Accurately modeling electric vehicle (EV) charging behavior is crucial for the design and operation of charging stations that offer multiple charging options to drivers. A realistic representation of driver decision-making enables better planning, load management, and energy efficiency. This paper proposes a weighted index-based model that captures the heterogeneous preferences of EV drivers, incorporating four key factors: battery state of charge (SoC), time sensitivity, price sensitivity, and environmental impact. Each factor is assigned a weight and combined into separate indices for slow and fast charging options. The final charging decision for each EV is determined by selecting the option with the lower weighted index, reflecting the drivers’ priorities and real-world behavior. Randomized input within predefined ranges allows the model to replicate variability among drivers. The proposed methodology captures both deterministic tendencies, such as preference for fast charging when the battery is low, and stochastic variations reflecting human behavior. Simulation results demonstrate that the model produces consistent, interpretable, and realistic charging patterns, with the distribution of slow and fast charging choices closely aligning with expected driver behavior across multiple scenarios. The simulation results indicate that the model converges well under stochastic input variables. Specifically, running 20 simulations for five EVs showed that, in most cases, either three EVs chose fast charging and two chose slow charging, or vice versa. A sensitivity analysis of the parameter weights further revealed that changing the weights of the environmental, price, and time indices led to a respective increase of 12%, 30%, and 54% in the probability of choosing slow charging.
