ARTIFICIAL INTELLIGENCE-BASED MULTI-OBJECTIVE OPTIMIZATION FOR SUPERCAPACITOR: A REVIEW.
Abstract
Supercapacitors have emerged as promising energy storage devices owing to their high-power density, rapid charge-discharge capability, and long operational lifespan. However, optimizing their design and operating conditions remains a complex task due to the presence of multiple conflicting objectives, including energy density, power density, cost efficiency, and cycle life. This study proposes an artificial intelligence (AI)-driven multi-objective optimization framework that integrates evolutionary algorithms such as Genetic Algorithms (GA), Particle Swarm Optimization (PSO), and the Non-dominated Sorting Genetic Algorithm II (NSGA-II) with neural network-based surrogate modeling. The surrogate model is trained to approximate the nonlinear relationships between design parameters and performance metrics, thereby reducing computational and experimental burden during optimization. NSGA-II is employed to generate Pareto-optimal solutions that capture the trade-offs among competing objectives, enabling informed decision-making in material selection and structural configuration. The results demonstrate that the proposed AI-based framework significantly accelerates the design process, minimizes experimental costs, and enhances the efficiency of supercapacitor performance optimization. This approach provides a robust, scalable, and data-driven methodology for advanced energy storage system development.
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Published in Salem Journal of Science, Information & Communication Technology
ISSN: 627-4467X
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