Apparatus and method for battery diagnosis based on impedance uncertainty

Abstract

The present invention relates to an apparatus and a method for battery diagnosis based on impedance uncertainty, and the apparatus comprises: a data collection unit for measuring frequency-specific battery impedance and impedance deviation in the entire frequency domain of a battery system assembly (BSA) including at least one battery module; a model construction unit for constructing a diagnostic model for predicting defective states of the battery system assembly by predicting impedance uncertainty on the basis of the impedance deviation and learning both the battery impedance and the impedance uncertainty; and a battery diagnosis unit for measuring diagnostic impedance from the diagnostic BSA at one or more given input frequencies, calculating diagnostic impedance deviation, and, as the result of providing the diagnostic impedance and diagnostic impedance deviation as input to the diagnostic model, generating the result of diagnosing defective state of the diagnostic BSA.

Description

Battery diagnosis device and method based on impedance uncertainty

[0001] The present invention relates to a battery diagnosis technology, and more particularly, to a battery diagnosis device and method based on impedance uncertainty that can generate a diagnosis result for a battery based on a diagnosis model constructed by learning battery impedance for various frequencies.

[0002] The final form of batteries used in electric vehicles or energy storage systems (ESS) may be a battery system assembly (BSA), which connects battery cells in series and parallel, along with electrical components. BSAs can serve as a power source for electric vehicle and energy storage systems.

[0003] BSA can be composed of an electrochemical system, such as a battery, an electrical system such as a welder, busbar, or wire that connects them in series or parallel, and finally an electrical system such as an electrical component.

[0004] Although various methods can be used to evaluate and diagnose the performance of these BSA systems, electrochemical testing methods can be used as one of the essential elements since they are composed of electrical or electrochemical systems.

[0005] Impedance is also a representative method for evaluating electrochemical systems. Impedance information can be separated into electrolyte and electrode resistances, compared to the simple internal resistance of a battery. This information can be very useful for diagnosing the battery's condition.

[0006] Additionally, impedance information may also enable diagnostic evaluation of electrical connection elements, etc. In other words, the connection status, such as welding or busbar assembly, of battery modules or battery cells within the BSA can be diagnosed using impedance information.

[0007] One embodiment of the present invention provides a battery diagnosis device and method based on impedance uncertainty, which can generate a diagnosis result for a battery based on a diagnosis model constructed by learning battery impedance for various frequencies.

[0008] Among the embodiments, a battery diagnosis device based on impedance uncertainty includes a data collection unit that measures battery impedance and impedance deviation by frequency in the entire frequency range for a battery system assembly (BSA) composed of at least one battery module; a model construction unit that predicts impedance uncertainty based on the impedance deviation and constructs a diagnosis model that predicts a failure state of the battery system assembly by learning the battery impedance and the impedance uncertainty together; and a battery diagnosis unit that measures diagnostic impedance from a diagnostic BSA at at least one given input frequency, calculates diagnostic impedance deviation, and provides the result as an input to the diagnosis model to generate a diagnosis result regarding a failure state of the diagnostic BSA.

[0009] The above data collection unit can generate an impedance waveform based on the distribution of the battery impedance by frequency measured in the entire frequency range.

[0010] The above data collection unit can collect the battery impedance for each frequency, including at least one of the zero crossing impedance frequency and the first and second inflection impedance frequencies defined in the impedance waveform.

[0011] The data collection unit can select at least one specific frequency from the entire frequency range and repeatedly measure the battery impedance for each specific frequency to generate the impedance deviation.

[0012] The above model building unit builds an impedance uncertainty model that receives a plurality of measurement values ​​regarding the impedance deviation as input and generates the impedance uncertainty as output, and the impedance uncertainty model can be defined to receive the plurality of measurement values ​​as input or to receive statistical values ​​between the plurality of measurement values ​​as input according to the definition of the impedance uncertainty.

[0013] The above diagnostic model may include a first diagnostic model that receives as input the cell impedance of a battery cell and generates as output a first diagnostic result regarding a defective state of the corresponding battery cell; a second diagnostic model that receives as input the module impedance of a battery module and the first diagnostic result and generates as output a second diagnostic result regarding a defective state and a connection state of the corresponding battery module; and a third diagnostic model that receives as input the diagnostic impedance of the diagnostic BSA and the second diagnostic result and generates a third diagnostic result including the defective state and connection state of the corresponding diagnostic BSA and the diagnostic prediction results of each of the corresponding battery cell and the corresponding battery module.

[0014] The above battery diagnostic unit diagnoses the connection status and electrochemical performance of the diagnostic BSA by using the status information and connection information of each battery module together with the diagnostic impedance, and the status information of the battery module includes status information of at least one battery cell constituting the module, and the connection information may include impedance information of an electrical connection means between modules or between cells.

[0015] Among the embodiments, a battery diagnosis method based on impedance uncertainty includes: a step of measuring, through a data collection unit, a battery impedance and an impedance deviation by frequency in the entire frequency range for a battery system assembly (BSA) composed of at least one battery module; a step of predicting, through a model construction unit, an impedance uncertainty based on the impedance deviation and constructing a diagnostic model for predicting a failure state of the battery system assembly by learning the battery impedance and the impedance uncertainty together; and a step of measuring, through a battery diagnosis unit, a diagnostic impedance from a diagnostic BSA at at least one given input frequency, calculating a diagnostic impedance deviation, and providing the result as an input to the diagnostic model to generate a diagnostic result regarding a failure state of the diagnostic BSA.

[0016] The disclosed technology may have the following effects. However, this does not mean that a particular embodiment must include all or only the following effects, and therefore the scope of the disclosed technology should not be construed as being limited thereby.

[0017] A battery diagnosis device and method based on impedance uncertainty according to one embodiment of the present invention can generate a diagnosis result for a battery based on a diagnosis model constructed by learning battery impedance for various frequencies.

[0018] FIG. 1 is a drawing illustrating a battery diagnosis system according to the present invention.

[0019] Fig. 2 is a drawing explaining the system configuration of the battery diagnostic device of Fig. 1.

[0020] Fig. 3 is a drawing explaining the functional configuration of the battery diagnostic device of Fig. 1.

[0021] Figure 4 is a flowchart illustrating a battery diagnosis method based on impedance uncertainty according to the present invention.

[0022] FIG. 5 is a drawing illustrating one embodiment of the structure of a battery system assembly according to the present invention.

[0023] Figure 6 is a drawing illustrating one embodiment of an impedance measurement result according to the present invention.

[0024] FIG. 7 is a drawing illustrating one embodiment of a battery diagnosis process according to the present invention.

[0025] FIG. 8 is a drawing illustrating one embodiment of a battery diagnostic structure according to the present invention.

[0026] The description of the present invention is merely an example for structural and functional explanation, and therefore, the scope of the present invention should not be construed as being limited by the embodiments described in the text. That is, since the embodiments can be modified in various ways and can take various forms, the scope of the present invention should be understood to include equivalents that can realize the technical idea. In addition, the purposes or effects presented in the present invention do not mean that a specific embodiment must include all of them or only such effects, and therefore, the scope of the present invention should not be construed as being limited thereby.

[0027] Meanwhile, the meaning of the terms described in this application should be understood as follows.

[0028] Terms such as "first" and "second" are intended to distinguish one component from another, and the scope of the rights should not be limited by these terms. For example, the first component may be referred to as the second component, and similarly, the second component may also be referred to as the first component.

[0029] When a component is said to be "connected" to another component, it should be understood that while it may be directly connected to that other component, there may also be other components intervening. Conversely, when a component is said to be "directly connected" to another component, it should be understood that there are no other intervening components. Similarly, other expressions describing relationships between components, such as "between" and "directly between," or "adjacent to" and "directly adjacent to," should be interpreted similarly.

[0030] Singular expressions should be understood to include plural expressions unless the context clearly indicates otherwise, and terms such as "comprises" or "have" should be understood to specify the presence of a feature, number, step, operation, component, part or combination thereof, but not to exclude the possibility of the presence or addition of one or more other features, numbers, steps, operations, components, parts or combinations thereof.

[0031] For each step, the identifiers (e.g., a, b, c, etc.) are used for convenience of explanation and do not describe the order of the steps. The steps may occur in a different order than stated unless the context clearly dictates a specific order. That is, the steps may occur in the same order as stated, may be performed substantially simultaneously, or may be performed in the opposite order.

[0032] The present invention can be implemented as computer-readable code on a computer-readable recording medium. The computer-readable recording medium includes all types of recording devices that store data that can be read by a computer system. Examples of computer-readable recording media include ROM, RAM, CD-ROM, magnetic tape, floppy disk, and optical data storage devices. Furthermore, the computer-readable recording medium can be distributed across network-connected computer systems, so that the computer-readable code can be stored and executed in a distributed manner.

[0033] Unless otherwise defined, all terms used herein have the same meaning as commonly understood by those of ordinary skill in the art to which this invention pertains. Terms defined in commonly used dictionaries should be interpreted to be consistent with their meaning within the context of the relevant technology, and should not be interpreted as having an idealized or overly formal meaning unless explicitly defined herein.

[0034] FIG. 1 is a drawing illustrating a battery diagnosis system according to the present invention.

[0035] Referring to FIG. 1, a battery diagnosis system (100) may include a battery (110), a battery diagnosis device (130), and a database (150).

[0036] The battery (110) may correspond to a battery device that can be repeatedly used by charging and discharging electric energy. For example, the battery (110) may be implemented as a battery system assembly (BSA). That is, in the case of an electric vehicle, the battery (110) may be installed inside the vehicle and may stably supply electric energy to the drive motor, which is the power device for driving the electric vehicle, while simultaneously storing electric energy generated through regenerative energy when the vehicle decelerates. The battery (110) may be implemented by being included in various devices operated by the user as needed.

[0037] In addition, when implemented as a BSA, the battery (110) may form a single unit battery by connecting multiple battery modules, may be combined with electrical components for vehicle installation, and may perform an operation of measuring or collecting status information of each battery module. In addition, the battery module may form a single unit module by connecting multiple battery cells. Each battery module and each battery cell forming a single unit may be electrically connected to each other. For example, the battery modules may be electrically connected through a bus bar, and the battery cells may be electrically connected through welding.

[0038] Although it is represented as one battery (110) in FIG. 1, it may be understood as multiple batteries (110) as needed. In this case, each battery (110) may be connected to a battery diagnostic device (130) via a network, and multiple batteries (110) may be connected to the battery diagnostic device (130) simultaneously.

[0039] In addition, the battery (110) may be implemented as a single device constituting the battery diagnosis system (100) according to the present invention, and the battery diagnosis system (100) may be implemented in various forms depending on the purpose of battery diagnosis based on impedance uncertainty. That is, the battery (110) may be implemented by being included in various devices that are connected to and operable with the battery diagnosis device (130).

[0040] The battery diagnostic device (130) may be implemented as a server corresponding to a computer or program that performs the battery diagnostic method based on impedance uncertainty according to the present invention. Furthermore, the battery diagnostic device (130) may be connected to the battery (110) via a wired network or a wireless network such as Bluetooth, WiFi, or LTE, and may transmit and receive data with the battery (110) via the network.

[0041] Additionally, the battery diagnostic device (130) may be implemented to operate in connection with an independent external system (not shown in FIG. 1). For example, the battery diagnostic device (130) may operate in conjunction with a battery management system (BMS) that manages battery charging and discharging, a learning server for learning data management and model learning, etc.

[0042] The database (150) may correspond to a storage device that stores various information required during the operation of the battery diagnosis device (130). For example, the database (150) may store impedance information measured from the battery (110) or various model information constructed through pre-learning. However, the database is not necessarily limited thereto, and the battery diagnosis device (130) may store information collected or processed in various forms during the battery diagnosis process based on impedance uncertainty according to the present invention.

[0043] In addition, in FIG. 1, the database (150) is depicted as a device independent of the battery diagnosis device (130), but it is not necessarily limited thereto, and it can be implemented as a logical storage device included in the battery diagnosis device (130).

[0044] Fig. 2 is a drawing explaining the system configuration of the battery diagnostic device of Fig. 1.

[0045] Referring to FIG. 2, the battery diagnostic device (130) may include a processor (210), memory (230), a user input / output unit (250), and a network input / output unit (270).

[0046] The processor (210) can execute a procedure for performing a battery diagnosis method based on impedance uncertainty according to an embodiment of the present invention, manage a memory (230) that is read or written in the process, and schedule a synchronization time between a volatile memory and a non-volatile memory in the memory (230). The processor (210) can control the overall operation of the battery diagnosis device (130), and is electrically connected to the memory (230), the user input / output unit (250), and the network input / output unit (270) to control data flow therebetween. The processor (210) can be implemented as a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), or an NPU (Neural Processing Unit) of the battery diagnosis device (130).

[0047] The memory (230) may include an auxiliary memory device implemented as a non-volatile memory such as an SSD (Solid State Disk) or an HDD (Hard Disk Drive) and used to store all data required for the battery diagnosis device (130), and may include a main memory device implemented as a volatile memory such as a RAM (Random Access Memory). In addition, the memory (230) may store a set of commands that are executed by an electrically connected processor (210) to execute a battery diagnosis method based on impedance uncertainty according to the present invention.

[0048] The user input / output unit (250) includes an environment for receiving user input and an environment for outputting specific information to the user, and may include, for example, an input device including an adapter such as a touchpad, a touch screen, a visual keyboard, or a pointing device, and an output device including an adapter such as a monitor or a touch screen. In one embodiment, the user input / output unit (250) may correspond to a computing device connected via remote access, and in such a case, the battery diagnostic device (130) may correspond to an independent node of a network to which the computing device is connected.

[0049] The network input / output unit (270) provides a communication environment for connecting to other devices via a network, and may include, for example, an adapter for communication such as a Local Area Network (LAN), a Metropolitan Area Network (MAN), a Wide Area Network (WAN), and a Value Added Network (VAN). In addition, the network input / output unit (270) may be implemented to provide a short-range communication function such as WiFi or Bluetooth, or a wireless communication function of 4G or higher for wireless transmission of data.

[0050] Fig. 3 is a drawing explaining the functional configuration of the battery diagnostic device of Fig. 1.

[0051] Referring to FIG. 3, the battery diagnostic device (130) can perform a battery diagnostic method based on impedance uncertainty according to the present invention. To this end, the battery diagnostic device (130) can include a data collection unit (310), a model construction unit (330), a battery diagnostic unit (350), and a control unit (370).

[0052] At this time, the embodiments of the present invention do not necessarily include all of the above-described components simultaneously. Depending on the embodiment, some of the above-described components may be omitted, or some or all of the above-described components may be selectively included. The operation of each component will be described in detail below.

[0053] The data collection unit (310) can measure the battery impedance and impedance deviation by frequency in the entire frequency range for a battery system assembly (BSA) composed of at least one battery module. Here, the battery impedance may correspond to information measured from the battery (110) using a plurality of different sinusoidal signals. For example, the battery impedance may be expressed as (frequency (f), real value (Re), imaginary value (Im)) as an array structure composed of real axis values ​​and imaginary axis values ​​according to frequency on a Nyquist diagram. If the battery impedance is measured in a single frequency band, the frequency information may be omitted.

[0054] Specifically, the data collection unit (310) can measure the battery impedance for each frequency by applying a series of sine wave signals according to the entire frequency range to the battery. To this end, the data collection unit (310) can operate in conjunction with a signal application module that applies a sine wave signal of a specific frequency to the battery. In addition, the data collection unit (310) can measure the voltage and current of the battery when the sine wave signal is applied to the battery. To this end, the data collection unit (310) can operate in conjunction with a voltage measurement module and a current measurement module.

[0055] Thereafter, the data collection unit (310) can calculate the impedance of the corresponding battery based on the measured voltage and current. For example, the data collection unit (310) can calculate the battery impedance by applying a Hamming window algorithm, a Discrete Fourier Transform (DFT), etc., using a plurality of current values ​​and voltage values ​​collected at different points in time at regular intervals, and using a Nyquist plot, etc. In addition, the data collection unit (310) can collect impedance measurement values ​​through a plurality of repeated measurements in the same frequency domain and collect the impedance deviation from the difference between consecutive impedance measurement values.

[0056] In one embodiment, the data acquisition unit (310) may generate an impedance waveform based on the distribution of battery impedances measured by frequency across the entire frequency range. In this case, the impedance waveform may be generated on a Nyquist plot, and the shape of the impedance waveform may be determined based on the distribution of battery impedances at various frequencies.

[0057] In one embodiment, the data collection unit (310) may collect battery impedance by frequency, including at least one of a zero-crossing impedance frequency and first and second inflection impedance frequencies defined in the impedance waveform. Here, the zero-crossing impedance frequency may correspond to a frequency at an intersection point between the impedance waveform and the x-axis (or Re-axis) on a two-dimensional graph (e.g., a Nyquist plot) in which the impedance waveform is defined. In addition, the first and second inflection impedance frequencies may correspond to frequencies at each of different inflection points formed on the impedance waveform. The data collection unit (310) may collect one or more frequency-specific battery impedances detected in the impedance waveform, and the collected frequency-specific battery impedances may be utilized as learning data for constructing a diagnostic model in a later step.

[0058] In one embodiment, the data acquisition unit (310) may select at least one specific frequency from the entire frequency range and repeatedly measure the battery impedance for each specific frequency to generate an impedance deviation. Since the impedance of a battery is sensitive to disturbances, when performing repeated impedance measurements on the same battery at the same frequency, a certain deviation may occur between consecutive measurements. In other words, the impedance deviation may correspond to the difference between consecutive impedance measurement values ​​generated by multiple repeated measurements.

[0059] The model building unit (330) can predict impedance uncertainty based on impedance deviation and build a diagnostic model that learns battery impedance and impedance uncertainty together to predict a defective state of a battery system assembly. At this time, the diagnostic model can be built by learning data collected at a specific frequency among the entire frequency range. That is, the learning data used to build the diagnostic model may correspond to data collected at a characteristic frequency used when judging whether the battery (110) is good or bad, and may correspond to data collected by frequency in one or more frequency bands. The model building unit (330) can select a frequency to be used for learning from the battery impedance based on the entire frequency range and then store it in the database (150). Therefore, the impedance information for each frequency stored in the database (150) can be used as learning data for building the diagnostic model.

[0060] In addition, the diagnostic model may correspond to an artificial neural network-based artificial intelligence model designed to predict the state information of the battery (110) by learning impedance uncertainty information predicted based on impedance deviation and selective impedance information. In this case, the diagnostic model may be implemented as a single artificial intelligence model, or may be implemented as a model set including one or more sub-models as needed. The diagnostic model may be pre-built by the model building unit (330), and each built model may be stored and managed in a database (150).

[0061] Here, the impedance uncertainty may correspond to the degree of error according to the difference between the measurement values ​​generated by multiple measurements. That is, the model construction unit (330) may use the impedance uncertainty model to compare multiple measurement results and derive the nonlinearity of uncertainty regarding impedance measurement through this. In addition, the impedance uncertainty model may correspond to a deep learning model that is pre-trained as an artificial intelligence model to receive multiple measurement values ​​as input and generate impedance uncertainty as output. The model construction unit (330) may reflect the impedance uncertainty in the fault condition diagnosis process, and thereby generate a diagnosis result that reflects the influence of battery characteristic factors such as the battery model and material, battery SoC (State of Charge), and environmental factors such as temperature and humidity.

[0062] In one embodiment, the model building unit (330) may perform a learning process of a diagnostic model by using a change in at least one impedance characteristic extracted from an impedance waveform as learning data. Here, the impedance characteristic may correspond to feature information extracted from the impedance waveform and may correspond to non-time series data calculated at a specific point in time. For example, the impedance characteristic may be composed of a real-axis value and an imaginary-axis value corresponding to a specific frequency on the impedance waveform, and may be expressed as (frequency (f), real-axis value (Re), imaginary-axis value (Im)). If the impedance characteristic is measured in a single frequency band, the frequency information may be omitted. When the impedance characteristic is determined, the model building unit (330) may track the corresponding impedance characteristic for each measurement point in time and detect a change therein.

[0063] In one embodiment, the model building unit (330) may build an impedance uncertainty model that receives multiple measurements of impedance deviation as input and generates impedance uncertainty as output. At this time, the impedance uncertainty model may be defined to receive multiple measurements as input or to receive statistical values ​​between the multiple measurements as input, depending on the definition of impedance uncertainty. For example, the statistical values ​​between the measurements may include the minimum, average, and maximum impedance of the measured battery impedance.

[0064] The battery diagnostic unit (350) can generate a diagnostic result regarding a defective state of the diagnostic BSA by measuring a diagnostic impedance from the diagnostic BSA at at least one given input frequency, calculating a diagnostic impedance deviation, and providing the result as an input to a diagnostic model. The diagnostic BSA may correspond to a battery (110) that is a target of a defective state diagnosis. Accordingly, the diagnostic impedance may correspond to the battery impedance measured from the diagnostic BSA, and the diagnostic impedance deviation may correspond to the difference between consecutive battery impedances measured from the diagnostic BSA.

[0065] That is, the battery diagnostic unit (350) can determine the defective state of the diagnostic BSA by analyzing the diagnostic impedance and the diagnostic impedance deviation measured from the diagnostic BSA. To this end, the battery diagnostic unit (350) can generate rapid and highly accurate diagnostic results using only the impedance information collected from the selected frequency by utilizing a pre-built diagnostic model. For example, the battery diagnostic result may include battery status information, connection part information, and electrical performance evaluation results.

[0066] In one embodiment, the diagnostic model may include a first diagnostic model that receives as input a cell impedance of a battery cell and generates as output a first diagnostic result regarding a fault condition of the corresponding battery cell, a second diagnostic model that receives as input a module impedance of a battery module and the first diagnostic result and generates as output a second diagnostic result regarding a fault condition and a connection state of the corresponding battery module, and a third diagnostic model that receives as input a diagnostic impedance of a diagnostic BSA and the second diagnostic result and generates a third diagnostic result including a fault condition and a connection state of the corresponding diagnostic BSA and a diagnostic prediction result of each of the corresponding battery cell and the corresponding battery module.

[0067] In one embodiment, the battery diagnostic unit (350) can diagnose the connection status and electrochemical performance of the diagnostic BSA by using the status information and connection information of each battery module together with the diagnostic impedance. At this time, the status information of the battery module may include status information of at least one battery cell constituting the module. In addition, the connection information may include impedance information of the electrical connection means between modules or between cells. That is, the battery diagnostic unit (350) can diagnose not only the failure status of the diagnostic BSA but also the cause thereof through a step-by-step inference process utilizing a pre-built diagnostic model. For example, the status information of the battery cell can be generated through impedance analysis of the cell impedance, and the status information of the battery cell can be generated through impedance analysis of the module impedance.

[0068] The control unit (370) controls the overall operation of the battery diagnostic device (130) and can manage the control flow or data flow between the data collection unit (310), the model construction unit (330), and the battery diagnostic unit (350).

[0069] Figure 4 is a flowchart illustrating a battery diagnosis method based on impedance uncertainty according to the present invention.

[0070] Referring to FIG. 4, the battery diagnostic device (130) can measure the battery impedance and impedance deviation for each frequency in the entire frequency range for a battery system assembly composed of at least one battery module through a data collection unit (310), and the measured impedance information and impedance deviation can be used as learning data for constructing a diagnostic model (step S410).

[0071] The battery diagnostic device (130) can predict impedance uncertainty based on impedance deviation through the model building unit (330) and build a diagnostic model that predicts a faulty state of the battery system assembly by learning the battery impedance and impedance uncertainty together (step S430). At this time, the diagnostic model can have its input and output data structures determined according to the configuration of the learning data, and can be composed of multiple sub-models as needed.

[0072] The battery diagnostic device (130) can measure the diagnostic impedance from the diagnostic BSA at at least one input frequency given through the battery diagnostic unit (350), calculate the diagnostic impedance deviation, and provide the result as an input to the diagnostic model to generate a diagnostic result regarding the defective state of the diagnostic BSA (step S450). The battery diagnostic device (130) can independently generate at least one of a cell-specific diagnostic result, a module-specific diagnostic result, and a BSA diagnostic result based on the output data output by the diagnostic model.

[0073] FIG. 5 is a drawing illustrating one embodiment of the structure of a battery system assembly according to the present invention.

[0074] Referring to FIG. 5, a battery (510) may be implemented by including a plurality of battery modules (530). In this case, the plurality of battery modules (530) may be connected in series or parallel, and may form a single unit battery depending on the connection result. The connection points between the battery modules (530) may be electrically connected through busbar bolting, etc.

[0075] In addition, each battery module (530) may be implemented to include a plurality of battery cells. At this time, the connection portions between the plurality of battery cells may be connected through welding, etc. The battery diagnostic device (130) may perform an electrical connection inspection on the connection status between modules and the connection status between cells within the battery (510), and for this purpose, may learn fault data on electrical connection failures and build a diagnostic model. Accordingly, the battery diagnostic device (130) may diagnose connection failures and cell failures of the modules through supervised learning based on fault data.

[0076] Figure 6 is a drawing illustrating one embodiment of an impedance measurement result according to the present invention.

[0077] Referring to Fig. 6, the battery impedance can be largely composed of an intercept, a semi-circle, and a Warburg that intersect the Real axis (x-axis) on the Nyquist plot, and can be effectively separated into electrode, separator, and electrolyte resistances to conduct a precise analysis.

[0078] In Fig. 6, a zero crossing point (610) and two inflection points (630, 650) intersecting the Real axis among the impedance measurement values ​​can be derived according to the impedance waveform. At this time, the frequency bands corresponding to the zero crossing point (610) and the inflection points (630, 650) may correspond to the zero crossing impedance frequency and the inflection impedance frequency, respectively. The battery diagnostic device (130) can calculate each measurement value through the absolute position and relative position with respect to the real part of the three derived points. For example, the zero crossing point may correspond to a value representing the electrolyte resistance, and the two inflection points may correspond to values ​​representing the electrode resistance.

[0079] In one embodiment, the battery diagnostic device (130) can derive key points from measurement data regarding an impedance battery and learn changes in the absolute and relative position values ​​of the key points to build a diagnostic model for diagnosing the state of the BSA. For example, the diagnostic model can be trained to diagnose a battery cell short among battery defects when the zero crossing point (610) momentarily increases and then decreases. In addition, the diagnostic model can be trained to diagnose a material defect when the first inflection point (630) increases, and conversely, a process defect or capacity defect when the change becomes smaller than a reference value. In addition, the diagnostic model can be trained to diagnose a material defect when the second inflection point (650) increases, and conversely, a process defect or capacity defect when the change becomes smaller than a reference value.

[0080] Meanwhile, depending on the battery type, key points and resistance values ​​may vary, and even the reference values ​​may change. Therefore, the battery diagnostic device (130) can build learning data on resistance values, reference values, and battery status information for various models by collecting numerous battery test results and big data, and can build diagnostic models with various structures through a pre-learning process that learns this data. Accordingly, the battery diagnostic device (130) can perform rapid and accurate battery diagnosis by utilizing the diagnostic models.

[0081] FIG. 7 is a drawing illustrating one embodiment of a battery diagnosis process according to the present invention.

[0082] Referring to Fig. 7, a single battery (110) can be implemented through multiple battery modules and electrical connections therebetween, and a single battery module can in turn be implemented through multiple battery cells and electrical connections therebetween. Accordingly, the battery diagnostic device (130) can generate analysis results for higher-level elements based on analysis data of lower-level elements according to the hierarchical structure of the battery (110).

[0083] In the case of Fig. 7, a step-by-step analysis process of battery diagnosis performed in a battery diagnosis device (130) can be illustrated. The battery diagnosis device (130) can build a diagnosis model by selectively learning impedance data of a specific frequency from a large amount of continuous impedance data, and can diagnose the state of the battery by collecting only impedance data of a given frequency through the diagnosis model.

[0084] FIG. 8 is a drawing illustrating one embodiment of a battery diagnostic structure according to the present invention.

[0085] Referring to FIG. 8, the battery diagnosis device (130) can diagnose the battery condition by additionally considering the influence of environmental factors according to the measurement environment of the battery impedance. To this end, the battery diagnosis device (130) can independently construct a first model for impedance uncertainty prediction and a second model for condition diagnosis. The first model may correspond to a model that receives impedance deviation as input and generates impedance uncertainty as output, and the second model may correspond to a diagnosis model that receives battery impedance and impedance uncertainty as input and generates a battery diagnosis result as output.

[0086] The battery diagnostic device (130) can measure diagnostic impedance and diagnostic impedance deviation from the diagnostic BSA, and in this case, can selectively collect impedance data for a given specific frequency. That is, the battery diagnostic device (130) can repeatedly measure and collect diagnostic impedance data for a combination of given input frequencies, and the diagnostic impedance deviation can be generated by the data collection unit (810) that receives the repeatedly measured diagnostic impedance. The impedance deviation can be transmitted from the data collection unit (810) to the battery diagnostic unit (850), and the battery diagnostic unit (850) can predict impedance uncertainty based on the impedance deviation. The predicted impedance uncertainty can be input into a diagnostic model together with the diagnostic impedance, and the diagnostic model can predict a defective state of the battery (110) based on the battery impedance and the impedance uncertainty.

[0087] Although the present invention has been described above with reference to preferred embodiments thereof, it will be understood by those skilled in the art that various modifications and changes may be made to the present invention without departing from the spirit and scope of the present invention as set forth in the claims below.

Claims

1. A data acquisition unit for measuring battery impedance and impedance deviation by frequency in a specific frequency range for a battery system assembly (BSA) consisting of at least one battery module; A model construction unit for predicting impedance uncertainty based on the impedance deviation and constructing a diagnostic model for predicting a defective state of the battery system assembly by learning the battery impedance, the impedance deviation, and the impedance uncertainty together, wherein the diagnostic model includes a first model that receives the impedance deviation as an input and generates the impedance uncertainty as an output, and a second model that receives the battery impedance and the impedance uncertainty as inputs and generates the defective state as an output; and A battery diagnosis device based on impedance uncertainty, comprising: a battery diagnosis unit for measuring a diagnostic impedance from a diagnostic BSA at at least one given input frequency, calculating a diagnostic impedance deviation, and generating a diagnosis result regarding a defective state of the diagnostic BSA through first and second models of the diagnostic model; 2. In paragraph 1, the data collection unit A battery diagnosis device based on impedance uncertainty, characterized in that it generates an impedance waveform based on the distribution of the battery impedance by frequency measured in the specific frequency range.

3. In the second paragraph, the data collection unit A battery diagnosis device based on impedance uncertainty, characterized in that it collects battery impedance by frequency, including at least one of a zero crossing impedance frequency and a first and second inflection impedance frequencies defined in the impedance waveform.

4. In paragraph 1, the data collection unit A battery diagnosis device based on impedance uncertainty, characterized in that it selects at least one specific frequency among the specific frequency ranges and repeatedly measures the battery impedance for each specific frequency to generate the impedance deviation.

5. In paragraph 4, the model construction unit An impedance uncertainty model is constructed as the second model, which receives multiple measurement values ​​of the above impedance deviation as input and generates the impedance uncertainty as output, A battery diagnosis device based on impedance uncertainty, characterized in that the impedance uncertainty model is defined to receive the plurality of measurement values ​​as inputs as they are or to receive statistical values ​​between the plurality of measurement values ​​as inputs according to the definition of the impedance uncertainty.

6. In paragraph 1, the diagnostic model A first diagnostic model that receives cell impedance of a battery cell as input and generates a first diagnostic result regarding a defective state of the corresponding battery cell as output; A second diagnostic model that receives the module impedance of the battery module and the first diagnostic result as input and generates a second diagnostic result regarding the faulty state and connection state of the battery module as output; and A battery diagnosis device based on impedance uncertainty, characterized in that it includes a third diagnosis model that receives the diagnostic impedance of the diagnostic BSA and the second diagnosis result as inputs and generates a third diagnosis result including a faulty state and connection state of the corresponding diagnostic BSA, and a diagnosis prediction result of each of the corresponding battery cell and the corresponding battery module.

7. In the first paragraph, the battery diagnostic unit Using the status information and connection part information of each battery module together with the above diagnostic impedance, the connection status and electrochemical performance of the diagnostic BSA are diagnosed. The status information of the above battery module includes status information of at least one battery cell constituting the module, A battery diagnostic device based on impedance uncertainty, characterized in that the above connection part information includes impedance information of an electrical connection means between modules or between cells.

8. A step of measuring battery impedance and impedance deviation by frequency in a specific frequency range for a battery system assembly (BSA) consisting of at least one battery module through a data collection unit; A step of constructing a diagnostic model that predicts impedance uncertainty based on the impedance deviation through a model construction unit and learns the battery impedance, the impedance deviation, and the impedance uncertainty together to predict a defective state of the battery system assembly, wherein the diagnostic model includes a first model that receives the impedance deviation as an input and generates the impedance uncertainty as an output and a second model that receives the battery impedance and the impedance uncertainty as inputs and generates the defective state as an output; and A battery diagnosis method based on impedance uncertainty, comprising: a step of measuring a diagnostic impedance from a diagnostic BSA at at least one given input frequency through a battery diagnosis unit, calculating a diagnostic impedance deviation, and generating a diagnosis result regarding a defective state of the diagnostic BSA through first and second models of the diagnostic model;

Images