With the rapid advancement of high-energy weapons and demanding maritime operations, naval platforms require robust and reliable energy supply systems. Energy storage lithium battery systems have emerged as a critical component due to their high energy density and fast response capabilities. However, the safety of these systems, particularly under extreme conditions, is paramount. Thermal runaway in energy storage lithium battery units can lead to catastrophic failures, including fires and explosions, posing significant risks to personnel and mission integrity. This study focuses on simulating thermal runaway in energy storage lithium battery systems designed for naval applications, comparing the effectiveness of immersion cooling and liquid cold plate cooling methods. By developing a co-simulation model using Fluent and Simulink, we analyze the thermal behavior under fault conditions and evaluate the cooling systems’ performance in mitigating thermal hazards. The results demonstrate that immersion cooling offers superior temperature control and stability, making it a promising solution for enhancing the safety of energy storage lithium battery systems on warships.
Energy storage lithium battery technology has gained traction in naval platforms for its ability to support high-power demands and improve operational endurance. For instance, hybrid propulsion systems in submarines and unmanned surface vessels rely on energy storage lithium battery packs to achieve extended underwater endurance and silent operation. Despite these advantages, the inherent risks associated with thermal runaway in energy storage lithium battery systems cannot be overlooked. In confined shipboard environments, a single battery failure can propagate rapidly, leading to widespread damage. Previous incidents, such as fires on Norwegian ferries, underscore the urgency of addressing thermal management challenges. This research aims to bridge the gap in existing studies by integrating battery system simulations with dynamic cooling system models, providing a comprehensive analysis of thermal runaway propagation and suppression strategies.
The structure of a typical marine energy storage lithium battery system includes multiple layers: battery cells organized into modules, packs, clusters, and finally, the battery cabinet. Each level is interconnected through series/parallel configurations to meet voltage, current, and power requirements. Supporting systems like the Battery Management System (BMS), cooling systems, and power distribution units are essential for safe operation. The cooling system, in particular, plays a vital role in maintaining optimal operating temperatures for energy storage lithium battery units, preventing thermal abuse that could trigger runaway events. This study delves into the simulation of these systems, with a focus on two cooling methodologies: immersion cooling using hydrocarbon oil and liquid cold plate cooling. The objective is to assess their efficacy in containing thermal runaway and ensuring system reliability under realistic naval operating conditions.
Thermal runaway in energy storage lithium battery systems is primarily triggered by mechanical, electrical, or thermal abuse. Mechanical abuse involves physical damage from impacts or vibrations, common in naval environments due to rough seas or combat scenarios. Electrical abuse results from overcharging, over-discharging, or internal short circuits, while thermal abuse occurs when localized overheating causes separator breakdown. The chain reaction begins with an internal short circuit, leading to exothermic reactions, gas generation, and ultimately, thermal runaway. Understanding these mechanisms is crucial for designing effective mitigation strategies. The simulation models developed in this work incorporate these aspects to replicate real-world failure modes and evaluate the cooling systems’ response.
The simulation framework combines Fluent for battery thermal modeling and Simulink for cooling system dynamics. The co-simulation approach enables real-time data exchange between the battery model and cooling system, reflecting the interactive nature of naval operations. Key equations governing fluid flow and heat transfer include the continuity, momentum, and energy equations. For instance, the continuity equation is expressed as:
$$\frac{\partial \rho}{\partial t} + \frac{\partial (\rho u_j)}{\partial x_j} = 0$$
where $\rho$ is fluid density, $t$ is time, and $u_j$ represents velocity components. The momentum equation accounts for viscous stresses:
$$\frac{\partial (\rho u_i)}{\partial t} + \frac{\partial (\rho u_i u_j)}{\partial x_j} = -\frac{\partial p}{\partial x_i} + \frac{\partial \tau_{ij}}{\partial x_j}$$
Here, $p$ denotes pressure, and $\tau_{ij}$ is the viscous stress tensor. The energy equation incorporates heat generation from the energy storage lithium battery:
$$\frac{\partial (\rho c_p T)}{\partial t} + \frac{\partial (\rho c_p u_j T)}{\partial x_j} = \frac{\partial}{\partial x_j} \left( \frac{\mu \lambda}{Pr} \frac{\partial (c_p T)}{\partial x_j} \right) + W_s Q_s$$
where $c_p$ is specific heat capacity, $T$ is temperature, $\mu$ is dynamic viscosity, $\lambda$ is thermal conductivity, $Pr$ is the Prandtl number, and $W_s Q_s$ represents heat sources. These equations are solved numerically in Fluent to simulate temperature distribution and fluid dynamics within the energy storage lithium battery pack.
For the cooling system, Simulink models the heat exchange between coolant and freshwater. The dynamic equations describe temperature changes based on flow rates and heat transfer coefficients. For the coolant side, the temperature evolution is given by:
$$\frac{dt_{co}}{dt} = \frac{1}{W_{cc}} \left[ m_c C_c (t_{ci} – t_{co}) – K_q A_q \Delta T_q \right]$$
where $t_{co}$ and $t_{ci}$ are coolant outlet and inlet temperatures, $m_c$ is coolant mass flow rate, $C_c$ is coolant specific heat, $K_q$ is the heat transfer coefficient, $A_q$ is heat exchange area, and $\Delta T_q$ is the logarithmic mean temperature difference. Similarly, the freshwater side equation balances heat exchange and flow dynamics. These models are parameterized with real naval operational data to ensure accuracy.
The energy storage lithium battery cell used in simulations is an LF280K LiFePO4 type, with key specifications summarized in Table 1. The battery pack consists of multiple cells arranged in a modular structure, immersed in hydrocarbon oil for cooling. The material properties of the battery and coolant are critical for realistic simulation and are listed in Table 2.
| Parameter | Value |
|---|---|
| Nominal Voltage | 3.2 V |
| Nominal Capacity | 280 Ah |
| Height | 207.2 mm |
| Width | 173.7 mm |
| Thickness | 71.7 mm |
| Property | Energy Storage Lithium Battery | Hydrocarbon Oil |
|---|---|---|
| Density (kg/m³) | 2715 | 810 |
| Specific Heat Capacity (J/kg·K) | 956.6 | 2100 |
| Thermal Conductivity (W/m·K) | (1.176, 16.128, 1.176) | 0.14 |
| Kinematic Viscosity at 20°C (St) | – | 19.4 |
| Flash Point (°C) | – | 190 |
The simulation setup mirrors actual naval power profiles, which include normal cruising, docking, and acceleration phases. Power demand fluctuations are significant during docking and departure, as shown in Table 3, which outlines the operational scenarios used in the study.
| Operational Phase | Time Interval (s) | Power Range (kW) |
|---|---|---|
| Normal Cruising | 0-100 | 20-60 |
| Docking | 100-150 | 0-100 |
| Berthing | 150-165 | 0 |
| Departure | 165-200 | 0-100 |
Thermal runaway is induced by simulating an internal short circuit in a single energy storage lithium battery cell at critical time points (e.g., 100 s and 165 s), corresponding to high power demand periods. The temperature response is monitored for both immersion and liquid cold plate cooling systems. The governing equations for heat generation during thermal runaway incorporate Arrhenius-based reaction kinetics, expressed as:
$$\frac{dQ}{dt} = A \exp\left(-\frac{E_a}{RT}\right) \cdot \Delta H$$
where $Q$ is heat generation, $A$ is pre-exponential factor, $E_a$ is activation energy, $R$ is universal gas constant, $T$ is temperature, and $\Delta H$ is enthalpy change. This model captures the exponential heat release characteristic of energy storage lithium battery failures.
Simulation results indicate that immersion cooling significantly outperforms liquid cold plate cooling in suppressing thermal runaway. For instance, when thermal runaway is triggered at 100 s, the maximum cell temperature with immersion cooling reaches 111.88°C, compared to 219.63°C with liquid cold plate cooling. The temperature rise rate is also lower for immersion cooling, at 0.24°C/s versus 1.25°C/s for liquid cold plate. Similarly, at 165 s, immersion cooling limits the peak temperature to 99.65°C, while liquid cold plate allows 188.01°C. These findings are summarized in Table 4, which compares key thermal metrics.
| Parameter | Immersion Cooling | Liquid Cold Plate Cooling |
|---|---|---|
| Peak Temperature at 100 s (°C) | 111.88 | 219.63 |
| Maximum Temperature Rise at 100 s (°C) | 86.88 | 194.63 |
| Temperature Slope at 100 s (°C/s) | 0.24 | 1.25 |
| Peak Temperature at 165 s (°C) | 99.65 | 188.01 |
| Maximum Temperature Rise at 165 s (°C) | 74.65 | 163.01 |
| Temperature Slope at 165 s (°C/s) | 0.61 | 1.25 |
The superior performance of immersion cooling can be attributed to its direct contact with energy storage lithium battery cells, enabling efficient heat absorption and uniform temperature distribution. In contrast, liquid cold plate cooling relies on indirect contact, leading to localized hot spots and slower heat dissipation. Flow trajectory analysis reveals that immersion cooling provides comprehensive coverage around cells, whereas cold plate cooling is confined to the base, resulting in inadequate upper cell cooling. The Reynolds number for coolant flow is calculated to ensure turbulent flow for enhanced heat transfer:
$$Re = \frac{\rho u L}{\mu}$$
where $u$ is flow velocity and $L$ is characteristic length. For immersion cooling, $Re$ values indicate turbulent flow, promoting effective mixing and heat removal.
Furthermore, the Simulink cooling system model demonstrates the dynamic response of the coolant loop. The freshwater side parameters, such as inlet temperature (20°C) and flow rate, are tuned to naval standards. The heat exchanger efficiency is quantified by the effectiveness-NTU method:
$$\epsilon = \frac{1 – \exp[-NTU(1-C)]}{1 – C \exp[-NTU(1-C)]}$$
where $\epsilon$ is effectiveness, $NTU$ is number of transfer units, and $C$ is capacity ratio. This approach ensures accurate modeling of real-world cooling performance for energy storage lithium battery systems.
In conclusion, this study establishes that immersion cooling is a robust solution for mitigating thermal runaway in energy storage lithium battery systems on naval platforms. The co-simulation framework provides a realistic assessment of thermal hazards and cooling efficacy, highlighting the importance of integrated system design. Future work will focus on scaling the model to full-scale battery cabinets and incorporating additional factors such as vessel motion and coolant combustion risks. By advancing thermal management strategies, we can enhance the safety and reliability of energy storage lithium battery systems, supporting the operational readiness of modern naval forces.
The implications of this research extend beyond naval applications to other domains where energy storage lithium battery systems are deployed under stringent safety requirements. The methodologies and findings presented here contribute to the broader field of battery safety engineering, offering insights into thermal runaway propagation and control. As energy storage lithium battery technology continues to evolve, ongoing simulation and experimental validation will be essential for addressing emerging challenges and ensuring sustainable energy solutions for critical infrastructure.