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Updated: Jun 29, 2026

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
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Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

Published on: September 8, 2023

Blockchain and bio-inspired deep learning for energy-efficient EV-to-grid optimization.

N V Ravindhar1, A Manju2, S Murugesan3

  • 1Department of Computer Science and Engineering, Saveetha Engineering College, Thandalam, Chennai, 602105, Tamil Nadu, India. ravindharnv1@gmail.com.

Scientific Reports
|April 6, 2026
PubMed
Summary
This summary is machine-generated.

This study introduces an energy-efficient framework for Electric Vehicle-to-Grid (V2G) systems using bio-inspired deep learning and blockchain. The optimized V2G coordination reduces costs, enhances grid stability, and improves EV charging efficiency.

Keywords:
Bio-inspired optimizationBlockchain authenticationEV load managementGRU forecastingSmart charging optimizationV2G coordination

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Last Updated: Jun 29, 2026

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
05:30

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

Published on: September 8, 2023

Area of Science:

  • Smart Grids and Renewable Energy Integration
  • Artificial Intelligence in Energy Systems
  • Cybersecurity in Energy Infrastructure

Background:

  • Electric Vehicle-to-Grid (V2G) technology enables bidirectional energy flow, crucial for modern smart grids.
  • Challenges in V2G include real-time decision-making, load balancing, and secure transaction validation.
  • Existing V2G systems require optimization for efficiency and reliability.

Purpose of the Study:

  • To propose an energy-efficient optimization framework for V2G charging and discharging schedules using Electric Vehicle (EV) fleets.
  • To enhance the security and trustworthiness of V2G transactions through blockchain integration.
  • To improve the overall performance and resilience of smart grids with integrated EVs.

Main Methods:

  • Developed a Bio-Inspired Deep Learning Controller utilizing a Monarch Butterfly Optimization (MBO) algorithm and Gated Recurrent Unit (GRU) network.
  • Employed GRU networks for forecasting grid demand and EV battery availability.
  • Integrated a blockchain layer with smart contracts for secure and tamper-resistant transaction logging (authentication, pricing, energy transfer).

Main Results:

  • Achieved a 19.6% reduction in charging costs and 23.2% peak load shaving efficiency.
  • Demonstrated a high forecast accuracy of 96.4% across various mobility scenarios.
  • Improved grid regulation response time by 28% and reduced EV queuing delay by 31%.

Conclusions:

  • The proposed framework offers a scalable and secure solution for V2G coordination, validated through MATLAB/Simulink, TensorFlow, and Ethereum-based blockchain simulations.
  • The system facilitates real-time adaptation to market conditions, strengthens grid resilience, and optimizes EV operations.
  • Further research could incorporate real-world deployment factors like communication latency and hardware non-idealities.