An online prediction method for remaining life of battery pack

Abstract

The present invention discloses a method for online prediction of the remaining life of a battery pack, comprising the following steps: S1, constructing and optimizing a charge capacity calculation model using offline battery pack cycle life test data; S2, using the charge capacity calculation model and actual battery pack operating data under actual operating conditions to online predict the remaining life of the battery pack. By combining laboratory research results with actual battery operating data, online modeling and correction of model parameters based on historical battery data, and predicting the battery pack's RUL using a cycle calculation method, the present invention achieves continuous improvement in the effectiveness of online prediction of the battery pack's RUL. This allows even machines with average computing power to accurately and quickly predict the remaining life of a battery pack online, building on existing commercially available battery management technologies.

Description

Technical Field

[0001] The present invention relates to the technical field of battery management, and in particular to an online prediction method for the remaining service life of a battery pack. Background Art

[0002] Real-time prediction of the remaining useful life (RUL) of lithium-ion batteries is crucial for the safe operation of lithium-ion battery systems and for reducing operation and maintenance costs. Currently, methods for online RUL prediction of lithium-ion battery packs, such as particle filtering, machine learning, and fusion prediction methods, are being researched in the laboratory. However, the difficulties in RUL prediction of lithium-ion battery packs lie in the following:

[0003] 1. There is a significant gap between laboratory testing conditions and actual operating conditions. For example, laboratory testing typically involves constant stress under a single condition, while the actual environment typically experiences complex stresses under complex dynamic operating conditions. Therefore, the single-cell RUL prediction model established using laboratory data lacks accuracy in predicting the RUL of a battery pack in its actual operating environment.

[0004] 2. The RUL of lithium-ion batteries not only depends on the minimum life of the single cell, but is also affected by the consistency of the RUL of each cell, which further increases the difficulty of online prediction of the RUL of lithium-ion battery packs. Summary of the Invention

[0005] The present invention aims to improve the online prediction accuracy of the RUL of a lithium-ion battery pack and provides an online prediction method for the remaining service life of a battery pack.

[0006] To achieve this object, the present invention adopts the following technical solutions:

[0007] A method for online prediction of the remaining life of a battery pack is provided, comprising the steps of:

[0008] S1, builds and optimizes the charge capacity calculation model using offline test data of battery pack cycle life;

[0009] S2, using the charge capacity calculation model and the battery pack operating data under actual working conditions to predict the remaining life of the battery pack online.

[0010] Preferably, in step S1, the method for constructing and optimizing the charge capacity calculation model includes the steps of:

[0011] S11, performing a constant current or constant power charge and discharge cycle test on the battery pack sample to obtain offline test data, including the battery pack single charging time t, charging current, charging power, remaining capacity increment dsoc, a first remaining capacity at the start of charging, a second remaining capacity at the end of charging, charged capacity, cumulative charged capacity tc, battery pack voltage, and number of cycle tests collected after the battery pack sample undergoes each constant current or constant power charge and discharge cycle;

[0012] S12, constructing the charge capacity calculation model expressed by the following formula (1):

[0013]

[0014] In formula (1), C n Indicates the current charge capacity of the battery pack, C n-1 Indicates the last charge of the battery pack;

[0015] w1, w2, w3 are weights, and w1+w2+w3=1;

[0016] k1 represents the equivalent charging current or charging power during the battery pack charging process;

[0017] k2 represents the maximum charge capacity of the battery pack for the first time. When this value cannot be obtained, the rated charge capacity of the battery pack Q is taken. r ;

[0018] k3 represents the battery pack single charge capacity attenuation rate;

[0019] f1(n) represents the product of the single charge capacity attenuation rates of the battery pack in the first n cycles in step S11, which is obtained by formula (2):

[0020]

[0021] in, Q i and Q i-1 represent the charge capacity of the i-th cycle and the charge capacity of the i-1-th cycle respectively;

[0022] S13, k1, k2, and k3 in formula (1) are optimized and inserted into formula (1) to obtain the optimized charging capacity calculation model expressed by the following formula (3):

[0023]

[0024] k1m, k2m, k3m are the optimal solutions corresponding to k1, k2, k3 respectively.

[0025] Preferably, the method for optimizing k1, k2, and k3 in formula (1) comprises the steps of:

[0026] S121, taking a plurality of continuous charge and discharge data segments collected from the battery pack sample, and calculating the charge and discharge data segment according to the C n , t, dsoc, tc data, obtain several solution equations about k1, k2, k3;

[0027] S122, determining whether the number of equations to be solved is equal to "4",

[0028] If so, the LU decomposition method is used to obtain the optimal solutions k1m, k2m, and k3m corresponding to k1, k2, and k3 respectively;

[0029] If not, go to step S123;

[0030] S123, judging whether the number of equations to be solved is greater than "4",

[0031] If so, apply the least squares method to obtain the optimal solutions k1m, k2m, and k3m corresponding to k1, k2, and k3 respectively;

[0032] If not, the optimal solution corresponding to at least one of k1, k2, and k3 is solved by the attenuation characteristic analysis method.

[0033] As a preference, under the "if no" judgment condition of step S122, let the optimal solution k1m of k1 be the current average value of the charging current of each of the charging and discharging data segments extracted in step S121; let the optimal solution k2m of k2 be the rated charge capacity Q of the battery pack sample. r The optimal solution k3m of k3 is calculated according to the following formula (4):

[0034]

[0035] Preferably, the method for assigning weights w1, w2, and w3 comprises the steps of:

[0036] A1, randomly select a set of battery pack charge and discharge data collected in the cycle test, and then calculate the k1×t and C in the charge and discharge data. n The absolute value of the first difference of the true value, with C n The absolute value of the second difference of the true value, k3×C n-1 with C n the absolute value of the third difference from the true value;

[0037] A2, calculating the sum of the absolute value of the first difference, the absolute value of the second difference, and the absolute value of the third difference;

[0038] A3, calculate the first ratio of the absolute value of the first difference to the sum as the weight value of w1; calculate the second ratio of the absolute value of the second difference to the sum as the weight value of w2; calculate the third ratio of the absolute value of the third difference to the sum as the weight value of w3.

[0039] Preferably, in step S2, the method for online predicting the remaining battery life of the battery pack includes the following steps:

[0040] S21, assigning an initial value of "0" to the remaining number of charge times L of the battery pack;

[0041] S22, for the battery pack, calculate the charge capacity C1 of the current charge using formula (3), and the model independent variables for calculating C1 are t=t1, dsoc=dsoc1, and tc=tc1, where tc1 represents the current cumulative charge capacity of the battery pack; dsoc1 represents the incremental value of the remaining charge of the battery pack from the start to the end of the current charge of the battery pack; and t1 represents the charging time of the battery pack in the current charge.

[0042] S23, calculating the actual maximum charge capacity of the battery pack at present

[0043] S24, determining the actual maximum chargeable capacity value Is it less than or equal to the minimum design capacity of the battery pack?

[0044] If yes, output the accumulated result of the remaining charge times L of the battery pack as the prediction result of the remaining life of the battery pack;

[0045] If not, then add “1” to the remaining charging times L, set tc1=C1+tc1, and then return to step S22.

[0046] Preferably, dsoc1≥0.8.

[0047] Preferably, t1 is obtained by solving the following formula (5):

[0048]

[0049] In formula (5), t0 represents the single charging time of the battery pack to which dsoc0, which has the value closest to dsoc1, belongs in the historical data of the online test of the battery pack;

[0050] dsoc0 represents the remaining power increment of the battery pack sample during a single charging process in the historical data.

[0051] Preferably, the minimum design capacity of the battery pack is the product of the rated voltage of each single cell in the battery pack, the initial capacity of the single cell, the total number of single cells, and a constant p.

[0052] The present invention combines laboratory research results with actual battery operation data, establishes online modeling and modifies model parameters based on historical battery data, and predicts battery pack RUL through a cyclic calculation method, thereby achieving continuous improvement in the online prediction effect of battery pack RUL. This allows machines with average computing power to accurately and quickly predict the remaining life of a battery pack online based on existing commercial battery management technology. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] To more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the embodiments of the present invention. Obviously, the drawings described below are only some embodiments of the present invention. Those skilled in the art can also derive other drawings based on these drawings without inventive effort.

[0054] Figure 1 This is a diagram of the steps for implementing the online prediction method for the remaining life of a battery pack provided by an embodiment of the present invention;

[0055] Figure 2 This is a graph showing the battery pack's room temperature charge capacity attenuation rate versus cycle number. DETAILED DESCRIPTION

[0056] The technical solution of the present invention will be further described below with reference to the accompanying drawings and through specific implementation methods.

[0057] Among them, the drawings are only used for illustrative purposes and represent only schematic diagrams rather than actual pictures, and should not be understood as limiting this patent; in order to better illustrate the embodiments of the present invention, some parts of the drawings may be omitted, enlarged or reduced, and do not represent the size of the actual product; for those skilled in the art, it is understandable that some well-known structures and their descriptions in the drawings may be omitted.

[0058] The same or similar numbers in the drawings of the embodiments of the present invention correspond to the same or similar parts; in the description of the present invention, it should be understood that if the terms "upper", "lower", "left", "right", "inside", "outside" and the like indicate an orientation or position relationship based on the orientation or position relationship shown in the drawings, it is only for the convenience of describing the present invention and simplifying the description, and does not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operate in a specific orientation. Therefore, the terms describing the position relationship in the drawings are only used for illustrative purposes and cannot be understood as limiting this patent. For ordinary technicians in this field, the specific meanings of the above terms can be understood according to specific circumstances.

[0059] In the description of the present invention, unless otherwise expressly specified or limited, when the term "connection" or the like appears to indicate a connection relationship between components, such term should be understood in a broad sense. For example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection or an indirect connection through an intermediate medium; it can be internal communication between two components or an interaction between two components. For those skilled in the art, the specific meanings of the above terms in the present invention can be understood in specific circumstances.

[0060] Taking a lithium-ion battery energy storage power station as an example, as an example, the online prediction application scenario of the remaining battery life is as follows: assuming that the battery pack (i.e., cluster) has historical charge and discharge data (for example, charge and discharge history data of more than one month), the remaining battery life of the battery pack can be continuously predicted online according to the following method. Taking a lithium iron phosphate energy storage battery pack as an example, an embodiment of the present invention provides a method for online prediction of the remaining battery life of a battery pack, which includes the following three technical parts:

[0061] 1. Battery pack cycle life offline test

[0062] 1. Take a battery pack sample and perform constant current or constant power charge and discharge cycle tests at room temperature at different charge and discharge depths (such as 20% dsoc, 60% dsoc, 100% dsoc) and different currents (such as 0.3C, 0.5C, 1C, 2C). Ensure that the operating voltage range of the single cells in the battery pack is within the lower and upper voltage limits (such as the 2.5V-3.65V limit range) until the battery pack charge or discharge capacity reaches the set limit (such as 80% of the initial charge capacity). The charge and discharge depth dsoc is the difference between the battery pack's soc (the ratio of remaining capacity to total capacity) at the start of charge or discharge and the battery pack's soc at the end of charge or discharge.

[0063] After a specific cycle (e.g., every 10 constant current charge and discharge cycles), the battery pack sample is tested at the rated current, first fully charged and then discharged, to determine the actual maximum charge capacity of the battery pack. This cycle is repeated until the battery pack's charge capacity reaches the preset end-of-life value (e.g., 80% of the initial charge capacity).

[0064] Finally, the battery pack voltage, charging current, charging power, number of cycle tests, the first remaining power at the start of charging, the second remaining power at the end of charging, the single charging time t, the remaining power increment dsoc, the charging capacity, and the cumulative charging capacity collected during each cycle test are obtained.

[0065] For example, assuming the interval for offline test data collection for a battery pack sample is every 10 constant-current charge-discharge cycles, then after completing 10 constant-current charge-discharge cycles, the battery pack sample is tested at rated current, first fully charged and then discharged. During the full charge process, the battery pack sample's charging current, the first remaining capacity at the start of charging, the battery charging duration t, and the second remaining capacity at the end of charging are collected. The difference between the second remaining capacity and the first remaining capacity is calculated as the remaining capacity increment dsoc, and the charge capacity of the battery pack sample is calculated based on parameters such as the charging current, charging power, and charging duration t. The cumulative charge capacity is the sum of the historical charge capacities of each charge cycle of the battery pack sample. The number of test cycles refers to the number of constant-current charge-discharge cycles the battery pack sample has undergone up to the time of offline test data collection.

[0066] 2. Battery Pack Charge Capacity Modeling

[0067] The battery pack's charge capacity, C, is related to the battery pack's single charge duration, t, the SOC delta, dsoc (i.e., the difference between the SOC at the end of charging and the SOC at the start), and the cumulative charge capacity, tc. As tc increases, the battery pack's charge capacity gradually decreases. This embodiment constructs an online charge capacity calculation model based on C, t, dsoc, and tc using the following formula (1).

[0068]

[0069] In formula (1), C n Indicates the current charge capacity of the battery pack, C n-1 Indicates the last charge of the battery pack;

[0070] w1, w2, w3 are weights, and w1+w2+w3=1;

[0071] k1 represents the equivalent charging current or charging power during the battery pack charging process;

[0072] k2 represents the maximum charge capacity of the battery pack for the first time. When this value cannot be obtained, the rated charge capacity of the battery pack Q is taken. r ;

[0073] k3 represents the battery pack single charge capacity attenuation rate;

[0074] f1(n) represents the product of the single charge capacity attenuation rates of the battery pack in the first n cycles in step S11, which is obtained by formula (2):

[0075]

[0076] in, Q i and Q i-1represent the charge capacity of the i-th cycle and the charge capacity of the i-1-th cycle respectively;

[0077] In the above offline test data, C n , t, dsoc, tc are known values. Therefore, formula (1) is converted into a multiple regression equation for k1, k2, and k3. Through multiple charging, such as charging data under one month of offline conditions, the equation group about k1, k2, and k3 expressed in the above formula (1) is solved. After solving the values ​​of k1, k2, and k3, specifying t, dsoc, and tc, the charge capacity C of the battery pack can be calculated. n value.

[0078] The method for solving k1, k2, and k3 in formula (1) is as follows:

[0079] (1) Assign specific weight values ​​to w1, w2, and w3. The assignment method is:

[0080] Take any set of battery pack charge and discharge data collected in the cycle test, and then calculate the k1×t and C in the charge and discharge data. n The absolute value of the first difference between the true value, k2×dsoc and C n The absolute value of the second difference between the true value, k3 and C n The absolute value of the third difference of the true value; then calculate the sum of the absolute value of the first difference, the absolute value of the second difference, and the absolute value of the third difference;

[0081] Then, the first ratio of the absolute value of the first difference to the sum is calculated as the weight value of w1; the ratio of the absolute value of the second difference to the sum is calculated as the weight value of w2; and the ratio of the absolute value of the third difference to the sum is calculated as the weight value of w3.

[0082] For example, the values ​​assigned to w1, w2, and w3 are 0.64, 0.21, and 0.15 respectively.

[0083] (2) Take several consecutive charge and discharge data segments from the battery pack sample, and then calculate the C n , t, dsoc, and tc data are used to obtain several equations for k1, k2, and k3. The battery pack has a rated capacity of 240Ah, a rated voltage of 768V, a single cell rated voltage of 3.2V, a single cell initial capacity of 240Ah, and a total of 240 cells in series.

[0084] The continuous charge and discharge data segments referred to here are: for example, assuming that the battery pack sample is tested with full charge and then empty at the rated current when completing 10 constant current charge and discharge cycles (for example, recorded as test a), then the completed full charge and then empty test is a charge and discharge data segment, and offline test data is collected once for the battery pack sample in this charge and discharge data segment; then, after 10 constant current charge and discharge cycles, the next full charge and then empty test is performed at the rated current (for example, recorded as test b). This full charge and then empty test b is a charge and discharge test segment continuous with test a.

[0085] The number of equations to be solved regarding k1, k2, and k3 is related to the number of continuous charge and discharge data segments extracted, and the number of continuous charge and discharge data segments affects the performance of the constructed online prediction model for the remaining life of the battery pack.

[0086] After obtaining several equations for k1, k2, and k3, determine whether the number of equations to be solved is equal to "4".

[0087] If so, the LU decomposition method is used to obtain the optimal solutions k1m, k2m, and k3m corresponding to k1, k2, and k3 respectively;

[0088] If not, go to the next step to determine whether the number of equations to be solved is greater than "4";

[0089] The following are examples of methods for solving the optimal solutions k1m, k2m, and k3m corresponding to k1, k2, and k3 respectively using the LU decomposition method:

[0090] 1) According to formula (1) and formula (2), the four charging data are organized into a matrix form about k1, k2, and k3:

[0091]

[0092] 2) Perform LU decomposition on the coefficient matrix on the left side of the matrix equation and solve it by computer in the MATLAB environment to obtain

[0093]

[0094] 3) Solve the a priori equations and obtain the intermediate solution, that is,

[0095]

[0096] 4) Solve the posterior equations and get the final solution, which is

[0097]

[0098] Under the above "if not" judgment condition, continue to judge whether the number of equations to be solved is greater than "4".

[0099] If so, apply the least squares method to obtain the optimal solutions k1m, k2m, and k3m corresponding to k1, k2, and k3 respectively;

[0100] If not, then the optimal solution corresponding to at least one of k1, k2, and k3 is solved by the decay characteristic analysis method. In this embodiment, it is preferred to solve the optimal solution k3m corresponding to k3; let the optimal solution k1m of k1 be the current average value of the charging current of each charging and discharging data segment extracted; let the optimal solution k2m of k2 be the rated charge capacity Q of the battery pack sample. r The optimal solution k3m of k3 is calculated according to formula (4):

[0101]

[0102] The following are examples of methods for solving the optimal solutions k1m, k2m, and k3m corresponding to k1, k2, and k3 respectively using the least squares method:

[0103] 1) According to formula (1) and formula (2), the five charging data are organized into a matrix form about k1, k2, and k3:

[0104]

[0105] 2) Apply the lsqnonneg function in the MATLAB environment to solve the problem and obtain the final solution, which is

[0106]

[0107] The following combination Figure 2 The solution method of k3m is specifically explained using formula (4):

[0108] First, obtain the array consisting of the number of cycles and the corresponding charge capacity in the offline test data, that is,

[0109] Apply formula (4) to obtain the array The first column is the number of cycles, and the second column is the k3m corresponding to the number of cycles.

[0110] 3. Battery Pack Remaining Life Prediction

[0111] The remaining life of a battery pack is a decreasing value. To calculate the remaining life of a battery pack at a certain point in its operation, first obtain the battery pack's historical operating data to complete the remaining life prediction model. Then, based on future operating data, simulate the battery cycle process, iterate the model, and then predict the remaining life. The specific steps include the following:

[0112] (1) The remaining number of charging times L is set to an initial value of "0";

[0113] (2) For the battery pack, the next charge capacity C1 that is continuous with the current charge is calculated using formula (3). The model independent variables for calculating C1 are t=t1, dsoc=dsoc1, and tc=tc1, where tc1 represents the current cumulative charge capacity of the battery pack; dsoc1 represents the incremental value of the remaining capacity of the battery pack from the start to the end of the current charge of the battery pack; and t1 represents the duration of the current charge of the battery pack.

[0114] t1 is calculated by the following formula (4):

[0115]

[0116] In formula (4), t0 represents the single charging time of the battery pack carried in the data belonging to dsoc0 which is closest to the value of dsoc1 in the historical data of the battery pack online test;

[0117] dsoc0 indicates the remaining power increment of the battery pack sample carried in the offline data.

[0118] For example, suppose that when a battery pack sample is tested offline, three dsoc0 values ​​are obtained, which are 0.78, 0.81, and 0.82 respectively, and dsoc1=0.8. Then dsoc0=0.81, which is closest to the dsoc1 value, is obtained. The single charging time of the battery pack sample carried in the offline data collected in the offline test to which dsoc0 belongs is obtained and recorded as t0.

[0119] (3) Calculate the current charging rate of the battery pack

[0120] (4) Determine the charging rate Is it less than or equal to the minimum design capacity of the battery pack?

[0121] If yes, the accumulated result of the remaining charge times L of the battery pack is output as the remaining life prediction result of the battery pack;

[0122] If not, the remaining number of charge times L of the battery pack is accumulated by "1", and tc1 is set to C1 + tc1, and then the process returns to step S22.

[0123] The minimum design capacity of the battery pack Cmin = total number of single cells × single cell rated voltage × single cell initial capacity × constant p. The constant p is set by the user. Usually, the battery pack ends its life when p = 0.8.

[0124] In summary, the present invention combines laboratory research results with actual battery operation data, establishes online modeling and modifies model parameters based on historical battery data, and predicts battery pack RUL through a cyclic calculation method, thereby achieving continuous improvement in the online prediction effect of battery pack RUL. This allows machines with average computing power to accurately and quickly predict the remaining life of a battery pack online based on existing commercial battery management technology.

[0125] It should be noted that the above-described specific embodiments are merely preferred embodiments of the present invention and the technical principles employed. Those skilled in the art will appreciate that various modifications, equivalent substitutions, and variations may be made to the present invention. However, as long as these modifications do not depart from the spirit of the present invention, they are intended to be within the scope of protection of the present invention. Furthermore, certain terms used in the specification and claims of this application are not intended to be limiting and are provided solely for ease of description.

Claims

1. A method for online prediction of remaining battery life, characterized in that: Including steps: S1, builds and optimizes the charge capacity calculation model using offline test data of battery pack cycle life; S2, using the charge capacity calculation model and the battery pack operating data under actual working conditions to predict the remaining life of the battery pack online; In step S1, the method for constructing and optimizing the charge capacity calculation model includes the following steps: S11, perform a constant current or constant power charge and discharge cycle test on the battery pack sample to obtain offline test data, including the single charge time of the battery pack collected after each constant current charge and discharge cycle of the battery pack sample , charging current, charging power, remaining power increment , the first remaining power at the start of charging, the second remaining power at the end of charging, the charging capacity, the cumulative charging capacity , battery pack voltage, number of cycle tests; S12, constructing the charging capacity calculation model expressed by the following formula (1): In formula (1), Indicates the current charge level of the battery pack. Indicates the last charge of the battery pack; is the weight, and ; Indicates the equivalent charging current or charging power during the battery pack charging process; Indicates the maximum charge capacity of the battery pack for the first time. If this value cannot be obtained, the rated charge capacity of the battery pack is used. ; Indicates the battery pack single charge capacity attenuation rate; Indicates the battery pack in step S11 n The product of the single charge capacity decay rate of the cycle is obtained by formula (2): in, , Q i and Q i-1 Respectively represent i times The charge capacity of the cycle and the i-1 The charge capacity of each cycle; S13, for formula (1) 、 、 After optimization, the optimized charging capacity calculation model expressed by formula (3) is obtained by inputting formula (1) into the optimized charging capacity calculation model: 、 for 、 The corresponding optimal solutions respectively; In step S2, the method for online predicting the remaining battery life of the battery pack includes the following steps: S21, the remaining number of charging times of the battery pack Assign the initial value to "0"; S22, for the battery pack, calculate the charge capacity of the current charge by formula (3) ,calculate The independent variables of the model are , , ,in, Indicates the current cumulative charge capacity of the battery pack; Indicates the incremental value of the remaining power of the battery pack from the start to the end of charging during the current charging process of the battery pack; Indicates the charging time of the battery pack at the current time; S23, calculating the actual maximum charge capacity of the battery pack at present ; S24, determining the actual maximum chargeable capacity value Is it less than or equal to the minimum design capacity of the battery pack? If so, output the remaining number of charging times for the battery pack The accumulated result is used as the prediction result of the remaining life of the battery pack; If not, the remaining number of charges Accumulate "1" and set Then, return to step S22.

2. The method for online prediction of remaining battery life according to claim 1, characterized in that: Optimize the formula (1) 、 、 The method comprises the steps of: S121, taking a plurality of continuous charge and discharge data segments collected from the battery pack sample, and performing a charge and discharge analysis based on the data contained in each charge and discharge data segment. Data, obtain several 、 Solving equations for S122, judging whether the number of equations to be solved is equal to "4", If so, use LU decomposition to obtain 、 The corresponding optimal solutions 、 ; If not, go to step S123; S123, judging whether the number of equations to be solved is greater than "4", If so, apply the least squares method to obtain 、 The corresponding optimal solutions 、 ; If not, solve it by attenuation characteristic analysis method 、 There is at least one optimal solution corresponding to .

3. The method for online prediction of remaining battery life according to claim 2, characterized in that: Under the "if no" judgment condition of step S122, let The optimal solution is the average current of the charging current of each of the charging and discharging data segments extracted in step S121; The optimal solution The rated charge capacity of the battery pack sample ; The optimal solution Calculate according to the following formula (4): Formula (4).

4. The method for online prediction of remaining battery life according to claim 1, characterized in that: Weight The method for assigning includes the steps of: A1, randomly select a set of battery pack charge and discharge data collected in the cycle test, and then calculate the charge and discharge data and The absolute value of the first difference of the true value, and The absolute value of the second difference of the true value, and the absolute value of the third difference from the true value; A2, calculating the sum of the absolute value of the first difference, the absolute value of the second difference, and the absolute value of the third difference; A3, calculating a first ratio of the absolute value of the first difference to the sum as Calculate the second ratio of the absolute value of the second difference to the sum as The weight value of Calculate the third ratio of the third difference absolute value to the sum value as The weight value of .

5. The method for online prediction of remaining battery life according to claim 1, characterized in that: 。 6. The method for online prediction of remaining battery life according to claim 1, characterized in that: Solve it by the following formula (5): In formula (5), Indicates the historical data of the battery pack online test, The value closest to Single charging time of the battery pack to which it belongs; Indicates the remaining power increment of the battery pack sample during a single charging process in the historical data.

7. The method for online prediction of remaining battery life according to claim 1, characterized in that: The minimum design capacity of the battery pack is the rated voltage, initial capacity, total number of cells, constant The product of .

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