Quantum mechanics

Context (archaeology)

Engineering

Bayesian probability

Biology

Bayesian network

Selection (genetic algorithm)

Power (physics)

Computer science

Electric vehicle

Risk analysis (engineering)

Business

Paleontology

Charging station

Physics

Operations research

Political science

Site selection

Artificial intelligence

Machine learning

International Journal of Electrical Power & Energy Systems

Fast charging sites play a crucial role for public acceptance of electric vehicle (EV). Selection of the most sustainable site positively contributes to the life cycle of electric vehicle charging station (EVCS), which requires considering some conflicting criteria. Previous researches mainly focused on utilizing optimization models to deal with EVCS site selection that only accounts for quantitative factors, while this paper proposes a Bayesian Network (BN) model that considers not only quantitative factors but also qualitative (subjective) ones. Based on academic literature and expert judgments, the assessment index for EVCS site selection was mainly made from sustainability point of view, which contains of economic, environmental, and social criteria with a total of eleven sub-criteria. BNs are powerful tools for handling risk assessment and decision making under uncertainty. The developed BN model is validated through sensitivity analysis approach. Finally, different propagation analyses have been performed to make special types of reasoning. This paper provides a new research perspective by considering uncertainty, qualitative and quantitative factors into the site selection assessment, and presents the mainstream penetration of BN as a powerful decision making tool in the context of electrical energy management.

Development of a Bayesian network model for optimal site selection of electric vehicle charging station