Despite the commercial success of lithium-ion batteries (LIBs), the risk of thermal runaway, which can lead to dangerous fires, has become more concerning as LIB usage increases. Research has focused on understanding the causes of thermal runaway and how to prevent or detect it. Additionally, novel thermal runaway-resistant materials are being researched, as are different methods of constructing LIBs that better isolate thermal runaway and prevent it from propagating. However, field firefighters are using hundreds of thousands of liters of water to control large runaway thermal emergencies, highlighting the need to merge research with practical observations. To study battery fire, this study utilized a temperature abuse method to increase LIB temperature and investigated whether thermal runaway can be suppressed by applying external cooling during heating. The batteries used were pouch-type ones and subjected to high states of charge (SOC), which primed the thermal runaway during battery temperature increase. A water spray method was then devised and tested to reduce battery temperature. Results showed that, without cooling, a thermal runaway fire occurred every time during the thermal abuse. However, external cooling successfully prevented thermal runaway. This observation shows that using water as a temperature reducer is more effective than using it as a fire suppressant, which can substantially improve battery performance and increase public safety.

The surge in lithium-ion battery (LIB) use, essential for mass-scale renewable energy storage, raises concerns about fire hazards. However, to date, there is a lack of industry-wide understanding of large-scale LIB fire propagation. This paper suggests a translational forensic approach to promote fire safety awareness and introduces the cellular automata (CA) model coupled with the Monte Carlo (MC) approach to address the complex fire propagation simulation within an energy storage system (ESS). The objective is to demonstrate that the CA-MC model can provide a flexible and scalable connection for all levels of battery fire studies. The numerical model is coupled with experimental tests which have been performed to establish the actual timing of fire propagation from a single source. Cellular automata simulation, conducted through hybrid modeling and an applied risk analysis approach to evaluate fire hazards associated with LIBs, offers crucial insights into potential risks. The results demonstrate that, with fire incident initiation at a probability of 0.1 (10%), 33% of batteries will burn, and at a probability of 0.6 (60%) and beyond, the entire battery module will face complete burndown. Achieving full combustion of the entire module will take only approximately 42 timesteps on average, indicating rapid fire propagation. The actual time for a complete fire to occur in the battery module has been estimated to be 304 s per timestep, or 3.5 h total. Using this example, it is shown that the CA-MC approach can be extended to many other aspects of battery fire studies and is ideal as a translational tool, spanning all domains of the LIB industry.