Multi-Phase Optimization of Shared Charging Infrastructure for Freight Electrification

Abstract

The transition to heavy-duty battery electric vehicles requires an efficient and cost-effective deployment of the charging infrastructure, particularly when multiple operators share resources. This paper presents a multi-phase optimization framework for the joint planning of charging stations in a shared network, using high-resolution empirical truck trajectory data from two freight companies with distinct operational characteristics. The model is formulated to minimize the total number of charging stations while ensuring that the predefined electrification targets are met over successive expansion stages. The analysis captures heterogeneity in fleet usage, with one company operating a spatially concentrated network with shorter and more consistent routes, and the other exhibiting more dispersed operations with longer and more variable driving patterns. The results show that early-stage infrastructure deployment primarily supports fleets with concentrated operations, while later expansion phases are essential to accommodate long-haul and geographically dispersed transport demand. Furthermore, shared infrastructure not only enables reductions in redundant investments, but also introduces dependencies where certain fleets rely heavily on the full network to sustain electrified operations. In general, the findings highlight the importance of coordinated and data-driven infrastructure planning, and demonstrate that fleet-specific characteristics strongly influence both infrastructure requirements and electrification outcomes. The proposed framework provides practical insights on how collaborative and phased deployment strategies can enhance the scalability and efficiency of freight transport electrification.