10.57647/ijeee.2024.1502.08

An Effective Requests-Based Charging Station Scheduling and Routing Model with HUI-MTA ANN and BD-SSO for EV Charging

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  • Rajat Yadav1,
  • Kalpana Chauhan*1,
  • Rajeev Kumar Chauhan2,
  1. Department of Electrical Engineering, Central University of Haryana, Mahendragarh, Haryana-123031, India
  2. Department of Electrical Engineering, Dayalbagh Educational Institute (Deemed to be University), Agra, UP-282005, India

Copyright (c) 2024 Rajat Yadav, Kalpana Chauhan, Rajeev Kumar Chauhan (Author)

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This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

Yadav, R., Chauhan, K., & Chauhan, R. K. (2024). An Effective Requests-Based Charging Station Scheduling and Routing Model with HUI-MTA ANN and BD-SSO for EV Charging. International Journal of Energy and Environmental Engineering, 15(02 (June 2024). https://doi.org/10.57647/ijeee.2024.1502.08
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Abstract

Electric Vehicles (EVs) rely on charging stations (CSs), but uncoordinated charging often leads to grid voltage deviations and long waiting times. This study proposes an integrated scheduling and routing framework that combines renewable-powered CSs with intelligent decision-making. A Singleton Membership Modified Fuzzy Logic Controller (SMM-FLC)-based MPPT ensures stable renewable power extraction, while a He-Uniform Initialized ModTanh Activated ANN (HUI-MTA-ANN) selects the optimal CS by considering queue lengths, travel deadlines, and cost. The Bayes Distributed Squirrel Search Optimization (BD-SSO) algorithm then identifies the most efficient route, and Quadratic Programming (QP) schedules charging slots. Results on an EV charging dataset show the framework achieves 97% accuracy with very low false selection rates, while significantly improving renewable energy utilization. The findings highlight a 
scalable and intelligent solution for reliable EV charging infrastructure. 

Keywords

  • Bayes distributed-Squirrel Search Optimization algorithm (BD-SSO),
  • Charging Station (CS),
  • Electric Vehicle (EV),
  • He-Uniform Initialized-ModTanh Activated-Artificial Neural Network (HUI-MTA-ANN),
  • Maximum Power-Point-Tracking (MPPT) controller,
  • Quadratic Programming (QP),
  • Renewable power sources,
  • Singleton Membership Modified Fuzzy Logic Controller (SMM-FLC)
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