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Optimal design of All-Vanadium Flow Batteries using Intelligent Techniques

Zarrabi Shirabad, Farnam | 2024

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  1. Type of Document: M.Sc. Thesis
  2. Language: Farsi
  3. Document No: 57069 (46)
  4. University: Sharif University of Technology
  5. Department: Energy Engineering
  6. Advisor(s): Boroushaki, Mehrdad
  7. Abstract:
  8. In recent decades, vanadium batteries have gained significant popularity in energy storage systems due to advantages such as safe and reliable performance, flexible design, high energy efficiency, and long lifecycles. However, these batteries may face challenges like early voltage cutoff, low charge/discharge rates, and reduced energy efficiency. Extensive research has been conducted to enhance the performance of these storage technologies. Previous operational strategies mainly focused on optimizing individual variables such as temperature or current rate, neglecting the interaction of these variables. This research introduces a multi-objective, multi-parameter optimization strategy to improve the energy efficiency and charge/discharge rates of the battery system. A two-dimensional, isothermal model of a vanadium redox flow battery is presented, examining processes such as self-discharge, pump work, as well as performance degradation due to hydrogen and oxygen evolution, to accurately represent the flow battery's behavior. The goal of this study is to find an optimal balance between maximizing energy efficiency and charge/discharge rates. For this purpose, the multi-objective genetic algorithm NSGA-II has been utilized. Subsequently, the TOPSIS method is applied to extract a unique solution under various charging conditions. Using the applied method, two multi-objective optimization processes identified 40 optimal response spectra according to Pareto principles. The primary optimization was conducted using all three input parameters, while the secondary optimization focused on the system temperature and fluid flow parameters. An optimal response assumed both efficiency and power to be optimized compared to the experimental setup. Overall, this study examined four scenarios: initial optimization for enhancing two objective functions, initial optimization according to the TOPSIS method, secondary optimization for enhancing two objective functions, and secondary optimization according to the TOPSIS method. Optimization results demonstrate that appropriate adjustment of input parameters can increase charging power by 1.115% and 1.0622%, discharging power by 0.1281% and 0.131%, and system efficiency by 0.08% and 0.1878% in scenarios 1 and 3, respectively. Another optimal scenario involves relative optimization of objective functions using the TOPSIS method, increasing charging power by 403% and 2.0426%, and discharging power by 325% and 0.3244% in scenarios 2 and 4, resulting in a decrease in system energy efficiency by 38% and 0.166%. Considering the significant increase in charge power when comparing scenarios one and two, this decrease in efficiency can be deemed cost-effective. This strategy, compared to traditional single-variable optimization methods, offers a precise approach to consider complex variables in vanadium redox flow batteries
  9. Keywords:
  10. Vanadium Redox Flow Battery ; Non-Dominate Sorting Genetic Algorithm (NSGAII) Method ; Efficiency ; Power ; Technique for Order-Perference by Similarity to Ideal Solution (TOPSIS)Method ; Pareto Optimization

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