Optimizing EV Energy Management Using Monarch ‎Butterfly and Quantum Genetic Algorithms

  • Authors

    • B. Ramesh Department of Electronics and Communication Engineering, Annapoorana Engineering College, Salem, Tamil Nadu, India
    • V. V. Kulkarni Department of Electrical Engineering, AISSMS College of Engineering, Pune, Maharashtra, India
    • Ashwini Shinde Department of Electronics and Telecommunication, PCET'S & NMVPM'S Nutan Maharashtra Institute of Engineering and ‎Technology, Pune, Vishnupuri, India
    • Dinesh Kumar J. R Department of ECE, Sri Krishna College of Engineering and Technology, Coimbatore, Tamil Nadu, India
    • Prasanthi Kumari Nunna School of Computer Sciences, University of Petroleum and Energy Studies, Dehradun, India
    • Rajendiran M Department of Computer Science and Engineering, Panimalar Engineering College, Chennai, Tamil Nadu 600123, India
    • Navdeep Kumar Hanumantu Researcher at IEEE, Banglore, Karnataka, India
    • Lalitha Chada Department of Mathematics, Aditya University, Surampalem, Andhra Pradesh, India
    • S. S. Sivasankari Department of AIML, Newhorizon College of Engineering, Bangalore, India
    • R. G. Vidhya Department of ECE, HKBK College of Engineering, Bangalore, India
    https://doi.org/10.14419/xaqk1294

    Received date: May 8, 2025

    Accepted date: June 6, 2025

    Published date: June 22, 2025

  • Monarch Butterfly Optimization; EV Charging or Discharging Scheduling; Quantum Genetic Algorithm; Energy ‎Management

  • Abstract

    In the evolving energy landscape, the integration of electric vehicles (EVs) presents both challenges and opportunities for efficient ‎energy management. This study introduces a hybrid optimization framework that combines Monarch Butterfly Optimization (MBO) ‎and Quantum Genetic Algorithm (QGA) to address the multi-objective problem of EV charging/discharging scheduling and energy ‎storage management in the context of dynamic market transactions. By leveraging the complementary strengths of MBO and QGA, ‎the proposed method delivers robust and adaptive solutions to complex optimization scenarios. The framework aims to minimize ‎charging costs, reduce peak electricity demand, and enhance grid stability, while accounting for the stochastic nature of EV usage ‎patterns and fluctuating energy prices. Extensive simulations and comparative analyses demonstrate the effectiveness of the ‎approach in diverse market environments. The results underscore the potential of integrating nature-inspired and quantum-inspired ‎algorithms for smart grid applications, offering a promising pathway for sustainable and intelligent EV energy management. The ‎integration of MBO and QGA improves the optimization process by using the inherent merits of both algorithms, allowing for ‎robust solutions to complicated optimization issues‎.

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    Ramesh , B. ., Kulkarni , V. V. ., Shinde , A. ., R, D. K. J., Nunna , P. K. ., M, R., Hanumantu , N. K. ., Chada , L. ., Sivasankari, S. S. ., & Vidhya, R. G. . (2025). Optimizing EV Energy Management Using Monarch ‎Butterfly and Quantum Genetic Algorithms. International Journal of Basic and Applied Sciences, 14(2), 311-318. https://doi.org/10.14419/xaqk1294