Dynamic Expansion Planning of Charging Stations With Fixed and Mobile Chargers

Truck mobile chargers (TMCs) with high-capacity batteries can alleviate peak grid pressure and reduce fixed charger idleness during non-peak periods. Although TMCs are already in use, existing charging station expansion plans rarely account for their integration. To meet growing but fluctuating charging demand and enhance station resilience, this study optimizes charger deployment by jointly considering fixed chargers and TMCs, with attention to expansion cost, service quality, and utilization. The model determines the optimal number of fixed chargers to install, the number of TMCs to rent, and their rental durations. Based on Monte Carlo-simulated scenarios generated from historical data, a sample average approximation (SAA) method is employed to support robust decision-making under uncertainty. Applied to a highway service area in China, the strategy keeps peak waiting under 10 minutes, improves robustness, and increases utilization by 17-18%. Management suggestions are also provided to support sustainable transportation planning.

Institutions

Publication Details

Journal
IEEE Transactions on Intelligent Transportation Systems
Published
2025-09-29
DOI
https://doi.org/10.1109/tits.2025.3611656
Primary Topic
Agriculture Sustainability and Environmental Impact
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Dynamic Expansion Planning of Charging Stations With Fixed and Mobile Chargers

IEEE Transactions on Intelligent Transportation Systems
Agriculture Sustainability and Environmental Impact
article

Dynamic Expansion Planning of Charging Stations With Fixed and Mobile Chargers

article en

Abstract

Truck mobile chargers (TMCs) with high-capacity batteries can alleviate peak grid pressure and reduce fixed charger idleness during non-peak periods. Although TMCs are already in use, existing charging station expansion plans rarely account for their integration. To meet growing but fluctuating charging demand and enhance station resilience, this study optimizes charger deployment by jointly considering fixed chargers and TMCs, with attention to expansion cost, service quality, and utilization. The model determines the optimal number of fixed chargers to install, the number of TMCs to rent, and their rental durations. Based on Monte Carlo-simulated scenarios generated from historical data, a sample average approximation (SAA) method is employed to support robust decision-making under uncertainty. Applied to a highway service area in China, the strategy keeps peak waiting under 10 minutes, improves robustness, and increases utilization by 17-18%. Management suggestions are also provided to support sustainable transportation planning.

IEEE Transactions on Intelligent Transportation SystemsVol. 26(12)
Sichuan University (CN)
Openalex Percentile: Top 42%
Agriculture Sustainability and Environmental Impact
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.