Graphene lead-acid battery health state assessment method and system

Abstract

The invention discloses a graphene lead-acid battery state-of-health assessment method and system, relates to the technical field of battery detection, and solves the problems that in the prior art, assessment of a single parameter and the whole battery has certain one-sidedness, so that an assessment result is different from reality, the condition of each battery pack in the lead-acid battery cannot be found in time, and the reliability of the battery pack is improved. And the service life of the battery is influenced. Real-time data of the lead-acid battery are collected through the data sensor; performing anomaly analysis on the running state of the lead-acid battery according to the real-time data; determining the health level of the lead-acid battery according to the analysis result; the lead-acid battery is processed according to the health level of the lead-acid battery, so that the health state of the lead-acid battery can be evaluated and processed in time, and the service life of the battery is prolonged.

Description

Technical Field

[0001] This invention belongs to the field of battery testing and relates to lead-acid battery health status assessment technology, specifically a graphene lead-acid battery health status assessment method and system. Background Technology

[0002] Graphene lead-acid batteries are an improved version of lead-acid battery technology. By introducing graphene materials into traditional lead-acid batteries, they aim to improve battery performance, extend lifespan, and optimize charging efficiency. Health assessments of graphene lead-acid batteries can help users understand the battery's current actual capacity, avoid unexpected equipment downtime due to capacity decay, and ensure stable system operation. In existing technologies, health assessments of graphene lead-acid batteries typically involve analyzing individual parameters and detecting abnormalities. While comprehensive assessments using overall indicators can identify abnormalities, these approaches are somewhat one-sided, leading to discrepancies between the results and reality. This can result in the inability to promptly detect issues in individual battery modules, impacting battery lifespan. Summary of the Invention

[0003] The present invention aims to solve at least one of the technical problems existing in the prior art; to this end, the present invention proposes a method and system for assessing the health status of graphene lead-acid batteries, which is used to solve the technical problem that the assessment of single parameters and the overall battery in the prior art has a certain degree of one-sidedness, resulting in a gap between the assessment results and the actual situation, causing the inability to detect the condition of each battery pack in the lead-acid battery in a timely manner, thus affecting the battery's service life.

[0004] To achieve the above objectives, a first aspect of the present invention provides a method for assessing the health status of a graphene lead-acid battery, comprising: Real-time data from lead-acid batteries is collected using data sensors; Anomaly analysis of the operating status of lead-acid batteries is performed based on real-time data. The health level of lead-acid batteries was determined based on the analysis results. Lead-acid batteries are treated according to their health status.

[0005] Preferably, the step of performing anomaly analysis on the operating status of the lead-acid battery based on real-time data includes: Retrieve real-time data, including real-time voltage, real-time current, and real-time temperature; compare the real-time data with the corresponding threshold ranges. When real-time data exceeds the threshold range, an abnormal signal is generated, and the abnormal battery pack is located; otherwise, the operating status of the lead-acid battery is continuously analyzed.

[0006] Preferably, the method of locating the abnormal battery pack includes: Several battery packs were analyzed using the pulse discharge method to obtain dynamic internal resistance data; the average value of several dynamic internal resistance data was calculated, and the average value was multiplied by the internal resistance threshold coefficient to obtain the internal resistance threshold; when there is dynamic internal resistance data that is greater than the internal resistance threshold, the corresponding battery pack is marked. After the lead-acid batteries are left to stand for a set period of time, the voltage of several battery packs is detected to obtain the voltage recovery rate; the average voltage recovery rate is calculated, and the average value is multiplied by the voltage threshold coefficient to obtain the recovery threshold; when there is a voltage recovery rate that is less than the recovery threshold, the corresponding battery pack is marked. The charging acceptance capability and AC impedance phase angle of several battery packs are analyzed, and abnormal batteries are marked according to the analysis results. The marked batteries are then analyzed, and if the number of times the same battery is marked exceeds the threshold, the battery is marked as an abnormal battery.

[0007] Preferably, the analysis of the charge acceptance capability and AC impedance phase angle of the plurality of battery packs includes: During the constant voltage charging phase of lead-acid batteries, the current of several battery packs is analyzed, and the current within a set time period is fitted into a current decay curve function; the derivative function of the current decay curve function is calculated; when the absolute value of the derivative function is greater than the change threshold, the corresponding battery is marked. The impedance angle of several battery packs was measured using the 1kHz AC injection method; when the impedance angle was greater than a set angle, the corresponding battery pack was marked as abnormal.

[0008] Preferably, determining the health level of the lead-acid battery based on the analysis results includes: Feature data of lead-acid batteries are extracted from real-time data; preliminary health coefficient of lead-acid batteries is analyzed based on feature data; the preliminary health value is corrected according to the number of abnormal battery packs to obtain the health assessment coefficient of lead-acid batteries. The health assessment coefficient of lead-acid batteries is matched with the corresponding mapping table to obtain the numerical range of the health assessment coefficient; the health level of lead-acid batteries is determined according to the corresponding numerical range; the health level includes: low, medium and high.

[0009] Preferably, the step of extracting feature data of lead-acid data based on real-time data of lead-acid batteries includes: The real-time voltage of the lead-acid battery is filtered and converted into a voltage signal using analog-to-digital conversion; the battery capacity is then calculated using the real-time voltage data to obtain the battery capacity data. The temperature difference between the real-time temperature and the optimum temperature of the lead-acid battery is calculated, and the absolute value of the temperature difference is used as the temperature coefficient. The voltage signal is fitted into a voltage change function, and the voltage change is calculated based on the voltage change function. The battery capacity data, temperature coefficient, and voltage change are integrated into feature data.

[0010] Preferably, the preliminary health coefficient analysis of the lead-acid battery based on feature data includes: Construct a health coefficient analysis function: ; Calculate the preliminary health coefficient of lead-acid batteries based on the health coefficient analysis function; in, This indicates battery capacity data; Indicates the rated capacity of the lead-acid battery; and The coefficient of influence is the temperature effect; T represents the temperature coefficient. and The voltage change rate influence coefficient; This represents the rate of change of voltage.

[0011] Preferably, the step of correcting the initial health value based on the number of abnormal battery packs includes: Construct a health value correction function: The initial health value is corrected according to the health value correction function to obtain the health assessment coefficient of the lead-acid battery. in, This represents the abnormal impact coefficient. YS is the non-linear penalty index; ZS is the number of abnormal battery packs; ZS is the total number of battery packs in the lead-acid battery pack.

[0012] Preferably, the process of treating the lead-acid battery according to its health rating includes: When the health level of the lead-acid battery is high, adjust the charging status of the lead-acid battery and perform equalization charging; when the health level of the lead-acid battery is low, replace the abnormal battery pack. When the health level of the lead-acid battery is medium, pulse repair is used to treat the lead-acid battery, and the repaired lead-acid battery is tested. If the lead-acid battery still exhibits abnormalities, the faulty battery pack inside the lead-acid battery will be replaced; the pulse repair function is as follows: .

[0013] The first aspect of the present invention provides a graphene lead-acid battery health status assessment system, comprising: a data acquisition module and a processing module; The data acquisition module is used to acquire real-time data from lead-acid batteries through data sensors. The processing module is used to perform anomaly analysis on the operating status of lead-acid batteries based on real-time data; determine the health level of lead-acid batteries based on the analysis results; and process lead-acid batteries according to their health level.

[0014] Compared with the prior art, the beneficial effects of the present invention are: 1. This invention, by retrieving real-time data and comparing it with thresholds, can promptly detect battery packs exceeding normal ranges, generate abnormal signals, and locate abnormal battery packs. Continuous analysis of the operating status helps to monitor battery status in real time and avoid potential dangers. Using the pulse discharge method to obtain dynamic internal resistance data, calculating the average value, and determining the internal resistance threshold, battery packs with dynamic internal resistance data exceeding the threshold are marked, effectively detecting changes in internal battery performance and identifying potentially problematic batteries early. After the battery has been left to stand, the voltage recovery rate is measured, the average value is calculated, and the recovery threshold is determined. Battery packs with voltage recovery rates less than the threshold are marked, reflecting the battery's self-recovery capability and health status, aiding in battery performance assessment. During the constant voltage charging phase, current is analyzed, a current decay curve function is fitted, and the derivative function is calculated. Batteries with the absolute value of the derivative function exceeding a change threshold are marked, providing in-depth understanding of battery characteristic changes during charging and timely detection of abnormal charging conditions. Using the 1kHz AC injection method to measure the impedance angle, battery packs with impedance angles greater than a set angle are marked, and the marked batteries are analyzed. When the number of times the same battery is marked exceeds a threshold, it is identified as an abnormal battery. By integrating multiple detection results, the accuracy of abnormal battery identification is improved, and false positives are reduced.

[0015] 2. This invention filters and performs analog-to-digital conversion on the real-time voltage to effectively remove noise interference, accurately converting the voltage into a voltage signal. Based on this signal, battery capacity data is calculated, providing accurate foundational data for subsequent analysis and aiding in accurate battery status assessment. The temperature coefficient is obtained by calculating the temperature difference, and the voltage change is obtained by fitting the voltage signal. The battery capacity data, temperature coefficient, and voltage change are then integrated into feature data. This multi-dimensional feature integration comprehensively considers various factors affecting battery health, making the description of battery status more accurate and complete. The established health coefficient analysis function comprehensively considers the impact of battery capacity, temperature, and voltage change rate on battery health. By introducing temperature and voltage change rate influence coefficients, the influence of different factors on battery health can be quantitatively calculated, yielding a more scientific preliminary health coefficient, laying the foundation for accurate battery health assessment. The constructed health value correction function considers the impact of the number of abnormal battery packs on overall battery health. By using anomaly impact coefficients and nonlinear penalty exponents to correct the initial health coefficients, the health assessment results can better reflect the actual condition of the batteries in real-world use, avoiding interference from individual abnormal battery packs on the overall assessment results. Furthermore, by treating the batteries according to their health levels, the battery lifespan can be effectively extended, the battery failure rate reduced, the stability and reliability of the battery system improved, and equipment downtime and maintenance costs caused by battery problems reduced, resulting in significant economic and social benefits. Attached Figure Description

[0016] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0017] Figure 1 This is a schematic diagram of the overall steps of the method of the present invention; Figure 2 This is a schematic diagram of the abnormal state detection and abnormal location steps of lead-acid batteries according to the present invention. Figure 3 This is a schematic diagram of the lead-acid battery health assessment steps of the present invention; Figure 4 This is a schematic diagram illustrating the working steps of the system module of the present invention. Detailed Implementation

[0018] The technical solution of the present invention will be clearly and completely described below with reference to the embodiments. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0019] Please see Figure 1 The first aspect of this invention provides a method for assessing the health status of a graphene lead-acid battery, comprising: S101. Real-time data of lead-acid batteries is collected through data sensors; S102. Perform anomaly analysis on the operating status of lead-acid batteries based on real-time data; S103. Determine the health level of lead-acid batteries based on the analysis results; S104. The lead-acid battery is processed according to its health level. Based on this, the present invention obtains key parameters of the lead-acid battery in real time; by quickly identifying abnormal modes, it determines whether it is overcharging, over-discharging, sulfation, internal short circuit, loose connection or other problems, and achieves accurate positioning; and by issuing alarms in a timely manner, it prevents safety accidents caused by abnormal battery conditions; it also avoids equipment shutdown or system interruption caused by sudden battery failure; and by evaluating the health status of the battery, it can process abnormal batteries in the battery pack, avoiding overall failure due to a group of abnormal batteries.

[0020] In one possible implementation of this invention, combined with Figure 1 , Figure 2 Step S102 can be implemented through the following steps S201-S205, which are explained in detail below: S201. Retrieve real-time data; compare the real-time data with the corresponding threshold range; when the real-time data exceeds the threshold range, generate an abnormal signal and locate the abnormal battery pack; otherwise, continue to analyze the operating status of the lead-acid battery.

[0021] The real-time data includes: real-time voltage, real-time current, and real-time temperature.

[0022] Example: The existing real-time data is as follows: lead-acid battery voltage = 1.88V, current = -5.1A, temperature = 28°C; Obtain the corresponding threshold ranges: Voltage threshold range: 1.90V~2.20V; Current threshold range: -50A to 0A (in discharge state); Temperature threshold range: 5°C-45°C; If the voltage of the lead-acid battery exceeds the voltage threshold range, then battery pack 15 is malfunctioning.

[0023] S202. Analyze several battery packs using the pulse discharge method to obtain dynamic internal resistance data; calculate the average value of several dynamic internal resistance data, multiply the average value by the internal resistance threshold coefficient to obtain the internal resistance threshold; when there is dynamic internal resistance data that is greater than the internal resistance threshold, mark the corresponding battery pack.

[0024] Example: Apply a short-duration high-current pulse discharge (10ms, 5A) to each of the 24 battery packs; measure the voltage drop ΔV at the moment of the pulse; calculate the dynamic internal resistance of each battery pack using the formula |ΔV| / |I_pulse|; assuming the average dynamic internal resistance is 1.15mΩ and the internal resistance threshold coefficient is 1.5, then the internal resistance threshold is 1.73mΩ.

[0025] The dynamic internal resistance of each battery pack is compared with the internal resistance threshold. The dynamic internal resistance of battery pack 15 is 3.5mΩ, which is greater than the internal resistance threshold of 1.73mΩ. Therefore, battery pack 15 is marked.

[0026] S203. After the lead-acid batteries have been left to stand for a set period of time, the voltage of several battery packs is detected to obtain the voltage recovery rate; the average value of the voltage recovery rate is calculated, and the average value is multiplied by the voltage threshold coefficient to obtain the recovery threshold; when there is a voltage recovery rate that is less than the recovery threshold, the corresponding battery pack is marked.

[0027] Example: Disconnect the system from the load and let it rest for 2 hours; record the voltage of all battery packs V_t0 at the start of the rest period (t0); record the voltage of all battery packs V_t2h at the end of the rest period (t2h); calculate the voltage recovery rate Rate_v=(V_t2h-V_t0) / (2 3600); Assuming the average voltage recovery rate is calculated to be 0.1mV / h; and the voltage threshold coefficient is set to 0.8 based on experience; then the calculated recovery threshold is 0.08.

[0028] The voltage recovery rate of each battery pack is compared with the recovery threshold. If the voltage recovery rate of battery pack 7 is 0.04, which is less than the recovery threshold, then battery pack 7 is marked.

[0029] S204. During the constant voltage charging stage of lead-acid batteries, analyze the current of several battery packs, fit the current within a set time period into a current decay curve function; calculate the derivative function of the current decay curve function; when the absolute value of the derivative function is greater than the change threshold, mark the corresponding battery.

[0030] Example: The system enters the constant voltage charging stage; select a stable charging period of 60 to 180 minutes after the start of charging; record the current I(t) of all battery packs every minute; The current of each battery group is fitted to the corresponding current decay curve function, and the derivative function is calculated. After analysis, it is found that the absolute value of the derivative function is less than the change threshold, then the current of all battery groups is normal.

[0031] S205. Measure the impedance angle of several battery packs using the 1kHz AC injection method; when the impedance angle is greater than the set angle, mark the corresponding battery pack as abnormal; analyze the marked batteries, and when the number of times the same battery is marked is greater than the number threshold, mark the battery as an abnormal battery.

[0032] Example: A 1kHz small-amplitude AC signal is injected into each battery pack using a dedicated device; the phase difference between the voltage and current signals, i.e., the impedance angle, is measured; the angle threshold is set to 10° based on experience; the impedance angle of battery pack 2 is 18° and the impedance angle of battery pack 15 is 12°, both of which are greater than the angle threshold, so battery pack 2 and battery pack 15 are marked; battery pack 15 is marked 3 times, which is greater than the number of times threshold 1, so battery pack 15 is marked as an abnormal battery.

[0033] Based on the above steps, this invention, by retrieving real-time data and comparing it with a threshold, can promptly detect situations where battery packs exceed the normal range, generate abnormal signals, and locate abnormal battery packs. Continuous analysis of the operating status helps to monitor battery status in real time and avoid potential dangers. Using the pulse discharge method to obtain dynamic internal resistance data, calculating the average value, and determining the internal resistance threshold, battery packs with dynamic internal resistance data greater than the threshold are marked, effectively detecting changes in internal battery performance and identifying potentially problematic batteries in advance. After the battery has been left to stand, the voltage recovery rate is measured, the average value is calculated, and the recovery threshold is determined. Battery packs with a voltage recovery rate less than the threshold are marked, reflecting the battery's self-recovery capability and health status, aiding in battery performance assessment. During the constant voltage charging phase, current is analyzed, a current decay curve function is fitted, and the derivative function is calculated. Batteries with an absolute value of the derivative function greater than a change threshold are marked, providing in-depth understanding of the battery's characteristic changes during charging and timely detection of abnormal charging situations. Using the 1kHz AC injection method to measure the impedance angle, battery packs with impedance angles greater than a set angle are marked, and the marked batteries are analyzed. When the number of times the same battery is marked exceeds a threshold, it is identified as an abnormal battery. By integrating multiple detection results, the accuracy of abnormal battery identification is improved, and false positives are reduced.

[0034] In one possible implementation of this invention, combined with Figure 1 , Figure 3 Steps S103-S104 can be implemented through the following steps S301-S307, which are explained in detail below: S301. The real-time voltage of the lead-acid battery is filtered and converted into a voltage signal using analog-to-digital conversion; the battery capacity is calculated using the real-time voltage data to obtain the battery capacity data.

[0035] Example: Battery system: 48V communication backup power (24 2V lead-acid batteries in series); Rated capacity: =100Ah; Optimal temperature 25℃; Based on experience, the parameters in the function are set as follows: Health analysis function parameters: =0.95, λ=0.04, =0.98, σ=120; Correction function parameters: γ=0.25, α=1.8.

[0036] The voltage of the lead-acid battery is collected in real time, and the real-time voltage is converted into a voltage signal through filtering and analog-to-digital conversion; the battery capacity data is calculated to be 96Ah.

[0037] S302. Calculate the temperature difference between the real-time temperature and the optimal temperature of the lead-acid battery, and use the absolute value of the temperature difference as the temperature coefficient; fit the voltage signal into a voltage change function, and calculate the voltage change based on the voltage change function; integrate the battery capacity data, temperature coefficient, and voltage change into feature data.

[0038] Example: Assume the real-time temperature of the lead-acid battery is 32°C; the calculated temperature coefficient is 7. Collect voltage data for 10 minutes (1 point per minute): [2.148,2.146,2.144,2.142,2.140,2.138,2.136,2.134,2.132,2.130]V; the calculated voltage change is 0.0018; the integrated feature data is [96,7,0.0018].

[0039] S303. Construct a health coefficient analysis function; calculate the preliminary health coefficient of the lead-acid battery based on the health coefficient analysis function.

[0040] The health coefficient analysis function is as follows: ; This indicates battery capacity data; Indicates the rated capacity of the lead-acid battery; and The coefficient of influence is the temperature effect; T represents the temperature coefficient. and The voltage change rate influence coefficient; This represents the rate of change of voltage.

[0041] Example: The preliminary health coefficient of the lead-acid battery calculated based on the characteristic data and the health coefficient analysis function is 43.5.

[0042] S304. Construct a health value correction function; correct the initial health value according to the health value correction function to obtain the health assessment coefficient of the lead-acid battery.

[0043] The health value correction function is as follows: ; This represents the abnormal impact coefficient. YS is the non-linear penalty index; ZS is the number of abnormal battery packs; ZS is the total number of battery packs in the lead-acid battery pack.

[0044] Example: The number of abnormal batteries is 1, and the health assessment coefficient of the lead-acid battery calculated by the health value correction function is 43.46.

[0045] S305. Match the health assessment coefficient of the lead-acid battery with the corresponding mapping table to obtain the numerical range of the health assessment coefficient; determine the health level of the lead-acid battery based on the corresponding numerical range.

[0046] The health levels are categorized as low, medium, and high.

[0047] Example: Based on practical experience, health levels are set as shown in the table below:

[0048] Table 1: Diagram of Health Level Ranges and Corresponding Levels

[0049] The battery's health level is determined to be low based on the health level range.

[0050] S306. When the health level of the lead-acid battery is high, adjust the charging status of the lead-acid battery and perform equalization charging; when the health level of the lead-acid battery is low, replace the abnormal battery pack.

[0051] S307. When the health level of the lead-acid battery is medium, pulse repair is used to process the lead-acid battery, and the repaired lead-acid battery is tested; if the lead-acid battery still has abnormalities, the abnormal battery pack inside the lead-acid battery is replaced.

[0052] The pulse repair function is as follows: .

[0053] For example: If the health level of the lead-acid battery is low, then replace the abnormal battery pack 15.

[0054] Based on the above steps, this invention filters and performs analog-to-digital conversion on the real-time voltage to effectively remove noise interference, accurately converting the voltage into a voltage signal, and calculating battery capacity data accordingly. This provides accurate basic data for subsequent analysis and helps to accurately assess battery status. The temperature coefficient is obtained by calculating the temperature difference, and the voltage change is obtained by fitting the voltage signal. The battery capacity data, temperature coefficient, and voltage change are then integrated into feature data. This multi-dimensional feature integration comprehensively considers various factors affecting battery health, making the description of battery status more accurate and complete. The established health coefficient analysis function comprehensively considers the impact of battery capacity, temperature, and voltage change rate on battery health. By introducing temperature influence coefficients and voltage change rate influence coefficients, the influence of different factors on battery health can be quantitatively calculated to obtain a more scientific preliminary health coefficient, laying the foundation for accurately assessing battery health status. The constructed health value correction function considers the impact of the number of abnormal battery packs on overall battery health. By using anomaly impact coefficients and nonlinear penalty exponents to correct the initial health coefficients, the health assessment results can better reflect the actual condition of the batteries in real-world use, avoiding interference from individual abnormal battery packs on the overall assessment results. Furthermore, by treating the batteries according to their health levels, the battery lifespan can be effectively extended, the battery failure rate reduced, the stability and reliability of the battery system improved, and equipment downtime and maintenance costs caused by battery problems reduced, resulting in significant economic and social benefits.

[0055] Please see Figure 4 A second aspect of the present invention provides a graphene lead-acid battery health status assessment system, comprising: a data acquisition module and a processing module; The data acquisition module is used to acquire real-time data from lead-acid batteries through data sensors. The processing module is used to perform anomaly analysis on the operating status of lead-acid batteries based on real-time data; determine the health level of lead-acid batteries based on the analysis results; and process lead-acid batteries according to their health level.

[0056] Some of the data in the above formula are calculated by removing dimensions and taking their numerical values. The formula is the closest to the real situation obtained by software simulation of a large amount of collected data. The preset parameters and preset thresholds in the formula are set by those skilled in the art according to the actual situation or obtained through simulation of a large amount of data.

[0057] The working principle of this invention is as follows: This invention collects real-time data of lead-acid batteries through data sensors; performs anomaly analysis on the operating status of lead-acid batteries based on the real-time data; determines the health level of lead-acid batteries based on the analysis results; and processes lead-acid batteries according to their health level.

[0058] The above embodiments are only used to illustrate the technical methods 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 methods of the present invention without departing from the spirit and scope of the technical methods of the present invention.

Claims

1. A method of state of health estimation for a graphene lead acid battery, characterized by, The application relates to a lead-acid battery health evaluation method and device. Real-time data of the lead-acid battery is collected through a data sensor; An abnormality analysis is performed on the running state of the lead-acid battery according to the real-time data; The health grade of the lead-acid battery is determined according to the analysis result; The lead-acid battery is processed according to the health grade of the lead-acid battery.

2. A method of state of health estimation of a graphene lead acid battery according to claim 1, characterized in that, The abnormality analysis on the running state of the lead-acid battery according to the real-time data comprises the following steps: The real-time data is called, wherein the real-time data comprises real-time voltage, real-time current and real-time temperature; the real-time data is compared with a corresponding threshold range; When the real-time data exceeds the threshold range, an abnormal signal is generated, and an abnormal battery pack is located; otherwise, the running state of the lead-acid battery is continuously analyzed.

3. A method of state of health estimation for a graphene lead acid battery according to claim 2, wherein, The abnormal battery pack is located by the following steps: The pulse discharge method is used to analyze a plurality of battery packs to obtain dynamic internal resistance data; the average value of the plurality of dynamic internal resistance data is calculated, and the average value is multiplied by an internal resistance threshold coefficient to obtain an internal resistance threshold value; when there is dynamic internal resistance data greater than the internal resistance threshold value, the corresponding battery pack is marked; The voltage of the plurality of battery packs is detected after the lead-acid battery is left to stand for a set period of time to obtain a voltage recovery rate; the average value of the voltage recovery rate is calculated, and the average value is multiplied by a voltage threshold coefficient to obtain a recovery threshold value; when there is voltage recovery rate less than the recovery threshold value, the corresponding battery pack is marked; The charge acceptance capability and the alternating current impedance phase angle of the plurality of battery packs are analyzed, and the abnormal battery is marked according to the analysis result; the marked battery is analyzed, and when the marking frequency of the same battery is greater than a frequency threshold value, the battery is marked as an abnormal battery.

4. A method of state of health estimation of a graphene lead acid battery according to claim 3, characterized in that, The analysis of the charge acceptance capability and the alternating current impedance phase angle of the plurality of battery packs comprises the following steps: The current of the plurality of battery packs is analyzed in the constant-voltage charging stage of the lead-acid battery, and the current in a set period of time is fitted into a current attenuation curve function; the derivative function of the current attenuation curve function is calculated; when the absolute value of the derivative function is greater than a change threshold value, the corresponding battery is marked; The impedance angle of the plurality of battery packs is measured by using the 1kHz alternating current injection method; when the impedance angle is greater than a set angle, the corresponding battery pack is marked as abnormal.

5. The method of claim 1, wherein the method further comprises: The health grade of the lead-acid battery is determined according to the analysis result by the following steps: Characteristic data of the lead-acid data is extracted according to the real-time data of the lead-acid battery; a preliminary health coefficient of the lead-acid battery is analyzed based on the characteristic data; the preliminary health value is corrected according to the number of abnormal battery packs to obtain a health evaluation coefficient of the lead-acid battery; The health evaluation coefficient of the lead-acid battery is matched with a corresponding mapping relationship table to obtain a numerical interval of the health evaluation coefficient; the health grade of the lead-acid battery is determined according to the corresponding numerical interval; wherein the health grade comprises low, medium and high.

6. A method of state of health estimation of a graphene lead acid battery according to claim 5, characterized in that, The characteristic data of the lead-acid data is extracted according to the real-time data of the lead-acid battery by the following steps: The real-time voltage of the lead-acid battery is filtered, and the real-time voltage is converted into a voltage signal by using an analog-digital conversion; the capacity of the battery is calculated by using the real-time voltage data to obtain battery capacity data; The temperature difference between the real-time temperature of the lead-acid battery and the optimum temperature is calculated, and the absolute value of the temperature difference is taken as a temperature coefficient; a voltage change function is fitted from the voltage signal, and a voltage change amount is calculated according to the voltage change function; and the battery capacity data, the temperature coefficient and the voltage change amount are integrated into feature data.

7. The method of claim 5, wherein the method further comprises: The preliminary health coefficient of the lead-acid battery is analyzed based on the feature data, including: Constructing a health coefficient analysis function: ; calculating a preliminary health coefficient of the lead-acid battery according to the health coefficient analysis function; wherein, represents the battery capacity data; represents the rated capacity of the lead-acid battery; and is a temperature influence coefficient; T represents a temperature coefficient; and is a voltage change rate influence coefficient; represents the voltage change rate.

8. The method of claim 5, wherein the method further comprises: The preliminary health value is corrected according to the number of abnormal battery groups, including: Constructing a health value correction function: ; correcting the preliminary health value according to the health value correction function to obtain the health evaluation coefficient of the lead-acid battery; wherein, is the abnormal influence coefficient; is the nonlinear penalty index; Ys is the number of abnormal battery packs; Zs is the total number of battery packs in the lead-acid battery.

9. The method of claim 1, wherein the method further comprises: The lead-acid battery is processed according to the health grade of the lead-acid battery, including: When the health grade of the lead-acid battery is high, the charging condition of the lead-acid battery is adjusted for equalization charging; when the health grade of the lead-acid battery is low, the abnormal battery group is replaced; When the health grade of the lead-acid battery is medium, the lead-acid battery is processed by pulse repair, and the repaired lead-acid battery is detected; When the lead-acid battery still has an anomaly, the abnormal battery group inside the lead-acid battery is replaced; wherein the pulse repair function is: .

10. A graphene lead-acid battery state-of-health evaluation system, applied to the graphene lead-acid battery state-of-health evaluation method of any one of claims 1-9, characterized in that, including: The acquisition module and the processing module; The acquisition module is used for acquiring real-time data of the lead-acid battery through a data sensor; The processing module is used for abnormity analysis on the running state of the lead-acid battery according to the real-time data, determining the health grade of the lead-acid battery according to the analysis result, and processing the lead-acid battery according to the health grade of the lead-acid battery.

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