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© Oleksandr Lysenko, Postgraduate Student, Department of Technical Operation of Automobiles and Car Service,
ORCID: 0009-0004-5704-9945,
e-mail: ravex1118@gmail.com
(National Transport University)
OPTIMIZATION OF THE NETWORK OF CHARGING STATIONS
FOR ELECTRIC VEHICLES IN THE CONTEXT OF NATIONAL
AND INTERNATIONAL TRANSPORT CORRIDORS
DOI: 10.33868/0365-8392-2026-1-286-64-70
- Abstract. The article investigates the scientific and applied problem of optimizing the network of charging stations for electric vehicles in the conditions of national and international transport corridors. The relevance of the topic is due to the rapid growth of the electric vehicle fleet, increased requirements for the decarbonization of road transport and the need to ensure the continuity of intercity and transit transportation in the process of Ukraine’s integration into the European transport space. Particular attention is paid to transport corridors, which are characterized by high traffic intensity, heterogeneous structure of transport flows and increased re-quirements for the reliability of infrastructure support. The purpose of the study is to develop and substantiate a comprehensive approach to optimizing the placement and parameters of the operation of charging stations along main routes, taking into account transport, energy, economic and environmental factors. The methodo-logical basis of the work is system analysis, transport modeling methods, graph theory, elements of the theory of queuing and multi-criteria optimization. The transport corridor is presented in the form of a directed graph, which made it possible to formalize the spatial structure of traffic and determine the demand for charging elec-tric vehicles depending on the intensity of flows, the length of sections and the average range of electric vehicles.
A multicriteria optimization model is proposed, in which the objective function combines the minimization of charging waiting time, total capital and operating costs, as well as energy and environmental losses. A system of constraints is introduced into the model, reflecting the permissible distances between charging stations, the throughput of charging equipment, the capabilities of electrical networks and the requirements for the stability of traffic flow maintenance. This ensures the practical orientation of the results and the possibility of their ap-plication for planning real infrastructure.
Numerical modeling was carried out for the international transport corridor Lviv – Krakovets – Rzeszow – Kra-ków, which demonstrated that the optimized placement of high-power charging stations allows significantly reducing the charging waiting time, ensuring energy continuity of electric transport traffic and increasing the throughput capacity of the main direction. The results obtained confirm the feasibility of transitioning from fragmented placement of charging infrastructure to a corridor approach focused on the needs of transit and intercity transportation.
The scientific novelty of the work lies in improving approaches to optimizing the charging station network by integrating the transport and flow characteristics of corridors, charging technology parameters and energy infrastructure constraints into a single formalized model. The practical significance of the results lies in the possibility of their use by state administration bodies, transport planners and infrastructure operators when developing strategies for the development of the electric vehicle network of Ukraine, taking into account the requirements of international transport corridors and European regulatory standards.
Keywords: electric transport, charging infrastructure, transport corridors, optimization, road transport, electric vehicles.
References
1. Deb, S., Tammi, K., Gao, X. Z., Kalita, K., Mahanta, P., & Cross, S. (2022). A robust two-stage planning model for the charging station placement problem considering road traffic uncertainty. IEEE Transactions on Intelligent Transportation Systems, 23, 7, 6571–6585. DOI: https://doi.org/10.1109/TITS.2021.3058419
2. EUR-Lex (2023). Regulation (EU) 2023/1804 of the European Parliament and of the Council of 13 September 2023 on the deployment of alternative fuels infrastruc-ture, and repealing Directive 2014/94/EU. Retrieved from https://eur-lex.europa.eu/eli/reg/2023/1804/oj/eng
3. EUR-Lex (2024). Deployment of alterna-tive fuels infrastructure: Summary of Regula-tion (EU) 2023/1804. Retrieved from https://eur-lex.europa.eu/EN/legal-content/summary/deployment-of-alternative-fuels-infrastructure.html
4. Tang, Y., Chau, K. T., & Liu, W. (2023). Charging station placement optimization using queueing model with time-varying ar-rival rate. In Proceedings of the 36th Interna-tional Electric Vehicle Symposium and Exhi-bition (EVS36) (Sacramento, USA, June 11–14, 2023)
5. Ullah, I., Liu, K., & Yamamoto, T. (2023). Scheduling-based time-dependent solution for electric vehicle charging station network. Journal of Advanced Transportation, Article ID 6103796. DOI: https://doi.org/10.1155/2023/6103796
6. Parent, P.-L., Carvalho, M., & Anjos, M. F. (2024). Maximum flow-based formulation for the optimal location of electric vehicle charging stations. Networks, 84, No. 2, 109–131.
DOI: https://doi.org/10.1002/net.22219
7. Pourvaziri, A., Faghih-Roohi, S., & Bari, A. (2024). Robust optimization of electric vehi-cle charging station locations considering grid stability and traffic patterns. Transpor-tation Research Part E: Logistics and Trans-portation Review, 187, Article 103568. DOI: https://doi.org/10.1016/j.tre.2024.103568
8. Zanvettor, G. G., et al. (2024). A stochastic approach for EV charging stations in demand response programs. Applied Energy, 373, 123862. DOI: https://doi.org/10.1016/j.apenergy.2024.123862
9. ICCT (2023). European Union alternative fuel infrastructure regulation (AFIR): Policy update. Retrieved from https://theicct.org/publication/afir-eu-april2023/
10. ACEA (2024). Charging ahead: Accelerat-ing the roll-out of EU electric vehicle charging infrastructure (snapshot end of 2023). Re-trieved from https://www.acea.auto/publication/automotive-insights-charging-ahead-accelerating-the-rollout-of-eu-electric-vehicle-charging-infrastructure/
11. IEA (2025). Regulation (EU) 2023/1804 alternative fuels infrastructure regulation (AFIR) – Policies. Retrieved from https://www.iea.org/policies/27375-regulation-eu-20231804-alternative-fuels-infrastructure-regulation
12. IEA (2025). Electric vehicle charging – Global EV outlook 2025. Retrieved from https://www.iea.org/reports/global-ev-outlook-2025/electric-vehicle-charging
13. Martínez, V. A., Sumper, A., Saldaña-Gonzalez, A. E., & Gadelha, V. (2025). Planning fast-charging stations along highways using probability distribution functions and traffic data. Sustainable Energy Technologies and Assessments, 104547.
DOI: https://doi.org/10.1016/j.seta.2025.104547
14. Wang, B., Ge, X., Jin, Y., Xu, M., & Huang, Z. (2025). Orderly charging scheduling for EVs with a novel queuing model under power capacity constraints. Applied Sciences, 15, 22, 12038.
DOI: https://doi.org/10.3390/app152212038
15. Nankali, A., & Levin, M. W. (2025). An exact solution algorithm for the bi-level op-timization of electric vehicle charging sta-tions and user charging behavior. arXiv.
Retrieved from https://arxiv.org/abs/2511.19884
ISSN 0365-8392
DOI: 10.33868/0365-8392-2026-1-286-64-70
Дата першого надходження статті: 10.01.2026
Дата прийняття до друку: 15.03.2026
Дата публікації: 31.03.2026