Optimizing Charger Placement in E-Bike Sharing Systems with State-of-Charge Dynamics

Abstract

The growing adoption of electric bike-sharing systems introduces new challenges in managing bike availability due to state-of-charge (SOC) dynamics. Existing models typically treat bikes as homogeneous and do not capture the interaction between SOC evolution and system performance. This study addresses charger placement through a unified framework that integrates SOC dynamics, ridership estimation, and infrastructure deployment within a two-dimensional Markovian system. Steady-state probabilities are derived to estimate expected ridership, which serves as the objective in a station-level location optimization problem. A heuristic solution method combined with a single-pooling approximation ensures scalability for large networks. Results show that the proposed approach achieves near-optimal performance, within 0.5–1.5% of the global optimum. Increasing charger deployment improves availability and accessibility, although with diminishing returns. Case studies in Pittsburgh, Vancouver, and San Francisco demonstrate that SOC-aware charger placement significantly reduces low-SOC occurrences and adapts to different demand patterns.