Energy storage battery degradation trend analysis method and system based on big data and medium

Abstract

The invention provides an energy storage battery degradation trend analysis method and system based on big data and a medium, and belongs to the technical field of energy storage batteries and big data. The method comprises the following steps: acquiring monitoring information of a battery cell, extracting voltage drop rate data, discharge depth data and internal resistance increase proportion data, and respectively judging whether the voltage drop rate of the battery cell meets the requirement, whether the discharge depth of the battery cell meets the requirement and the internal resistance aging degree of the battery cell; and processing according to the voltage drop rate data, the discharge depth data and the internal resistance growth proportion data to obtain a cell degradation index of the battery, and comparing the cell degradation index with a preset degradation index threshold to judge the cell degradation grade of the battery. According to the method, accurate battery performance evaluation is provided, the state of the single cell of the battery is comprehensively evaluated, potential problems can be found in advance, preventive maintenance measures can be specifically taken, sudden battery faults are avoided, and the service life of the battery is effectively prolonged.

Description

Technical Field

[0001] The present application relates to the field of energy storage batteries and big data technology, and in particular, to a method, system and medium for analyzing degradation trends of energy storage batteries based on big data. Background Art

[0002] At present, energy storage battery technology is the key to the development of the power industry. For example, lithium-ion battery technology in electrochemical energy storage has become relatively mature and has begun to take shape in commercialization. Energy storage battery technology plays an important role in large-scale access of new energy to the power grid, peak load shaving and valley filling, and voltage and frequency regulation. However, the degradation of energy storage batteries will bring a series of disadvantages and challenges.

[0003] As the use time of energy storage batteries increases, battery degradation is bound to occur. Battery degradation will cause its energy density to decrease, the cycle life will gradually shorten, and the safety may be reduced. There is a lack of a technical method for multi-level and comprehensive status monitoring of energy storage batteries and intuitive reflection of degradation trends.

[0004] In view of the above problems, effective technical solutions are currently awaited. Summary of the invention

[0005] The purpose of this application is to provide a method, system and medium for analyzing the degradation trend of energy storage batteries based on big data. By acquiring the monitoring information of a single battery cell, the voltage drop rate data, the discharge depth data and the internal resistance growth percentage data can be extracted to judge whether the voltage drop rate of a single battery cell meets the requirements, whether the discharge depth of a single battery cell meets the requirements and the degree of internal resistance aging of the single battery cell, and then the voltage drop rate data, the discharge depth data and the internal resistance growth percentage data are processed to obtain the battery cell degradation index, which is compared with the preset degradation index threshold to judge the battery cell degradation level. By monitoring multiple parameters such as the voltage drop rate, the discharge depth and the internal resistance growth percentage, this application not only provides an accurate battery performance evaluation, but also a comprehensive evaluation of the battery cell status. Potential problems can be discovered in advance, and preventive maintenance measures can be taken in a targeted manner to avoid sudden battery failures and effectively extend the battery life.

[0006] This application provides a method for analyzing the degradation trend of energy storage batteries based on big data, comprising the following steps:

[0007] Obtain battery cell monitoring information within a preset time period under a preset environment, and extract voltage drop rate data, discharge depth data, and internal resistance growth percentage data;

[0008] Comparing the voltage drop rate data with a preset drop rate threshold, determining whether the battery cell voltage drop rate meets the requirement;

[0009] Comparing the discharge depth data with a preset discharge depth threshold, determining whether the discharge depth of a single cell of the battery meets the requirement;

[0010] According to the comparison between the internal resistance growth percentage data and the preset growth percentage threshold, the aging degree of the internal resistance of the battery cell is determined;

[0011] Processing the voltage drop rate data, the discharge depth data and the internal resistance growth ratio data to obtain a battery cell degradation index;

[0012] The battery cell degradation level is determined by comparing the battery cell degradation index with a preset degradation index threshold.

[0013] Among them, in the energy storage battery degradation trend analysis method based on big data described in the present application, the voltage drop rate data is compared with the preset drop rate threshold to determine whether the battery cell voltage drop rate meets the requirements, specifically:

[0014] Comparing the voltage drop rate data with a preset drop rate threshold value to obtain a drop rate deviation rate;

[0015] comparing the descent rate deviation rate with a preset descent rate deviation rate threshold;

[0016] If the drop rate deviation rate is greater than or equal to the drop rate deviation rate threshold, the battery cell voltage drop rate does not meet the requirement and the discharge capacity is insufficient;

[0017] If the drop rate deviation rate is less than the drop rate deviation rate threshold, the battery cell voltage drop rate meets the requirement and the discharge capacity is normal.

[0018] Among them, in the energy storage battery degradation trend analysis method based on big data described in the present application, the discharge depth data is compared with the preset discharge depth threshold to determine whether the battery single cell discharge depth meets the requirements, specifically:

[0019] Comparing the discharge depth data with a preset first discharge depth threshold and a second discharge depth threshold, respectively, the first discharge depth threshold being less than the second discharge depth threshold;

[0020] If the discharge depth data is less than the first discharge depth threshold, the battery cell discharge depth is too low, affecting the battery cell performance;

[0021] If the discharge depth data is greater than or equal to the first discharge depth threshold and less than or equal to the second discharge depth threshold, the battery single cell discharge depth meets the requirement;

[0022] If the discharge depth data is greater than the second discharge depth threshold, the discharge depth of the battery cell is too high, which affects the service life of the battery cell.

[0023] Among them, in the energy storage battery degradation trend analysis method based on big data described in the present application, the internal resistance growth ratio data is compared with the preset growth ratio threshold to judge the aging degree of the battery cell internal resistance, specifically:

[0024] Comparing the internal resistance growth proportion data with a preset first growth proportion threshold and a second growth proportion threshold respectively, the first growth proportion threshold is less than the second growth proportion threshold;

[0025] If the internal resistance growth percentage data is less than or equal to the first growth percentage threshold, the battery cell internal resistance is in good condition;

[0026] If the internal resistance growth ratio data is greater than the first growth ratio threshold and less than or equal to the second growth ratio threshold, the internal resistance aging degree of the battery cell is low and is marked;

[0027] If the internal resistance growth rate data is greater than the second growth rate threshold, the internal resistance of the battery cell is highly aged and needs to be replaced.

[0028] Among them, in the energy storage battery degradation trend analysis method based on big data described in the present application, the voltage drop rate data, discharge depth data and internal resistance growth ratio data are processed to obtain the battery cell degradation index, specifically:

[0029] Acquire ambient temperature data under the preset environment;

[0030] The voltage drop rate data, the discharge depth data and the internal resistance growth ratio data are processed in combination with the ambient temperature data through a preset degradation trend model to obtain a battery cell degradation index.

[0031] Among them, in the energy storage battery degradation trend analysis method based on big data described in the present application, the battery cell degradation index is compared with the preset degradation index threshold to determine the battery cell degradation level, specifically:

[0032] The battery cell degradation index is compared with a preset first degradation index threshold and a preset second degradation index threshold, respectively, and the first degradation index threshold is less than the second degradation index threshold;

[0033] If the battery cell degradation index is less than the first degradation index threshold, the battery cell is slightly degraded and the degradation level is low;

[0034] If the battery cell degradation index is greater than or equal to the first degradation index threshold and less than the second degradation index threshold, the battery cell is moderately degraded and the degradation level is medium;

[0035] If the battery cell degradation index is greater than or equal to the second degradation index threshold, the battery cell is severely degraded and the degradation level is high.

[0036] In a second aspect, the present application provides a system for analyzing degradation trends of energy storage batteries based on big data, the system comprising: a memory and a processor, the memory comprising a program of an energy storage battery degradation trend analysis method based on big data, and the program of the energy storage battery degradation trend analysis method based on big data is executed by the processor to implement the following steps:

[0037] Obtain battery cell monitoring information within a preset time period under a preset environment, and extract voltage drop rate data, discharge depth data, and internal resistance growth percentage data;

[0038] Comparing the voltage drop rate data with a preset drop rate threshold, determining whether the battery cell voltage drop rate meets the requirement;

[0039] Comparing the discharge depth data with a preset discharge depth threshold, determining whether the discharge depth of a single cell of the battery meets the requirement;

[0040] According to the comparison between the internal resistance growth percentage data and the preset growth percentage threshold, the aging degree of the internal resistance of the battery cell is determined;

[0041] Obtaining a battery cell degradation index according to the voltage drop rate data, the discharge depth data, and the internal resistance growth ratio data;

[0042] The battery cell degradation level is determined by comparing the battery cell degradation index with a preset degradation index threshold.

[0043] Among them, in the energy storage battery degradation trend analysis system based on big data described in the present application, the voltage drop rate data is compared with the preset drop rate threshold to determine whether the battery cell voltage drop rate meets the requirements, specifically:

[0044] Comparing the voltage drop rate data with a preset drop rate threshold value to obtain a drop rate deviation rate;

[0045] comparing the descent rate deviation rate with a preset descent rate deviation rate threshold;

[0046] If the drop rate deviation rate is greater than or equal to the drop rate deviation rate threshold, the battery cell voltage drop rate does not meet the requirement and the discharge capacity is insufficient;

[0047] If the drop rate deviation rate is less than the drop rate deviation rate threshold, the battery cell voltage drop rate meets the requirement and the discharge capacity is normal.

[0048] Among them, in the energy storage battery degradation trend analysis system based on big data described in the present application, the discharge depth data is compared with the preset discharge depth threshold to determine whether the battery single cell discharge depth meets the requirements, specifically:

[0049] Comparing the discharge depth data with a preset first discharge depth threshold and a second discharge depth threshold, respectively, the first discharge depth threshold being less than the second discharge depth threshold;

[0050] If the discharge depth data is less than the first discharge depth threshold, the battery cell discharge depth is too low, affecting the battery cell performance;

[0051] If the discharge depth data is greater than or equal to the first discharge depth threshold and less than or equal to the second discharge depth threshold, the battery single cell discharge depth meets the requirement;

[0052] If the discharge depth data is greater than the second discharge depth threshold, the discharge depth of the battery cell is too high, which affects the service life of the battery cell.

[0053] In a third aspect, the present application also provides a computer-readable storage medium, which includes a program for analyzing a degradation trend of a storage battery based on big data. When the program for analyzing a degradation trend of a storage battery based on big data is executed by a processor, the steps of the method for analyzing a degradation trend of a storage battery based on big data as described in any one of the above items are implemented.

[0054] As can be seen from the above, the energy storage battery degradation trend analysis method, system and medium based on big data provided by the embodiment of the present application obtains the battery cell monitoring information within the preset time period under the preset environment, extracts the voltage drop rate data, the discharge depth data and the internal resistance growth ratio data, compares the voltage drop rate data with the preset drop rate threshold, judges whether the battery cell voltage drop rate meets the requirements, compares the discharge depth data with the preset discharge depth threshold, judges whether the battery cell discharge depth meets the requirements, judges the battery cell internal resistance aging degree according to the internal resistance growth ratio data and the preset growth ratio threshold, and processes the voltage drop rate data, the discharge depth data and the internal resistance growth ratio data in combination with the ambient temperature data under the preset environment to obtain the battery cell degradation index, and finally compares it with the preset degradation index threshold to judge the battery cell degradation level. The present application not only provides accurate battery performance evaluation by monitoring multiple parameters such as voltage drop rate, discharge depth and internal resistance growth ratio, but also comprehensively evaluates the battery cell status, can discover potential problems in advance, can take targeted preventive maintenance measures, avoid sudden battery failures, and effectively extend the battery life.

[0055] Other features and advantages of the present application will be described in the following description, and partly become apparent from the description, or understood by practicing the embodiments of the present application. The purpose and other advantages of the present application can be realized and obtained by the structures specifically pointed out in the written description and the drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required for use in the embodiments of the present application will be briefly introduced below. It should be understood that the following drawings only show certain embodiments of the present application and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without paying creative work.

[0057] Figure 1 A flowchart of a method for analyzing degradation trends of energy storage batteries based on big data provided in an embodiment of the present application;

[0058] Figure 2 A flow chart of determining whether the voltage drop rate of a single cell of a battery meets the requirements of the energy storage battery degradation trend analysis method based on big data provided in an embodiment of the present application;

[0059] Figure 3 A flow chart of a method for analyzing the degradation trend of a storage battery based on big data provided in an embodiment of the present application for determining whether the discharge depth of a single cell of a battery meets the requirements;

[0060] Figure 4 A flow chart of determining the degree of aging of the internal resistance of a battery cell according to the method for analyzing the degradation trend of an energy storage battery based on big data provided in an embodiment of the present application;

[0061] Figure 5 A flow chart of obtaining a battery cell degradation index in a method for analyzing degradation trends of energy storage batteries based on big data provided in an embodiment of the present application. DETAILED DESCRIPTION

[0062] The technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all of the embodiments. The components of the embodiments of the present application described and shown in the drawings here can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present application provided in the drawings is not intended to limit the scope of the application claimed for protection, but merely represents the selected embodiments of the present application. Based on the embodiments of the present application, all other embodiments obtained by those skilled in the art without making creative work belong to the scope of protection of the present application.

[0063] It should be noted that similar numbers and letters represent similar items in the following figures. Therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures. At the same time, in the description of this application, the terms "first", "second", etc. are only used to distinguish the description and cannot be understood as indicating or implying relative importance. It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data need to comply with relevant laws, regulations and standards of relevant countries and regions.

[0064] Please refer to Figure 1 , Figure 1 The flowchart of the energy storage battery degradation trend analysis method based on big data in some embodiments of the present application. The energy storage battery degradation trend analysis method based on big data is used in terminal devices, such as computers, mobile phone terminals, etc. The energy storage battery degradation trend analysis method based on big data includes the following steps:

[0065] S101, obtaining battery cell monitoring information within a preset time period under a preset environment, and extracting voltage drop rate data, discharge depth data, and internal resistance growth ratio data;

[0066] S102, comparing the voltage drop rate data with a preset drop rate threshold to determine whether the battery cell voltage drop rate meets the requirement;

[0067] S103, comparing the discharge depth data with a preset discharge depth threshold to determine whether the discharge depth of a single cell of the battery meets the requirement;

[0068] S104, comparing the internal resistance growth percentage data with a preset growth percentage threshold to determine the aging degree of the battery cell internal resistance;

[0069] S105, obtaining a battery cell degradation index by processing the voltage drop rate data, the discharge depth data, and the internal resistance growth ratio data;

[0070] S106 , comparing the battery cell degradation index with a preset degradation index threshold to determine the battery cell degradation level.

[0071] Among them, the present application obtains the battery cell monitoring information within the preset time period under the preset environment, extracts the voltage drop rate data, the discharge depth data and the internal resistance growth percentage data, compares the voltage drop rate data with the preset drop rate threshold, judges whether the battery cell voltage drop rate meets the requirements, compares the discharge depth data with the preset discharge depth threshold, judges whether the battery cell discharge depth meets the requirements, judges the degree of internal resistance aging of the battery cell according to the internal resistance growth percentage data and the preset growth percentage threshold, and processes the voltage drop rate data, the discharge depth data and the internal resistance growth percentage data in combination with the ambient temperature data under the preset environment to obtain the battery cell degradation index, and finally compares it with the preset degradation index threshold to judge the battery cell degradation level. The present application not only provides accurate battery performance evaluation by monitoring multiple parameters such as voltage drop rate, discharge depth and internal resistance growth percentage, but also comprehensively evaluates the battery cell status, can discover potential problems in advance, can take targeted preventive maintenance measures, avoid sudden battery failures, and effectively extend the battery life.

[0072] Please refer to Figure 2 , Figure 2 This is a flowchart of a method for analyzing the degradation trend of energy storage batteries based on big data in some embodiments of the present application for determining whether the voltage drop rate of a battery cell meets the requirements. According to an embodiment of the present invention, the voltage drop rate data is compared with a preset drop rate threshold to determine whether the voltage drop rate of a battery cell meets the requirements, specifically:

[0073] S201, comparing the voltage drop rate data with a preset drop rate threshold to obtain a drop rate deviation rate;

[0074] S202, comparing the descent rate deviation rate with a preset descent rate deviation rate threshold;

[0075] S203: If the drop rate deviation rate is greater than or equal to the drop rate deviation rate threshold, the battery cell voltage drop rate does not meet the requirement and the discharge capacity is insufficient;

[0076] S204: If the drop rate deviation rate is less than the drop rate deviation rate threshold, the battery cell voltage drop rate meets the requirement and the discharge capacity is normal.

[0077] Among them, in order to determine whether the voltage drop rate of the battery cell meets expectations, the voltage drop rate data is compared with the preset drop rate threshold to obtain the drop rate deviation rate, and the drop rate deviation rate is compared with the preset drop rate deviation rate threshold. If the drop rate deviation rate is greater than or equal to the drop rate deviation rate threshold, the battery cell voltage drop rate does not meet the requirements, the discharge capacity is insufficient, and maintenance or replacement may be required. If the drop rate deviation rate is less than the drop rate deviation rate threshold, the battery cell voltage drop rate meets the requirements, the discharge capacity is normal, and the battery cell performance meets the requirements. The above method can timely detect the abnormality of the battery cell performance, so as to take corresponding measures to ensure the reliability and safety of the battery system, help to extend the service life of the battery cell, and reduce damage to the battery cell by avoiding excessive discharge.

[0078] Please refer to Figure 3 , Figure 3 It is a flowchart of judging whether the discharge depth of a battery cell meets the requirements of the energy storage battery degradation trend analysis method based on big data in some embodiments of the present application. According to an embodiment of the present invention, the comparison between the discharge depth data and the preset discharge depth threshold is used to judge whether the discharge depth of a battery cell meets the requirements, specifically:

[0079] S301, comparing the discharge depth data with a preset first discharge depth threshold and a second discharge depth threshold, respectively, the first discharge depth threshold being less than the second discharge depth threshold;

[0080] S302: If the discharge depth data is less than a first discharge depth threshold, the battery cell discharge depth is too low, which affects the battery cell performance;

[0081] S303: If the discharge depth data is greater than or equal to the first discharge depth threshold and less than or equal to the second discharge depth threshold, the battery cell discharge depth meets the requirement;

[0082] S304: If the discharge depth data is greater than a second discharge depth threshold, the discharge depth of the battery cell is too high, which affects the service life of the battery cell.

[0083] Among them, in order to evaluate the performance and service life of the battery cell, the discharge depth data is compared with the preset first discharge depth threshold and the second discharge depth threshold respectively. The first discharge depth threshold is less than the second discharge depth threshold. For example, the first discharge depth threshold is 65% and the second discharge depth threshold is 75%. If the discharge depth data is less than the first discharge depth threshold, the discharge depth of the battery cell is too low, which may affect the immediate performance of the battery cell because the battery cell is not fully utilized. If the discharge depth data is greater than or equal to the first discharge depth threshold and less than or equal to the second discharge depth threshold, it indicates that the discharge depth of the battery cell is within a healthy range and meets the requirements. If the discharge depth data is greater than the second discharge depth threshold, the discharge depth of the battery cell is too high. Being in this state for a long time may shorten the service life of the battery because excessive discharge accelerates battery aging.

[0084] Please refer to Figure 4 , Figure 4 The flowchart of the method for analyzing the degradation trend of energy storage batteries based on big data in some embodiments of the present application is to judge the degree of aging of the internal resistance of a battery cell. According to an embodiment of the present invention, the degree of aging of the internal resistance of a battery cell is judged by comparing the internal resistance growth percentage data with a preset growth percentage threshold, specifically:

[0085] S401, comparing the internal resistance growth proportion data with a preset first growth proportion threshold and a second growth proportion threshold, respectively, the first growth proportion threshold being less than the second growth proportion threshold;

[0086] S402: If the internal resistance growth percentage data is less than or equal to a first growth percentage threshold, the battery cell internal resistance status is good;

[0087] S403: If the internal resistance growth ratio data is greater than the first growth ratio threshold and less than or equal to the second growth ratio threshold, the internal resistance aging degree of the battery cell is low and is marked;

[0088] S404: If the internal resistance growth rate is greater than the second growth rate threshold, the internal resistance of the battery cell is highly aged and needs to be replaced.

[0089] Among them, in order to classify and evaluate the state of the internal resistance of the battery cell, the internal resistance growth percentage data is compared with the preset first growth percentage threshold and the second growth percentage threshold respectively, and the first growth percentage threshold is less than the second growth percentage threshold. If the internal resistance growth percentage data is less than or equal to the first growth percentage threshold, the internal resistance state of the battery cell is considered to be good, and the performance and efficiency of the battery are not significantly affected. If the internal resistance growth percentage data is greater than the first growth percentage threshold and less than or equal to the second growth percentage threshold, the internal resistance aging degree of the battery cell is low and marked. At this stage, the performance of the battery may begin to decline, but the battery cell does not usually need to be replaced immediately. The battery cell can be marked for closer monitoring and maintenance. If the internal resistance growth percentage data is greater than the second growth percentage threshold, the internal resistance aging degree of the battery cell is high, the battery performance may have declined significantly, and there is a safety hazard. The battery cell needs to be replaced in time to avoid failures or accidents.

[0090] Please refer to Figure 5 , Figure 5 The flowchart of obtaining the battery cell degradation index of the energy storage battery degradation trend analysis method based on big data in some embodiments of the present application. According to the embodiment of the present invention, the battery cell degradation index is obtained by processing the voltage drop rate data, the discharge depth data and the internal resistance growth ratio data, specifically:

[0091] S501, obtaining ambient temperature data under the preset environment;

[0092] S502: Process the voltage drop rate data, the discharge depth data and the internal resistance growth ratio data in combination with the ambient temperature data through a preset degradation trend model to obtain a battery cell degradation index.

[0093] Among them, in order to comprehensively evaluate and intuitively reflect the degradation degree and trend of the battery cell, the ambient temperature data under the preset environment is first obtained, and the voltage drop rate data, the discharge depth data and the internal resistance growth ratio data are combined with the ambient temperature data through a preset degradation trend model to obtain the battery cell degradation index;

[0094] The calculation formula of the degradation trend model is:

[0095]

[0096] Among them, B dg is the battery cell degradation index, v ds ,d dp 、r zb ,t wThey are voltage drop rate data, discharge depth data, internal resistance growth percentage data and ambient temperature data respectively, and δ, η, θ, κ, ξ are preset characteristic coefficients (the characteristic coefficients are obtained by querying the preset degradation trend database).

[0097] According to an embodiment of the present invention, the battery cell degradation level is determined by comparing the battery cell degradation index with a preset degradation index threshold, specifically:

[0098] The battery cell degradation index is compared with a preset first degradation index threshold and a preset second degradation index threshold, respectively, and the first degradation index threshold is less than the second degradation index threshold;

[0099] If the battery cell degradation index is less than the first degradation index threshold, the battery cell is slightly degraded and the degradation level is low;

[0100] If the battery cell degradation index is greater than or equal to the first degradation index threshold and less than the second degradation index threshold, the battery cell is moderately degraded and the degradation level is medium;

[0101] If the battery cell degradation index is greater than or equal to the second degradation index threshold, the battery cell is severely degraded and the degradation level is high.

[0102] Among them, in order to judge the degree of degradation and degradation level of the battery cell, the battery cell degradation index is compared with the preset first degradation index threshold and second degradation index threshold respectively. The first degradation index threshold is less than the second degradation index threshold. If the battery cell degradation index is less than the first degradation index threshold, the battery cell is slightly degraded, the degradation level is low, the battery performance is slightly affected, and no immediate measures are required. If the battery cell degradation index is greater than or equal to the first degradation index threshold and less than the second degradation index threshold, the battery cell is moderately degraded, the degradation level is medium, the battery performance is affected to a certain extent, and attention and maintenance are required. If the battery cell degradation index is greater than or equal to the second degradation index threshold, the battery cell is severely degraded, the degradation level is high. The battery performance is severely affected and the battery needs to be replaced.

[0103] According to an embodiment of the present invention, it also includes:

[0104] Extracting capacity attenuation data according to the battery cell monitoring information;

[0105] Comparing the capacity decay data with a preset decay threshold;

[0106] If the capacity attenuation data is greater than or equal to the attenuation threshold, the battery cell needs to be replaced;

[0107] If the capacity decay data is less than the decay threshold, the battery cell does not need to be replaced.

[0108] Among them, in order to determine the degree of capacity decay of a battery cell, the capacity decay data is extracted according to the battery cell monitoring information, and the capacity decay data is compared with the preset decay threshold. If the capacity decay data is greater than or equal to the decay threshold, it indicates that the battery performance has dropped to the preset limit value and the battery cell needs to be replaced. If the capacity decay data is less than the decay threshold, it indicates that the battery performance is still within an acceptable range and the battery cell does not need to be replaced. This evaluation method can help determine whether the battery is still suitable for use or needs to be replaced to ensure the overall performance and reliability of the battery system.

[0109] The present invention also discloses a system for analyzing degradation trend of energy storage batteries based on big data, including a memory and a processor, wherein the memory includes a program for analyzing degradation trend of energy storage batteries based on big data, and when the program for analyzing degradation trend of energy storage batteries based on big data is executed by the processor, the following steps are implemented:

[0110] Obtain battery cell monitoring information within a preset time period under a preset environment, and extract voltage drop rate data, discharge depth data, and internal resistance growth percentage data;

[0111] Comparing the voltage drop rate data with a preset drop rate threshold, determining whether the battery cell voltage drop rate meets the requirement;

[0112] Comparing the discharge depth data with a preset discharge depth threshold, determining whether the discharge depth of a single cell of the battery meets the requirement;

[0113] According to the comparison between the internal resistance growth percentage data and the preset growth percentage threshold, the aging degree of the internal resistance of the battery cell is determined;

[0114] Processing the voltage drop rate data, the discharge depth data and the internal resistance growth ratio data to obtain a battery cell degradation index;

[0115] The battery cell degradation level is determined by comparing the battery cell degradation index with a preset degradation index threshold.

[0116] Among them, the present application obtains the battery cell monitoring information within the preset time period under the preset environment, extracts the voltage drop rate data, the discharge depth data and the internal resistance growth percentage data, compares the voltage drop rate data with the preset drop rate threshold, judges whether the battery cell voltage drop rate meets the requirements, compares the discharge depth data with the preset discharge depth threshold, judges whether the battery cell discharge depth meets the requirements, judges the degree of internal resistance aging of the battery cell according to the internal resistance growth percentage data and the preset growth percentage threshold, and processes the voltage drop rate data, the discharge depth data and the internal resistance growth percentage data in combination with the ambient temperature data under the preset environment to obtain the battery cell degradation index, and finally compares it with the preset degradation index threshold to judge the battery cell degradation level. The present application not only provides accurate battery performance evaluation by monitoring multiple parameters such as voltage drop rate, discharge depth and internal resistance growth percentage, but also comprehensively evaluates the battery cell status, can discover potential problems in advance, can take targeted preventive maintenance measures, avoid sudden battery failures, and effectively extend the battery life.

[0117] According to an embodiment of the present invention, the voltage drop rate data is compared with a preset drop rate threshold to determine whether the battery cell voltage drop rate meets the requirement, specifically:

[0118] Comparing the voltage drop rate data with a preset drop rate threshold value to obtain a drop rate deviation rate;

[0119] comparing the descent rate deviation rate with a preset descent rate deviation rate threshold;

[0120] If the drop rate deviation rate is greater than or equal to the drop rate deviation rate threshold, the battery cell voltage drop rate does not meet the requirement and the discharge capacity is insufficient;

[0121] If the drop rate deviation rate is less than the drop rate deviation rate threshold, the battery cell voltage drop rate meets the requirement and the discharge capacity is normal.

[0122] Among them, in order to determine whether the voltage drop rate of the battery cell meets expectations, the voltage drop rate data is compared with the preset drop rate threshold to obtain the drop rate deviation rate, and the drop rate deviation rate is compared with the preset drop rate deviation rate threshold. If the drop rate deviation rate is greater than or equal to the drop rate deviation rate threshold, the battery cell voltage drop rate does not meet the requirements, the discharge capacity is insufficient, and maintenance or replacement may be required. If the drop rate deviation rate is less than the drop rate deviation rate threshold, the battery cell voltage drop rate meets the requirements, the discharge capacity is normal, and the battery cell performance meets the requirements. The above method can timely detect the abnormality of the battery cell performance, so as to take corresponding measures to ensure the reliability and safety of the battery system, help to extend the service life of the battery cell, and reduce damage to the battery cell by avoiding excessive discharge.

[0123] According to an embodiment of the present invention, comparing the discharge depth data with a preset discharge depth threshold to determine whether the discharge depth of a battery cell meets the requirement is specifically as follows:

[0124] Comparing the discharge depth data with a preset first discharge depth threshold and a second discharge depth threshold, respectively, the first discharge depth threshold being less than the second discharge depth threshold;

[0125] If the discharge depth data is less than the first discharge depth threshold, the battery cell discharge depth is too low, affecting the battery cell performance;

[0126] If the discharge depth data is greater than or equal to the first discharge depth threshold and less than or equal to the second discharge depth threshold, the battery single cell discharge depth meets the requirement;

[0127] If the discharge depth data is greater than the second discharge depth threshold, the discharge depth of the battery cell is too high, which affects the service life of the battery cell.

[0128] Among them, in order to evaluate the performance and service life of the battery cell, the discharge depth data is compared with the preset first discharge depth threshold and the second discharge depth threshold respectively. The first discharge depth threshold is less than the second discharge depth threshold. For example, the first discharge depth threshold is 65% and the second discharge depth threshold is 75%. If the discharge depth data is less than the first discharge depth threshold, the discharge depth of the battery cell is too low, which may affect the immediate performance of the battery cell because the battery cell is not fully utilized. If the discharge depth data is greater than or equal to the first discharge depth threshold and less than or equal to the second discharge depth threshold, it indicates that the discharge depth of the battery cell is within a healthy range and meets the requirements. If the discharge depth data is greater than the second discharge depth threshold, the discharge depth of the battery cell is too high. Being in this state for a long time may shorten the service life of the battery because excessive discharge accelerates battery aging.

[0129] According to an embodiment of the present invention, the internal resistance growth percentage data is compared with a preset growth percentage threshold to determine the aging degree of the battery cell internal resistance, specifically:

[0130] Comparing the internal resistance growth proportion data with a preset first growth proportion threshold and a second growth proportion threshold respectively, the first growth proportion threshold is less than the second growth proportion threshold;

[0131] If the internal resistance growth percentage data is less than or equal to the first growth percentage threshold, the battery cell internal resistance is in good condition;

[0132] If the internal resistance growth ratio data is greater than the first growth ratio threshold and less than or equal to the second growth ratio threshold, the internal resistance aging degree of the battery cell is low and is marked;

[0133] If the internal resistance growth rate data is greater than the second growth rate threshold, the internal resistance of the battery cell is highly aged and needs to be replaced.

[0134] Among them, in order to classify and evaluate the state of the internal resistance of the battery cell, the internal resistance growth percentage data is compared with the preset first growth percentage threshold and the second growth percentage threshold respectively, and the first growth percentage threshold is less than the second growth percentage threshold. If the internal resistance growth percentage data is less than or equal to the first growth percentage threshold, the internal resistance state of the battery cell is considered to be good, and the performance and efficiency of the battery are not significantly affected. If the internal resistance growth percentage data is greater than the first growth percentage threshold and less than or equal to the second growth percentage threshold, the internal resistance aging degree of the battery cell is low and marked. At this stage, the performance of the battery may begin to decline, but the battery cell does not usually need to be replaced immediately. The battery cell can be marked for closer monitoring and maintenance. If the internal resistance growth percentage data is greater than the second growth percentage threshold, the internal resistance aging degree of the battery cell is high, the battery performance may have declined significantly, and there is a safety hazard. The battery cell needs to be replaced in time to avoid failures or accidents.

[0135] According to an embodiment of the present invention, the voltage drop rate data, the discharge depth data and the internal resistance growth ratio data are processed to obtain the battery cell degradation index, specifically:

[0136] Acquire ambient temperature data under the preset environment;

[0137] The voltage drop rate data, the discharge depth data and the internal resistance growth ratio data are processed in combination with the ambient temperature data through a preset degradation trend model to obtain a battery cell degradation index.

[0138] Among them, in order to comprehensively evaluate and intuitively reflect the degradation degree and trend of the battery cell, the ambient temperature data under the preset environment is first obtained, and the voltage drop rate data, the discharge depth data and the internal resistance growth ratio data are combined with the ambient temperature data through a preset degradation trend model to obtain the battery cell degradation index;

[0139] The calculation formula of the degradation trend model is:

[0140]

[0141] Among them, B dg is the battery cell degradation index, v ds ,d dp 、r zb ,t w They are voltage drop rate data, discharge depth data, internal resistance growth percentage data and ambient temperature data respectively, and δ, η, θ, κ, ξ are preset characteristic coefficients (the characteristic coefficients are obtained by querying the preset degradation trend database).

[0142] According to an embodiment of the present invention, the battery cell degradation level is determined by comparing the battery cell degradation index with a preset degradation index threshold, specifically:

[0143] The battery cell degradation index is compared with a preset first degradation index threshold and a preset second degradation index threshold, respectively, and the first degradation index threshold is less than the second degradation index threshold;

[0144] If the battery cell degradation index is less than the first degradation index threshold, the battery cell is slightly degraded and the degradation level is low;

[0145] If the battery cell degradation index is greater than or equal to the first degradation index threshold and less than the second degradation index threshold, the battery cell is moderately degraded and the degradation level is medium;

[0146] If the battery cell degradation index is greater than or equal to the second degradation index threshold, the battery cell is severely degraded and the degradation level is high.

[0147] Among them, in order to judge the degree of degradation and degradation level of the battery cell, the battery cell degradation index is compared with the preset first degradation index threshold and second degradation index threshold respectively. The first degradation index threshold is less than the second degradation index threshold. If the battery cell degradation index is less than the first degradation index threshold, the battery cell is slightly degraded, the degradation level is low, the battery performance is slightly affected, and no immediate measures are required. If the battery cell degradation index is greater than or equal to the first degradation index threshold and less than the second degradation index threshold, the battery cell is moderately degraded, the degradation level is medium, the battery performance is affected to a certain extent, and attention and maintenance are required. If the battery cell degradation index is greater than or equal to the second degradation index threshold, the battery cell is severely degraded, the degradation level is high. The battery performance is severely affected and the battery needs to be replaced.

[0148] According to an embodiment of the present invention, it also includes:

[0149] Extracting capacity attenuation data according to the battery cell monitoring information;

[0150] Comparing the capacity decay data with a preset decay threshold;

[0151] If the capacity attenuation data is greater than or equal to the attenuation threshold, the battery cell needs to be replaced;

[0152] If the capacity decay data is less than the decay threshold, the battery cell does not need to be replaced.

[0153] Among them, in order to determine the degree of capacity decay of a battery cell, the capacity decay data is extracted according to the battery cell monitoring information, and the capacity decay data is compared with the preset decay threshold. If the capacity decay data is greater than or equal to the decay threshold, it indicates that the battery performance has dropped to the preset limit value and the battery cell needs to be replaced. If the capacity decay data is less than the decay threshold, it indicates that the battery performance is still within an acceptable range and the battery cell does not need to be replaced. This evaluation method can help determine whether the battery is still suitable for use or needs to be replaced to ensure the overall performance and reliability of the battery system.

[0154] A third aspect of the present invention provides a computer-readable storage medium, which includes a program for analyzing the degradation trend of a storage battery based on big data. When the program for analyzing the degradation trend of a storage battery based on big data is executed by a processor, the steps of the method for analyzing the degradation trend of a storage battery based on big data as described in any one of the above items are implemented.

[0155] The present invention discloses a method, system and medium for analyzing the degradation trend of energy storage batteries based on big data, by acquiring the monitoring information of a battery cell within a preset time period under a preset environment, extracting the voltage drop rate data, the discharge depth data and the internal resistance growth ratio data, comparing the voltage drop rate data with the preset drop rate threshold, judging whether the voltage drop rate of the battery cell meets the requirements, comparing the discharge depth data with the preset discharge depth threshold, judging whether the discharge depth of the battery cell meets the requirements, comparing the internal resistance growth ratio data with the preset growth ratio threshold, judging the degree of internal resistance aging of the battery cell, processing the voltage drop rate data, the discharge depth data and the internal resistance growth ratio data in combination with the ambient temperature data under the preset environment, obtaining the battery cell degradation index, and finally comparing it with the preset degradation index threshold to judge the battery cell degradation level. The present application not only provides accurate battery performance evaluation by monitoring multiple parameters such as voltage drop rate, discharge depth and internal resistance growth ratio, but also comprehensively evaluates the battery cell status, can discover potential problems in advance, can take targeted preventive maintenance measures, avoid sudden battery failures, and effectively extend the battery life.

[0156] In the several embodiments provided in the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. The device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as: multiple units or components can be combined, or can be integrated into another system, or some features can be ignored or not executed. In addition, the coupling, direct coupling, or communication connection between the components shown or discussed can be through some interfaces, and the indirect coupling or communication connection of the devices or units can be electrical, mechanical or other forms.

[0157] The units described above as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units; they may be located in one place or distributed on multiple network units; some or all of the units may be selected according to actual needs to achieve the purpose of the present embodiment.

[0158] In addition, all functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may be separately used as a unit, or two or more units may be integrated into one unit; the above-mentioned integrated units may be implemented in the form of hardware or in the form of hardware plus software functional units.

[0159] A person of ordinary skill in the art can understand that: all or part of the steps of implementing the above-mentioned method embodiment can be completed by hardware related to program instructions, and the aforementioned program can be stored in a readable storage medium, which, when executed, executes the steps of the above-mentioned method embodiment; and the aforementioned storage medium includes: mobile storage devices, read-only memories, random access memories, magnetic disks or optical disks, and other media that can store program codes.

[0160] Alternatively, if the above-mentioned integrated unit of the present invention is implemented in the form of a software function module and sold or used as an independent product, it can also be stored in a readable storage medium. Based on this understanding, the technical solution of the embodiment of the present invention can be essentially or partly reflected in the form of a software product that contributes to the prior art. The software product is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the methods described in each embodiment of the present invention. The aforementioned storage medium includes: various media that can store program codes, such as mobile storage devices, ROM, RAM, magnetic disks or optical disks.

Claims

1. A method for analyzing the degradation trend of energy storage batteries based on big data, characterized in that: The following steps are involved: Obtain battery cell monitoring information within a preset time period under a preset environment, and extract voltage drop rate data, discharge depth data, and internal resistance growth percentage data; Comparing the voltage drop rate data with a preset drop rate threshold, determining whether the battery cell voltage drop rate meets the requirement; Comparing the discharge depth data with a preset discharge depth threshold, determining whether the discharge depth of a single cell of the battery meets the requirement; According to the comparison between the internal resistance growth percentage data and the preset growth percentage threshold, the aging degree of the internal resistance of the battery cell is determined; Processing the voltage drop rate data, the discharge depth data and the internal resistance growth ratio data to obtain a battery cell degradation index; The battery cell degradation level is determined by comparing the battery cell degradation index with a preset degradation index threshold.

2. The method for analyzing the degradation trend of energy storage batteries based on big data according to claim 1, characterized in that: The voltage drop rate data is compared with a preset drop rate threshold to determine whether the battery cell voltage drop rate meets the requirement, specifically: Comparing the voltage drop rate data with a preset drop rate threshold value to obtain a drop rate deviation rate; comparing the descent rate deviation rate with a preset descent rate deviation rate threshold; If the drop rate deviation rate is greater than or equal to the drop rate deviation rate threshold, the battery cell voltage drop rate does not meet the requirement and the discharge capacity is insufficient; If the drop rate deviation rate is less than the drop rate deviation rate threshold, the battery cell voltage drop rate meets the requirement and the discharge capacity is normal.

3. The method for analyzing the degradation trend of energy storage batteries based on big data according to claim 2 is characterized in that: The comparison between the discharge depth data and a preset discharge depth threshold is used to determine whether the discharge depth of a battery cell meets the requirement, specifically: Comparing the discharge depth data with a preset first discharge depth threshold and a second discharge depth threshold, respectively, the first discharge depth threshold being less than the second discharge depth threshold; If the discharge depth data is less than the first discharge depth threshold, the battery cell discharge depth is too low, affecting the battery cell performance; If the discharge depth data is greater than or equal to the first discharge depth threshold and less than or equal to the second discharge depth threshold, the battery single cell discharge depth meets the requirement; If the discharge depth data is greater than the second discharge depth threshold, the discharge depth of the battery cell is too high, which affects the service life of the battery cell.

4. The method for analyzing the degradation trend of energy storage batteries based on big data according to claim 3 is characterized in that: The internal resistance aging degree of the battery cell is determined by comparing the internal resistance growth ratio data with a preset growth ratio threshold value, specifically: Comparing the internal resistance growth proportion data with a preset first growth proportion threshold and a second growth proportion threshold respectively, the first growth proportion threshold is less than the second growth proportion threshold; If the internal resistance growth percentage data is less than or equal to the first growth percentage threshold, the battery cell internal resistance is in good condition; If the internal resistance growth ratio data is greater than the first growth ratio threshold and less than or equal to the second growth ratio threshold, the internal resistance aging degree of the battery cell is low and is marked; If the internal resistance growth rate data is greater than the second growth rate threshold, the internal resistance of the battery cell is highly aged and needs to be replaced.

5. The method for analyzing the degradation trend of energy storage batteries based on big data according to claim 4 is characterized in that: The battery cell degradation index is obtained by processing the voltage drop rate data, the discharge depth data and the internal resistance growth ratio data, specifically: Acquire ambient temperature data under the preset environment; The voltage drop rate data, the discharge depth data and the internal resistance growth ratio data are processed in combination with the ambient temperature data through a preset degradation trend model to obtain a battery cell degradation index.

6. The method for analyzing the degradation trend of energy storage batteries based on big data according to claim 5, characterized in that: The step of comparing the battery cell degradation index with a preset degradation index threshold to determine the battery cell degradation level is specifically as follows: The battery cell degradation index is compared with a preset first degradation index threshold and a preset second degradation index threshold, respectively, and the first degradation index threshold is less than the second degradation index threshold; If the battery cell degradation index is less than the first degradation index threshold, the battery cell is slightly degraded and the degradation level is low; If the battery cell degradation index is greater than or equal to the first degradation index threshold and less than the second degradation index threshold, the battery cell is moderately degraded and the degradation level is medium; If the battery cell degradation index is greater than or equal to the second degradation index threshold, the battery cell is severely degraded and the degradation level is high.

7. The energy storage battery degradation trend analysis system based on big data is characterized by: The system comprises a memory and a processor, wherein the memory comprises a method program for analyzing the degradation trend of an energy storage battery based on big data, and when the method program for analyzing the degradation trend of an energy storage battery based on big data is executed by the processor, the following steps are implemented: Obtain battery cell monitoring information within a preset time period under a preset environment, and extract voltage drop rate data, discharge depth data, and internal resistance growth percentage data; Comparing the voltage drop rate data with a preset drop rate threshold, determining whether the battery cell voltage drop rate meets the requirement; Comparing the discharge depth data with a preset discharge depth threshold, determining whether the discharge depth of a single cell of the battery meets the requirement; According to the comparison between the internal resistance growth percentage data and the preset growth percentage threshold, the aging degree of the internal resistance of the battery cell is determined; Obtaining a battery cell degradation index according to the voltage drop rate data, the discharge depth data, and the internal resistance growth ratio data; The battery cell degradation level is determined by comparing the battery cell degradation index with a preset degradation index threshold.

8. The energy storage battery degradation trend analysis system based on big data according to claim 7 is characterized in that: The voltage drop rate data is compared with a preset drop rate threshold to determine whether the battery cell voltage drop rate meets the requirement, specifically: Comparing the voltage drop rate data with a preset drop rate threshold value to obtain a drop rate deviation rate; comparing the descent rate deviation rate with a preset descent rate deviation rate threshold; If the drop rate deviation rate is greater than or equal to the drop rate deviation rate threshold, the battery cell voltage drop rate does not meet the requirement and the discharge capacity is insufficient; If the drop rate deviation rate is less than the drop rate deviation rate threshold, the battery cell voltage drop rate meets the requirement and the discharge capacity is normal.

9. The energy storage battery degradation trend analysis system based on big data according to claim 8, characterized in that: The comparison between the discharge depth data and a preset discharge depth threshold is used to determine whether the discharge depth of a battery cell meets the requirement, specifically: Comparing the discharge depth data with a preset first discharge depth threshold and a second discharge depth threshold, respectively, the first discharge depth threshold being less than the second discharge depth threshold; If the discharge depth data is less than the first discharge depth threshold, the battery cell discharge depth is too low, affecting the battery cell performance; If the discharge depth data is greater than or equal to the first discharge depth threshold and less than or equal to the second discharge depth threshold, the battery cell discharge depth meets the requirement; If the discharge depth data is greater than the second discharge depth threshold, the discharge depth of the battery cell is too high, which affects the service life of the battery cell.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium includes a program for analyzing the degradation trend of an energy storage battery based on big data. When the program for analyzing the degradation trend of an energy storage battery based on big data is executed by a processor, the steps of the method for analyzing the degradation trend of an energy storage battery based on big data as described in any one of claims 1 to 6 are implemented.

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