Battery Electric Truck Infrastructure Co-design via Joint Optimization and Agent-based Simulation

TL;DR

This paper introduces a joint optimization and agent-based simulation framework for designing electric truck charging infrastructure, demonstrating cost and power savings in a Dutch case study.

Contribution

It develops a mixed-integer linear programming model combined with agent-based simulation to optimize charging infrastructure and schedules for electric freight fleets.

Findings

Central co-design reduces total installed power by 20.1%.

Optimized charge scheduling cuts queuing times by 99%.

Commercial solutions are sufficient for middle-mile logistics.

Abstract

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 frame-work for jointly designing charging infrastructure and schedules within a logistics distribution network, validated through agent-based simulations. We formulate the problem as a mixed-integer linear program and develop an agent-based model 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% to 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.