Investigating the Cell Result Multiplication Method for Emission Test of Battery Module

Abstract

The thermal safety of lithium-ion traction batteries is a highly concerning issue in the field of electric transportation. The large amount of gas emissions during the thermal runaway process of batteries has high safety hazards, such as fire and explosion. The quantitative analysis of emissions is one of the important challenges in testing and evaluating battery safety. Focusing on quantifying gas emissions using large-scale thermal propagation in battery modules and packs, based on the idea of cell result multiplication, this article conducts a thermal runaway emission analysis of a single cell and a module and compares the behavior of thermal runaway and gas emissions of the cell and module from the perspectives of temperature, pressure, gas composition, and battery morphology. The feasibility of the cell result multiplication method is verified from the perspective of experimental data.

1. Introduction

The global inventory of new energy vehicles has exceeded 20 million, with lithium-ion traction batteries being the predominant energy storage system for electric vehicles (EVs) [1,2,3]. Despite rapid technological advancements, traction-battery technologies still face many challenges to meet the requirements of the next generation of electric transportation. Some of these challenges include enhancing energy density, cycle life, and recyclability [4,5,6]. Safety stands as one of the most significant factors affecting consumers’ choice of electric vehicles, considering that fire accidents caused by traction batteries in EVs occur occasionally [7,8,9]. To overcome these challenges, researchers must continue prioritizing safety while aiming to improve battery performance and sustainability [10,11].

Despite the current electrochemical system of lithium-ion batteries, there is still a risk of thermal runaway. Limiting safety risks within a controllable range is an urgent problem to solve. Thermal runaway and thermal propagation represent typical failure behaviors of lithium-ion batteries that create a barrier to the widespread application of EVs [12,13,14]. When there is an internal short circuit or another situation that causes heat accumulation, a series of chain reactions can occur, including the decomposition of the solid electrolyte interface (SEI), separator breakage, electrolyte combustion, cathode and anode material thermal decomposition, and more [15,16,17]. These heat-generating reactions also involve the emission of gases such as CO, CO₂, H₂, and CH₄, as well as the release of a large amount of smoke [18,19]. It is crucial to identify and address these risks to enhance the safety of lithium-ion batteries and increase their accessibility for various applications.

From an electric vehicle driver’s safety perspective, developing traction batteries that do not experience thermal runaway or can suppress thermal propagation with active/passive strategies is critical for safety technologies [20,21]. Achieving this goal requires extensive research on the thermal runaway and thermal propagation behavior of batteries. As the group that first proposed the concept of thermal runaway propagation, we have conducted numerous investigations on the impact of SOC, abuse, damage, and other factors on the thermal safety of batteries [22,23,24]. Our studies have included mechanism analyses of thermal runaway processes based on simulation methods, such as those conducted by Ouyang et al. [25]. In addition, other researchers, like Al-Hallaj et al., have investigated the use of phase-change materials to block thermal propagation [26]. These studies have highlighted the importance of analyzing battery emissions after thermal runaway, as it is integral to quantitatively assessing disaster hazards after battery failure and establishing corresponding safety testing methods [27,28,29]. Through a collaborative effort by researchers and organizations, we can enhance the safety of lithium-ion batteries used in electric vehicles, ultimately supporting the sustainable development of transportation systems worldwide.

The accurate quantification of battery thermal runaway emissions poses a significant challenge due to the intense reaction during the process and the complex underlying mechanism. Galushkin et al. undertook a detailed analysis of the gas generated by lithium-ion batteries of different chemistries under a range of aging or abuse conditions (including cycling, overcharging, and heating) employing GC-MS and FTIR [30,31,32]. Baird et al. focused on the potential explosion hazards associated with battery gas generation, and Wang et al. investigated the dynamic changes in gas generation combustibility [33,34]. However, it is worth noting that these research topics primarily concentrate on the cell level, leaving the task of quantifying emissions for batteries beyond the module level yet to be satisfactorily resolved [35,36,37]. When thermal runaway and thermal propagation occur at the module level, the gases emitted from different battery cells in series or parallel bring composition and content factors into play, and larger-scale thermal propagation escalates the risk of fire and explosion. This presents significant challenges to conducting emission research at the module and pack levels, which calls for continued research efforts and close collaborative engagement between researchers and other stakeholders to enhance the safety of the lithium-ion batteries used in electric vehicles.

Currently, the establishment of larger, sealed test chambers is the most popular direction for research on quantifying thermal runaway emissions. This method is highly feasible for expanding emission testing from the monomer level to the module level. Nonetheless, due to the current utilization of traction batteries without modular structures, such as Cell to Pack or Cell to Chassis, it is challenging to quantify emissions at a higher level. This is because conducting emission tests at the system level using sealed test chambers requires significant investment costs and poses greater safety risks, for instance, the risk of gas explosions. Therefore, further research and the development of safer and cheaper methods for assessing the gas emissions of these batteries at a higher level are needed in the near future.

The cell result multiplication method can be used to estimate the total emission of a module or pack based on the consistency of battery cells in the module or pack. This method observes the number of failed cells and thermal propagation tests to determine the emission level at the cell level, which is then converted to estimate the total emission of the module or pack. However, there is currently no research report on the validity of this method.

To verify the cell result multiplication (CRM) method theoretically, we conducted an analysis of the thermal runaway emission pressure, composition, and battery failure morphology between a cell and three series modules of a nickel–cobalt–manganese–lithium battery. The research results indicate that this method is highly feasible for cells with sufficient thermal runaway. The difference between individual gas production and average gas production among modules only has a small percentage difference, and the main components of gas production also have a high degree of consistency. Overall, the CRM method has potential to be an effective way to estimate the total emission of a module or pack, but further research is needed to validate its use for other battery types and configurations.

2. Experimental Section

Battery information: A square shell lithium-ion battery with rated capacity of 42 Ah was selected for this experiment. The chemistry of the battery sample is LiNi_1/3Co_1/3Mn_1/3O₂-based cathode and graphite-based anode, with a working voltage range of 2.75–4.2 V.

Thermal failure test chamber: We developed a battery thermal failure test chamber to conduct gas-emission analysis of the heating thermal runaway of cells and modules. The chamber has a maximum working pressure of 2 MPa and a content volume of 80 L, with an inner diameter of 600 mm. The chamber body features 8 external testing channels, 2 copper terminals, exhaust ports, and sampling ports. To monitor the heating, temperature, and pressure of the battery during the experiment, we equipped the thermal runaway test chamber with gas pressure sensors and battery temperature and voltage sensors. The heating strip is positioned at the center of the battery surface, and its heating, temperature, and pressure are monitored using the device’s reserved port. The test device also features a dedicated port for gas discharge, allowing us to extract gas samples using a gas sampling bag after the thermal runaway reaction is complete.

Gas composition analysis: For the gas-emission quantification of batteries after thermal runaway, we used high-temperature gas collection bags to sample the gas and used gas chromatography with a thermal conductivity detector (TCD) and a flame ionization detector (FID) for the quantitative analysis of gas composition.

CT information: X-ray computed tomography (X-ray CT) was conducted using a Nano Voxel 5000 instrument (Sanying Precision, Tianjin, China) with an operating voltage of 300 kV.

3. Result and Discussion

This experiment utilized a batch of lithium-ion traction batteries with rated capacity of 42 Ah. The cathode’s active material was LiNi_1/3Co_1/3Mn_1/3O₂, while the anode’s active material was graphite. Figure 1a displays the battery sample’s image. The experiment used batteries from the same batch, ensuring battery performance consistency and equivalent test results for all samples. Each battery cell underwent three 1C charge–discharge cycles before the test to maintain consistency in battery status. Figure 1b represents the capacity–voltage curve of the battery sample for both charging and discharging processes. The battery underwent two charging stages: constant current and constant voltage. The charging capacity during the constant-current stage was 40.26 Ah, while that in the constant-voltage stage was 1.09 Ah. The discharge process utilized constant-current mode, with discharge capacity of 41.33 Ah. The battery efficiency surpassed 99.95% in this cycle, indicating satisfactory charge and discharge states.

To gain a better understanding of the test sample’s internal structure, computer tomography was performed on the battery sample, as exhibited in Figure 2. The battery’s internal structure is visible, and we use an YZ-axis cross-section of the fresh battery as an example to explain the findings. Typically, X-ray CT imaging is more responsive to high-density materials, like the anode’s Cu current collector and the cathode’s active materials. These materials show high contrast in the image and appear brighter than others. Conversely, the active materials of the anode and the cathode’s aluminum current collector have lower density and display a darker-colored region with lower contrast in the image. As a result of its lower density, the separator is barely visible in X-CT images.

Upon analyzing the CT image of a single battery cell, we observed that the battery’s interior comprised two parallel, coiled cells. The internal coil core features a regular layered structure, with no safety risks such as wrinkles or fractures that could trigger thermal runaway of the battery. Figure 2b depicts the overall structural diagrams of the battery cell’s front side. The battery cell’s internal electrode structure is flat, without any anomalies such as metal objects or misalignment of electrode.

The battery thermal failure test chamber, as presented in Figure 3, was utilized to observe the battery’s thermal runaway and emission process. The equipment comprises main components, including the chamber, flow meter, chamber temperature sensor, pressure sensor, battery temperature sensor, battery voltage sensor, data acquisition system, and computer. The test was conducted in sealed mode, whereby all valves, such as pressure inside the chamber, average temperature within the chamber, and temperature and voltage of the tested battery shell, were directly measured during the test. According to the ideal gas state equation, the gas mass flow can be calculated, thus estimating the battery gas emission based on the provided data.

Concerning the testing process details, we placed a heating element at the battery’s center, and the required sensors were extended to the outside via the chamber’s pre-set port, with data collection being performed by a data collector. The battery was heated at the constant power of 300 W to trigger thermal runaway. Figure 3c,d illustrate the temperature and pressure alterations during the thermal runaway process.

As illustrated in Figure 3c, the black curve displays the temperature change during the thermal runaway process of the battery, while the blue curve shows the rate at which temperature changes. From the figure, we can discern two stages of temperature rise in the battery. The first stage lasted approximately 415 s, with a slow temperature increase. During this stage, the fluctuation in temperature change rate may be attributed to the internal reactions within the battery, causing interior temperature differences. The second stage followed after 415 s, characterized by a rapid increase in the battery’s temperature, hitting a maximum value of 643 °C in 587 s. Notably, the highest temperature change rate occurred at 416 s, indicating a severe state of thermal runaway, with a maximum temperature rise rate of 63 °C s⁻¹.

Figure 3d displays the pressure change in the test chamber during the battery’s thermal runaway process. From the graph, it can be clearly observed that before 415 s, the pressure within the chamber was stable, gradually increasing at a rate of about 0.1 kPa s⁻¹. However, we can determine, based on the temperature change in the battery during this period, that there was no emission. Rather, the increase in pressure was due to heating and the consequent rise in ambient gas temperature. After 415 s, the pressure significantly increased within 14 s, measuring a maximum value of 98.6 kPa. It is noteworthy that the highest pressure increase rate occurred at 422 s (13.7 kPa s⁻¹), serving as an indication of the severe thermal runaway of the battery. Thereafter, the pressure gradually decreased, with minimal emission after the first 14 s of the process. Concurrently, the gas pressure in the chamber averaged, leading to a drop in pressure at the measured point. By combining the temperature and pressure changes of the battery-cell samples during the thermal runaway process, we can comprehend the entire evolutionary process of thermal runaway.

To gain insight into the battery thermal runaway process and emission, we performed simulations. We constructed a 3D geometric model of cell thermal runaway and gas emission within a test chamber, as depicted in Figure 4. The chamber and battery-cell parameters were consistent with those in our experiment; in detail, the battery was modeled in the shape of a cuboid, with the same size as Figure 1a shows, and the test chamber was modeled in the shape of a cylinder with an arc top, with the same size as shown in Figure 3b, with the supporting structure of the cell being simplified. We divided the above geometric model into a pure polyhedral unstructured mesh, which has higher generation efficiency than pure hexahedral structured mesh and pure tetrahedral unstructured mesh. This mesh has fewer meshes and higher mesh quality on the same unit scale. To ensure accurate calculations of flow and heat transfer during the simulation, we arranged six boundary layers on the chamber wall, and the thickness of the first layer of the boundary layer was determined using the smooth transition offset method, with a growth rate of 1.2. Figure 4a,b shows the grid of the boundary layer around the cell. We defined a volume mesh size to obtain a high-quality unstructured mesh of 754,000 mesh elements, 3.075 million nodes, a minimum orthogonal mass of 0.18, and a maximum aspect ratio of 25.0.

Concerning physical models, the simulation model is governed by the continuity equation, the N-S equation, and the energy equation, with the turbulence model being the SST k-ω model, as shown in Equations (1)–(4), respectively.

$$\frac{\partial \rho }{\partial t}+\nabla ·\left(\rho \overrightarrow{\nu }\right)=S_m$$

(1)

$$\frac{\partial }{\partial t}\left(\rho \overrightarrow{v}\right)+\nabla ·\left(\rho \overrightarrow{v}\right)=-\nabla p+\nabla ·\left(\stackrel{=}{\tau }\right)+\rho \overrightarrow{g}+\overrightarrow{F}$$

(2)

$$\frac{\partial }{\partial t}\left(\rho \left(e+\frac{v^2}{2}\right)\right)+\nabla ·\left(\rho \nu \left(h+\frac{v^2}{2}\right)\right)=\nabla ·\left(k_{eff}\nabla \mathrm{T}-{\displaystyle \sum }_j{h}_j{\overrightarrow{J}}_j+{\dot{\tau }}_{eff}·\overrightarrow{v}\right)+S_h$$

(3)

$$\frac{\partial \left(\rho \gamma \right)}{\partial t}+\frac{\partial \left(\rho U_j\gamma \right)}{\partial X_j}=P_{\mathsf{\gamma }1}-E_{\mathsf{\gamma }1}+P_{\mathsf{\gamma }2}-E_{\mathsf{\gamma }2}+\frac{\partial }{\partial x_j}\left[\left(\mathsf{\mu }+\frac{{\mathsf{\mu }}_{\mathrm{t}}}{{\mathsf{\sigma }}_{\mathsf{\gamma }}}\right)\frac{\partial \gamma }{\partial X_j}\right]$$

(4)

where the source, $S_m$, is the mass added to the continuous phase from the dispersed second phase; $p$ is the static pressure; $\stackrel{=}{\tau }$ is the stress tensor; $\rho \overrightarrow{g}$ and $\overrightarrow{F}$ are the gravitational body force and external body force; $k_{eff}$ is the effective conductivity; ${\overrightarrow{J}}_j$ is the diffusion flux of species j; and $S_h$ includes volumetric heat sources.

The average gas-emission rate and transient temperature were computed based on the ideal gas state equation and test results. We utilized a pressure-based solver and a coupled algorithm, resulting in a second-order implicit discretization of the time term of the control equation and a second-order upwind discretization of the spatial term. The simulation physical time was set to 20 s, with the first 14.1 s representing the thermal runaway gas-emission process, using a time step of 0.01 s, and a maximum of 20 iteration steps for each time step. We defined the temperature monitoring points as shown in the following figure and monitored the volume-weighted average gas pressure within the sealed chamber.

Having established the aforementioned simulation model, we conducted simulations of the battery sample’s thermal runaway process. The simulation results of gas-emission velocity, temperature, and pressure during the thermal runaway process at 1, 5, 10, and 15 s are presented in Figure 5, Figure 6 and Figure 7. At the first three time points, gas was emitted, while at the 15th second, it was in a state where the emission had just ended.

From Figure 5, it can be seen that the different colors in the figure represent the velocity at different positions inside the thermal runaway test chamber during the emission process. The center serves as the cross-section, and the small black arrows indicate the velocity direction. At 1 s, the image reveals the highest gas velocity to be located at the pressure relief valve, with a speed of 22.45 m s⁻¹, which gradually decreases vertically. Due to the chamber’s cylindrical structure and vertical upward ejection process, a turbulent flow of velocity circulation forms around the chamber. This process persists until 14 s of emission, at which point the gas flow rate in the chamber abruptly declines, with the maximum speed reducing to 0.62 m s⁻¹, situated above the chamber. This marks the process whereby the gas in the test chamber progressively returns to uniformity following the cessation of the emission source.

Figure 6 demonstrates the simulation results of pressure inside the thermal runaway test chamber. From a pressure distribution perspective, the gas emission barely impacted the pressure difference in the test chamber, as shown in the pressure distribution diagrams in Figure 6 at 1 s, 5 s, 10 s. Apart from the slightly higher relief valve nozzle and chamber top, other parts remained stable.

Further, we focus on the overall pressure value in these three time frames. The chamber pressure increased from ca. 6000 Pa to ca. 60,000 Pa within 10 s, and to ca. 97,000 Pa after 15 s; the average pressure continually increased during the gas-emission process. This outcome is consistent with the actual test pressure transformation pattern throughout the thermal runaway process. Additionally, as the gas-emission process terminated at approximately 15 s, the pressure steadily approached equilibrium. In this process, no local pressure accumulation point was formed in the test chamber, and the overall pressure of the chamber was mainly influenced by the gas-emission process.

Figure 7 depicts the temperature simulation outcomes in the thermal runaway test chamber. The emission of high-temperature gas causes significant temperature changes inside the chamber. The initial temperature was 25 °C and gradually increased to approximately 170 °C during the emission process. The temperature distribution clearly shows the gas diffusion process. To indicate temperature changes within the tank effectively, we chose five points from which to output temperature curves over time, as illustrated in Figure 8. T1 represents the temperature at the emission point, which showed a rapid rise from 113 °C to 225 °C within 2 s and then increased gradually to 309 °C within the following 12 s. T2 and T4 are temperature points along the gas-emission path, exhibiting temperature trends similar to T1, with temperatures of 213 °C and 182 °C, respectively. T3, located nearer to the chamber’s inner wall and at the same height as T2, showed more pronounced temperature fluctuations, owing to the continuous exchange of high- and low-temperature gases during gas emission. Unlike T3, T5 showed a more stable temperature increase trend, with temperature fluctuations mainly occurring from 10 s to 14 s, the end of the emission process. At the 14th second, the temperatures of T3 and T5 were 145 °C and 157 °C, respectively, indicating similar temperatures. By conducting a comprehensive analysis of the temperature change during the entire emission process, we found that the gas-emission path plays a key role in the transfer and distribution of temperature in the early stages of thermal runaway emissions. Therefore, many power battery systems have adopted structural designs that prevent flow diversion, heat conduction, and flame propagation along the gas-emission path. After thermal runaway emission, the temperature around the battery gradually reached equilibrium. Simulating the velocity, pressure, and temperature data during the thermal runaway gas-emission process enables us to attain a comprehensive understanding of the dynamic changes during this process.

In addition to single cells, we conducted thermal runaway testing on a module consisting of three identical cells. The initial state of each battery cell was adjusted to be the same as that of the single-cell test, with an SOC of 100%. We numbered the cells 1#–3# from left to right. For the thermal propagation test, we placed the same heating plate at the center of the surface of cell 1# and heated it with 300 W heating power. We conducted temperature monitoring on each battery cell, as shown in Figure 9a. The red, black, and blue curves correspond to the temperatures of cells 1#–3#, respectively. According to the red curve, the time for thermal runaway of cell 1# was approximately 400 s, and the maximum temperature for thermal runaway was 676 °C, which was very close to the thermal runaway result of the single-cell test. The times for thermal runaway of cells 2# and 3# were 421 s and 463 s, respectively, with the highest temperatures of thermal runaway being 586 °C and 428 °C, respectively. The heat generated by the thermal runaway of the first cell initiated the thermal propagation of cell 2# and continued to trigger the thermal propagation of cell 3#. The pressure in the test chamber during the thermal runaway and thermal propagation process is shown in Figure 9b. The graph clearly depicts the three stages of pressure rise and fall, corresponding to the thermal runaway and thermal propagation processes of the three cells over time. The pressure inside the environmental chamber increased by 171.7 kPa (calculated based on the stabilized pressure) during the entire thermal failure process. From the curve, we observe that these pressure increases can be distinctly identified and attributed to the thermal runaway process of the three battery cells, being 60.7 kPa, 57.6 kPa, and 53.4 kPa, respectively. According to the ideal gas equation, the gas-production pressure is proportional to the gas emission. Based on this understanding, we conclude that the gas produced by the three electric cores during the thermal runaway process was relatively similar.

The presented study employed computed tomography (CT) to observe the internal structure of a module that underwent thermal runaway testing, shown in Figure 10. Figure 10b illustrates the cross-sectional image of cell 1#, where the electrode’s structure exhibits a mottled shape due to high-temperature baking. Remarkably, the middle part of the battery cell presents a black area, marked by red in the figure, which is the primary zone for thermal runaway emission. Notably, the emission of high-temperature gas near the pressure relief valve can almost entirely destroy this area. Thus, this area is also marked on the lateral view (see Figure 10c) and the bottom view (see Figure 10d) for proper positioning. The inspection of the right-side structure in cell 1# reveals an inward concave shape, whereas cell 2# displays a concave structure on the right and a convex one on the left. Both sides of cell 3# exhibit a convex state, as illustrated in Figure 10c. Based on morphology, we can analyze the thermal runaway process. The convex morphology on both sides of cell 1# is the result of the thermal runaway generated in that cell. During the thermal runaway of cell 2#, it squeezed the right side of cell 1#, causing it to become concave. Similarly, cell 3#’s thermal runaway formed compression on the right side of cell 2#. This progression of morphology is clearly observed in Figure 10d.

To determine whether the gas emission during the thermal runaway process of the battery cell and module is consistent, we performed an analysis of its primary components. The gas emitted by the battery cell and module underwent GC testing for component analysis, using TCD and FID detectors. The results (

Table 1. Results of GC-TCD-FID test.

Gas	Contents (%)
	Cell	Module
CO	15.85	14.76
CO2	15.52	15.11
H2	10.51	10.3
CH4	2.62	2.86
C2H6	0.34	0.39
C2H4	3.45	3.54
C3H8	0.08	0.09
Others	51.63	52.95

) indicate that CO, CO₂, and H₂ are the primary components generating gas, consistently with previous studies. The gas composition of both the cell and module shows high consistency. To better understand the other components, we conducted a mass spectrometry (MS) analysis of the gas produced by the module (

Table 2. Results of MS test.

Compound	CAS
Cyclopropane	75-19-4
Trans-2-pentene	646-04-8
3,4-Epoxy-1-butene	930-22-3
2-Methyl-1-pentene	763-29-1
Methyl peroxide	690-02-8
1-Heptyne	628-71-7
Benzene	71-43-2
1-Heptene	592-76-7
Ethyl methyl carbonate	623-53-0
Toluene	108-88-3
1,1-Dimethylcyclopropane	1630-94-0
2,3-Dimethyl-1-pentene	3404-72-6
Bromocyclohexane	108-85-0
1,1-Dimethylcyclopropane	1630-94-0

), as sample shortages prevented their analysis in the cell. The MS testing of the gas samples revealed the presence of organic compounds with a carbon count of five and more. These results suggest that these compounds resulted from incomplete electrolyte combustion, as electrolytes primarily exist in a liquid state at room temperature and only become detectable when mixed into the gas under high-temperature conditions.

Furthermore, we analyzed the thermal runaway emissions process. Due to the size limitation of the test chamber, we conducted the thermal runaway emission test of the battery pack on an open bench. The test bench, temperature alteration, and gas (CO and CO₂) emission processes are illustrated in Figure 11. It was a small pack with 18 battery cells in series, and the pack was fully charged. The heating program was 300 W constant power heating. Six sets of CO/CO₂ sensors were placed in different positions on the test bench. Specifically, sensors 1#–4# were placed around the battery pack, and sensor 5 was 2.1 m above the battery pack; we choose 2.1 m due to the consistency of the vehicle level test last time. Sensor 6# was arranged inside the battery pack. We arranged six thermocouples for the temperature analysis of the battery thermal runaway process. Channel 1# was in the center of the target battery, and channels 2~5 were located on the cathode, anode, and two narrow sides of the battery. From Figure 11b, we see that the battery pack cracked due to the deflagration gas during thermal runaway, and violent flames erupted from the rift. Figure 11c shows the CO₂ results. The six labels here are different gas sensors. The dark blue line, the most obvious one, is the sensor inside the pack, and the dark green line is the sensor above the pack. The increase in the value of CO₂ started around the 400th second of sensor 6#, and the two stages of significant increase correspond to cell emission and pack combustion. Here, the sudden interruption in the curve is due to the high temperature burning out the gas sensor. Subsequently, there was a significant increase in sensor 5 (above of the battery pack), until the battery pack sank into the basin. Regarding CO (Figure 11d), its trend of concentration change is similar to that of CO₂. On the other hand, around the battery pack, among sensors 1#–4#, only 2# detected relatively few signals.

4. Conclusions

In summary, we assessed the gas emission of a single cell and module of the same battery type during thermal runaway, considering temperature, gas pressure, and gas composition. Our findings suggest that the cell result multiplication method is generally workable when thermal runaway triggering conditions are consistent. Nevertheless, due to differences in temperature, environment, and other factors, gas-production content and rate among the cells may exhibit some deviation during thermal runaway and thermal propagation. Notably, our analysis indicates that particularly in the initial emission process of thermal runaway—which accounts for a significant portion of gas emission—cell consistency is relatively high. Hence, this method is practical for evaluating gas production throughout the entire process, particularly in the face of several thermal runaway modules involving battery cells or battery systems.