## 1 Introduction  
In the global transition toward cleaner energy systems, compressed natural gas (CNG) continues to serve as a viable alternative fuel for the transportation sector due to its relatively low emissions, mature infrastructure, and economic feasibility [1]. Compared to conventional fuels, CNG exhibits superior combustion characteristics and environmental benefits, particularly in urban mobility applications [2, 3, 4]. Its usage not only reduces greenhouse gas emissions but also provides a reliable transitional pathway within existing engine technologies [5]. Recent comparative studies have evaluated CNG, battery electric vehicle (BEV), and fuel cell electric vehicle (FCEV) technologies in public transport, emphasizing that CNG remains a competitive and transitional option in terms of both environmental and economic performance [6]. While CNG remains a viable low-emission alternative in many urban -freight applications, recent cost-benefit analyses suggest that electric light commercial vehicles (LCVs) may offer lower lower total cost of ownership under certain conditions. For example, [PERSON] et al. [7] found that EVs had a cost advantage of 2% over CNG vehicles in urban distribution scenarios, although infrastructure and policy support remain critical factors in the adoption process. In addition to performance considerations, recent research has also explored the safety and risk aspects of CNG infrastructure by applying integrated decision-making methods. For example, [PERSON] et al. [8] analyzed the CNG sector in Pakistan using fuzzy AHP and TOPSIS, identifying system design limitations, human error, and economic instability as key issues, especially in developing countries. Meanwhile, dynamic modeling has become a useful tool for capturing how gas storage and distribution systems respond under variable conditions. [PERSON] et al. [9; 10] demonstrated that incorporating time-varying parameters into simulations improves the accuracy of system behavior predictions. In a similar context, [PERSON] et al. [11] used scenario-driven risk assessments to show how dynamic models can support better safety planning in integrated energy systems. Hybrid systems that combine CNG and electricity are drawing increasing interest, especially in the broader area of multi-energy systems. [PERSON] et al. [12] proposed an energy management method that uses deep learning to control a virtual energy hub, which connects vehicles that run on both CNG and electricity with a gas compression station. Their results showed that CNG can help improve system flexibility when used together with electric and thermal energy sources. At the same time, comparisons between different refueling technologies show a growing focus on hydrogen systems. For example, [PERSON] et al. [13] described a hydrogen station powered by solar energy, which includes an electrolyzer, a compressor, and a storage setup. They also used life cycle and full-chain efficiency analyses to study its environmental and economic impact. These modeling efforts reflect the growing demand for cleaner and more efficient fuel systems. Simulation tools such as MATLAB and Simulink are widely used in engine and control system development. For instance, [PERSON] et al. [14] used this platform to evaluate passenger comfort by measuring vibration levels in vehicles that use CNG. Despite these advantages, the operation of CNG refueling stations introduces several engineering challenges. These include high-pressure storage requirements, energy-intensive compression cycles, transient thermal effects during fast filling, and the need for efficient system control [15; 16; 17; 18]. One of the primary difficulties lies in managing the dynamic behavior of thermodynamic parameters, such as pressure, temperature, and gas density, which vary significantly during the refueling process [19; 20; 21; 22]. Rapid temperature rise due to gas compression can lead to lower fill efficiency, reduced safety margins, and increased energy consumption [23; 24; 25; 26].