Abstract
The rapid upsurge in the espousal of electric vehicles in the global market for improving the sustainability of transportation systems raised apprehensions regarding the impact of the electric network during peak load hours that may affect the embargo of a power blackout, voltage drop, etc. The effect of these types of demand-side energy paraphernalia like Electric vehicles on the grid is the cause of locating charging infrastructure, which confines with producing cost, battery charging time, and the limitations in the battery. We propose in this paper a new-fangled methodology that charges an Electric Vehicle on an Adaptive Neuro-Fuzzy Inference System (ANFIS). It efficiently controls non-linear suspension classification and reduces power fluctuation. Also, Hybridization is effective in the progression of prediction models, predominantly for renewable energy systems. Furthermore, with the familiarization of Blockchain Technology, it will accomplish a protected and transparent provision with an acceptable expectancy cost through decentralization of the network.
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Sur, T., Dhar, S., Naskar, S., Adhikari, C., Chakraborty, I. (2022). Adaptive Neuro Fuzzy Inference System for Monitoring Activities in Electric Vehicles Through a Hybrid Approach and Blockchain Technology. In: Giri, D., Mandal, J.K., Sakurai, K., De, D. (eds) Proceedings of International Conference on Network Security and Blockchain Technology. ICNSBT 2021. Lecture Notes in Networks and Systems, vol 481. Springer, Singapore. https://doi.org/10.1007/978-981-19-3182-6_23
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DOI: https://doi.org/10.1007/978-981-19-3182-6_23
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