Abstract
As global interest shifts toward sustainable transportation with the proliferation of electric vehicles (EVs), the demand for an efficient, real-time, and robust charging infrastructure becomes increasingly pronounced. This paper introduces an approach to address the imbalance between the surging EV demand and the existing charging infrastructure: the concept of Mobile Charging Stations (MCSs). The research develops an algorithm for the dynamic placement of MCSs to significantly reduce the waiting time for EV owners. The core of this research is the Two-stage Placement and Management with Multi-Agent Reinforcement Learning (2PM-MARL) for a dynamic balancing of charging demand and supply. The complexity of the problem is elaborated by showing the NP-hard nature of the MCS placement issue through a relation to the Uncapacitated Facility Location Problem (UFLP), underscoring the computational challenges and emphasizing the need for intelligent real-time solutions. Our framework is validated through comprehensive experiments using real-world charging session data. The results exhibit significant reductions in the waiting time, suggesting the potential practicality and efficiency of our proposed model.
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Notes
- 1.
LIGHTNING MOBILE, https://lightningemotors.com/.
- 2.
SparkCharge, https://www.sparkcharge.io/.
- 3.
References
Charging stations by state (2021). https://evadoption.com/ev-charging-stations-statistics/charging-stations-by-state/
Afshar, S., Disfani, V.R.: Optimal scheduling of electric vehicles in the presence of mobile charging stations. In: IEEE PESGM (2022)
Cornuéjols, G., Nemhauser, G.L., Wolsey, L.A.: The uncapacitated facility location problem (1990). https://api.semanticscholar.org/CorpusID:8880493
Elghitani, F., El-Saadany, E.F.: Efficient assignment of electric vehicles to charging stations. IEEE Trans. Smart Grid 12, 761–773 (2021)
Hardman, S., et al.: A review of consumer preferences of and interactions with electric vehicle charging infrastructure. Transp. Res. Part D: Transp. Environ. 62, 508–523 (2018)
Hussain, S., Kim, Y.S., Thakur, S., Breslin, J.G.: Optimization of waiting time for electric vehicles using a fuzzy inference system. IEEE T-ITS 23, 15396–15407 (2022)
Irle, R.: Global EV sales for 2022 (2023). https://www.ev-volumes.com/
Lee, K.B., Ahmed, M.A., Kang, D.K., Kim, Y.C.: Deep reinforcement learning based optimal route and charging station selection. Energies 13, 6255 (2020)
Lin, K., Zhao, R., Xu, Z., Zhou, J.: Efficient large-scale fleet management via multi-agent deep reinforcement learning. In: ACM SIGKDD (2018)
Liu, L., Xi, Z., Zhu, K., Wang, R., Hossain, E.: Mobile charging station placements in internet of electric vehicles: a federated learning approach. IEEE T-ITS 23, 24561–24577 (2022)
Liu, Q., Zeng, Y., Chen, L., Zheng, X.: Social-aware optimal electric vehicle charger deployment on road network. In: ACM SIGSPATIAL (2019)
Moradipari, A., Alizadeh, M.: Pricing and routing mechanisms for differentiated services in an electric vehicle public charging station network. IEEE Trans. Smart Grid 11, 1489–1499 (2019)
Sadeghianpourhamami, N., Deleu, J., Develder, C.: Definition and evaluation of model-free coordination of electrical vehicle charging with reinforcement learning. IEEE Trans. Smart Grid 11, 203–214 (2018)
Sadreddini, Z., Guner, S., Erdinç, O.: Design of a decision-based multicriteria reservation system for the EV parking lot. IEEE TTE 7, 2429–2438 (2021)
Sunehag, P., et al.: Value-decomposition networks for cooperative multi-agent learning (2017). arXiv:abs/1706.05296
Tang, X., et al.: Value function is all you need: a unified learning framework for ride hailing platforms. In: ACM SIGKDD (2021)
Ting, L.P.Y., Wu, P.H., Chung, H.Y., Chuang, K.T.: An incentive dispatch algorithm for utilization-perfect EV charging management. In: Gama, J., Li, T., Yu, Y., Chen, E., Zheng, Y., Teng, F. (eds.) Advances in Knowledge Discovery and Data Mining. Lecture Notes in Computer Science(), vol. 13282, pp. 132–146. Springer, Cham (2022). https://doi.org/10.1007/978-3-031-05981-0_11
von Wahl, L., Tempelmeier, N., Sao, A., Demidova, E.: Reinforcement learning-based placement of charging stations in urban road networks. In: ACM SIGKDD (2022)
Yan, L., Shen, H., Kang, L., Zhao, J., Xu, C.: Reinforcement learning based scheduling for cooperative EV-to-EV dynamic wireless charging. In: IEEE MASS (2020)
Zheng, B., et al.: Soup: spatial-temporal demand forecasting and competitive supply in transportation. IEEE TKDE 35, 2034–2047 (2023)
Acknowledgement
This paper was supported in part by National Science and Technology Council (NSTC), R.O.C., under Contract 112-2221-E-006-158 and 113-2622-8-006-011-TD1.
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Ting, L.PY., Lin, CC., Lin, SH., Chu, YL., Chuang, KT. (2024). Multi-agent Reinforcement Learning for Online Placement of Mobile EV Charging Stations. In: Yang, DN., Xie, X., Tseng, V.S., Pei, J., Huang, JW., Lin, J.CW. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2024. Lecture Notes in Computer Science(), vol 14649. Springer, Singapore. https://doi.org/10.1007/978-981-97-2262-4_23
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