A mobile energy storage device online resistance detection method

Abstract

The application relates to the technical field of internal resistance detection of energy storage devices, in particular to a mobile energy storage device online internal resistance detection method, an AC signal is sent to a battery through a signal generator, and a voltage sensor and a current sensor respectively measure response signals; a Fourier transform algorithm is used to extract characteristic parameters in the AC signal; in combination with electrochemical impedance spectroscopy technology, the internal resistance value of the battery is calculated; the health state of the battery is evaluated according to the internal resistance value; the application realizes high-precision and real-time battery internal resistance detection, and in combination with a data analysis algorithm, the health state of the battery can be evaluated in detail. This method not only improves the accuracy and reliability of measurement, but also can provide effective early warning and maintenance suggestions, and significantly improves the overall performance and safety of the energy storage device.

Description

Technical Field

[0001] This invention relates to the technical field of internal resistance detection for energy storage devices, and more particularly to an online internal resistance detection method for mobile energy storage devices. Background Technology

[0002] With the continuous growth of global energy demand and the increasing awareness of environmental protection, energy storage technology is playing an increasingly important role in modern power systems. Energy storage devices, especially mobile energy storage devices, have become crucial means of addressing energy instability and load regulation due to their flexibility and efficiency. In recent years, mobile energy storage devices have been widely used in emergency power supply, renewable energy grid connection regulation, and power system peak-valley balancing. Traditional energy storage technologies mainly rely on electrochemical batteries, whose performance and lifespan directly affect the reliability and economy of energy storage devices. Battery internal resistance is a crucial parameter for assessing battery performance and health; changes in internal resistance can effectively predict battery lifespan and failure rates. Therefore, how to accurately and in real-time detect battery internal resistance has become an important research direction in the field of energy storage technology.

[0003] Most existing battery internal resistance detection methods are offline, requiring the battery to be removed from the energy storage device for measurement. This method not only increases operational complexity and time costs but also fails to acquire real-time data on changes in battery internal resistance, leading to lag in battery state monitoring. Furthermore, traditional internal resistance detection methods have limitations in measurement accuracy and data processing capabilities, making it difficult to meet the high-precision, real-time internal resistance detection requirements of modern energy storage systems. Especially in complex application scenarios, such as electric vehicles, smart grids, and emergency energy storage systems, offline detection methods struggle to provide continuous and reliable data support. Therefore, developing an efficient and accurate online internal resistance detection method is crucial to solving these problems.

[0004] Existing technologies for internal resistance detection suffer from the following shortcomings: First, offline detection methods are complex to operate and cannot achieve real-time monitoring of battery internal resistance, affecting the accuracy and timeliness of battery state assessment. Second, existing detection technologies have limited measurement accuracy, making it difficult to capture minute changes in internal resistance and failing to provide high-precision battery health status assessments. Finally, traditional methods lack sufficient data processing capabilities, cannot effectively analyze internal resistance change trends, and lack the ability to predict the future state of the battery. Our invention sends an AC signal to the battery via a signal generator, and voltage and current sensors measure the response signals respectively. A Fourier transform algorithm is used to extract characteristic parameters from the AC signal, and combined with electrochemical impedance spectroscopy, the battery's internal resistance value is calculated. This overcomes the aforementioned shortcomings of existing technologies, achieving high-precision, real-time internal resistance detection. Summary of the Invention

[0005] The purpose of this section is to outline some aspects of embodiments of the present invention and to briefly describe some preferred embodiments. Simplifications or omissions may be made in this section, as well as in the abstract and title of this application, to avoid obscuring the purpose of these documents; however, such simplifications or omissions should not be construed as limiting the scope of the invention.

[0006] In view of the aforementioned existing problems, the present invention is proposed.

[0007] Therefore, the technical problem solved by the present invention is that the existing technology has problems such as poor real-time performance, low accuracy and insufficient data processing capability in internal resistance detection.

[0008] To solve the above-mentioned technical problems, the present invention provides the following technical solution: an online internal resistance detection method for a mobile energy storage device, comprising:

[0009] An AC signal is sent to the battery via a signal generator, and the voltage sensor and current sensor measure the response signal respectively.

[0010] Use the Fourier transform algorithm to extract feature parameters from AC signals;

[0011] The internal resistance of the battery is calculated by combining electrochemical impedance spectroscopy.

[0012] The health status of a battery is assessed based on its internal resistance value.

[0013] As a preferred embodiment of the online internal resistance detection method for mobile energy storage devices of the present invention, wherein:

[0014] AC signals include sine wave signals, square wave signals, triangular wave signals, and frequency scanning signals.

[0015] As a preferred embodiment of the online internal resistance detection method for mobile energy storage devices of the present invention, wherein:

[0016] Signal generation: An AC signal with a preset frequency and amplitude is applied to the battery via a signal generator;

[0017] Signal measurement: Voltage and current sensors measure the voltage and current responses after a signal is applied, respectively; ensure good sensor connection to avoid contact resistance affecting measurement accuracy.

[0018] As a preferred embodiment of the online internal resistance detection method for mobile energy storage devices of the present invention, wherein:

[0019] The Fourier transform algorithm is expressed by the following formula:

[0020]

[0021] in:

[0022] F(x) represents the Fourier transform of the input signal, P(t) represents the feature parameter values ​​extracted at time t, n represents the harmonic number of the signal, T represents the period of the signal, x(t) represents the voltage response signal at time t, k represents the harmonic index, i represents the imaginary unit, f represents the frequency, and f0 represents the center frequency of the signal. The standard deviation of the frequency. This is the normalization constant.

[0023] As a preferred embodiment of the online internal resistance detection method for mobile energy storage devices of the present invention, wherein:

[0024] If P(t) is high, it means that the characteristic parameter strength of the signal at that moment is high, indicating that the battery internal resistance is low and the health status is good.

[0025] If P(t) is low, it indicates that the characteristic parameter strength of the signal at that moment is low, suggesting that the battery internal resistance is high and there may be aging or fault.

[0026] As a preferred embodiment of the online internal resistance detection method for mobile energy storage devices of the present invention, wherein:

[0027] The internal resistance of a battery can be calculated using the following formula:

[0028]

[0029] in:

[0030] Z(f) is the impedance value at frequency f, x(t) is the voltage response signal at time t, T is the period of the signal, i is the imaginary unit, and f is the frequency. k Let σ be the center frequency of the kth harmonic. k Let be the standard deviation of the frequency of the kth harmonic, N be the total number of harmonics, and t be the standard deviation of the frequency of the kth harmonic. n Let n be the time point. Let n be the mean value at time point n. Let n be the standard deviation of time point n, M be the total number of time points, and y(t) be the current response signal at time t.

[0031] As a preferred embodiment of the online internal resistance detection method for mobile energy storage devices of the present invention, wherein:

[0032] The range of Z(f) is a complex number, representing the impedance value at frequency f;

[0033] If the real part of Z(f) is high, it indicates that the ohmic resistance of the battery is large, which may indicate poor contact or material deterioration.

[0034] If the imaginary part of Z(f) is high, it indicates that the polarization resistance of the battery is large, which may indicate battery aging or capacity reduction.

[0035] The beneficial effects of this invention are as follows: This invention achieves high-precision, real-time battery internal resistance detection, and combined with data analysis algorithms, it can comprehensively assess the battery's health status. This method not only improves the accuracy and reliability of measurements but also provides effective early warnings and maintenance recommendations, significantly enhancing the overall performance and safety of energy storage devices. Attached Figure Description

[0036] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the description of the embodiments 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. Wherein:

[0037] Figure 1 This is a flowchart of the method of the present invention. Detailed Implementation

[0038] To make the above-mentioned objects, features, and advantages of the present invention more apparent and understandable, specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the protection scope of the present invention.

[0039] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of the invention. Therefore, the invention is not limited to the specific embodiments disclosed below.

[0040] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in different places in this specification does not necessarily refer to the same embodiment, nor is it a single or selective embodiment that is mutually exclusive with other embodiments.

[0041] This invention is described in detail with reference to the schematic diagrams. When detailing the embodiments of this invention, for ease of explanation, the cross-sectional views illustrating the device structure may be partially enlarged, not adhering to the usual scale. Furthermore, the schematic diagrams are merely examples and should not be construed as limiting the scope of protection of this invention. In actual fabrication, the three-dimensional spatial dimensions of length, width, and depth should be included.

[0042] Furthermore, in the description of this invention, it should be noted that the terms "upper," "lower," "inner," and "outer," etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. These terms are used solely for the convenience of describing the invention and for simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on the invention. In addition, the terms "first," "second," or "third" are used for descriptive purposes only and should not be construed as indicating or implying relative importance.

[0043] Unless otherwise explicitly specified and limited, the terms "installation," "connection," and "joining" in this invention should be interpreted broadly. For example, they can refer to fixed connections, detachable connections, or integral connections; similarly, they can refer to mechanical connections, electrical connections, or direct connections, or indirect connections through an intermediate medium, or internal connections between two components. Those skilled in the art can understand the specific meaning of the above terms in this invention based on the specific circumstances.

[0044] Example 1

[0045] Step 1: Send an AC signal to the battery via a signal generator, and measure the response signal using a voltage sensor and a current sensor respectively.

[0046] Analysis: By sending an AC signal to the battery through a signal generator, the frequency characteristics of the AC signal can be used to stimulate different electrochemical processes within the battery, allowing for the effective detection of various components of its internal resistance. Voltage and current sensors measure the response signals, accurately capturing the battery's response characteristics under AC signals. This method not only provides an overall picture of the battery's internal resistance but also allows for analysis of impedance characteristics at different frequencies, providing a detailed understanding of the various impedance components within the battery.

[0047] Beneficial effects: This method enables comprehensive excitation and response measurement of the internal electrochemical processes of a battery. It effectively captures the frequency response characteristics of the battery's internal resistance, providing a high-quality data foundation for subsequent accurate calculations and analysis. Furthermore, this method avoids the polarization effects that may exist in DC signal measurements, thereby improving the accuracy and reliability of the measurements.

[0048] Step 2: Extract feature parameters from the AC signal using the Fourier transform algorithm.

[0049] Analysis: The Fourier transform algorithm is used to convert the time-domain signal into a frequency-domain signal, extracting the characteristic parameters from the AC signal. This process can separate the various frequency components in the signal, accurately reflecting the battery's response characteristics at different frequencies. The Fourier transform is a mature signal processing technique that can efficiently and accurately extract characteristic parameters.

[0050] Beneficial effects: By using Fourier transform, frequency domain analysis of complex time-domain signals can be achieved, thereby obtaining the frequency response characteristics of the battery's internal resistance. This method can effectively extract useful information from AC signals, avoiding interference from noise and other irrelevant signals, and improving the accuracy and reliability of the data. Ultimately, it provides key characteristic parameters for the accurate calculation of internal resistance.

[0051] Step 3: Calculate the internal resistance of the battery using electrochemical impedance spectroscopy.

[0052] Analysis: By combining electrochemical impedance spectroscopy (EIS) with impedance data at different frequencies, the internal resistance of the battery can be calculated. EIS is a powerful analytical tool capable of revealing in detail the various electrochemical processes and mechanisms within a battery. This technique allows for the separation of the various components of the battery's internal resistance, including ohmic impedance, charge transfer impedance, and diffusion impedance.

[0053] Beneficial effects: This method enables precise calculation of battery internal resistance and distinguishes the contributions of different impedance components. It not only improves the accuracy of internal resistance measurement but also provides deeper insights into the electrochemical processes within the battery, offering strong support for battery performance optimization and fault diagnosis. Ultimately, it provides high-quality data support for battery health status assessment.

[0054] Step 4: Assess the battery's health by evaluating its internal resistance.

[0055] Analysis: By analyzing the measurement results of internal resistance, the health status of the battery can be assessed. The trend of internal resistance changes can reflect the aging process and usage conditions of the battery. By monitoring changes in internal resistance over a long period, battery life and potential failures can be predicted in advance, allowing for timely maintenance measures.

[0056] Beneficial effects: This method enables real-time monitoring and assessment of battery health. It effectively predicts battery lifespan and potential failures, providing early warnings and preventing system downtime or safety incidents caused by battery malfunctions. This improves the reliability and safety of the battery system, extends battery life, and reduces maintenance costs.

[0057] Through the above steps, this invention achieves high-precision, real-time battery internal resistance detection, and combined with data analysis algorithms, can provide a detailed assessment of the battery's health status. This method not only improves the accuracy and reliability of measurements but also provides effective early warnings and maintenance recommendations, significantly enhancing the overall performance and safety of energy storage devices. Ultimately, it provides a strong guarantee for the stable operation of modern power systems.

[0058] Types or parameter representations of AC signals and response signals

[0059] Sine wave signal: the most commonly used type of AC signal. The frequency range can be set between 1Hz and 10kHz to cover different frequency response characteristics of the battery's internal resistance. The advantage of a sine wave signal is its pure spectrum and ease of analysis.

[0060] Square wave signal: It has rich harmonic components and can excite the nonlinear characteristics of the battery, but the signal processing complexity is high.

[0061] Triangular wave signal: Its spectral characteristics are between those of a sine wave and a square wave, and it can be used in certain special application scenarios.

[0062] Frequency Sweep Signal: A sinusoidal signal that varies continuously from low frequency to high frequency, which can comprehensively detect the impedance characteristics of the battery at different frequencies.

[0063] Voltage response signal: Represents the voltage change of a battery after an AC signal is applied. It is represented by a voltage-time curve, and is usually further analyzed using frequency and phase.

[0064] Current response signal: This represents the change in battery current after an AC signal is applied. It is represented by a curve showing the change in current over time, and is analyzed using frequency and phase.

[0065] Amplitude: The peak or effective value (RMS) of the voltage and current response signal, reflecting the strength of the signal.

[0066] Phase difference: The phase difference between voltage and current signals reflects the impedance characteristics of the battery.

[0067] Voltage sensor: Select a high-precision, high-input-impedance sensor, such as a differential amplifier or a precision ADC module, to ensure measurement accuracy.

[0068] Sensors: Select high-precision, low-noise sensors, such as Hall effect sensors or precision current shunts, to ensure the accuracy of current measurement.

[0069] Voltage sensor connection: Directly connected to the positive and negative terminals of the battery to measure the voltage change after an AC signal is applied.

[0070] Current sensor connection: connected in series in the battery circuit to measure changes in AC current passing through the battery.

[0071] Data Acquisition (DAQ) System: Employs a high sampling rate, low-noise acquisition system to ensure the accuracy and real-time performance of signal measurements. A sampling rate of 1 kHz or higher is commonly used; the appropriate sampling rate should be selected based on the frequency of the AC signal.

[0072] Synchronous sampling: Ensure synchronous sampling of voltage and current signals to avoid phase errors caused by asynchronous sampling.

[0073] Filtering and noise reduction: The acquired raw signal is filtered to remove high-frequency noise and other interference signals, thereby improving the purity of the measurement signal.

[0074] Signal processing algorithm: The Fourier transform (FFT) is used to convert the time-domain signal into a frequency-domain signal, extracting the amplitude and phase information from the AC signal. The FFT algorithm can efficiently separate different frequency components and accurately calculate impedance characteristics.

[0075] Signal generation: Applying an AC signal with a preset frequency and amplitude to the battery via a signal generator.

[0076] Signal Measurement: Voltage and current sensors measure the voltage and current responses after a signal is applied, respectively. Ensure the sensors are properly connected to avoid contact resistance affecting measurement accuracy.

[0077]

[0078] in:

[0079] F(x) represents the Fourier transform of the input signal, P(t) represents the feature parameter values ​​extracted at time t, n represents the harmonic number of the signal, T represents the period of the signal, x(t) represents the voltage response signal at time t, k represents the harmonic index, i represents the imaginary unit, f represents the frequency, and f0 represents the center frequency of the signal. The standard deviation of the frequency. This is the normalization constant.

[0080] Formula Explanation:

[0081] The Fourier transform of signal x(t) is used to convert it into a frequency domain signal;

[0082] The calculation of the k-th harmonic coefficients of the Fourier series is used to extract characteristic parameters of the signal at different frequencies.

[0083] This represents a Gaussian filter in the frequency domain, used to filter out noise and irrelevant frequency components while retaining the main frequency components of the signal;

[0084] This represents an exponential function used to eliminate the influence of different signal amplitudes on the calculation of characteristic parameters.

[0085] Range analysis:

[0086] The range of F(x) is a complex number, representing the signal value in the frequency domain.

[0087] The range of P(t) is positive real numbers, representing the feature parameter values ​​extracted at time t.

[0088] If P(t) is high, it means that the characteristic parameter strength of the signal at that moment is high, indicating that the battery internal resistance is low and the health status is good.

[0089] If P(t) is low, it indicates that the characteristic parameter strength of the signal at that moment is low, suggesting that the battery internal resistance is high and there may be aging or fault.

[0090] The internal resistance of a battery can be calculated using the following formula:

[0091]

[0092] in:

[0093] Z(f) is the impedance value at frequency f, x(t) is the voltage response signal at time t, T is the period of the signal, i is the imaginary unit, and f is the frequency. k Let σ be the center frequency of the kth harmonic. k Let be the standard deviation of the frequency of the kth harmonic, N be the total number of harmonics, and t be the standard deviation of the frequency of the kth harmonic. n Let n be the time point. Let n be the mean value at time point n. Let n be the standard deviation of time point n, M be the total number of time points, and y(t) be the current response signal at time t.

[0094] Formula Explanation:

[0095] The Fourier transform of signal x(t) is converted into a frequency domain signal, which is used to extract the voltage response at a specific frequency.

[0096] This represents a Gaussian filter in the frequency domain, used to filter out noise and irrelevant frequency components while retaining the main frequency components of the signal.

[0097] These represent normalization and exponential functions, used to normalize current response signals and eliminate the influence of different signal amplitudes on the calculation of characteristic parameters.

[0098] Explanation of the range:

[0099] The range of Z(f) is a complex number, representing the impedance value at frequency f;

[0100] If the real part of Z(f) is high, it indicates that the ohmic resistance of the battery is large, which may indicate poor contact or material deterioration.

[0101] If the imaginary part of Z(f) is high, it indicates that the polarization resistance of the battery is large, which may indicate battery aging or capacity reduction.

[0102] Electrochemical impedance spectroscopy specifically includes the following steps:

[0103] Generate an internal resistance calculation model that presupposes the theoretical response of the battery based on its chemical and physical properties;

[0104] The characteristic parameters are matched with the internal resistance calculation model to determine the actual impedance characteristics of the battery.

[0105] The internal resistance of the battery is calculated by comparing the theoretical response with the actual measurement results.

[0106] The theoretical response includes at least the voltage-current curve relationship and the impedance spectrum.

[0107] As an example, voltage and current curves: based on theoretical analysis of battery materials and structure, the voltage and current relationship of a battery under specific load conditions is predicted.

[0108] As an example, impedance spectrum: simulating battery response at different frequencies reveals the electrochemical kinetics and electronic conduction mechanisms inside the battery.

[0109] Using these theoretical response models, batteries can be actually measured in experiments using electrochemical impedance spectroscopy. By comparing the measured data with the theoretical response, not only can the internal resistance of the battery be calculated, but also a deeper understanding of the battery's health status and potential performance problems can be obtained.

[0110] The Fourier transform algorithm specifically includes:

[0111] The collected time-series data, namely the response signals of voltage and current, are digitally processed to convert the continuous signals into discrete signals.

[0112] Discrete signals are processed using the Discrete Fourier Transform to analyze their frequency components and obtain frequency domain data.

[0113] Feature parameters are identified and extracted from frequency domain data, including at least frequency, amplitude, and phase.

[0114] The innovation of this invention lies in its comprehensive utilization of electrochemical impedance spectroscopy technology and advanced data processing algorithms to achieve high-precision real-time monitoring of battery internal resistance. By analyzing the trend of internal resistance changes over time, it can provide accurate battery health status assessment and effectively predict battery lifespan and potential failures. This not only improves the reliability and maintenance efficiency of energy storage devices but also provides strong support for the stable operation of power systems. Existing technologies for internal resistance detection suffer from problems such as poor real-time performance, low accuracy, and insufficient data processing capabilities. Our invention solves these problems through an online detection method, representing an innovative achievement in the field of power and energy storage technology.

[0115] (1) Experimental subjects

[0116] Existing battery internal resistance detection technologies mainly fall into two categories: DC impedance measurement and simple AC signal testing. These methods have the following drawbacks:

[0117] DC impedance measurement: It is simple to operate, but it is easily affected by the battery status (such as charging status, temperature, etc.) and cannot provide frequency-related battery response information, which is insufficient for battery performance evaluation under dynamic load conditions.

[0118] Simple AC signal testing: This method typically uses only a single frequency or signal type, which limits the comprehensiveness and accuracy of the measurement and fails to fully identify battery characteristics under different operating conditions.

[0119] This invention proposes an online detection method for battery internal resistance using various AC signal types (sine wave, square wave, triangular wave, and frequency scanning signal). Combined with Fourier transform algorithm and electrochemical impedance spectroscopy, this method can effectively assess the battery's health status. Specifically:

[0120] It provides more comprehensive battery response analysis, adaptable to a wider range of test conditions, and more accurate evaluation of battery performance. Using Fourier transform to extract characteristic parameters of the AC signal, combined with electrochemical impedance spectroscopy, improves the accuracy and reliability of internal resistance measurement.

[0121] (2) Experimental environment

[0122] Signal generator: generates various AC signals as needed, including sine waves, square waves, triangle waves, and frequency scanning signals.

[0123] Voltage and current sensors: High-precision sensors that ensure the accuracy of measurement data.

[0124] Data acquisition system: Records experimental data for subsequent analysis.

[0125] Signal application: The signal generator sends different types of AC signals to the battery according to a preset program.

[0126] Data logging: Voltage and current sensors record the battery's response to each signal in real time.

[0127] Data processing: The recorded data is processed using the Fourier transform algorithm to extract key feature parameters.

[0128] Internal resistance calculation and analysis: The internal resistance of the battery is calculated based on electrochemical impedance spectroscopy to assess its health status.

[0129] (3) Experimental results

[0130] Table 1 shows the battery response data when using different signal types, including the measured voltage, current, and calculated internal resistance.

[0131] Table 1 Battery response data for different signal types

[0132] signal type Frequency (Hz) Amplitude (V) Measure voltage (V) Measuring current (A) Calculate the internal resistance (Ω). sine wave 50 0.5 1.00 0.20 5.00 Fang Bo 50 0.5 1.02 0.21 4.86 Triangular wave 50 0.5 0.98 0.19 5.16 Frequency Scan 10-1000 0.5 1.01 0.20 5.05

[0133] In an alternative implementation, a signal generator, such as the Agilent 33500B waveform generator, is used to generate the required AC signal. This device can precisely control the frequency, amplitude, and waveform type of the output signal and is an indispensable tool in battery internal resistance measurement.

[0134] In an alternative implementation, high-precision battery response data can be obtained by using a high-precision voltage and current sensor, such as the Fluke 289 true RMS multimeter.

[0135] Preferably, referring to Table 1, it can be intuitively seen that different signal types have different excitation effects on the battery, which shows a specific advantage in the measurement of internal resistance; for example, square wave signals, due to their rapid change characteristics, can more effectively reveal the response characteristics of the battery under rapid load changes.

[0136] This invention discloses an online internal resistance detection method for mobile energy storage devices, comprising: sending an AC signal to the battery via a signal generator, and measuring the response signals by voltage and current sensors respectively; extracting characteristic parameters from the AC signal using a Fourier transform algorithm; calculating the battery's internal resistance value using electrochemical impedance spectroscopy; and assessing the battery's health status based on the internal resistance value. This invention achieves high-precision, real-time battery internal resistance detection, and, combined with data analysis algorithms, can provide a detailed assessment of the battery's health status. This method not only improves the accuracy and reliability of measurements but also provides effective early warnings and maintenance suggestions, significantly enhancing the overall performance and safety of energy storage devices.

[0137] It should be noted that 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 preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A method for online internal resistance detection of a mobile energy storage device, characterized in that: An AC signal is sent to the battery via a signal generator, and the voltage sensor and current sensor measure the response signal respectively. Use the Fourier transform algorithm to extract feature parameters from AC signals; The internal resistance of the battery is calculated by combining electrochemical impedance spectroscopy. The battery's health status is assessed based on the internal resistance value. The AC signals include sine wave signals, square wave signals, triangular wave signals, and frequency scanning signals; The Fourier transform algorithm is expressed by the following formula: in: F(x) represents the Fourier transform of the input signal, P(t) represents the feature parameter values ​​extracted at time t, n represents the harmonic number of the signal, T represents the period of the signal, x(t) represents the voltage response signal at time t, k represents the harmonic index, i represents the imaginary unit, f represents the frequency, and f0 represents the center frequency of the signal. The standard deviation of the frequency, This is a normalization constant; The internal resistance of a battery can be calculated using the following formula: in: Z(f) is the impedance value at frequency f, x(t) is the voltage response signal at time t, T is the period of the signal, i is the imaginary unit, and f is the frequency. k Let σ be the center frequency of the kth harmonic. k Let be the standard deviation of the frequency of the kth harmonic, N be the total number of harmonics, and t be the standard deviation of the frequency of the kth harmonic. n Let n be the time point. Let n be the mean value at time point n. Let n be the standard deviation of time point n, M be the total number of time points, and y(t) be the current response signal at time t.

2. The online internal resistance detection method for mobile energy storage devices according to claim 1, characterized in that: An AC signal with a preset frequency and amplitude is applied to the battery through a signal generator; Voltage and current sensors measure the voltage and current responses after a signal is applied, respectively.

3. The online internal resistance detection method for mobile energy storage devices according to claim 1, characterized in that: If P(t) is high, it indicates that the characteristic parameter strength of the signal at that time is high, which means that the battery internal resistance is low and the battery is in good health. If P(t) is low, it indicates that the characteristic parameter strength of the signal at that time is low, suggesting that the battery internal resistance is high and there may be aging or fault.

4. The online internal resistance detection method for mobile energy storage devices according to claim 1, characterized in that: The range of Z(f) is a complex number, representing the impedance value at frequency f; If the real part of Z(f) is high, it indicates that the ohmic resistance of the battery is large, which may indicate poor contact or material deterioration. If the imaginary part of Z(f) is high, it indicates that the polarization resistance of the battery is large, which may indicate battery aging or capacity reduction.

5. The online internal resistance detection method for mobile energy storage devices according to claim 4, characterized in that: The electrochemical impedance spectroscopy technique specifically includes the following steps: Generate an internal resistance calculation model that presupposes the theoretical response of the battery based on its chemical and physical properties; The characteristic parameters are matched with the internal resistance calculation model to determine the actual impedance characteristics of the battery; The internal resistance of the battery is calculated by comparing the theoretical response with the actual measurement results.

6. The online internal resistance detection method for mobile energy storage devices according to claim 5, characterized in that: The theoretical response includes at least the voltage-current curve relationship and the impedance spectrum.

7. The online internal resistance detection method for mobile energy storage devices according to claim 6, characterized in that: The Fourier transform algorithm specifically includes: The collected time-series data, namely the response signals of voltage and current, are digitally processed to convert the continuous signals into discrete signals. Discrete signals are processed using the Discrete Fourier Transform to analyze their frequency components and obtain frequency domain data. Feature parameters are identified and extracted from the frequency domain data, and the feature parameters include at least frequency, amplitude and phase.

Images