Noise separation detection method and system for electrochemical impedance spectroscopy

Abstract

The invention provides a noise separation detection method and system for an electrochemical impedance spectroscopy in the technical field of electrochemical impedance spectroscopy detection. The method comprises the following steps: S1, setting a test frequency set of an excitation signal; s2, collecting a first voltage / current sequence signal for each test frequency when the excitation signal is not applied; when the excitation signal is applied, a second voltage / current sequence signal is collected; s3, Fourier transform is carried out, and a first voltage amplitude, a first current amplitude, a second voltage amplitude and a second current amplitude corresponding to the test frequency are extracted; s4, calculating a difference value of the second / first voltage amplitude to obtain a net voltage amplitude, and calculating a difference value of the second / first current amplitude to obtain a net current amplitude; and S5, calculating the impedance value of each test frequency to obtain the electrochemical impedance spectrum. The electrochemical impedance spectroscopy detection device has the advantages that the anti-interference capability and accuracy of electrochemical impedance spectroscopy detection are greatly improved, and meanwhile, the problems of high equipment cost, large size and potential battery damage are avoided.

Description

Technical Field

[0001] This invention relates to the field of electrochemical impedance spectroscopy detection technology, and in particular to a noise separation and detection method and system for electrochemical impedance spectroscopy. Background Technology

[0002] Electrochemical impedance spectroscopy (EIS) is an important method for battery detection and analysis. Its basic principle involves applying an AC sinusoidal excitation signal (voltage or current) of a specific frequency to the battery under test, and then simultaneously acquiring the voltage and current responses at the battery terminals. Based on the definition of impedance (Z=V / I), the impedance value at that frequency is calculated. By scanning a series of frequency points, the battery's impedance spectrum can be obtained, allowing analysis of key internal parameters such as polarization resistance, capacitance, and diffusion coefficient. EIS is widely used to evaluate battery performance indicators such as state of health (SOH) and state of charge (SOC).

[0003] In a laboratory environment, when performing EIS testing on individual cells, modules, or battery cabinets, traditional EIS testing methods can obtain relatively accurate measurement results due to minimal external electromagnetic interference. However, when batteries are integrated into actual operating systems (such as electric vehicles or electrochemical energy storage power stations), the measurement environment becomes extremely complex and harsh. Components within the system, such as high-voltage circuits, motors, converters, and switching power supplies, generate a large amount of broadband electromagnetic interference noise. This strong noise couples into the voltage and current sampling signals of the EIS testing circuit, causing a severe deterioration in the signal-to-noise ratio. This makes it difficult for traditional EIS testing methods to accurately extract the weak excitation signal response from the strong noise background, ultimately resulting in large errors or even complete distortion of the measurement results.

[0004] To improve detection capabilities in noisy environments, a common solution in existing technologies is to increase the power of the injected excitation signal to enhance its amplitude and make it stand out in the noise. However, this "brute force" approach has significant drawbacks: First, a high-power excitation signal source means higher equipment cost, size, and energy consumption, hindering the miniaturization, portability, and cost reduction of detection equipment; second, an excessively strong excitation signal may cause irreversible damage to the battery's electrode interface, thereby affecting the battery's lifespan and performance, which contradicts the original purpose of EIS detection—to assess and extend battery life.

[0005] In summary, existing traditional EIS detection technologies are severely limited in complex real-world applications due to their inherent insufficient anti-interference capabilities. Furthermore, methods that improve the signal-to-noise ratio by increasing hardware power suffer from drawbacks such as high cost, bulky equipment, and potential battery damage.

[0006] Therefore, how to provide a noise separation and detection method and system for electrochemical impedance spectroscopy to improve the anti-interference capability and accuracy of electrochemical impedance spectroscopy detection, while avoiding the problems of high equipment cost, large size and potential battery damage caused by increasing the excitation signal power, has become an urgent technical problem to be solved. Summary of the Invention

[0007] The technical problem to be solved by the present invention is to provide a noise separation and detection method and system for electrochemical impedance spectroscopy, thereby improving the anti-interference capability and accuracy of electrochemical impedance spectroscopy detection, while avoiding the problems of high equipment cost, large size and potential battery damage caused by increasing the excitation signal power.

[0008] In a first aspect, the present invention provides a noise separation and detection method for electrochemical impedance spectroscopy, comprising the following steps: Step S1: Set a test frequency set for the excitation signal, which includes several test frequencies; Step S2: For each of the test frequencies, when the excitation signal is not applied to the battery under test, acquire the first voltage sequence signal and the first current sequence signal of the battery under test; when the excitation signal is applied to the battery under test, acquire the second voltage sequence signal and the second current sequence signal of the battery under test. Step S3: Perform Fourier transform on the first voltage sequence signal and the first current sequence signal respectively to extract the first voltage amplitude and the first current amplitude corresponding to the current test frequency; perform Fourier transform on the second voltage sequence signal and the second current sequence signal respectively to extract the second voltage amplitude and the second current amplitude corresponding to the current test frequency. Step S4: Calculate the difference between the second voltage amplitude and the first voltage amplitude to obtain the net voltage amplitude; calculate the difference between the second current amplitude and the first current amplitude to obtain the net current amplitude. Step S5: Based on the net voltage amplitude and net current amplitude, calculate the impedance value at the current test frequency, and generate an electrochemical impedance spectrum based on the impedance values ​​at all test frequencies.

[0009] Furthermore, in step S1, the excitation signal is a sine wave signal.

[0010] Furthermore, in step S2, the first voltage sequence signal, the first current sequence signal, the second voltage sequence signal, and the second current sequence signal are all discrete time sequence signals with the same number of sampling points.

[0011] Furthermore, in step S2, the first voltage sequence signal is collected and stored in the voltage sampling data unit, the first current sequence signal is collected and stored in the current sampling data unit, the second voltage sequence signal is collected and stored in the voltage noise floor data unit, and the second current sequence signal is collected and stored in the current noise floor data unit.

[0012] Furthermore, in step S3, the Fourier transform is either a discrete Fourier transform or a fast Fourier transform.

[0013] Secondly, the present invention provides a noise separation and detection system for electrochemical impedance spectroscopy, comprising the following modules: The test frequency set setting module is used to set a test frequency set, which includes several test frequencies of the excitation signal; The voltage and current signal acquisition module is used to acquire the first voltage sequence signal and the first current sequence signal of the battery under test when no excitation signal is applied to the battery under test for each of the test frequencies; and to acquire the second voltage sequence signal and the second current sequence signal of the battery under test when the excitation signal is applied to the battery under test. The signal Fourier transform module is used to perform Fourier transforms on the first voltage sequence signal and the first current sequence signal respectively, and extract the first voltage amplitude and the first current amplitude corresponding to the current test frequency; and to perform Fourier transforms on the second voltage sequence signal and the second current sequence signal respectively, and extract the second voltage amplitude and the second current amplitude corresponding to the current test frequency. The net amplitude calculation module is used to calculate the difference between the second voltage amplitude and the first voltage amplitude to obtain the net voltage amplitude, and to calculate the difference between the second current amplitude and the first current amplitude to obtain the net current amplitude; An electrochemical impedance spectroscopy generation module is used to calculate the impedance value at the current test frequency based on the net voltage amplitude and net current amplitude, and to generate an electrochemical impedance spectrum based on the impedance values ​​at all test frequencies.

[0014] Furthermore, in the test frequency set setting module, the excitation signal is a sine wave signal.

[0015] Furthermore, in the voltage and current signal acquisition module, the first voltage sequence signal, the first current sequence signal, the second voltage sequence signal, and the second current sequence signal are all discrete time sequence signals with the same number of sampling points.

[0016] Furthermore, in the voltage and current signal acquisition module, the first voltage sequence signal acquired is stored in the voltage sampling data unit, the first current sequence signal acquired is stored in the current sampling data unit, the second voltage sequence signal acquired is stored in the voltage noise floor data unit, and the second current sequence signal acquired is stored in the current noise floor data unit.

[0017] Furthermore, in the signal Fourier transform module, the Fourier transform is either a discrete Fourier transform or a fast Fourier transform.

[0018] The advantages of this invention are: 1. By setting a test frequency set including several test frequencies for the excitation signal, for each test frequency, when the excitation signal is not applied to the battery under test, a first voltage sequence signal and a first current sequence signal of the battery under test are acquired; when the excitation signal is applied to the battery under test, a second voltage sequence signal and a second current sequence signal of the battery under test are acquired; then, Fourier transforms are performed on the first voltage sequence signal and the first current sequence signal respectively to extract the first voltage amplitude and the first current amplitude corresponding to the current test frequency; Fourier transforms are performed on the second voltage sequence signal and the second current sequence signal respectively to extract the second voltage amplitude and the second current amplitude corresponding to the current test frequency; then, the difference between the second voltage amplitude and the first voltage amplitude is calculated to obtain the net voltage amplitude, and the difference between the second current amplitude and the first current amplitude is calculated to obtain the net current amplitude; Then, based on the net voltage amplitude and net current amplitude, the impedance value at the current test frequency is calculated, and an electrochemical impedance spectroscopy is generated based on the impedance values ​​at all test frequencies. Specifically, the background noise signal of the battery under test (first voltage / current sequence) is first acquired when no excitation signal is applied, and then the mixed signal (second voltage / current sequence) is acquired when an excitation signal is applied. Subsequently, Fourier transform is performed on the two sets of signals, and the amplitude of the background noise is subtracted from the amplitude of the mixed signal at each test frequency to calculate the net amplitude of the pure response signal. This process actively cancels environmental noise at the algorithm level, significantly improving the signal-to-noise ratio and accuracy. Since the core advantage of this scheme lies in the signal processing algorithm rather than hardware power, there is no need to use a high-power excitation source, which ultimately greatly improves the anti-interference capability and accuracy of electrochemical impedance spectroscopy detection, while avoiding the problems of high equipment cost, large size and potential battery damage.

[0019] 2. By acquiring the first voltage and current sequence signals of the battery (i.e., the background noise signal) without applying an excitation signal, and acquiring the second voltage and current sequence signals when an excitation signal is applied, and then calculating the net amplitude (the difference between the second amplitude and the first amplitude) in the frequency domain, the background noise is directly removed from the measurement data. This method can effectively eliminate the influence of environmental electromagnetic interference, inherent noise inside the battery, or background noise of the test system on impedance measurement, so that the final generated net voltage amplitude and net current amplitude more realistically reflect the electrochemical response of the battery under excitation. Compared with the traditional method of directly using the original signal to calculate impedance in electrochemical impedance spectroscopy (EIS) measurement, by actively separating noise, amplitude distortion and phase error caused by noise superposition are avoided, thereby greatly improving the accuracy and reliability of impedance spectroscopy, especially in application scenarios with low signal-to-noise ratio or high precision requirements.

[0020] 3. By calculating the net amplitude, the effective components and noise components in the signal are effectively distinguished, thus significantly improving the signal-to-noise ratio of the final impedance data. Since background noise (such as thermal noise, power frequency interference, etc.) usually changes with time or environment, traditional methods may lead to unstable measurement results due to noise fluctuations. This solution synchronously collects the background noise and subtracts it in real time during each frequency test, which can dynamically compensate for noise changes. This makes the impedance value calculation more dependent on the actual response characteristics of the battery. This enhances the stability of the measurement results and the repeatability of the test process. For applications that require long-term tracking and comparison, such as battery status monitoring, performance evaluation, or aging studies, it can provide a more consistent and reliable data foundation.

[0021] 4. The noise separation process is decomposed into clearly defined steps (frequency setting, time-division acquisition, transformation extraction, difference calculation, impedance generation), forming a standardized testing process. This modular design facilitates fully automated operation through software algorithms, eliminating the need for manual intervention in noise estimation or complex post-processing. At the same time, the configurability of the test frequency set allows the method to flexibly adapt to EIS testing requirements with different resolutions or frequency ranges, improving the method's versatility and engineering applicability.

[0022] 5. The voltage and current amplitudes at the test frequency points are directly extracted using Fourier transform (such as Discrete Fourier Transform or Fast Fourier Transform), avoiding time-consuming algorithms such as full-band scanning or iterative fitting. In particular, the application of Fast Fourier Transform (FFT) can significantly reduce the amount of computation and achieve near real-time processing, which is especially important for multi-frequency point testing or online monitoring scenarios. By extracting the amplitude of a single frequency point first and then performing noise subtraction and impedance calculation, instead of filtering or denoising the entire time domain signal, unnecessary computational overhead is reduced. While ensuring accuracy, the overall detection speed is improved, making it suitable for rapid testing on battery production lines or real-time diagnosis of vehicle battery management systems.

[0023] 6. Since batteries are often in complex electromagnetic environments in practical applications (such as electric vehicles and energy storage systems), traditional EIS measurements are easily affected by random noise. This solution uses an alternating acquisition strategy of "with excitation" and "without excitation" to capture the ambient noise and the battery's own background noise at the moment of testing, and uses them as a benchmark for subtraction, thereby effectively suppressing the influence of periodic or non-periodic interference. This allows the method to maintain high measurement robustness in industrial sites, mobile devices, or scenarios with fluctuating noise backgrounds, reducing the dependence on external shielding equipment or harsh laboratory conditions, and expanding the application scope of electrochemical impedance spectroscopy in on-site detection and online monitoring.

[0024] 7. By storing signals in different states into dedicated voltage / current sampling data units and noise floor data units, structured data storage is achieved. This not only avoids signal confusion but also provides a clear data source for subsequent data analysis, fault diagnosis, or algorithm improvement. For example, the stored noise floor data can be used for long-term noise trend analysis or equipment status calibration. In addition, the entire processing flow (acquisition, transformation, and calculation) is standardized, making it easy to synchronize and correlate with other battery parameters (such as temperature and state of charge) or integrate machine learning algorithms to further optimize the noise model, providing a scalable technical foundation for the development of intelligent battery management systems. Attached Figure Description

[0025] The present invention will be further described below with reference to the accompanying drawings and embodiments.

[0026] Figure 1 This is a flowchart of a noise separation and detection method for electrochemical impedance spectroscopy according to the present invention.

[0027] Figure 2 This is a schematic diagram of the structure of an electrochemical impedance spectroscopy noise separation and detection system according to the present invention.

[0028] Figure 3 This is a schematic diagram of the principle of the present invention.

[0029] Figure 4 This is a flowchart illustrating the present invention.

[0030] Figure 5 This is a block diagram illustrating the principle of performing a Fourier transform in this invention.

[0031] Figure 6 This is a schematic diagram of the process of performing Fourier transform in this invention. Detailed Implementation

[0032] The technical solution in this application embodiment has the following general idea: First, the background noise signal of the battery under test is acquired when no excitation signal is applied, and then the mixed signal is acquired when an excitation signal is applied; then, Fourier transform is performed on the two sets of signals, and the amplitude of the background noise is subtracted from the amplitude of the mixed signal at each test frequency to calculate the net amplitude of the pure response signal. This process actively cancels environmental noise at the algorithm level, significantly improving the signal-to-noise ratio and accuracy. Since the core advantage of this solution lies in the signal processing algorithm rather than hardware power, there is no need to use a high-power excitation source, thereby improving the anti-interference capability and accuracy of electrochemical impedance spectroscopy detection, while avoiding the problems of high equipment cost, large size and potential battery damage.

[0033] Please refer to Figures 1 to 6 As shown, a preferred embodiment of the noise separation and detection method for electrochemical impedance spectroscopy of the present invention includes the following steps: Step S1: Set a test frequency set for the excitation signal, which includes several test frequencies; Step S2: For each of the test frequencies, when the excitation signal is not applied to the battery under test, acquire the first voltage sequence signal and the first current sequence signal of the battery under test; when the excitation signal is applied to the battery under test, acquire the second voltage sequence signal and the second current sequence signal of the battery under test. Step S3: Perform Fourier transform on the first voltage sequence signal and the first current sequence signal respectively to extract the first voltage amplitude and the first current amplitude corresponding to the current test frequency; perform Fourier transform on the second voltage sequence signal and the second current sequence signal respectively to extract the second voltage amplitude and the second current amplitude corresponding to the current test frequency. Step S4: Calculate the difference between the second voltage amplitude and the first voltage amplitude to obtain the net voltage amplitude; calculate the difference between the second current amplitude and the first current amplitude to obtain the net current amplitude. Step S5: Based on the net voltage amplitude and net current amplitude, calculate the impedance value at the current test frequency, and generate an electrochemical impedance spectrum based on the impedance values ​​at all test frequencies.

[0034] In step S1, the excitation signal is a sine wave signal.

[0035] In step S2, the first voltage sequence signal, the first current sequence signal, the second voltage sequence signal, and the second current sequence signal are all discrete time sequence signals with the same number of sampling points.

[0036] In step S2, the first voltage sequence signal is collected and stored in the voltage sampling data unit, the first current sequence signal is collected and stored in the current sampling data unit, the second voltage sequence signal is collected and stored in the voltage noise floor data unit, and the second current sequence signal is collected and stored in the current noise floor data unit.

[0037] In step S3, the Fourier transform is either a discrete Fourier transform or a fast Fourier transform.

[0038] A preferred embodiment of the noise separation and detection system for electrochemical impedance spectroscopy of the present invention includes the following modules: The test frequency set setting module is used to set a test frequency set, which includes several test frequencies of the excitation signal; The voltage and current signal acquisition module is used to acquire the first voltage sequence signal and the first current sequence signal of the battery under test when no excitation signal is applied to the battery under test for each of the test frequencies; and to acquire the second voltage sequence signal and the second current sequence signal of the battery under test when the excitation signal is applied to the battery under test. The signal Fourier transform module is used to perform Fourier transforms on the first voltage sequence signal and the first current sequence signal respectively, and extract the first voltage amplitude and the first current amplitude corresponding to the current test frequency; and to perform Fourier transforms on the second voltage sequence signal and the second current sequence signal respectively, and extract the second voltage amplitude and the second current amplitude corresponding to the current test frequency. The net amplitude calculation module is used to calculate the difference between the second voltage amplitude and the first voltage amplitude to obtain the net voltage amplitude, and to calculate the difference between the second current amplitude and the first current amplitude to obtain the net current amplitude; An electrochemical impedance spectroscopy generation module is used to calculate the impedance value at the current test frequency based on the net voltage amplitude and net current amplitude, and to generate an electrochemical impedance spectrum based on the impedance values ​​at all test frequencies.

[0039] In the test frequency set setting module, the excitation signal is a sine wave signal.

[0040] In the voltage and current signal acquisition module, the first voltage sequence signal, the first current sequence signal, the second voltage sequence signal, and the second current sequence signal are all discrete time sequence signals with the same number of sampling points.

[0041] In the voltage and current signal acquisition module, the first voltage sequence signal acquired is stored in the voltage sampling data unit, the first current sequence signal acquired is stored in the current sampling data unit, the second voltage sequence signal acquired is stored in the voltage noise floor data unit, and the second current sequence signal acquired is stored in the current noise floor data unit.

[0042] In the signal Fourier transform module, the Fourier transform is either a discrete Fourier transform or a fast Fourier transform.

[0043] The implementation process of this invention is illustrated using a lithium-ion battery as the battery under test. For example, the test frequency set is set to a frequency range from 0.01Hz to 10kHz, and 10 frequency points are selected at logarithmic intervals (e.g., 0.01Hz, 0.1Hz, 1Hz, 10Hz, 100Hz, 1kHz, 10kHz). The excitation signal is a sinusoidal voltage signal with an amplitude of 5mV (typical value, adjustable according to battery voltage), generated by a signal generator. During signal acquisition, a 16-bit ADC (analog-to-digital converter) is used to synchronously sample the voltage and current at the battery terminals at a sampling frequency of 1kHz, with 1024 sampling points for each sequence to ensure the same number of sampling points. When no excitation signal is applied, the first voltage sequence signal and the first current sequence signal (i.e., the noise floor signal) are acquired for a duration of 1.024 seconds; when an excitation signal is applied, the second voltage sequence signal and the second current sequence signal (mixed signal) are acquired for a duration of 1.024 seconds. The acquired signals are stored in the corresponding data units.

[0044] The Fourier transform employs the Fast Fourier Transform (FFT) algorithm and applies the Hanning window function to reduce spectral leakage. After performing an FFT on the stored signal, the amplitude corresponding to each test frequency point is extracted. For example, at a test frequency of 100Hz, the first voltage amplitude at 100Hz is extracted from the FFT result of the first voltage sequence signal, and other amplitudes are extracted in the same way. The net amplitude is calculated using simple arithmetic subtraction: net voltage amplitude = second voltage amplitude - first voltage amplitude, net current amplitude = second current amplitude - first current amplitude. The impedance value is calculated as the ratio of the net voltage amplitude to the net current amplitude, i.e., Z = |V| / |I|, yielding the impedance magnitude at that frequency; if phase information is needed, the phase difference after the FFT can be further calculated. All test frequencies are repeated to generate a complete electrochemical impedance spectroscopy (EIS).

[0045] In practice, to save storage space, a Fourier transform can be performed first, such as... Figure 5 , 6 As shown.

[0046] In summary, the advantages of this invention are as follows: 1. By setting a test frequency set including several test frequencies for the excitation signal, for each test frequency, when the excitation signal is not applied to the battery under test, a first voltage sequence signal and a first current sequence signal of the battery under test are acquired; when the excitation signal is applied to the battery under test, a second voltage sequence signal and a second current sequence signal of the battery under test are acquired; then, Fourier transforms are performed on the first voltage sequence signal and the first current sequence signal respectively to extract the first voltage amplitude and the first current amplitude corresponding to the current test frequency; Fourier transforms are performed on the second voltage sequence signal and the second current sequence signal respectively to extract the second voltage amplitude and the second current amplitude corresponding to the current test frequency; then, the difference between the second voltage amplitude and the first voltage amplitude is calculated to obtain the net voltage amplitude, and the difference between the second current amplitude and the first current amplitude is calculated to obtain the net current amplitude; Then, based on the net voltage amplitude and net current amplitude, the impedance value at the current test frequency is calculated, and an electrochemical impedance spectroscopy is generated based on the impedance values ​​at all test frequencies. Specifically, the background noise signal of the battery under test (first voltage / current sequence) is first acquired when no excitation signal is applied, and then the mixed signal (second voltage / current sequence) is acquired when an excitation signal is applied. Subsequently, Fourier transform is performed on the two sets of signals, and the amplitude of the background noise is subtracted from the amplitude of the mixed signal at each test frequency to calculate the net amplitude of the pure response signal. This process actively cancels environmental noise at the algorithm level, significantly improving the signal-to-noise ratio and accuracy. Since the core advantage of this scheme lies in the signal processing algorithm rather than hardware power, there is no need to use a high-power excitation source, which ultimately greatly improves the anti-interference capability and accuracy of electrochemical impedance spectroscopy detection, while avoiding the problems of high equipment cost, large size and potential battery damage.

[0047] 2. By acquiring the first voltage and current sequence signals of the battery (i.e., the background noise signal) without applying an excitation signal, and acquiring the second voltage and current sequence signals when an excitation signal is applied, and then calculating the net amplitude (the difference between the second amplitude and the first amplitude) in the frequency domain, the background noise is directly removed from the measurement data. This method can effectively eliminate the influence of environmental electromagnetic interference, inherent noise inside the battery, or background noise of the test system on impedance measurement, so that the final generated net voltage amplitude and net current amplitude more realistically reflect the electrochemical response of the battery under excitation. Compared with the traditional method of directly using the original signal to calculate impedance in electrochemical impedance spectroscopy (EIS) measurement, by actively separating noise, amplitude distortion and phase error caused by noise superposition are avoided, thereby greatly improving the accuracy and reliability of impedance spectroscopy, especially in application scenarios with low signal-to-noise ratio or high precision requirements.

[0048] 3. By calculating the net amplitude, the effective components and noise components in the signal are effectively distinguished, thus significantly improving the signal-to-noise ratio of the final impedance data. Since background noise (such as thermal noise, power frequency interference, etc.) usually changes with time or environment, traditional methods may lead to unstable measurement results due to noise fluctuations. This solution synchronously collects the background noise and subtracts it in real time during each frequency test, which can dynamically compensate for noise changes. This makes the impedance value calculation more dependent on the actual response characteristics of the battery. This enhances the stability of the measurement results and the repeatability of the test process. For applications that require long-term tracking and comparison, such as battery status monitoring, performance evaluation, or aging studies, it can provide a more consistent and reliable data foundation.

[0049] 4. The noise separation process is decomposed into clearly defined steps (frequency setting, time-division acquisition, transformation extraction, difference calculation, impedance generation), forming a standardized testing process. This modular design facilitates fully automated operation through software algorithms, eliminating the need for manual intervention in noise estimation or complex post-processing. At the same time, the configurability of the test frequency set allows the method to flexibly adapt to EIS testing requirements with different resolutions or frequency ranges, improving the method's versatility and engineering applicability.

[0050] 5. The voltage and current amplitudes at the test frequency points are directly extracted using Fourier transform (such as Discrete Fourier Transform or Fast Fourier Transform), avoiding time-consuming algorithms such as full-band scanning or iterative fitting. In particular, the application of Fast Fourier Transform (FFT) can significantly reduce the amount of computation and achieve near real-time processing, which is especially important for multi-frequency point testing or online monitoring scenarios. By extracting the amplitude of a single frequency point first and then performing noise subtraction and impedance calculation, instead of filtering or denoising the entire time domain signal, unnecessary computational overhead is reduced. While ensuring accuracy, the overall detection speed is improved, making it suitable for rapid testing on battery production lines or real-time diagnosis of vehicle battery management systems.

[0051] 6. Since batteries are often in complex electromagnetic environments in practical applications (such as electric vehicles and energy storage systems), traditional EIS measurements are easily affected by random noise. This solution uses an alternating acquisition strategy of "with excitation" and "without excitation" to capture the ambient noise and the battery's own background noise at the moment of testing, and uses them as a benchmark for subtraction, thereby effectively suppressing the influence of periodic or non-periodic interference. This allows the method to maintain high measurement robustness in industrial sites, mobile devices, or scenarios with fluctuating noise backgrounds, reducing the dependence on external shielding equipment or harsh laboratory conditions, and expanding the application scope of electrochemical impedance spectroscopy in on-site detection and online monitoring.

[0052] 7. By storing signals in different states into dedicated voltage / current sampling data units and noise floor data units, structured data storage is achieved. This not only avoids signal confusion but also provides a clear data source for subsequent data analysis, fault diagnosis, or algorithm improvement. For example, the stored noise floor data can be used for long-term noise trend analysis or equipment status calibration. In addition, the entire processing flow (acquisition, transformation, and calculation) is standardized, making it easy to synchronize and correlate with other battery parameters (such as temperature and state of charge) or integrate machine learning algorithms to further optimize the noise model, providing a scalable technical foundation for the development of intelligent battery management systems.

[0053] While specific embodiments of the present invention have been described above, those skilled in the art should understand that the specific embodiments described are merely illustrative and not intended to limit the scope of the present invention. Equivalent modifications and variations made by those skilled in the art in accordance with the spirit of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A method for noise separation and detection using electrochemical impedance spectroscopy, characterized in that: Includes the following steps: Step S1: Set a test frequency set for the excitation signal, which includes several test frequencies; Step S2: For each of the test frequencies, when the excitation signal is not applied to the battery under test, acquire the first voltage sequence signal and the first current sequence signal of the battery under test; when the excitation signal is applied to the battery under test, acquire the second voltage sequence signal and the second current sequence signal of the battery under test. Step S3: Perform Fourier transform on the first voltage sequence signal and the first current sequence signal respectively, and extract the first voltage amplitude and the first current amplitude corresponding to the current test frequency; Perform Fourier transforms on the second voltage sequence signal and the second current sequence signal respectively to extract the second voltage amplitude and the second current amplitude corresponding to the current test frequency; Step S4: Calculate the difference between the second voltage amplitude and the first voltage amplitude to obtain the net voltage amplitude; calculate the difference between the second current amplitude and the first current amplitude to obtain the net current amplitude. Step S5: Based on the net voltage amplitude and net current amplitude, calculate the impedance value at the current test frequency, and generate an electrochemical impedance spectrum based on the impedance values ​​at all test frequencies.

2. The method for noise separation and detection by electrochemical impedance spectroscopy as described in claim 1, characterized in that: In step S1, the excitation signal is a sine wave signal.

3. The method for noise separation and detection by electrochemical impedance spectroscopy as described in claim 1, characterized in that: In step S2, the first voltage sequence signal, the first current sequence signal, the second voltage sequence signal, and the second current sequence signal are all discrete time sequence signals with the same number of sampling points.

4. The method for noise separation and detection by electrochemical impedance spectroscopy as described in claim 1, characterized in that: In step S2, the first voltage sequence signal is collected and stored in the voltage sampling data unit, the first current sequence signal is collected and stored in the current sampling data unit, the second voltage sequence signal is collected and stored in the voltage noise floor data unit, and the second current sequence signal is collected and stored in the current noise floor data unit.

5. The method for noise separation and detection by electrochemical impedance spectroscopy as described in claim 1, characterized in that: In step S3, the Fourier transform is either a discrete Fourier transform or a fast Fourier transform.

6. A noise separation and detection system for electrochemical impedance spectroscopy, characterized in that: Includes the following modules: The test frequency set setting module is used to set a test frequency set, which includes several test frequencies of the excitation signal; The voltage and current signal acquisition module is used to acquire the first voltage sequence signal and the first current sequence signal of the battery under test when no excitation signal is applied to the battery under test for each of the test frequencies; and to acquire the second voltage sequence signal and the second current sequence signal of the battery under test when the excitation signal is applied to the battery under test. The signal Fourier transform module is used to perform Fourier transform on the first voltage sequence signal and the first current sequence signal respectively, and extract the first voltage amplitude and the first current amplitude corresponding to the current test frequency; Perform Fourier transforms on the second voltage sequence signal and the second current sequence signal respectively to extract the second voltage amplitude and the second current amplitude corresponding to the current test frequency; The net amplitude calculation module is used to calculate the difference between the second voltage amplitude and the first voltage amplitude to obtain the net voltage amplitude, and to calculate the difference between the second current amplitude and the first current amplitude to obtain the net current amplitude; An electrochemical impedance spectroscopy generation module is used to calculate the impedance value at the current test frequency based on the net voltage amplitude and net current amplitude, and to generate an electrochemical impedance spectrum based on the impedance values ​​at all test frequencies.

7. The noise separation and detection system for electrochemical impedance spectroscopy as described in claim 6, characterized in that: In the test frequency set setting module, the excitation signal is a sine wave signal.

8. The noise separation and detection system for electrochemical impedance spectroscopy as described in claim 6, characterized in that: In the voltage and current signal acquisition module, the first voltage sequence signal, the first current sequence signal, the second voltage sequence signal, and the second current sequence signal are all discrete time sequence signals with the same number of sampling points.

9. The noise separation and detection system for electrochemical impedance spectroscopy as described in claim 6, characterized in that: In the voltage and current signal acquisition module, the first voltage sequence signal acquired is stored in the voltage sampling data unit, the first current sequence signal acquired is stored in the current sampling data unit, the second voltage sequence signal acquired is stored in the voltage noise floor data unit, and the second current sequence signal acquired is stored in the current noise floor data unit.

10. The noise separation and detection system for electrochemical impedance spectroscopy as described in claim 6, characterized in that: In the signal Fourier transform module, the Fourier transform is either a discrete Fourier transform or a fast Fourier transform.

Images