A Cyber-Physical Systems Framework for Tracking Post Thermal-Runaway Temperature and Smoke Dynamics in Underground Mines

TL;DR

This paper introduces a cyber-physical systems framework that models temperature and smoke dynamics in underground mines during LIB failures, enabling real-time safety monitoring and decision-making.

Contribution

It develops CPS models trained on CFD data to efficiently estimate temperature and smoke evolution, addressing computational challenges in large, complex mine environments.

Findings

CPS models accurately predict temperature and smoke dynamics.

The framework enables real-time monitoring during LIB failure scenarios.

It supports informed emergency decision-making for mine safety.

Abstract

Underground mining operations are actively exploring the use of large-format lithium-ion batteries (LIBs) to power their equipment. LIBs have high energy density, long cycle life, and favorable safety record. They also have low noise, heat, and emission footprints. This fosters a conducive workplace environment for underground mining personnel. However, many occurrences of LIB failure have resulted in dangerous situations in underground mines. The combustion products, including toxic emissions, can rapidly travel throughout the mine using the ventilation network. Therefore, it is critical to monitor the temperature and smoke concentration underground at all times to ensure the safety of the miners. High-fidelity models can be developed for specific scenarios of LIB failure, but are computationally prohibitive for large underground mine volumes, complex geometries, and long duration combustion events. To mitigate computation-related issues associated with high-fidelity models, we developed cyber-physical systems (CPS) models to examine temperature and smoke dynamics. The mine supervisory control center, acting as the cyber framework, operates in conjunction with the physical underground mine. The CPS models, trained on high-fidelity computational fluid dynamics (CFD) model data sets, present an exceptional estimate of the evolution of temperature and smoke concentration in the underground mine tunnel. Once implemented, the research results can help mine operators make informed decisions during emergencies.