A method for evaluating the available capacity of a flexible battery pack based on a Monte Carlo algorithm

Abstract

The application relates to a method for evaluating the available capacity of a flexible battery pack based on a Monte Carlo algorithm and belongs to the technical field of battery management. On the basis of an empirical model, the relationship between temperature and battery state of health SOH and influencing factors thereof are analyzed through experimental data, nonlinear regression is used to fit the data, model parameters are determined, and finally a storage life prediction model of irreversible capacity loss and temperature and time is established. Based on the differences between single cells and measurement errors in the storage test process, an error model is established, and Monte Carlo simulation testing is performed on the storage life of the battery. Based on the above, the capacity attenuation probability distribution of a single flexible battery is given, and then the probability distribution of the available capacity of the flexible battery pack is obtained in combination with the grouping mode of the battery pack and the algorithm rule of the available electric quantity of the battery pack.

Description

Technical Field

[0001] This invention belongs to the field of battery management technology and relates to a method for evaluating the available capacity of flexible battery packs based on the Monte Carlo algorithm. Background Technology

[0002] The advantages of ultra-thin flexible printed batteries are their ultra-thin and flexible design, high safety performance (they will not burn or explode under extreme physical conditions such as high temperature, bending, puncture, shearing, and crushing, and can still be used normally), and environmental friendliness (they use environmentally friendly electrode materials, aqueous electrolyte systems, and advanced encapsulation technology, and the finished battery does not contain heavy metals and does not pollute the environment). Therefore, flexible batteries are used in many fields such as smart wearables, medical health, and the Internet of Things.

[0003] Most existing methods are based on simply multiplying the battery capacity decay rate by the number of parallel branches. While this method can provide a rough estimate of the usable capacity of the battery pack, it ignores the randomness of capacity changes during the battery pack assembly process.

[0004] Monte Carlo algorithm, also known as statistical simulation or statistical experiment, is a numerical simulation method that takes probabilistic phenomena as the research object and uses statistical values ​​obtained by sampling survey to estimate unknown characteristic quantities. Summary of the Invention

[0005] The technical problem to be solved by this invention is:

[0006] To address the impact of random capacity variations during battery pack assembly on the accuracy of usable capacity assessment, this invention provides a method for evaluating the usable capacity of flexible battery packs based on the Monte Carlo algorithm. Based on the Monte Carlo algorithm, numerous independent simulated real-world tests are conducted under the same test time, method, and conditions to eliminate the randomness of capacity variations during battery pack assembly.

[0007] To solve the above-mentioned technical problems, the technical solution adopted by the present invention is as follows:

[0008] A method for evaluating the usable capacity of a flexible battery pack based on the Monte Carlo algorithm, characterized by comprising:

[0009] Cyclic testing of the capacity decay rate of individual cells after discharge at different storage times;

[0010] The randomness coefficient β of the proportional effect of power decay during the storage process is calculated based on the capacity decay rate.

[0011] Design capacity degradation models under various storage time conditions and calculate the maximum usable capacity of each battery cell;

[0012] The Monte Carlo algorithm is used to obtain the usable capacity of the flexible battery pack by combining the battery pack assembly method with the algorithm rules of the available power of the assembly.

[0013] A further technical solution of the present invention: the capacity decay rate of the single cell in the cycle test after discharge at different storage times is specifically as follows:

[0014] When the storage time reaches the experimental design value, remove the battery, place it at 25°C for 2 hours, and then discharge it with a constant current until the cutoff voltage is reached. Record the discharge capacity C. dis ;

[0015] Perform two 1C standard charge-discharge cycles, record the usable discharge capacity, and calculate the capacity decay rate Y. i :

[0016]

[0017] Where C0 is the initial maximum available capacity, C n This represents the maximum available capacity after the corresponding time, where i represents the number of different measurements;

[0018] Charge the battery with a constant current of 1C until the remaining battery capacity reaches C. dis After standing for 1 hour, put it back into the original constant temperature incubator;

[0019] When changing the experimental design values, repeat the above process 8 times until the test is completed.

[0020] A further technical solution of the present invention: The random coefficient β of the proportional effect of power decay during the storage process is calculated based on the capacity decay rate as follows:

[0021] The capacity decay rate Y at each time point was obtained. i (t) take the average value

[0022]

[0023] Calculate Y using the average value i The variance Var(Y) of (t) i (t)), Var(Y i Substituting (t) into the following equation:

[0024]

[0025] The regression curve is obtained from the experimental data. The intercept and slope of the regression line are then derived from the graph, where the slope represents... The intercept represents Thus, a variance is constructed. β is a normally distributed variable with a mean of 0.

[0026] A further technical solution of the present invention: The capacity degradation model designed under various storage time conditions, and the calculation of the maximum usable capacity of each battery cell, specifically involves:

[0027] Calculate the capacity degradation model under various storage time conditions: α0, α1, and ρ are given parameter values, determined by the type of battery; T represents the temperature during storage, t represents the duration of storage, and μ(t) is the expected value of the capacity degradation ratio.

[0028] The maximum usable capacity of each battery cell when stored in a single cell is generated by combining the stochastic factor β, which affects the proportional effect, and the expected value of the capacity degradation ratio μ(t): Q. true (t)=μ(t)+β·μ(t);

[0029] When batteries are stored as battery packs, each individual cell in the pack is affected by the pack as a whole, and its randomness is reduced. It is necessary to deduct the randomness of SOH during degradation caused by the randomness of SOC decay; Q true (t)=μ(t)+ε·β·μ(t), where ε represents the measurement error value related to the measuring equipment.

[0030] A further technical solution of the present invention: The use of the Monte Carlo algorithm, combined with the algorithm rules of the battery pack assembly method and the available power of the assembly, to obtain the available capacity of the flexible battery pack is specifically as follows:

[0031] Let C Pij (t)=Q true(t) The maximum usable capacity C of each battery after storage is derived based on the capacity degradation ratio μ(t) of each battery cell in the battery pack. Pij (t), where i and j represent the positions of individual cells in the battery pack, i.e., the i-th row and j-th column;

[0032] The battery pack is connected in series first, then in parallel:

[0033]

[0034] The battery packs are connected in parallel first, then in series:

[0035]

[0036] Among them, C mPnS (t) represents the capacity of the battery pack connected in parallel first and then in series, C nSmP (t) represents the capacity of the battery pack connected in series and then in parallel, and m represents the number of modules connected in series.

[0037] By combining Monte Carlo simulations and repeating the single-cell experiments multiple times, the results were statistically calculated to obtain the probability distribution of the usable capacity of the battery pack.

[0038] A computer system is characterized by comprising: one or more processors, and a computer-readable storage medium for storing one or more programs, wherein when the one or more programs are executed by the one or more processors, the one or more processors cause the one or more processors to implement the method described above.

[0039] A computer-readable storage medium is characterized by storing computer-executable instructions, which, when executed, are used to implement the above-described method.

[0040] The beneficial effects of this invention are as follows:

[0041] This invention provides a method for evaluating the usable capacity of flexible battery packs based on the Monte Carlo algorithm. Building upon an empirical model, it analyzes the relationship between temperature and the state of health (SOH) of the battery, along with its influencing factors, using experimental data. Nonlinear regression is used to fit the data, determining the model parameters and ultimately establishing a prediction model for irreversible capacity loss in relation to temperature and time. An error model is established based on individual cell differences and measurement errors during storage testing, and Monte Carlo simulation tests are conducted on the battery's storage life. Based on the above, the probability distribution of capacity decay for a single flexible battery is given. Combining this with the battery pack assembly method and the algorithmic rules governing the usable capacity of the assembled pack, the probability distribution of the usable capacity of the flexible battery pack is obtained. Attached Figure Description

[0042] The accompanying drawings are for illustrative purposes only and are not intended to limit the invention. Throughout the drawings, the same reference numerals denote the same parts.

[0043] Figure 1 Distribution of SOH in the battery pack after a certain storage time.

[0044] Figure 2 Distribution of available capacity of battery packs under different assembly methods.

[0045] Figure 3 Flowchart of the experiment of this invention.

[0046] Figure 4 Regression curve. Detailed Implementation

[0047] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention. Furthermore, the technical features involved in the various embodiments of this invention described below can be combined with each other as long as they do not conflict with each other.

[0048] This invention provides a method for evaluating the usable capacity of flexible battery packs based on the Monte Carlo algorithm, specifically including the following steps:

[0049] S1: Battery Test

[0050] S11: When the storage time reaches the designed experimental value (15 days, 30 days, 45 days, 60 days...), remove the battery, place it at 25°C for 2 hours, then discharge it with a constant current to the cutoff voltage, and record the discharge capacity C. dis ;

[0051] S12: Perform two 1C standard charge-discharge cycles, record the available discharge capacity, and calculate the capacity decay rate Y. i :

[0052]

[0053] Where C0 is the initial maximum available capacity, C n This represents the maximum available capacity after the corresponding time, where i represents different numbers of measurements. For example, i = 1 represents a design value of 15 days.

[0054] S13: Charge the battery with a constant current of 1C until the remaining battery capacity reaches C. dis After standing for 1 hour, put it back into the original constant temperature incubator;

[0055] S14: When changing the experimental design value of S11, repeat process S11-S13 8 times until the test ends;

[0056] S15: Calculate the randomness coefficient β of the proportional effect of power decay during storage, specifically:

[0057] The capacity decay rate Y at each time point was obtained. i (t) take the average value

[0058]

[0059] Calculate Y using the average value i The variance Var(Y) of (t) i (t)), Var(Y i Substituting (t) into the following equation:

[0060]

[0061] Regression curves are obtained from experimental data, such as Figure 4 As shown in the figure. The intercept and slope of the regression line are obtained from the graph, where the slope represents... The intercept represents Thus, a variance is constructed. β is a normally distributed variable with a mean of 0.

[0062] S2: Random simulation to obtain the probability distribution of battery capacity

[0063] S21: Initial inconsistency in the timing and capacity of the selected storage test;

[0064] S22: Calculate the capacity degradation model under various storage time conditions: α0, α1, and ρ are given parameter values, determined by the type of battery; T represents the temperature during storage, t represents the duration of storage, and μ(t) is the expected value of the capacity degradation ratio.

[0065] S23: Combining the stochastic factor β, which affects the proportional effect, and the expected value μ(t) of the capacity degradation ratio, generates the maximum usable capacity of each battery cell when stored in a single cell: Q true (t)=μ(t)+β·μ(t);

[0066] S24: When batteries are stored as battery packs, each individual cell in the pack is affected by the pack as a whole, thus reducing its randomness. Therefore, it is necessary to deduct the randomness of SOH during degradation caused by the randomness of SOC decay; Q true (t)=μ(t)+ε·β·μ(t), where ε represents the measurement error value related to the measuring equipment.

[0067] Let C Pij (t)=Q true(t) The maximum usable capacity C of each battery after storage is derived based on the capacity degradation ratio μ(t) of each battery cell in the battery pack. Pij (t), where i and j represent the positions of individual cells in the battery pack, i.e., the i-th row and j-th column.

[0068] S25: Repeat steps S21-S24 i*j times; obtain i*j maximum available capacities C. Pij (t).

[0069] S26: Combining the battery pack assembly method with the algorithm for calculating the available capacity of the assembled battery pack, the usable capacity of the flexible battery pack can be obtained, specifically:

[0070] Series connection followed by parallel connection:

[0071]

[0072] Parallel connection followed by series connection:

[0073]

[0074] Among them, C mPnS (t) represents the capacity of the battery pack connected in parallel first and then in series, C nSmP(t) represents the capacity of the battery pack connected in series and then in parallel, and m represents the number of modules connected in series.

[0075] S27: Combining Monte Carlo simulation, repeat S21-S26 1000 times, and perform statistical calculations on the 1000 results to obtain the probability distribution of the battery pack's usable capacity.

[0076] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any person skilled in the art can easily conceive of various equivalent modifications or substitutions within the scope of the technology disclosed in the present invention, and such modifications or substitutions should all be covered within the scope of protection of the present invention.

Claims

1. A method for evaluating the usable capacity of a flexible battery pack based on the Monte Carlo algorithm, characterized in that, include: Cyclic testing of the capacity decay rate of individual cells after discharge at different storage times; The randomness coefficient β of the proportional effect of power decay during the storage process is calculated based on the capacity decay rate. Design capacity degradation models under various storage time conditions and calculate the maximum usable capacity of each battery cell; Specifically: Calculate the capacity degradation model under various storage time conditions: ; , , The given parameter values ​​are determined by the type of battery. T This represents the temperature during the storage process. t This represents the duration of the stored procedure. This is the expected value for the capacity degradation rate; Random factors incorporating proportional influence β and the expected value of the capacity decline ratio The maximum usable capacity of each battery cell when stored in a single unit: ; When batteries are stored as battery packs, each individual cell in the battery pack is affected by the pack, and its randomness is reduced. It is necessary to deduct the randomness of SOH during decay caused by the randomness of SOC decay. , ; The Monte Carlo algorithm is used, combined with the battery pack assembly method and the algorithm rules for the available capacity of the assembly, to obtain the usable capacity of the flexible battery pack; specifically: make = Based on the capacity degradation ratio of each battery cell in the battery pack Determine the maximum usable capacity of each battery after storage. ,in This represents the position of a single cell within the battery pack, i.e., the [number]th cell. Line 1 List; The battery pack is connected in series first, then in parallel: The battery packs are connected in parallel first, then in series: in, This indicates the capacity of a battery pack that is first connected in parallel and then in series. This indicates the capacity of a battery pack that is connected in series and then in parallel. Indicates the number of modules connected in series; By combining Monte Carlo simulations and repeating the single-cell experiments multiple times, the results were statistically calculated to obtain the probability distribution of the usable capacity of the battery pack.

2. The method for evaluating the usable capacity of a flexible battery pack based on the Monte Carlo algorithm according to claim 1, characterized in that, The capacity decay rate of the single cell in the cycle test after discharge at different storage times is specifically as follows: When the storage time reaches the experimental design value, remove the battery, place it at 25°C for 2 hours, and then discharge it with a constant current to the cutoff voltage, recording the discharge capacity. ; Perform two 1C standard charge-discharge cycles, record the usable discharge capacity, and calculate the capacity decay rate. : in, This is the initial maximum available capacity. This represents the maximum available capacity after the corresponding time. Representing different measurements; Charge the battery with a constant current of 1C until the remaining battery capacity reaches [the specified value]. After standing for 1 hour, put it back into the original constant temperature incubator; When changing the experimental design values, repeat the above process 8 times until the test is completed.

3. The method for evaluating the usable capacity of a flexible battery pack based on the Monte Carlo algorithm according to claim 1, characterized in that, The randomness coefficient of the proportional effect of power decay during the storage process is calculated based on the capacity decay rate. β Specifically ： The capacity decay rate at each time point was obtained. Take the average value Calculate using average value variance ,Will Substitute into the following formula: The regression curve is obtained from the experimental data. The intercept and slope of the regression line are then derived from the graph, where the slope represents... The intercept represents Thus, a variance of is constructed. A normally distributed variable with a mean of 0 .

4. A computer system, characterized in that... include: One or more processors, a computer-readable storage medium for storing one or more programs, wherein, when the one or more programs are executed by the one or more processors, the one or more processors cause the one or more processors to implement the method of claim 1.

5. A computer-readable storage medium, characterized in that... The device stores computer-executable instructions, which, when executed, are used to implement the method of claim 1.

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