Abstract
Electric vehicles (EVs) are important for most countries and regions that rely on imported energy. Unfortunately, slow recharge and improper deployment at charging stations have limited the widespread use of EVs. To improve the recharging speed of charging stations and optimize their deployment, we propose a Minimized optimization Deployment algorithm based on EV Dynamic Changes (MDDC) to optimize the deployment of eXtremely Fast Charging (XFC) stations. The method selects multiple EV distribution maps to simulate EV movement and utilizes a grid partitioning method to divide the deployment area. To reduce the deployment scope of XFC stations, the grids with small and stable EV numbers variance are excluded from service areas. Then, we design the minimum required XFC station optimization function to achieve coverage for all EVs. Three optimization rules are designed to reduce the overlapping coverage of XFC stations, which minimizes the number of XFC stations. Finally, we use an efficient and accurate method named Minimum dIstance Sum of Unique Public area location (MISUP) in MDDC to redetermine the deployment location of XFC stations. We verify the rationality, effectiveness, and robustness of MDDC through extensive simulations, and the results show that the MDDC outperforms the comparison algorithm by 54.2% and 52.0% when using the Euclidean algorithm and the A-star algorithm, respectively.
Similar content being viewed by others
References
Xiong Y et al (2017) Optimal electric vehicle fast charging station placement based on game theoretical framework. IEEE Trans Intell Transp Syst 19(8):2493–2504
Wang X et al (2016) Electric vehicle charging station placement for urban public bus systems. IEEE Trans Intell Transp Syst 18(1):128–139
Fang C et al (2020) Dynamic pricing for electric vehicle extreme fast charging. IEEE Trans Intell Transp Syst 22(1):531–541
Shareef H et al (2016) A review of the stage-of-the-art charging technologies placement methodologies and impacts of electric vehicles. Renew Sustain Energy Rev 64:403–420
He J et al (2018) An optimal charging station location model with the consideration of electric vehicle’s driving range. Transp Res C Emerg Technol 86:641–654
Cao Y et al (2018) Toward distributed battery switch based electro-mobility using publish/subscribe system. IEEE Trans Veh Technol 67(11):10204–10217
Zhang Y et al (2018) Optimal charging scheduling by pricing for ev charging station with dual charging modes. IEEE Trans Intell Transp Syst 20(9):3386–3396
Efthymiou D et al (2017) Electric vehicles charging infrastructure location: a genetic algorithm approach. Eur Transp Res Rev 9(2):27
Pinto F et al (2016) Space-aware modeling of two-phase electric charging stations. IEEE Trans Intell Transp Syst 18(2):450–459
Iyer V et al (2018) Extreme fast charging station architecture for electric vehicles with partial power processing. In: 2018 IEEE Applied Power Electronics Conference and Exposition (APEC), San Antonio, TX, pp 659–665
Liu Y et al (2019) Challenges and opportunities towards fast-charging battery materials. Nat Energy 4(7):540–550
Chen X et al (2021) Enabling extreme fast charging technology for electric vehicles. IEEE Trans Intell Transp Syst 22(1):466–470
Cui Q et al (2019) Electric vehicle charging station placement method for urban areas. IEEE Trans Smart Grid 10(6):6552–6565
Davidov S, Pantoš M (2017) Planning of electric vehicle infrastructure based on charging reliability and quality of service. Energy 118:1156–1167
Xiang Y et al (2016) Economic planning of electric vehicle charging stations considering traffic constraints and load profile templates. Appl Energy 178:647–659
Gopalakrishnan R et al (2016) Demand prediction and placement optimization for electric vehicle charging stations. In: Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence, pp 3117–3123
Zhao H, Li N (2016) Optimal siting of charging stations for electric vehicles based on fuzzy delphi and hybrid multi-criteria decision making approaches from an extended sustainability perspective. Energies 9(4):270
Zhang Y et al (2019) Expanding ev charging networks considering transportation pattern and power supply limit. IEEE Trans Smart Grid 10(6):6332–6342
Yang W et al (2020) Joint planning of ev fast charging stations and power distribution systems with balanced traffic flow assignment. IEEE Trans Ind Inf 17(3):1795–1809
Luo C et al (2015) Placement of ev charging stations–balancing benefits among multiple entities. IEEE Trans Smart Grid 8(2):759–768
Wu X et al (2021) A novel fast-charging stations locational planning model for electric bus transit system. Energy 224:120106
Andrenacci N et al (2016) A demand-side approach to the optimal deployment of electric vehicle charging stations in metropolitan areas. Appl Energy 182:39–46
Vazifeh M et al (2019) Optimizing the deployment of electric vehicle charging stations using pervasive mobility data. Transp Res Part A Policy Pract 121:75–91
Wei W et al (2017) Expansion planning of urban electrified transportation networks: a mixed-integer convex programming approach. IEEE Trans Transport Electrif 3(1):210–224
Wang S et al (2018) Stochastic collaborative planning of electric vehicle charging stations and power distribution system. IEEE Trans Ind Informat 14(1):321–331
de Quevedo P et al (2019) Impact of electric vehicles on the expansion planning of distribution systems considering renewable energy storage and charging stations. IEEE Trans Smart Grid 10(1):794–804
Zhang H et al (2017) Optimal planning of pev charging station with single output multiple cables charging spots. IEEE Trans Smart Grid 8(5):2119–2128
Chen H et al (2019) Design and planning of a multiple-charger multiple-port charging system for pev charging station. IEEE Trans Smart Grid 10(1):173–183
Yang Q et al (2019) Optimal sizing of pev fast charging stations with Markovian demand characterization. IEEE Trans Smart Grid 10(4):4457–4466
Liu X et al (2022) Time efficient tag searching in large-scale rfid systems: a compact exclusive validation method. IEEE Trans Mob Comput 21(4):2891–2905
Liu X et al (2021) Time-efficient target tags information collection in large-scale rfid systems. IEEE Trans Mob Comput 20(4):2891–2905
Xue Y et al (2016) Adopting strategic niche management to evaluate ev demonstration projects in china. Sustainability 8(2):142
Aljaidi M (2019) Optimal placement and capacity of electric vehicle charging stations in urban areas: survey and open challenges. In: 2019 IEEE Jordan International Joint Conference on Electrical Engineering and Information Technology (JEEIT), pp 238–243
Zhu Z et al (2018) Charging station planning for plug-in electric vehicles. J Syst Sci Syst Eng 27(1):24–45
Alhazmi Y et al (2017) Optimal allocation for electric vehicle charging stations using trip success ratio. Int J Electr Power Energy Syst 91:101–116
Li J et al (2018) Planning electric vehicle charging stations based on user charging behavior. In: 2018 IEEE/ACM Third International Conference on Internet-of-Things Design and Implementation (IoTDI), pp 225–236
Rajabi-Ghahnavieh A, Sadeghi-Barzani P (2016) Optimal zonal fast-charging station placement considering urban traffic circulation. IEEE Trans Veh Technol 66(1):45–56
Ge S et al (2012) The planning of electric vehicle charging stations in the urban area. In: 2nd International Conference on Electronic & Mechanical Engineering and Information Technology, pp 1598–1604
Sadeghi-Barzani P et al (2014) Optimal fast charging station placing and sizing. Appl Energy 125:289–299
Karp R (1972) Reducibility among combinatorial problems. In: Miller RE, Thatcher JW, Bohlinger JD (eds) Complexity of Computer Computations. Springer, Boston, pp 85–103
Tseng C, Siewiorek D (1986) Automated synthesis of data paths in digital systems. IEEE Trans Comput Aided Des Integr Circuits Syst 5(3):379–395
Wang Q et al (2017) Optimized charging scheduling with single mobile charger for wireless rechargeable sensor networks. Symmetry 9(11):285
Khelladi L et al (2017) Efficient on-demand multi-node charging techniques for wireless sensor networks. Comput Commun 101:44–56
Funding
This work was supported in part by the National Science Foundation of Hunan Province under Grant 2018JJ3692; by the National Science Foundation of Changsha City under Grant 63004; by the National Natural Science Foundation of China under Grant 61772559 and 62172443; by the Fundamental Research Funds for the Central Universities of Central South University (China) under Grant 2021zzts0735 and 2021zzts0753..
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
The authors declare that there is no conflict of interest regarding the publication of this paper.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Zhong, P., Xu, A., Kang, Y. et al. An optimal deployment scheme for extremely fast charging stations. Peer-to-Peer Netw. Appl. 15, 1486–1504 (2022). https://doi.org/10.1007/s12083-022-01306-7
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s12083-022-01306-7