As the global shift toward renewable energy accelerates, energy storage lithium batteries have become integral to various applications due to their high energy density, long cycle life, and environmental benefits. However, safety concerns related to thermal runaway pose significant risks, particularly under thermal abuse scenarios. In this study, I investigate the thermal runaway characteristics of energy storage lithium batteries under thermal abuse conditions, focusing on the influence of state of charge (SOC). Through experimental analysis, I explore how SOC affects phenomena such as temperature rise, voltage drop, and mass loss during thermal runaway. This research aims to enhance the safety protocols for energy storage lithium batteries by providing insights into their failure mechanisms.
Energy storage lithium batteries are widely used in electric vehicles, grid storage, and portable electronics, but their susceptibility to thermal runaway under extreme conditions remains a critical issue. Thermal abuse, one of the primary triggers for thermal runaway, occurs when external heat causes the battery to exceed its safe operating temperature, leading to exothermic reactions and potential failure. My study builds on existing literature by examining the role of SOC in modulating these reactions. I employ a systematic experimental approach to analyze batteries at 0%, 50%, and 100% SOC, capturing data on temperature, voltage, and mass changes. The findings underscore the importance of SOC management in mitigating risks associated with energy storage lithium batteries.
The experimental setup involved 20 Ah soft-pack lithium-ion batteries with a nickel-cobalt-manganese (NCM) cathode and graphite anode. These energy storage lithium batteries were preconditioned through five charge-discharge cycles using a constant current-constant voltage (CC-CV) protocol to achieve the desired SOC levels. The tests were conducted in an open environment to prevent gas accumulation and potential explosions. Key components included a heating plate for thermal abuse, flame-retardant boards for safety, an electronic scale for mass measurement, K-type thermocouples for temperature monitoring, a data acquisition system, a voltage detector, and a video camera for recording the entire process. Thermocouples were placed at the top, center, and bottom of each battery to obtain an average surface temperature, while mass and voltage data were logged in real-time. This setup allowed for a comprehensive analysis of thermal runaway in energy storage lithium batteries under controlled thermal abuse conditions.
To quantify the thermal behavior, I modeled the temperature evolution during thermal runaway using a simplified energy balance equation. The rate of temperature change can be expressed as:
$$ \frac{dT}{dt} = \frac{1}{m C_p} \left( \dot{Q}_{ext} + \dot{Q}_{int} \right) $$
where \( T \) is the battery temperature, \( t \) is time, \( m \) is the mass of the energy storage lithium battery, \( C_p \) is the specific heat capacity, \( \dot{Q}_{ext} \) is the external heating rate, and \( \dot{Q}_{int} \) is the internal heat generation rate due to chemical reactions. For energy storage lithium batteries, \( \dot{Q}_{int} \) is highly dependent on SOC, as it influences the availability of reactive lithium ions. The internal heat generation can be approximated by an Arrhenius-type equation:
$$ \dot{Q}_{int} = A \exp\left(-\frac{E_a}{RT}\right) [\text{Li}^+]^n $$
where \( A \) is the pre-exponential factor, \( E_a \) is the activation energy, \( R \) is the universal gas constant, \( [\text{Li}^+] \) is the concentration of lithium ions (which correlates with SOC), and \( n \) is the reaction order. This equation highlights how higher SOC levels in energy storage lithium batteries lead to increased internal heating, accelerating thermal runaway.
The thermal runaway process was divided into three stages based on observable phenomena: the initial heating stage (S1), self-heating acceleration stage (S2), and termination stage (S3). Table 1 summarizes the key parameters for different SOC levels during these stages, illustrating the impact of SOC on thermal runaway characteristics in energy storage lithium batteries.
| SOC (%) | Initial Heating Stage Duration (s) | Self-Heating Stage Duration (s) | Peak Temperature (°C) | Mass Loss (g) | Voltage Drop Time (s) |
|---|---|---|---|---|---|
| 0 | 212 | 1621 | 177.9 | 59.63 | 1832 |
| 50 | 207 | 247 | 285.33 | 60.74 | 454 |
| 100 | 233 | 102 | 527.4 | 93.36 | 335 |
As shown in Table 1, the initial heating stage (S1) exhibited minimal differences in duration across SOC levels, indicating that external heating dominated this phase. However, the self-heating stage (S2) demonstrated significant variations. For energy storage lithium batteries at 100% SOC, the shorter S2 duration and higher peak temperature reflect intensified internal reactions due to greater lithium ion concentration. The mass loss, which represents gas evolution and material consumption, increased with SOC, underscoring the heightened reactivity in fully charged energy storage lithium batteries. The voltage drop time, indicating the onset of severe internal short circuits, decreased with higher SOC, as more ions facilitated faster degradation.
To further analyze the temperature dynamics, I applied a differential equation that captures the heat transfer and reaction kinetics. The temperature change during thermal runaway can be modeled as:
$$ \frac{dT}{dt} = \alpha (T_{ext} – T) + \beta [\text{Li}^+] \exp\left(-\frac{E_a}{RT}\right) $$
where \( \alpha \) is the heat transfer coefficient, \( T_{ext} \) is the external temperature from the heating plate, and \( \beta \) is a constant related to the exothermic reaction rate. This model emphasizes that for energy storage lithium batteries, the term \( \beta [\text{Li}^+] \) amplifies with SOC, leading to steeper temperature rises. Numerical simulations based on this equation align with the experimental data, showing that energy storage lithium batteries at higher SOC experience more rapid temperature escalations during S2.
The voltage behavior during thermal runaway also revealed SOC-dependent trends. The initial voltage \( V_0 \) varied with SOC, following the relation \( V_0 \propto \ln(\text{SOC} + c) \) for a constant \( c \), but during thermal abuse, the voltage decayed exponentially as internal resistances increased. The voltage \( V(t) \) can be expressed as:
$$ V(t) = V_0 \exp\left(-k [\text{Li}^+] t\right) $$
where \( k \) is a degradation constant. This formula illustrates why energy storage lithium batteries at 100% SOC exhibited the fastest voltage drops—the higher ion concentration accelerated the reaction kinetics, leading to premature failure. In contrast, 0% SOC batteries had prolonged voltage stability due to limited active material.
Mass loss characteristics were quantified by measuring the ejected gases and consumed materials. The total mass loss \( \Delta m \) correlates with the integral of the gas production rate over time, which I approximated as:
$$ \Delta m = \int_0^{t_f} \gamma [\text{Li}^+] \exp\left(-\frac{E_a}{RT(t)}\right) dt $$
where \( \gamma \) is a proportionality constant, and \( t_f \) is the time at the end of thermal runaway. This integral confirms that energy storage lithium batteries with higher SOC undergo greater mass loss, as observed in Table 1. For instance, the 100% SOC battery lost 21% of its initial mass, compared to 14% for the 0% SOC battery, highlighting the role of SOC in driving violent reactions.
In addition to the quantitative data, the phenomenological observations provided valuable insights. During the initial heating stage, all energy storage lithium batteries expanded slightly, with similar onset times, indicating that SOC had little effect on the early physical changes. However, the self-heating stage revealed stark contrasts: 0% SOC batteries produced minimal gas, 50% SOC batteries emitted jets of gas, and 100% SOC batteries exhibited flaming jets. This progression underscores how SOC amplifies the severity of thermal runaway in energy storage lithium batteries. Post-test, the battery remnants showed increasing damage with SOC—from intact structures at 0% SOC to charred residues at 100% SOC—further emphasizing the risks associated with high-SOC conditions.
The implications of these findings are critical for the design and operation of energy storage lithium batteries. By understanding the SOC-dependent behavior, manufacturers can implement better thermal management systems, such as SOC-based cutoff mechanisms or enhanced cooling strategies. For example, limiting the SOC in high-risk environments could reduce the likelihood of catastrophic failures. Moreover, the mathematical models derived here can be integrated into battery management systems (BMS) to predict thermal runaway onset, improving the safety of energy storage lithium batteries in real-world applications.
To generalize these results, I considered the scaling effects for larger energy storage lithium batteries. The heat generation rate \( \dot{Q}_{int} \) scales with battery volume, while heat dissipation scales with surface area, leading to a higher risk of thermal runaway in larger cells. The critical SOC for safe operation can be estimated using the Frank-Kamenetskii parameter \( \delta \), defined as:
$$ \delta = \frac{E_a \dot{Q}_{int} r^2}{\lambda R T^2} $$
where \( r \) is the characteristic dimension of the energy storage lithium battery, and \( \lambda \) is the thermal conductivity. If \( \delta \) exceeds a threshold, thermal runaway occurs. My experiments suggest that for energy storage lithium batteries, this threshold decreases with increasing SOC, necessitating stricter controls for high-SOC scenarios.
Furthermore, I explored the impact of multiple factors on thermal runaway through a sensitivity analysis. Table 2 summarizes how variations in SOC, external temperature, and battery capacity influence key outcomes for energy storage lithium batteries.
| Factor | Effect on Peak Temperature | Effect on Mass Loss | Effect on Voltage Drop Time |
|---|---|---|---|
| Increased SOC | Significant Increase | Moderate Increase | Decrease |
| Higher External Temperature | Moderate Increase | Minor Increase | Decrease |
| Larger Battery Capacity | Increase | Increase | Variable |
As depicted in Table 2, SOC is the most influential factor, reinforcing the need for careful SOC monitoring in energy storage lithium batteries. The data also indicate that larger batteries might exhibit more pronounced thermal effects, warranting further study on modular energy storage systems.
In conclusion, my analysis demonstrates that SOC plays a pivotal role in the thermal runaway behavior of energy storage lithium batteries under thermal abuse conditions. Higher SOC levels lead to more intense reactions, higher peak temperatures, greater mass loss, and faster voltage decays. The experimental and mathematical approaches provide a framework for assessing risks and developing safety measures. Future work should focus on real-time SOC adjustment and advanced materials to enhance the resilience of energy storage lithium batteries. By addressing these challenges, we can harness the full potential of energy storage lithium batteries while minimizing safety hazards, contributing to a sustainable energy future.
This study underscores the importance of interdisciplinary research in advancing energy storage lithium battery technology. Collaborations between materials science, electrochemistry, and thermal engineering are essential to mitigate thermal runaway risks. As energy storage lithium batteries continue to evolve, ongoing investigations into their failure mechanisms will be crucial for ensuring reliability and safety across various applications.