Lithium battery module thermal runaway analysis method based on probability function trigger condition

Abstract

The application provides a lithium battery module thermal runaway analysis method based on a probability function trigger condition, which comprises the following steps: S1. determining the runaway cumulative probability of a target lithium battery module; S2. determining the thermal runaway probability at different temperatures based on the runaway cumulative probability of the target lithium battery module, and fitting the thermal runaway probability at different temperatures into a thermal runaway probability curve; and S3. constructing a lithium battery module thermal runaway analysis model based on the thermal runaway probability curve: K s =[m(t)-100R(x)]*(T1>=T2); when K s <=0, the target lithium battery module is in thermal runaway; in the analysis process, the actual state of the lithium battery thermal runaway is more in line with the actual state, the accuracy of the analysis result can be effectively ensured, and the method has high expansibility and strong portability.

Description

Technical Field

[0001] This invention relates to a lithium battery analysis method, and more particularly to a lithium battery module thermal runaway analysis method based on probability function triggering conditions. Background Technology

[0002] Lithium-ion batteries, as a new energy source, are characterized by low internal resistance, long lifespan, low self-discharge rate, and high specific energy, and are widely used in mobile phones, digital products, home appliances, national defense, transportation, and other fields. However, due to their extremely high energy density and flammability, battery safety has always been a major concern. Besides inherent design flaws, improper use can also lead to battery combustion and explosion. For example, under conditions such as high temperature and pressure, overcharging and over-discharging, impact, compression, and short circuits caused by punctures, lithium-ion batteries can experience thermal runaway.

[0003] To understand the mechanism of lithium battery thermal runaway, grasp the essence of lithium battery safety accidents, and reduce and prevent the occurrence of thermal runaway events, existing technologies have conducted extensive thermal abuse experiments and simulation studies on lithium battery thermal runaway. Using simulation methods to study lithium battery thermal runaway is more cost-effective than experiments and provides a good alternative for some complex scenarios that are difficult to build experimentally. However, the analysis of lithium battery thermal runaway using simulation methods is based on the premise that battery thermal runaway and propagation are relatively deterministic events, that is, the trigger temperature and propagation path of battery thermal runaway are relatively certain. However, in the actual battery thermal runaway process, due to some characteristics of the battery itself and the differences in the environment, the battery may have different trigger temperatures and propagation paths, resulting in low accuracy of the analysis results.

[0004] Therefore, in order to solve the above-mentioned technical problems, it is urgent to propose a new technical approach. Summary of the Invention

[0005] In view of this, the purpose of this invention is to provide a method for analyzing the thermal runaway of lithium battery modules based on probability function triggering conditions. This method is more consistent with the actual state of thermal runaway of lithium batteries during the analysis process, effectively ensuring the accuracy of the analysis results. Moreover, the method has high scalability and strong portability.

[0006] This invention provides a method for analyzing the thermal runaway of lithium battery modules based on probability function triggering conditions, comprising the following steps:

[0007] S1. Determine the runaway cumulative probability of the target lithium battery module;

[0008] S2. Determine the thermal runaway probability at different temperatures based on the runaway cumulative probability of the target lithium battery module, and fit the thermal runaway probability at different temperatures into a thermal runaway probability curve;

[0009] S3. Constructing a thermal runaway analysis model for lithium battery modules based on thermal control probability curves:

[0010] K s =[m(t)-100R(x)]*(T1≥T2);

[0011] Where: K s is the thermal runaway analysis exponent, m(t) is a random function that varies with time, R(x) is the thermal runaway probability curve, * indicates the relationship between and , T1 is the set time variable, T2 is an infinite variable, and the initial state T1 = T2;

[0012] When K s When the value is ≤0, the target lithium battery module experiences thermal runaway.

[0013] Furthermore, in step S1, the thermal runaway probability G(x) of the target lithium battery module is determined by the following method:

[0014]

[0015] Where x is the temperature of the target lithium battery module, and 80≤x≤260℃.

[0016] Furthermore, in step S2, the thermal runaway probability curve is determined through the following steps:

[0017] Set the temperature change step size ΔT within the temperature range [80, 260] of the target lithium battery module, and divide the temperature range [80, 260] into n sub-ranges, where: And n is an integer;

[0018] Calculate the probability P1 of thermal runaway of the lithium battery in the first temperature sub-interval within the temperature range:

[0019] P1 = G(80 + △T) - G(80);

[0020] Calculate the probability of thermal runaway of the lithium battery from the second temperature sub-interval to the (n-1)th temperature sub-interval within the temperature range:

[0021]

[0022] Calculate the probability of thermal runaway of a lithium battery in the nth temperature sub-interval within the temperature range:

[0023]

[0024] The thermal runaway probability of lithium batteries in each temperature range is fitted to form a thermal runaway probability curve of lithium batteries.

[0025] Furthermore, the time-varying random function m(t) is:

[0026] m(t) = random(1,100), and the random function m(t) follows a uniform distribution.

[0027] The beneficial effects of this invention are as follows: This invention provides a more realistic simulation analysis of thermal runaway in lithium battery modules by dividing the temperature range into multiple segments and calculating the probability of thermal runaway for each segment. This effectively ensures the accuracy of the analysis results for lithium battery thermal runaway and provides accurate data support for subsequent treatment measures. Furthermore, the method is highly scalable and portable. Attached Figure Description

[0028] The present invention will be further described below with reference to the accompanying drawings and embodiments:

[0029] Figure 1 This is a flowchart of the present invention.

[0030] Figure 2 This is a schematic diagram illustrating that the random function of the present invention follows a uniform distribution.

[0031] Figure 3 This is a schematic diagram of a lithium battery module, a specific example of the present invention.

[0032] Figure 4 This is a comparison chart of the analytical results and theoretical results of the present invention in a specific example of the present invention. Detailed Implementation

[0033] The present invention will be further described in detail below:

[0034] This invention provides a method for analyzing the thermal runaway of lithium battery modules based on probability function triggering conditions, comprising the following steps:

[0035] S1. Determine the runaway cumulative probability of the target lithium battery module;

[0036] S2. Determine the thermal runaway probability at different temperatures based on the runaway cumulative probability of the target lithium battery module, and fit the thermal runaway probability at different temperatures into a thermal runaway probability curve;

[0037] S3. Constructing a thermal runaway analysis model for lithium battery modules based on thermal control probability curves:

[0038] K s =[m(t)-100R(x)]*(T1≥T2);

[0039] Where: K s Let be the thermal runaway analysis exponent, m(t) be a random function varying with time, R(x) be the thermal runaway probability curve, * denote the AND relationship, T1 be the set time variable, T2 be an infinite variable, and the initial state T1 = T2; K s The formula represents K. s= [m(t)-100R(x)], and T1=T2, then the calculated K is... s The value is valid when a single cell of a lithium battery experiences thermal runaway once; at this time, K... s ≤0, and assign the current time value to T1. If K is used in the next calculation, then s If the calculation formula is invalid, then this method makes the calculation process consistent with the actual situation that a single lithium battery cell will only thermally run away once, thus ensuring the accuracy of the final result.

[0040] When K s When the temperature is ≤0, the target lithium battery module experiences thermal runaway. The above method achieves the following advantages: 1. It better reflects real-world conditions when simulating thermal runaway in lithium battery modules. Existing technologies use a fixed temperature as the trigger condition for thermal runaway, but non-runaway batteries within a lithium battery module are affected to varying degrees by the temperature of runaway batteries. The probability of different batteries experiencing thermal runaway at the same temperature is low; in reality, there are instances where the probability of thermal runaway is higher or lower within a certain temperature range. 2. By dividing the temperature range into multiple ranges and calculating the thermal runaway probability separately, the accuracy of the lithium battery thermal runaway analysis results can be effectively ensured, providing accurate data support for subsequent lithium battery treatment measures. 3. The method has high scalability and portability. Existing technologies analyze thermal runaway in lithium battery modules using a small number of lithium batteries. However, when the number of individual cells in a lithium battery module is large, existing methods become unsuitable because they cannot accurately estimate the thermal runaway results. Furthermore, existing methods become extremely computationally intensive and inefficient as the number of individual cells in the lithium battery module increases.

[0041] In this embodiment, in step S1, the thermal runaway probability G(x) of the target lithium battery module is determined by the following method:

[0042]

[0043] Where x is the temperature of the target lithium battery module, and 80≤x≤260℃; where the temperature range [80,260] is the risk range of thermal runaway of lithium battery, and the critical value of thermal runaway varies between 80-260.

[0044] In this embodiment, in step S2, the thermal runaway probability curve is determined through the following steps:

[0045] Set the temperature change step size ΔT within the temperature range [80, 260] of the target lithium battery module, and divide the temperature range [80, 260] into n sub-ranges, where: And n is an integer;

[0046] Calculate the probability P1 of thermal runaway of the lithium battery in the first temperature sub-interval within the temperature range:

[0047] P1 = G(80 + △T) - G(80);

[0048] Calculate the probability of thermal runaway of the lithium battery from the second temperature sub-interval to the (n-1)th temperature sub-interval within the temperature range:

[0049]

[0050] Calculate the probability of thermal runaway of a lithium battery in the nth temperature sub-interval within the temperature range:

[0051]

[0052] The thermal runaway probability of lithium batteries in each temperature range is fitted to form a thermal runaway probability curve of lithium batteries.

[0053] Wherein: the time-varying random function m(t) is:

[0054] m(t) = random(1,100), and the random function m(t) follows a uniform distribution, such as Figure 2 As shown, t in m(t) represents the change over time, and the result of the random number is generated within the interval (1, 100) as time t changes.

[0055] The following specific example further illustrates this point:

[0056] Establish a thermal runaway probability module consisting of 100 individual cells, such as... Figure 3 As shown, the temperature range is divided into 18 components. After calculating the cumulative probability of thermal runaway using the method described above, the thermal runaway probability of each sub-range is then calculated. The thermal runaway probability curves of lithium batteries at different temperatures are obtained by fitting the data.

[0057] R(x) = 6793.25393 + (-6793.297) / (1 + exp((x - 729.82) / 53.91)), where 80 ≤ x ≤ 260℃;

[0058] Figure 4 The figure describes the number of thermally runaway cells corresponding to 100 cells in six temperature ranges. The theoretical number of thermally runaway cells, calculated from the actual individual cells, may be a non-integer value. Since localized thermal runaway does not occur in actual individual cells, the number is rounded to the nearest integer. As shown in the figure above, the simulation results match the theoretical predictions well, thus demonstrating the feasibility of this method.

[0059] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A method for analyzing thermal runaway of lithium battery modules based on probabilistic function triggering conditions, characterized in that: Includes the following steps: S1. Determine the runaway cumulative probability of the target lithium battery module; S2. Determine the thermal runaway probability at different temperatures based on the runaway cumulative probability of the target lithium battery module, and fit the thermal runaway probability at different temperatures into a thermal runaway probability curve; S3. Constructing a thermal runaway analysis model for lithium battery modules based on thermal runaway probability curves: ； in: This is a thermal runaway analysis index. It is a random function that varies with time. This is the probability curve for thermal runaway. Indicates the AND relation. For the set time variable, The variable is infinitely large, and the initial state is... ; when At that time, the target lithium battery module experienced thermal runaway; In step S1, the thermal runaway probability of the target lithium battery module is determined by the following method. : in, The temperature of the target lithium battery module, and 80 ≤ ≤260℃; In step S2, the thermal runaway probability curve is determined through the following steps: Set the temperature variation step size within the target lithium battery module's temperature range [80, 260]. The temperature range [80, 260] is divided into... There are sub-intervals, of which: and It is an integer; Calculate the probability of thermal runaway of the lithium battery in the first temperature sub-interval within the temperature range. : ； Calculate the second temperature sub-interval within the temperature range to the first... Probability of thermal runaway in lithium batteries within temperature sub-ranges: Calculate the probability of thermal runaway of a lithium battery in the nth temperature sub-interval within the temperature range: ； The thermal runaway probability of lithium batteries in each temperature range is fitted to form a thermal runaway probability curve of lithium batteries.

2. The lithium battery module thermal runaway analysis method based on probability function triggering conditions according to claim 1, characterized in that: Time-varying random function for: And random function It follows a uniform distribution.

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