to use any single method mentioned so far to reflect the series-parallel structural characteristics of lithium-ion batteries of kWh class or higher and ensure measurement reliability. The DC method (DCIR) calculates the internal resistance of a battery using a fixed voltage-current ratio, and is suitable for cells and modules at the battery manufacturing stage. In other words, lithium-ion battery-based ESS sites require a series-parallel battery configuration and additional equipment, such as large-capacity charging and discharging equipment with a capacity of kWh or more. Therefore, the DC method (DCIR) cannot be applied to ESSs connected to the power grid. Since the ESS equipment must be operating (charging and discharging) during DC method measurements, problems can occur with the equipment.

[0003] The AC method (ACIR) measures internal resistance mainly in a specific frequency range (1 kHz), so the measurement itself is simple, but the measurement value is not accurate enough. In the field, lithium-ion battery-based ESS capacities range from one million to ten thousand kWh, and electrical circuits vary, so analysis using simple internal resistance measurements in a specific frequency range can be inaccurate. Furthermore, traditional AC-based measurements require the operator to directly measure hundreds of modules, which takes a considerable amount of time, and there are discrepancies in the measurements depending on the operator.

[0004] Electrochemical impedance spectroscopy (EIS) is used to analyze the state of a single cell. That is, EIS is a precision analysis function, but the measurement speed is slow due to the current and voltage application method and liquid / solid component analysis function. Currently, applying EIS to large-capacity lithium-ion batteries requires separate applications (measurement jigs, power system data analysis, etc.), and related equipment is currently unavailable.

[0005] Therefore, a diagnostic system for understanding the condition of lithium-ion batteries requires an internal resistance measurement and analysis method that reflects the on-site characteristics of lithium-ion battery-based ESSs, and technology is required to ensure measurement reliability and equipment and human protection at sites where large-capacity (kWh to MWh) lithium-ion batteries are installed. [Prior art documents] [Patent documents]

[0006] [Patent Document 1] Korean Patent Publication No. 10-2022-0104949 (July 26, 2022) Summary of the Invention [Problem to be solved by the invention]

[0007] In order to solve the above-mentioned problems, the present invention aims to propose an internal resistance diagnosis system and method for a kWh class or higher lithium ion battery that diagnoses the electrical state of a kWh class or higher lithium ion battery for an ESS by reflecting the structural characteristics of the kWh class or higher lithium ion battery. [Means for solving the problem]

[0008] According to one aspect of the present invention, a system for diagnosing the internal resistance of a kWh-class or higher lithium-ion battery includes an internal resistance measurement unit that measures the AC internal resistance of the battery in a frequency domain and sets a reference value for the internal resistance; a battery module state diagnosis unit that evaluates the state of a battery module using the reference value for internal resistance set by the internal resistance measurement unit and identifies an abnormal battery module by analyzing a deviation in the internal resistance value; a module replacement determination unit that determines whether to replace the battery module by performing a precise analysis using electrochemical impedance spectroscopy on a battery module classified as an abnormal state based on the internal resistance measurement result; and a control unit that monitors normal and abnormal battery modules using the internal resistance measurement result and battery module state diagnosis information.

[0009] The control unit corrects the error rate of the instantaneous value measured through the internal resistance measuring unit, and controls the battery status to be diagnosed through the battery module status diagnosis unit based on the internal resistance change rate at a fixed period (weekly, monthly, quarterly, yearly).

[0010] The battery module status diagnosis unit performs a comparative analysis of the internal resistance change rate by period (day / month / year) and ESS information such as SOH (battery deterioration index), SOC (battery charge rate), and temperature and humidity.

[0011] According to another aspect of the present invention, a method for diagnosing the internal resistance of a kWh-class or higher lithium-ion battery includes an internal resistance measurement unit, a battery module state diagnosis unit, a module replacement determination unit, and a control unit, and includes the steps of: causing the control unit to measure the AC internal resistance of the battery in a frequency domain via the internal resistance measurement unit and set a reference value for the internal resistance; causing the control unit to evaluate the state of the battery module using the reference value for the internal resistance set in the internal resistance measurement unit via the battery module state diagnosis unit and analyze deviations in the internal resistance values ​​to identify battery modules in an abnormal state; and causing the control unit to perform a separate precise analysis using electrochemical impedance spectroscopy on battery modules classified as in an abnormal state based on the internal resistance measurement results, thereby determining whether to replace the battery modules. [Effects of the Invention]

[0012] According to the present invention, it is possible to diagnose the electrical condition of a kWh-class or higher lithium-ion battery for an ESS by reflecting the structural characteristics of the kWh-class or higher lithium-ion battery, and to improve the reliability of internal resistance measurement through remote measurement while ensuring the protection of the person making the measurement. [Brief explanation of the drawings]

[0013] [Figure 1] 1 is a block diagram of a system for diagnosing the internal resistance of a kWh-class or higher lithium-ion battery according to an embodiment of the present invention; [Figure 2] 1 is a remote measurement conceptual diagram of a system for diagnosing the internal resistance of a kWh-class or higher lithium-ion battery according to an embodiment of the present invention; [Figure 3] 1 is an overall flow chart illustrating a method for diagnosing the internal resistance of a kWh-class or higher lithium-ion battery according to an embodiment of the present invention. [Figure 4]1 is a schematic flowchart illustrating a method for diagnosing the internal resistance of a kWh-class or higher lithium-ion battery according to an embodiment of the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0014] A system for diagnosing the internal resistance of a kWh-class or higher lithium-ion battery according to one embodiment of the present invention proposes an internal resistance measurement method and analysis method that reflects the structural characteristics of a kWh-class or higher lithium-ion battery as an electrical status diagnosis technology for a kWh-class or higher lithium-ion battery for an ESS, and improves the reliability of internal resistance measurement through a remote measurement system while ensuring the protection of the person making the measurement.

[0015] According to this embodiment, the main functions of ACIR (Alternating Current Inversion Rate) and Electrochemical Impedance Spectroscopy (EIS), which are suitable for the structure of kWh large-capacity lithium-ion batteries, are combined to evaluate and diagnose the battery state using the measured internal resistance of kWh large-capacity lithium-ion batteries.

[0016] In addition, when diagnosing the battery, the error rate of the instantaneous measurement value, which is a drawback of ACIR, is corrected, and the battery condition is diagnosed based on the rate of change in internal resistance over a certain period (weekly, monthly, quarterly, yearly).

[0017] In addition, in this embodiment, by remotely automating internal resistance measurement and improving the existing method of direct measurement by a human operator, it is possible to not only prevent accidents to ESS equipment and humans through remote measurement, but also to realize a real-time battery status diagnosis function.

[0018] Hereinafter, an embodiment of the present invention will be described in detail with reference to the drawings. FIG. 1 is a configuration diagram of a system for diagnosing the internal resistance of a kWh-class or higher lithium-ion battery according to an embodiment of the present invention. As shown in FIG. 1, a system 10 for diagnosing the internal resistance of a kWh-class or higher lithium-ion battery according to an embodiment of the present invention includes an internal resistance measuring unit 110, a battery module status diagnosing unit 120, a module replacement determining unit 130, and a control unit 140.

[0019] The internal resistance measurement unit 110 measures the internal resistance based on the AC method to grasp basic information and the resonant frequency range of the lithium-ion battery at the site, and after confirming the resonant frequency range, measures again to set the reference value of the internal resistance. That is, the first internal resistance value becomes the reference value of the internal resistance at the site, and the status of each battery module is diagnosed based on this.

[0020] The AC internal resistance measurement (hereinafter referred to as "ACIR measurement") of the internal resistance measurement unit 110 according to this embodiment applies an AC current to the battery in the frequency domain and measures the voltage response to the AC current. This ACIR measurement shows the real part of the impedance value as a function of frequency and can be expressed in mΩ (milliohms).

[0021] Such frequency response analysis allows us to determine how the internal resistance of a battery changes with frequency, and the frequency response reflects the battery's electrolyte state, chemical reaction state, electrode and electrolyte structure, etc.

[0022] In this case, ACIR measurement may include errors due to contact resistance between the measurement equipment and the battery module and other external factors. It is important to obtain an accurate ACIR value by correcting the error rate of the instantaneous value measured by ACIR measurement and diagnosing the battery condition based on the rate of change of internal resistance over a certain period (weekly, monthly, quarterly, or yearly).

[0023] Next, the operation of the internal resistance measurement unit 110 according to this embodiment will be described with reference to an example. A battery module is selected and ACIR measurement is initiated. An AC current is applied to the battery module using a frequency set in the frequency domain. The battery module responds to the AC current according to the applied frequency and generates a corresponding voltage response. The internal resistance measurement unit measures and records the voltage response of the battery module, calculates the ACIR value using the measured voltage response data, and displays this value in milliohms.

[0024] The internal resistance measurement unit also performs error correction on the ACIR measurement result to obtain an accurate ACIR value, and the measured ACIR value is recorded, analyzed, and used to evaluate the internal resistance state of the battery module. The ACIR value obtained in the first measurement is set as the internal resistance reference value for the module and is used for comparison in subsequent measurements. Thereafter, the battery module status diagnosis unit compares the ACIR value with the internal resistance reference value to diagnose the status of the battery module and detect changes in the status. The ACIR measurement result and battery module status diagnosis information are sent to the control unit and reported to a system manager or operator.

[0025] The battery module status diagnosis unit 120 diagnoses the status of each battery module based on the internal resistance reference value set by the internal resistance measurement unit and classifies the status into normal and abnormal. At this time, a battery module that has a large deviation from the internal resistance reference value is classified as abnormal. In particular, a battery module that has a large deviation from the internal resistance reference value, i.e., a high resistance value, is classified as abnormal.

[0026] The operation of the battery module state diagnosis unit 120 will now be described. The internal resistance value obtained by the initial ACIR measurement in the internal resistance measurement unit 110 becomes the internal resistance reference value at the site, and this reference value is used as a standard for evaluating the state of the battery module.

[0027] The battery module status diagnosis unit 120 compares the ACIR measurement result with the internal resistance reference value to diagnose the status of each battery module. The status diagnosis is performed by comparing the internal resistance value with the reference value to determine whether there is a deviation. If the deviation between the internal resistance value and the reference value is large, the battery module is classified as abnormal. In particular, a battery module whose internal resistance value deviates greatly from the internal resistance reference value and corresponds to a high resistance value is classified as abnormal. A battery module classified as abnormal based on the status diagnosis result is reported to the control unit, and this information is used in a later step of determining whether to replace the module. The module replacement determination unit 130 determines whether to replace the battery module by performing a separate detailed analysis using the EIS on the battery module classified as abnormal by the battery module state diagnosis unit.

[0028] The operation of the module replacement decision unit 130 will now be described. The battery module status diagnosis unit 120 receives information about a battery module classified as abnormal. Such a battery module is likely to have an internal resistance value that is significantly different from the reference value or that corresponds to a high resistance value.

[0029] The module replacement determination unit 130 performs a separate, detailed analysis using EIS (Electrochemical Impedance Spectroscopy) on battery modules classified as abnormal. EIS is used to analyze the physical and electrochemical characteristics of the battery module in more detail. At this time, it determines whether to replace the module based on the EIS analysis results. In-depth information can be obtained through EIS, which helps to understand the actual status of the battery module and the cause of the problem. The module replacement determination result can be reported to the control unit, which can identify the module that needs to be replaced and coordinate the replacement work accordingly.

[0030] Next, a determination as to whether or not to replace the battery module according to this embodiment will be described. The module replacement determination unit measures impedance values ​​in a frequency range through electrochemical impedance spectroscopy, and connects electrodes and sensors to measure the battery module while taking into account the state of the electrodes and electrolyte inside the battery module.

[0031] The data obtained by measuring the initial state of the battery module then serves as a baseline for comparison with subsequent measurements. To assess the initial state of the battery module, the battery is brought to a specific state (charged state) and the initial impedance data is measured in a stopped state. Such data represents a healthy starting point for the battery module and can be considered baseline data.

[0032] Next, in relation to impedance measurement, the battery module is measured at various temperatures and voltage conditions within a specific frequency range, and the impedance measurement is mainly performed at various frequencies, and the impedance values ​​are measured within the frequency range to obtain information related to the state of the electrodes and electrolyte inside the battery, the ion migration rate, etc.

[0033] The module replacement determination unit analyzes the previously measured impedance data to determine the health state of the battery module and decide whether to replace the battery module. Changes in impedance values ​​are related to the state of the electrodes and electrolyte inside the battery, the rate of ion migration, etc. If the impedance value shows a tendency to increase over time, it may be determined that there is a possibility of damage inside the battery, which is a signal that replacement is necessary. When determining whether to replace the battery module, the unit quantifies the health state of the battery module based on the impedance data, and if the impedance is abnormally high or unstable during this process, it may decide to replace the battery module.

[0034] The control unit 140 performs centralized monitoring of battery modules that have deviations from a reference value that determines not to replace the battery module through the module replacement determination unit, and when diagnosing a battery module that has been classified as normal through the battery module status diagnosis unit, performs a comparative analysis of the internal resistance change rate by period (day / month / year) with the ESS main information such as SOH (battery deterioration index), SOC (battery conduction rate), temperature and humidity, etc. If a data deviation occurs, the battery module is reclassified as abnormal and a decision is made again on whether to replace the module through an EIS-based detailed analysis of the battery module status. The control unit of this embodiment not only prevents accidents to ESS equipment and humans through remote measurement using the existing direct measurement method by remotely automating internal resistance measurement, but also enables the real-time battery status diagnosis function.

[0035] FIG. 2 is a conceptual diagram of a remote measurement system for diagnosing the internal resistance of a kWh-class or higher lithium-ion battery according to an embodiment of the present invention. Figure 2 shows a conceptual diagram of the internal resistance remote measurement system for kWh-class or higher lithium-ion batteries, illustrating the main components and functions for measurement. The main components are the internal resistance measurement device (including the power supply device), measurement terminals (cables), and the internal resistance remote data collection device.

[0036] Here, the internal resistance measuring device and jig are basically installed in the same number as the number of modules that make up the ESS rack, and there is one remote data collecting device. However, the internal resistance remote data collecting device must have wireless communication functionality and send data to the ESS EMS server.

[0037] FIG. 3 is an overall flow chart illustrating a method for diagnosing the internal resistance of a kWh or higher lithium ion battery according to an embodiment of the present invention. As shown in Figure 3, the internal resistance measurement method and diagnosis flowchart for kWh-class or higher lithium-ion batteries first combines the main functions of the AC method and EIS. By measuring the internal resistance using the AC method, basic information about the lithium-ion battery at the site and the resonant frequency range are identified. Once the resonant frequency range is confirmed, another measurement is performed to set the reference value for internal resistance. In other words, the first internal resistance value becomes the reference value for internal resistance at the site, and the status of each battery module is diagnosed based on this value.

[0038] Here, the status diagnosis for each battery module is broadly divided into normal and abnormal states, and for this status classification, modules with significant deviation from the internal resistance reference value (especially high resistance values) are classified as abnormal, and a separate detailed analysis is performed using EIS. A precise analysis of the module status using EIS determines whether or not to replace the module. If the module is not to be replaced, centralized monitoring of the module causing the deviation is performed.

[0039] In addition, module diagnosis under normal conditions involves a comparative analysis of the rate of change in internal resistance by period (day / month / year) with the key information of the ESS, such as SOH (battery health index), SOC (battery charge rate), temperature and humidity, etc. If a data deviation occurs, it is classified as an abnormal state and a decision is made as to whether the module should be replaced through a detailed analysis of the module status based on EIS.

[0040] FIG. 4 is a schematic flowchart illustrating a method for diagnosing the internal resistance of a kWh or higher class lithium ion battery according to an embodiment of the present invention. As shown in FIG. 4, a method for diagnosing the internal resistance of a kWh or higher lithium ion battery including an internal resistance measuring unit, a battery module state diagnosing unit, a module replacement determining unit, and a control unit according to an embodiment of the present invention will be described as follows.

[0041] First, the control unit measures the AC internal resistance of the battery in the frequency domain via the internal resistance measuring unit, and sets a reference value of the internal resistance (S2).

[0042] Next, the control unit evaluates the state of the battery module using the internal resistance reference value set in the internal resistance measurement unit through the battery module state diagnosis unit, and analyzes the deviation of the internal resistance value to identify the battery module in an abnormal state (S4).

[0043] Next, the control unit performs a separate precise analysis using electrochemical impedance spectroscopy on the battery modules classified as being in an abnormal state based on the internal resistance measurement results, thereby determining whether to replace the battery modules (S6).

[0044] The system for diagnosing the internal resistance of kWh-class or higher lithium-ion batteries according to this embodiment efficiently diagnoses the status of hundreds to thousands of battery modules through diagnostic technology optimized for on-site application of lithium-ion battery-based ESSs, and prevents equipment and human injury through a remote measurement system when measuring internal resistance. In addition, the system can be applied to various ESS projects due to its versatility, which overcomes the capacity constraints of lithium-ion batteries.

[0045] In addition, the electrical condition of a kWh-class or higher lithium-ion battery for ESS can be diagnosed by reflecting the structural characteristics of the kWh-class or higher lithium-ion battery, and the reliability of internal resistance measurement can be improved through remote measurement, while ensuring the protection of the person making the measurement.

Claims

1. an internal resistance measurement unit that measures the AC internal resistance of the battery in the frequency domain and sets a reference value for the internal resistance; a battery module state diagnosis unit that evaluates the state of a battery module using the internal resistance reference value set by the internal resistance measurement unit, analyzes deviations in the internal resistance values, and identifies an abnormal battery module; a module replacement determination unit that determines whether to replace the battery module by performing a precise analysis using electrochemical impedance spectroscopy on the battery module that is classified as being in an abnormal state based on the internal resistance measurement result; A system for diagnosing the internal resistance of a lithium-ion battery of kWh class or higher, comprising: a control unit that monitors normal and abnormal battery modules using internal resistance measurement results and battery module state diagnosis information.

2. 2. The system for diagnosing the internal resistance of a lithium-ion battery of kWh class or higher as set forth in claim 1, wherein the control unit corrects an error rate of the instantaneous value measured through the internal resistance measurement unit and controls the battery state to be diagnosed through the battery module state diagnosis unit based on a rate of change in the internal resistance for a certain period (weekly, monthly, quarterly, or yearly).

3. 2. The system for diagnosing the internal resistance of a kWh-class or higher lithium-ion battery according to claim 1, wherein the battery module status diagnosis unit performs a comparative analysis of the internal resistance change rate by period (day / month / year) with ESS information such as SOH (battery deterioration index), SOC (battery charging rate), and temperature and humidity.

4. A method for diagnosing the internal resistance of a kWh or higher lithium ion battery, comprising an internal resistance measuring unit, a battery module state diagnosing unit, a module replacement determining unit, and a control unit, The control unit measures the AC internal resistance of the battery in the frequency domain via the internal resistance measurement unit, and sets a reference value of the internal resistance; the control unit evaluates the state of the battery module using the internal resistance reference value set in the internal resistance measurement unit through a battery module state diagnosis unit, and analyzes deviations in the internal resistance values ​​to identify an abnormal battery module; and determining whether to replace the battery module by performing a separate precise analysis using electrochemical impedance spectroscopy on the battery module classified as being in an abnormal state based on the internal resistance measurement result.

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