Optimization of Multi-Power Supply using Quantum Artificial Intelligence for Chemical Industries
DOI:
https://doi.org/10.62486/978-9915-9851-0-7_202631Keywords:
Optimized Energy Sources, Internet of Things, Energy efficiency, Hybrid energy management, Oil and Gas IndustryAbstract
Maximizing the utilization of multiple renewable energy power sources, tied to storage systems, presents an opportunity, as well as a challenge, when optimizing the system efficiency of charge and energy distribution of electric vehicles (EVs). This physics focuses on the improved design of a system whereby the power input and coordination of multiple power sources used in an electric and industrial vehicle are prime considerations. Implementing the IoT in the system ensures advanced automation and a step-down in energy waste of the ecosystems in focus. To enhance the sustainable energy practice, the system incorporates PV battery storage for the restriction of active use of grid energy. The system architecture incorporates batteries with a non-isolated multi-port DC-DC converter, which manages two distinct energy inputs and two outputs of distinct voltage levels to power multiple subsystems of the electric vehicle. The deployed IoT modems facilitate the optimization, real-time monitoring, and minimized system control of the resonator while reducing harmonic distortions and ripple torque in the motor drives. This integration of Electric vehicles with PV systems has been shown to enhance the use of energy and the life of the battery, as shown in the proposed design and systems test results. The system has a good efficiency when incorporated to Chemical Industries.The new system of a hybrid energy management system defines and augments the seamless integration of sustainable electric mobility and enables refined and efficient integration of IoT-based actuators for step-scaled real-time electric drive systems.
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Copyright (c) 2026 Dhanush Kumaran M, Aryaan Ganesan S, C.Bhuvaneswari, W.Abitha Memala, M.Pushpavalli, Christo Ananth (Author)
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The article is distributed under the Creative Commons Attribution 4.0 License. Unless otherwise stated, associated published material is distributed under the same licence.
