Optimizing Charging Infrastructure for Battery Electric Trucks in Zero-Carbon Freight Corridors Under State-of-Charge and Service-Capacity Constraints

Abstract

The large-scale deployment of battery electric trucks (BETs) requires well-developed charging infrastructure; however, existing planning approaches often neglect capacity constraints and the uncertainty inherent in microscopic charging behavior. This paper proposes a four-stage charging infrastructure planning methodology comprising energy analysis, single-vehicle Monte Carlo simulation, mixed-integer linear programming (MILP) optimization, and M/G/c post-calibration for long-haul highway freight transport. The method aims to achieve 100% flow capture by embedding path energy constraints into the MILP formulation and introducing peak-hour capacity constraints derived from queueing theory to address service instability. A case study demonstrates that the existing network without optimization is severely overloaded, achieving a capture rate of only 72.7%. Under the baseline scenario with 30% BET penetration, the optimized solution deploys eight charging stations. After station-level M/G/c calibration, the final configuration comprises 54 fast chargers and 271 slow chargers, representing a 33.8% reduction in total charger count compared to the initial MILP solution, effectively eliminating resource redundancy caused by the global capacity assumption. As the penetration rate increases to 60% and 100%, accompanied by technological progress, the required number of stations decreases to five, and the charging network evolves toward a “core node concentration” topology, with total costs exhibiting significant increasing returns to scale. This study provides a quantitative basis for zero-carbon freight corridor infrastructure planning and differentiated investment strategies.