ORIGINAL RESEARCH article
Front. Energy Res.
Sec. Solar Energy
Volume 13 - 2025 | doi: 10.3389/fenrg.2025.1556726
Performance Enhancement of Photovoltaic and Thermoelectric Generator Combined Hybrid Energy System based on hCOSS-MPPT Algorithm
Provisionally accepted
- VIT University, Vellore, India
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Recently, there has been a big demand for cleaner energy production. Renewable energy systems such as photovoltaic (PV), thermoelectric generators (TEG), and hybrid PV-TEG (hPV-TEG) systems achieved cost parity with non-renewable fuels. The hPV-TEG system is more efficient compared to the standard PV or TEG. However, it is not possible to reach the maximum achievable power by the hybrid system due to its nonlinear nature. Hence, this paper proposes an HCOSS-MPPT based hybrid PV-TEG system to increase power efficiency. The proposed hybrid system consists of the PV array, Buck-Boost converter in which an HCOSS-based MPPT controller is integrated, TEG with battery, 3-phase VSI inverter, and a grid. In the proposed methodology, the Harmonic Coyote Optimal Step Size-based Maximum Power Point Tracking (HCOSS -MPPT) method adjusts the duty cycle of the DC-DC Buck-Boost converter to maximize the power output of the grid-connected PV system. The Harmonic mean-based Coyote Optimization Algorithm (HCOA) is used for the Maximum Power Point (MPP) problem by obtaining optimal step size instead of fixed step size. The simulation results are presented to demonstrate the efficacy of the design. The result indicated that the proposed hPV-TEG in EV has obtained a maximum power of nearly 10x 10 4 Watt. The proposed model under standard test conditions has nearly achieved above 8x10 4 Watt power. Moreover, the proposed hPV-TEG with battery showed good performance when compared to the hPV-TEG model without battery and the PV model with battery. Thus, this analysis proves that the proposed hPV-TEG system with HCOSS -MPPT shows better performance and optimal results.
Keywords: Photovoltaic (PV) Cell, Thermoelectric generators (TEGs), Maximum Power Point Tracking (MPPT), Perturb and Observe (P&O) and Harmonic mean based Coyote Optimization Algorithm (HCOA), Maximum power point tracking (MPPT)
Received: 08 Jan 2025; Accepted: 24 Jul 2025.
Copyright: © 2025 R and Pathipooranam. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Ponnambalam Pathipooranam, VIT University, Vellore, India
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