Abstract
In the near future, Electric Vehicles (EVs) are anticipated to develop into fantastic modes of transportation. Due to their limited range and under powered batteries, EVs are crucial for lowering the use of conventional fuel. When the battery charge is about to reach a critical level, it is essential to be aware of local Charging Stations (CS). As a result, we could spot two issues: (1) Secured Cluster based CS allocation and routing to CS (2) Scheduling vehicle at CS based on delay prediction. First, a Cluster based Vacant charging slot is searched in clustered charging stations using cloud and Vehicular Adhoc Network (VANET) model, along with evolutionary Social Ski Driven (SSD) optimized algorithm using Deep Recurrent Neural Network (DRNN) as a new optimal routing for EVs to reach CS based on established fitness function computing distance, battery power and traffic congestion. Second, at CS, vehicle time scheduling is done using the DRNN approach, considering delay-based distance computation. When compared to the stochastic Particle Swarm Optimization (PSO) algorithm for routing, the proposed DRNN-SSD routing algorithm optimizes delay and traffic congestion significantly achieving better successful allocation rate of CS during On-peak and Off-peak hours.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Dow, L., Liu, R., Liu, E.: A survey of PEV impacts on electric utilities, pp. 1–8 (2017)
Céspedes, S., Taha, S., Shen, X.: A multihop-authenticated proxy mobile IP scheme for asymmetric VANETs. IEEE Trans. Veh. Technol. 62(7), 3271–3286 (2013)
Tharwat, Liang, H., Zhuang, W.: Efficient on-demand data service delivery to high-speed trains in cellular/info station integrated networks. IEEE J. Sel. Areas Commun. 30(4), 780–791 (2012)
Pierri, E., Cirillo, V., Vietor, T., Sorrentino, M.: Adopting a conversion design approach to maximize the energy density of battery packs in electric vehicles. Energies 14 (2021). https://doi.org/10.3390/en14071939
Tharwat, A.: Social Ski-Driver (SSD) optimization algorithm: python code (2019)
Leontiadis, I., Marfia, G., Mack, D., Pau, G., Mascolo, C., Gerla, M.: On the effectiveness of an opportunistic traffic management system for vehicular networks. IEEE Trans. Intell. Transp. Syst. 12(4), 1537–1548 (2011)
Liu, A., Li, C., Xia, B., Yue, W., Miao, Z.: G-MACO: a multi-objective route planning algorithm on green wave effect for electric vehicles. In: 2018 IEEE 87th Vehicular Technology Conference (VTC Spring), pp. 1–5 (2018)
Pourazarm, S., Cassandras, C.G., Malikopoulos, A.: Optimal routing of electric vehicles in networks with charging nodes: a dynamic programming approach. In: 2014 IEEE International Electric Vehicle Conference (IEVC), pp. 1–7 (2014)
Yang, H., Yang, S., Xu, Y., Cao, E., Lai, M., Dong, Z.: Electric vehicle route optimization considering time-of-use electricity price by learnable partheno-genetic algorithm. IEEE Trans. Smart Grid 6(2), 657–666 (2015)
Nimalsiri, N., Smith, D., Ratnam, E., Mediwaththe, C., Halgamuge, S.: A decentralized electric vehicle charge scheduling scheme for tracking power profiles. In: 2020 IEEE Power & Energy Society Innovative Smart Grid Technologies Conference (ISGT), pp. 1–5 (2020)
Hiremath, S.C., Mallapur, J.D.: A cloud interfaced social ski driver optimized routing for future electric motor vehicles. In: Tuba, M., Akashe, S., Joshi, A. (eds.) ICT Systems and Sustainability. LNNS, vol. 321, pp. 385–395. Springer, Singapore (2022)
Hiremath, S.C., Mallapur, J.D.: Fractional-social ski driver optimization-driven routing protocol for routing electric vehicle under server hosted VANET. Multimed. Tools Appl. 81, 17437–17456 (2022). https://doi.org/10.1007/s11042-022-12543-6
Hiremath, S.C., Mallapur, J.D.: Social SKI driver based efficient parking dynamics and computation using deep reinforcement learning in vehicular cloud. J. Theor. Appl. Inf. Technol. 100(10), 4615–4627 (2022). ISSN 1992-8645. E-ISSN 1817-3195
Author information
Authors and Affiliations
Corresponding authors
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Hiremath, S.C., Mallapur, J.D. (2023). Optimization of Secured Cluster Based Charging Dynamics and Scheduling of EV Using Deep RNN. In: Prabhu, S., Pokhrel, S.R., Li, G. (eds) Applications and Techniques in Information Security . ATIS 2022. Communications in Computer and Information Science, vol 1804. Springer, Singapore. https://doi.org/10.1007/978-981-99-2264-2_14
Download citation
DOI: https://doi.org/10.1007/978-981-99-2264-2_14
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-99-2263-5
Online ISBN: 978-981-99-2264-2
eBook Packages: Computer ScienceComputer Science (R0)