Electric vehicle fire risk assessment based on WBS-RBS and fuzzy BN coupling.
An object of class bn.fit. Refer to the documentation of bnlearn for details.
A discrete Bayesian network to evaluate the risk of electric vehicle fire accidents. Probabilities were given within the referenced paper. The vertices are:
Air conditioning equipment failure (yes, no);
Artificial modification (yes, no);
Not aware of early fire (yes, no);
Blocked exhaust pipe (yes, no);
Battery failure (yes, no);
Battery overcharge (yes, no);
The car body is ignited (yes, no);
Charging equipment fault (yes, no);
Collision ignition (yes, no);
Defroster temperature too high (yes, no);
Electrical circuit failure (yes, no);
Electronic component failure (yes, no);
The vehicle is not equipped with fire-fighting equipment (yes, no);
Human factor (yes, no);
Ignition source (yes, no);
Risk of internal spontaneous combustion of electric vehicles (yes, no);
Man made arson (yes, no);
The early open fire was not extinguished (yes, no);
Risk of external ignition (yes, no);
(yes, no);
Short circuit in battery (yes, no);
Transmission line damage (yes, no);
Electric vehicle fire disaster (yes, no);
Chen, J., Li, K., & Yang, S. (2022). Electric vehicle fire risk assessment based on WBS-RBS and fuzzy BN coupling. Mathematics, 10(20), 3799.