Designing battery swapping and charging networks for electric freight transport under demand uncertainty
Abstract
Long-haul truck electrification requires rapid energy replenishment near freight demand, although dispersed hubs may lack sufficient grid capacity. We study a "Swap-Locally, Charge-Centrally" architecture embedded in a relay freight network: swapping remains close to freight flows, charging can be shifted to grid-suitable hubs, and co-location is retained where economical. A multi-period, two-stage stochastic location-inventory-fleet model coordinates station deployment, replenishment assignments and grid-bounded charging capacity with scenario-dependent battery stocks, batch sizes and incremental fleet requirements. Corridor electrification competes with temporary diesel operation. Building on submodularity-based constraint generation for location-inventory systems, we give an alternative scalar partial-minimization proof that the battery value function is submodular, show that the fleet recourse is affine in the binary swap-station decisions, and develop a Lovász-cut Benders decomposition with hub-period block cuts and a convex battery oracle with analytic gradients and memoization. On a seven-state southeastern U.S. freight network, the method reaches gaps below 0.01% on all five instances, the largest in about four minutes, whereas a strengthened extensive formulation reaches this tolerance only on the two smallest instances within three hours. Stochastic planning avoids the capacity shortfalls of a mean-value plan, and joint planning avoids the battery and fleet costs that sequential siting shifts downstream. The optimized network uses remote charging selectively, keeps most replenishment local, and expands geographic coverage before intensifying capacity within its committed deployment plan.
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BibTeX
@misc{xu2026designing,
title = {{Designing battery swapping and charging networks for electric freight transport under demand uncertainty}},
author = {Yujia Xu and Walid Klibi and Benoit Montreuil},
howpublished = {arXiv},
year = {2026},
month = oct,
doi = {10.48550/arxiv.2610.39652},
eprint = {2610.39652},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2610.39652},
}
RIS
TY - UNPB
TI - Designing battery swapping and charging networks for electric freight transport under demand uncertainty
AU - Yujia Xu
AU - Walid Klibi
AU - Benoit Montreuil
PY - 2026
DA - 2026/10/29
PB - arXiv
DO - 10.48550/arxiv.2610.39652
UR - https://arxiv.org/abs/2610.39652
AB - Long-haul truck electrification requires rapid energy replenishment near freight demand, although dispersed hubs may lack sufficient grid capacity. We study a "Swap-Locally, Charge-Centrally" architecture embedded in a relay freight network: swapping remains close to freight flows, charging can be shifted to grid-suitable hubs, and co-location is retained where economical. A multi-period, two-stage stochastic location-inventory-fleet model coordinates station deployment, replenishment assignments and grid-bounded charging capacity with scenario-dependent battery stocks, batch sizes and incremental fleet requirements. Corridor electrification competes with temporary diesel operation. Building on submodularity-based constraint generation for location-inventory systems, we give an alternative scalar partial-minimization proof that the battery value function is submodular, show that the fleet recourse is affine in the binary swap-station decisions, and develop a Lovász-cut Benders decomposition with hub-period block cuts and a convex battery oracle with analytic gradients and memoization. On a seven-state southeastern U.S. freight network, the method reaches gaps below 0.01% on all five instances, the largest in about four minutes, whereas a strengthened extensive formulation reaches this tolerance only on the two smallest instances within three hours. Stochastic planning avoids the capacity shortfalls of a mean-value plan, and joint planning avoids the battery and fleet costs that sequential siting shifts downstream. The optimized network uses remote charging selectively, keeps most replenishment local, and expands geographic coverage before intensifying capacity within its committed deployment plan.
ER -