Zhang, Yongqi (Monash University), Ngoduy, Dong (Monash University)

A Stochastic and Heterogeneous Dynamic Pricing Strategy for Electric Vehicle Charging Stations

Scheduled for presentation during the Regular Session "S38a-Towards Scalable and Trustworthy AI in Connected Mobility" (FR-LM-T38), Friday, November 21, 2025, 11:10−11:30, Coolangata 2

2025 IEEE 28th International Conference on Intelligent Transportation Systems (ITSC), November 18-21, 2025, Gold Coast, Australia

This information is tentative and subject to change. Compiled on October 18, 2025

Abstract

The rapid expansion of electric vehicles (EVs) introduces spatiotemporal demand management challenges for charging station operators (CSOs), exacerbated by demand imbalance, behavioral heterogeneity, and system uncertainty. Traditional dynamic pricing models, reliant on deterministic EV-CS pairings and network equilibrium assumptions, oversimplify user behavior and lack scalability. This study proposes a stochastic, behaviorally heterogeneous dynamic pricing framework formulated as a bi-level Stackelberg game. The upper level optimizes time-dependent pricing to maximize system-wide utility, while the lower level models decentralized EV users via a multinomial logit (MNL) choice model, incorporating price sensitivity, battery aging, risk attitudes, and network travel costs—explicitly avoiding equilibrium constraints for scalability. Congestion effects are quantified using queuing-theoretic approximations for waiting times and rejection probabilities. By integrating dynamic pricing, stochastic choice, and congestion-aware modeling, the framework enables adaptive, large-scale charging infrastructure management while balancing realism and computational efficiency, offering a robust tool for strategic and operational EV charging optimization in urban networks.