Hours-of-service-aware siting of charging and battery-swapping stations for long-haul electric trucks under adoption uncertainty

2026-10-06

AI Summary

This study addresses the challenge of coordinating charging durations with legally mandated driving rest periods for long-haul electric trucks. It proposes a multi-stage stochastic mixed-integer programming framework to jointly optimize the siting and scheduling of charging and battery-swapping stations. Notably, this work is the first to integrate hours-of-service regulations into infrastructure planning, enabling the overlap of charging activities with mandatory rest breaks. To handle demand uncertainty, a Stochastic Dual Dynamic integer Programming (SDDiP) algorithm based on Lagrangian cuts is developed. Validation along Australia’s East Coast corridor demonstrates that under ±30% adoption rate fluctuations, the network configuration varies by only 12%–13%, with over 95% of stations deferred to the second construction phase. These findings indicate substantial reductions in total operational time and enhanced investment robustness.

Abstract

Planning en-route charging and battery-swapping infrastructure for long-haul battery-electric trucks (BETs) requires models that reflect how trucks actually operate. This paper develops a mixed-integer programming framework that jointly sites charging or swapping stations and schedules each truck's charging, swapping and mandatory driver rest, so that charging time overlaps with regulated rest instead of being added to it. Energy use is derived segment by segment from road terrain with a tractive-force model, and the truck battery is modelled as a set of independently swappable packs. Staged investment under uncertain BET adoption is formulated as a multistage stochastic program with Markovian demand and solved by stochastic dual dynamic integer programming (SDDiP) with Lagrangian cuts; we show that the Lagrangian multipliers can be bounded by each station's annualised cost without weakening the cuts. Applied to twelve freight corridors on Australia's East Coast, the algorithm jointly optimises charging and battery-swapping events as well as mandatory break events. A +/- 30\% change in adoption alters the final charge-only network by only -12\% to +13\% of stations, with over 95\% of stations built by the second stage. Hedging against uncertainty mainly changes which sites are chosen (71\% overlap with a deterministic rolling-horizon model), not when they are built.

Problem

• charging infrastructure siting
• battery-swapping stations
• long-haul electric trucks
• hours-of-service regulations
• adoption uncertainty

Innovation

• mixed-integer programming
• stochastic dual dynamic integer programming
• battery-swapping
• hours-of-service
• multistage stochastic program