Managing Electric Vehicle Charging Networks: Cooperative Servicing Utilizing Mobile Charging Stations

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Authors

1 Department of Industrial Engineering, K. N. Toosi University of Technology, Tehran, Iran.

2 Faculty of Industrial Engineering, K. N. Toosi University of Technology (KNTU), Tehran, Iran.

3 Department of Industrial Engineering, Shahed University, Tehran, Iran.

Abstract

At present, electric vehicles (EVs) are increasingly recognized as a viable alternative to conventional internal combustion engine vehicles, primarily due to their superior environmental sustainability, particularly regarding carbon emissions, and their cost-effectiveness attributed to lower energy consumption. Consequently, the market share of electric vehicles has witnessed substantial growth in recent years, which has in turn heightened the demand for charging infrastructure. Conversely, the rising number of electric vehicles necessitating recharging-especially during peak demand periods- poses challenges such as prolonged waiting times at public charging stations and increased strain on the power distribution network. To address these issues and enhance network efficiency, the concept of Mobile Charging Stations (MCS) has emerged, offering flexible charging solutions in terms of both time and location. This paper introduces an innovative approach for the allocation and deployment of MCSs in areas with high demand, aimed at alleviating the burden on public charging stations. A mathematical model grounded in the Location-or-Routing Problem (LoRP) has been formulated, employing various truck-based and van-based mobile charging stations to collaboratively service demand points near public charging facilities. This strategy seeks to attain various achievements, including the reduction of network load and waiting times at charging stations while simultaneously expanding coverage to improve customer satisfaction. Based on conducted experiments, a comprehensive evaluation and analysis of the proposed model demonstrate that the LoRP significantly outperforms traditional models in terms of both coverage and cost efficiency.

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Advances in Industrial Engineering

Articles in Press, Accepted Manuscript
Available Online from 18 November 2024

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