A method and related apparatus for locating a defective battery cell position of a battery pack based on impedance spectroscopy

Abstract

The application provides a method for locating the position of a defective battery in a battery pack based on impedance spectrum and a related device, and belongs to the technical field of battery monitoring. The method comprises applying a multi-frequency superimposed excitation current signal to the battery pack; obtaining the battery voltage response of the battery pack under the multi-frequency superimposed signal; determining the superposition of impedances at different frequencies based on the battery voltage response to obtain a superimposed impedance; and identifying the position of the defective battery in the battery pack according to the superimposed impedance by using a defective battery identification model, which is a model trained by using different positions of the defective battery in the battery pack and corresponding impedance spectrum. The application can more finely capture the abnormal conditions of a single battery in the battery pack by analyzing the battery voltage response under multi-frequency superimposed excitation. By using the change characteristics of the battery impedance at different frequencies and combining superimposed impedance analysis, the accuracy of the positioning of the defective battery can be significantly improved, which is crucial for maintaining the overall performance and safety of the battery pack.

Description

Technical Field

[0001] This invention belongs to the field of battery monitoring technology, specifically relating to a method and related apparatus for locating defective batteries in a battery pack based on impedance spectroscopy. Background Technology

[0002] Widely used as backup DC power in substations, valve-regulated lead-acid (VRLA) batteries are in a float charge state to ensure they are fully charged. This stage maintains the battery pack's state of charge, preventing self-discharge and keeping the pack in a ready state. In float charge, the charging current of each battery is very small. However, due to slight differences between individual batteries in a lead-acid battery pack, such as internal resistance and material variations, the charging voltage and current can be inconsistent. This can damage some batteries, affecting the performance and lifespan of the entire battery pack. Therefore, accurately identifying the location of defective batteries and replacing them promptly is crucial.

[0003] Common methods of determining battery capacity using voltage cannot effectively capture parameter changes caused by internal battery variations. Electrochemical impedance spectroscopy (EIS) is a method for assessing battery state by measuring the frequency response of charge and discharge processes. However, traditional impedance measurements employ frequency sweeping methods, requiring individual measurements for each battery, which is time-consuming. Summary of the Invention

[0004] In view of this, the present invention aims to provide a method and related apparatus for locating defective cells in a battery pack based on impedance spectrum, so as to overcome the shortcomings of the prior art.

[0005] To achieve the above objectives, the technical solution provided by the present invention is as follows:

[0006] In a first aspect, the present invention provides a method for locating defective cells in a battery pack based on impedance spectroscopy, comprising the following steps:

[0007] Apply multi-frequency superimposed excitation current signals to the battery pack;

[0008] Obtain the battery voltage response of the battery pack under multi-frequency superimposed signals;

[0009] The superposition of impedances at different frequencies is determined based on the battery voltage response, and the superimposed impedance is obtained.

[0010] Based on the superimposed impedance, the location of defective cells in the battery pack is identified using a defective cell identification model. The defective cell identification model is trained using the different locations of defective cells in the battery pack and their corresponding impedance spectra.

[0011] Furthermore, the expression for the excitation current signal is as follows:

[0012]

[0013] In the formula, i is the excitation signal, I1, ..., I n Let w be the maximum value of n different frequency currents. 1t ,…,w nt There are n different signal frequencies.

[0014] Furthermore, the battery voltage response of the battery pack under multi-frequency superimposed signals is obtained, including:

[0015] Set the measuring terminal to the positive terminal of the first battery and the negative terminal of the last battery in the battery pack, or the negative terminal of the first battery and the positive terminal of the last battery.

[0016] The battery voltage response of the battery pack is obtained from the measurement terminal.

[0017] Furthermore, the defective battery identification model is based on a model trained using an artificial neural network. The training process includes:

[0018] Set up defective batteries at different locations in the battery pack and measure the corresponding battery impedance;

[0019] By using the location and impedance of the defective battery as input, an artificial neural network is used for training to obtain a defective battery identification model.

[0020] Secondly, the present invention provides a device for locating defective cells in a battery pack based on impedance spectroscopy, comprising:

[0021] The hybrid excitation module is used to apply multi-frequency superimposed excitation current signals to the battery pack;

[0022] The measurement module is used to acquire the battery voltage response of the battery pack under multi-frequency superimposed signals;

[0023] The calculation module is used to determine the superposition of impedances at different frequencies based on the battery voltage response, and obtain the superimposed impedance;

[0024] The analysis module is used to identify the location of defective cells in the battery pack based on the superimposed impedance and the defective cell identification model. The defective cell identification model is trained using the different locations of defective cells in the battery pack and their corresponding impedance spectra.

[0025] Furthermore, the battery voltage response of the battery pack under multi-frequency superimposed signals is obtained, including:

[0026] Set the measuring terminal to the positive terminal of the first battery and the negative terminal of the last battery in the battery pack, or the negative terminal of the first battery and the positive terminal of the last battery.

[0027] The battery voltage response of the battery pack is obtained from the measurement terminal.

[0028] Furthermore, the defective battery identification model is based on a model trained using an artificial neural network. The training process includes:

[0029] Set up defective batteries at different locations in the battery pack and measure the corresponding battery impedance;

[0030] By using the location and impedance of the defective battery as input, an artificial neural network is used for training to obtain a defective battery identification model.

[0031] Furthermore, it also includes: an alarm module and a display module;

[0032] The alarm module is used to issue an alarm signal when the analysis module detects a defective battery;

[0033] The display module is used to display the serial number of the defective battery in the battery pack.

[0034] Accordingly, the present invention provides a computer device, the device including a processor and a memory:

[0035] The memory is used to store computer programs and send the instructions of the computer programs to the processor;

[0036] The processor executes, according to the instructions of the computer program, a method for locating defective cells in a battery pack based on impedance spectrum, as described in the first aspect.

[0037] Accordingly, the present invention also provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements a method for locating defective batteries in a battery pack based on impedance spectrum, as described in the first aspect.

[0038] In summary, this invention provides a method and related apparatus for locating defective cells in a battery pack based on impedance spectrum analysis. The method includes applying a multi-frequency superimposed excitation current signal to the battery pack; acquiring the battery voltage response of the battery pack under the multi-frequency superimposed signal; determining the superposition of impedances at different frequencies based on the battery voltage response to obtain the superimposed impedance; and identifying the location of the defective cell in the battery pack using a defective cell identification model based on the superimposed impedance. The defective cell identification model is trained using the different locations of the defective cells in the battery pack and their corresponding impedance spectra. This invention, by analyzing the battery voltage response under multi-frequency superimposed excitation, can more precisely capture anomalies in individual cells within the battery pack. Utilizing the characteristics of battery impedance variation at different frequencies, combined with superimposed impedance analysis, can significantly improve the accuracy of defective cell location, which is crucial for maintaining the overall performance and safety of the battery pack. Attached Figure Description

[0039] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0040] Figure 1 A flowchart illustrating a method for locating defective cells in a battery pack based on impedance spectroscopy, provided in an embodiment of the present invention;

[0041] Figure 2 This is a schematic diagram of battery impedance measurement provided in an embodiment of the present invention;

[0042] Figure 3 This is a schematic diagram illustrating the acquisition of impedance data for training an artificial neural network, provided in an embodiment of the present invention.

[0043] Figure 4 A schematic diagram of an artificial neural network provided in an embodiment of the present invention;

[0044] Figure 5 A block diagram of the device for locating defective cells in a battery pack based on impedance spectroscopy, provided in an embodiment of the present invention.

[0045] Figure 6 This is a block diagram of a computer device provided in an embodiment of the present invention. Detailed Implementation

[0046] To make the objectives, features, and advantages of this invention more apparent and understandable, the technical solutions of the embodiments of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the embodiments described below are only some embodiments of this invention, and not all embodiments. Based on the embodiments of this invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this invention.

[0047] Please see Figure 1 This embodiment provides a method for locating defective cells in a battery pack based on impedance spectroscopy, including the following steps:

[0048] S:1: Apply a multi-frequency superimposed excitation current signal to the battery pack.

[0049] It should be noted that, in order to comprehensively assess the battery state, this step applies current signals containing multiple frequency components simultaneously to the battery pack. Currents of different frequencies can induce electrochemical reactions at different depths and mechanisms within the battery, thereby revealing the battery's multi-level health status.

[0050] Multi-frequency signals can more comprehensively elicit the impedance characteristics of a battery, including ohmic resistance, electrochemical reaction resistance, and double-layer capacitance effect. This information is crucial for identifying battery defects.

[0051] S12: Obtain the battery voltage response of the battery pack under multi-frequency superimposed signals.

[0052] It should be noted that the voltage response of the battery pack was measured and recorded under multi-frequency superimposed excitation. The voltage response encompasses the battery's comprehensive response to currents of various frequencies and forms the basis for subsequent analysis.

[0053] The characteristic of voltage changing with current directly reflects the impedance characteristics of the battery. Batteries in different states (such as healthy, aged, short-circuited, open-circuited, etc.) will exhibit different characteristics in voltage response.

[0054] S13: Determine the superposition of impedances at different frequencies based on the battery voltage response to obtain the superimposed impedance.

[0055] It should be noted that by analyzing the voltage response obtained in S2, the impedance values ​​of the battery at different frequencies are separated, and these impedance values ​​are superimposed according to frequency. This process may involve signal processing techniques such as Fourier transform to convert the signal from the time domain to the frequency domain, facilitating impedance extraction.

[0056] The resulting superimposed impedance is an impedance spectrum that integrates multi-frequency information, which can more comprehensively reflect the state of the battery than the impedance of a single frequency.

[0057] S14: Based on the superimposed impedance, the location of defective cells in the battery pack is identified using a defective cell identification model. The defective cell identification model is trained using the different locations of defective cells in the battery pack and their corresponding impedance spectra.

[0058] It should be noted that the pre-trained defective battery identification model uses superimposed impedance data as input. This model is trained on a large number of defective battery samples with known locations and impedance spectra, and is able to learn and identify the correlation between different impedance characteristics and battery defect locations.

[0059] Model recognition can not only quickly locate problematic batteries, but also adapt to various types of defects, such as capacity decay, increased internal resistance, and local short circuits, thus improving the accuracy and efficiency of diagnosis.

[0060] This embodiment provides a method for locating defective cells in a battery pack based on impedance spectroscopy. This method analyzes the battery voltage response under multi-frequency superimposed excitation, enabling more precise detection of anomalies in individual cells within the battery pack. By utilizing the characteristics of battery impedance variations at different frequencies, combined with advanced superimposed impedance analysis methods, the accuracy of defective cell location can be significantly improved, which is crucial for maintaining the overall performance and safety of the battery pack.

[0061] The superimposed excitation current signal of multiple frequencies can acquire impedance information at multiple frequency points in a single test, which greatly shortens the detection time and improves work efficiency compared to testing each frequency one by one. This method is particularly suitable for online monitoring and rapid screening of large-scale battery packs, helping to promptly identify and eliminate potential problems.

[0062] In some embodiments, the excitation signal is superimposed with multiple current signals of different frequencies and amplitudes according to a certain pattern, while ensuring that the signal amplitude is more than 5 times the charging current. The expression for the excitation signal is:

[0063]

[0064] In the formula, i is the excitation signal, I1, ..., I n Let w be the maximum value of n different frequency currents. 1t ,…,w nt There are n different signal frequencies.

[0065] like Figure 2 As shown, in some embodiments, obtaining the battery voltage response of the battery pack under multi-frequency superimposed signals includes:

[0066] Set the measuring terminal to the positive terminal of the first battery and the negative terminal of the last battery in the battery pack, or the negative terminal of the first battery and the positive terminal of the last battery; obtain the battery voltage response of the battery pack from the measuring terminal.

[0067] In other words, battery voltage response measures the overall voltage signal of the entire battery pack. The Fast Fourier Transform algorithm can be used to decompose the battery voltage response into different frequencies, yielding impedance values ​​at various frequencies.

[0068] In some embodiments, the defective battery identification model is a model trained based on an artificial neural network, and the training process includes:

[0069] S21: Set up defective batteries at different locations in the battery pack and measure the corresponding battery impedance;

[0070] S22: Using the location and impedance of the defective battery as input, an artificial neural network is used for training to obtain a defective battery identification model.

[0071] During training, the defective battery identification model measures the impedance (EIS) of different defective batteries at different battery locations to obtain combinations of impedances for different types of batteries. For example... Figure 3 As shown, battery number 2 is a defective battery. The impedance of a group of batteries is measured at this time, and battery position 2 is used as the input of the artificial intelligence network. The other battery positions are measured once, and the network is trained once. Finally, a well-trained artificial neural network is obtained.

[0072] The architecture of artificial neural networks is as follows Figure 4 As shown, it has an input layer, a hidden layer, and an output layer.

[0073] Based on the same inventive concept, embodiments of the present invention also provide an apparatus for locating the location of defective batteries in a battery pack based on impedance spectroscopy, which is used to implement the method for locating the location of defective batteries in a battery pack as described above. The solution provided by this apparatus is similar to the solution described in the above method; therefore, the specific limitations in the embodiments of the apparatus for locating the location of defective batteries in a battery pack based on impedance spectroscopy provided below can be found in the limitations of the method for locating the location of defective batteries in a battery pack based on impedance spectroscopy described above, and will not be repeated here.

[0074] Please see Figure 5 This embodiment provides a device for locating the position of defective cells in a battery pack based on impedance spectroscopy, comprising:

[0075] The hybrid excitation module is used to apply multi-frequency superimposed excitation current signals to the battery pack;

[0076] The measurement module is used to acquire the battery voltage response of the battery pack under multi-frequency superimposed signals;

[0077] The calculation module is used to determine the superposition of impedances at different frequencies based on the battery voltage response, and obtain the superimposed impedance;

[0078] The analysis module is used to identify the location of defective cells in the battery pack based on the superimposed impedance and the defective cell identification model. The defective cell identification model is trained using the different locations of defective cells in the battery pack and their corresponding impedance spectra.

[0079] In some embodiments, obtaining the battery voltage response of the battery pack under multi-frequency superimposed signals includes:

[0080] Set the measuring terminal to the positive terminal of the first battery and the negative terminal of the last battery in the battery pack, or the negative terminal of the first battery and the positive terminal of the last battery.

[0081] The battery voltage response of the battery pack is obtained from the measurement terminal.

[0082] In some embodiments, the defective battery identification model is a model trained based on an artificial neural network, and the training process includes:

[0083] Set up defective batteries at different locations in the battery pack and measure the corresponding battery impedance;

[0084] By using the location and impedance of the defective battery as input, an artificial neural network is used for training to obtain a defective battery identification model.

[0085] In some embodiments, such as Figure 5As shown, it also includes: an alarm module and a display module;

[0086] The alarm module is used to issue an alarm signal when the analysis module detects a defective battery;

[0087] The display module is used to display the serial number of the defective battery in the battery pack.

[0088] Those skilled in the art will understand that, for the sake of convenience and brevity, the above-described division of functional units and modules is merely an example. In practical applications, the functions described above can be assigned to different functional units and modules as needed, that is, the internal structure of the system can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiments can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit. Furthermore, the specific names of the functional units and modules are only for easy differentiation and are not intended to limit the scope of protection of this invention. The specific working process of the units and modules in the above system can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0089] Reference Figure 6 The present invention also provides a computer device 1, including a memory 12 and a processor 11 and a computer program 13 stored on the memory 12. When the computer program 13 is executed on the processor 11, it implements the method for locating the location of defective batteries in a battery pack based on impedance spectrum as described in any of the above methods.

[0090] The computer device 1 may be a desktop computer, laptop, handheld computer, or cloud server, etc. The computer device 1 may include, but is not limited to, a processor 11 and a memory 12. Those skilled in the art will understand that... Figure 6 The computer device 1 is merely an example and does not constitute a limitation on the computer device 1. It may include more or fewer components than shown in the figure, or combine certain components, or different components, such as input / output devices, network access devices, etc.

[0091] The processor 11 may be a Central Processing Unit (CPU), or it may be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor.

[0092] In some embodiments, the memory 12 may be an internal storage unit of the computer device 1, such as a hard disk or memory of the computer device 1. In other embodiments, the memory 12 may be an external storage device of the computer device 1, such as a plug-in hard disk, smart media card (SMC), secure digital (SD) card, flash card, etc., equipped on the computer device 1. Furthermore, the memory 12 may include both internal and external storage units of the computer device 1. The memory 12 is used to store the operating system, applications, bootloader, data, and other programs, such as the program code of the computer program. The memory 12 can also be used to temporarily store data that has been output or will be output.

[0093] This invention also provides a computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the method for locating defective batteries in a battery pack based on impedance spectroscopy, as described in any of the above methods.

[0094] In this embodiment, if the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the methods of the above embodiments of the present invention can be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include at least: any entity or device capable of carrying computer program code to a photographic device / terminal device, a recording medium, a computer memory, a read-only memory (ROM), a random access memory (RAM), an electrical carrier signal, a telecommunication signal, and a software distribution medium. Examples include USB flash drives, portable hard drives, magnetic disks, or optical disks. In some jurisdictions, according to legislation and patent practice, computer-readable media cannot be electrical carrier signals or telecommunication signals.

[0095] In the above embodiments, the descriptions of each embodiment have different focuses. For parts that are not described in detail or recorded in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0096] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this invention.

[0097] In the embodiments disclosed in this invention, it should be understood that the disclosed devices / terminal equipment and methods can be implemented in other ways. For example, the device / terminal equipment embodiments described above are merely illustrative. For instance, the division of modules or units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between devices or units may be electrical, mechanical, or other forms.

[0098] The above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for locating defective cells in a battery pack based on impedance spectroscopy, characterized in that, Includes the following steps: Apply multi-frequency superimposed excitation current signals to the battery pack; Obtain the battery voltage response of the battery pack under multi-frequency superimposed signals; The superposition of impedances at different frequencies is determined based on the battery voltage response, and the superimposed impedance is obtained. Based on the superimposed impedance, the location of defective cells in the battery pack is identified using a defective cell identification model, which is a model trained using the different locations of defective cells in the battery pack and their corresponding impedance spectra. Obtaining the battery voltage response of the battery pack under multi-frequency superimposed signals includes: Set the measuring terminal to the positive terminal of the first battery and the negative terminal of the last battery in the battery pack, or the negative terminal of the first battery and the positive terminal of the last battery. The battery voltage response of the battery pack is obtained from the measuring terminal; The defective battery identification model is a model trained based on an artificial neural network. The training process includes: Defective batteries were placed at different locations within the battery pack, and the corresponding battery impedances were measured. The location of the defective battery and its impedance are used as inputs, and an artificial neural network is used for training to obtain the defective battery identification model.

2. The method for locating defective cells in a battery pack based on impedance spectroscopy according to claim 1, characterized in that, The expression for the excitation current signal is as follows: ； In the formula, i is the excitation signal, I1, ..., I n Let w be the maximum value of n different frequency currents. 1t ,…,w nt There are n different signal frequencies.

3. A device for locating defective cells in a battery pack based on impedance spectroscopy, characterized in that, include: The hybrid excitation module is used to apply multi-frequency superimposed excitation current signals to the battery pack; The measurement module is used to acquire the battery voltage response of the battery pack under multi-frequency superimposed signals; The calculation module is used to determine the superposition of impedances at different frequencies based on the battery voltage response, and to obtain the superimposed impedance; The analysis module is used to identify the location of defective cells in the battery pack based on the superimposed impedance using a defective cell identification model. The defective cell identification model is a model trained using the different locations of defective cells in the battery pack and their corresponding impedance spectra. Obtaining the battery voltage response of the battery pack under multi-frequency superimposed signals includes: Set the measuring terminal to the positive terminal of the first battery and the negative terminal of the last battery in the battery pack, or the negative terminal of the first battery and the positive terminal of the last battery. The battery voltage response of the battery pack is obtained from the measuring terminal; The defective battery identification model is a model trained based on an artificial neural network. The training process includes: Defective batteries were placed at different locations within the battery pack, and the corresponding battery impedances were measured. The location of the defective battery and its impedance are used as inputs, and an artificial neural network is used for training to obtain the defective battery identification model.

4. The device for locating defective cells in a battery pack based on impedance spectroscopy according to claim 3, characterized in that, Also includes: Alarm module and display module; The alarm module is used to issue an alarm signal when the analysis module detects a defective battery. The display module is used to display the serial number of the defective battery in the battery pack.

5. A computer device, characterized in that, The device includes a processor and a memory: The memory is used to store computer programs and send the instructions of the computer programs to the processor; The processor executes, according to the instructions of the computer program, a method for locating defective batteries in a battery pack based on impedance spectrum as described in any one of claims 1-2.

6. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, implements a method for locating defective cells in a battery pack based on impedance spectroscopy as described in any one of claims 1-2.

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