Risk quantification assessment for fuel cell buses based on component leakage space risk analysis

Luming Yang, Jianwei Li*, Haiqiang Liang, Guoqi Liang, Zhonghao Tian, Jun Shen

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

Classic risk quantitative analysis (QRA) and simulation utilizing computational fluid dynamics (CFD) software encounter issues such as insufficient intuitiveness and high resource costs respectively when it comes to analyzing hydrogen safety of hydrogen fuel cell vehicles (HFCV). Therefore, a risk potential field superposition analysis method by considering the leakage space of typical leaking components is proposed in this paper. The simplified leakage space is developed by analyzing the leakage distribution law and spatial characteristics of typical leaking components. In addition, the thermal radiation, overpressure, and impulse risks of hydrogen leakage and explosion in the leakage space is calculated, and a risk potential field for the hydrogen system to intuitively quantify the consequences of hydrogen leakage risk is established. Finally, the risk mitigation effect of the protective cover in the hydrogen storage tank area is evaluated. The results show that the risk value of the hydrogen storage tank area when all tanks leak simultaneously is the largest, reaching 0.913, followed by the multi-stage pressure-reducing valve area at 0.323, the safety valve area at 0.236 and the fuel cell hydrogen inlet area at 0.135. Moreover, the protective cover of the hydrogen storage tank area has reduced the impact range of overpressure risk by 81.25 % with minimal weight and cost increase.

Original languageEnglish
Article number124046
JournalRenewable Energy
Volume256
DOIs
Publication statusPublished - 1 Jan 2026
Externally publishedYes

Keywords

  • Fuel cell vehicle
  • Hydrogen leakage
  • Quantitative risk analysis
  • Risk potential field

Fingerprint

Dive into the research topics of 'Risk quantification assessment for fuel cell buses based on component leakage space risk analysis'. Together they form a unique fingerprint.

Cite this