Health level estimation system and method for lead-acid battery for transformer substation

Abstract

A health level estimation system and method for a lead-acid battery for a transformer substation. The system comprises: an environment configuration module (100), a multi-channel constant-current module (200), a multi-channel constant-voltage module (300), a battery selection module (400), and a control module (500). The environment configuration module (100) is configured to adjust the temperature of the health level estimation system; the multi-channel constant-current module (200) is configured to output a constant current for each battery to be tested, and detect a current; the multi-channel constant-voltage module (300) is configured to output a constant voltage for each battery to be tested, and detect voltage; the battery selection module (400) is configured to select a battery to be tested and perform testing; and the control module (500) is configured to control the modules to cyclically charge and discharge the battery to be tested, and acquire battery data to perform calculation and evaluation of the health level of the battery to be tested. The estimation system can implement health level estimation of a lead-acid battery, and remaining service life of the battery can be effectively predicted.

Description

Health estimation system and method for lead-acid batteries used in substations

[0001] This application claims priority to the Chinese patent application filed with the State Intellectual Property Office on September 14, 2023, with application number 202311187321.2 and invention name “Health estimation system and method for lead-acid batteries for substations”, the entire contents of which are incorporated by reference into this application. Technical Field

[0002] The present application relates to the technical field of health estimation of lead-acid batteries, and in particular to a health estimation system and method for lead-acid batteries used in substations. Background Art

[0003] After a period of use, backup batteries for communication systems will gradually lose capacity due to factors such as the loss and deterioration of active materials within the battery, electrolyte depletion, corrosion or sulfation of the positive grid, and other factors. To understand the actual discharge performance of the battery pack, confirm the guaranteed power supply duration after a utility power outage, and ensure safe power supply to equipment, regular discharge tests should be performed on the battery pack in use. There are two types of battery discharge tests: a verification discharge test and a capacity test, also known as a capacity verification test.

[0004] A verification discharge test is performed annually using actual load, discharging 30%-40% of the battery pack's rated capacity. This test verifies the reliability of connections between individual cells, any internal short circuits or disconnections, and any significant degradation in the overall battery pack's discharge performance, including the presence of lagging cells. A capacity test is performed every three years using a dummy load, discharging 80% of the battery pack's rated capacity. This is a comprehensive testing method, and only through this test can one truly determine a battery's discharge performance.

[0005] Because the valve-regulated sealed lead-acid batteries (VRLA) in substations serve as DC backup power sources, they cannot be frequently tested for capacity. Currently, in actual substation use, the health status of lead-acid batteries is estimated only by performing capacity tests at regular intervals. This results in batteries being damaged before reaching the capacity limit when they approach their lower capacity limit, seriously threatening the safe operation of substations.

[0006] Summary of the Invention

[0007] This application aims to solve the problem that when existing batteries approach the lower capacity limit, the batteries are damaged before the capacity cycle is reached, which seriously threatens the safety of substation operation. On the one hand, a health estimation system for lead-acid batteries used in substations is provided, which includes: an environment setting module, a multi-channel constant current module, a multi-channel constant voltage module, a battery selection module and a control module;

[0008] The multi-channel constant current module and the multi-channel constant voltage module are both electrically connected to the battery to be tested, the positive and negative electrodes of the battery to be tested are respectively connected to the two ends of the discharge resistor, and the battery selection module is provided between each battery to be tested and the multi-channel constant current module and the multi-channel constant voltage module;

[0009] The environment setting module, the multi-channel constant current module, the multi-channel constant voltage module and the battery selection module are all communicatively connected to the control module;

[0010] The environment setting module is configured to: adjust the temperature of the health estimation system and output temperature data;

[0011] The multi-channel constant current module is configured to: output a constant current for each battery to be tested, detect the current of each battery to be tested, and output battery charge and discharge current data and battery charge and discharge current time data;

[0012] The multi-channel constant voltage module is configured to: output a constant voltage for each battery to be tested, detect the voltage of each battery to be tested, and output battery charge and discharge voltage data and battery charge and discharge voltage time data;

[0013] The battery selection module is configured to: select the battery to be tested for testing;

[0014] The control module is configured to: control the environment setting module, the multi-channel constant current module, the multi-channel constant voltage module and the battery selection module to cyclically perform charge and discharge tests on the battery to be tested;

[0015] The temperature data, battery charge and discharge current data, battery charge and discharge current time data, battery charge and discharge voltage data, and battery charge and discharge voltage time data are obtained to calculate and evaluate the health of the battery to be tested.

[0016] In a feasible implementation, the temperature of the health estimation system is adjusted in a range of -20-100°C.

[0017] In a feasible implementation, the current adjustment range of the multi-channel constant current module is: 0-100A, and the current adjustment resolution is: 0.04A.

[0018] In a feasible implementation, the current adjustment range of the multi-channel constant voltage module is 0-5V, and the voltage adjustment resolution is 0.001V.

[0019] In one possible implementation,

[0020] The control module is further configured to: send a control instruction, wherein the control instruction includes a sequence of preset values;

[0021] The battery selection module includes: a relay and a connecting piece;

[0022] The connecting piece is used to connect the battery to be tested with the multi-channel constant current module and the multi-channel constant voltage module;

[0023] The relay is used to receive the control instruction and open or close the connecting piece according to the preset value sequence.

[0024] Another aspect of the present application provides a method for estimating the health of a lead-acid battery for a substation, the method applying any of the above-mentioned systems for estimating the health of a lead-acid battery for a substation, the method comprising the steps of:

[0025] Randomly select 10% of the batteries to be tested from the same manufacturer and the same model as the sample, and perform a periodic floating charge accelerated aging test on the sample;

[0026] Obtaining the mean and variance of the Gaussian distribution model parameters of the battery capacity to be tested after any cycle of aging to obtain a Gaussian distribution model of the sample;

[0027] According to the Gaussian distribution model of the sample, an upper limit curve, a lower limit curve and a median curve of the capacity of the battery to be tested changing with time during the periodic floating charge accelerated aging test are obtained;

[0028] The remaining service life of the lead-acid battery is estimated according to the upper limit curve, the lower limit curve and the median curve.

[0029] In a feasible implementation, the step of randomly selecting 10% of the batteries to be tested from batteries of the same manufacturer and model as samples for periodic floating charge accelerated aging testing includes:

[0030] Randomly select 10% of the batteries to be tested and number them as 1, 2, 3...n-1, n respectively;

[0031] The capacity of all sample batteries is calibrated and recorded as C i，0 , i=1, 2, 3...n-1, n;

[0032] Perform periodic floating charge aging with heating cycle t = 6 days and temperature T = 60°C;

[0033] After each cycle of floating charge heating aging is completed, the capacity is calibrated once, and the capacity after the jth cycle of heating floating charge aging is recorded as C i，j ,j=1,2,3...k-1,k.

[0034] In a feasible implementation, the method for obtaining the mean and variance of the Gaussian distribution model parameters of the battery capacity after any cycle aging and obtaining the Gaussian distribution model of the sample is as follows:

[0035] Assume that the total sample of lead-acid battery capacity after the jth cycle obeys N(μ j , σ j );

[0036] Assume (C 1，j , C 2，j , C 3，j ...C n-1 , j , C n，j ) is a simple sample of size n taken from the population, then the joint probability density function is:

[0037] The maximum likelihood function is thus determined as:

[0038] Select parameter μ j , σ j , maximize the maximum likelihood function value, and select the parameter μ j , σ j as the mean and variance of the Gaussian model.

[0039] In a feasible implementation, the step of obtaining an upper limit curve, a lower limit curve, and a median curve of the lead-acid battery capacity changing with time during the substation float charging process according to the Gaussian distribution model of the sample includes:

[0040] With capacity C = μ j +σ j is a known point on the upper bound curve, capacity C = μ j -σ j is a known point on the lower bound curve, capacity C = μ j is a known point on the median curve;

[0041] The battery health change curve data obtained by fitting the aged battery samples using cubic spline interpolation was used to draw the lower limit curve, upper limit curve and median curve of the battery capacity change.

[0042] According to the above content, the present application provides a health estimation system for lead-acid batteries used in substations, wherein the environment setting module is configured to: adjust the temperature of the health estimation system; the multi-channel constant current module is configured to: output a constant current for each battery to be tested and detect the current of each battery to be tested; the multi-channel constant voltage module is configured to: output a constant voltage for each battery to be tested and detect the voltage of each battery to be tested; the battery selection module is configured to: select the battery to be tested for testing; the control module is configured to: control the environment setting module, the multi-channel constant current module, the multi-channel constant voltage module and the battery selection module to cyclically perform charge and discharge tests on the battery to be tested; obtain battery data for calculation and evaluation of the health of the battery to be tested. The estimation system of the present application can realize remote health estimation of lead-acid batteries and effectively predict the remaining service life of the battery. On the other hand, the present application provides a method for estimating the health of lead-acid batteries used in substations, which uses Gaussian model parameters to detect battery health. This method does not require a large amount of lead-acid battery operating data. It only requires aging tests on some lead-acid batteries of the same model from the same manufacturer to obtain a curve of the lead-acid battery capacity changing over time, calculate the Gaussian model parameters, and determine the upper and lower bounds of the battery aging capacity change curve. This method can estimate the service life of the lead-acid battery and improve the operational stability of the substation. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] The accompanying drawings, which constitute part of this application, are intended to provide a further understanding of the present invention. The exemplary embodiments of the present invention and their descriptions are intended to explain the present invention and do not constitute an undue limitation of the present invention. In the accompanying drawings:

[0044] FIG1 is a schematic diagram of a health estimation system for a lead-acid battery for a substation according to an exemplary embodiment of the present application;

[0045] FIG2 is a flow chart of a method for estimating the health of a lead-acid battery for a substation, shown in an exemplary embodiment of the present application.

[0046] FIG3 shows an upper limit curve, a lower limit curve, and a median curve of the capacity of a battery to be tested changing with time, according to an exemplary embodiment of the present application.

[0047] In the figure: 100-environment setting module; 200-multi-channel constant current module; 300-multi-channel constant voltage module; 400-battery selection module; 500-control module. DETAILED DESCRIPTION

[0048] The technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments.

[0049] Example embodiments will now be described more fully with reference to the accompanying drawings. However, example embodiments can be implemented in many forms and should not be construed as limited to the examples set forth herein; rather, these embodiments are provided so that the present invention will be more comprehensive and complete and to fully convey the concepts of the example embodiments to those skilled in the art. The described features, structures, or characteristics may be combined in any suitable manner in one or more embodiments. In the following description, numerous specific details are provided to provide a thorough understanding of the implementation of the example embodiments of the present invention.

[0050] The health of lead-acid batteries is usually estimated by measuring parameters such as battery voltage, internal resistance, electrolyte density or temperature. These parameters can reflect the performance and status of the battery, but they are usually affected by many factors, such as battery load, temperature and aging. Gaussian models can be used to analyze and describe the probability distribution of these parameters. The present application provides a health estimation system for lead-acid batteries for substations, which obtains various parameters of lead-acid batteries and brings them into the Gaussian model to calculate the health of the lead-acid batteries.

[0051] 1 , the system includes: an environment setting module 100, a multi-channel constant current module 200, a multi-channel constant voltage module 300, a battery selection module 400, and a control module 500. The multi-channel constant current module 200 and the multi-channel constant voltage module 300 are both electrically connected to the battery to be tested, the positive and negative electrodes of the battery to be tested are respectively connected to the two ends of the discharge resistor, and the battery selection module 400 is arranged between each battery to be tested and the multi-channel constant current module 200 and the multi-channel constant voltage module 300; the environment setting module 100, the multi-channel constant current module 200, the multi-channel constant voltage module 300, and the battery selection module 400 are all in communication with the control module 500.

[0052] The environment setting module 100 is configured to adjust the temperature of the health estimation system and output temperature data. In some embodiments of the present application, the temperature of the health estimation system is adjusted within a range of -20-100°C. The ambient temperature during battery aging testing can affect battery performance.

[0053] For example, in high-temperature environments, such as 45-50°C, batteries undergo an aging process. This aging process exposes potential adverse battery phenomena, such as changes in voltage, thickness, and internal resistance. These changes can serve as comprehensive indicators for directly testing battery safety and electrochemical performance. On the other hand, if the ambient temperature is too high, such as above 50°C, it may also affect battery performance and accelerate the reaction of active substances within the battery, thereby accelerating battery aging.

[0054] In the present application, the environment setting module 100 is used to configure the ambient temperature of the battery to be tested, so that the temperature data is substituted into the Gaussian model to calculate the health of the battery at the actual temperature.

[0055] The multi-channel constant current module 200 is configured to output a constant current for each battery under test, detect the current of each battery under test, and output battery charge and discharge current data and battery charge and discharge current time data. The multi-channel constant voltage module 300 is configured to output a constant voltage for each battery under test, detect the voltage of each battery under test, and output battery charge and discharge voltage data and battery charge and discharge voltage time data.

[0056] The multi-channel constant current module 200 and the multi-channel constant voltage module 300 in this application adopt a multi-channel integrated control strategy, which can simultaneously perform constant voltage float charging on multiple batteries. By precisely controlling the float charge voltage and charging current, the battery to be tested can be effectively protected. In some embodiments of the present application, the current adjustment range of the multi-channel constant current module 200 is: 0-100A, and the current adjustment resolution is: 0.04A. The current adjustment range of the multi-channel constant voltage module 300 is: 0-5V, and the voltage adjustment resolution is: 0.001V.

[0057] Specifically, the current regulation range of the multi-channel constant current module 200 can be from 0-100A, depending on the power level and load capacity of the battery under test. The regulation range can be set according to actual conditions. Similarly, the voltage regulation range of the multi-channel constant voltage module 300 can range from a few volts to hundreds of volts. In this application, the lead-acid batteries used in substations are generally low-voltage batteries, so the voltage regulation range is set to 0-5V.

[0058] The battery selection module 400 is configured to: select a battery to be tested for testing; the control module 500 is configured to: control the environment setting module 100, the multi-channel constant current module 200, the multi-channel constant voltage module 300 and the battery selection module 400 to cyclically perform charge and discharge tests on the battery to be tested; obtain temperature data, battery charge and discharge current data, battery charge and discharge current time data, battery charge and discharge voltage data and battery charge and discharge voltage time data to calculate and evaluate the health of the battery to be tested.

[0059] Among them, the control module 500 is integrated in the host computer and has the control and processing capabilities of the terminal. In this application, the main functions of the control module 500 are: data acquisition, data processing and data storage, etc. The control module 500 can collect various data of the battery, namely temperature data, battery charge and discharge current data, battery charge and discharge current time data, battery charge and discharge voltage data and battery charge and discharge voltage time data. And in this application, the control module 500 forms a Gaussian model after processing the collected data, which can intuitively understand the operating status and change trend of the battery. It is understandable that the control module 500 can also have a data storage function, and the collected data can be stored locally or in a cloud server for easy viewing and analysis at any time.

[0060] In some embodiments of the present application, the control module 500 is further configured to: send a control instruction, which includes a preset value sequence; the battery selection module 400 includes: a relay and a connecting piece; the connecting piece is used to connect the battery to be tested with the multi-channel constant current module 200 and the multi-channel constant voltage module 300; the relay is used to receive the control instruction and disconnect or close the connecting piece according to the preset value sequence.

[0061] The relays in the battery selection module 400 can be set to trigger high or low levels via jumpers. The interface is connected to the connector. The control module 500 arranges the preset value sequence of the batteries to be tested. Each battery has a corresponding channel number, which serves as the connection number for the battery selection module 400, the channel constant current module 200, and the multi-channel constant voltage module 300. During testing, the preset value sequence is input into the system, and the battery selection module 400 then closes or opens the channels of the corresponding numbered batteries to be tested according to the preset value sequence, ensuring that the test data corresponds correctly.

[0062] According to the above content, the present application provides a health estimation system for lead-acid batteries used in substations, wherein the environment setting module is configured to: adjust the temperature of the health estimation system; the multi-channel constant current module is configured to: output a constant current for each battery to be tested and detect the current of each battery to be tested; the multi-channel constant voltage module is configured to: output a constant voltage for each battery to be tested and detect the voltage of each battery to be tested; the battery selection module is configured to: select the battery to be tested for testing; the control module is configured to: control the environment setting module, the multi-channel constant current module, the multi-channel constant voltage module and the battery selection module to cyclically perform charge and discharge tests on the battery to be tested; obtain battery data for calculation and evaluation of the health of the battery to be tested. The estimation system of the present application can measure the selected parameters and record data. It is necessary to obtain enough data points for each battery to establish an effective Gaussian model, realize the health estimation of the lead-acid battery, and effectively predict the remaining service life of the lead-acid battery.

[0063] On the other hand, the present application provides a method for estimating the health of a lead-acid battery for a substation, as shown in FIG2 , the method comprising the steps of:

[0064] S100: Randomly select 10% of the batteries to be tested from the same manufacturer and model as samples, and perform periodic floating charge accelerated aging test on the samples.

[0065] The batteries used to build the Gaussian model must be from the same manufacturer and model as the batteries to be calculated. Batteries from different manufacturers or models may have different parameters, and the calculated results may not match the actual conditions, which can easily lead to errors. The specific steps for sampling include:

[0066] S110: Randomly select 10% of the batteries to be tested and number them as 1, 2, 3, ... n-1, n;

[0067] S120: Calibrate the capacity of all sample batteries and record it as C i，0 , i=1, 2, 3...n-1, n;

[0068] S130: Perform periodic heating float charge aging, period t = 6 days, temperature T = 60°C;

[0069] S140: After each cycle of floating charge heating aging is completed, the capacity is calibrated and the capacity after the jth cycle of heating floating charge aging is recorded as C i，j ,j=1,2,3...k-1,k.

[0070] In this step, the health estimation system of the lead-acid battery for the substation completes the core capacity test of the sample and obtains experimental data, and a Gaussian distribution model can be established based on the experimental data.

[0071] S200: Obtain the mean and variance of the Gaussian distribution model parameters of the battery capacity to be tested after any cycle of aging to obtain a Gaussian distribution model of the sample. In this application, after obtaining the Gaussian distribution model, it is necessary to use appropriate evaluation indicators, such as mean square error (MSE) or R-square value, to evaluate the model fit. If the model fit is poor, it is necessary to adjust the model or re-collect data.

[0072] Specifically, the mean and variance of the Gaussian distribution model parameters of the battery capacity after any cycle of aging are obtained. The specific method for parameter estimation is as follows:

[0073] S210: Assume that the total sample of lead-acid battery capacity after the jth cycle obeys N(μ j , σ j ), where μ is the mean of the Gaussian distribution model parameters and σ is the variance of the Gaussian distribution model parameters;

[0074] S220:Assume (C 1，j , C 2，j , C 3，j ...C n-1 , j , C n，j ) is a simple sample of size n taken from the population, then the joint probability density function is:

[0075] S230: The maximum likelihood function is thus determined to be:

[0076] S240: Select parameter μ j , σ j , maximize the maximum likelihood function value, and select the parameter μ j , σ j As the mean and variance of the Gaussian model, the Gaussian distribution model of the sample is obtained.

[0077] S300: Obtaining, according to the Gaussian distribution model of the sample, an upper limit curve, a lower limit curve, and a median curve of the capacity of the battery to be tested changing with time during the periodic floating charge accelerated aging test.

[0078] Specifically, as shown in FIG3 , the upper curve, middle curve and lower curve of the battery capacity to be tested changing with time correspond to the initial stage, stable stage and final stage of the battery capacity, respectively.

[0079] In the initial stages of battery life, as shown in the upper curve, the battery's capacity decreases rapidly. This is because the active materials within the battery are not yet fully activated, leading to a decline in battery performance. However, once the active materials are fully activated, the upper curve flattens out, entering a plateau. The middle curve, corresponding to the plateau phase, shows that the rate of capacity decline has slowed and stabilized. This is because the active materials within the battery have become relatively stable during this stage, and the rate of performance decline has also slowed accordingly. Although battery performance is still declining, the rate of decline has significantly slowed.

[0080] Finally, as the battery nears the end of its life, the next curve begins to appear. At this stage, the rate of battery capacity decline accelerates again. This is because the battery's performance further declines after the active material inside the battery is completely depleted.

[0081] In general, the upper curve, middle curve and lower curve depict different stages of battery capacity decline during battery aging. These curves help to better understand and evaluate how battery capacity changes over time.

[0082] The specific steps of obtaining the upper limit curve, lower limit curve, and median curve of the capacity of the battery to be tested changing with time include:

[0083] S310: Capacity C = μ j +σ j is a known point on the upper bound curve, capacity C = μ j -σ j is a known point on the lower bound curve, capacity C = μ j is a known point on the median curve.

[0084] S320: Using cubic spline interpolation to fit the battery health change curve data obtained from the aged battery sample, draw the lower limit curve, upper limit curve and median curve of the battery capacity change.

[0085] S400: Estimate the remaining service life of the lead-acid battery according to the upper limit curve, the lower limit curve, and the median curve.

[0086] As can be seen from the above embodiments, the present application provides a system for estimating the health of lead-acid batteries for substations, which can achieve remote health estimation of lead-acid batteries and effectively predict the remaining battery life. Another aspect of the present application provides a method for estimating the health of lead-acid batteries for substations, which uses Gaussian model parameters to detect battery health. This method does not require a large amount of lead-acid battery operating data. Instead, it only requires aging tests on a portion of lead-acid batteries of the same model from the same manufacturer to obtain a curve of the lead-acid battery capacity change over time. The Gaussian model parameters are calculated, and the upper and lower bounds of the battery aging capacity change curve are determined. This method can estimate the service life of the lead-acid battery and improve the operational stability of the substation.

[0087] The above-described embodiments of the present application do not constitute a limitation on the scope of protection of the present application.

Claims

1. A health estimation system for lead-acid batteries for substations, characterized in that: include: Environment setting module, multi-channel constant current module, multi-channel constant voltage module, battery selection module and control module; The multi-channel constant current module and the multi-channel constant voltage module are both electrically connected to the battery to be tested, the positive and negative electrodes of the battery to be tested are respectively connected to the two ends of the discharge resistor, and the battery selection module is arranged between each battery to be tested and the multi-channel constant current module and the multi-channel constant voltage module; The environment setting module, the multi-channel constant current module, the multi-channel constant voltage module and the battery selection module are all connected to the control module in communication; The environment setting module is configured to: adjust the temperature of the health estimation system and output temperature data; The multi-channel constant current module is configured to: output a constant current for each battery to be tested, detect the current of each battery to be tested, and output battery charge and discharge current data and battery charge and discharge current time data; The multi-channel constant voltage module is configured to: output a constant voltage for each battery to be tested, detect the voltage of each battery to be tested, and output battery charge and discharge voltage data and battery charge and discharge voltage time data; The battery selection module is configured to: select the battery to be tested for testing; The control module is configured to: control the environment setting module, the multi-channel constant current module, the multi-channel constant voltage module and the battery selection module to cyclically perform charge and discharge tests on the battery to be tested; The temperature data, battery charge and discharge current data, battery charge and discharge current time data, battery charge and discharge voltage data, and battery charge and discharge voltage time data are obtained to calculate and evaluate the health of the battery to be tested.

2. The health estimation system of lead-acid batteries for substations according to claim 1, characterized in that: The temperature of the health estimation system is adjusted in a range of -20-100°C.

3. The health estimation system for lead-acid batteries for substations according to claim 1, characterized in that: The current adjustment range of the multi-channel constant current module is: 0-100A, and the current adjustment resolution is: 0.04A.

4. The health estimation system for lead-acid batteries for substations according to claim 1, characterized in that: The current adjustment range of the multi-channel constant voltage module is 0-5V, and the voltage adjustment resolution is: 0.001V.

5. The health estimation system for lead-acid batteries for substations according to claim 1, characterized in that: The control module is further configured to: send a control instruction, wherein the control instruction includes a sequence of preset values; The battery selection module includes: a relay and a connecting piece; The connecting piece is used to connect the battery to be tested with the multi-channel constant current module and the multi-channel constant voltage module; The relay is used to receive the control instruction and open or close the connecting piece according to the preset value sequence.

6. A method for estimating the health of a lead-acid battery for a substation, characterized in that: The method is applied to the health estimation system of the lead-acid battery for a substation according to any one of claims 1 to 5, and the method comprises the steps of: Randomly select 10% of the batteries to be tested from the same manufacturer and the same model as samples, and perform a periodic floating charge accelerated aging test on the samples; Obtain the mean and variance of the Gaussian distribution model parameters of the battery capacity to be tested after any cycle of aging, and obtain the Gaussian distribution model of the sample; According to the Gaussian distribution model of the sample, an upper limit curve, a lower limit curve and a median curve of the capacity of the battery to be tested changing with time during the periodic floating charge accelerated aging test are obtained; The remaining service life of the lead-acid battery is estimated according to the upper limit curve, the lower limit curve and the median curve.

7. The method for estimating the health of a lead-acid battery for a substation according to claim 6, characterized in that: The step of randomly selecting 10% of the batteries to be tested from the same manufacturer and the same model as samples for periodic floating charge accelerated aging test to obtain the Gaussian distribution model of all samples includes: Randomly select 10% of the batteries to be tested and number them as 1, 2, 3...n-1, n respectively; The capacity of all sample batteries is calibrated and recorded as C i，0 , i=1, 2, 3...n-1, n; Perform periodic heating floating charge aging, period t = 6 days, temperature T = 60 ° C; After each cycle of floating charge heating aging is completed, the capacity is calibrated once, and the capacity after the jth cycle of heating floating charge aging is recorded as C i，j ,j=1,2,3...k-1,k.

8. The method for estimating the health of a lead-acid battery for a substation according to claim 7, characterized in that: The method for obtaining the mean and variance of the Gaussian distribution model parameters of the battery capacity after any cycle aging and obtaining the Gaussian distribution model of the sample is as follows: Assume that the total sample of lead-acid battery capacity after the jth cycle obeys N(μ j , σ j ); Assume (C 1，j , C 2，j , C 3，j ……C n-1，j , C n，j ) is a simple sample of size n taken from the population, then the joint probability density function is: The maximum likelihood function is thus determined as: Select parameter μ j , σ j , maximize the maximum likelihood function value, and select the parameter μ j , σ j As the mean and variance of the Gaussian model, the Gaussian distribution model of the sample is obtained.

9. The method for estimating the health of a lead-acid battery for a substation according to claim 8, characterized in that: The step of obtaining an upper limit curve, a lower limit curve and a median curve of the lead-acid battery capacity changing with time during the substation floating charge process according to the Gaussian distribution model of the sample comprises: With capacity C = μ j +σ j is a known point on the upper bound curve, capacity C = μ j -σ j is a known point on the lower bound curve, capacity C = μ j is a known point on the median curve; The battery health change curve data obtained by fitting the aged battery samples was obtained by cubic spline interpolation, and the lower limit curve, upper limit curve and median curve of the battery capacity change were drawn.

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