A single battery rapid detection device based on radio measurement

Abstract

The application relates to the technical field of battery detection, in particular to a single battery rapid detection device based on wireless electric measurement; the device comprises an excitation source module, a power amplifier module, a test probe, a data processing module and a model analysis module; the excitation source module is used for generating wireless electric signals of specific frequency bands; the power amplifier module is used for processing the wireless electric signals; the test probe is used for forming a complete signal transmission path with a lithium battery to be detected; the data processing module is used for acquiring key wireless electric measurement parameters; the model analysis module is used for analyzing the key wireless electric measurement parameters and obtaining core parameters reflecting battery health through inversion; an analysis model is arranged in the model analysis module; the analysis model comprises a full connection neural network, a transmission line propagation model and an electrochemical impedance spectrum model; and the application can improve the detection efficiency of lithium batteries.

Description

Technical Field

[0001] This invention relates to the field of battery testing technology, and in particular to a rapid testing device for single-cell batteries based on radio measurement. Background Technology

[0002] Currently, the mainstream testing equipment in the lithium battery capacity testing industry is the lithium battery capacity grading equipment. This equipment can be used to screen the capacity of lithium batteries to meet the packing requirements, ensuring the performance stability of the reconstituted battery packs. However, existing lithium battery capacity grading equipment suffers from drawbacks in actual capacity grading processes, including low testing efficiency, high energy consumption, limited functionality, and impact on design lifespan.

[0003] Radio measurement technology is a common measurement method. Its core is to transmit radio waves of a specific frequency and utilize their propagation characteristics in a medium to explore the frequency response variations at the interface of different battery materials and states, measuring the externally observable signal resulting from the interaction between the radio waves and the battery. Specifically, it measures frequency parameters such as complex impedance, phase, amplitude, resonant frequency, and cutoff frequency of the measured object at different frequency points through the transfer functions of reflection, refraction, and transmission. Furthermore, it uses morphological analysis, modeling, and inversion calculations to evaluate the morphology, materials, and structure of the measured object (e.g., changes in inorganic salt adhesion on electrode materials, the smoothness of electrode materials, changes in electrolyte concentration, internal defects, etc.). With the development of measurement and control technology and the continuous improvement of measurement accuracy, radio measurement technology has been widely researched and applied in fields such as geophysical exploration, lightning, cloud and rain detection, and road detection.

[0004] The existing technology has at least the following unresolved technical problems or defects:

[0005] Currently, radio measurement technology is not used in the field of lithium battery testing. The testing efficiency and energy consumption during the lithium battery capacity grading process need to be improved. Summary of the Invention

[0006] The purpose of this invention is to provide: A rapid testing device for single-cell batteries based on radio measurement, and related technologies, to solve technical problems such as low testing efficiency and high energy consumption during lithium battery capacity grading, or a combination thereof.

[0007] Terminology Explanation: Unless otherwise defined, all technical terms used herein have the same meanings as commonly understood by one of ordinary skill in the art to which the subject matter pertains. Unless otherwise stated, all patents, patent inventions, and publications cited throughout this document are incorporated herein by reference in their entirety. Where multiple definitions exist for terms in this document, the definitions provided in this chapter shall prevail.

[0008] It should be understood that the above brief description and the following detailed description are exemplary and for illustrative purposes only, and do not limit the subject matter of the invention in any way. In this invention, the singular is used in conjunction with the plural unless otherwise specifically stated. It should also be noted that, unless otherwise stated, the use of “or” or “or” means “and / or”. Furthermore, the use of the term “comprising” and other forms such as “including,” “containing,” and “contains” are not limiting.

[0009] Definitions of standard terms can be found in the reference "Electrochemical Testing Technology".

[0010] Unless otherwise stated, conventional methods within the scope of the art, such as constant current and constant voltage charge and discharge settings, shall be used.

[0011] Unless otherwise defined, the use of various commercially available products as described herein employs standard techniques. These techniques and methods can generally be implemented according to conventional methods well-known in the art, based on the descriptions in the numerous general and more specific documents cited and discussed in this specification.

[0012] The terms “optional / arbitrary” or “optionally / arbitrarily” mean that the event or situation described below may or may not occur, including both the occurrence and non-occurrence of the event or situation.

[0013] The term "voltage" used in this article refers to voltage difference, which is the potential difference between two points.

[0014] The term "electric current" as used in this article refers to the directional flow of electric charge.

[0015] The term "lithium battery" as used in this article refers to a battery that uses lithium metal or lithium alloy as electrode material and a non-aqueous electrolyte as the conductive medium.

[0016] This invention provides a rapid testing device for single-cell batteries based on radio measurement.

[0017] The system includes an excitation source module, a power amplifier module, a test probe, a data processing module, and a model analysis module. The excitation source module generates radio signals in a specific frequency band. The power amplifier module processes the radio signals. The test probe forms a complete signal transmission path with the lithium battery under test. The data processing module acquires key radio measurement parameters. The model analysis module analyzes the key radio measurement parameters and, after inversion, obtains the core parameters reflecting the battery's health. The model analysis module includes analysis models such as fully connected neural networks, transmission line propagation models, and electrochemical impedance spectroscopy models.

[0018] Among them, the key radio measurement parameters include the impedance characteristics of the signal between the first probe and the second probe at multiple frequencies, and the impedance characteristics include voltage, current, battery internal resistance and remaining capacity; The impedance characteristic parameters at all frequencies are sequentially input into the fully connected neural network model. Based on the output of the fully connected neural network model corresponding to each frequency, the propagation of the electrical signal between the first and second probes in the test circuit is adjusted in combination with the transmission line propagation model. After processing the impedance characteristic parameters at all frequencies, impedance characteristic parameter inversion is performed. The inversion yields impedance characteristic parameters used to reflect the health status of the lithium battery.

[0019] The electrochemical impedance spectroscopy model includes a set of impedance characteristic parameters, denoted as follows: , ,in, , , These represent the first impedance characteristic parameter, the second impedance characteristic parameter, and the Nth impedance characteristic parameter, respectively.

[0020] The fully connected neural network includes an input layer and multiple hidden layers, with the last hidden layer serving as the output layer, which stores the impedance characteristic parameters. The input is fed into the input layer, processed through multiple hidden layers, and the output layer outputs the processing result.

[0021] The output of the l-th hidden layer in a fully connected neural network is specifically represented by the following formula, where l ranges from 1 to E, and E represents the total number of hidden layers: ; In the above formula, This represents the output after passing through the l-th hidden layer. Indicates the impedance of the input to the l-th hidden layer. The weight matrix of the parameter set, This represents the set of impedance characteristic parameters of the input l-th hidden layer. Indicates the bias term. This indicates the activation function, which is the sigmoid function.

[0022] The test probe includes a first probe and a second probe. The first probe and the second probe are coupled to the surface of the lithium battery under test, respectively. The propagation of the electrical signal between the first probe and the second probe in the test circuit satisfies the transmission line propagation model.

[0023] The propagation characteristic equation of the transmission line propagation model is as follows: ; ; In the above formula, This represents the voltage after processing by a fully connected neural network. This represents the current after processing by a fully connected neural network. Represents the imaginary part of voltage. Represents the real part of the voltage. Indicates the foundation impedance. Indicates the direction of propagation. This represents the distributed resistance after processing by a fully connected neural network. Indicates distributed conductivity. The value of C represents the equivalent inductance of the circuit, and the value of C represents the equivalent capacitance.

[0024] The foundation impedance is expressed as: ; In the above formula, denoted by , where C represents the equivalent inductance of the circuit, w represents the phase difference between voltage and current, and j represents the rate of change of the signal over time.

[0025] In this process, multiple characteristic frequency points are selected in the electrical signal between the first and second probes, covering the resonant sensitive frequency band and the attenuation characteristic frequency band. For each frequency point, the line parameters are dynamically adjusted through an impedance adaptive matching algorithm to achieve impedance matching between the signals of each frequency band and the system.

[0026] The power amplifier module uses a power amplifier with a power of 10W or less. Compared with the prior art, the beneficial effects of the present invention are as follows: This invention introduces radio measurement technology into battery testing. A radio signal is emitted by an excitation source module, then regulated by a power amplifier module. A first and second probe form a circuit with the lithium battery under test. A data processing module captures key radio measurement parameters at a high-frequency sampling rate, and a model analysis module analyzes these parameters. Through inversion, core parameters reflecting battery health are obtained. This invention can quickly detect the internal resistance, voltage, and capacity parameters of a lithium battery for assessing its health status. Furthermore, it boasts high testing efficiency, low energy consumption, non-destructive testing capabilities, and low safety risks. Attached Figure Description

[0027] Figure 1 This is a flowchart of the present invention.

[0028] Figure 2 This is a topology diagram of the fully connected neural network of this invention. Detailed Implementation

[0029] The technical solution of the present invention will be clearly described below with reference to the accompanying drawings. Obviously, the described embodiments are not all embodiments of the present invention. All other embodiments obtained by those skilled in the art without creative effort are within the protection scope of the present invention.

[0030] like Figure 1 As shown, this invention provides a rapid testing device for single-cell batteries based on radio measurement, including an excitation source module, a power amplifier module, a test probe, a data processing module, and a model analysis module. The excitation source module generates radio signals in a specific frequency band. The power amplifier module adjusts the gain and optimizes the waveform of the generated radio signals to ensure stable transmission power and anti-interference capability. The test probe has a first probe and a second probe, both of which are coupled to the surface of the lithium battery under test. The radio signal after passing through the power amplifier module penetrates the core components of the lithium battery under test, such as the battery electrodes, electrolyte, and separator, through the first and second probes, forming a complete signal transmission path. The data processing module captures key radio measurement parameters at a high-frequency sampling rate, and the model analysis module analyzes the key radio measurement parameters to obtain core parameters reflecting battery health.

[0031] The power amplifier module, specifically a power amplifier, is the core unit for signal amplification and energy driving in a lithium battery non-destructive testing system. It receives the radio signal output from the excitation source module, and its power is less than or equal to 10W. The power amplifier module directly determines the signal penetration depth, anti-interference capability, and subsequent testing accuracy, serving as a crucial technological bridge connecting the excitation source module and the test probe. The excitation source module and the power amplifier module work in coordination, with parameters automatically configured via a host computer.

[0032] The test probe employs a dual-probe collaborative design, utilizing multi-dimensional technologies such as cabling optimization, DC blocking, amplification and filtering, frequency selection, and fixture design. In the cabling design phase, adhering to electromagnetic compatibility (EMC) principles, differential cabling and shielded cabling techniques are employed to isolate the transmission lines of the transmitting probe, receiving probe, and signal processing circuitry, reducing electromagnetic coupling interference between lines. Simultaneously, line impedance continuity is optimized, shortening the signal transmission path and minimizing the impact of parasitic parameters on weak signals, providing a stable and reliable physical link for small signal transmission. One of the first and second probes is designated as the transmitting probe, and the other as the receiving probe, connected via the signal processing circuitry. DC blocking precisely filters out DC components and low-frequency drift signals introduced by the power supply system, preventing signal baseline shifts caused by DC interference and ensuring the integrity of the original characteristics of small signals. An integrated amplification and filtering circuit architecture is adopted, balancing signal amplification and noise suppression capabilities to ensure high-fidelity processing of small signals. In frequency selection, based on the electromagnetic response characteristics analysis of key internal components (electrodes, electrolyte, and separator) of the lithium battery under test, multiple characteristic frequency points are selected, covering the resonant sensitive frequency band and attenuation characteristic frequency band. For each frequency point, the circuit parameters are dynamically adjusted through an impedance adaptive matching algorithm to achieve accurate impedance matching between the signal and the system in each frequency band, reducing signal reflection loss and transmission distortion. In fixture design, the fixture can be adjusted in size according to lithium batteries of different sizes under test. The test probes of the first and second probes can also be moved up, down, left, and right to match the testing requirements of different lithium batteries under test.

[0033] The data processing module performs impedance-frequency characteristic measurements on the signals collected by the first and second probes to obtain impedance characteristic parameters of the signals between the first and second probes at multiple frequencies. The impedance characteristic parameters include voltage, current, battery internal resistance, and remaining capacity (SOC).

[0034] The model analysis module sets up an analysis model, which is constructed by combining a fully connected neural network (FCNN), a transmission line propagation model, and an electrochemical impedance spectroscopy model. Impedance characteristic parameters at different frequencies are input into the analysis model, and state parameters are output to reflect the health status of the tested lithium battery.

[0035] In the analysis model, a fully connected neural network is used to predict and compensate for voltage and current. The topology of the fully connected neural network is as follows: Figure 2 As shown, it includes an input layer and multiple hidden layers. There are E hidden layers, and the Eth hidden layer is the output layer.

[0036] The electrochemical impedance spectroscopy model is a set of impedance characteristic parameters. The input set of impedance characteristic parameters is set as follows: , ,in, , , Let these represent the first, second, and Nth impedance characteristic parameters, respectively. The output of the l-th hidden layer is: ; In the above formula, This represents the output after passing through the l-th hidden layer. The weight matrix represents the set of impedance characteristic parameters of the input l-th hidden layer. This represents the set of impedance characteristic parameters of the input l-th hidden layer. Indicates the bias term. This represents the activation function, which is the sigmoid function, with l ranging from 1 to E.

[0037] The propagation of the electrical signal between the first and second probes in the test circuit follows the transmission line propagation model, and the propagation characteristic equation of the transmission line propagation model is as follows: ; ; ; In the above formula, This represents the voltage after processing by a fully connected neural network. This represents the current after processing by a fully connected neural network. Represents the imaginary part of voltage. Represents the real part of the voltage. Indicates the foundation impedance. The signal represents the direction of propagation, w represents the phase difference between voltage and current, and j represents the rate of change of the signal over time. This represents the distributed resistance after processing by a fully connected neural network. Indicates distributed conductivity. The value of C represents the equivalent inductance of the circuit, and the value of C represents the equivalent capacitance.

[0038] In the model analysis module, the impedance characteristic parameters of all frequencies obtained from the data processing module are sequentially input into the fully connected neural network model. Based on the output results of the fully connected neural network model corresponding to each frequency, the propagation of the electrical signal between the first and second probes in the test circuit is adjusted in combination with the transmission line propagation model. After the impedance characteristic parameters at all frequencies are processed as described above, the health status of the lithium battery is inverted. The inversion yields the impedance characteristic parameters that reflect the health status of the lithium battery, thereby accurately reflecting the health status of the tested lithium battery.

[0039] This invention introduces radio measurement technology into battery testing. A radio signal is emitted by an excitation source module, then regulated by a power amplifier module. A first and second probe form a circuit with the lithium battery under test. A data processing module captures key radio measurement parameters at a high-frequency sampling rate, and a model analysis module analyzes these parameters. Through inversion, core parameters reflecting battery health are obtained. This invention can quickly detect the internal resistance, voltage, and capacity parameters of a lithium battery for assessing its health status. Furthermore, it boasts high testing efficiency, low energy consumption, non-destructive testing capabilities, and low safety risks.

[0040] Finally, it should be noted that the above content is only used to illustrate the technical solution of the present invention, and is not intended to limit the scope of protection of the present invention. Simple modifications or equivalent substitutions made by those skilled in the art to the technical solution of the present invention do not depart from the essence and scope of the technical solution of the present invention.

Claims

1. A rapid testing device for single-cell batteries based on radio measurement, characterized in that, It includes an excitation source module, a power amplifier module, a test probe, a data processing module, and a model analysis module. The excitation source module is used to generate radio signals in a specific frequency band. The power amplifier module is used to process the radio signals. The test probe is used to form a complete signal transmission path with the lithium battery under test. The data processing module is used to acquire key radio measurement parameters. The model analysis module is used to analyze the key radio measurement parameters and, after inversion, obtain the core parameters reflecting the battery health. The model analysis module includes analysis models such as fully connected neural networks, transmission line propagation models, and electrochemical impedance spectroscopy models.

2. The rapid testing device for single-cell batteries based on radio measurement according to claim 1, characterized in that, The key radio measurement parameters include impedance characteristics of the signal between the first and second probes at multiple frequencies, including voltage, current, battery internal resistance, and remaining capacity. The impedance characteristic parameters at all frequencies are sequentially input into the fully connected neural network model. Based on the output of the fully connected neural network model corresponding to each frequency, the propagation of the electrical signal between the first and second probes in the test circuit is adjusted in combination with the transmission line propagation model. After processing the impedance characteristic parameters at all frequencies, impedance characteristic parameter inversion is performed. The inversion yields impedance characteristic parameters used to reflect the health status of the lithium battery.

3. The rapid testing device for single-cell batteries based on radio measurement according to claim 2, characterized in that, The electrochemical impedance spectroscopy model includes a set of impedance characteristic parameters, denoted as . , ,in, , , These represent the first impedance characteristic parameter, the second impedance characteristic parameter, and the Nth impedance characteristic parameter, respectively.

4. The rapid testing device for single-cell batteries based on radio measurement according to claim 3, characterized in that, The fully connected neural network includes an input layer and multiple hidden layers, with the last hidden layer serving as the output layer, which stores the impedance characteristic parameters. The input is fed into the input layer, processed through multiple hidden layers, and the output layer outputs the processing result.

5. The rapid testing device for single-cell batteries based on radio measurement according to claim 4, characterized in that, The output of the l-th hidden layer in a fully connected neural network is specifically represented by the following formula, where l ranges from 1 to E, and E represents the total number of hidden layers: ； In the above formula, This represents the output after passing through the l-th hidden layer. The weight matrix represents the set of impedance characteristic parameters of the input l-th hidden layer. This represents the set of impedance characteristic parameters of the input l-th hidden layer. Indicates the bias term. This indicates the activation function, which is the sigmoid function.

6. The rapid testing device for single-cell batteries based on radio measurement according to claim 2, characterized in that, The test probe includes a first probe and a second probe. The first probe and the second probe are coupled to the surface of the lithium battery under test, respectively. The propagation of the electrical signal between the first probe and the second probe in the test circuit satisfies the transmission line propagation model.

7. The rapid testing device for single-cell batteries based on radio measurement according to claim 6, characterized in that, The propagation characteristic equation of the transmission line propagation model is as follows: ； ； In the above formula, This represents the voltage after processing by a fully connected neural network. This represents the current after processing by a fully connected neural network. Represents the imaginary part of voltage. Represents the real part of the voltage. Indicates the foundation impedance. Indicates the direction of propagation. This represents the distributed resistance after processing by a fully connected neural network. Indicates distributed conductivity. The value of C represents the equivalent inductance of the circuit, and the value of C represents the equivalent capacitance.

8. The rapid testing device for single-cell batteries based on radio measurement according to claim 7, characterized in that, The fundamental impedance is expressed as: ； In the above formula, denoted by , where C represents the equivalent inductance of the circuit, w represents the phase difference between voltage and current, and j represents the rate of change of the signal over time.

9. A rapid testing device for single-cell batteries based on radio measurement according to claim 2, characterized in that, In the electrical signal between the first and second probes, multiple characteristic frequency points are selected, covering the resonant sensitive frequency band and the attenuation characteristic frequency band. For each frequency point, the line parameters are dynamically adjusted through an impedance adaptive matching algorithm to achieve impedance matching between the signals of each frequency band and the system.

10. A rapid testing device for single-cell batteries based on radio measurement according to claim 1, characterized in that, The power amplifier module uses a power amplifier with a power of less than or equal to 10W.

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