## 1 Introduction
Experimental and numerical studies have shown that initial gas conditions, pressure staging, and heat transfer mechanisms play critical roles in determining system performance during fast filling operations [27; 28; 29; 30; 31]. In CNG infrastructure, the proper design and control of compressor systems are also crucial. Compressor cycling, control algorithms, and reservoir pressure staging significantly affect station efficiency and operational cost [32; 33; 34]. Additionally, component-level reliability issues, such as valve material degradation or improper mounting structures, can impact safety and maintenance performance [35; 36; 37]. Although several studies have addressed individual aspects of CNG station operation, such as fuel transmission [38], energy cost [39; 40], and thermodynamic losses [41; 42], a comprehensive approach that simultaneously considers multiple engineering objectives, such as pressure level, storage volume allocation, and thermal behavior, remains underexplored [43; 44; 45]. Another important factor influencing the performance of CNG refueling systems is the optimization of pressure levels and volume distributions within cascade storage configurations. [PERSON] and [PERSON] [46] investigated the effects of different pressure stages on station efficiency using an ideal gas model and demonstrated that determining optimal pressure levels can enhance compressor energy usage and refueling time. Similarly, [PERSON] et al. [47] presented a comprehensive analysis on optimizing the volume ratios of storage tanks in multi-line systems to improve overall station performance. However, most of these studies were conducted under steady-state or quasi-steady assumptions, with limited attention to the dynamic behavior of the system. In recent years, multi-objective optimization techniques have gained attention as a means to holistically improve energy systems. In particular, the combination of Taguchi experimental design with gray relational analysis (GRA) has proven effective in a wide range of thermal and mechanical applications, such as refrigeration systems [48], battery thermal management [49], absorption cooling [50] and organic Rankine cycles [51]. This approach allows for simultaneous consideration of several conflicting performance metrics, making it highly suitable for systems like CNG refueling stations, where trade-offs between pressure utilization, energy efficiency, and temperature control must be carefully balanced. In this study, the well-established Taguchi-GRA framework was adapted to optimize a dynamically modeled CNG refueling station. The model evaluates system behavior over a 24 h operating cycle and targets simultaneous optimization of reservoir pressure levels and volume distribution to minimize compressor cycling and ensure sufficient fill pressure for vehicle tanks. All thermodynamic properties of natural gas, including pressure-dependent density and enthalpy, were calculated based on real gas properties to ensure accurate simulation of compression and filling dynamics. This ensures that the simulation reflects the actual behavior of natural gas during compression and filling. By combining dynamic simulation with multi-objective optimization, this work contributes a novel framework for improving the technical performance, energy efficiency, and operational stability of CNG refueling systems. In doing so, it addresses critical gaps in the current literature and offers insights for future infrastructure development.