Coordinated Battery Electric Vehicle Charging Scheduling across Multiple Charging Stations
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866912599248994304 |
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| author | Mehrnia, Saman Song, Hui Khafaf, Nameer Al Jalili, Mahdi Meegahapola, Lasantha McGrath, Brendan |
| author_facet | Mehrnia, Saman Song, Hui Khafaf, Nameer Al Jalili, Mahdi Meegahapola, Lasantha McGrath, Brendan |
| contents | The uptake of battery electric vehicles (BEVs) is increasing to reduce greenhouse gas emissions in the transport sector. The rapid adoption of BEVs depends significantly on the coordinated charging/discharging infrastructure. Without it, uncontrolled and erratic charging patterns could lead to increased power losses and voltage fluctuations beyond acceptable thresholds. BEV charge scheduling presents a multi-objective optimization (MOO) challenge, demanding a balance between minimizing network impact and maximizing the benefits for electric vehicle charging station (EVCS) operators and BEV owners. In this paper, we develop an MOO framework incorporating a carbon emission program and a dynamic economic dispatch problem, allowing BEV users to respond by charging and discharging through grid-to-vehicle (G2V) and vehicle-to-grid (V2G) technologies according to the optimal electricity price and compensation. Furthermore, we integrate dynamic economic dispatch with time-of-use tariffs to obtain optimal market electricity prices and reduce total costs over 24 hours. Our experimental results on a sample network show that the proposed scheduling increases participation in V2G services by over 10%, increases EVCS benefits by over 20%, and reduces network losses. Furthermore, increased rates of charging/discharging, coupled with more significant carbon revenue benefits for BEV users and EVCS, contribute to better offsetting battery degradation costs. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_17607 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Coordinated Battery Electric Vehicle Charging Scheduling across Multiple Charging Stations Mehrnia, Saman Song, Hui Khafaf, Nameer Al Jalili, Mahdi Meegahapola, Lasantha McGrath, Brendan Systems and Control The uptake of battery electric vehicles (BEVs) is increasing to reduce greenhouse gas emissions in the transport sector. The rapid adoption of BEVs depends significantly on the coordinated charging/discharging infrastructure. Without it, uncontrolled and erratic charging patterns could lead to increased power losses and voltage fluctuations beyond acceptable thresholds. BEV charge scheduling presents a multi-objective optimization (MOO) challenge, demanding a balance between minimizing network impact and maximizing the benefits for electric vehicle charging station (EVCS) operators and BEV owners. In this paper, we develop an MOO framework incorporating a carbon emission program and a dynamic economic dispatch problem, allowing BEV users to respond by charging and discharging through grid-to-vehicle (G2V) and vehicle-to-grid (V2G) technologies according to the optimal electricity price and compensation. Furthermore, we integrate dynamic economic dispatch with time-of-use tariffs to obtain optimal market electricity prices and reduce total costs over 24 hours. Our experimental results on a sample network show that the proposed scheduling increases participation in V2G services by over 10%, increases EVCS benefits by over 20%, and reduces network losses. Furthermore, increased rates of charging/discharging, coupled with more significant carbon revenue benefits for BEV users and EVCS, contribute to better offsetting battery degradation costs. |
| title | Coordinated Battery Electric Vehicle Charging Scheduling across Multiple Charging Stations |
| topic | Systems and Control |
| url | https://arxiv.org/abs/2509.17607 |