Mechanism-Based Fire Hazard Chain Risk Assessment for Roll-On/Roll-Off Passenger Vessels Transporting Electric Vehicles: A Fault Tree–Fuzzy Bayesian Network Approach

Abstract

Roll-on/Roll-off passenger vessels transporting electric vehicles (Ro-Ro EVs) face unique fire hazards, challenging traditional fire risk management strategies. This study integrates fault tree analysis (FTA) with Fuzzy Bayesian Network (FBN) to assess the fire risks of Ro-Ro EVs across the entire hazard chain. Given limited historical accident data, five experts familiar with the Shanghai Baoshan–Chongming ferry route refine fault tree models to visualize key fire hazard chain mechanisms and estimate risk probabilities. The FBN incorporates fault tree hierarchical structures, EV and Ro-Ro vessel-related risk factors, and applies a nine-level fuzzy scoring system to assess these risks. The FTA-FBN model offers a comprehensive framework for evaluating emerging fire risks specific to Ro-Ro EVs. Findings indicate that the highest risk occurs during the ignition phase. Primary triggers include external heat sources, improper vehicle securing, and vehicle collisions, leading to thermal runaway in lithium batteries. Failures in extinguishing and detecting lithium battery fires exacerbate fire spread. Effective fire compartmentalization and flammable material management are essential to prevent uncontrolled fires. Recommendations for fire prevention and control include shipboard battery level monitoring, charging restrictions, explosion-proof electrical installations, enhanced ventilation, lithium battery fire suppression systems, and vehicle securing.