electricvehicle {bnRep} | R Documentation |
electricvehicle Bayesian Network
Description
Electric vehicle fire risk assessment based on WBS-RBS and fuzzy BN coupling.
Format
A discrete Bayesian network to evaluate the risk of electric vehicle fire accidents. Probabilities were given within the referenced paper. The vertices are:
- ACF
Air conditioning equipment failure (yes, no);
- AM
Artificial modification (yes, no);
- AWE
Not aware of early fire (yes, no);
- BEP
Blocked exhaust pipe (yes, no);
- BF
Battery failure (yes, no);
- BO
Battery overcharge (yes, no);
- CBI
The car body is ignited (yes, no);
- CEF
Charging equipment fault (yes, no);
- CI
Collision ignition (yes, no);
- DTH
Defroster temperature too high (yes, no);
- EC
Electrical circuit failure (yes, no);
- ECF
Electronic component failure (yes, no);
- FFE
The vehicle is not equipped with fire-fighting equipment (yes, no);
- HF
Human factor (yes, no);
- IS
Ignition source (yes, no);
- ISC
Risk of internal spontaneous combustion of electric vehicles (yes, no);
- MMA
Man made arson (yes, no);
- OFE
The early open fire was not extinguished (yes, no);
- REI
Risk of external ignition (yes, no);
- SBB
(yes, no);
- SCB
Short circuit in battery (yes, no);
- TLD
Transmission line damage (yes, no);
- VFD
Electric vehicle fire disaster (yes, no);
Value
An object of class bn.fit
. Refer to the documentation of bnlearn
for details.
References
Chen, J., Li, K., & Yang, S. (2022). Electric vehicle fire risk assessment based on WBS-RBS and fuzzy BN coupling. Mathematics, 10(20), 3799.