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Magnesium-based hydrogen storage has garnered significant attention in the field of hydrogen storage due to its notable advantages, including high hydrogen storage capacity and low material cost. However, the relatively high thermodynamic stability of magnesium-based hydrogen storage materials necessitates heating the material to a specific temperature during the hydrogen charging and discharging processes, severely limiting the practical application and further development of this technology.Therefore, this paper aims to design a temperature control system for a magnesium-based solid-state hydrogen storage bottle. The system employs an optimized particle swarm optimization (PSO) algorithm to determine PID parameters, enabling rapid preheating of the hydrogen storage bottle and maintaining a stable operating temperature. Furthermore, by incorporating an appropriate penalty function, the system effectively suppresses temperature overshoot, preventing adverse effects on hydrogen storage performance caused by transient high temperatures, thereby ensuring the safety and stability of the hydrogen storage process. [Display omitted] • Enhanced PSO algorithm improves PID control for dynamic temperature environments. • New method significantly reduces temperature overshoot from 29.2% to 2%. • Improved hydrogen storage system performance and reliability with PSO algorithm. • Reduced need for thermal buffers and increased system efficiency. [ABSTRACT FROM AUTHOR]

As a global-optimized and naturally inspired algorithm, particle swarm optimization (PSO) is characterized by its high quality and easy application in practical optimization problems. However, PSO has some obvious drawbacks, such as early convergence and slow convergence speed. Therefore, we introduced some appropriate improvements to PSO and proposed a novel chaotic PSO variant with arctangent acceleration coefficient (CPSO-AT). A total of 10 numerical optimization functions were employed to test the performance of the proposed CPSO-AT algorithm. Extensive contrast experiments were conducted to verify the effectiveness of the proposed methodology. The experimental results showed that the proposed CPSO-AT algorithm converges quickly and has better stability in numerical optimization problems compared with other PSO variants and other kinds of well-known optimal algorithms. [ABSTRACT FROM AUTHOR]