Simulation of Thermal Runaway in Ternary Lithium-Ion Batteries Based on an Electrochemical–Thermal Coupling Model

Abstract

To address the issue of thermal runaway in ternary lithium-ion batteries under overcharging conditions, this paper establishes a multi-physics simulation model based on electrochemical–thermal coupling theory to systematically investigate the thermal behavior and runaway mechanisms of the battery. A P2D electrochemical model and the Bernardi heat generation model were combined to construct an electrochemical–thermal coupling model suitable for overcharging conditions. Simulation results indicate that under normal charging conditions, the battery temperature rise is small and uniformly distributed; however, under overcharging conditions, side reactions significantly intensify, leading to a rapid increase in heat generation. The battery temperature exhibits a distinct inflection point and rises rapidly, displaying typical thermal runaway characteristics. Charging rate and ambient temperature have a significant impact on the thermal runaway process; both high charging rates and high ambient temperatures accelerate heat accumulation and reduce battery thermal safety. The study demonstrates that the established model effectively reveals the evolution of thermal runaway in overcharged ternary lithium-ion batteries, providing a theoretical basis for battery thermal management design and safety early warning systems.

1. Introduction

Owing to their high specific energy and excellent cycle life, Li-ion cells have found extensive application across a range of sectors, including battery electric vehicles (BEVs), hybrid electric vehicles (HEVs), plug-in hybrid electric vehicles (PHEVs), and stationary energy storage installations [1,2]. Safety concerns regarding Li-ion battery packs have become increasingly prominent, particularly under extreme or abuse conditions [3,4]. Thermal runaway—identified as a primary cause of fire and explosion—can result in catastrophic damage [5]. This phenomenon arises from a cascade of severe, self-propagating reactions that ultimately lead to combustion [6]. It should be noted that thermal runaway is a consequence of the battery’s response to extreme conditions, rather than the underlying trigger. It is generally believed that thermal runaway is caused by a generalized internal short circuit resulting from contact between the positive and negative electrodes, which is a common factor in mechanical, thermal, and electrical abuse [7,8]. Therefore, designing internal short-circuit induction experiments under various conditions is of great significance for thermal runaway research.

Based on the mechanism of action, induction tests can be classified into non-self-initiated tests and self-initiated tests. The former focus on the impact of extreme external conditions, such as mechanical stress and elevated temperatures, on internal short circuits in batteries. The latter mainly examine how internal dendrite growth, resulting from prolonged electrical abuse or other factors, affects internal short circuits. Since thermal runaway induced by self-initiated internal short circuits is currently difficult to predict and control and can occur at virtually any stage of a battery’s lifecycle, investigating the mechanisms of these short circuits and developing preventive measures is of significant practical importance. Such efforts are essential for addressing thermal runaway issues in electric vehicle batteries and facilitating their widespread adoption.

There are various subjects of study regarding lithium-ion batteries under abusive conditions, and the specifics may vary depending on the battery type. Lithium iron phosphate (LFP) batteries and ternary lithium-ion (NCM) batteries have distinct electrode materials, characteristics, and application scenarios, and they exhibit significant differences in thermal runaway behavior. Research by Zhenpo Wang et al. [9,10,11,12] indicates that under the same conditions, LFP batteries experience earlier thermal runaway, primarily characterized by smoke emission without easy ignition or explosion, but they have poor tolerance and higher risk. In contrast, NCM batteries undergo thermal runaway later, but the process is more severe. Hatchard et al. [13] conducted temperature simulations on lithium-ion batteries of different shapes and developed a furnace thermal runaway model to track the temperature distribution of the batteries in the experiment. While these comparative studies provide valuable insights into material-dependent behavior, they primarily focus on experimental observation rather than predictive modeling, and do not quantify the individual thermal contributions of specific side reactions during overcharging.

Some scholars have studied the thermal runaway behavior of individual cells and battery packs under overcharge conditions. Dongsheng Ren et al. [14,15] used an electrochemical–thermal coupled model to simulate the electrochemical and thermal behavior of lithium-ion cells under overcharge conditions. Their test results revealed the mechanism by which overcharging induces thermal runaway. Chuang Qi et al. [16] established an electrochemical–thermal abuse model and used an overcharge model to simulate the behavior of battery packs. Their findings showed that the onset temperature of thermal runaway in overcharged batteries increases with higher current rates, and that when the convection coefficient is high, the battery does not experience significant temperature rise during charging. Overdischarge is also a form of electrical abuse. Dongxu Ouyang et al. [17,18,19] conducted a series of experiments to study the thermal and electrical characteristics of commercial LFP batteries under overdischarge-induced failure. They concluded that during overdischarge, the battery temperature rise increases significantly and is positively correlated with the discharge rate. Despite these important contributions, Ren’s model concentrated on a single overcharge rate without systematic rate comparison, and Qi’s work, while addressing battery packs, did not resolve the four side reactions individually. Furthermore, neither study quantified how elevated ambient temperatures would accelerate the side reaction cascade.

In specific electrical abuse testing experiments, external environmental conditions and charge/discharge rates are critical influencing factors. Jiana Ye et al. [20,21] combined a multichannel battery cycler with an accelerating rate calorimeter to investigate the dynamic thermal effects during overcharge of lithium-ion batteries in a closed environment. Their results indicated that to prevent thermal runaway, cooling measures should be applied within two minutes after the cell voltage passes the inflection point. Other related research primarily focuses on simulation experiments and comparative tests under different external conditions and electrical abuse methods. Mahipal Bukya et al. [22] used ANSYS finite element analysis software to construct a heat conduction model for a single battery and extended the model to a battery pack, obtaining the temperature distribution under corresponding conditions. C.X. He et al. [23] focused on battery modules and similarly used modeling and simulation to test thermal runaway behavior. Zeng, GH et al. [24] addressed the issue of electrical abuse in automotive lithium-ion batteries at a 1C rate and proposed a non-standard test method for long-term high-rate electrical abuse, obtaining numerical results for the battery’s temperature field through simulation. These simulation-based studies have advanced the field, but they share common limitations: side reactions are typically lumped rather than individually characterized; the comparison across different charging rates is often missing; and the feedback of overcharge-induced internal resistance variation into heat generation is seldom implemented.

Comparative testing of physical batteries is also an important method for studying the thermal runaway mechanism. Zhu Wang et al. [25] compared thermal runaway phenomena under two conditions: single overcharge and cyclic overcharge. By comparing the time to thermal runaway and the battery surface temperature, they reasonably evaluated the thermal safety of NCM batteries. Jinlong Zhao et al. [26] did not rely on simulation but instead used physical experiments to record relevant data during thermal runaway. By analyzing the changes in these data, they divided the thermal runaway process into three stages, summarized the characteristics of the battery at each stage, and developed an overcharge warning algorithm for NCM batteries. Physical experiments provide irreplaceable real-world data, yet they are often constrained by cost, safety, and the ability to capture internal temperature/voltage distributions.

Direct comparison between IR thermography and FE thermal predictions remains challenging in battery thermal modeling. Li et al. [27] attributed discrepancies between simulations and IR measurements to surface emissivity variations and simplified boundary conditions. Sequino et al. [28] showed that surface coatings alter emissivity, causing measurable differences in IR readings. Niu et al. [29] reported temperature prediction errors of several degrees Celsius for large-format pouch cells due to spatial thermal gradients. These studies confirm that difficulty in validating FE-predicted internal temperature gradients with IR images is a well-recognized limitation.

Based on the above research, a finite element simulation method was developed to analyze thermal runaway in ternary lithium-ion batteries under various overcharging conditions. Effective parameters were extracted from the simulation results to provide relevant recommendations for the prevention and early warning of thermal runaway. The key novelties of this approach compared to previous studies are as follows. Unlike previous studies that primarily focus on single-rate overcharge experiments or empirical thermal models, the novelty of this work lies in: (1) a fully coupled electrochemical–thermal model incorporating four temperature-dependent side reactions with parameters validated against literature; (2) quantitative analysis of thermal runaway evolution under multiple charging rates and extreme ambient temperatures; and (3) integration of overcharge-induced internal resistance variation into the heat generation calculation.

2. Mathematical Modeling

2.1. Electrochemical Model

The pseudo-two-dimensional (P2D) model is established on the basis of mass conservation, charge conservation, and energy conservation. The Butler–Volmer equation and Fick’s law are employed to characterize the electrochemical phenomena at the electrode–electrolyte interface in lithium-ion batteries and the transport process of lithium in the solid phase within the battery, respectively. This model can largely reflect the charge and discharge behavior of the battery under normal operating conditions [30].

Ternary lithium-ion power batteries employ porous solid-phase electrodes. The contact between these electrodes and the electrolyte provides a pathway for ions to transport from one electrode to the other. However, the electrons generated by the electrode reactions also need to cross the electrode–electrolyte interface to achieve charge balance in the battery. To overcome the resistance at this interface and accomplish charge transfer, an overpotential is generated, which is defined as (1).

$$\eta ={\varphi }_s-{\varphi }_e-U_{eq}$$

(1)

Among them, ${\varphi }_s$ and ${\varphi }_e$ are the voltages of the electrode and electrolyte, respectively, and $U_{eq}$ is the equilibrium potential, which can be regarded as a function related to the solid-phase concentration at the particle surface. The overpotential generated by electrode reactions differs between high-current-density and low-current-density charge/discharge processes. The Butler–Volmer equation combines these two characteristics of electrode kinetics, as shown in (2).

$$j=j_0[\exp ({\displaystyle \frac{{\alpha }_{\alpha }F}{RT}}\eta )-\exp (-{\displaystyle \frac{{\alpha }_{c}F}{RT}}\eta )]$$

(2)

In the equation, $j_0$ is the exchange current density, $R$ is the gas constant, and ${\alpha }_a,{\alpha }_c$ is the transfer coefficient.

The solid-phase ions are uniformly distributed within the electrode. The conservation of lithium ions in spherical active particles can be described by Fick’s law [31]. The specific formula is shown in (3).

$${\displaystyle \frac{\partial c_s}{\partial t}}={\displaystyle \frac{D_s}{r^2}}{\displaystyle \frac{\partial }{\partial r}}(r^2{\displaystyle \frac{\partial c_s}{\partial r}})$$

(3)

The boundary conditions are shown in (4).

$$\begin{array}{l}{\displaystyle \frac{\partial C_s}{\partial r}}{|}_{r=0}=0\\ D_s{\displaystyle \frac{\partial C_s}{\partial r}}{|}_{r=R_s}=-{\displaystyle \frac{j}{F}}\end{array}$$

(4)

Here, $C_s$ is the lithium concentration in the solid phase, and $D_s$ is the diffusion coefficient.

The equation for the conservation of mass of lithium ions in the electrolyte phase is given by (5).

$${\epsilon }_{\mathrm{e}}{\displaystyle \frac{\partial C_{\mathrm{e}}}{\partial t}}=D_{\mathrm{e}}^{\mathrm{eff}}{\displaystyle \frac{{\partial }^2{C}_{\mathrm{e}}}{{\partial }^2{x}^2}}+{\displaystyle \frac{a_{\mathrm{s}}(1-t_{+}^{°})}{F}}j$$

(5)

In the above equation, $C_e$ is the concentration of lithium ions in the electrolyte phase, and ${\epsilon }_e$ is the volume fraction of the electrolyte phase; and $t_{+}^0,a_s$ are the lithium ion transfer coefficients and the specific interfacial area, respectively, taking into account the solvent rate.

The law of charge conservation in the electrolyte phase follows (6).

$${\kappa }^{\mathrm{eff}}{\displaystyle \frac{{\partial }^2{\varphi }_{\mathrm{e}}}{\partial x^2}}+{\kappa }_{\mathrm{d}}^{\mathrm{eff}}{\displaystyle \frac{{\partial }^2{C}_{\mathrm{e}}}{\partial x^2}}+a_{\mathrm{s}}j=0$$

(6)

${\varphi }_e$ is the phase potential of the electrolyte, and $k^{eff}$ is the effective ionic conductivity; both $k^{eff}=k{\epsilon }_e^p$ can also be derived from the Bruggman relation.

The P2D pseudo-two-dimensional model establishes a multi-physics-coupling electrochemical model for lithium-ion batteries from three aspects: electrode kinetics, solid-phase diffusion, and electrolyte transport.

2.2. Thermal Model

Under normal operating conditions, the heat generated by lithium-ion batteries primarily stems from three sources: heat from electrochemical reactions, heat from reaction polarization, and ohmic heat resulting from the battery’s internal resistance. Lithium-ion batteries generate heat during charging and discharging, and this heat generation process is typically described using the Bernardi heat generation model [32].

During the coupling process, the heat generation rate of the battery is calculated using an electrochemical model, as expressed in Equation (7).

$$Q=I(U_{oc}-U)+IT{\displaystyle \frac{\partial U_{oc}}{\partial T}}$$

(7)

The first term represents irreversible heat, and the second term represents reversible heat. These heat-generating terms are substituted into the energy conservation equation as heat source terms to determine the temperature distribution inside the cell.

Heat transfer within the battery occurs primarily through thermal conduction, and its temperature distribution satisfies the energy conservation Equation (8).

$$\rho C_p{\displaystyle \frac{\partial T}{\partial t}}=\nabla (k\nabla T)+Q$$

(8)

where $\rho$ is the density, $C_p$ is the specific heat capacity, $k$ is the thermal conductivity, and $Q$ is the heat generation rate per unit volume.

These two modes of heat transfer can occur between all states of matter, whereas thermal convection occurs when fluids come into contact with one another [33]; it represents the energy exchanged as fluids flow and can be classified into two forms: natural convection and forced convection (9).

$$q_c=h(T-T_{\infty })$$

(9)

Here, $h$ represents the heat transfer coefficient, and $T$ is the temperature of the object.

The internal heat generation of the battery is calculated based on the Bernardi heat generation model, and the temperature field governing equations are established in conjunction with the energy conservation equation, thereby enabling a description of the battery’s thermal behavior.

Under overcharging, side reactions besides normal heat generation must be considered. These reactions are mainly related to SEI film decomposition, which releases heat and accelerates temperature rise, increasing thermal runaway risk. The four main side reactions are SEI decomposition, anode-electrolyte, cathode-electrolyte, and electrolyte decomposition [34]. The heat generation rate of each side reaction is calculated by the Arrhenius Equation (10):

$$Q_i=H_i{W}_i{A}_i\exp (-{\displaystyle \frac{E_{a,i}}{RT}})C_i$$

(10)

where $Q_i$ is the heat generation rate, $H_i$ the specific heat release, $W_i$ the active material content, $A_i$ the pre-exponential factor, $E_{a,i}$ the activation energy, $R$ the gas constant, $T$ the temperature, and $C_i$ the dimensionless concentration of the reacting species. The total heat generation rate under overcharging conditions is the sum of reversible heat, irreversible heat, and the four side reactions [35] (11):

$$Q_{total}=Q_{rev}+Q_{irrev}+{{\displaystyle \sum }}_{i=1}^4{Q}_i$$

(11)

These side reaction terms are coupled into the thermal model via domain ordinary differential equations (ODEs) in COMSOL, Multiphysics 5.6.

2.3. Electrochemical–Thermal Coupling Model

Under overcharging conditions, both the voltage and current of a battery directly influence its temperature distribution, and battery temperature, in turn, triggers physicochemical changes within the battery, thereby altering its electrical performance. Therefore, an electrochemical–thermal coupling model is established based on electrochemical and thermal models; this model achieves bidirectional coupling between the electric field and the temperature field by incorporating the heat generation term from electrochemical reactions into the heat conduction equation [36].

Changes in temperature, in turn, affect the battery’s electrochemical behavior. An increase in temperature alters the battery’s internal resistance, reaction rates, and electrolyte transport properties, thereby influencing the battery’s voltage response and heat generation rate. Consequently, relevant parameters in the electrochemical model are defined as functions of temperature to enable feedback from the thermal field to the electric field. The governing equations and boundary conditions of the electrochemical–thermal coupling model are shown in

Table 1. Control Equations for the Electrochemical–Thermal Coupling Model.

Equation	Equation Format
Charge conservation	$\nabla ·\left({\sigma }^{\mathrm{e}\mathrm{f}\mathrm{f}}\nabla {\varphi }_s\right)=j^{\mathrm{L}\mathrm{i}}$ $\nabla ·\left({\sigma }^{\mathrm{e}\mathrm{f}\mathrm{f}}\nabla {\varphi }_e\right)+\nabla ·\left(k_D^{\mathrm{e}\mathrm{f}\mathrm{f}}\nabla \ln c_e\right)=-j^{\mathrm{L}\mathrm{i}}$
Mass conservation	${\displaystyle \frac{\partial c_s}{\partial t}}={\displaystyle \frac{D_s}{r^2}}{\displaystyle \frac{\partial }{\partial r}}\left(r^2{\displaystyle \frac{\partial c_s}{\partial r}}\right)$ ${\displaystyle \frac{\partial {\epsilon }_e{c}_e}{\partial t}}=\nabla ·\left(D_{\mathrm{e}}^{\mathrm{e}\mathrm{f}\mathrm{f}}\nabla c_e\right)+{\displaystyle \frac{1-t_{+}^0}{F}}{j}^{\mathrm{L}\mathrm{i}}$
Energy conservation	${\displaystyle \frac{\partial \left(\rho c_{p}T\right)}{\partial t}}=\nabla ·\left(k\nabla T\right)+q_e+q_r+q_j$

and

Table 2. Boundary conditions of the governing equations.

Boundary Conditions	Equation Format
Charge conservation	${\displaystyle \frac{\partial {\varphi }_s}{\partial n}}=0;{\displaystyle \frac{\partial {\varphi }_e}{\partial n}}=0$
Mass conservation	${\displaystyle \frac{\partial C_s}{\partial r}}{|}_{r=0}={\displaystyle \frac{\partial C_e}{\partial n}}=0;{\displaystyle \frac{\partial C_s}{\partial r}}{|}_{r=R_s}=-{\displaystyle \frac{J^{Li}}{a_{s}F}}$
Energy conservation	$-k{\displaystyle \frac{\partial T}{\partial n}}=h(T-T_{\infty })+\epsilon \sigma (T^4-T_{\infty }^4)$

.

3. Model Validation

3.1. Model Parameter Settings

An electrochemical simulation model was developed using the lithium-ion battery interface and the mathematics interface in COMSOL. The battery tested in this study is a pouch cell, which has a relatively complex structure consisting of multiple individual cells stacked along the z-axis direction. The basic parameters are listed in

Table 3. Basic battery parameter.

Battery Parameter	Value
Rated capacity	22.8 Ah
Battery dimensions	212 × 104 × 13 mm
Upper cut-off voltage	4.3 V
Lower cut-off voltage	2.8 V
Ambient temperature	24.85 °C
Cathode material	NMC ($LiN{i}_{1/3}M{n}_{1/3}C{o}_{1/3}{O}_2$)
Anode material	Graphite ($L{i}_x{C}_{6}MCMB$)
Electrolyte material	$LiP{F}_6$(1:2 $EC:DMC$)

.

Table 4. Geometric Thicknesses of Relevant Structures in a Single-Layer Cell.

Physical Layer	Positive Electrode	Negative Electrode	Positive Current Collector	Negative Current Collector	Separator
Thickness (μm)	60	60	10	10	30

lists the thicknesses of each structural layer in the model. As can be seen, the individual cells are relatively small; compared to the overall battery, this significantly reduces the computational resources required for complex calculations, and the accuracy of the results is also validated by relevant performance tests.

On the basis of the existing parameters, the thermophysical parameters of each battery component are added, and the overall performance parameters are obtained through a homogenization method. The specific thermodynamic values are listed in

Table 5. Thermophysical parameters of the electrochemical–thermal coupled model.

Component	Density $\left({\mathbf{kg}/\mathbf{m}}^3\right)$	Specific Heat Capacity $(\mathbf{J}/(\mathbf{kg}·\mathbf{K}\left)\right)$	Thermal Conductivity $(\mathbf{W}/(\mathbf{m}·\mathbf{K}\left)\right)$
Cathode	2328.5	1269.2	1.58
Anode	1347.3	1437.4	1.04
Separator	1009	1978.2	0.344
Cathode current collector	2702	875	170
Anode current collector	8920	385	398

.

3.2. Mesh Generation of Geometric Model

The pouch cell is composed of multiple layers of repetitive structures stacked together. To simplify the computation while improving the accuracy of the results, three layers are selected for calculation in this simulation, and the geometric physical model shown in Figure 1 is established. For display convenience, the battery in the schematic diagram is scaled by a factor of 45 along the z-axis direction. Before performing computational simulation, it is necessary to first mesh the geometric model of the battery. The quality of the mesh directly affects the accuracy of the calculation and the efficiency of the simulation.

Kim et al. [37] recommended testing at least three mesh densities to ensure solution independence. Following their practice, three mesh densities were tested. The temperature difference between the medium and fine meshes was 1.2 °C, confirming that the medium mesh provides sufficient accuracy. Based on this validation, the internal mesh of the battery was generated using an arithmetic sequence, with the number of elements set to five and the element aspect ratio set to four. The current collectors and tabs at the battery boundaries were meshed using a mapped grid, The mesh of the geometric model is shown in Figure 1. The total number of mesh elements is 4560, with a maximum element size of 0.021 m, a minimum element size of 0.00378 m, an element growth rate of 1.5, a curvature factor of 0.6, and a resolution of 0.5 in narrow regions.

4. Result and Discussion

4.1. Temperature Changes Under Normal Charging Conditions

This simulation focuses on a three-layer single-cell battery at an ambient temperature of 25 °C. The test measured the average and maximum battery temperatures at different charging rates, with the results shown in Figure 2. The general trend of the curves indicates that higher charging rates result in more significant temperature increases, with a faster rate of temperature rise during the early stages of charging. However, as charging progresses, the battery’s capacity approaches saturation. Specifically, the average battery temperature at a 1C rate was 31 °C, while the corresponding temperatures at 2C and 3C rates reached 43 °C and 50 °C, respectively. This indicates that, due to a certain degree of heat exchange between the battery and its surroundings, the overall battery temperature remains relatively stable throughout the charging process. However, certain internal structures experience higher temperature rises due to their specific physical properties and locations.

To gain a more accurate understanding of the heat generation of the battery during the charging process, Figure 3 presents the three-dimensional temperature distribution of the battery when it reaches a fully charged state. From left to right, the temperatures correspond to charging rates of 1C, 2C, and 3C, respectively.

During the entire charging process, ohmic heat is more significantly affected by the current rate. Consequently, the temperature at the tabs, where the resistance is fixed, is higher. Furthermore, a higher current rate implies a shorter charging time, which affects both the variation in internal resistance and the polarization heat. Multiple factors contribute to the differences in temperature distribution of the battery under different rates.

Overall, under normal charging conditions, the temperature variation in the battery is relatively small and the temperature distribution is uniform, with no significant heat accumulation observed. This indicates that the battery exhibits good thermal stability under these conditions.

4.2. Temperature Variation Under Overcharge Conditions

The overcharge threshold is defined as SOC > 100%, which corresponds to the limit of lithium intercalation capacity of the negative electrode. Beyond this point, excess lithium ions deposit on the anode surface and trigger side reactions. The 130% SOC endpoint follows the GB/T 31467.3-2015 standard [38]. As demonstrated by Hu et al. [39], this standard represents a moderate overcharge condition that allows complete observation of thermal runaway evolution without immediate cell failure.

Assuming that the effects of changes in battery volume and structure are reflected in the battery’s internal resistance, we quantify the impact of these factors by measuring changes in internal resistance at different states of charge, and validate this assumption using subsequent simulation results. Given the complexity of heat generation in batteries under overcharging conditions, probes are placed at various locations within the battery to monitor corresponding temperature changes; the probe distribution is shown in Figure 4.

In this simulation, a partially discharged battery was charged to 130% SOC at a current rate of 1C. The temperature changes were observed in the voltage range of 3.4 V to 6 V, and the results are shown in Figure 5. After reaching the rated voltage, the temperature increased significantly. This is because the anode has a limited capacity for lithium-ion insertion. As the reaction continues, more and more voids are occupied. Lithium ions that cannot react with the anode will adsorb onto the anode surface in a different manner, forming so-called dendritic structures. At the same time, the continuous rise in temperature causes changes in the electrolyte’s conductivity and the porosity of the separator, which manifests externally as an increase in internal resistance.

By setting point probes and domain probes at different locations, the temperature variations at different parts of the battery during the entire overcharging process can be obtained. Based on the results, Figure 6 is plotted as shown below.

Once the battery reaches the overcharge stage, the electrode materials are nearing their lithium-insertion limit. Continued charging causes excess electrical energy to be converted primarily into thermal energy, while triggering a series of side reactions, including electrolyte decomposition, lithium plating at the anode, and decomposition of the cathode material. These side reactions are all highly exothermic, generating heat far exceeding that produced during normal charging and discharging, and thus become the primary source of rapid temperature rise in the battery. To quantify the impact of side reactions on battery heat generation, the heat release profiles of four side reactions over the 0–3000 s time window were extracted, as shown in Figure 7.

To verify the previous hypothesis, changes in the battery’s internal resistance were recorded during the course of the side reaction. As shown in Figure 8, this demonstrates that the battery structure has suffered significant damage, and the impact of this damage on the battery is reflected in the resistance values. It can be seen that once the side reaction is complete, the battery heats up rapidly while its internal resistance increases significantly.

Figure 9 shows a 3D plot of the temperature distribution during battery overcharging. By recording the temperature distribution when the battery is charged to 50%, 100%, and 130% capacity, one can better understand the progression of thermal runaway during overcharging.

Simulation results show that, at a certain current-to-capacity ratio, the battery temperature rises slowly during the initial stage; however, once the temperature reaches a certain critical value, the rate of temperature rise increases significantly, and a distinct inflection point appears in the temperature curve, indicating that side reactions begin to dominate. Subsequently, the battery temperature enters a rapid rise phase and reaches a high level within a short period of time, exhibiting typical thermal runaway characteristics.

4.3. Effect of Charging Rate on Thermal Runaway

To investigate the impact of different charging current rates on battery thermal runaway, this experiment selected three different current levels (1C, 2C, and 3C). The curve trends for all three cases were generally consistent, but the 1C rate showed significant differences. At the 1C rate, the voltage and temperature exhibited distinct phased increases. This was primarily due to the relatively long charging duration and moderate current, allowing changes at each stage of the battery to be reflected in the rising voltage and temperature, as shown in Figure 10.

Figure 11 presents the temporal evolution of different heat generation components (Q_ele, Q_neg, Q_pos, and Q_sei). It can be observed that prior to thermal runaway, the SEI-related heat (Q_sei) gradually increases, indicating the onset of irreversible side reactions within the cell. As the system approaches the critical point, the positive electrode reaction heat (Q_pos) rises sharply and becomes the dominant heat source, driving rapid temperature escalation and triggering thermal runaway. Meanwhile, the negative electrode heat (Q_neg) also increases but contributes less significantly. From a mechanistic perspective, SEI reactions act as an early-stage trigger, while positive electrode reactions dominate the thermal runaway process.

4.4. Effect of Overcharging Behavior on Battery Thermal Runaway Under Different High-Temperature Environments

The 100–200 °C range is selected based on the thermal runaway literature. As reviewed by Xu et al. [40], SEI decomposition, separator melting, and cathode decomposition all occur within this window. Below 100 °C, no side reactions are triggered; above 200 °C, thermal runaway becomes immediate. Based on this consideration, the following simulation conditions are set. In the experiment, the ambient temperature at which the battery operated was set to 100 °C, 150 °C, and 200 °C, respectively. With the battery’s initial temperature at 25 °C, the changes in battery temperature and the heat generated by side reactions were calculated for a charging current of 2C. The results are shown in Figure 12.

Figure 12 shows that the battery voltage is closely related to ambient temperature. Changes in battery temperature can affect the internal chemical reactions and physical changes, resulting in noticeable periodic variations in battery voltage at the same charging rate. Figure 13 shows that, at this point, the battery’s ohmic heating exceeds the heat dissipation from the external environment, resulting in no noticeable phase of temperature decline. Subsequently, as charging proceeds and the battery structure continues to degrade, the thermal effects generated by the battery current become increasingly significant at 150 °C and 200 °C; the temperature rises again and eventually peaks at a relatively slow rate.

The simulation results show that thermal runaway occurs only after SOC exceeds 100%, with an inflection point appearing approximately 180–200 s after reaching full charge.

Specifically, if a BMS can estimate SOC with an error of less than 2%, it can detect the 100% SOC threshold and terminate charging before side reactions onset. The feasibility of such precision has been demonstrated. Locorotondo et al. [41] proposed a model-adaptive Kalman filter that achieves robust SOC tracking under dynamic loads, confirming that modern algorithms possess the accuracy required for overcharge prevention.

5. Conclusions

By establishing an electrochemical–thermal coupling model, a simulation study was conducted on the thermal runaway behavior of ternary lithium-ion batteries under overcharging conditions. The main conclusions are as follows:

• An electrochemical model based on the P2D model and an electrochemical–thermal coupling model were constructed using the finite element method in COMSOL.
• Under normal charging at 1C, 2C, and 3C rates, the battery temperature rise remains below 10 °C, 18 °C, and 25 °C, respectively, with a maximum temperature of 50 °C at 3C. Temperature differences between different locations reach up to 18 °C.
• Under overcharging at 1C, side reactions initiate after 2680 s, generating a peak heat power of $6.5\times {10}^7 {\mathrm{W}/\mathrm{m}}^3$. This causes a temperature jump of over 200 °C within 30 s, exhibiting typical thermal runaway characteristics.
• Higher charging rates accelerate thermal runaway. At 3C, the temperature rise is more concentrated and the onset of thermal runaway occurs 40–60% earlier compared to 1C.
• Elevated ambient temperatures significantly promote side reactions. The SEI decomposition reaction starts earlier and releases more energy, accelerating thermal runaway progression.

In summary, the proposed electrochemical–thermal coupling model effectively characterizes the thermal behavior and thermal runaway evolution of lithium-ion batteries under overcharging conditions, providing quantitative references for battery thermal management design and safety early warning systems. However, the model still relies on certain simplifying assumptions. Future work should incorporate multi-scale structures, stress coupling, and more complex side reaction mechanisms to improve model accuracy and engineering applicability.