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<p>This study presents the development of an adaptive jellyfish search algorithm (AJSA) for maximum power point (MPP) tracking in proton exchange membrane fuel cell (PEMFC)-based electric vehicles (EVs). PEMFCs are widely recognized for their high efficiency, extended lifespan, and suitability in renewable energy applications such as EVs. However, their performance is impacted by dynamic conditions, such as temperature (T), fuel pressure (FP), and membrane hydration, necessitating an effective method for tracking MPP to optimize power output. The AJSA algorithm, inspired by Jellyfish’s adaptive and collaborative behavior, introduces a novel approach to overcome the limitations of conventional and bio-inspired optimization techniques. By balancing exploration and exploitation capabilities, AJSA addresses challenges such as slow convergence, power oscillations, and inefficiencies in traditional methods, including particle swarm optimization (PSO) and the standard jellyfish search algorithm (JSA). The AJSA enhances system stability, reduces failure rates, and maximizes energy generation. Simulations on a 2.76&#xa0;kW PEMFC system, integrated with a DC/DC boost converter, demonstrate AJSA’s superior performance, reducing the average convergence time by 79% compared to PSO and 75% compared to JSA. This reduction minimizes the risk of system failure and reduces power, voltage, and current oscillations across the load. Additionally, AJSA enhances energy efficiency by 25.7% compared to PSO and 17.5% compared to JSA. The proposed method achieves smooth MPP tracking with minimal power oscillations and improved energy conservation, directly impacting system reliability and operational costs. This research establishes AJSA as a robust and efficient solution for PEMFC MPP tracking, offering significant advancements in renewable energy applications for EVs. By addressing technical and economic challenges, AJSA supports sustainable energy solutions, paving the way for enhanced adoption of fuel cell technologies in the automotive industry.</p>

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An adaptive jellyfish search algorithm based on maximizing power tracking of a PEM fuel cell-based electric vehicle application

  • 期刊论文

  • 发表日期：

    2025年8月26日

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  • Balmukund Kumar,
  • Amitesh Kumar

摘要

This study presents the development of an adaptive jellyfish search algorithm (AJSA) for maximum power point (MPP) tracking in proton exchange membrane fuel cell (PEMFC)-based electric vehicles (EVs). PEMFCs are widely recognized for their high efficiency, extended lifespan, and suitability in renewable energy applications such as EVs. However, their performance is impacted by dynamic conditions, such as temperature (T), fuel pressure (FP), and membrane hydration, necessitating an effective method for tracking MPP to optimize power output. The AJSA algorithm, inspired by Jellyfish’s adaptive and collaborative behavior, introduces a novel approach to overcome the limitations of conventional and bio-inspired optimization techniques. By balancing exploration and exploitation capabilities, AJSA addresses challenges such as slow convergence, power oscillations, and inefficiencies in traditional methods, including particle swarm optimization (PSO) and the standard jellyfish search algorithm (JSA). The AJSA enhances system stability, reduces failure rates, and maximizes energy generation. Simulations on a 2.76 kW PEMFC system, integrated with a DC/DC boost converter, demonstrate AJSA’s superior performance, reducing the average convergence time by 79% compared to PSO and 75% compared to JSA. This reduction minimizes the risk of system failure and reduces power, voltage, and current oscillations across the load. Additionally, AJSA enhances energy efficiency by 25.7% compared to PSO and 17.5% compared to JSA. The proposed method achieves smooth MPP tracking with minimal power oscillations and improved energy conservation, directly impacting system reliability and operational costs. This research establishes AJSA as a robust and efficient solution for PEMFC MPP tracking, offering significant advancements in renewable energy applications for EVs. By addressing technical and economic challenges, AJSA supports sustainable energy solutions, paving the way for enhanced adoption of fuel cell technologies in the automotive industry.