Method for diagnosing imbalance of battery pack of electric vehicle and apparatus therefor

Abstract

A method for diagnosing imbalance of a battery pack of an electric vehicle and an apparatus thereof are provided. The method includes receiving current and voltage signals corresponding to the battery pack from a battery management system (BMS), performing signal processing based on the received signals, extracting passive electrochemical impedance spectroscopy (PEIS) data based on a result of the signal processing, setting a threshold for an imbalance cell diagnosis based on the PEIS data, and performing the imbalance diagnosis of individual cells in the battery pack based on the threshold.

Description

[0001] Cross-references to related applications

[0002] This application claims the benefit of priority to Korean Patent Application No. 10-2025-0046328, filed with the Korean Intellectual Property Office on April 9, 2025, the entire contents of which are incorporated herein by reference. Technical Field

[0003] This disclosure relates to battery diagnostic techniques for electric vehicles, and more specifically to techniques for deriving passive electrochemical impedance spectroscopy (PEIS) data and diagnosing battery pack imbalances via signal processing of raw voltage and current signals from the battery pack of an electric vehicle. Background Technology

[0004] The descriptions in this background section are intended only to enhance the understanding of the background of this disclosure and should not be construed as an admission that they correspond to prior art known to those skilled in the art.

[0005] Imbalance between battery cells in the battery module of an electric vehicle can be one of the main problems, leading to degradation of battery performance and safety issues.

[0006] When battery cells are unbalanced, the total capacity and energy density of the battery pack decrease. This leads to a reduction in the range of electric vehicles and a deterioration in battery performance.

[0007] Furthermore, imbalances in the behavior of individual battery cells within a battery pack can lead to overcharging or over-discharging of specific cells. This can be a major factor in battery fires and damage to durability.

[0008] Therefore, battery pack imbalance diagnostic technology is used for the performance and safety of electric vehicles.

[0009] DC-based battery pack imbalance diagnostic algorithms may lack a standard for clearly identifying battery pack imbalance and may simply perform the diagnosis based on voltage and resistance deviations to obtain deviation information between battery cells only at the corresponding specific time.

[0010] A more accurate and reliable battery pack imbalance diagnostic technique was developed, incorporating new factors that can represent electrochemical states as well as voltage and resistance deviations.

[0011] Electrochemical impedance spectroscopy (EIS) diagnostic technology may have limitations in its application to actual electric vehicles (EVs) due to hardware specification limitations and the need for separate equipment to generate and measure EIS signals.

[0012] Because EIS, which facilitates electrochemical measurements of batteries, requires data acquisition across several frequency bands, it can take a considerable amount of time. The time consumption of EIS can cause problems in situations where real-time monitoring is required.

[0013] When using AC data via BMS input signal processing, robustness and reliability of imbalance diagnosis can be ensured.

[0014] In particular, because the battery pack imbalance diagnosis method using AC data can obtain deviation information in the frequency domain at a single point in time, it can ensure higher reliability than the DC solution. Summary of the Invention

[0015] This disclosure has been made to address the aforementioned issues.

[0016] According to this disclosure, a method performed by a device in a vehicle may include: acquiring current and voltage signals associated with a battery pack of the vehicle via one or more sensors; processing the acquired current and voltage signals; extracting passive electrochemical impedance spectroscopy (PEIS) data based on the processing of the acquired current and voltage signals; setting a threshold for unbalanced battery cell diagnosis based on the PEIS data; performing unbalanced battery cell diagnosis on individual battery cells in the battery pack based on the threshold; and outputting a signal indicating the result of the unbalanced battery cell diagnosis on each battery cell.

[0017] The method may further include: determining a lower threshold by subtracting a value obtained by multiplying the median absolute deviation (MAD) by a constant from the median impedance value, and determining an upper threshold by adding the median impedance value to a value obtained by multiplying the MAD by the constant.

[0018] In this method, outputting the signal may include generating a warning message based on at least one unbalanced battery cell detected as a result of unbalanced battery cell diagnosis.

[0019] In this method, the warning message may include: group identification information corresponding to a group containing at least one unbalanced battery cell, and battery cell identification information corresponding to at least one unbalanced battery cell.

[0020] According to this disclosure, a device for a vehicle may include: a processor; and a memory storing at least one instruction, wherein the at least one instruction, when executed by the processor communicating with the memory, is configured to cause the device to: acquire current and voltage signals associated with a battery pack of the vehicle via one or more sensors; process the acquired current and voltage signals; extract passive electrochemical impedance spectroscopy (PEIS) data based on the processing of the acquired current and voltage signals; set a threshold for unbalanced battery diagnosis based on the PEIS data; perform unbalanced battery cell diagnosis on individual battery cells in the battery pack based on the threshold; and output a signal indicating the result of the unbalanced battery cell diagnosis on each battery cell.

[0021] At least one instruction, when executed by a processor communicating with the memory, can be configured to cause the device to: perform a discrete wavelet transform by removing noise from at least one of the obtained current and voltage signals and decomposing the noise-removed signal for each frequency level to obtain a time-domain signal, and perform a short-time Fourier transform by segmenting the time-domain signal into a frequency-domain signal.

[0022] The PEIS data may include AC impedance data for each frequency point in the PEIS data, and at least one instruction, when executed by a processor communicating with the memory, may be configured to cause the device to: determine the threshold based on the real impedance data in the AC impedance data using statistical techniques.

[0023] At least one instruction, when executed by a processor communicating with memory, can be configured to cause the device to: derive, via statistical techniques, the median absolute deviation (MAD) as the median value of absolute differences, wherein each absolute difference is the difference between the impedance value of a corresponding battery cell in a battery cell group in a battery pack and the median impedance value of all battery cells in the battery cell group.

[0024] The threshold may include a lower threshold and an upper threshold, and at least one instruction, when executed by a processor communicating with the memory, may be configured to cause the device to determine the lower threshold and the upper threshold based on the median impedance value and MAD.

[0025] It can export a median impedance value for each frequency point in the PEIS data, and it can also export an absolute difference and a corresponding MAD for each battery cell for each frequency point in the PEIS data.

[0026] The median impedance value can be derived as the median of the real impedance data of the battery cells in the group at each frequency point in the PEIS data.

[0027] According to this disclosure, a vehicle may include: a battery pack; a processor; and a memory storing at least one instruction, which, when executed by the processor communicating with the memory, is configured to cause the vehicle to: receive voltage and current signals associated with a plurality of battery cells in the battery pack; process the voltage and current signals to remove noise; and derive frequency domain data associated with the plurality of battery cells; obtain passive battery cell impedance data based on the frequency domain data, wherein the passive battery cell impedance data includes real impedance values ​​of the plurality of battery cells; determine the median of the real impedance values ​​and the median of the absolute deviations of the real impedance values; set a threshold range for identifying at least one battery cell among the plurality of battery cells as unbalanced based on the median of the real impedance values ​​and the median of the absolute deviations; identify at least one battery cell as an unbalanced battery cell based on the real impedance value of at least one battery cell being outside the threshold range; and output a warning signal indicating information about the unbalanced battery cell.

[0028] At least one instruction, when executed by a processor communicating with memory, can be configured to cause the vehicle to determine a threshold range by the following steps: determining an upper threshold by adding the median of the real impedance value to a value obtained by multiplying the median of the absolute deviation by a constant, and determining a lower threshold by subtracting the value obtained by multiplying the median of the absolute deviation by a constant from the median of the real impedance value.

[0029] At least one instruction, when executed by a processor communicating with the memory, can be configured to include in the warning signal: group identification information corresponding to the battery cell group containing the unbalanced battery cell, and battery cell identification information corresponding to the unbalanced battery cell. Attached Figure Description

[0030] The above and other objects, features, and advantages of this disclosure will become more apparent from the following detailed description taken in conjunction with the accompanying drawings:

[0031] Figure 1 An example configuration of a battery pack imbalance diagnostic system is shown;

[0032] Figure 2 An example of the entire battery pack imbalance diagnosis process is shown;

[0033] Figure 3 An example of a detailed configuration of a battery pack imbalance diagnostic device is shown;

[0034] Figure 4 An example of a method for diagnosing imbalance on a cell-by-cell basis in a battery pack imbalance diagnostic device is shown, by applying a statistical technique based on the real impedance of the extracted individual battery cell PEIS data and setting a threshold.

[0035] Figure 5 An example of a method for setting a threshold based on median absolute deviation (MAD) is shown;

[0036] Figure 6 An example of a method for diagnosing battery pack imbalance is shown; and

[0037] Figure 7 An example of a computing system is shown. Detailed Implementation

[0038] In the following, some embodiments of the present disclosure are described in detail with reference to the accompanying drawings. When adding reference numerals to components in the various drawings, it should be noted that the same components include the same reference numerals, even though they are shown in another drawing. In describing embodiments of the present disclosure, detailed descriptions associated with well-known functions or configurations are omitted where such omissions may unnecessarily obscure the subject matter of the disclosure.

[0039] In describing components according to embodiments of this disclosure, terms such as first, second, "A", "B", (a), (b), etc., may be used. These terms are intended only to distinguish one component from another, and they do not limit the nature, sequence, or order of the respective components. Furthermore, unless otherwise defined, all terms used herein, including technical and scientific terms, have the same meaning as commonly understood by one of ordinary skill in the art to which this disclosure pertains. Terms as defined in commonly used dictionaries should be interpreted as having the meaning equivalent to their meaning in the context of the relevant field and should not be interpreted as having an ideal or overly formal meaning unless explicitly defined as having such an ideal or overly formal meaning in this application.

[0040] For the purposes of this application and claims, the exemplary phrases “at least one: A; B; or C” or “at least one of A, B, or C” are used, which means “at least one A, or at least one B, or at least one C, or any combination of at least one A, at least one B, and at least one C.” Furthermore, as used herein, exemplary phrases such as “A, B, or C,” “at least one of A, B, and C,” “at least one of A, B, or C,” etc., may represent each listed item or all possible combinations of listed items. For example, “at least one of A or B” may refer to (1) at least one A; (2) at least one B; or (3) at least one A and at least one B.

[0041] As used in this specification, the terms "module" or "unit" refer to software and / or hardware components, and a "module" or "unit" performs certain operations / functions / roles. However, a "module" or "unit" is not to be construed as limited to software or hardware. A "module" or "unit" may be configured to reside in addressable storage media or to execute on one or more processors. Thus, by way of example, a "module" or "unit" may include at least one of the following: software components, object-oriented software components, class components, and task components; processes, functions, attributes, programs, subroutines, program code segments, drivers, firmware, microcode, circuits, data, databases, data structures, tables, arrays, or variables. The functionality provided in a component, "module," or "unit" may be combined into a smaller number of components, "modules," or "units," or further divided into additional components, "modules," or "units."

[0042] In this disclosure, a “module” or “unit” can be implemented as a processor and a memory. “Processor” should be broadly interpreted to include general-purpose processors, central processing units (CPUs), microprocessors, digital signal processors (DSPs), microcontrollers, state machines, etc. In some contexts, “processor” can refer to application-specific integrated circuits (ASICs), programmable logic devices (PLDs), or field-programmable gate arrays (FPGAs). For example, “processor” can refer to a combination of processing devices, such as a combination of a DSP and a microprocessor, a combination of multiple microprocessors, a combination of one or more microprocessors combined with a DSP core, or any other such combination. Furthermore, “memory” should be broadly interpreted to include any electronic component capable of storing electronic information. “Memory” can refer to various types of processor-readable media, such as random access memory (RAM), read-only memory (ROM), non-volatile random access memory (NVRAM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), flash memory, magnetic or optical data storage devices, and registers. The memory can be in a state of electronic communication with the processor when the processor can read information from the memory and / or record information in the memory. The memory integrated into the processor is in a state of electronic communication with the processor.

[0043] One or more features described herein can be provided as a computer program stored in a computer-readable recording medium for execution on a computer. This medium can persistently store a computer-executable program or temporarily store a program for execution or download. Furthermore, the medium can be a variety of recording or storage instruments in the form of a single hardware device or multiple combined hardware devices, and is not limited to media directly connected to some computer systems, but can also be distributed across a network. Examples of such media include magnetic media such as hard disks, floppy disks, or magnetic tapes; optical recording media such as CD-ROMs or DVDs; magneto-optical media such as floppy disks; and ROMs, RAMs, or flash memory configured to store program instructions. Additional examples of such media include media or storage media managed by application stores that distribute applications or by different other sites or servers that provide or distribute software.

[0044] In a hardware implementation, the processing unit for performing these techniques may be implemented in one or more ASICs, DSPs, digital signal processing devices, programmable logic devices, field-programmable gate arrays, processors, controllers, microcontrollers, microprocessors, electronic devices, or computers or combinations thereof, designed to perform the functions described in this disclosure.

[0045] According to the Society of Automotive Engineers (SAE), the automation levels of autonomous vehicles can be classified as follows: At Level 0, the SAE classification corresponds to "No Automation," where the autonomous driving system intervenes only temporarily in emergency situations (e.g., automatic emergency braking) and / or only provides warnings (e.g., blind spot warning, lane departure warning, etc.), and expects the driver to operate the vehicle. At Level 1, the SAE classification corresponds to "Driver Assistance," where the system performs some driving functions (e.g., steering, acceleration, braking, lane centering, adaptive cruise control, etc.) when the driver operates the vehicle in normal operating conditions, and expects the driver to determine the system's operating status and / or timing, perform other driving functions, and handle (e.g., resolve) emergency situations. At Level 2, the SAE classification corresponds to "Partial Automation," where the system performs steering, acceleration, and / or braking under driver supervision, and expects the driver to determine the system's operating status and / or timing, perform other driving functions, and handle (e.g., resolve) emergency situations. At Level 3 of Automated Driving, the SAE classification standard can correspond to "Conditional Automation," where the system drives the vehicle under limited conditions (e.g., performing driving functions such as steering, acceleration, and / or braking), but when the required conditions are not met, it transfers driving control to the driver, expecting the driver to determine the system's operating state and / or timing, and to take over control in emergency situations without performing other vehicle operations (e.g., steering, acceleration, and / or braking). At Level 4 of Automated Driving, the SAE classification standard can correspond to "High Automation," where the system performs all driving functions, and expects the driver to control the vehicle only in emergency situations. At Level 5 of Automated Driving, the SAE classification standard can correspond to "Full Automation," where the system performs all driving functions without any assistance from the driver, including in emergency situations, and expects the driver to perform any driving functions other than determining the system's operating state. While this disclosure applies the SAE classification standard to the classification of automated driving, other classification methods and / or algorithms can be used in one or more configurations described herein.

[0046] One or more features associated with autonomous driving control can be activated based on configured autonomous driving control settings (e.g., based on at least one of autonomous driving classification, selection of the vehicle's autonomous driving level, etc.). Based on one or more features described herein (e.g., features for imbalance diagnosis in the battery pack using statistical thresholds based on PEIS), vehicle operation can be controlled. Vehicle control can include various operational controls associated with the vehicle (e.g., autonomous driving control, sensor control, braking control, braking time control, acceleration control, acceleration rate of change control, warning time control, forward collision warning time control, etc.).

[0047] For example, based on one or more features described herein (e.g., features for imbalance diagnosis in a battery pack using statistical thresholds based on PEIS), one or more auxiliary devices (e.g., engine braking, exhaust braking, hydraulic decelerator, electric decelerator, regenerative braking, etc.) can also be controlled.

[0048] For example, based on one or more features described herein (e.g., features for imbalance diagnosis in a battery pack using statistical thresholds based on PEIS), one or more communication devices (e.g., modems, network adapters, radio transceivers, antennas, etc., capable of communicating via one or more wired or wireless communication protocols such as Ethernet, Wi-Fi, Near Field Communication (NFC), Bluetooth, Long Term Evolution (LTE), 5G New Radio (NR), Vehicle to Everything (V2X), etc.) can also be controlled.

[0049] For example, based on one or more features described herein (e.g., features for imbalance diagnosis in a battery pack using PEIS-based statistical thresholds), minimum risk maneuvering (MRM) operations can also be controlled. Minimum risk maneuvering operations (e.g., minimum risk maneuvering, lowest risk maneuvering) can be vehicle maneuvers designed to minimize (e.g., reduce) the risk of collisions with surrounding vehicles to achieve a reduced (e.g., minimum) risk state. Minimum risk maneuvering can be an operation activated during autonomous driving when the driver is unable to respond to an intervention request. During minimum risk maneuvering, one or more processors in the vehicle can control the duration of the vehicle's driving operations.

[0050] For example, biased driving operations can also be controlled based on one or more features described herein (e.g., features for imbalance diagnosis in a battery pack using PEIS-based statistical thresholds). A driving control unit can perform biased driving control. To perform biased driving, the driving control unit can control the vehicle to travel within the lane by maintaining a lateral distance between the vehicle's center position and the lane center. For example, the driving control unit can control the vehicle to remain within the lane but not in the lane center. The driving control unit can identify or determine a target lateral distance for biased driving control. For example, the target lateral distance may include an intentionally adjusted lateral distance that the vehicle can maintain from a reference point (such as the center of the lane or another vehicle) during maneuvers such as lane changes. This adjustment can be made to improve the vehicle's stability, safety, and / or performance under changing driving conditions. For example, during lane changes, the driving control system can bias the lateral distance to maintain a safer clearance from adjacent vehicles, taking into account factors such as vehicle speed, road conditions, and / or the presence of obstacles.

[0051] For example, based on one or more features described herein (e.g., features for imbalance diagnosis in a battery pack using PEIS-based statistical thresholds), one or more sensors (e.g., IMU sensors, cameras, LiDAR, RADAR, blind spot monitoring sensors, lane departure warning sensors, parking sensors, light sensors, rain sensors, traction control sensors, anti-lock braking system sensors, tire pressure monitoring sensors, seat belt sensors, airbag sensors, fuel sensors, emission sensors, throttle position sensors, inverters, converters, motor controllers, power distribution units, high-voltage wiring and connectors, auxiliary power modules, charging interfaces, etc.) can also be controlled. Operational control for autonomous driving of a vehicle can include various driving controls of the vehicle by the vehicle control unit (e.g., acceleration, deceleration, steering control, gear shifting control, braking system control, traction control, stability control, cruise control, lane keeping assist control, collision avoidance system control, emergency braking assist control, traffic sign recognition control, adaptive headlight control, etc.).

[0052] Based on one or more features described herein (e.g., features for imbalance diagnosis in a battery pack using statistical thresholds based on PEIS), autonomous driving activation / deactivation can be controlled. Specifically, the diagnostic control device can determine the imbalance state of the battery pack based on PEIS data (e.g., the module median and median absolute deviation (MAD) thresholds using real impedance at multiple frequency points) and control autonomous driving accordingly. When the imbalance state is normal (e.g., all battery cells are within the upper / lower impedance thresholds), autonomous driving can be enabled. When the imbalance state is warning (e.g., one or more battery cells slightly exceed the threshold at a limited number of frequency points or for a very short time window), autonomous driving can be limited (e.g., speed / acceleration limits, route constraints, or a reduced feature set), and a driver takeover request can be prepared. When the imbalance state is critical (e.g., battery cells span several frequency points or samples exceed the upper / lower thresholds of a set multiple of MAD), autonomous driving can be deactivated and minimal-risk maneuvers can be initiated (e.g., controlled deceleration, lane keeping towards the shoulder, or safe stop), while issuing warnings including module and battery cell identification information.

[0053] This disclosure relates to a technique for diagnosing imbalances in battery packs of electric vehicles. In this technique, discrete wavelet transform (DWT) is used to process voltage and current signals from a battery management system (BMS) to remove noise and decompose the signals into frequency levels, and short-time Fourier transform (STFT) is used to convert the time-domain signals to frequency-domain signals. Based on the processed data, passive battery cell impedance data is obtained for each battery cell without the need for a separate impedance spectroscopy device. Statistical methods, such as median absolute deviation (MAD) are used to analyze the real impedance values ​​from the passive battery cell impedance data to determine upper and lower threshold values ​​for each frequency point in the passive battery cell impedance data. A frequency point can be a specific frequency value. Individual battery cells with impedance values ​​outside the determined thresholds can be classified as unbalanced battery cells. When an unbalanced battery cell is detected, a warning message identifying the corresponding module (e.g., a group of battery cells containing the unbalanced battery cell) and the battery cell can be generated and output to a vehicle display or alarm system. This method enables real-time, cost-effective, and reliable imbalance diagnosis while also providing information about battery health and safety.

[0054] In the following text, reference will be made to Figures 1 to 7 Examples of this disclosure are described in detail.

[0055] Figure 1 An example configuration of an electric vehicle battery diagnostic system according to an embodiment of the present disclosure is shown.

[0056] refer to Figure 1 The electric vehicle battery diagnostic system 100 can be generally configured to include a battery pack 10, a battery management system (BMS) 20, and a battery pack imbalance diagnostic device 30 (e.g., implemented as a dedicated hardware device, an integrated control unit, or a software module running on an existing ECU).

[0057] The battery pack 10 can be the final form of a battery located in an electric vehicle. The battery can be formed by bonding several modules (e.g., front-mounted modules, rear-mounted modules, or under-floor modules). Each module can be configured to include several battery cells (e.g., cylindrical, prismatic, or pouch-type battery cells).

[0058] A module (e.g., a bundle of battery cells bundled together) is a battery component formed by bundling together a number of battery cells and inserting them into a frame to protect them from external shocks, heat, vibration, moisture, dust or other environmental factors (e.g., road debris, humidity or temperature cycling).

[0059] A battery cell can be the basic unit of a battery that can use electrical energy for charging / discharging, and can be realized by inserting a cathode, anode, separator, and electrolyte into an aluminum casing (or other suitable casing material, such as steel or laminated polymer).

[0060] BMS 20 can manage parameters of the battery module, such as temperature, voltage, and current (e.g., by controlling cooling fans, pumps, or heaters, and adjusting charging or discharging rates).

[0061] The BMS 20 according to the embodiment can be installed in the battery pack 10. Various control and protection systems, such as cooling devices (e.g., liquid cooling plates, air ducts, or refrigerant-based systems), can be installed in the battery pack 10.

[0062] When a voltage imbalance is detected between battery cells, the BMS 20 according to the embodiment can detect the voltage difference between battery cells and can perform battery cell balancing (e.g., passive balancing using resistors or active balancing using inductors or capacitors).

[0063] Typically, when the voltage difference between battery cells is not maintained uniformly, overload is more likely to be applied to a particular battery cell, thereby increasing the risk of fire. Therefore, accurate and rapid detection of voltage imbalance between battery cells is important for ensuring the safety of electric vehicles (e.g., preventing thermal runaway, avoiding reduced driving range, or extending battery life).

[0064] The BMS 20 can monitor the voltage and current of each battery cell in the battery pack 10 during vehicle operation, and can generate voltage and current signals and transmit them to the battery pack imbalance diagnostic device 30 for real-time or periodic analysis.

[0065] The battery pack imbalance diagnostic device 30 can perform signal processing on the voltage and current signals received from the BMS 20 to derive passive electrochemical impedance spectroscopy (PEIS) data for each battery cell. Here, the voltage and current signals received from the BMS 20 can be direct current (DC) signals, and the signal processing can include discrete wavelet transform (DWT) and short-time Fourier transform (STFT).

[0066] The PEIS according to this disclosure has the advantage of being able to extract EIS data at the battery cell level via vehicle BMS voltage and / or current signal processing, without the need for a separate EIS device (e.g., a laboratory-grade frequency response analyzer).

[0067] DWT is a transform used to decompose a given signal into several sets. Here, each set is a time series of coefficients describing the time variation of the signal in the corresponding frequency band. DWT is a mathematical process used to quantify how much energy is included in a specific frequency band at a specific time in a signal (e.g., detecting noise spikes, transient events, or gradual drift).

[0068] In other words, DWT can be implemented as logic to remove noise included in the voltage and current signals input from the BSM 20 and to decompose the data for each frequency level to extract the time-domain signal for subsequent analysis.

[0069] STFT is one of the Fourier transforms used to transform time-domain signals into frequency-domain signals. As the signal changes over time, STFT can be used to determine the sinusoidal frequency and phase components of local portions of the signal. Specifically, STFT can be implemented as logic for dividing a long time-domain signal into shorter segments (each segment having the same length) and performing a Fourier transform separately for each short segment (e.g., enabling local frequency analysis during vehicle acceleration, braking, or charging events).

[0070] The battery pack imbalance diagnostic device 30 can derive PEIS data for each battery cell in the aforementioned signal processing module (e.g., battery cell group), and can apply the median absolute deviation (MAD) as a statistical technique to the derived PEIS data to set a threshold for imbalance diagnosis for each battery cell. Reference will be made below to the description below. Figure 5 Describe in detail the method for setting thresholds based on MAD.

[0071] According to the embodiment, the battery pack imbalance diagnostic device 30 can apply only the real impedance component in the derived PEIS data to MAD. This is because the real component represents the resistance component that varies according to the frequency domain (e.g., related to ohmic resistance, electrolyte conductivity, or contact resistance).

[0072] The battery pack imbalance diagnostic device 30 can compare the real impedance of a battery cell with a threshold to detect and diagnose unbalanced battery cells.

[0073] According to one embodiment, the battery pack imbalance diagnostic device 30 can be installed in the battery pack 10. According to another embodiment, the battery pack imbalance diagnostic device 30 can be integrated with the BMS 20.

[0074] Figure 2 An example of a battery pack imbalance diagnosis process according to an embodiment of this disclosure is shown.

[0075] refer to Figure 2The battery pack imbalance diagnosis process may include: a BMS voltage and / or current raw signal reception step S210, a signal processing step S220 (e.g., filtering noise, separating frequency bands, or converting the signal into a usable analysis format), a PEIS data extraction step S230 (e.g., deriving the real impedance component and virtual impedance component of each battery cell at multiple frequencies), a threshold setting step S240 (e.g., determining an upper impedance threshold and a lower impedance threshold based on the median and median absolute deviation values ​​for battery cells in the same module), and an unbalanced battery cell diagnosis step S250 (e.g., marking the battery cell as unbalanced if the impedance of the battery cell exceeds the threshold at one or more frequencies).

[0076] In the BMS voltage and / or current raw signal receiving step S210, the battery pack imbalance diagnostic device 30 can receive the voltage and current raw signals from the BMS 20. Here, the voltage and current raw signals can be DC signals (e.g., sampled at 10Hz, 100Hz, 1kHz, or other rates).

[0077] In signal processing step S220, the battery pack imbalance diagnostic device 30 may perform discrete wavelet transform (DWT) (e.g., using Daubechies wavelet, Symlet wavelet, or Coiflet wavelet) to remove noise components (e.g., noise caused by inverter switching, electromagnetic interference, or sensor errors) included in the received original signal and decompose the data for each frequency level (e.g., low frequency band, mid frequency band, or high frequency band) to extract the time-domain signal.

[0078] Furthermore, in signal processing step S220, the battery pack imbalance diagnostic device 30 can perform a short-time Fourier transform (STFT) (e.g., using a Hamming window or Hann window) to extract AC impedance data for each frequency (e.g., 1 Hz, 10 Hz, or 100 Hz) based on the DC voltage and current signal data of each battery cell in the battery pack 10 by transforming the time-domain signal into a frequency-domain signal.

[0079] In the PEIS data extraction step S230, the battery pack imbalance diagnostic device 30 can extract PEIS data for each individual battery cell frequency point in the battery pack 10 (e.g., impedance values ​​at 0.1 Hz, 1 Hz, and 10 Hz for a given battery cell). Here, the number of individual battery cell frequency points can be multiple (e.g., multiple discrete measurement frequencies for each battery cell). As an example, the number of individual battery cell frequency points can be, but is not limited to, a total of 20 (e.g., 10, 20, 40, or 100 points, etc.). The PEIS data according to the embodiment can be extracted for each battery cell in the module and can include real impedance data and imaginary impedance data.

[0080] In the threshold setting step S240, the battery pack imbalance diagnostic device 30 can apply statistical techniques to set a threshold based on real impedance data (e.g., resistance values ​​at specific frequencies such as 0.1 Hz, 1 Hz, or 10 Hz) extracted from the PEIS data for each individual battery cell. As an example, the median absolute deviation (MAD) can be applied to the statistical techniques (e.g., calculating the median impedance value for the battery cells in the module and determining the deviation from the median to define an upper and lower threshold). The threshold for imbalanced battery cell diagnostics can be determined based on the module median and MAD, and can include an upper and lower threshold (e.g., calculated as module median ± K × MAD, where K is a constant, etc.).

[0081] As in the following equation, MAD according to the implementation can be defined as the median value of the absolute difference, where the i-th absolute difference is the impedance value X of the individual i-th battery cell in the X-th module. i The difference between the median impedance (median(x)) of all battery cells in the Xth module and the median impedance of the Xth module.

[0082]

[0083] For example, if the module contains battery cells with impedance values ​​of 5.1 mΩ, 5.3 mΩ, 5.5 mΩ, and 6.0 mΩ, the median (x) would be 5.4 mΩ, and the absolute deviations would be 0.3 mΩ, 0.1 mΩ, 0.1 mΩ, and 0.6 mΩ, respectively. The MAD is then calculated as the median of these deviations (e.g., 0.2 mΩ in this case).

[0084] The battery pack imbalance diagnostic device 30 can calculate the module median based on the estimated impedance of each battery cell in the battery pack 10 for each frequency point. Here, a module median can be derived for each frequency point.

[0085] The battery pack imbalance diagnostic device 30 can calculate the absolute deviation between the calculated module median and the impedance of each battery cell in the module at each frequency point (e.g., the difference between the 1 Hz module median and the 1 Hz impedance value of each battery cell in the module). Here, an absolute deviation can be derived for each battery cell at each frequency point.

[0086] The battery pack imbalance diagnostic device 30 can calculate the MAD based on the median of the absolute deviations (e.g., summing all the absolute deviations of battery cells in the same module at a given frequency and determining their median). Here, one MAD can be derived for each battery cell per frequency point.

[0087] Lower threshold Threshold and Upper Threshold Threshold It can be determined by the following equation.

[0088] Lower Threshold = Module median - 3 × MAD

[0089] Upper Threshold = Module median + 3 × MAD

[0090] According to the embodiment, the battery pack imbalance diagnostic device 30 can apply MAD based on the real part impedance data (e.g., values ​​measured at low frequencies such as 0.1 Hz, mid frequencies such as 10 Hz, or high frequencies such as 1 kHz) of the PEIS data extracted for each battery cell in the battery pack 10 at each frequency point.

[0091] In the unbalanced battery cell diagnostic step 250, the battery pack unbalanced diagnostic device 30 can classify the impedance data of a given battery cell that deviates from the set upper / lower threshold (e.g., exceeding the upper limit by more than 15%, falling below the lower limit by more than 10%, or exceeding the absolute impedance limit (such as 50mΩ)) as abnormal data and can diagnose the battery cell as an unbalanced battery cell.

[0092] Figure 3 An example of a detailed configuration of a battery pack imbalance diagnostic device according to an embodiment of the present disclosure is shown.

[0093] refer to Figure 3 The battery pack imbalance diagnostic device 30 can be configured to include: a BMS signal receiving device 310, a signal processing device 320, a PEIS data extraction device 330, a threshold setting device 340, an imbalance battery cell diagnostic device 350, and an output device 360 ​​(e.g., a display unit, a wireless transmitter, a data logger, or a vehicle control interface).

[0094] BMS signal receiving device 310 can receive raw voltage and current signals from BMS 20. Here, the raw voltage and current signals can be DC signals (e.g., a steady-state group voltage of 350 V, a continuous current draw of 50 A, or other non-AC measurement signals).

[0095] The signal processing device 320 can perform discrete wavelet transform (DWT) to remove noise components (e.g., electromagnetic interference, sensor drift, or random fluctuations) included in the received raw signal and decompose the data for each frequency level to extract the time-domain signal. It can also perform short-time Fourier transform (STFT) to extract AC impedance data for each frequency based on the DC voltage and current signal data of the individual battery cells in the battery pack 10 by transforming the time-domain signal into a frequency-domain signal (e.g., converting transient voltage spikes into corresponding frequency peaks).

[0096] The PEIS data extraction device 330 can extract PEIS data for each individual battery cell frequency point in the battery pack 10. Here, the number of individual battery cell frequency points can be multiple. As an example, the number of individual battery cell frequency points can be, but is not limited to, a total of 20 (e.g., frequency points at 0.01 Hz, 0.1 Hz, 1 Hz, or 10 Hz, etc.). According to the embodiment, the PEIS data can be extracted for each battery cell in the module and can include real impedance data and imaginary impedance data (e.g., representing the resistive component and the reactive component, respectively).

[0097] The threshold setting device 340 can apply statistical techniques to set a threshold based on the real impedance data in the PEIS data extracted for each individual battery cell. As an example, the median absolute deviation (MAD) can be applied to the statistical technique. The threshold for unbalanced battery cell diagnosis can be determined based on the module median and MAD, and can include an upper threshold and a lower threshold (e.g., ±15% of the module median impedance value, etc.).

[0098] As shown in the following equation, MAD according to the implementation can be defined as the impedance value X of the individual i-th battery cell in the X-th module. i The median value of the absolute difference between the median impedance (median(x)) of all battery cells in the Xth module and the median value of the median impedance (median(x)).

[0099]

[0100] The threshold setting device 340 can calculate the module median based on the estimated impedance value for each individual battery cell in the battery pack 10 for each frequency point in the PEIS measurement data (e.g., 5 Hz, 10 Hz, 20 Hz, or 50 Hz). Here, a module median can be derived for each frequency point.

[0101] The threshold setting device 340 can calculate the absolute deviation between the calculated module median at each frequency point and the impedance value of each battery cell in the module. Here, an absolute deviation can be derived for each battery cell at each frequency point of the PEIS measurement data.

[0102] The threshold setting device 340 can calculate the MAD based on the median of the absolute deviation. Here, a MAD can be derived for each battery cell at each frequency point.

[0103] Lower threshold Threshold ) and upper threshold (Upper) Threshold It can be determined by the following equation.

[0104] Lower Threshold = Module median - 3 × MAD

[0105] Upper Threshold = Module median + 3 × MAD

[0106] According to the embodiment, the threshold setting device 340 can apply MAD to set a threshold based on the real impedance data of each frequency point in the PEIS data extracted for each battery cell in the battery pack 10 (e.g., using the resistance value at a specific frequency instead of the complex impedance value).

[0107] The unbalanced battery cell diagnostic device 350 can classify impedance data that deviates from a set upper / lower threshold as abnormal data and can diagnose unbalanced battery cells (e.g., battery cells with abnormally high internal resistance or battery cells with significantly lower impedance than their counterparts).

[0108] When an unbalanced battery cell is diagnosed, the output device 360 ​​can generate a warning alarm message indicating that an imbalance in the battery pack has been detected, and can transmit the generated warning alarm message to an output device located in the vehicle (e.g., instrument cluster, infotainment display, or maintenance log system).

[0109] Figure 4 An example is shown of a process in which a statistical technique based on the real impedance of the extracted PEIS data of each battery cell is used to set a threshold and diagnose unbalanced battery cells in a battery pack imbalance diagnostic device according to an embodiment of the present disclosure.

[0110] refer to Figure 4 The battery pack imbalance diagnostic device 30 can apply MAD based on the real part impedance data of each frequency point in the PEIS data of each battery cell extracted by signal processing of the voltage and / or current signals received from the BMS 20 to set an upper / lower threshold for imbalance battery cell diagnosis (e.g., a range of ±3×MAD around the module median) and can diagnose battery cells that deviate from the set upper / lower threshold as imbalance battery cells (e.g., battery cells with high internal resistance, degraded capacity, or abnormal electrochemical behavior).

[0111] Figure 5 An example of a process for setting a threshold based on median absolute deviation (MAD) according to an embodiment of the present disclosure is shown (e.g., for detecting voltage imbalance, identifying abnormal impedance values, or triggering battery safety alarms, etc.).

[0112] like Figure 5 As shown in reference numeral 510, the battery pack imbalance diagnostic device 30 can extract PEIS data corresponding to each battery cell in the module.

[0113] Referring to reference numeral 520, PEIS data may include impedance data for the real and imaginary components of each cell in the battery pack at each frequency point of the PEIS measurement data (e.g., 5 Hz, 10 Hz, 20 Hz, or 50 Hz). Here, a median can be derived for each module at each frequency point. As an example, when the module consists of 6 cell units, the module median can be derived based on the impedance data of the 6 cell units at each frequency point of the PEIS measurement data.

[0114] Referring to reference numeral 530, MAD can be calculated as the impedance value X of the individual i-th battery cell in the X-th module, as shown in the following equation. i The median value of the absolute deviation between the median impedance value (median(x)) of all battery cells in the Xth module and the median value of the median impedance value.

[0115]

[0116] Here, the impedance value X of the individual i-th battery cell in the X-th module can be derived for each frequency point of the PEIS measurement data. i And the median impedance value (median(x)) of all battery cells in the Xth module.

[0117] The upper / lower threshold can be calculated using the following equation based on a module midpoint impedance value and a MAD value, which are derived for all battery cells in a module at each frequency point.

[0118] Lower Threshold = Module median - 3 × MAD

[0119] Upper Threshold = Module median + 3 × MAD

[0120] The battery pack imbalance diagnostic device 30 can apply MAD to set a threshold based on the real part impedance data (e.g., resistance impedance at a given frequency) of the PEIS data extracted for each battery cell in the battery pack 10 at each frequency.

[0121] The battery pack imbalance diagnostic device 30 can classify battery cell impedance measurement data that deviates from the set upper / lower threshold as abnormal data and diagnose the corresponding battery cell as an unbalanced battery cell.

[0122] Figure 6 An example of a method for operating a battery pack imbalance diagnostic device according to an embodiment of the present disclosure is shown.

[0123] refer to Figure 6 In S610, the battery pack imbalance diagnostic device 30 can receive voltage and current signals of the battery pack 10 from the BMS 20 (e.g., during charging, discharging, or regenerative braking).

[0124] In S620, the battery pack imbalance diagnostic device 30 can perform signal processing on the received voltage and current signals. Here, signal processing may include DWT and STFT as described above to remove noise and extract frequency data specific to the battery cell (e.g., low-frequency impedance for SoH estimation or high-frequency impedance for fault detection, etc.).

[0125] In S630, the battery pack imbalance diagnostic device 30 can extract PEIS data corresponding to each battery cell in the module for multiple frequency points.

[0126] In S640, the battery pack imbalance diagnostic device 30 can apply statistical techniques based on the real impedance data in the extracted PEIS data. Statistical techniques according to the implementation may include MAD (e.g., for detecting outliers in the battery cell impedance distribution).

[0127] In S650, the battery pack imbalance diagnostic device 30 can set thresholds for diagnosing unbalanced battery cells based on the results of applied statistical techniques. These thresholds can include an upper threshold and a lower threshold to define the acceptable impedance range for each frequency point.

[0128] In S660, the battery pack imbalance diagnostic device 30 can perform imbalance diagnosis of all battery cells in the battery pack 10 based on a set threshold by comparing the real impedance value of each battery cell with an acceptable range.

[0129] In S670, the battery pack imbalance diagnostic device 30 can output the result of the imbalance diagnosis. As an example, if the presence of unbalanced battery cells in the battery pack 10 is the result of the imbalance diagnosis, the battery pack imbalance diagnostic device 30 can generate a warning alarm message including the result of the battery pack imbalance diagnosis and transmit the generated warning alarm message to an output device installed in the vehicle. Here, the output device may include, but is not limited to, at least one of an instrument panel, an audio-visual navigation (AVN) system, a head-up display, or a speaker (e.g., emitting a ringing sound, flashing a warning icon, or displaying detailed battery cell / module status). The result of the battery pack imbalance diagnosis according to the embodiment may include: module identification information of the module containing the unbalanced battery cells and battery cell identification information of the battery cells in the module (e.g., "Module 3 - Battery Cell 5" or "Module 1 - Battery Cell 2", etc.).

[0130] Figure 7 An example of a computing system according to an embodiment of the present disclosure is shown.

[0131] refer to Figure 7 The computing system 700 may include at least one processor 720, memory 730, user interface input device 740, user interface output device 750, storage device 760 and network interface 770 connected to each other via a bus 710 (e.g., a high-speed serial bus or parallel bus depending on design requirements).

[0132] According to the embodiments, the network interface 770 can perform at least one of diagnostic communication, vehicle-to-vehicle communication, and / or communication with a server outside the vehicle (e.g., a remote diagnostic server, a fleet management platform, or an over-the-air update server). The network interface 770 can interact with the BMS 20, navigation system (e.g., an audio, video, and navigation (AVN) system), instrument panel, etc., via in-vehicle communication protocols (e.g., CAN, LIN, FlexRay, MOST, or Ethernet). In-vehicle communication according to the embodiments may include, but is not limited to, at least one of Controller Area Network (CAN) communication, Local Area Network (LIN) communication, FlexRay communication, Media Guided System Transmission (MOST) communication, or Ethernet communication.

[0133] Processor 720 may be a central processing unit (CPU) or a semiconductor device (e.g., a microcontroller, system-on-a-chip (SoC), or digital signal processor (DSP)) that processes instructions stored in memory 730 and / or storage device 760. Memory 730 and storage device 760 may include various types of volatile or non-volatile storage media. For example, memory 730 may include ROM (read-only memory) 731 and RAM (random access memory) 732 (e.g., SRAM or DRAM).

[0134] Therefore, the operation of the methods or algorithms described in conjunction with the embodiments disclosed herein can be directly embodied in hardware or software modules executed by processor 720, or in a combination of hardware and software modules. Software modules may reside on storage media (i.e., memory 730 and / or storage device 760), such as RAM, flash memory, ROM, EPROM, EEPROM, registers, hard disks, removable disks, and CD-ROMs (e.g., storing executable code for MAD threshold calculation or PEIS data processing).

[0135] An exemplary storage medium may be coupled to processor 720. Processor 720 may read information from the storage medium and may write information to the storage medium. Alternatively, the storage medium may be integrated with processor 720. Processor 720 and storage medium may reside in an application-specific integrated circuit (ASIC) (e.g., a custom-designed battery diagnostic control ASIC). The ASIC may reside in a control unit in the vehicle. Alternatively, processor 720 and storage medium may reside as separate components in an electric vehicle (e.g., housed within a BMS housing or a dedicated diagnostic ECU).

[0136] As an example, computing system 700 can be implemented to perform the above... Figures 1 to 6At least one function and method disclosed herein, and may be applied to at least one of the components of the electric vehicle battery diagnostic system 100 described above (e.g., integrated into a BMS, a telematics control unit, or an external service tool).

[0137] One aspect of this disclosure provides a method and apparatus for diagnosing imbalances in battery packs of electric vehicles.

[0138] Another aspect of this disclosure provides an electric vehicle battery pack diagnostic technique for extracting real-time AC impedance at each frequency via signal processing of raw voltage and current signals input from the BMS and for diagnosing battery pack imbalance cell by cell based on the real part impedance of the passive electrochemical impedance spectroscopy (PEIS) of each individual battery cell in the battery pack.

[0139] Another aspect of this disclosure provides a method and apparatus for diagnosing battery pack imbalance based on BMS signal processing impedance, in order to perform battery pack imbalance diagnosis by using AC impedance information extracted via signal processing of the raw voltage and current signals input from the BMS as an indicator, and to obtain various information in the battery module, such as state of health (SOH) and safety deviation.

[0140] Another aspect of this disclosure provides a method and apparatus for diagnosing imbalances in a battery pack based on BMS signal processing impedance, applying statistical techniques based on the real part impedance of PEIS (AC impedance extracted via BMS input signal processing even without a separate EIS device) to diagnose imbalances in the battery pack.

[0141] The technical problems to be solved by this disclosure are not limited to those described above, and any other technical problems not mentioned herein will be clearly understood by those skilled in the art from the following description.

[0142] According to one aspect of this disclosure, a method for diagnosing imbalances in a battery pack of an electric vehicle may include: receiving current and voltage signals corresponding to the battery pack from a battery management system (BMS); performing signal processing based on the received signals; extracting passive electrochemical impedance spectroscopy (PEIS) data based on the result of the signal processing; setting a threshold for diagnosing imbalanced battery cells based on the PEIS data; and performing imbalance diagnosis of individual battery cells in the battery pack based on the threshold.

[0143] As an example, performing signal processing may include: a discrete wavelet transform step to remove noise from a received direct current (DC) signal and decompose the data for each frequency level to extract a time-domain signal; and a short-time Fourier transform step to segment the time-domain signal into short segments to convert the time-domain signal into a frequency-domain signal.

[0144] As an example, PEIS data can include alternating current (AC) impedance data for each frequency point. Thresholds can be determined by applying statistical techniques based on the real impedance data in the AC impedance data.

[0145] As an example, the median absolute deviation (MAD) can be derived via statistical techniques. The median absolute deviation is the median of the absolute deviation between the module median of the x-th module in the battery pack and the impedance observation of the i-th battery cell in the x-th module.

[0146] As an example, a threshold can include a lower threshold and an upper threshold. The lower and upper thresholds can be determined based on the module median and MAD.

[0147] As an example, a module median can be exported for each frequency point, and an absolute deviation and a corresponding MAD can be exported for each battery cell for each frequency point.

[0148] As an example, the module median can be derived as the median of the real impedance data of all battery cells included in the module at each frequency point.

[0149] As an example, the lower threshold can be calculated by subtracting the value obtained by multiplying MAD by a specific constant from the module median. The upper threshold can be calculated by adding the module median to the value obtained by multiplying MAD by that specific constant.

[0150] As an example, the method may also include: generating and outputting a specific warning alarm message based on detecting at least one abnormal battery cell as a result of imbalance diagnosis of the individual battery cells in the battery pack.

[0151] As an example, a warning alert message may include: module identification information corresponding to the abnormal battery cell and battery cell identification information corresponding to the abnormal battery cell.

[0152] According to another aspect of this disclosure, a computing device for diagnosing imbalances in a battery pack of an electric vehicle may include a processor for executing instructions and a memory for storing instructions. These instructions may be implemented to: receive current and voltage signals corresponding to the battery pack from a battery management system (BMS); perform signal processing based on the received signals; extract passive electrochemical impedance spectroscopy (PEIS) data based on the results of the signal processing; set a threshold for diagnosing imbalanced battery cells based on the PEIS data; and perform imbalance diagnosis of individual battery cells in the battery pack based on the threshold.

[0153] As an example, signal processing can include discrete wavelet transform and short-time Fourier transform. Discrete wavelet transform is used to remove noise from the received DC signal and decompose the data for each frequency level to extract the time-domain signal. Short-time Fourier transform is used to segment the time-domain signal into short segments to convert the time-domain signal into a frequency-domain signal.

[0154] As an example, PEIS data can include AC impedance data for each frequency point. The processor can apply statistical techniques to determine a threshold based on the real impedance data in the AC impedance data.

[0155] As an example, the processor can derive the median absolute deviation (MAD) via statistical techniques. The median absolute deviation is the median of the absolute deviation between the module median of the x-th module in the battery pack and the impedance observation of the i-th battery cell in the x-th module.

[0156] As an example, a threshold can include a lower threshold and an upper threshold. The processor can determine the lower and upper thresholds based on the module median and MAD.

[0157] As an example, a module median can be exported for each frequency point, and an absolute deviation and a corresponding MAD can be exported for each battery cell for each frequency point.

[0158] As an example, the module median can be derived as the median of the real impedance data of all battery cells included in the module at each frequency point.

[0159] As an example, the lower threshold can be calculated by subtracting the value obtained by multiplying MAD by a specific constant from the module median. The upper threshold can be calculated by adding the module median to the value obtained by multiplying MAD by that specific constant.

[0160] As an example, the processor can generate and output a specific warning alarm message based on the result of imbalance diagnosis of individual battery cells in the battery pack, which detects at least one abnormal battery cell.

[0161] As an example, a warning alert message may include: module identification information corresponding to the abnormal battery cell and battery cell identification information corresponding to the abnormal battery cell.

[0162] This technology can provide methods and equipment for diagnosing imbalances in battery packs of electric vehicles.

[0163] Furthermore, this technology can provide a battery pack imbalance diagnostic method that can extract real-time AC impedance at each frequency via signal processing of raw voltage and current signals input from the BMS (e.g., CAN-based voltage data or direct analog measurements), and diagnose battery pack imbalance cell by cell based on the real part impedance of the passive electrochemical impedance spectroscopy (PEIS) of each cell in the battery pack (e.g., detecting abnormal rises in impedance at 10 Hz, 50 Hz, or 1 kHz).

[0164] Furthermore, this technology can provide a method and apparatus for diagnosing battery pack imbalance based on BMS signal processing impedance. The method uses AC impedance information extracted via signal processing of the raw voltage and current signals from the BMS input as an indicator (e.g., frequency-specific impedance amplitude) to perform battery pack imbalance diagnosis and obtain various information in the battery module, such as state of health (SOH) and safety deviations (e.g., capacity decay, internal resistance increase, thermal anomalies, or voltage drift).

[0165] Furthermore, this technology can provide a method and apparatus for diagnosing battery pack imbalances based on BMS signal processing impedance, applying statistical techniques based on PEIS real part impedance (AC impedance extracted via BMS input signal processing even without a separate EIS device (e.g., a laboratory-grade electrochemical impedance spectroscopy analyzer) to diagnose imbalances in battery packs (e.g., using median absolute deviation (MAD) to identify anomalous battery cells).

[0166] Furthermore, this technology can provide a method for real-time diagnosis of battery pack imbalances based on PEIS, which is more cost-effective and reliable than existing DC-based diagnostic methods (e.g., voltage drop measurement under load) and existing EIS-based diagnostic methods (e.g., in-service center external frequency scan testing).

[0167] Furthermore, this technology can simultaneously apply DC and AC signals to ensure the accuracy of battery pack imbalance diagnosis (e.g., applying a small AC disturbance to the DC operating current to improve measurement accuracy).

[0168] In addition, various effects that can be directly or indirectly determined through this disclosure may be provided.

[0169] While this disclosure has been described above with reference to embodiments and accompanying drawings, it is not limited thereto. Various modifications and alterations may be made by those skilled in the art without departing from the spirit and scope of this disclosure as claimed in the appended claims.

[0170] Therefore, the examples in this disclosure are not intended to limit but rather to explain the technical concept of this disclosure, and the scope and spirit of the invention are not limited to the examples above. The scope of this disclosure should be interpreted based on the appended claims, and all technical concepts within the scope of the claims should be included within the scope of this disclosure.

Claims

1. A method for diagnosing imbalance in a battery pack of an electric vehicle, the method comprising: Current and voltage signals associated with the vehicle's battery pack are obtained via one or more sensors; Process the obtained current and voltage signals; Passive electrochemical impedance spectroscopy data are extracted based on the processing of the obtained current and voltage signals. Based on the passive electrochemical impedance spectroscopy data, a threshold is set for diagnosing unbalanced battery cells; Based on the threshold, the unbalanced battery cell diagnosis is performed on each battery cell in the battery pack; and Output a signal indicating the result of the diagnosis of the unbalanced battery cells in each of the battery cells.

2. The method according to claim 1, wherein, The current and voltage signals obtained by the processing include: A time-domain signal is obtained by performing a discrete wavelet transform on at least one of the obtained current and voltage signals, removing noise from the signal and decomposing the noise-removed signal for each frequency level, wherein the at least one signal includes a DC signal; and The time-domain signal is converted into a frequency-domain signal by performing a short-time Fourier transform by segmenting the time-domain signal into segments.

3. The method according to claim 1, wherein, The passive electrochemical impedance spectroscopy data includes AC impedance data for each frequency point in the passive electrochemical impedance spectroscopy data, wherein the method further includes: The threshold is determined by applying statistical techniques based on the real impedance data in the AC impedance data.

4. The method according to claim 3, further comprising: Using the statistical technique, the median absolute deviation is derived as the median value of the absolute difference, where each absolute difference is the difference between the impedance value of a corresponding battery cell in a battery cell group in the battery pack and the median impedance value of all battery cells in the battery cell group.

5. The method according to claim 4, wherein, The threshold includes a lower threshold and an upper threshold, wherein the method further includes: The lower threshold and the upper threshold are determined based on the median impedance value and the median absolute deviation.

6. The method according to claim 4, wherein, For each frequency point in the passive electrochemical impedance spectroscopy data, a corresponding median impedance value is derived, and for each frequency point in the passive electrochemical impedance spectroscopy data, a corresponding absolute difference and a corresponding median absolute deviation are derived for each battery cell.

7. The method according to claim 6, wherein, The median impedance value is derived as: the median of the real impedance data of all battery cells in the battery cell group for each frequency point in the passive electrochemical impedance spectroscopy data.

8. The method according to claim 5, further comprising: The lower limit threshold is determined by subtracting the value obtained by multiplying the median absolute deviation by a constant from the median impedance value. as well as The upper limit threshold is determined by adding the median impedance value to the value obtained by multiplying the median absolute deviation by a constant.

9. The method according to claim 1, wherein, The output signal includes: A warning message is generated based on at least one unbalanced battery cell detected as a result of the diagnosis of the unbalanced battery cell.

10. The method according to claim 9, wherein, The warning message includes: Group identification information corresponding to a group containing at least one unbalanced battery cell, and Battery cell identification information corresponding to the at least one unbalanced battery cell.

11. An apparatus for diagnosing imbalance in a battery pack of an electric vehicle, the apparatus comprising: processor; as well as A memory that stores at least one instruction, which, when executed by a processor communicating with the memory, is configured to cause the device to: Current and voltage signals associated with the vehicle's battery pack are obtained via one or more sensors. Process the obtained current and voltage signals. Passive electrochemical impedance spectroscopy data are extracted based on the processing of the obtained current and voltage signals. Based on the passive electrochemical impedance spectroscopy data, a threshold is set for diagnosing unbalanced battery cells. Based on the threshold, the unbalanced battery cell diagnosis is performed on each battery cell in the battery pack, and Output a signal indicating the result of the diagnosis of the unbalanced battery cells in each of the battery cells.

12. The device according to claim 11, wherein, When executed by the processor communicating with the memory, the at least one instruction is configured to cause the device to: The time-domain signal is obtained by performing a discrete wavelet transform by removing noise from at least one of the obtained current and voltage signals and decomposing the noise-removed signal for each frequency level. and The time-domain signal is converted into a frequency-domain signal by performing a short-time Fourier transform by segmenting the time-domain signal into segments.

13. The device according to claim 11, wherein, The passive electrochemical impedance spectroscopy data includes AC impedance data for each frequency point in the passive electrochemical impedance spectroscopy data, and Wherein, the at least one instruction, when executed by the processor communicating with the memory, is configured to cause the device to: The threshold is determined by applying statistical techniques based on the real impedance data in the AC impedance data.

14. The device according to claim 13, wherein, When executed by the processor communicating with the memory, the at least one instruction is configured to cause the device to: Using the statistical technique, the median absolute deviation is derived as the median value of the absolute difference, where each absolute difference is the difference between the impedance value of a corresponding battery cell in a battery cell group in the battery pack and the median impedance value of all battery cells in the battery cell group.

15. The device according to claim 14, wherein, The threshold includes a lower threshold and an upper threshold, and Wherein, the at least one instruction, when executed by the processor communicating with the memory, is configured to cause the device to: The lower threshold and the upper threshold are determined based on the median impedance value and the median absolute deviation.

16. The device according to claim 14, wherein, For each frequency point in the passive electrochemical impedance spectroscopy data, a corresponding median impedance value is derived, and for each frequency point in the passive electrochemical impedance spectroscopy data, a corresponding absolute difference and a corresponding median absolute deviation are derived for each battery cell.

17. The device according to claim 16, wherein, The median impedance value is derived as: the median of the real impedance data of all battery cells in the battery cell group for each frequency point in the passive electrochemical impedance spectroscopy data.

18. A vehicle comprising: Battery pack; processor; as well as A memory that stores at least one instruction, which, when executed by a processor in communication with the memory, is configured to cause the vehicle to: Receive voltage and current signals associated with multiple battery cells in the battery pack. The voltage and current signals are processed to remove noise, and frequency domain data associated with the plurality of battery cells is derived. The passive battery cell impedance data is obtained based on the frequency domain data, wherein the passive battery cell impedance data includes the real part impedance values ​​of the plurality of battery cells. Determine the median of the real impedance value and the median of the absolute deviation of the real impedance value. A threshold range for identifying imbalance in at least one of the plurality of battery cells is set based on the median of the real impedance values ​​and the median of the absolute deviation. Based on the fact that the real impedance value of the at least one battery cell is outside the threshold range, the at least one battery cell is identified as an unbalanced battery cell, and Output a warning signal indicating information about the unbalanced battery cells.

19. The vehicle according to claim 18, wherein, When executed by the processor communicating with the memory, the at least one instruction is configured to cause the vehicle to determine the threshold range by the following steps: The upper limit threshold is determined by adding the median of the real impedance values ​​to a value obtained by multiplying the median of the absolute deviation by a constant. The lower limit threshold is determined by subtracting the value obtained by multiplying the median of the absolute deviation by a constant from the median of the real impedance values.

20. The vehicle according to claim 18, wherein, When executed by the processor communicating with the memory, the at least one instruction is configured to cause the vehicle to include the following in the warning signal: Group identification information corresponding to the battery cell group containing the unbalanced battery cells, and Battery cell identification information corresponding to the unbalanced battery cell.

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