The high capacity and voltage properties demonstrated by lithium-ion batteries render them as the preferred energy carrier in portable electronic devices. The application of the lithium-ion batteries which previously circulating and contained around small-scale electronics is now expanding into large scale emerging markets such as electromobility and stationary energy storage. Therefore, the understanding of the risk involved is imperative. Thermal runaway is the most common failure mode of lithium-ion battery which may lead to safety incidents. Transport process of immense amounts of heat released during thermal runaway of lithium-ion battery to neighboring batteries in a module can lead to cascade failure of the whole energy storage system. In this work, a model is developed to predict the propagation of lithium-ion battery in a module for large scale applications. For this purpose, kinetic of material thermal decomposition is combined with heat transfer modelling. The simulation is built based on chemical kinetics at component level of a singular cell and energy balance that accounts for conductive and convective heat transfer.

Pool fires cause immense damage to fuel storage tank farms. Reduced fire escalation risk in tank farms improves fire safety. Computational fluid dynamics (CFD) has proven effective in assessing escalation of fire-related domino effects and is being utilized for pool fire consequences in tank farms. The past CFD-based analysis focused on primary fire effects on secondary targets. This study used fire dynamics simulator (FDS) to model complete evolution of the domino effect under different wind speeds and primary pool fire locations. Dynamic escalation probability (DEP) and fire spread probability of the tank farm were calculated. Offset tank failure increased by 3% and 31%, while inline tank failure dropped by 36% and 90%, at 2 and 8 m/s, respectively. An artificial neural network (ANN) incorporating the Levenberg–Marquardt algorithm is used to predict fire spread probability based on numerical data set. The use of ANNs for this purpose is one of the first attempts in this regard. ANNs can reliably predict dynamic fire spread probability and could be utilized to manage fire-induced domino effects. Moreover, dynamic fire spread probability in tank farms obtained from ANN modelling can be used for safety applications, such as updating mitigation time when fire spread probability is unacceptable for a specific wind speed.