Optimal deployment of battery-less truck mobile charging stations on motorways

Although fixed chargers have been installed on motorways, they often fail to meet the charging demand of electric vehicles (EV) on peak days. The battery-less truck mobile charging station (BL-TMCS) is a truck that carries chargers for EV and can be moved along motorways to provide additional energy. Based on a detailed analysis of the feasibility of using BL-TMCS, this paper proposed a solution to the problem. By integrating traffic simulation, the Monte Carlo method, the EV energy consumption model, and the user decision model for charging, a comprehensive simulation-based framework was established to estimate the spatio-temporal distribution of charging demand on motorways. A precise method was developed to calculate waiting time of users based on the real-time status of chargers. To minimise the average waiting time of users, an optimal deployment model for BL-TMCS was formulated considering constraints such as the number, operating costs and transfer times, and solved using a genetic algorithm. The numerical study shows that with a 50% increase in traffic volume on the test motorway, the deployment of two BL-TMCSs reduces the average waiting time from 21.76 to 0.75 min. These results highlight the effectiveness of the proposed solution and method.

Authors

Institutions

Publication Details

Journal
Proceedings of the Institution of Civil Engineers - Municipal Engineer
Published
2025-11-25
DOI
https://doi.org/10.1680/jmuen.25.00021
Primary Topic
Electric Vehicles and Infrastructure
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Optimal deployment of battery-less truck mobile charging stations on motorways

Zhaolei Zhang, Zhixue Li, Wei Hao, Songlin Zhou et al.
Proceedings of the Institution of Civil Engineers - Municipal Engineer
Electric Vehicles and Infrastructure
article

Optimal deployment of battery-less truck mobile charging stations on motorways

Zhaolei Zhang, Zhixue Li, Wei Hao, Songlin Zhou, Bin Yu
article en

Abstract

Although fixed chargers have been installed on motorways, they often fail to meet the charging demand of electric vehicles (EV) on peak days. The battery-less truck mobile charging station (BL-TMCS) is a truck that carries chargers for EV and can be moved along motorways to provide additional energy. Based on a detailed analysis of the feasibility of using BL-TMCS, this paper proposed a solution to the problem. By integrating traffic simulation, the Monte Carlo method, the EV energy consumption model, and the user decision model for charging, a comprehensive simulation-based framework was established to estimate the spatio-temporal distribution of charging demand on motorways. A precise method was developed to calculate waiting time of users based on the real-time status of chargers. To minimise the average waiting time of users, an optimal deployment model for BL-TMCS was formulated considering constraints such as the number, operating costs and transfer times, and solved using a genetic algorithm. The numerical study shows that with a 50% increase in traffic volume on the test motorway, the deployment of two BL-TMCSs reduces the average waiting time from 21.76 to 0.75 min. These results highlight the effectiveness of the proposed solution and method.

Proceedings of the Institution of Civil Engineers - Municipal Engineer
Department of Science and Technology of Hunan Province (CN), Changsha University of Science and Technology (CN), Beihang University (CN)
Openalex Percentile: Top 64%
Electric Vehicles and Infrastructure
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.