In recent years, the rapid growth of electric vehicles and renewable energy systems has heightened the importance of energy storage lithium batteries due to their high energy density and efficiency. However, the dynamic evolution of parameters such as state of charge (SOC), internal resistance, and voltage during charging and discharging processes exhibits a complex non-linear relationship with thermal runaway risks. Thermal runaway in energy storage lithium batteries can lead to fires or explosions, posing significant threats to safety and property. This study investigates the coupling mechanisms between thermal runaway and multiple parameters, including SOC, internal resistance, voltage, aging degree, and capacitance, through systematic experiments. We aim to provide a comprehensive analysis that enhances the thermal safety of energy storage lithium batteries by integrating experimental data with theoretical models.
We conducted experiments using a cylindrical LiFePO4 energy storage lithium battery with a capacity of 5 Ah. The tests were performed in a controlled environment using a battery tester and a thermostat to set different temperatures (20°C, 30°C, and 40°C) and charging/discharging currents (1 A, 3 A, 5 A, 10 A, and 15 A). Real-time data on internal resistance, voltage, SOC, and other parameters were collected to analyze their evolution and correlation with thermal runaway. The experimental setup ensured precise monitoring of the energy storage lithium battery under various operational conditions, simulating real-world scenarios to understand the multi-parameter dynamics.
The internal resistance of an energy storage lithium battery is a critical parameter that influences its performance and safety. During charging, we observed that internal resistance varies with SOC and temperature. For instance, at lower SOC levels, internal resistance remains relatively stable, but it increases significantly towards the end of charging due to polarization effects. The relationship between internal resistance (R) and SOC can be approximated using the following empirical formula: $$ R = R_0 + \alpha \cdot e^{\beta \cdot (1 – SOC)} $$ where \( R_0 \) is the baseline internal resistance, and \( \alpha \) and \( \beta \) are constants dependent on temperature and current. This equation highlights the non-linear behavior of internal resistance in energy storage lithium batteries, which exacerbates heat generation and raises thermal runaway risks.
To quantify the internal resistance changes, we compiled data from charging experiments at different currents and temperatures. The table below summarizes the average internal resistance values at the end of charging for various conditions:
| Current (A) | Temperature (°C) | Average Internal Resistance (Ω) |
|---|---|---|
| 1 | 20 | 0.24 |
| 1 | 30 | 0.23 |
| 1 | 40 | 0.22 |
| 3 | 20 | 0.25 |
| 3 | 30 | 0.24 |
| 3 | 40 | 0.23 |
| 5 | 20 | 0.26 |
| 5 | 30 | 0.25 |
| 5 | 40 | 0.24 |
| 10 | 20 | 0.28 |
| 10 | 30 | 0.27 |
| 10 | 40 | 0.26 |
| 15 | 20 | 0.30 |
| 15 | 30 | 0.29 |
| 15 | 40 | 0.28 |
Similarly, during discharging, internal resistance shows a comparable trend, with significant increases at low SOC levels. The heat generated due to internal resistance can be described by Joule’s law: $$ Q = I^2 R t $$ where \( Q \) is the heat energy, \( I \) is the current, \( R \) is the internal resistance, and \( t \) is time. This heat accumulation in energy storage lithium batteries, if not dissipated properly, can trigger thermal runaway, especially in high SOC conditions where the energy release is more intense.
The voltage and SOC relationship in energy storage lithium batteries is another vital aspect. During charging, voltage rises with increasing SOC, but the rate of change varies with current and temperature. We derived a model to represent voltage (V) as a function of SOC: $$ V = V_0 + k \cdot \ln(SOC) + m \cdot I $$ where \( V_0 \) is the open-circuit voltage, and \( k \) and \( m \) are coefficients that account for electrochemical dynamics. This model helps in predicting voltage behavior under different operating conditions for energy storage lithium batteries. For example, at high currents, voltage polarization becomes more pronounced, leading to steeper voltage changes and increased thermal risks.
We also analyzed the discharge characteristics, where voltage decreases as SOC drops. The table below presents voltage data at different SOC levels during discharging at a constant current of 5 A and temperature of 30°C:
| SOC (%) | Voltage (V) |
|---|---|
| 100 | 3.65 |
| 80 | 3.50 |
| 60 | 3.40 |
| 40 | 3.30 |
| 20 | 3.15 |
| 10 | 3.00 |
| 5 | 2.80 |
This data indicates that voltage stability is maintained in the mid-SOC range, but sharp declines occur at low SOC, which can lead to over-discharge and accelerate aging in energy storage lithium batteries. The coupling between voltage fluctuations and thermal runaway is critical; abnormal voltage rises during charging can indicate副 reactions that generate excess heat, while sudden drops during discharging may signal internal shorts.
Aging effects on energy storage lithium batteries were evaluated through cycle life testing. We performed repeated charge-discharge cycles (10, 1000, and 2000 cycles) at 20°C and 40°C, measuring capacitance and energy retention. The capacitance degradation follows an exponential decay model: $$ C = C_0 \cdot e^{-\lambda n} $$ where \( C \) is the current capacitance, \( C_0 \) is the initial capacitance, \( \lambda \) is the decay constant, and \( n \) is the number of cycles. This model highlights the irreversible capacity loss in energy storage lithium batteries over time, which correlates with increased internal resistance and heightened thermal runaway susceptibility.
The table below compares capacitance and energy values at different aging stages for an energy storage lithium battery tested at 20°C:
| Cycle Count | Capacitance (mAh) | Energy (mWh) |
|---|---|---|
| 10 | 5039 | 15248 |
| 1000 | 4819 | 14939 |
| 2000 | 4310 | 13839 |
As cycles increase, the reduction in capacitance and energy is evident, emphasizing the importance of monitoring aging in energy storage lithium batteries to prevent thermal runaway. The thickening of the solid electrolyte interface (SEI) layer and electrode material degradation contribute to this decline, raising the risk of internal shorts and heat generation.
Thermal runaway in energy storage lithium batteries is a multi-stage process influenced by the interplay of SOC, internal resistance, voltage, and aging. We developed a risk assessment model based on the Arrhenius equation to describe the temperature rise rate: $$ \frac{dT}{dt} = A \cdot e^{-\frac{E_a}{RT}} \cdot f(SOC, R, V) $$ where \( A \) is the pre-exponential factor, \( E_a \) is the activation energy, \( R \) is the gas constant, \( T \) is temperature, and \( f(SOC, R, V) \) is a function incorporating SOC, internal resistance, and voltage. This model allows for predicting thermal runaway thresholds in energy storage lithium batteries, particularly under high SOC conditions where the triggering energy is lower.
To mitigate thermal runaway risks, we propose several strategies focused on energy storage lithium batteries. First, optimizing thermal management systems using liquid cooling or phase-change materials can maintain temperature uniformity and dissipate heat effectively. The heat dissipation rate can be modeled as: $$ \dot{Q}_{diss} = h A (T_{battery} – T_{ambient}) $$ where \( h \) is the heat transfer coefficient and \( A \) is the surface area. Second, real-time monitoring with sensors for voltage, current, and temperature, combined with machine learning algorithms, can enable early warning systems. For instance, anomaly detection in parameter trends can trigger alerts before thermal runaway occurs in energy storage lithium batteries.
Third, improving battery materials and structures enhances thermal stability. Using additives in electrolytes or advanced separators can suppress副 reactions. The energy release during thermal runaway can be quantified as: $$ E_{release} = \int I^2 R dt + \Delta H_{reaction} $$ where \( \Delta H_{reaction} \) represents the enthalpy change from chemical reactions. Fourth, intelligent charging strategies, such as reinforcement learning-based control, can adapt charging currents based on real-time data to minimize stress on energy storage lithium batteries. These approaches collectively reduce the likelihood of thermal runaway by addressing the root causes identified in our multi-parameter analysis.
In conclusion, our study demonstrates that the evolution of SOC, internal resistance, voltage, and aging in energy storage lithium batteries is intricately linked to thermal runaway risks. Through experimental data and mathematical models, we have shown how these parameters interact under various conditions. The findings underscore the need for integrated safety measures, including advanced thermal management, continuous monitoring, material innovations, and smart control systems. By implementing these strategies, the reliability and safety of energy storage lithium batteries can be significantly improved, supporting their sustainable application in electric vehicles and energy storage systems. Future work should explore real-time simulation of thermal runaway mechanisms to further enhance predictive capabilities.