Method and system for estimating state of health of lithium iron phosphate graphite battery

Abstract

The invention provides a lithium iron phosphate graphite battery health state estimation method and system, and the method comprises the steps: carrying out the long-cycle stress measurement experiment of a lithium iron phosphate graphite soft package battery, and collecting the mechanical and electrical data of different stages; based on the correlation between a stress change rule and cyclic capacity attenuation, establishing a linear relationship between a longitudinal height evolution characteristic parameter of the stress curve and a health state: SOH = m (c-b) + n, m being a slope, and n being an intercept; and based on the obtained linear relationship, performing health state estimation after cyclic attenuation on the lithium iron phosphate battery in combination with analysis results of the mechanical data and the electrical data. According to the method, linear monotonous correspondence between the mechanical characteristics of the lithium iron phosphate battery and the health state is realized by attenuating a good linear relation between the deformation scale of the stress curve in the longitudinal height at different stages and the capacity attenuation degree.

Description

Technical Field

[0001] The present invention belongs to the technical field of new energy battery management, and in particular relates to a method and system for estimating the health status of a lithium iron phosphate graphite battery in a partially charged state. Background Art

[0002] Due to its advantages such as good stability, high safety, environmental friendliness and relatively low cost, lithium iron phosphate graphite power batteries have become the preferred battery in many fields. In recent years, they have been widely used in electric vehicles, energy storage, portable electronic devices and other fields. Battery management system is the core technology for large-scale application of lithium-ion batteries. Its functions cover multiple aspects such as safety assurance, performance optimization, life extension and real-time monitoring. It improves the reliability, economy and sustainability of batteries through efficient battery management, and is an important support for promoting the widespread application of lithium battery technology. Among them, accurate battery status monitoring is the basis for battery management decision-making and intelligent battery management. State of health (SOH) is an important aspect of battery management system monitoring. It reflects the degree of degradation of battery performance and life compared with new batteries after a certain use and aging process. It provides real-time SOH information for the battery management system to formulate the optimal charging and balancing strategy to extend battery life, improve charging efficiency and protect battery safety, thereby supporting advanced functions such as fault diagnosis and predictive maintenance. Due to the unique voltage platform, the internal resistance does not change significantly, it is easily affected by temperature, and the complex nonlinear aging mechanism of lithium iron phosphate batteries, it is difficult to accurately describe the real-time health status estimation requirements of batteries using simple models. In addition, most of the current studies on the health status of lithium iron phosphate graphite batteries are conducted under fully charged and discharged conditions, and the resulting health status estimation method lacks accuracy in actual application scenarios. Therefore, a new health status estimation method is urgently needed to solve the long-standing problem of the difficulty in accurately estimating the health status of lithium iron phosphate graphite batteries under partial charge conditions in real working conditions. Summary of the invention

[0003] The technical problem to be solved by the present invention is how to estimate the health of a lithium iron phosphate graphite battery under a partial state of charge.

[0004] The present invention solves the above technical problems through the following technical means:

[0005] A method for estimating the health status of a lithium iron phosphate graphite battery comprises the following steps:

[0006] After pre-treatment of the lithium iron phosphate graphite soft pack battery, the long cycle stress measurement experiment was started to collect mechanical and electrical data at different stages, and analyze the stress change law of the battery at different attenuation levels;

[0007] Based on the correlation between the stress variation law and the cycle capacity decay, a linear relationship between the stress curve longitudinal height evolution characteristic parameter and the health state is established; the stress curve longitudinal height evolution characteristic parameters at different stages of the cycle capacity decay include: the stress value at the end of charging and the difference b and c of the stress values ​​at two extreme points representing that the stress increase rate of the graphite negative electrode and the stress decrease rate of the lithium iron phosphate positive electrode during charging are equal; after fitting, the linear function obtained is: SOH=m(cb)+n, m is the slope, and n is the intercept;

[0008] Based on the obtained linear relationship and combined with the analysis results of mechanical data and electrical data, the health status of the lithium iron phosphate battery after cycle attenuation is estimated.

[0009] Furthermore, the process of pre-treating the lithium iron phosphate graphite battery includes: at a given ambient temperature, performing formation treatment on the battery and calibrating the initial capacity; after the initial capacity is stable, placing the battery on an in-situ mechanical testing device and setting an initial preload for shelving; collecting mechanical data at the same time, analyzing the mechanical data; and completing the pre-treatment when the stress drop ends and reaches a platform, i.e., the baseline is stable.

[0010] Furthermore, the steps of performing a long cycle on the lithium iron phosphate battery are:

[0011] Before the cycle test, the mechanical data of the complete charging process before the cycle is collected at a low rate for the pretreated lithium iron phosphate graphite battery to be tested. Then, a long cycle with a high charge and discharge rate is adopted. In the long cycle, two complete low-rate charge and discharge processes are set at a certain interval of a certain number of cycles to collect mechanical data at different cycle attenuation stages for the analysis of stress characteristics.

[0012] Furthermore, based on the mechanical data and electrical data, a stress curve of the lithium iron phosphate graphite battery that changes with the state of charge at different stages of cycle attenuation is obtained. Compared with the original stress curve before the cycle, the stress curve with a state of charge between 20% and 80% has a good linear relationship between the longitudinal height evolution characteristics and the degree of capacity attenuation.

[0013] The present invention also provides a health status estimation system for a lithium iron phosphate graphite battery, comprising the following steps:

[0014] Data acquisition module: After pre-treating the lithium iron phosphate graphite soft-pack battery, the long-cycle stress measurement experiment begins, the mechanical and electrical data at different stages are collected, and the stress change law of the battery at different attenuation levels is analyzed;

[0015] Linear relationship analysis module: Based on the correlation between stress variation law and cycle capacity attenuation, a linear relationship between the characteristic parameters of longitudinal height evolution of stress curve and health state is established; the characteristic parameters of longitudinal height evolution of stress curve at different stages of cycle capacity attenuation include: the stress value at the end of charging and the difference b and c of the stress values ​​at two extreme points representing that the stress increase rate of graphite negative electrode and the stress decrease rate of lithium iron phosphate positive electrode are equal during charging; the linear function obtained after fitting is: SOH=m(cb)+n, m is the slope, n is the intercept;

[0016] Battery health status estimation module: Based on the obtained linear relationship, combined with the analysis results of mechanical data and electrical data, the health status of the lithium iron phosphate battery after cycle attenuation is estimated.

[0017] Furthermore, the process of pre-treating the lithium iron phosphate graphite battery includes: at a given ambient temperature, performing formation treatment on the battery and calibrating the initial capacity; after the initial capacity is stable, placing the battery on an in-situ mechanical testing device and setting an initial preload for shelving; collecting mechanical data at the same time, analyzing the mechanical data; and completing the pre-treatment when the stress drop ends and reaches a platform, i.e., the baseline is stable.

[0018] Furthermore, the steps of performing a long cycle on the lithium iron phosphate battery are:

[0019] Before the cycle test, the mechanical data of the complete charging process before the cycle is collected at a low rate for the pretreated lithium iron phosphate graphite battery to be tested. Then, a long cycle with a high charge and discharge rate is adopted. In the long cycle, two complete low-rate charge and discharge processes are set at a certain interval of a certain number of cycles to collect mechanical data at different cycle attenuation stages for the analysis of stress characteristics.

[0020] Furthermore, based on the mechanical data and electrical data, a stress curve of the lithium iron phosphate graphite battery that changes with the state of charge at different stages of cycle attenuation is obtained. Compared with the original stress curve before the cycle, the stress curve with a state of charge between 20% and 80% has a good linear relationship between the longitudinal height evolution characteristics and the degree of capacity attenuation.

[0021] The present invention also provides a processing device, comprising at least one processor and at least one memory communicatively connected to the processor, wherein: the memory stores program instructions executable by the processor, and the processor can execute the above-mentioned method by calling the program instructions.

[0022] The present invention also provides a computer-readable storage medium, wherein the computer-readable storage medium stores computer instructions, and the computer instructions enable the computer to execute the above method.

[0023] The advantages of the present invention are:

[0024] The present invention conducts a long cycle stress measurement experiment on the pretreated battery, collects mechanical data and electrical data of the lithium iron phosphate graphite battery at different stages of long cycle, and analyzes the stress change characteristics of the battery after different degrees of attenuation; based on the correlation between the stress curve shape evolution law and capacity attenuation, a linear relationship between the longitudinal height characteristics of the stress curve and the health state is established; based on this, the health state of the lithium iron phosphate graphite battery after cycle attenuation is estimated in combination with the analysis results of the mechanical data and electrical data. The present invention proposes a new method for estimating the health state by using the mechanical characteristics of different stages of battery cycle attenuation for the health state estimation method under partial charge state, and realizes the linear monotonic correspondence between the mechanical characteristics and the health state of the lithium iron phosphate battery by the good linear relationship between the deformation scale of the stress curve in the longitudinal height at different stages of attenuation and the capacity attenuation degree. The method has the characteristics of simple operation, strong adaptability, high accuracy, etc., which makes it possible to solve the problem that the health state of lithium iron phosphate graphite batteries has long been difficult to accurately estimate under partial charge state in real working conditions, and provides a theoretical basis for the next generation of lithium iron phosphate battery health state estimation technology for battery management systems. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] Figure 1 A stress curve comparison diagram of the charging process within the full state of charge range of the lithium iron phosphate graphite battery at different cycle decay stages according to an embodiment of the present invention;

[0026] Figure 2 A stress curve comparison diagram of the charging process of the lithium iron phosphate graphite battery in the range of 20%-80% state of charge at different cycle attenuation stages of the embodiment of the present invention;

[0027] Figure 3 This is a long cycle capacity attenuation diagram of a lithium iron phosphate battery according to an embodiment of the present invention;

[0028] Figure 4 It is a longitudinal height marking diagram of the pre-cycle stress curve of an embodiment of the present invention;

[0029] Figure 5 A linear relationship diagram between the capacity retention rate and the longitudinal height characteristic parameter of an embodiment of the present invention;

[0030] Table 1 shows the longitudinal height characteristic parameters and their values ​​of the stress curve of the lithium iron phosphate battery at different cycle decay stages in the embodiment of the present invention. DETAILED DESCRIPTION

[0031] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in combination with the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0032] This embodiment provides a method for estimating the health state of a lithium iron phosphate graphite battery in a partially charged state based on in-situ stress monitoring, including:

[0033] Carry out long cycle stress measurement experiments on pretreated batteries, collect mechanical and electrical data of lithium iron phosphate soft-pack batteries at different stages of long cycle, and analyze stress change characteristics of batteries after different degrees of attenuation;

[0034] In some embodiments, the process of pre-treating lithium iron phosphate batteries includes: at a given ambient temperature, performing formation treatment on the battery and calibrating the initial capacity. After the initial capacity is stable, the battery is placed on an in-situ mechanical testing device and an initial preload is set for shelving. At the same time, mechanical data is collected and analyzed. The pre-treatment is completed when the stress drop ends and reaches a platform, that is, the baseline is stable.

[0035] Specifically, the battery is first discharged to the cut-off voltage, and then the four-layer lithium iron phosphate graphite soft-pack battery assembled in the laboratory is placed on the test splint, the initial stress is set to 106.82N, and the standing time is set. Usually the battery will relax under the initial stress. After about 12 hours, it can be observed through the mechanical sensor that the stress drops to a flat value, and then the charge and discharge test is started.

[0036] This embodiment uses a self-built in-situ stress measurement device to collect mechanical and electrical data of pre-processed lithium iron phosphate graphite batteries at different stages of pre-cycle and long cycle decay. The main module of the device is a high-precision mechanical sensor, and the middle clamp fixes the test battery.

[0037] like Figure 1As shown in the figure, the stress of the battery before cycling shows a nonlinear and non-monotonic change during the charging process. In general, the stress of the battery rises during the charging process, but in the medium state of charge range, the stress will experience a period of decline. This is because the stress increase rate of the graphite negative electrode is greater than the stress decrease rate of the lithium iron phosphate positive electrode in the initial and final charging stages, and the stress increase rate of the graphite negative electrode in the medium state of charge is less than the stress decrease rate of the lithium iron phosphate positive electrode. Therefore, the stress curve has two extreme points (maximum point and minimum point) representing the stress increase rate of the graphite negative electrode during charging and the stress decrease rate of the lithium iron phosphate positive electrode are equal. With the increase of the cycle speed, the stress curve of the charging process has undergone obvious deformation, which is mainly manifested in the decrease of the longitudinal height and the change of the position of the two extreme points. Therefore, through the evolution of these stress characteristics that can reflect the process of lithium insertion and extraction in the battery, it is reliable to estimate the health state after cycle attenuation. In addition, after the battery cycle decays, the stress at the starting point of charging also increases significantly, from 103.92N before the cycle to 118.51N after 300 cycles. The stress increase of 14.59N before and after 300 cycles may be caused by the collapse of the crystal structure of lithium iron phosphate and graphite and even particle cracking caused by repeated lithium insertion and extraction of positive and negative active materials during the long cycle of the battery. At the same time, the thickening of the graphite negative electrode solid electrolyte interface (SEI) after the cycle will also lead to an increase in system stress. The evolution of the above stress curve shape can be attributed to the battery capacity loss caused by active material loss (LAM) and lithium inventory loss (LLI). Based on this, the method of predicting SOH using the longitudinal height evolution characteristics of the stress curve is solid and reliable. The following is a detailed analysis of the longitudinal height of the stress curve at different stages of the cycle to establish a linear relationship between the stress curve and capacity decay, and use it to estimate SOH.

[0038] based on Figure 1 The stress curve characteristics at different cyclic decay stages are analyzed in detail for their longitudinal height evolution.

[0039] In some embodiments, the detailed analysis process of the stress curves at different stages of the long cycle includes:

[0040] After every 100 cycles of 1C charge and discharge, a complete charge and discharge is performed with a small current of 0.3C to obtain a stress curve that is minimally affected by temperature and can reflect the actual electrochemical reaction process inside the battery.

[0041] Specifically, Figure 3As shown in the figure, taking the stress curve before the cycle as an example, the charging process is annotated with longitudinal height, a represents the height difference between the charging end point and the charging starting point, b represents the height difference between the maximum point and the charging end point, and c represents the height difference between the minimum point and the charging end point. In order to analyze the longitudinal height characteristics of the curve more deeply, the values ​​of b / a, c / a, and cb in the four stages are calculated respectively. The specific values ​​of a, b, c, b / a, c / a, and cb in the four stages are shown in Table 1. With the increase of the number of cycles, the first four parameters decrease to varying degrees, while cb increases. The evolution of the parameters is caused by the change of the internal electrochemical reaction process after battery aging.

[0042] In some embodiments, the process of establishing a linear relationship between the longitudinal height characteristic of the stress curve and the capacity decay includes:

[0043] Based on the electrical data of lithium iron phosphate graphite batteries during long cycles, the trend of charging capacity changing with the number of cycles can be obtained, such as Figure 4 As shown in the figure, the overall charging capacity decreases with the increase of the number of cycles, but the capacity fluctuates greatly in the first 100 cycles, which may be caused by the unstable factors such as the battery being in the formation stage and pressure fluctuations. In addition, the phenomenon that the capacity is significantly higher than that in the local area at the cycle numbers of 0, 100, 200, and 300 is caused by two complete cycles of 0.3C charging and discharging with measuring the real pressure signal every 100 cycles. Under the condition of 0.3C low current charging and discharging, the battery polarization is small, and the speed of lithium extraction and insertion of positive and negative active materials is slow, so that the active lithium and active materials can be more fully utilized, so the capacity will recover, but the charging capacity at the 0.3C rate is still decreasing with the increase of the number of cycles.

[0044] The 0.3C charging capacity retention rate and stress characteristic parameters at cycle numbers of 0, 100, 200, and 300 were selected for fitting. The reason is that when charging and discharging at a 1C rate, a larger current will be accompanied by relatively intense heat generation, and heat generation will also cause the battery to expand, thereby generating additional stress increase, making it impossible for the stress curve to reflect the actual lithium deintercalation electrochemical reaction of the positive and negative active materials. The small current heat generation at low rates is relatively weak, which can reflect a more realistic electrochemical reaction process inside the battery and thus achieve a more accurate prediction of the health status.

[0045] By fitting the 0.3C charge capacity retention rate in four stages with five different stress curve longitudinal height parameters in the same stage, it is found that the 0.3C charge capacity retention rate has a better linear relationship with cb than a, b, c, b / a, and c / a, such as Figure 5 In addition, considering that the battery will not be fully charged or discharged under actual working conditions, the acquisition of parameter cb is as follows Figure 2This can also be accomplished under partial states of charge (20%-80%) as shown, which facilitates the application of this health state estimation method in real operating conditions.

[0046] The health status of lithium iron phosphate graphite battery in partially charged state is estimated based on the analysis results of mechanical data, electrical data, and linear fitting of the longitudinal height characteristics of the stress curve and capacity decay.

[0047] In some embodiments, a curve of the overall stress of a lithium iron phosphate graphite battery changing with the state of charge at different cycle attenuation stages is obtained based on mechanical data and electrical data, and a linear stress curve characteristic parameter-health status relationship is obtained based on a correlation analysis curve between the stress curve shape evolution and the battery health status parameters.

[0048] In some embodiments, the health status is estimated based on a linear stress curve shape characteristic-capacity decay relationship combined with a statistical method.

[0049] Specifically, after completing the linear fitting of the longitudinal height characteristic parameters of the stress curve at different cycle attenuation stages and the 0.3C charging capacity retention rate, the health status of the lithium iron phosphate graphite soft-pack battery can be estimated.

[0050] According to the experimentally measured curves of battery stress curves changing with state of charge at different cycle decay stages and the curves of charge capacity retention rate changing with cycle number, the linear relationship between 0.3C charge capacity retention rate and stress curve longitudinal height parameter cb is fitted, which can be expressed by a simple linear function: SOH = m (cb) + n. The slope m and intercept n can be obtained by fitting the curve with the 0.3C charge capacity retention rate and cb value within 300 cycles.

[0051] SOH estimation: Through the 0.3C charge capacity retention rate at different cycle decay stages and the stress curve longitudinal height parameter cb, a relatively linear stress curve characteristic-SOH relationship can be obtained. This relationship can be used to estimate SOH.

[0052] Based on the stress curve feature-SOH relationship of this embodiment, in the future, according to different application scenarios, intelligent algorithms can be used to comprehensively consider all stress curve longitudinal height parameters. For example, a linear regression model can be used to fit other extracted stress curve longitudinal height parameters (such as a, b, c, b / a, cb) with the battery capacity estimation curve, and the fitting results of different stress curve longitudinal height parameters and capacity retention rates can be combined to provide a more accurate battery SOH estimation. In addition, more complex models such as support vector machines, neural networks, etc. can be used to analyze nonlinear relationships and perform pattern recognition. Verification and adjustment: Verify whether the linear relationship between the obtained SOH and parameters such as cb can be used for SOH estimation after a long cycle of 300 cycles. If necessary, the model can be adjusted and optimized according to the actual test data. Data acquisition and preprocessing: First, various data from the battery are obtained through devices such as mechanical sensors, voltage sensors, current sensors, and temperature sensors. These data are preprocessed, such as filtering, calibration, and normalization, to eliminate noise and make them suitable for the input of the SOH estimation algorithm. Model calibration: The selected electrochemical model is calibrated to ensure that the model can accurately reflect the behavior of the actual battery. Calibration may involve methods such as laboratory testing, historical data fitting, or online parameter estimation. Data fusion: Fusing multiple data from sensors to obtain more accurate and reliable SOH estimation results. Fusion may involve weight assignment, Kalman filtering, and other techniques. Real-time adjustment: Based on real-time sensor data and estimation results, the SOH estimation algorithm is adjusted and corrected in real time to cope with changes and uncertainties in battery operating conditions.

[0053] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for estimating the health status of a lithium iron phosphate graphite battery, characterized in that: The following steps are involved: After pre-treatment of the lithium iron phosphate graphite soft pack battery, the long cycle stress measurement experiment was started to collect mechanical and electrical data at different stages, and analyze the stress change law of the battery at different attenuation levels; Based on the correlation between stress variation and cycle capacity decay, a linear relationship between the characteristic parameters of the longitudinal height evolution of the stress curve and the health status is established; The characteristic parameters of the longitudinal height evolution of the stress curve at different stages of the cycle capacity decay include: the difference b and c between the stress value at the end of charging and the stress value at two extreme points representing that the stress increase rate of the graphite negative electrode and the stress decrease rate of the lithium iron phosphate positive electrode are equal during charging; the linear function obtained after fitting is: SOH=m(cb)+n, where m is the slope and n is the intercept; Based on the obtained linear relationship and combined with the analysis results of mechanical data and electrical data, the health status of the lithium iron phosphate battery after cycle attenuation is estimated.

2. The health status estimation method of the lithium iron phosphate graphite battery according to claim 1, characterized in that: The process of pre-treating the lithium iron phosphate graphite battery includes: at a given ambient temperature, performing formation treatment on the battery and calibrating the initial capacity. After the initial capacity is stable, the battery is placed on an in-situ mechanical testing device and an initial preload is set for shelving. At the same time, mechanical data is collected and analyzed. The pre-treatment is completed when the stress drop ends and reaches a platform, that is, the baseline is stable.

3. The health status estimation method of the lithium iron phosphate graphite battery according to claim 1, characterized in that: The steps of long-cycle the lithium iron phosphate battery are as follows: Before the cycle test, the mechanical data of the complete charging process before the cycle is collected at a low rate for the pretreated lithium iron phosphate graphite battery to be tested. Then, a long cycle with a high charge and discharge rate is adopted. In the long cycle, two complete low-rate charge and discharge processes are set at a certain interval of a certain number of cycles to collect mechanical data at different cycle attenuation stages for the analysis of stress characteristics.

4. The health status estimation method of the lithium iron phosphate graphite battery according to claim 1, characterized in that: According to the mechanical data and electrical data, the stress curve of the lithium iron phosphate graphite battery changing with the state of charge at different stages of cycle attenuation is obtained. Compared with the original stress curve before the cycle, the stress curve with a state of charge between 20% and 80% has a good linear relationship between the longitudinal height evolution characteristics and the capacity attenuation degree.

5. A health status estimation system for lithium iron phosphate graphite batteries, characterized in that: The following steps are involved: Data acquisition module: After pre-treating the lithium iron phosphate graphite soft-pack battery, the long-cycle stress measurement experiment begins, the mechanical and electrical data at different stages are collected, and the stress change law of the battery at different attenuation levels is analyzed; Linear relationship analysis module: Based on the correlation between stress change law and cycle capacity decay, the linear relationship between the longitudinal height evolution characteristic parameters of the stress curve and the health status is established; The characteristic parameters of the longitudinal height evolution of the stress curve at different stages of the cycle capacity decay include: the difference b and c between the stress value at the end of charging and the stress value at two extreme points representing that the stress increase rate of the graphite negative electrode and the stress decrease rate of the lithium iron phosphate positive electrode are equal during charging; the linear function obtained after fitting is: SOH=m(cb)+n, where m is the slope and n is the intercept; Battery health status estimation module: Based on the obtained linear relationship, combined with the analysis results of mechanical data and electrical data, the health status of the lithium iron phosphate battery after cycle attenuation is estimated.

6. The health status estimation system of the lithium iron phosphate graphite battery according to claim 5, characterized in that: The process of pre-treating the lithium iron phosphate graphite battery includes: at a given ambient temperature, performing formation treatment on the battery and calibrating the initial capacity. After the initial capacity is stable, the battery is placed on an in-situ mechanical testing device and an initial preload is set for shelving. At the same time, mechanical data is collected and analyzed. The pre-treatment is completed when the stress drop ends and reaches a platform, that is, the baseline is stable.

7. The health status estimation system of the lithium iron phosphate graphite battery according to claim 5, characterized in that: The steps of long-cycle the lithium iron phosphate battery are as follows: Before the cycle test, the mechanical data of the complete charging process before the cycle is collected at a low rate for the pretreated lithium iron phosphate graphite battery to be tested. Then, a long cycle with a high charge and discharge rate is adopted. In the long cycle, two complete low-rate charge and discharge processes are set at a certain interval of a certain number of cycles to collect mechanical data at different cycle attenuation stages for the analysis of stress characteristics.

8. The health status estimation system of the lithium iron phosphate graphite battery according to claim 5, characterized in that: According to the mechanical data and electrical data, the stress curve of the lithium iron phosphate graphite battery changing with the state of charge at different stages of cycle attenuation is obtained. Compared with the original stress curve before the cycle, the stress curve with a state of charge between 20% and 80% has a good linear relationship between the longitudinal height evolution characteristics and the capacity attenuation degree.

9. A processing device, characterized in that: It comprises at least one processor and at least one memory in communication with the processor, wherein the memory stores program instructions executable by the processor, and the processor calls the program instructions to execute any method according to claims 1 to 4.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer instructions, and the computer instructions enable the computer to execute the method according to any one of claims 1 to 4.

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