Prediction of PEV Adoption with Agent-Based Parameterized Bass Network Diffusion Model

TL;DR

This paper develops an agent-based parameterized Bass network diffusion model to accurately predict electric vehicle adoption in Washington, considering income and social influence, aiding planning for sustainable transportation.

Contribution

The study introduces a novel agent-based diffusion model incorporating income and social factors, achieving high accuracy in predicting EV adoption geographically and temporally.

Findings

High estimation accuracy for EV adoption in Washington

Model effectively incorporates income and social influence factors

Potential to adapt the model for other regions

Abstract

Although the growing electric vehicle (EV) population is leading us into a more sustainable world, it is also bringing challenges for the manufacturers's production planning, the charging facility providers's expansion plan, and the energy system's adaption to greater electricity demand. To tackle these challenges, a model to predict EV growth in geographical scope would be helpful. In this study, an agent-based parameterized bass network diffusion model was developed for EV population data in Washington. The model included income levels and number of neighbors adopted as two key factors in determining EV diffusion probabilities. With the parameters estimated from simulation, the resulting model achieve a high estimation accuracy for EV adoption in Washington in both temporal and geographical scopes. This model could be used to predict EV growth in Washington, and to be adopted to other geographical areas.