Author:
Ivanovich Vatin Nikolai,Ram Kumar R.P.
Abstract
This article investigates the integration of blockchain technology into cybersecurity frameworks in electric transportation systems, evaluating the implications and advancements achieved in constructing a safe and resilient infrastructure. An analysis of electric vehicle (EV) specifications reveals a diverse range of battery capacity and driving ranges. As an example, the Tesla Model S has a battery capacity of 100 kWh, enabling it to go a distance of up to 320 miles. In contrast, the Nissan Leaf has a battery capacity of 40 kWh and a range of 150 miles. An examination of charging station data uncovers inconsistencies in power generation and transaction expenses. Charging stations with a higher power output of 100 kW have a price of up to $7, whilst stations with a lower power output of 50 kW charge $5. Moreover, the evaluation of cybersecurity metrics demonstrates significant improvements in data encryption and access control in high-security systems, demonstrating a 26% increase compared to low-security settings. An analysis of blockchain transaction records uncovers discrepancies in energy transfers between electric vehicles (EVs) and charging stations. It has been observed that some electric vehicles (EVs) transmit 50 kilowatt-hours (kWh) of energy, while others only transfer 25 kWh, leading to a notable 100% difference. These findings underscore the need of providing consumers with a diverse selection of electric vehicle options, the impact of location-based choices on charging infrastructure, the imperative of robust cybersecurity measures, and the many methods of energy transmission in electric transportation networks. This report offers comprehensive and crucial data that is necessary for stakeholders and policymakers seeking to enhance infrastructure and security systems to establish secure and efficient electric transportation ecosystems.
Reference30 articles.
1. New Protection Schemes in Smarter Power Grids With Higher Penetration of Renewable Energy Systems
2. Intelligent power grid monitoring and management strategy using 3D model visual computation with deep learning
3. “Intelligent Agents for Advanced Power System Protection Schemes – Search | ScienceDirect.com.” Accessed: Jan. 05, 2024. [Online]. Available: https://www.sciencedirect.com/search?qs=Intelligent%20Agents%20for%20Advanced%20Power%20System%20Protection%20Schemes
4. Azeroual M. et al., “Fault location and detection techniques in power distribution systems with distributed generation: Kenitra City (Morocco) as a case study,” Electric Power Systems Research, vol. 209, Aug. 2022, doi: 10.1016/j.epsr.2022.108026.
5. Baidya S. and Nandi C., “A comprehensive review on DC Microgrid protection schemes,” Electric Power Systems Research, vol. 210, Sep. 2022, doi: 10.1016/j.epsr.2022.108051.