Joint Optimization of Charging Infrastructure Placement and Operational Schedules for a Fleet of Battery Electric Trucks

TL;DR

This paper introduces a joint optimization framework for designing charging infrastructure and scheduling operations of electric truck fleets, demonstrating cost savings and operational feasibility through a case study.

Contribution

It presents a mixed-integer linear programming model for co-optimizing infrastructure placement and operational schedules, a novel approach in electric fleet planning.

Findings

Co-design reduces total costs by approximately 3.51%.

Electric trucks can perform similar routes as diesel trucks with current technology.

The framework effectively balances infrastructure costs and grid constraints.

Abstract

This paper examines the challenges and requirements for transitioning logistic distribution networks to electric fleets. To maintain their current operations, fleet operators need a clear understanding of the charging infrastructure required and its relationship to existing power grid limitations and fleet schedules. In this context, this paper presents a modeling framework to optimize the charging infrastructure and charging schedules for a logistic distribution network in a joint fashion. Specifically, we cast the joint infrastructure design and operational scheduling problem as a mixed-integer linear program that can be solved with off-the-shelf optimization algorithms providing global optimality guarantees. For a case study in the Netherlands, we assess the impact of different parameters in our optimization problem, specifically, the allowed deviation from existing operations with conventional diesel trucks and the cost factor for daily peak energy usage. We examine the effects on infrastructure design and power requirements, comparing our co-design algorithm with planned infrastructure solutions. The results indicate that current charging and electric machine technologies for trucks can perform the itineraries of conventional trucks for our case study, but to maintain critical time requirements and navigate grid congestion co-design can have a significant impact in reducing total cost of ownership (average 3.51% decrease in total costs compared to rule-based design solutions).