.png){kind=icon}
.png){kind=icon}
Return
A Robust Optimization Framework for eBus Charging Infrastructure Planning
.png){kind=icon}
PRE
AI
DOI：10.1007/s10732-026-09582-9.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Efficient and robust charging infrastructure plays a key role in accelerating the adoption of electric buses (eBuses) in urban transit systems. Unlike diesel buses, eBuses depend on strategically placed fast-charging stations to maintain continuous operation while ensuring high service quality. Planners must balance infrastructure investment, network reliability, and operational feasibility when determining optimal charging locations. Providing redundant charging access prevents disruptions from station failures, energy consumption fluctuations, and scheduling uncertainties. This work introduces an Iterated Local Search (ILS) algorithm that optimises the number and placement of charging stations. The approach improves robustness by incorporating backup charging stations and flexible energy redistribution techniques. We evaluate performance using real-world public transportation data from Cork and Dublin, comparing results against the state-of-the-art Large Neighborhood Search (LNS) method. Our experiments show that ILS outperforms LNS in 81.2% of cases by requiring fewer charging stations, whereas LNS performs better in 7.8% of cases. ILS is particularly effective in high-energy-demand scenarios with strict timetable constraints and realistic discharging rates, demonstrating its effectiveness in ensuring viable and scalable charging station placement.
Keywords:
Iterated Local Search
Electric Buses
Charging Location Problem
Combinatorial Optimization
Journal
J
IF:
1.4
Papers:
30
Citations:
1.3K