Method and system for evaluating health state of valve-regulated lead-acid storage battery

Abstract

According to the valve-regulated lead-acid storage battery state-of-health assessment method and system provided by the invention, the characteristic frequency points which are not sensitive to the change of the charge state but sensitive to the aging degree in the relaxation time distribution map of the battery are innovatively screened, and the multi-parameter fusion assessment model based on the polarization impedance, the ohmic internal resistance and the residual capacity is established; the defect that a single capacity index cannot reflect a historical aging path of the battery is overcome, the future attenuation trend of the battery can be predicted while the current health state is accurately quantified, the influence of charge state fluctuation on health assessment is effectively avoided based on a dual-stability screening mechanism of a characteristic frequency point, the assessment reliability under a complex working condition is improved, and the assessment accuracy is improved. The full-band impedance test is simplified into specific target frequency point measurement, so that the detection time is greatly shortened, and the on-site rapid evaluation requirement is met.

Description

Technical Field

[0001] This invention relates to the field of DC system technology for gas turbine power plants, and in particular to a method and system for assessing the health status of valve-regulated lead-acid batteries. Background Technology

[0002] Lead-acid batteries, as an important component of the DC system of power plants, bear the heavy responsibility of providing emergency power to important equipment in the event of AC power outages, and play a vital role in the safe and stable operation of power plants.

[0003] Valve-regulated lead-acid batteries are a common type of chemical power source. Due to their advantages such as small size, light weight, long service life, and high safety performance, they are widely used in communication systems, power systems, lighting systems, security systems, and medical equipment.

[0004] In power systems, to prevent sudden power outages, valve-regulated lead-acid batteries are generally used as the main component of the DC power supply system of substations. The batteries will provide emergency power to important equipment in the substation during power outages until the grid power supply is restored to normal.

[0005] In theory, valve-regulated lead-acid batteries have a lifespan of 10-12 years, but in reality, the lifespan of batteries in most substations is generally no more than 10 years, far below the theoretical design lifespan.

[0006] The national standard GB / T19639.1 states that lead-acid battery failure is caused by two factors: external shape distortion and internal structural decay. External shape distortion can be observed in a timely manner, while structural decay is more insidious and sporadic, making it difficult to identify in the early stages of decay. Furthermore, structural decay often leads to a precipitous drop in battery health, causing battery failure in a short period of time without triggering a safety incident.

[0007] From a microscopic perspective, the main causes of lead-acid battery failure are corrosion of the positive electrode grid and sulfation of the negative electrode. These factors reduce the number of migratable particles in the battery, leading to a decrease in battery capacity, reduce the flatness of the positive and negative electrode plates, and damage the tunnel structure of the negative electrode plate, resulting in an increase in the battery's static internal resistance and dynamic polarization impedance, which in turn reduces battery conductivity and causes abnormal battery temperature rise.

[0008] The failure criterion for structural distortion is that the current battery capacity drops to 80% of the rated capacity. However, the current battery capacity is correlated with the battery health status, and the current capacity cannot fully characterize the battery health status.

[0009] Therefore, in order to improve the reliability of lead-acid batteries in power plant applications and optimize the whole life cycle management of batteries, there is an urgent need for a comprehensive, accurate, and non-invasive health status evaluation method for lead-acid batteries. Summary of the Invention

[0010] A first aspect of this disclosure provides a method for assessing the health status of a valve-regulated lead-acid battery, characterized by comprising:

[0011] Measure the internal resistance of the battery under test and its current remaining capacity;

[0012] Apply a target frequency AC signal to the battery and obtain the polarization impedance values ​​at the first and second characteristic frequency points;

[0013] The polarization impedance, ohmic internal resistance, and remaining capacitance are input into a pre-constructed health status assessment model, which outputs an assessment value that simultaneously characterizes the current health level and future degradation trend. The health status assessment model is constructed in the following manner:

[0014] Based on impedance spectrum data of historical battery samples under different aging conditions, characteristic frequency points are extracted.

[0015] Establish a mapping relationship between the polarization impedance at the characteristic frequency point and the battery life termination state.

[0016] An evaluation model is formed through multi-parameter nonlinear fitting.

[0017] In conjunction with the first aspect, the first characteristic frequency and the second characteristic frequency are determined in the following manner:

[0018] Based on the broadband impedance test data of the benchmark battery pack, the first characteristic peak with a peak width change of ≤5% when the state of charge changes by ±20% was identified in the relaxation time distribution spectrum, and the second characteristic peak with a peak value change of ≥20% when the remaining battery capacity decays by ≥10% was identified in the relaxation time distribution spectrum.

[0019] The peak boundary point of the first characteristic peak is converted into the first characteristic frequency point, and the peak boundary point of the second characteristic peak is converted into the second characteristic frequency point.

[0020] In conjunction with the first aspect, converting the peak boundary points of the first characteristic peak into the first characteristic frequency points and converting the peak boundary points of the second characteristic peak into the second characteristic frequency points includes:

[0021] Identify the characteristic boundary points on both sides of the characteristic peak that satisfy the preset amplitude attenuation position;

[0022] Based on the physical relationship between relaxation time and frequency, it is converted into characteristic frequency points.

[0023] In conjunction with the first aspect, determining the first characteristic peak and the second characteristic peak further includes:

[0024] Exclude polarization peaks that exhibit inter-peak adhesion when the charge state changes;

[0025] Exclude polarization peaks whose peak volatility is below a threshold when the remaining capacity changes;

[0026] Prioritize the frequency bands corresponding to the new characteristic peaks that appear when the battery is abnormally aging.

[0027] In conjunction with the first aspect, the target frequency AC signal includes a first target frequency AC signal corresponding to the relaxation time of the first characteristic peak boundary and a second target frequency AC signal corresponding to the relaxation time of the second characteristic peak boundary.

[0028] In conjunction with the first aspect, the health status assessment model satisfies the following functional relationship:

[0029]

[0030] Where R p1 R is the polarization impedance at the first characteristic frequency point. p2 The second characteristic frequency point polarization impedance, Q is the current remaining capacitance.

[0031] a1 = 3.14 × 10 -1 a² = 6.358 × 10 -5 a3 = 2.23 × 10 -1 ,

[0032] a4 = 6.635 × 10 -2 a5 = -9.815, a6 = 3.91 × 10 -1 ,

[0033] a7 = -3.38 × 10 -1 a8 = -2.597 × 10 2 a9 = 6.0287 × 10 4 .

[0034] A second aspect of this disclosure provides a valve-regulated lead-acid battery health status assessment system, comprising:

[0035] Impedance testing unit is used to apply a target frequency AC signal to the battery and obtain the polarization impedance values ​​at the first characteristic frequency point and the second characteristic frequency point.

[0036] The parameter acquisition unit is used to measure the ohmic internal resistance and current remaining capacity of the battery under test.

[0037] The model processing unit stores the pre-built health status assessment model and is used to receive the polarization impedance value, ohmic internal resistance and remaining capacitance, and output the health status assessment value.

[0038] The result output unit is used to display the evaluation value and warning information.

[0039] In conjunction with the second aspect, the system further includes:

[0040] The frequency control module is used to store the first characteristic frequency point and the second characteristic frequency point.

[0041] The boundary point recognition module is used to perform feature boundary point recognition.

[0042] A third aspect of this disclosure provides an electronic device comprising:

[0043] One or more processors;

[0044] A storage unit is used to store one or more programs, which, when executed by one or more processors, enable the one or more processors to implement the valve-regulated lead-acid battery health status assessment method.

[0045] A fourth aspect of this disclosure provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, enables the implementation of the valve-regulated lead-acid battery health status assessment method.

[0046] Beneficial Effects: This disclosure provides a method and system for assessing the health status of valve-regulated lead-acid batteries. By innovatively screening characteristic frequency points in the battery relaxation time distribution spectrum that are insensitive to changes in charge state but sensitive to the degree of aging, a multi-parameter fusion assessment model based on polarization impedance, ohmic internal resistance, and remaining capacity is established. This overcomes the deficiency that a single capacity index cannot reflect the battery's historical aging path. While accurately quantifying the current health status, it can predict the future degradation trend of the battery. The dual stability screening mechanism based on characteristic frequency points effectively avoids the impact of charge state fluctuations on health assessment, improves the reliability of assessment under complex operating conditions, and significantly shortens the detection time by simplifying the full-band impedance test to a specific target frequency point measurement, meeting the needs of rapid on-site assessment. Attached Figure Description

[0047] Figure 1 This is a flowchart illustrating a method for assessing the health status of a valve-regulated lead-acid battery according to an embodiment of this disclosure.

[0048] Figure 2 This is a schematic diagram of the structure of a valve-regulated lead-acid battery health status assessment system according to an embodiment of the present disclosure;

[0049] Figure 3 An electronic device according to an embodiment of this disclosure. Detailed Implementation

[0050] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with those disclosed herein.

[0051] The terminology used in this disclosure is for the purpose of describing particular embodiments only and is not intended to be limiting of the present disclosure. The singular forms “a,” “the,” and “the” as used in this disclosure and the appended claims are also intended to include the plural forms unless the context clearly indicates otherwise. It should also be understood that the term “and / or” as used herein refers to and includes any and all possible combinations of one or more of the associated listed items.

[0052] It should be understood that although the terms first, second, third, etc., may be used to describe various information in embodiments of this disclosure, such information should not be limited to these terms. These terms are only used to distinguish information of the same type from one another. For example, first information may also be referred to as second information without departing from the scope of embodiments of this disclosure, and similarly, second information may also be referred to as first information. Depending on the context, the word "if" as used herein may be interpreted as "when," "when," or "in response to a determination."

[0053] like Figure 1 The diagram shown is a flowchart illustrating a method for assessing the health status of a valve-regulated lead-acid battery according to an embodiment of this disclosure, including:

[0054] S101: Measure the ohmic internal resistance and current remaining capacity of the battery under test;

[0055] For example, DC pulse technology is used to measure ohmic internal resistance: a short-duration high-current pulse is applied when the battery is at rest, and the interference of wire resistance is eliminated by a four-wire connection, accurately capturing the instantaneous voltage drop amplitude, and combining the current value to obtain the internal resistance value.

[0056] The remaining capacity can be obtained in two scenarios: online monitoring mode directly reads the real-time ampere-hour integral data stream of the battery management system and dynamically calculates the current remaining power percentage;

[0057] The offline detection mode performs a standard constant current discharge test to the cutoff voltage, and calculates the actual capacity by multiplying the discharge current by the time.

[0058] S102: Apply a target frequency AC signal to the battery and obtain the polarization impedance values ​​at the first and second characteristic frequency points;

[0059] The first characteristic frequency and the second characteristic frequency are determined in the following way:

[0060] Based on the broadband impedance test data of the benchmark battery pack, the first characteristic peak with a peak width change of ≤5% when the state of charge changes by ±20% was identified in the relaxation time distribution spectrum, and the second characteristic peak with a peak value change of ≥20% when the remaining battery capacity decays by ≥10% was identified in the relaxation time distribution spectrum.

[0061] Specifically, based on broadband impedance test data of a reference battery pack (covering multiple models and aging paths), relaxation time distribution spectrum analysis was used to screen characteristic peaks that remained stable within a charge state fluctuation range of ±20%. The specific criterion was: when the charge state changed from 10% to 90%, the peak width change rate of the target characteristic peak did not exceed 5%. After selecting the characteristic peaks that met this condition, the boundary points on both sides were located (the boundary points were defined as the positions where the peak intensity was 10%), and the relaxation times corresponding to the boundary points were converted into characteristic frequency values.

[0062] In the relaxation time distribution spectrum of the same benchmark battery pack, identify the characteristic peak that is highly sensitive to capacity decay. The criterion is: when the remaining battery capacity decays by 10% (e.g., from 100% to 90%), the peak intensity change rate of the target characteristic peak is not less than 20%. After locking the characteristic peak, locate its two boundary points (at the 10% intensity position) and derive the characteristic frequency value using the relaxation time-frequency conversion formula.

[0063] Converting the peak boundary points of the first characteristic peak to the first characteristic frequency points, and converting the peak boundary points of the second characteristic peak to the second characteristic frequency points, includes:

[0064] Identify the characteristic boundary points on both sides of the characteristic peak that satisfy the preset amplitude attenuation position;

[0065] Based on the physical relationship between relaxation time and frequency, it is converted into characteristic frequency points.

[0066] Specifically, the apex of the characteristic peak is located in the relaxation time distribution spectrum, and amplitude attenuation is scanned along both sides of the peak shape. The preset amplitude attenuation position is defined as the characteristic boundary point. The specific criterion is: when the spectral intensity at a certain position drops to 10% of the intensity of the characteristic peak apex, it is marked as the left / right boundary point of the characteristic peak.

[0067] The target frequency AC signal includes a first target frequency AC signal corresponding to the relaxation time of the first characteristic peak boundary and a second target frequency AC signal corresponding to the relaxation time of the second characteristic peak boundary.

[0068] Based on the fundamental laws of electromagnetism, the relaxation time of the boundary points is converted into characteristic frequency values.

[0069] Furthermore, before determining the first characteristic peak and the second characteristic peak, the following steps are also included:

[0070] Exclude polarization peaks that exhibit inter-peak adhesion when the charge state changes;

[0071] Exclude polarization peaks whose peak volatility is below a threshold when the remaining capacity changes;

[0072] Prioritize the frequency bands corresponding to the new characteristic peaks that appear when the battery is abnormally aging.

[0073] Specifically, when the state of charge (SOC) of the battery varies within the range of 10% to 90%, if the peak-to-valley intensity ratio of two adjacent polarization peaks is ≥0.8 (i.e., the peak-to-valley height difference is <20%), it is determined to be inter-peak adhesion.

[0074] When the remaining capacity decays by 10%, a peak change rate of less than 15% for the target characteristic peak is considered a low-sensitivity peak.

[0075] In addition to the standard DRT spectrum with 6 peaks, we checked whether a seventh characteristic peak appeared and correlated the causal relationship between this peak and abnormal aging, including confirmation of diaphragm puncture by scanning electron microscopy and temperature rise monitoring records >50°C.

[0076] S103: Input the polarization impedance value, ohmic internal resistance, and remaining capacitance into a pre-constructed health status assessment model, and output an assessment value that simultaneously characterizes the current health status and future degradation trend, wherein the health status assessment model is constructed in the following manner:

[0077] Based on impedance spectrum data of historical battery samples under different aging conditions, characteristic frequency points are extracted.

[0078] Establish a mapping relationship between the polarization impedance at the characteristic frequency point and the battery life termination state.

[0079] An evaluation model is formed through multi-parameter nonlinear fitting.

[0080] Specifically, after the battery has undergone a specified number of charge-discharge cycles, it is capacitated. EIS testing is then performed when the remaining capacity reaches 95%, 90%, 85%, and 80% of the nominal capacity, respectively.

[0081] Record the changes in remaining capacity and the polarization resistance R of p1 during typical polarization processes of different aging pathways. p1 Typical polarization process p2 polarization internal resistance R p2 The value of, and the current ohmic resistance R0 and current remaining capacity Q of the battery. R The composition includes SOH and remaining capacity Q. R Ohmic resistance R0, polarization internal resistance R p1 Polarization internal resistance R p2 The dataset (SOH uses impedance values ​​calculated based on standard cycles)

[0082] Using the battery health status as the dependent variable in the dataset, the polarization internal resistance R p1 Polarization internal resistance R p2 With remaining capacity Q R Using SOH as the independent variable, the battery state after complex operating conditions is normalized to the standard aging path, thereby effectively identifying the aging stage of the battery under different operating conditions. When the SOH value is 0, the battery is declared to be in failure. The higher the SOH value, the better the battery health.

[0083] Furthermore, a health state estimation model for valve-type lead-acid batteries is established using power function form:

[0084] The health status assessment model satisfies the following functional relationship:

[0085]

[0086] Where R p1 R is the polarization impedance at the first characteristic frequency point. p2 The polarization impedance at the second characteristic frequency point, Q R This represents the current remaining capacity.

[0087] a1 = 3.14 × 10 -1 a² = 6.358 × 10 -5 a3 = 2.23 × 10 -1 ,

[0088] a4 = 6.635 × 10 -2 a5 = -9.815, a6 = 3.91 × 10 -1 ,

[0089] a7 = -3.38 × 10 -1 a8 = -2.597 × 10 2 a9 = 6.0287 × 10 4 .

[0090] like Figure 2 The diagram shown is a flowchart illustrating a valve-regulated lead-acid battery health status assessment system according to an embodiment of this disclosure, including:

[0091] Impedance testing unit 210 is used to apply a target frequency AC signal to the battery and obtain the polarization impedance values ​​at the first characteristic frequency point and the second characteristic frequency point.

[0092] The core function of this unit is to apply two specific frequency AC signals to the battery and obtain the polarization impedance value. The hardware is configured with a direct digital frequency synthesizer to generate a sinusoidal signal with an amplitude strictly controlled at 30mV to avoid disturbing the battery electrode state. The signal application employs a four-electrode system, eliminating contact resistance errors through independent current injection and voltage detection terminals. The target frequency values ​​are pre-stored in a database, corresponding to the right boundary frequency of the first characteristic peak and the left boundary frequency of the second characteristic peak, respectively. When the signal is applied, the unit's internal high-precision lock-in amplifier synchronously analyzes the phase relationship between voltage and current, directly calculating the real component of the complex impedance as the polarization impedance value output. A narrowband filtering mechanism is embedded throughout the testing process to effectively suppress environmental electromagnetic interference.

[0093] The parameter acquisition unit 220 is used to measure the ohmic internal resistance and current remaining capacity of the battery under test.

[0094] This unit is responsible for synchronously acquiring the battery's ohmic internal resistance and remaining capacity. Ohmic internal resistance measurement employs DC pulse technology: a 1C high-current pulse is applied to the battery for 3 seconds, and a four-wire detection system accurately captures the instantaneous voltage drop, combining the current value to derive the internal resistance data. The remaining capacity acquisition design utilizes a dual-mode mechanism: for batteries operating online, it analyzes the ampere-hour integral data stream from the battery management system in real time; for batteries under offline testing, it initiates a constant-current discharge program (0.2C rate to cutoff voltage) to calculate the actual capacity. All acquired data is calibrated using a temperature sensor to ensure comparability of results under a standard temperature reference.

[0095] The model processing unit 230 stores the pre-built health status assessment model and is used to receive the polarization impedance value, ohmic internal resistance and remaining capacitance, and output the health status assessment value.

[0096] This unit incorporates a pre-trained health assessment model, receiving polarization impedance, ohmic internal resistance, and remaining capacity data from the front end. First, the input parameters undergo normalization preprocessing: the current ohmic internal resistance is converted into a rate of change relative to the initial value, and the remaining capacity is standardized to a percentage scale. Then, a multi-parameter fusion algorithm embedded in the chip is invoked, trained on 300 samples from different aging paths. The core model analyzes the sensitivity of polarization impedance at characteristic frequency points to electrode interface decay, the degradation of the conductive network due to changes in ohmic internal resistance, and the macroscopic decay of remaining capacity, outputting a health status assessment value from 0-100%. When an abnormal shift in characteristic frequency impedance is detected, a structural degradation analysis subroutine is automatically triggered.

[0097] The result output unit 240 displays the assessed values ​​and early warning information. The final unit converts the health assessment values ​​into actionable decision-making information. The current health level and historical change curves are displayed in real-time on an LCD screen, and a red alarm is issued when the value falls below the 80% failure threshold. An innovative three-level early warning mechanism is designed: Level 1 alerts maintenance when the health level is normal but the degradation rate is abnormal; Level 2 alerts performance testing when the health level drops to 85%; and Level 3 alerts forced shutdown for maintenance when a characteristic frequency impedance mutation (±30% offset) is detected. All early warning information is synchronously uploaded to the power plant's central monitoring system, forming a complete battery life management system. The output interface is compatible with the industrial standard Modbus protocol and can be directly connected to existing power plant control systems.

[0098] Furthermore, the system also includes:

[0099] The frequency control module is used to store the first characteristic frequency point and the second characteristic frequency point.

[0100] The boundary point recognition module is used to perform feature boundary point recognition.

[0101] This module serves as the core database of the system, pre-storing characteristic frequency values ​​determined through historical big data analysis. Each battery model corresponds to an independent data file, recording the precise Hertz values ​​of the first characteristic frequency point (originating from the right boundary of the charge-insensitive peak) and the second characteristic frequency point (originating from the left boundary of the aging-sensitive peak). The module has a built-in self-learning mechanism: when the relaxation time distribution of a new batch of batteries differs from the benchmark by more than 5%, a recalibration program is automatically initiated, updating the characteristic frequency by taking the average of three repeated tests. All data is stored encrypted and supports remote reading and writing via industrial bus, ensuring data consistency across different inspection terminals within the power plant.

[0102] This module is dedicated to the intelligent analysis of the relaxation time distribution (DRT) spectrum, performing feature boundary localization through an embedded signal processing algorithm. The workflow consists of three steps: first, identifying the vertex coordinates of stable feature peaks in the DRT spectrum; then, scanning along both sides of the peak shape to detect amplitude attenuation; when the spectral intensity drops to 10% of the peak value, it is marked as a valid boundary point. The key technological breakthrough lies in the noise reduction algorithm design: a sliding window smoothing process is used to eliminate high-frequency glitches, combined with second-derivative zero-crossing verification to ensure boundary localization accuracy error is less than 0.1 seconds. The identification results are output in real-time to the frequency conversion interface as relaxation time values ​​(in seconds).

[0103] Electronic device 300 can be a desktop computer, laptop, handheld computer, cloud server, or other electronic device. Electronic device 300 may include, but is not limited to, processor 301 and memory 302. Those skilled in the art will understand that... Figure 3This is merely an example of electronic device 300 and does not constitute a limitation on electronic device 300. It may include more or fewer components than shown, or combine certain components, or different components. For example, electronic device may also include input / output devices, network access devices, buses, etc.

[0104] Processor 301 can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor can be a microprocessor or any conventional processor.

[0105] The memory 302 can be an internal storage unit of the electronic device 300, such as a hard disk or RAM of the electronic device 300. The memory 302 can also be an external storage device of the electronic device 300, such as a plug-in hard disk, Smart Media Card (SMC), Secure Digital (SD) card, or Flash Card equipped on the electronic device 300. Furthermore, the memory 302 can include both internal and external storage units of the electronic device 300. The memory 302 is used to store the computer program 303 and other programs and data required by the electronic device. The memory 302 can also be used to temporarily store data that has been output or will be output.

[0106] The above embodiments are only used to illustrate the technical solutions of this disclosure, and are not intended to limit it. Although this disclosure has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this disclosure, and should all be included within the protection scope of this disclosure.

Claims

1. A method for assessing the health status of a valve-regulated lead-acid battery, characterized in that... include: Measure the internal resistance of the battery under test and its current remaining capacity; Apply a target frequency AC signal to the battery and obtain the polarization impedance values ​​at the first and second characteristic frequency points; The polarization impedance, ohmic internal resistance, and remaining capacitance are input into a pre-constructed health status assessment model, which outputs an assessment value that simultaneously characterizes the current health level and future degradation trend. The health status assessment model is constructed in the following manner: Based on impedance spectrum data of historical battery samples under different aging conditions, characteristic frequency points are extracted. Establish a mapping relationship between the polarization impedance at the characteristic frequency point and the battery life termination state. An evaluation model is formed through multi-parameter nonlinear fitting.

2. The method according to claim 1, characterized in that, The first characteristic frequency and the second characteristic frequency are determined in the following way: Based on the broadband impedance test data of the benchmark battery pack, the first characteristic peak with a peak width change of ≤5% when the state of charge changes by ±20% was identified in the relaxation time distribution spectrum, and the second characteristic peak with a peak value change of ≥20% when the remaining battery capacity decays by ≥10% was identified in the relaxation time distribution spectrum. The peak boundary point of the first characteristic peak is converted into the first characteristic frequency point, and the peak boundary point of the second characteristic peak is converted into the second characteristic frequency point.

3. The method according to claim 2, characterized in that, The step of converting the peak boundary point of the first characteristic peak into the first characteristic frequency point and converting the peak boundary point of the second characteristic peak into the second characteristic frequency point includes: Identify the characteristic boundary points on both sides of the characteristic peak that satisfy the preset amplitude attenuation position; Based on the physical relationship between relaxation time and frequency, it is converted into characteristic frequency points.

4. The method according to claim 2, characterized in that, Before determining the first characteristic peak and the second characteristic peak, the following steps are also included: Exclude polarization peaks that exhibit inter-peak adhesion when the charge state changes; Exclude polarization peaks whose peak volatility is below a threshold when the remaining capacity changes; Prioritize the frequency bands corresponding to the new characteristic peaks that appear when the battery is abnormally aging.

5. The method according to claim 1, characterized in that, The target frequency AC signal includes a first target frequency AC signal corresponding to the relaxation time of the first characteristic peak boundary and a second target frequency AC signal corresponding to the relaxation time of the second characteristic peak boundary.

6. The method according to claim 1, characterized in that, The health status assessment model satisfies the following functional relationship: Where R p1 R is the polarization impedance at the first characteristic frequency point. p2 The polarization impedance at the second characteristic frequency point, Q R This represents the current remaining capacity. a1=3.14×10 -1 a2=6.358×10 -5 a3=2.23×10 -1 , a4 ﴝ6.635×10 -2 a5=-9.815, a6=3.91×10 -1 , a7=-3.38×10 -1 a8=-2.597×10 2 a9=6.0287×10 4 。 7. A valve-regulated lead-acid battery health status assessment system, used to implement any one of claims 1-6, characterized in that, The system includes: Impedance testing unit is used to apply a target frequency AC signal to the battery and obtain the polarization impedance values ​​at the first characteristic frequency point and the second characteristic frequency point. The parameter acquisition unit is used to measure the ohmic internal resistance and current remaining capacity of the battery under test. The model processing unit stores the pre-built health status assessment model and is used to receive the polarization impedance value, ohmic internal resistance and remaining capacitance, and output the health status assessment value. The result output unit is used to display the evaluation value and warning information.

8. The system according to claim 7, characterized in that, The system also includes: The frequency control module is used to store the first characteristic frequency point and the second characteristic frequency point. The boundary point recognition module is used to perform feature boundary point recognition.

9. An electronic device, characterized in that, include: One or more processors; A storage unit for storing one or more programs, which, when executed by one or more processors, enable the one or more processors to implement the valve-regulated lead-acid battery health status assessment method according to any one of claims 1 to 6.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it can implement the valve-regulated lead-acid battery health status assessment method according to any one of claims 1 to 6.

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