Risk quantification framework of hydride-based hydrogen storage systems for light-duty vehicles

Journal article

This study aims to develop a quantitative risk assessment (QRA) framework for on-board hydrogen storage systems in light-duty fuel cell vehicles, with focus on hazards from potential vehicular collision affecting hydride-based hydrogen storage vessels. Sodium aluminum hydride (NaAlH4) has been selected as a representative reversible hydride for hydrogen storage. Functionality of QRA framework is demonstrated by presenting a case study of a postulated vehicle collision (VC) involving the onboard hydrogen storage system. An event tree (ET) model is developed for VC as the accident initiating event. For illustrative purposes, a detailed FT model is developed for hydride dust cloud explosion as part of the accident progress. Phenomenologically-driven ET branch probabilities are estimated based on an experimental program performed for this purpose. Safety-critical basic events (BE) in the FT model are determined using conventional risk importance measures. The Latin Hypercube sampling (LHS) technique has been employed to propagate the aleatory (i.e., stochastic) and epistemic (i.e., phenomenological) uncertainties associated with the probabilistic ET and FT models. Extrapolation of the proposed QRA framework and its core risk-informed insights to other candidate on-board reversible and off-board regenerable hydrogen storage systems could provide better understanding of risk consequences and mitigation options associated with employing this hydrogen-based technology in the transportation sector.

Authors: Khalil, Y

• Publication status: Published
• Peer review status: Peer reviewed
• Publisher: Elsevier
• Journal: Journal of Loss Prevention in the Process Industries
• Volume: 38
• Pages: 187-198
• Publication date: 2015-09-14
• Acceptance date: 2015-09-13
• DOI: 10.1016/j.jlp.2015.09.008
• EISSN: 1873-3352
• ISSN: 0950-4230
• Language: English
• Keywords: off-board regenerable; event tree; on-board reversible; importance measures; dust cloud explosion; fault tree