Abstract

This paper presents ZYNERGY, an AI-enabled hybrid solar–hydrogen energy system designed to improve short- and long-duration backup capability by combining photovoltaic (PV) generation, maximum power point tracking (MPPT), LiFePO4 battery storage, water electrolysis, hydrogen storage, a Zn–H2 fuel-cell backup unit, power conditioning, and ESP32-based IoT monitoring. The proposed design uses a 50-W PV panel and a 12-V, 20-Ah LiFePO4 battery, corresponding to a nominal battery energy of 240 Wh. Under an assumed 5 peak-sun-hour condition and a 0.75 system derating factor, the PV subsystem is estimated to produce about 187.5 Wh/day. With an usable-energy factor of 80% and 90% conversion efficiency, approximately 172.8 Wh of battery energy is available, giving an estimated 8.64 h of backup for a 20-W load. The hydrogen subsystem will be analyzed on the basis of a sample of 0.01-kg H2 which represents chemical energy of 333.3 Wh; at a hypothetical 50% fuel-cell efficiency, 166.7 Wh of electrical energy can potentially be harvested. The ESP32 monitoring layer captures PV and battery electrical parameters, temperature, hydrogen parameters, and fault conditions, while the AI/ML layer extension will enable load prediction, state-of-charge estimation, abnormal condition detection, and energy management. The values are design values and need to be verified by instrumented prototypes.

Authors

C. Tharunkumar, R. Naveen, V. Sanjay, P. Annamalai, Jayanthan Raj
Madras Institute of Technology, India

Keywords

Green Hydrogen, Photovoltaic System, LiFePO4 Battery, Fuel Cell, MPPT, IoT, ESP32, Energy Management, Hybrid Renewable Energy, Hydrogen Storage