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Stream/Area:
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Subject:
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Paper ID:
IJSREM66328
Volume:
10 |
7 |
Jul 2026
Pages:
1-7
Publication Frequency:
Monthly (12 issues per year)
License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.
Electrical Engineering
CSMSS College of Engineering, Chh. Sambhaji Nagar
Electrical Engineering
CSMSS College of Engineering, Chh. Sambhaji Nagar
This paper presents the design and experimental validation of an IoT-enabled Wireless Electric Vehicle (EV) Charging Station integrated with a smart grid comprising Solar Photovoltaic (PV), Wind Turbine, and Maharashtra State Electricity Board (MSEB) grid supply. An ESP32 (38-pin) microcontroller serves as the central intelligence, continuously monitoring DC voltage from solar and wind sources and scaled AC voltage from the MSEB supply through resistive sensing circuits. A solar tracking mechanism driven by dual LDR sensors and servo motors maximizes photovoltaic energy capture by orienting the panel toward peak irradiance. Priority-based relay logic selects the optimal available source — Solar > Wind > MSEB — and routes power to a wireless inductive charging transmitter coil (resonant frequency ~100 kHz, 5W), which inductively transfers energy to a receiver coil mounted on a demonstration toy EV model. The receiver coil connects to a TP4056 lithium-ion battery charging module that charges a 3.7V/1000 mAh cell. A sliding switch on the toy car toggles a miniature voltage display between coil-received voltage and battery terminal voltage, providing immediate on-board feedback. A 16×2 I²C LCD displays live voltage readings locally; a SIM800L GSM module issues SMS alerts on source transitions; and the ThingSpeak cloud platform logs all channel data for remote analytics. Experimental results over a 5-day evaluation period demonstrate 99.5% source-switching success, ≤32 ms average relay transition time, ±0.6% voltage measurement accuracy, and 62.7% renewable energy utilization. The system provides a scalable, cost-effective blueprint (total BOM ≈ ₹9,200) for green-energy-powered wireless EV charging infrastructure.
KEYWORDS
This paper has presented and experimentally validated an IoT-based Wireless EV Charging Station underpinned by a three-source smart grid and a solar tracking system. The ESP32 microcontroller orchestrates real-time voltage monitoring, priority-based relay switching, LDR-driven solar panel orientation, wireless coil power delivery, local LCD visualization, ThingSpeak cloud logging, and GSM SMS alerting within a unified, cost-effective platform (BOM ≈ ₹9,200). The prototype demonstrated: 62.7% renewable energy utilization, 100% relay switching success (mean 30.8 ms), ±0.6% voltage accuracy, 21.3% improvement in solar energy capture via tracking, and complete wireless charging of a 3.7 V Li-ion cell in 68 minutes. The on-board toy EV receiver assembly — with TP4056 charging module, sliding-switch dual-reading voltmeter, and flush-mounted receiver coil — demonstrates the end-to-end viability of the concept. Future work will extend the system to SAE J2954-compliant 3.3 kW GaN-based wireless charging, incorporate a lithium battery storage buffer for uninterrupted charging during source transitions, deploy machine learning-based energy forecasting on the ESP32-S3 edge AI platform, and implement bi-directional V2G (Vehicle-to-Grid) capability. The architecture is directly scalable to real EV parking bays and roadway dynamic charging scenarios.
[1] A. Kurs et al., "Wireless power transfer via strongly coupled magnetic resonances," Science, vol. 317, no. 5834, pp. 83–86, 2007. [2] M. Budhia, G. A. Covic, and J. T. Boys, "Design and optimization of circular magnetic structures for lumped inductive power transfer systems," IEEE Trans. Power Electron., vol. 26, no. 11, pp. 3096–3108, 2011. [3] SAE International, "SAE J2954: Wireless Power Transfer for Light-Duty Plug-In/Electric Vehicles and Alignment Methodology," 2022. [4] X. Fang et al., "Smart grid—the new and improved power grid: A survey," IEEE Commun. Surv. Tutorials, vol. 14, no. 4, pp. 944–980, 2012. [5] S. Lata, S. Vadhera, and M. Mehta, "Energy management system for grid-connected HRES using fuzzy logic," J. Renew. Sustain. Energy, vol. 12, no. 1, p. 015301, 2020. [6] B. B. Sahoo, R. Panda, and D. Das, "ESP32-based IoT monitoring system for solar–wind hybrid microgrid," IEEE Access, vol. 9, pp. 78345–78358, 2021. [7] S. Abdallah and S. Salem, "The effects of controlled full and half day sun tracking systems on the voltage-current characteristics and power generation of flat-plate solar collectors," Renew. Energy, vol. 28, no. 10, pp. 1511–1521, 2003. [8] H. Rezk and A. M. Eltamaly, "A comprehensive comparison of different MPPT techniques for photovoltaic systems," Sol. Energy, vol. 112, pp. 1–11, 2015. [9] N. Kumar and M. Vijayalakshmi, "SMS-based remote monitoring system for solar power plants using SIM900A," Int. J. Electron. Commun. Eng., vol. 10, no. 2, pp. 45–52, 2017. [10] S. Alam et al., "IoT-based smart grid communication system for electric vehicle charging," IEEE Syst. J., vol. 12, no. 4, pp. 3663–3672, 2018.
Publication Date
7/24/2026
Peer Review Type
Double-Blind Peer Review
Review Rounds: 2
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