Toward Sustainable Electromobility: Planning Electric Vehicle Charging Infrastructure with a Hierarchical Bayesian Model, Agent-Based Simulation and Multi-Criteria Decision Making

Electromobility is central to urban decarbonisation, but its charging infrastructure must be sized under substantial uncertainty about user behaviour that varies across stations, time of day and user type. This study couples a hierarchical Bayesian model with an agent-based, discrete-event simulation of a charging network. It is fitted by Markov chain Monte Carlo to the public ACN-Data dataset (13,694 sessions across 52 stations; 16,468 user requests), with partial pooling across stations. Posterior parameters drive a 24 h simulation of 500 vehicles across nine configurations and 30 to 180 slots. Service success rises from 19% to 81% and mean waiting falls from 110 to 62 min; long workplace dwell times limit turnover, so capacity rather than energy binds. TOPSIS with a paired bootstrap selects 160 slots under balanced weighting, but that optimum holds for only a tenth of the weight simplex, and the recommendation spans 140–180 slots. Spreading the arrival peak at fixed hardware raises service from 67% to 83%, matching a 29% expansion. Spatially explicit assignment costs three percentage points when stations are evenly sited, and six when clustered. The framework makes the cost of over-provisioning explicit and preference-conditional rather than naming a single optimum, giving a reproducible basis for sustainable capacity planning.