A hybrid model of prairie dog optimization and closed-form continuous-time neural networks for next generation lithium-ion and sodium-ion batteries
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Nagarjuna Tandra
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Padmanaban Kuppan
Abstract
This study aims to enhance the performance and sustainability of Li-ion and Na-ion batteries by developing a hybrid PDO-CCTNN technique focused on optimizing energy density and efficiency for advanced energy storage applications across diverse sectors. The proposed method integrates Prairie Dog Optimization (PDO) to fine-tune operational parameters and Closed-Form Continuous-Time Neural Networks (CCTNN) to model battery dynamics, ensuring effective energy management, prolonged lifespan, and improved charging/discharging performance in MATLAB simulations. Simulation results demonstrate that the PDO-CCTNN method achieves a high energy density of 210 kWh/g and an efficiency of 94.1 %, outperforming conventional methods such as BOA, ICBO, GA, PSO, and BHMCO across multiple evaluation metrics. The PDO-CCTNN technique significantly boosts battery performance and sustainability, making it a promising solution for next-generation Li-ion and Na-ion batteries in energy storage systems, with high accuracy, optimized operation, and better adaptability for real-world energy applications.
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Research ethics: This article does not contain any studies with human participants or animals performed by any of the authors.
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Informed consent: Informed consent was obtained from all individuals included in this study.
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Author contributions: All authors have accepted responsibility for the entire content of this manuscript and approved its submission.
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Use of Large Language Models, AI and Machine Learning Tools: None declared.
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Conflict of interest: Authors state no conflict of interest.
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Research funding: No funding is provided for the preparation of manuscript.
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Data availability: Not applicable.
References
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Articles in the same Issue
- Frontmatter
- Review
- Quantum dots for wastewater treatment for the removal of heavy metals
- Articles
- A hybrid model of prairie dog optimization and closed-form continuous-time neural networks for next generation lithium-ion and sodium-ion batteries
- Mass transfer intensification and kinetics of o-xylene nitration in the microreactor
- Transesterification process and biofuel blending actions on performance of compression ignition engine under different loading conditions
- Phosphate, TDS and BOD removal from industrial wastewater using combined sono-pulsed-electrochemical oxidation: optimization by response surface methodology
- Evaluation of degradation in lubricating oil and engine wear using Jatropha oil blended with diesel in stationary compression ignition (CI) engines
- Thermophysical characterization and chemical stability of Ag2O-enhanced eutectic nano-PCMs for moderate-temperature applications
- Experimental evaluation of a solar-assisted heat pump system as a hybrid thermal reactor for energy-efficient drying of agricultural biomass
- Short Communication
- Design of terminator-shaped chamber-based micromixers with different obstructions: a CFD approach
