Battery Electric Truck Infrastructure Co-Design via Joint Optimization and Agent-Based Simulation

As zero-emission zones emerge in European cities, fleet operators are shifting to electric vehicles. To maintain their current operations, a clear understanding of the charging infrastructure required and its relationship to existing power grid limitations is needed. This study presents an optimization framework to jointly design charging infrastructure and schedules within a logistics distribution network, validated through agent-based simulations. We formulate the problem as a mixed-integer linear program (MILP) and develop an agent-based model (ABM) to evaluate various designs and operations under stochastic conditions. Our experiments compare rule-based and optimized strategies in a case study of The Netherlands. Results show that current commercial solutions suffice for middle-mile logistics, with central co-design yielding average cost reductions of 5.2%–6.4% and an average 20.1% decrease in total installed power. While rule-based control effectively manages charging operations and mitigates delays, optimizing charge scheduling significantly reduces queuing times (99%), charging costs (13.5%), and time spent near capacity (10.9%). Our optimization-simulation framework paves the way for combining optimized infrastructure planning and realistic fleet operations in digital-twin environments.