CN111597747A - Multipoint-triggering ternary lithium power battery module thermal runaway simulation and prediction method - Google Patents

Abstract

The invention discloses a multi-point trigger ternary lithium power battery module thermal runaway simulation and prediction method, comprising: establishing a three-dimensional model of a power battery module, selecting a thermal runaway multi-point trigger position in the battery module; The 3D model is imported into the finite element software, the battery heat generation model is established for the battery pack according to the thermal abuse model, and the thermal runaway trigger heat source Q is applied to the selected multi-point trigger position. The heat transfer methods are convection and conduction, and thermal runaway simulation is used to obtain battery module temperature, trigger point, heat source, and thermal runaway time data; the number of different trigger points n and the thermal runaway trigger heat source Q are collected. The nonlinear least squares method is used to fit the relationship between the calculation time t and the thermal runaway heat source Q and the number of trigger points n to predict the thermal runaway time of the battery module; Runaway time is calculated.

Description

A multi-point trigger ternary lithium power battery module thermal runaway simulation and prediction method

technical field

The invention relates to the technical field of electric vehicle power batteries, in particular to a method for simulating and predicting thermal runaway of a ternary lithium power battery module based on multi-point triggering.

Background technique

As the core component of electric vehicles, the safety of power battery plays an important role in the development of electric vehicles. The power battery is prone to thermal runaway under mechanical abuse, electrical abuse and thermal abuse, causing serious harm to the safety of electric vehicles and personnel. Thermal runaway condition, that is, thermal abuse, is the root cause of thermal runaway in power batteries. As an important component of the power battery system, the power battery module is of great significance to the research on its safety. Therefore, a simulation of the multi-point triggering of the power battery module under different collision conditions is required, causing the thermal runaway of the module. and forecasting methods.

At present, when the thermal runaway safety test of power lithium batteries is carried out in China, the method of safety test of power battery cells is mostly adopted. Although this method can safely control the battery, it does not take into account the practical application in the form of modules. The safety of the power battery module existing in the car does not take into account the multi-point overheating generated when the electric vehicle encounters various collision conditions during actual driving, which leads to the thermal runaway of the power battery.

1) In the paper "Research on battery thermal runaway and its expansion characteristics under heating conditions" published by Liu Qingchen (2018) of Hefei University of Technology, the center point heating method is adopted for the 3×3 structure of nickel-cobalt-manganese lithium-ion power battery modules , to explore the thermal runaway law of power battery modules under different distances and different heat exchange methods. In the literature, heating the battery at the center of the module causes the thermal runaway of the power battery module. Although this method can be used to observe the thermal runaway law between the battery modules more intuitively, it does not consider the collision of electric vehicles in actual working conditions. It is ideal for isochronous conditions that cause multiple points of overheating in the battery module. Under the premise of considering the overheating of different positions in the battery module caused by different collision conditions, the present invention adopts the ternary lithium battery with high energy density as the research object, and explores and researches the law of the overall thermal runaway of the power battery module. It has practical applicability, and the parameters can be flexibly changed according to different battery materials used, so as to realize the prediction of the overall thermal runaway time of the power battery module under different collision conditions.

2), "An early warning method for thermal runaway of a power lithium-ion battery", patent number CN110911772A. The invention provides an early warning method for thermal runaway of a power lithium-ion battery. The temperature, smoke and characteristic gas are respectively detected by sensors, a three-layer power lithium battery thermal runaway early warning mechanism is constructed, and the existing monitoring and early warning system is simple in form and mode. However, this early warning mechanism uses the battery system as the test object to conduct a thermal runaway test and verify the accuracy of the mechanism. The test cost is high, and the sensor position and type need to be changed for batteries of different materials and structures, and the versatility is insufficient. The thermal runaway prediction method involved in the present invention adopts simulation as the main means, and can be flexibly set according to batteries of different materials, with low cost and high accuracy.

3) "Research on Thermal Runaway of NCM Ternary Lithium Power Batteries" published in 2018 by Tu Chao and Huang Qingsheng of Chang'an University in Journal of Jiamusi University, Volume 36, Issue 5. Thermal runaway behavior studies were carried out. The literature studies the thermal runaway time of ternary lithium square power battery cells under different heating furnace temperatures and different heat dissipation conditions, but the objects considered are limited to battery cells, and the triggering method for thermal runaway of battery cells is the environment. When heated at high temperature, it is considered that the battery cells are uniformly heated. However, in practical applications, the heating position of the battery is mostly uneven, and in electric vehicles, the single cells are grouped as the basic unit of the battery system. Therefore, only the power battery cells are used. Conducting thermal runaway research cannot have a good grasp of the thermal runaway law of power batteries in actual use. The present invention performs thermal runaway simulation on the NCM ternary lithium power battery module composed of the most common cylindrical batteries on the market, and the thermal runaway triggering method adopts precise heating by a heat source, which is more in line with the uneven heating of the battery in practical applications, and considers the The thermal effects of different heating positions of the battery on the module as a whole under different collision conditions make the simulation situation more in line with the actual application situation and more credible.

SUMMARY OF THE INVENTION

In order to solve the above-mentioned technical problems, the purpose of the present invention is to provide a simulation and prediction method for thermal runaway of a ternary lithium power battery module based on multi-point triggering. The thermal runaway condition is simulated to study the process and prediction method of the overall thermal runaway of the module caused by the multi-point overheating of the power battery, and provide a reliable basis for the thermal runaway blocking method and safety design of the power battery.

The object of the present invention is achieved through the following technical solutions:

A method for simulating and predicting thermal runaway of a ternary lithium power battery module based on multi-point triggering, the method comprises the following steps:

S1 establishes a three-dimensional model of the power battery module according to the size, shape and arrangement of the sample battery, and selects the thermal runaway multi-point trigger position in the power battery module according to different collision conditions;

S2 imports the three-dimensional model of the power battery module into the finite element software, establishes a battery heat production model for the power battery pack based on the thermal abuse model, applies thermal runaway trigger heat source Q to the battery at the selected multi-point trigger position, and converts the battery domain to the battery. Divide a quadrilateral as the main free grid, set the heat transfer mode between the batteries as convection and conduction, conduct thermal runaway simulation, and obtain battery module temperature, trigger point, heat source, and thermal runaway time data;

通过非线性最小二乘法拟合计算得到时 间

与热失控热源Q、触发点个数n关系式，对动力电池模组整体发生热失控 的时间进行预测；S3 collects the average value of the overall thermal runaway time in the module caused by the number of different trigger points n and the thermal runaway trigger heat source Q under different collision conditions

Calculated time by nonlinear least squares fitting

The relationship between the thermal runaway heat source Q and the number of trigger points n is used to predict the overall thermal runaway time of the power battery module;

S4 calculates the overall thermal runaway time of the power battery module caused by different trigger positions and heat sources under each collision condition according to the overall thermal runaway prediction relationship of the power battery module calculated under different collision conditions.

One or more embodiments of the present invention may have the following advantages over the prior art:

It can simulate the situation of multi-point overheating caused by different collision conditions and cause the overall thermal runaway of the power battery module, and predict the time when the overall thermal runaway of the power battery module is caused by this position, which is the thermal runaway resistance of the power battery. Provide a reliable basis for breaking method and safety design.

Description of drawings

Fig. 1 is the working flow chart of the thermal runaway simulation and prediction method of ternary lithium power battery module based on multi-point triggering;

Figure 2 is a program frame diagram of the thermal runaway simulation and prediction method of a ternary lithium power battery module based on multi-point triggering;

Fig. 3 is the thermal runaway simulation and prediction method based on multi-point triggering of ternary lithium power battery module thermal runaway trigger point selection and calculation schematic diagram;

4 is a three-dimensional schematic diagram of a ternary lithium power battery module;

Figure 5 is a three-dimensional temperature cloud diagram of the battery module when n is 3, heat source Q is 1e ⁷ (W/m ³ ), t=0(s) and t=12(s);

Figures 6a and 6b are the temperature probe diagrams of the battery module when n is taken as 3 and the heat source Q is taken as 1e ⁷ (W/m ³ );

Figures 7 and 8 are schematic diagrams of fitting the data using the nonlinear least squares method.

Detailed ways

In order to make the objectives, technical solutions and advantages of the present invention clearer, the present invention will be described in further detail below with reference to the embodiments and accompanying drawings.

关系式。具体包括如下步骤：As shown in Figures 1 and 2, the workflow of the thermal runaway simulation method for a multi-point triggering power battery module includes: building a three-dimensional model of the power battery module, selecting a multi-point triggering position, modeling with a quadrilateral free grid, applying The heat source calculates the battery thermal field, collects data, and fits the calculation to predict the overall thermal runaway time of the power battery module

relational. Specifically include the following steps:

Step 10 Build a three-dimensional model of the power battery module according to the size, shape and arrangement of the sample battery, and select thermal runaway in the power battery module according to different collision conditions (rear-end collision, side collision, bottom collision, corner collision, etc.). Multi-point trigger position, the specific method is:

As shown in Figure 3, a rectangular coordinate system is constructed with the corners of the power battery module, and the distances from the center of the battery cell to the x and y axes are dl and dh, respectively. When selecting thermal trigger points according to different collision conditions, the number of trigger points The battery at the position corresponding to n should meet:

Figure 4 shows the three-dimensional model of the power battery module constructed for the sample battery ternary lithium cylindrical battery in the present invention, and the battery size values in the figure are shown in Table 1.

Table 1

Single point pool radius r (unit: mm) Single point pool height h (unit: mm) Battery spacing d (unit: mm) 16 65 10

因此， 图4中所示1号电池、2号电池和5号电池为n＝3时，所选取多点热触发电 池。Taking the corner impact condition as an example, when the power battery module is impacted by the corner, it may cause thermal runaway of n batteries in the battery module. When there are 3 batteries in thermal runaway, according to the above calculation method of thermal runaway position, the positions of the 3 batteries should meet the requirements of

Therefore, when the No. 1 battery, the No. 2 battery and the No. 5 battery shown in FIG. 4 are n=3, the multi-point thermal trigger battery is selected.

Step 20: Import the established three-dimensional model into finite element software, establish a battery heat production model for the power battery module according to the thermal abuse model, apply thermal runaway trigger heat source Q to the battery at the selected multi-point trigger position, and divide the battery domain into With quadrilateral as the main free grid, set the heat exchange mode between batteries as convection and conduction, conduct thermal runaway simulation, and obtain battery module temperature, trigger point, heat source, thermal runaway time and other data;

Among them, the indicators for determining the thermal runaway of the power battery are:

Battery cell: monitoring point temperature > T _{critical temperature} ;

Battery module: the temperature of each battery in the module > T _{critical temperature} .

The selection method for collecting the temperature position of the battery cell in the power lithium battery module is:

As shown in Figure 5, in the cylindrical coordinate system, the battery is equivalent to a cylinder with a height of h and a radius of r. The random monitoring point positions of (hi, θ _i ) are generated on the surface of the battery by the Monte Carlo method _. N, where _{hi ∈(0,h) and θ i ∈} (0,360°). Let ΔT be the difference between the temperature of the monitoring point and the surface temperature of the battery cell, Δt is the difference between the thermal trigger time of the monitoring point and the surface trigger time of the battery cell, t ₀ is the start time of the battery thermal trigger, and t _r is the time corresponding to the maximum temperature of the battery thermal runaway .

Normalize the two indicators ∑ΔT(h _i , θ _i ) and Δt( _hi , θ _i ):

The two indicators are summed, and the position at i corresponding to the minimum value (h _i , θ _i ) is the position of the selected battery surface temperature monitoring point:

计算方法如 下：The average time of thermal runaway of other batteries in the module caused by multi-point thermal runaway

The calculation method is as follows:

The thermal runaway time of the first battery in the power battery module caused by the heat source is defined as t ₁ , and the thermal runaway time of the other batteries is t ₂ , t ₃ . . . t _n respectively. The average time for the thermal runaway of the power battery module as a whole caused by the thermal runaway battery is:

Under the condition of corner impact, take the number of trigger points n=3 (No. 1, No. 2, and No. 5 batteries), set the trigger heat source Q=1e ⁷ (W/m ³ ), T _{critical temperature} =200℃, use Figure 5 and Figures 6a and 6b show the temperature cloud map of the battery surface obtained by the simulation software for thermal runaway simulation of the power battery module and the temperature probe maps of each battery. It can be seen from the figure that under the influence of thermal runaway triggering the high temperature of the battery, the battery module absorbs heat and the temperature rises, thereby causing the overall thermal runaway of the module.

数据进行计算，所得数据如下表2。Using the above-mentioned overall thermal runaway calculation time of the power battery module, take 0.5e ⁷ , 0.6e ⁷ , 0.75e ⁷ , 0.9e ⁷ , 1e ⁷ , 1.25e ⁷ for the heat source Q (W/m ³ ), and n take 1 respectively In -6 cases, the time average of thermal runaway of other batteries in the module

The data is calculated, and the obtained data are as follows in Table 2.

Table 2

通过非线性最小二乘法拟合计算得 到时间

与热失控热源Q、触发点个数n关系式，对动力电池模组整体发生热 失控的时间进行预测；Step 30 collects the average value of the overall thermal runaway time in the module caused by the number n of different trigger points and the thermal runaway trigger heat source Q under different collision conditions

Calculated time by nonlinear least squares fitting

The relationship between the thermal runaway heat source Q and the number of trigger points n is used to predict the overall thermal runaway time of the power battery module;

与热源Q、触发点个数n的关系式方法如下：calculating time

The relationship between the heat source Q and the number of trigger points n is as follows:

为热失控 预测系统输出，触发点n与热源Q为系统输入，a,b,c,d为参数，则经过x次 试验所得数据(n₁，Q₁，t₁)，(n₂，Q₂，t₂)…(n_x，Q_x，t_x)之间经非线性最小二乘 法拟合后具有以下关系:Defines the time average that causes thermal runaway of other batteries in the power battery module

is the output of the thermal runaway prediction system, the trigger point n and the heat source Q are the system input, a, b, c, d are the parameters, then the data obtained after x times of experiments (n ₁ , Q ₁ , t ₁ ), (n ₂ , Q ₂ , t ₂ )...(n _x , Q _x , t _x ) have the following relationship after fitting by nonlinear least squares method:

关系：Since f[(n _x ,Q _x ),(a,b,c,e))] is a nonlinear function, the iterative algorithm is used to solve the problem, so that the objective function is the smallest sum of squared errors, that is, when ε is the smallest, the required The parameter value is its solution, and the heat source Q in the power battery module, the number of trigger points n and the predicted overall thermal runaway time of the battery module can be obtained.

relation:

Among them, a, b, c, d, f, and g are coefficients, which are obtained by nonlinear least squares fitting.

According to the above fitting method, nonlinear least squares fitting is performed on the data in Table 2. After fitting as shown in Figure 7 and Figure 8, the overall thermal runaway time relationship of the predicted power lithium battery module is finally obtained:

Step 40 calculates the overall thermal runaway time of the power battery module caused by different trigger positions and heat sources under different collision conditions according to the predicted relational expression of the overall thermal runaway of the power battery module calculated under different collision conditions.

时整体发生热失控。Under the condition of edge and corner collision, the above calculation is obtained by fitting the number of thermal runaway trigger points n and the heat source Q to predict the overall thermal runaway time relationship of the power lithium battery module. When the number of trigger points n=7, the heat source Q= 1e ⁷ (W/m ³ ), the power battery module will be

Thermal runaway occurs as a whole.

Although the embodiments disclosed in the present invention are as above, the described contents are only the embodiments adopted to facilitate the understanding of the present invention, and are not intended to limit the present invention. Any person skilled in the art to which the present invention belongs, without departing from the spirit and scope disclosed by the present invention, can make any modifications and changes in the form and details of the implementation, but the scope of patent protection of the present invention, The scope as defined by the appended claims shall still prevail.

Claims (7)

1. a multi-point trigger ternary lithium power battery module thermal runaway simulation and prediction method, is characterized in that, described method comprises the following steps: S1 establishes a three-dimensional model of the power battery module according to the size, shape and arrangement of the sample battery, and selects the thermal runaway multi-point trigger position in the power battery module according to different collision conditions; S2 imports the three-dimensional model of the power battery module into the finite element software, establishes a battery heat production model for the power battery pack based on the thermal abuse model, applies thermal runaway trigger heat source Q to the battery at the selected multi-point trigger position, and converts the battery domain to the battery. Divide a quadrilateral as the main free grid, set the heat transfer mode between the batteries as convection and conduction, conduct thermal runaway simulation, and obtain battery module temperature, trigger point, heat source, and thermal runaway time data; S3 collects the average value of the overall thermal runaway time in the module caused by the number of different trigger points n and the thermal runaway trigger heat source Q under different collision conditions

Calculated time by nonlinear least squares fitting

The relationship between the thermal runaway heat source Q and the number of trigger points n is used to predict the overall thermal runaway time of the power battery module; S4 calculates the overall thermal runaway time of the power battery module caused by different trigger positions and heat sources under each collision condition according to the overall thermal runaway prediction relationship of the power battery module calculated under different collision conditions. 2. The multi-triggered ternary lithium power battery module thermal runaway simulation and prediction method according to claim 1, wherein in the step S1: The shape of the battery is cylindrical; Different collision scenarios include rear-end collision, side collision, bottom collision and corner collision. 3. The multi-point trigger ternary lithium power battery module thermal runaway simulation and prediction method according to claim 1, wherein in the step S1: the thermal runaway multi-point trigger position selection method comprises: using the power battery model A rectangular coordinate system is constructed for the corners of the group, and the distances from the center of the battery cell to the x and y axes are dl and dh, respectively. When the thermal trigger point is selected according to different collision conditions, the battery at the position corresponding to the number of trigger points n should meet:

4. The multi-point-triggered ternary lithium power battery module thermal runaway simulation and prediction method according to claim 1, characterized in that, in the step S2, the index for determining the thermal runaway of the power battery is: the battery cell and the battery module, where: Battery cell: monitoring point temperature > T _{critical temperature} ; Battery module: the temperature of each battery in the module > T _{critical temperature} . 5. The multi-point trigger ternary lithium power battery module thermal runaway simulation and prediction method according to claim 1, wherein the step S2 further comprises: the selection of the temperature position of the battery cell collected in the power battery module The method is: In the cylindrical coordinate system, the battery is equivalent to a cylinder with a height of h and a radius of r. The Monte Carlo method is used to generate ( _{hi, θ i )} random monitoring point positions on the surface of the battery, where h _i ∈(0,h), θ _i ∈(0,360°), let ΔT be the difference between the temperature of the monitoring point and the surface temperature of the battery cell, Δt is the difference between the thermal trigger time of the monitoring point and the surface temperature of the battery cell, and t ₀ is the battery Thermal trigger start time, t _r is the time corresponding to the maximum temperature of the battery thermal runaway; Normalize the two indicators ∑ΔT(h _i , θ _i ) and Δt( _hi , θ _i ):

The two indicators are summed, and the position at i corresponding to the minimum value (h _i , θ _i ) is the position of the selected battery surface temperature monitoring point:

6. The multi-point trigger ternary lithium power battery module thermal runaway simulation and prediction method according to claim 1, wherein in the step S3, the thermal runaway time of other batteries in the module is caused by the multi-point thermal runaway. average value

The calculation method is as follows: The thermal runaway time of the first battery in the power battery module caused by the heat source is defined as t ₁ , and the thermal runaway time of the other batteries is t ₂ , t ₃ . . . t _n respectively. The average time for the thermal runaway of the power battery module as a whole caused by the thermal runaway battery is:

7. The multi-point-triggered ternary lithium power battery module thermal runaway simulation and prediction method according to claim 1, wherein the calculation time in the step S3

The relationship between the heat source Q and the number of trigger points n is as follows: Defines the time average that causes thermal runaway of other batteries in the power battery module

is the output of the thermal runaway prediction system, the trigger point n and the heat source Q are the system input, a, b, c, d are the parameters, then the data obtained after x times of experiments (n ₁ , Q ₁ , t ₁ ), (n ₂ , Q ₂ , t ₂ )...(n _x , Q _x , t _x ) have the following relationship after fitting by nonlinear least squares method:

Since f[(n _x ,Q _x ),(a,b,c,d))] is a nonlinear function, an iterative algorithm is used to solve the problem, so that the objective function is the smallest sum of squared errors, that is, when ε is the smallest, the required The parameter value is its solution, and the heat source Q in the power battery module, the number of trigger points n and the predicted overall thermal runaway time of the battery module can be obtained.

relation:

Among them, a, b, c, d, f, and g are coefficients, which are obtained by nonlinear least squares fitting.

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