Time and energy driven online scheduling problem in EV charging
•Proposal of an online scheduling algorithm for electric vehicle (EV) charging that minimizes maximum flow time during peak charging periods.•The algorithm achieves a competitive ratio of γ=3.4528⋅(1+ϵ) compared to the optimal offline solution.•Utilization of the EV charging power curve characterist...
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| Published in: | Theoretical computer science Vol. 1044; p. 115266 |
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| Abstract | •Proposal of an online scheduling algorithm for electric vehicle (EV) charging that minimizes maximum flow time during peak charging periods.•The algorithm achieves a competitive ratio of γ=3.4528⋅(1+ϵ) compared to the optimal offline solution.•Utilization of the EV charging power curve characteristics to optimize both time efficiency and energy intake.•Significant reduction in queuing congestion and optimization of charging time during peak hours.•Effective balance between time cost and energy intake, enhancing charging station throughput.
Electric vehicles (EVs) become more and more popular, along with the higher and higher efficiency demand for charging scheduling. This paper studies the online scheduling problem of EV charging that minimizes the maximum flow time, solving the problem of queuing congestion at charging stations during peak hours. Based on the characteristics of the charging power curve of electric vehicles, we design an online scheduling algorithm for EV charging and prove that the ratio of this algorithm to the optimal offline algorithm is γ=3.4528⋅(1+ϵ). We analyse and find that the proposed algorithm can adjust the charging time proportionally, allowing the electric vehicle EV j to gradually reach the final State of Charge (SOC), which is 0.7313+0.2687⋅jini, from its initial charging state (jini). According to the battery overheating protection mechanism, charging will stop when the battery level starts to decrease. This method effectively balances the trade-off between time cost and energy intake, significantly improving the queuing situation for charging electric vehicles during peak hours. |
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| AbstractList | •Proposal of an online scheduling algorithm for electric vehicle (EV) charging that minimizes maximum flow time during peak charging periods.•The algorithm achieves a competitive ratio of γ=3.4528⋅(1+ϵ) compared to the optimal offline solution.•Utilization of the EV charging power curve characteristics to optimize both time efficiency and energy intake.•Significant reduction in queuing congestion and optimization of charging time during peak hours.•Effective balance between time cost and energy intake, enhancing charging station throughput.
Electric vehicles (EVs) become more and more popular, along with the higher and higher efficiency demand for charging scheduling. This paper studies the online scheduling problem of EV charging that minimizes the maximum flow time, solving the problem of queuing congestion at charging stations during peak hours. Based on the characteristics of the charging power curve of electric vehicles, we design an online scheduling algorithm for EV charging and prove that the ratio of this algorithm to the optimal offline algorithm is γ=3.4528⋅(1+ϵ). We analyse and find that the proposed algorithm can adjust the charging time proportionally, allowing the electric vehicle EV j to gradually reach the final State of Charge (SOC), which is 0.7313+0.2687⋅jini, from its initial charging state (jini). According to the battery overheating protection mechanism, charging will stop when the battery level starts to decrease. This method effectively balances the trade-off between time cost and energy intake, significantly improving the queuing situation for charging electric vehicles during peak hours. |
| ArticleNumber | 115266 |
| Author | Xu, Yicheng Guo, Xinru Dai, Sijia Han, Xinxin Zhang, Yong |
| Author_xml | – sequence: 1 givenname: Xinru orcidid: 0000-0002-5719-1659 surname: Guo fullname: Guo, Xinru email: xr.guo@siat.ac.cn organization: Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, PR China – sequence: 2 givenname: Sijia surname: Dai fullname: Dai, Sijia email: sj.dai@siat.ac.cn organization: Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, PR China – sequence: 3 givenname: Xinxin surname: Han fullname: Han, Xinxin email: hanxin@szpu.edu.cn organization: School of Artificial Intelligence, Shenzhen Polytechnic University, Shenzhen, PR China – sequence: 4 givenname: Yicheng surname: Xu fullname: Xu, Yicheng email: yc.xu@siat.ac.cn organization: Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, PR China – sequence: 5 givenname: Yong surname: Zhang fullname: Zhang, Yong email: zhangyong@siat.ac.cn organization: Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, PR China |
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| Keywords | Scheduling Online algorithm Competitive analysis Flow-time EV charging |
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