A computationally efficient jaya optimization for fuel cell maximum power tracking. Issue 1 (31st March 2022)

• Record Type: Journal Article
• Authors: Gugulothu, Ramesh; Nagu, Bhookya; Pullaguram, Deepak
• Is Part Of: Energy sources. Volume 44:Issue 1(2022)
• Page Start: 1541
• Page End: 1565
• Publication Date: 2022-03-31
• Subjects: Fuel cells -- jaya algorithm -- maximum power point tracking (MPPT) -- meta-heuristic -- optimization; Natural resources -- Periodicals; Energy consumption -- Periodicals; Energy consumption -- Climatic factors -- Periodicals; Energy conversion -- Periodicals; Energy conversion -- Environment aspects -- Periodicals; Power (Mechanics) -- Periodicals; 333.7905
• DOI: 10.1080/15567036.2022.2055229
• Languages: English
• ISSNs: 1556-7036

Abstract

ABSTRACT: Fuel cells typically exhibit non-linear, convex P − I characteristics with a single peak power-point for a constant temperature, membrane water content (MWC), hydrogen gas, and oxygen partial pressure. In this paper, a Jaya algorithm-based maximum power point tracking (MPPT) is developed for fast and accurate peak power tracking of a proton exchange membrane fuel cell (PEMFC). Most of the conventional MPPT algorithms are prone to continual steady-state oscillations. Further, most meta-heuristic MPPT algorithms use a PID controller to track the peak power-point. The use of combined meta-heuristic and PID controller affects the efficiency of MPPT since it is strongly dependent on PID controller gains and meta-heuristic optimization parameters. In this paper, a Jaya algorithm-based MPPT tracking approach without a PID controller is developed to fulfill the MPP of a PEMFC. The Jaya algorithm-based MPPT solution ensures a global maximum peak power-point solution that is independent of solver parameters. The efficacy of the proposed Jaya MPPT is evaluated by performing various simulation studies under various operating conditions with different perturbations and compared against widely accepted particle swarm optimization (PSO), conventional perturb and observe (P&O)-based MPPT techniques. The proposed method can track a maximum power of 1411.02 W within two iterations as compared to method particle swarm optimization (PSO), conventional perturb and observe (P&O), which could track a maximum power of 1376.11 W and 1370.4 W, respectively. Thus, giving an additional increase in power efficiency 2.53% Jaya algorithm. Furthermore, the proposed approach delivered an improved output power efficiency of 11.28% compared to the fuel cell operation without MPPT. Further, the real-time feasibility of the proposed algorithm is also validated by developing a hardware prototype and performing various case studies to track MPPT under different operating conditions and perturbations.