Renewable Energy Based Wireless EV Charging Infrasturture
DOI:
https://doi.org/10.15662/IJEETR.2026.0802260Keywords:
Renewable energy, wireless power transfer, electric vehicles, inductive charging, EV infrastructure, smart grid, energy storage, solar power, charging efficiency, sustainable transportationAbstract
The increasing demand for electrical energy and the rapid growth of portable electronic devices and electric vehicles have created a strong need for efficient and sustainable charging systems. Conventional charging methods depend heavily on wired connections and electricity generated from non-renewable energy sources such as fossil fuels. These methods often lead to energy losses, environmental pollution, and limitations in mobility due to the requirement of physical cables. To address these challenges, renewable energy sources and wireless power transfer technologies are being explored as alternative solutions. This project proposes a Solar Wireless Electrical Charging System that combines solar energy generation with wireless power transfer technology to provide a convenient, eco-friendly, and efficient charging method.
The proposed system utilizes solar panels to capture sunlight and convert it into electrical energy. Solar energy is one of the most abundant and renewable sources of energy available in nature. By using solar panels, the system can generate electricity without relying on conventional power grids. The electrical energy generated by the solar panel is stored in a rechargeable battery through a charge controller that regulates the voltage and prevents overcharging. This stored energy serves as the power source for the wireless charging system.
Wireless power transfer is achieved through inductive coupling, which allows electrical energy to be transmitted between two coils without direct physical contact. The transmitter coil is connected to the power source, and when alternating current flows through the coil, it generates a magnetic field. This magnetic field induces a voltage in the receiver coil placed nearby. The induced voltage is then converted into usable electrical energy to charge electronic devices or batteries. This method eliminates the need for cables and connectors, making the charging process safer and more convenient. The system includes electronic components such as oscillators, transmitter and receiver coils, rectifiers, and voltage regulators to ensure efficient wireless power transfer. The transmitter circuit converts the stored DC energy into high-frequency AC signals, which are then transmitted through the transmitter coil. The receiver coil captures the transmitted magnetic energy and converts it back into electrical energy using rectification and voltage regulation circuits. The regulated output voltage can then be used to charge small electronic devices such as mobile phones, sensors, or rechargeable batteries.
References
1. R. K. Sharma and S. Verma, “Wireless Power Transfer for Mobile Charging Applications,” International Journal of Engineering Research & Technology (IJERT), vol. 9, no. 3, pp. 120–125, 2020.
2. A. Kumar and S. Gupta, “Design and Implementation of Solar Powered Wireless Charging System,” International Journal of Innovative Research in Electrical, Electronics, Instrumentation and Control Engineering, vol. 7, no. 5, pp. 45–50, 2019.
3. M. Patel and R. Desai, “Solar Energy Based Wireless Charging System for Portable Devices,” International Journal of Advanced Research in Electrical, Electronics and Instrumentation Engineering, vol. 8, no. 4, pp. 210–215, 2019.
4. S. Banerjee and T. K. Das, “Inductive Wireless Power Transfer System for Low Power Applications,” International Journal of Recent Technology and Engineering (IJRTE), vol. 8, no. 3, pp. 350–355, 2019.
5. C.Nagarajan and M.Madheswaran - ‘Stability Analysis of Series Parallel Resonant Converter with Fuzzy Logic Controller Using State Space Techniques’- Taylor &Francis, Electric Power Components and Systems, Vol.39 (8), pp.780-793, May 2011. DOI: 10.1080/15325008.2010.541746
6. C.Nagarajan and M.Madheswaran - ‘Experimental verification and stability state space analysis of CLL-T Series Parallel Resonant Converter’ - Journal of Electrical Engineering, Vol.63 (6), pp.365-372, Dec.2012. DOI: 10.2478/v10187-012-0054-2
7. C.Nagarajan and M.Madheswaran - ‘Performance Analysis of LCL-T Resonant Converter with Fuzzy/PID Using State Space Analysis’- Springer, Electrical Engineering, Vol.93 (3), pp.167-178, September 2011. DOI 10.1007/s00202-011-0203-9
8. S.Tamilselvi, R.Prakash, C.Nagarajan,“Solar System Integrated Smart Grid Utilizing Hybrid Coot-Genetic Algorithm Optimized ANN Controller” Iranian Journal Of Science And Technology-Transactions Of Electrical Engineering, DOI10.1007/s40998-025-00917-z,2025
9. S.Tamilselvi, R.Prakash, C.Nagarajan,“ Adaptive sliding mode control of multilevel grid-connected inverters using reinforcement learning for enhanced LVRT performance” Electric Power Systems Research 253 (2026) 112428, doi.org/10.1016/j.epsr.2025.112428
10. S.Thirunavukkarasu, C. Nagarajan, 2024, “Performance Investigation on OCF and SCF study in BLDC machine using FTANN Controller," Journal of Electrical Engineering And Technology, Volume 20, pages 2675–2688, (2025), doi.org/10.1007/s42835-024-02126-w
11. C. Nagarajan, M.Madheswaran and D.Ramasubramanian- ‘Development of DSP based Robust Control Method for General Resonant Converter Topologies using Transfer Function Model’- Acta Electrotechnica et Informatica Journal , Vol.13 (2), pp.18-31,April-June.2013, DOI: 10.2478/aeei-2013-0025.
12. C.Nagarajan and M.Madheswaran - ‘DSP Based Fuzzy Controller for Series Parallel Resonant converter’- Springer, Frontiers of Electrical and Electronic Engineering, Vol. 7(4), pp. 438-446, Dec.12. DOI 10.1007/s11460-012-0212-0.
13. C.Nagarajan and M.Madheswaran - ‘Experimental Study and steady state stability analysis of CLL-T Series Parallel Resonant Converter with Fuzzy controller using State Space Analysis’- Iranian Journal of Electrical & Electronic Engineering, Vol.8 (3), pp.259-267, September 2012.
14. C.Nagarajan and M.Madheswaran, “Analysis and Simulation of LCL Series Resonant Full Bridge Converter Using PWM Technique with Load Independent Operation” has been presented in ICTES’08, a IEEE / IET International Conference organized by M.G.R.University, Chennai.Vol.no.1, pp.190-195, Dec.2007
15. Suganthi Mullainathan, Ramesh Natarajan, “An SPSS and CNN modelling based quality assessment using ceramic materials and membrane filtration techniques”, Revista Materia (Rio J.) Vol. 30, 2025, DOI: https://doi.org/10.1590/1517-7076-RMAT-2024-0721
16. M Suganthi, N Ramesh, “Treatment of water using natural zeolite as membrane filter”, Journal of Environmental Protection and Ecology, Volume 23, Issue 2, pp: 520-530,2022
17. [5] V. Singh and P. Sharma, “Development of Solar Powered Charging Station Using Renewable Energy,” International Journal of Scientific Research in Engineering and Technology, vol. 6, no. 2, pp. 98–102, 2018.
18. [6] J. A. Shafique and M. Ahmed, “Wireless Power Transfer Technology for Portable Electronic Devices,” IEEE Access, vol. 7, pp. 112–118, 2019.
19. [7] N. Gupta and R. Mehta, “Solar Powered Smart Charging System Using Inductive Coupling,” International Journal of Computer Applications, vol. 178, no. 39, pp. 15–20, 2020.
20. [8] P. S. Reddy and K. Kumar, “Design of Inductive Wireless Power Transfer System for Mobile Devices,” International Journal of Engineering and Advanced Technology (IJEAT), vol. 9, no. 2, pp. 420–425, 2020.
21. [9] L. Wang and Y. Li, “Wireless Charging System Based on Magnetic Resonance Coupling,” IEEE Transactions on Power Electronics, vol. 33, no. 7, pp. 610–618, 2018.
22. [10] S. K. Verma and A. Mishra, “Solar Powered Wireless Energy Transfer System for Sustainable Applications,” International Journal of Electrical and Electronics Research, vol. 7, no. 4, pp. 55–60, 2019.
23. Kiran, A., Rubini, P., & Kumar, S. S. (2025). Comprehensive review of privacy, utility and fairness offered by synthetic data. IEEE Access.
24. Gopinathan, V. R. (2024). Real-Time Financial Risk Intelligence Using Secure-by-Design AI in SAP-Enabled Cloud Digital Banking. International Journal of Computer Technology and Electronics Communication, 7(6), 9837-9845.
25. Udayakumar, R., Elankavi, R., Vimal, R., & Sugumar, R. (2023). Improved Particle Swarm Optimization with Deep Learning-Based Municipal Solid Waste Management in Smart Cities. Environmental & Social Management Journal, 17(4).
26. Anand, L. (2023). An Intelligent AI and ML–Driven Cloud Security Framework for Financial Workflows and Wastewater Analytics. International Journal of Humanities and Information Technology, 5(02), 87-94.
27. Soundappan, S. J. (2020). Big Data Analytics in Healthcare: Applications for Pandemic Forecasting. International Journal of Advanced Research in Computer Science & Technology, 3(1), 2248-2253.
28. Rajasekar, M. (2024). Real-Time Predictive DevOps Intelligence for Risk-Aware Digital Business Processes in Cloud and SAP Ecosystems. International Journal of Advanced Research in Computer Science & Technology, 7(4), 10713-10718.
29. Poornima, G., & Anand, L. (2024, May). Novel AI Multimodal Approach for Combating Against Pulmonary Carcinoma. In 2024 5th International Conference for Emerging Technology (INCET) (pp. 1-6). IEEE.
30. Prabha, P. S., & Rengarajan, A. (2025). Adaptive Cloud Resource Allocation Using Attention-Driven Deep Reinforcement Learning. Engineering, Technology & Applied Science Research, 15(6), 29334-29340.
31. Jagadeesh, S., & Sugumar, R. (2017). A Comparative study on Artificial Bee Colony with modified ABC algorithm. European Journal of Applied Sciences, 9(5), 243-248.
32. Varma, K. K., & Anand, L. (2025, March). Deep Learning Driven Proactive Auto Scaler for High-Quality Cloud Services. In International Conference on Computing and Communication Systems for Industrial Applications (pp. 329-338). Singapore: Springer Nature Singapore.
33. Kumar, S. A., & Anand, L. (2025). A Novel EEG-Based Deep Learning Framework for Enhancing Communication in Locked-In Syndrome Using P300 Speller and Attention Mechanisms. KSII TRANSACTIONS ON INTERNET AND INFORMATION SYSTEMS, 19(11), 3841-3855.
34. Poornima, G., & Anand, L. (2025). Medical image fusion model using CT and MRI images based on dual scale weighted fusion based residual attention network with encoder-decoder architecture. Biomedical Signal Processing and Control, 108, 107932.
35. Archana, R., & Anand, L. (2025). Residual u-net with Self-Attention based deep convolutional adaptive capsule network for liver cancer segmentation and classification. Biomedical Signal Processing and Control, 105, 107665.Kumar, S. A., & Anand, L. (2025). A Novel EEG-Based Deep Learning Framework for Enhancing Communication in Locked-In Syndrome Using P300 Speller and Attention Mechanisms. KSII Transactions on Internet and Information Systems, 19(11), 3841-3855.
36. Rengarajan, A. (2025). Cloud-Based AI-Driven Threat Detection Framework for Smart Grid Cybersecurity. International Journal of Future Innovative Science and Technology, 8(6), 16065.
37. Murugeshwari, B., Sudharson, K., Panimalar, S. P., Shanmugapriya, M., & Abinaya, M. (2020). SAFE–Secure Authentication in Federated Environment using CEG Key code.
38. Raj A. A., & Sugumar, R. (2023). Early Detection of COVID-19 with Impact on Cardiovascular Complications using CNN Utilising Pre-Processed Chest X-Ray Images. 2023 International Conference on Applied Intelligence and Sustainable Computing (ICAISC), IEEE.
39. Jagadeesh, S., & Sugumar, R. (2017). A Comparative study on Artificial Bee Colony with modified ABC algorithm. European Journal of Applied Sciences, 9(5), 243-248.
40. Selvi, G. V., Anbarasan, A. B., Murthy, B. A., & Prabavathy, S. (2023). An Application Oriented Integrated Unequal Clustering Algorithm for Wireless Sensor Network. In Underwater Vehicle Control and Communication Systems Based on Machine Learning Techniques (pp. 140-154). CRC Press.
41. Sruthi, R. S., Ananya, S., & Murugeshwari, B. (2010). Web Based Virtual Control System Laboratory and On-Line Temperature Control of Electrophoresis Equipment using LabVIEW. International Journal of Computer Applications, 975, 8887.
42. Vimal Raja, G. (2021). Mining Customer Sentiments from Financial Feedback and Reviews using Data Mining Algorithms. International Journal of Innovative Research in Computer and Communication Engineering, 9(12), 14705-14710.
43. MATHEW, A. R. (2025). Neurosecurity and Brain-Computer Interfaces.
44. Soundappan, S. J. (2024). AI-Driven Customer Intelligence in Enterprise Lakehouse Systems Sentiment Mining Governance-Aware Analytics and Real-Time Data Synchronization. International Journal of Advanced Engineering Science and Information Technology (IJAESIT), 7(5), 14905.
45. Mathew, A. (2025). Human–AI Collaboration in Security Operations: Measuring Alert Trust, Automation Bias, and Analyst Upskilling in AI-Augmented SOC Environments. International Journal of Computer Technology and Electronics Communication, 8(5), 11375-11380.
46. Soundappan, S. J. (2022). AI-Based Fault Detection and Isolation for Reliability in Modern Power Systems. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 5(4), 7106-7110.
47. Poornima, G., & Anand, L. (2024, April). Effective Machine Learning Methods for the Detection of Pulmonary Carcinoma. In 2024 Ninth International Conference on Science Technology Engineering and Mathematics (ICONSTEM) (pp. 1-7). IEEE.Garg, V. K., Soundappan, S. J., & Kaur, E. M. (2020). Enhancement in intrusion detection system for WLAN using genetic algorithms. South Asian Research Journal of Engineering and Technology, 2(6), 62–64.
48. Rengarajan, A., Jayakumar, C., & Sugumar, R. (2012). Optimization Of Recent Attacks Using Internet Protocol. National Journal of System and Information Technology, 5(1), 8.
49. Mathew, A. (2024). AI TRiSM: Trust, Risk, and Security Management in Cybersecurity. Cybersecurity, 4(3), 84-90.
50. Mathew, A. (2025). Deep seek vs. ChatGPT: A deep dive into AI Language mastery. Int J Multidisciplinary Res, 7(1), 1-5.





