ML-Driven Optimization Framework for the Analysis, Design, and Development of Efficient Wireless Power Transfer Systems for EV Charging

Year : 2026 | Volume : 04 | Issue : 01 | Page : 1 9
    By

    Maridas Pillai,

  • Anil Pal,

  • Mukesh Kumar Gupta,

  1. Research Scholar, Department of Computer Science, Suresh Gyan Vihar University, Jaipur, Rajasthan, India
  2. Assistant Professor, Department of Computer Science, Suresh Gyan Vihar University, Jaipur, Rajasthan, India
  3. Professor, Department of Electrical Engineering, Suresh Gyan Vihar University, Jaipur, Rajasthan, India

Abstract

The fast uptake of electric vehicles (EVs) has heightened the necessity of effective, dependable and convenient charging systems. The Wireless Power Transfer (WPT) systems can be taken as a potential solution as they allow charging cells without contact, without any risks, and without any overcrowding; the efficiency of the system is strongly influenced by the alignment of coils, the fluctuations of air-gaps, the conditions of the loads, and geometrical arrangements of the system. The study offers an optimization framework of WPT systems based on the analysis, design, and optimization of its performance to serve EV charging purposes with the involvement of Machine Learning (ML). Under the proposed method, the deep learning regression model is trained to be able to predict the power transfer efficiency in different operational conditions with a high degree of accuracy with R 2 = 0.994. The model helps in maximizing major parameters in coil alignment tolerance, air-gap levels, and magnetic coupling. The experimental and simulated outcomes prove that the ML-optimal system can give up to 6.9% transfer efficiency improvement, 14-percent gain in output power, and 20-percent shorter EV charge time than traditional design methods. Moreover, the enhancement of the field uniformity and the enhancement of the coupling are justified by the magnetic flux density distribution analysis that supports the optimization of coil geometry. The suggested ML based framework shows scalable and smart design approach that can greatly enhance the performance of real world WPT system and thus it is most suitable to current generation smart and autonomous EV charging systems.

Keywords: Wireless Power Transfer, Electric Vehicle Charging, Machine Learning Optimization, Inductive Power Transfer, Efficiency Enhancement

[This article belongs to International Journal of Manufacturing and Production Engineering ]

How to cite this article:
Maridas Pillai, Anil Pal, Mukesh Kumar Gupta. ML-Driven Optimization Framework for the Analysis, Design, and Development of Efficient Wireless Power Transfer Systems for EV Charging. International Journal of Manufacturing and Production Engineering. 2026; 04(01):1-9.
How to cite this URL:
Maridas Pillai, Anil Pal, Mukesh Kumar Gupta. ML-Driven Optimization Framework for the Analysis, Design, and Development of Efficient Wireless Power Transfer Systems for EV Charging. International Journal of Manufacturing and Production Engineering. 2026; 04(01):1-9. Available from: https://journals.stmjournals.com/ijmpe/article=2026/view=236347


References

  1. Sagar et al., “A Comprehensive Review of the Recent Development of Wireless Power Transfer Technologies for Electric Vehicle Charging Systems,” in IEEE Access, vol. 11, pp. 83703-83751, 2023, doi: 10.1109/ACCESS.2023.3300475.
  2. Abuajwa, O., Thiagarajah, S.P., Ambak, Z. et al. Comprehensive review of wireless power transfer systems for electric vehicle charging applications. Discov Appl Sci 7, 1176 (2025). https://doi.org/10.1007/s42452-025-07738-z
  3. Hossain, M.S.; Kumar, L.; Islam, M.M.; Selvaraj, J. A comprehensive review on the integration of electric vehicles for sustainable development. J. Adv. Transp. 2022, 2022, 3868388.
  4. Aslam, S., Aung, P.P., Rafsanjani, A.S. et al. Machine learning applications in energy systems: current trends, challenges, and research directions. Energy Inform 8, 62 (2025). https://doi.org/10.1186/s42162-025-00524-6
  5. Ben Fadhel, Y., & Marques Cardoso, A. J. (2025). Intelligent Optimization and Real-Time Control of Wireless Power Transfer for Electric Vehicles. Electronics, 14(22), 4478. https://doi.org/10.3390/electronics14224478
  6. Mohamed, A.A.S.; Shaier, A.A.; Metwally, H.; Selem, S.I. An Overview of Dynamic Inductive Charging for Electric Vehicles. Energies 2022, 15, 5613.
  7. Wei, S.; Xu, F.; Yuan, D.; Chen, K.; Liu, B.; Li, J. Review on the Applications of Intelligent Algorithm in Wireless Charging System for Electric Vehicles. Energies 2025, 18, 592.
  8. Zhang, H.; Liu, S.; Liu, J. Advanced Magnetic Coupling Resonance Model Optimization for Enhanced Wireless Power Transfer. Electronics 2025, 14, 1152.
  9. Zhang, H.; Liao, M.; He, L.; Lee, C.-K. Parameter Optimization of Wireless Power Transfer Based on Machine Learning. Electronics 2024, 13, 103.
  10. Shern, S. J., Sarker, M. T., Ramasamy, G., Thiagarajah, S. P., Al Farid, F., & Suganthi, S. T. (2024). Artificial Intelligence-Based Electric Vehicle Smart Charging System in Malaysia. World Electric Vehicle Journal, 15(10), 440. https://doi.org/10.3390/wevj15100440

11. Rachid, A.; El Fadil, H.; Gaouzi, K.; Rachid, K.; Lassioui, A.; El Idrissi, Z.; Koundi, M. Electric vehicle charging systems: Comprehensive review. Energies 2022, 16, 255.

Regular Issue Subscription Original Research
Volume 04
Issue 01
Received 21/01/2026
Accepted 28/01/2026
Published 10/02/2026
Publication Time 20 Days
94

Login


My IP

PlumX Metrics