Method and system for testing and analyzing capacity of single battery based on big data, and medium

Abstract

The invention discloses a single battery capacity test analysis method and system based on big data and a medium, and relates to the technical field of battery capacity attenuation analysis, and the method comprises the following steps: testing a single battery through different charging and discharging tests, and recording test data at the same time; acquiring test data, and analyzing a capacity attenuation curve of the single battery; based on the test data, analyzing the influence of different use habits on the capacity attenuation of the single battery, and matching an optimal discharge threshold value based on the electric quantity of the battery charged by the user; the method is used for solving the problem that the attenuation loss analysis of the charged battery capacity is not accurate enough because the reference factors for the capacity attenuation analysis are not comprehensive enough in the existing battery capacity attenuation analysis technology.

Description

Technical Field

[0001] The present invention relates to the technical field of battery capacity attenuation analysis, and in particular to a single cell capacity testing and analysis method, system and medium based on big data. Background Art

[0002] Battery capacity decay analysis technology refers to the technology used to evaluate and analyze the capacity decay of lithium-ion batteries and other batteries during use. As the number of battery charge and discharge cycles increases, the battery capacity will gradually decrease. This capacity decay will affect the performance and service life of the battery.

[0003] Existing battery capacity decay analysis technologies usually test the battery capacity decay with a fixed charge and discharge threshold. In fact, during the use of the battery, the battery capacity decay is different due to the different charging habits of the users. Good charging habits make the battery capacity decay much less than bad charging habits. The existing battery capacity decay analysis technology can only roughly predict the approximate number of charge and discharge cycles of the battery corresponding to good charging habits and bad charging habits, and cannot accurately analyze the attenuation loss of the battery capacity due to each different charging behavior of the user. For example, in a patent application with publication number CN116660756A, a battery capacity decay curve generation method based on a conditional generative adversarial network is disclosed. The scheme obtains the real capacity decay curve of the lithium battery for analysis. The existing battery capacity decay analysis technology usually analyzes the approximate value for the capacity decay curve of the lithium battery, and does not consider the impact of charging habits on capacity decay. There is no real capacity decay curve. The existing battery capacity decay analysis technology also has the problem that the reference factors for capacity decay analysis are not comprehensive enough, resulting in inaccurate analysis of the attenuation loss of the battery capacity after charging. Summary of the invention

[0004] The present invention aims to solve one of the technical problems in the prior art to at least a certain extent, by setting different charge and discharge state groups, testing single cells based on the charge and discharge state groups, recording test data at the same time, then acquiring the test data, analyzing the capacity decay curve of the single cell, and then constructing the nth decay trend graph based on the nth test group and analyzing the influence of different usage habits on the capacity decay of the single cell, and finally analyzing the discharge start value with the least influence on the capacity decay when the single cell is charged at different charging start values ​​based on the nth decay trend graph, so as to solve the problem that the existing battery capacity decay analysis technology still has insufficient reference factors for capacity decay analysis, resulting in inaccurate analysis of the attenuation loss of the battery capacity after charging.

[0005] To achieve the above objectives, in a first aspect, the present application provides a single cell capacity test and analysis method based on big data, comprising the following steps:

[0006] Test the single battery through different charge and discharge tests and record the test data;

[0007] Obtain test data and analyze the capacity decay curve of single cells;

[0008] Based on the test data, the impact of different usage habits on the capacity decay of single cells is analyzed and the optimal discharge threshold is matched based on the battery power at which the user starts charging.

[0009] Further, testing the single battery through different charge and discharge tests and recording the test data includes the following sub-steps:

[0010] Set different charge and discharge status groups;

[0011] The single battery is tested based on the charge and discharge status group, and the test data is recorded at the same time.

[0012] Furthermore, setting different charge and discharge state groups includes the following sub-steps:

[0013] The charge and discharge state group includes a charge threshold and a discharge threshold;

[0014] Setting a first power threshold, a second power threshold, a third power threshold and a fourth power threshold, wherein the first power threshold is smaller than the second power threshold, the second power threshold is smaller than the third power threshold, and the third power threshold is smaller than the fourth power threshold;

[0015] The range in which the charging threshold is less than or equal to the first power threshold is set as the first threshold range, and the range in which the charging threshold is greater than the first power threshold and less than or equal to the second power threshold is set as the second threshold range;

[0016] Mark a range where the discharge threshold is greater than or equal to the third power threshold and less than the fourth power threshold as a third threshold range, and mark a range where the discharge threshold is greater than or equal to the fourth power threshold as a fourth threshold range;

[0017] The first threshold range and the second threshold range are collectively referred to as a charging range, and the third threshold range and the fourth threshold range are collectively referred to as a discharging range;

[0018] The first threshold range and the third threshold range are a charge and discharge state group, named the first test group; the first threshold range and the fourth threshold range are a charge and discharge state group, named the second test group; the second threshold range and the third threshold range are a charge and discharge state group, named the third test group; the second threshold range and the fourth threshold range are a charge and discharge state group, named the fourth test group.

[0019] Further, testing the single battery based on the charge and discharge status group and recording the test data at the same time includes the following sub-steps:

[0020] For each test group, a first test quantity of charge and discharge tests are performed, and during each test, the power in the single battery is monitored in real time and marked as the battery power;

[0021] During each charge and discharge test, a value is randomly selected from the charge range corresponding to the test group and marked as the charge start value, and then a value is randomly selected from the discharge range corresponding to the test group and marked as the discharge start value, wherein the charge start value is smaller than the discharge start value;

[0022] When the battery power is detected to be equal to the charging start value, the single battery is charged; when the battery power is detected to be equal to the discharging start value, the single battery is discharged;

[0023] The charge start value and the discharge start value are numbered and marked as C(n,m) and F(n,m) respectively according to the order of the tests, where n and m are natural numbers and (n,m) are the sequence numbers of C and F, where C(n,m) represents the charge start value of the mth charge and discharge test of the nth test group, and F(n,m) represents the discharge start value of the mth charge and discharge test of the nth test group;

[0024] After each charge and discharge test, the battery capacity of the single cell is detected, marked as E(n,m). Before the test, the initial battery capacity of the battery is obtained, marked as E(n,0).

[0025] Furthermore, obtaining test data and analyzing the capacity decay curve of a single battery includes the following sub-steps:

[0026] A plane rectangular coordinate system is established with m as the X-axis and E(n,m) as the Y-axis, named as the capacity decay trend chart, and all E(n,m) are entered into the capacity decay trend chart according to m;

[0027] A polynomial regression is performed on the capacity decay trend graph, and the obtained regression function is named as the capacity decay function. The capacity decay function is plotted in the capacity decay trend graph, and the obtained curve is named as the capacity decay curve.

[0028] Furthermore, based on the test data, analyzing the influence of different usage habits on the capacity decay of the single battery and matching the best discharge threshold based on the battery power at which the user starts charging includes the following sub-steps:

[0029] Construct the nth attenuation trend chart based on the nth test group and analyze the impact of different usage habits on the capacity attenuation of the single battery;

[0030] Based on the nth decay trend chart, the discharge starting value that has the least impact on capacity decay when charging the single battery at different charging starting values ​​is analyzed.

[0031] Furthermore, constructing an nth attenuation trend chart based on the nth test group and analyzing the influence of different usage habits on the capacity attenuation of the single battery includes the following sub-steps:

[0032] For the nth test group, a plane coordinate rectangular system is established with m as the horizontal axis and E(n,m) as the vertical axis, named the nth attenuation trend chart, and E(n,m) is entered into the nth attenuation trend chart according to n and m;

[0033] Perform polynomial regression on the nth attenuation trend graph respectively, name the obtained regression function as the nth attenuation function, create a new capacity attenuation trend graph and remove all data therein, name the newly created coordinate system as the attenuation comparison graph, plot the nth attenuation function on the attenuation comparison graph, and obtain the nth attenuation curve;

[0034] The nth attenuation curve in the attenuation comparison diagram is sorted from top to bottom to show the influence of different test groups on the capacity attenuation of the single battery. The higher the nth attenuation curve is, the smaller the influence is, and the lower the nth attenuation curve is, the greater the influence is.

[0035] Furthermore, analyzing the discharge start value with the least effect on capacity decay when charging the single battery at different charge start values ​​based on the nth decay trend graph includes the following sub-steps:

[0036] Add the coordinate point (0, E(n, 0)) to all n-th attenuation trend graphs, and refer to all the coordinate points in the n-th attenuation trend graphs as attenuation coordinate points;

[0037] Connect m adjacent attenuation coordinate points with a straight line, and name the straight line as the attenuation line;

[0038] Get the slope of the attenuation line, which is represented by the symbol L(n,m) in the order of the attenuation lines from left to right. L(n,m) represents the mth attenuation line in the nth attenuation trend graph, and L(n,m) corresponds to E(n,m);

[0039] Get C(n,m) and L(n,m) corresponding to E(n,m), and divide L(n,m) with the same C(n,m) into the same group, named charging group;

[0040] Find the maximum value of L(n,m) in the charging group and mark it as the optimal discharge threshold;

[0041] When the user needs to charge, the optimal discharge threshold is automatically matched according to the battery power at the beginning of charging, and charging stops when the battery power reaches the optimal discharge threshold.

[0042] In a second aspect, the present application provides a single cell capacity test and analysis system based on big data, including a charge and discharge test module, a decay curve analysis module and a decay impact analysis module; the decay curve analysis module and the decay impact analysis module are respectively connected to the charge and discharge test module data;

[0043] The charge and discharge test module is used to test the single battery through different charge and discharge tests and record the test data at the same time;

[0044] The attenuation curve analysis module is used to obtain test data and analyze the capacity attenuation curve of the single battery;

[0045] The attenuation impact analysis module is used to analyze the impact of different usage habits on the capacity attenuation of the single battery based on the test data and match the optimal discharge threshold based on the battery power at which the user starts charging.

[0046] In a third aspect, the present application provides a storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the steps in the above method are performed.

[0047] Beneficial effects of the present invention: The present invention sets different charge and discharge state groups, tests the single battery based on the charge and discharge state groups, records the test data at the same time, then obtains the test data, and analyzes the capacity decay curve of the single battery. The advantage is that a capacity decay trend chart is constructed through the overall test data for analysis, and a rough decay trend curve of the battery capacity under the condition of comprehensive multiple charging habits can be obtained, thereby improving the accuracy and effectiveness of the battery capacity decay analysis;

[0048] The present invention constructs an nth attenuation trend graph based on the nth test group and analyzes the influence of different usage habits on the capacity attenuation of a single cell battery. Finally, based on the nth attenuation trend graph, the discharge starting value with the least influence on the capacity attenuation is analyzed when the single cell battery starts charging at different charging starting values. The advantage lies in that the influence of different usage habits on the capacity attenuation of the battery is analyzed through different test groups. After the analysis is completed, it can be determined which usage habit has the least influence on the capacity attenuation of the battery, thereby obtaining the best usage habit for the user to choose. When the user charges the battery, it is impossible to charge the battery with the same power every time. Different initial powers have different influences on the capacity attenuation of the battery when charged to different powers. By analyzing the discharge starting value with the least influence on the capacity attenuation when the single cell battery starts charging at different charging starting values ​​based on the nth attenuation trend graph, the capacity attenuation of the battery can be reduced to the greatest extent, thereby improving the practicality and comprehensiveness of the battery capacity attenuation analysis. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] Figure 1 is a functional block diagram of the system of the present invention;

[0050] Figure 2 It is the capacity attenuation trend diagram of the present invention;

[0051] Figure 3 is a schematic diagram of a capacity decay curve of the present invention;

[0052] Figure 4 This is the attenuation comparison diagram of the present invention;

[0053] Figure 5 is a first attenuation trend diagram of the present invention;

[0054] Figure 6 is a schematic diagram of the attenuation coordinate points and the attenuation straight line of the present invention;

[0055] Figure 7 The figure is a flow chart of the steps of the method of the present invention. DETAILED DESCRIPTION

[0056] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only 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.

[0057] Example 1, please refer to Figure 1 As shown, the present application provides a single cell capacity test and analysis system based on big data, including a charge and discharge test module, a decay curve analysis module and a decay impact analysis module; the decay curve analysis module and the decay impact analysis module are respectively connected to the charge and discharge test module data;

[0058] The charge and discharge test module is used to test the single battery through different charge and discharge tests and record the test data at the same time; the charge and discharge test module includes a test group division unit and a charge and discharge test unit;

[0059] The test group division unit is used to set different charge and discharge state groups;

[0060] The test group division unit is configured with a test group division strategy, which includes:

[0061] The charge and discharge state group includes a charge threshold and a discharge threshold;

[0062] Setting a first power threshold, a second power threshold, a third power threshold and a fourth power threshold, wherein the first power threshold is less than the second power threshold, the second power threshold is less than the third power threshold, and the third power threshold is less than the fourth power threshold;

[0063] The range in which the charging threshold is less than or equal to the first power threshold is set as the first threshold range, and the range in which the charging threshold is greater than the first power threshold and less than or equal to the second power threshold is set as the second threshold range;

[0064] Mark a range where the discharge threshold is greater than or equal to the third power threshold and less than the fourth power threshold as a third threshold range, and mark a range where the discharge threshold is greater than or equal to the fourth power threshold as a fourth threshold range;

[0065] The first threshold range and the second threshold range are collectively referred to as a charging range, and the third threshold range and the fourth threshold range are collectively referred to as a discharging range;

[0066] The first threshold range and the third threshold range are used as a charge-discharge state group, which is named the first test group; the first threshold range and the fourth threshold range are used as a charge-discharge state group, which is named the second test group; the second threshold range and the third threshold range are used as a charge-discharge state group, which is named the third test group; the second threshold range and the fourth threshold range are used as a charge-discharge state group, which is named the fourth test group;

[0067] In actual applications, big data shows that charging from 20% to 80% is the best charging range for charging a battery, which can improve the battery life. However, most users charge when the battery is below 20%, and will charge the battery to more than 80%. Most users will charge the battery directly. Therefore, in this embodiment, the first power threshold is set to 10%, the second power threshold is set to 20%, the third power threshold is set to 80%, and the fourth power threshold is set to 90%; thus, the first threshold range is [0, 10%], the second threshold range is (10%, 20%], the third threshold range is [80%, 90%), and the fourth threshold range is [9 0%, 100%]; the charging range and the discharging range are combined in pairs, thereby obtaining 4 test groups with different usage habits, wherein the first test group represents the habit of charging the battery when the battery power is less than 10%, and will be charged to 80% to 90%; the second test group represents the habit of charging the battery when the battery power is less than 10%, and will be charged to more than 90%; the third test group represents the habit of charging the battery when the battery power is between 10% and 20%, and will be charged to 80% to 90%; the fourth test group represents the habit of charging the battery when the battery power is between 10% and 20%, and will be charged to more than 90%;

[0068] The charge and discharge test unit is used to test the single battery based on the charge and discharge status group and record the test data at the same time;

[0069] The charge and discharge test unit is configured with a charge and discharge test strategy, which includes:

[0070] For each test group, a first test quantity of charge and discharge tests are performed, and during each test, the power in the single battery is monitored in real time and marked as the battery power;

[0071] During each charge and discharge test, a value is randomly selected from the charge range corresponding to the test group and marked as the charge start value, and then a value is randomly selected from the discharge range corresponding to the test group and marked as the discharge start value. The charge start value is smaller than the discharge start value.

[0072] When the battery power is detected to be equal to the charging start value, the single battery is charged; when the battery power is detected to be equal to the discharging start value, the single battery is discharged;

[0073] In practical applications, the first test quantity is set to obtain sufficient test data to cover all charging conditions, and the number of cycles of the battery cell usually needs to be maintained at more than 1000 times, and the capacity attenuation shall not be less than 80% of the initial capacity. Therefore, the first test quantity in this embodiment is set to 1000 to simulate the complete life cycle of a battery; in the test, the charging start value and the discharging start value are both two decimal places and expressed in percentage form. Taking the first test group as an example, in the first test group, the charging range is [0,10%], and the discharging range is [80%,90%). In a certain charge and discharge test, 6% is randomly selected from the charging range [0,10%] as the charging start value, and 85% is randomly selected from the discharging range [80%,90%) as the discharging start value. Therefore, when it is monitored that the battery power is equal to 6%, the battery is charged, and when the charging reaches 85%, the charging is stopped and the discharge is performed;

[0074] The charge start value and the discharge start value are numbered and marked as C(n,m) and F(n,m) respectively according to the order of the tests, where n and m are natural numbers and (n,m) are the sequence numbers of C and F, where C(n,m) represents the charge start value of the mth charge and discharge test of the nth test group, and F(n,m) represents the discharge start value of the mth charge and discharge test of the nth test group;

[0075] After each charge and discharge test, the battery capacity of the single cell is detected, marked as E(n,m). Before the test, the initial battery capacity of the battery is obtained, marked as E(n,0);

[0076] In practical applications, due to too much test data, this embodiment does not display the specific values ​​of C(n,m) and F(n,m). In this embodiment, there are the first to fourth test groups, so 1≤n≤4, the first test number is 1000, so 1≤m≤1000, and the sequence numbers (n,m) in C(n,m), F(n,m) and E(n,m) are (1,1) to (1,1000), (2,1) to (2,1000), (3,1) to (3,1000) and (4,1) to (4,1000). Due to the limitation of detection technology, it is difficult to detect the specific attenuation ratio of battery capacity in a single charge. At this time, another battery of the same battery can be subjected to multiple charge and discharge tests with the same charge start value and discharge start value until the battery capacity decreases. The decrease value is calculated and divided by the number of charge and discharge tests to obtain the decrease ratio of battery capacity in each charge and discharge test.

[0077] The attenuation curve analysis module is used to obtain test data and analyze the capacity attenuation curve of the single battery;

[0078] The attenuation curve analysis module is configured with an attenuation curve analysis strategy, which includes:

[0079] See also Figures 2 to 3 As shown, a plane rectangular coordinate system is established with m as the X-axis and E(n,m) as the Y-axis, named as the capacity decay trend chart, and all E(n,m) are entered into the capacity decay trend chart according to m;

[0080] Perform polynomial regression on the capacity decay trend chart, name the obtained regression function as the capacity decay function, plot the capacity decay function on the capacity decay trend chart, and name the obtained curve as the capacity decay curve;

[0081] In practical applications, polynomial regression is a type of linear regression model in discrete mathematics. Linear regression studies the regression problem between a dependent variable and an independent variable. However, in real life, most problems involve multiple variables, resulting in the relationship between the dependent variable and the independent variable not being a linear relationship. A single linear regression cannot explain the relationship between them. At this time, multiple regression analysis, namely the polynomial regression method, is introduced to interpret the relationship between the dependent variable and the independent variable under the influence of multiple variables. Polynomial regression is widely used in data analysis and data prediction. The capacity attenuation trend chart is constructed as follows: Figure 2 As shown, the capacity decay function obtained by polynomial regression is EG = -9 × 10 -8 ×X 2 -10 -4 ×X+1, where X represents the number of charge and discharge cycles, i.e. m, and EG represents the battery capacity. Due to the large amount of data, the capacity decay curve is obscured, so Figure 3 Display capacity decay curve, Figure 3 It is a capacity decay trend chart without coordinate points.

[0082] The attenuation impact analysis module is used to analyze the impact of different usage habits on the capacity attenuation of the single battery based on the test data and match the optimal discharge threshold based on the battery power at which the user starts charging; the attenuation impact analysis module includes an attenuation impact analysis unit and a minimum attenuation analysis unit;

[0083] The attenuation impact analysis unit is used to construct the nth attenuation trend chart based on the nth test group and analyze the impact of different usage habits on the capacity attenuation of the single battery;

[0084] The attenuation impact analysis unit is configured with an attenuation impact analysis strategy, which includes:

[0085] For the nth test group, a plane coordinate rectangular system is established with m as the horizontal axis and E(n,m) as the vertical axis, named the nth attenuation trend chart, and E(n,m) is entered into the nth attenuation trend chart according to n and m;

[0086] See also Figure 4 As shown, polynomial regression is performed on the nth attenuation trend graph, the obtained regression function is named the nth attenuation function, a new capacity attenuation trend graph is created and all the data therein are removed, the new coordinate system is named the attenuation comparison graph, the nth attenuation function is plotted on the attenuation comparison graph, and the nth attenuation curve is obtained;

[0087] The nth attenuation curves in the attenuation comparison diagram are sorted from top to bottom to show the influence of different test groups on the capacity attenuation of the single battery. The higher the nth attenuation curve is, the smaller the influence is, and the lower the nth attenuation curve is, the greater the influence is.

[0088] In practical applications, the analysis process of the nth attenuation trend graph and the nth attenuation function is similar to the analysis process of the capacity attenuation trend graph and the capacity attenuation function, so it is not specifically demonstrated in this embodiment, and only the analysis result is shown by the final nth attenuation curve; the attenuation comparison diagram is constructed as shown in FIG. Figure 4 As shown, Figure 4 The nth attenuation curves in the figure are the third test group, the fourth test group, the first test group and the second test group from top to bottom. According to the analysis results, the third test group has the smallest impact on battery capacity attenuation, followed by the fourth test group, and then the first test group. The second test group has the greatest impact on battery capacity attenuation.

[0089] The minimum decay analysis unit is used to analyze the discharge starting value with the least effect on capacity decay when the single battery starts charging at different charging starting values ​​based on the nth decay trend graph;

[0090] The minimum attenuation analysis unit is configured with a minimum attenuation analysis strategy, which includes:

[0091] See also Figure 5 to Figure 6 As shown, a coordinate point (0, E(n, 0)) is added to all n-th attenuation trend graphs, and the coordinate points in all n-th attenuation trend graphs are collectively referred to as attenuation coordinate points;

[0092] Connect m adjacent attenuation coordinate points with a straight line, and name the straight line as the attenuation line;

[0093] In practical applications, this embodiment takes the first attenuation trend graph as an example to illustrate the analysis of the attenuation straight line. Since the amount of data is too large and presented in a small graph, it is difficult to observe the attenuation straight line and attenuation coordinate points. Figure 6 right Figure 5 The circular part in the figure is enlarged to show the attenuation coordinate points and attenuation straight line in detail. Figure 6 Each straight line and dotted line in is an attenuation line. Adjacent attenuation lines are distinguished by straight lines and dotted lines. The attenuation line before the attenuation coordinate point is the attenuation line corresponding to the attenuation coordinate point.

[0094] Get the slope of the attenuation line, which is represented by the symbol L(n,m) in the order of the attenuation lines from left to right. L(n,m) represents the mth attenuation line in the nth attenuation trend graph, and L(n,m) corresponds to E(n,m);

[0095] Get C(n,m) and L(n,m) corresponding to E(n,m), and divide L(n,m) with the same C(n,m) into the same group, named charging group;

[0096] Find the maximum value of L(n,m) in the charging group and mark it as the optimal discharge threshold;

[0097] When the user needs to charge, the optimal discharge threshold is automatically matched according to the battery power at the start of charging, and charging stops when the battery power reaches the optimal discharge threshold;

[0098] In practical applications, obtain C(n,m) and L(n,m) corresponding to E(n,m), and group L(n,m) based on C(n,m). For example, L(n,m) with a C(n,m) of 18% is grouped into a group to obtain an 18% charging group. When the user starts charging with 18% battery power, find the maximum value of L(n,m) in the 18% charging group as the optimal discharge threshold. Since the battery capacity is always decreasing, the slope of the attenuation line must be less than or equal to 0. At this time, the larger the slope, the smaller the attenuation.

[0099] Example 2, please refer to Figure 7 As shown, the present application provides a single cell capacity test and analysis method based on big data, comprising the following steps:

[0100] Step S1, testing the single battery through different charge and discharge tests, and recording the test data; Step S1 includes the following sub-steps:

[0101] Step S101, setting different charge and discharge state groups;

[0102] Step S101 includes the following sub-steps:

[0103] Step S1011, the charge and discharge state group includes a charge threshold and a discharge threshold;

[0104] Step S1012, setting a first power threshold, a second power threshold, a third power threshold and a fourth power threshold, wherein the first power threshold is less than the second power threshold, the second power threshold is less than the third power threshold, and the third power threshold is less than the fourth power threshold;

[0105] Step S1013, setting a range where the charging threshold is less than or equal to the first power threshold as the first threshold range, and setting a range where the charging threshold is greater than the first power threshold and less than or equal to the second power threshold as the second threshold range;

[0106] Step S1014, marking a range where the discharge threshold is greater than or equal to the third power threshold and less than the fourth power threshold as a third threshold range, and marking a range where the discharge threshold is greater than or equal to the fourth power threshold as a fourth threshold range;

[0107] Step S1015, the first threshold range and the second threshold range are collectively referred to as a charging range, and the third threshold range and the fourth threshold range are collectively referred to as a discharging range;

[0108] Step S1016, the first threshold range and the third threshold range are used as a charge-discharge state group, named as the first test group; the first threshold range and the fourth threshold range are used as a charge-discharge state group, named as the second test group; the second threshold range and the third threshold range are used as a charge-discharge state group, named as the third test group; the second threshold range and the fourth threshold range are used as a charge-discharge state group, named as the fourth test group;

[0109] Step S102, testing the single battery based on the charge and discharge status group, and recording the test data;

[0110] Step S102 includes the following sub-steps:

[0111] Step S1021, for each test group, performing a first test number of charge and discharge tests, and during each test, monitoring the power in the single battery in real time, and marking it as battery power;

[0112] Step S1022, during each charge and discharge test, a value is randomly selected from the charge range corresponding to the test group and marked as the charge start value, and then a value is randomly selected from the discharge range corresponding to the test group and marked as the discharge start value, and the charge start value is smaller than the discharge start value;

[0113] Step S1023, when it is monitored that the battery power is equal to the charging start value, the single battery is charged, and when it is monitored that the battery power is equal to the discharging start value, the single battery is discharged;

[0114] Step S1024, numbering the charge start value and the discharge start value, and marking them as C(n,m) and F(n,m) respectively according to the order of the tests, wherein n and m are natural numbers and (n,m) are the sequence numbers of C and F, wherein C(n,m) represents the charge start value of the mth charge and discharge test of the nth test group, and F(n,m) represents the discharge start value of the mth charge and discharge test of the nth test group;

[0115] Step S1025, detecting the battery capacity of the single battery after each charge and discharge test, marked as E(n,m), and obtaining the initial battery capacity of the battery before the test, marked as E(n,0);

[0116] Step S2, obtaining test data and analyzing the capacity decay curve of the single battery; Step S2 includes the following sub-steps:

[0117] Step S201, establish a plane rectangular coordinate system with m as the X-axis and E(n,m) as the Y-axis, name it as the capacity decay trend chart, and enter all E(n,m) into the capacity decay trend chart according to m;

[0118] Step S202, performing polynomial regression on the capacity decay trend graph, naming the obtained regression function as the capacity decay function, plotting the capacity decay function in the capacity decay trend graph, and naming the obtained curve as the capacity decay curve;

[0119] Step S3, based on the test data, analyze the impact of different usage habits on the capacity decay of the single battery and match the optimal discharge threshold based on the battery power at which the user starts charging; Step S3 includes the following sub-steps:

[0120] Step S301, constructing an nth attenuation trend chart based on the nth test group and analyzing the influence of different usage habits on the capacity attenuation of the single battery;

[0121] Step S301 includes the following sub-steps:

[0122] Step S3011, for the nth test group, a plane coordinate rectangular system is established with m as the horizontal axis and E(n,m) as the vertical axis, named as the nth attenuation trend chart, and E(n,m) is entered into the nth attenuation trend chart according to n and m;

[0123] Step S3012, performing polynomial regression on the nth attenuation trend graph respectively, naming the obtained regression function as the nth attenuation function, creating a new capacity attenuation trend graph and removing all data therein, naming the newly created coordinate system as the attenuation comparison graph, plotting the nth attenuation function on the attenuation comparison graph, and obtaining the nth attenuation curve;

[0124] Step S3013, sorting the nth attenuation curves in the attenuation comparison diagram from top to bottom, thereby showing the influence of different test groups on the capacity attenuation of the single battery. The higher the nth attenuation curve is, the smaller the influence is, and the lower the nth attenuation curve is, the greater the influence is.

[0125] Step S302, analyzing the discharge starting value that has the least impact on capacity decay when charging of the single battery at different charging starting values ​​based on the nth decay trend graph;

[0126] Step S302 includes the following sub-steps:

[0127] Step S3021, adding a coordinate point (0, E(n, 0)) to all n-th attenuation trend graphs, and collectively referring to the coordinate points in all n-th attenuation trend graphs as attenuation coordinate points;

[0128] Step S3022, connecting m adjacent attenuation coordinate points with a straight line, and naming the straight line as an attenuation straight line;

[0129] Step S3023, obtaining the slope of the attenuation line, which is represented by the symbol L(n,m) in the order of the attenuation lines from left to right, where L(n,m) represents the mth attenuation line in the nth attenuation trend graph, and L(n,m) corresponds to E(n,m);

[0130] Step S3024, obtaining C(n,m) and L(n,m) corresponding to E(n,m), and dividing L(n,m) with the same C(n,m) into the same group, which is named charging group;

[0131] Step S3025, finding the maximum value of L(n,m) in the charging group and marking it as the optimal discharge threshold;

[0132] Step S3026, when the user needs to charge, the optimal discharge threshold is automatically matched according to the battery power at the beginning of charging, and charging is stopped when the battery power reaches the optimal discharge threshold.

[0133] Embodiment 3, this embodiment provides an electronic device, which may include: a processor, a communication interface, a memory and a communication bus, wherein the processor, the communication interface and the memory communicate with each other through the communication bus. The memory stores computer-readable instructions, and the processor can call the instructions in the memory. When the computer-readable instructions are executed by the processor, the steps in the single-cell battery capacity test and analysis method based on big data are executed to achieve the following functions: test the single-cell battery through different charge and discharge tests, and record the test data at the same time; obtain the test data and analyze the capacity decay curve of the single-cell battery; based on the test data, analyze the impact of different usage habits on the capacity decay of the single-cell battery and match the best discharge threshold based on the battery power at which the user starts charging.

[0134] In addition, the logic instructions in the above-mentioned memory can be implemented in the form of software functional units and can be stored in a computer-readable storage medium when sold or used as an independent product. Based on this understanding, the technical solution of the present application can be essentially or partly embodied in the form of a software product that contributes to the prior art, and the computer software product is stored in a storage medium, including several instructions to enable a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes: various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a disk or an optical disk.

[0135] Embodiment 4, the present application also provides a computer-readable storage medium, the present application provides a storage medium, on which a computer program is stored. When the computer program is executed by a processor, the steps in the single-cell battery capacity test and analysis method based on big data are executed to achieve the following functions: test the single-cell battery through different charge and discharge tests, and record the test data at the same time; obtain the test data and analyze the capacity decay curve of the single-cell battery; based on the test data, analyze the impact of different usage habits on the capacity decay of the single-cell battery and match the optimal discharge threshold based on the battery power at which the user starts charging.

[0136] Through the description of the above implementation methods, the embodiments of the present invention can be provided as methods, systems or computer program products. Based on such an understanding, the above technical solutions can be essentially or partly contributed to the prior art in the form of software products, which can be stored in a computer-readable storage medium, such as ROM / RAM, a magnetic disk, an optical disk, etc., and include several instructions for enabling a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment or some parts of the embodiments.

[0137] In the embodiments provided in the present application, it should be understood that the disclosed system or method can be implemented in other ways. The embodiments described above are merely schematic. For example, the division of modules or units is only a logical function division. There may be other division methods in actual implementation. For example, multiple modules or units can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some communication interfaces, and the indirect coupling or communication connection of systems, modules and units can be electrical, mechanical or other forms.

[0138] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit it. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. 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 application.

Claims

1. A single cell battery capacity test and analysis method based on big data, characterized in that: The steps include: Test the single battery through different charge and discharge tests and record the test data; Obtain test data and analyze the capacity decay curve of single cells; Based on the test data, the impact of different usage habits on the capacity decay of single cells is analyzed and the optimal discharge threshold is matched based on the battery power at which the user starts charging.

2. The single cell capacity testing and analysis method based on big data according to claim 1, characterized in that: Testing the single battery through different charge and discharge tests and recording the test data includes the following sub-steps: Set different charge and discharge status groups; The single battery is tested based on the charge and discharge status group, and the test data is recorded at the same time.

3. The single cell capacity testing and analysis method based on big data according to claim 2 is characterized in that: Setting different charge and discharge status groups includes the following sub-steps: The charge and discharge state group includes a charge threshold and a discharge threshold; Setting a first power threshold, a second power threshold, a third power threshold and a fourth power threshold, wherein the first power threshold is smaller than the second power threshold, the second power threshold is smaller than the third power threshold, and the third power threshold is smaller than the fourth power threshold; The range in which the charging threshold is less than or equal to the first power threshold is set as the first threshold range, and the range in which the charging threshold is greater than the first power threshold and less than or equal to the second power threshold is set as the second threshold range; Mark a range where the discharge threshold is greater than or equal to the third power threshold and less than the fourth power threshold as a third threshold range, and mark a range where the discharge threshold is greater than or equal to the fourth power threshold as a fourth threshold range; The first threshold range and the second threshold range are collectively referred to as a charging range, and the third threshold range and the fourth threshold range are collectively referred to as a discharging range; The first threshold range and the third threshold range are a charge and discharge state group, named the first test group; the first threshold range and the fourth threshold range are a charge and discharge state group, named the second test group; the second threshold range and the third threshold range are a charge and discharge state group, named the third test group; the second threshold range and the fourth threshold range are a charge and discharge state group, named the fourth test group.

4. The single cell capacity testing and analysis method based on big data according to claim 3 is characterized in that: Testing a single cell based on a charge and discharge status group and recording test data includes the following sub-steps: For each test group, a first test quantity of charge and discharge tests are performed, and during each test, the power in the single battery is monitored in real time and marked as the battery power; During each charge and discharge test, a value is randomly selected from the charge range corresponding to the test group and marked as the charge start value, and then a value is randomly selected from the discharge range corresponding to the test group and marked as the discharge start value, wherein the charge start value is smaller than the discharge start value; When the battery power is detected to be equal to the charging start value, the single battery is charged; when the battery power is detected to be equal to the discharging start value, the single battery is discharged; The charge start value and the discharge start value are numbered and marked as C(n,m) and F(n,m) respectively according to the order of the tests, where n and m are natural numbers and (n,m) are the sequence numbers of C and F, where C(n,m) represents the charge start value of the mth charge and discharge test of the nth test group, and F(n,m) represents the discharge start value of the mth charge and discharge test of the nth test group; After each charge and discharge test, the battery capacity of the single cell is detected, marked as E(n,m). Before the test, the initial battery capacity of the battery is obtained, marked as E(n,0).

5. The method for testing and analyzing the capacity of a single battery based on big data according to claim 4, characterized in that: Obtaining test data and analyzing the capacity decay curve of a single battery includes the following sub-steps: A plane rectangular coordinate system is established with m as the X-axis and E(n,m) as the Y-axis, named as the capacity decay trend chart, and all E(n,m) are entered into the capacity decay trend chart according to m; A polynomial regression is performed on the capacity decay trend graph, and the obtained regression function is named as the capacity decay function. The capacity decay function is plotted in the capacity decay trend graph, and the obtained curve is named as the capacity decay curve.

6. The single cell capacity testing and analysis method based on big data according to claim 5, characterized in that: Based on the test data, analyzing the impact of different usage habits on the capacity decay of the single battery and matching the optimal discharge threshold based on the battery power at which the user starts charging includes the following sub-steps: Construct the nth attenuation trend chart based on the nth test group and analyze the impact of different usage habits on the capacity attenuation of the single battery; Based on the nth decay trend chart, the discharge starting value that has the least impact on capacity decay when charging the single battery at different charging starting values ​​is analyzed.

7. The single cell capacity testing and analysis method based on big data according to claim 6 is characterized in that: Constructing the nth attenuation trend chart based on the nth test group and analyzing the impact of different usage habits on the capacity attenuation of the single battery includes the following sub-steps: For the nth test group, a plane coordinate rectangular system is established with m as the horizontal axis and E(n,m) as the vertical axis, named the nth attenuation trend chart, and E(n,m) is entered into the nth attenuation trend chart according to n and m; Perform polynomial regression on the nth attenuation trend graph respectively, name the obtained regression function as the nth attenuation function, create a new capacity attenuation trend graph and remove all data therein, name the newly created coordinate system as the attenuation comparison graph, plot the nth attenuation function on the attenuation comparison graph, and obtain the nth attenuation curve; The nth attenuation curve in the attenuation comparison diagram is sorted from top to bottom to show the influence of different test groups on the capacity attenuation of the single battery. The higher the nth attenuation curve is, the smaller the influence is, and the lower the nth attenuation curve is, the greater the influence is.

8. The single cell capacity testing and analysis method based on big data according to claim 7, characterized in that: Analyzing the discharge starting value with the least influence on capacity decay when charging the single battery at different charging starting values ​​based on the nth decay trend graph includes the following sub-steps: Add the coordinate point (0, E(n, 0)) to all n-th attenuation trend graphs, and refer to all the coordinate points in the n-th attenuation trend graphs as attenuation coordinate points; Connect m adjacent attenuation coordinate points with a straight line, and name the straight line as the attenuation line; Get the slope of the attenuation line, which is represented by the symbol L(n,m) in the order of the attenuation lines from left to right. L(n,m) represents the mth attenuation line in the nth attenuation trend graph, and L(n,m) corresponds to E(n,m); Get C(n,m) and L(n,m) corresponding to E(n,m), and divide L(n,m) with the same C(n,m) into the same group, named charging group; Find the maximum value of L(n,m) in the charging group and mark it as the optimal discharge threshold; When the user needs to charge, the optimal discharge threshold is automatically matched according to the battery power at the beginning of charging, and charging stops when the battery power reaches the optimal discharge threshold.

9. A single cell battery capacity test and analysis system based on big data, used to implement the single cell battery capacity test and analysis method based on big data according to any one of claims 1 to 8, characterized in that: It includes a charge and discharge test module, an attenuation curve analysis module and an attenuation impact analysis module; the attenuation curve analysis module and the attenuation impact analysis module are respectively connected to the charge and discharge test module data; The charge and discharge test module is used to test the single battery through different charge and discharge tests and record the test data at the same time; The attenuation curve analysis module is used to obtain test data and analyze the capacity attenuation curve of the single battery; The attenuation impact analysis module is used to analyze the impact of different usage habits on the capacity attenuation of the single battery based on the test data and match the optimal discharge threshold based on the battery power at which the user starts charging.

10. A storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps in the method according to any one of claims 1 to 7 are executed.

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