# AI-driven advances in composite materials for hydrogen storage vessels: A review

Pedram Aminharati, Mohammadali Shirinbayan, Khaled Benfriha, Fodil

Meraghni, Joseph Fitoussi

► To cite this version:

Pedram Aminharati, Mohammadali Shirinbayan, Khaled Benfriha, Fodil Meraghni, Joseph Fitoussi. AI-driven advances in composite materials for hydrogen storage vessels: A review. International Journal of Hydrogen Energy, 2025, 171, pp.151288. 10.1016/j.ijhydene.2025.151288. hal-05250270

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# AI-driven advances in composite materials for hydrogen storage vessels:
A review

Pedram Aminharati<sup>a, b, c</sup>, Mohammadali Shirinbayan<sup>b, *, d</sup>, Khaled Benfriha<sup>c</sup>, Fodil Meraghni<sup>d</sup>,
Joseph Fitoussi<sup>b</sup>

## ARTICLE INFO

### Keywords:

- Hydrogen storage vessels
- Artificial intelligence
- Finite element analysis
- Multiscale modeling
- Composite materials
- Optimization methods

## ABSTRACT

This review provides a comprehensive examination of artificial intelligence methods applied to the design, optimization, and performance prediction of composite-based hydrogen storage vessels, with a focus on composite overwrapped pressure vessels. Targeted at researchers, engineers, and industrial stakeholders in materials science, mechanical engineering, and renewable energy sectors, the paper aims to bridge traditional mechanical modeling with evolving AI tools, while emphasizing alignment with standardization and certification requirements to enhance safety, efficiency, and lifecycle integration in hydrogen infrastructure. The review begins by introducing HSV types, their material compositions, and key design challenges, including high-pressure durability, weight reduction, hydrogen embrittlement, leakage prevention, and environmental sustainability. It then analyzes conventional approaches, such as finite element analysis, multiscale modeling, and experimental testing, which effectively address aspects like failure modes, fracture strength, liner damage, dome thickness, winding angle effects, crash behavior, crack propagation, charging/discharging dynamics, burst pressure, durability, reliability, and fatigue life. On the other hand, it has been shown that to optimize and predict the characteristics of hydrogen storage vessels, it is necessary to combine the conventional methods with artificial intelligence methods, as conventional methods often fall short in multi-objective optimization and rapid predictive analytics due to computational intensity and limitations in handling uncertainty or complex datasets. To overcome these gaps, the paper evaluates hybrid frameworks that integrate traditional techniques with AI, including machine learning, deep learning, artificial neural networks, evolutionary algorithms, and fuzzy logic. Recent studies demonstrate AI's efficacy in failure prediction, design optimization to mitigate structural risks, structural health monitoring, material property evaluation, burst pressure forecasting, crack detection, composite lay-up arrangement, weight minimization, material distribution enhancement, metal foam ratio optimization, and optimal material selection. By synthesizing these advancements, this work underscores AI's potential to accelerate development, reduce costs, and improve HSV performance, while advocating for physics-informed models, robust datasets, and regulatory alignment to facilitate industrial adoption.

## 1. Introduction

The increase in the global population, on one hand, and the limitation of fossil fuels, on the other, have led researchers and scientists to move towards the use of renewable energy sources [1,2]. Although the use of fossil fuels has resulted in numerous advancements in various scientific fields, the pollution caused by their usage has led to respiratory

diseases, cardiovascular problems, and neurological disorders in humans [3,4]. Furthermore, the consumption of fossil fuels has contributed to the issue of global warming, which in turn has caused climate change worldwide [5,6]. In addition, geopolitical tensions have always resulted in severe fluctuations in the prices of fossil fuels, including oil and gas, which in turn have caused inflation and economic uncertainty [7,8]. These reasons have motivated researchers to explore renewable energy sources to mitigate these challenges. Hydrogen, as a

<footnote><sup>a</sup> Arts et Métiers Institute of Technology, CNAM, LCPI, F-75013, Paris, France</footnote>

<footnote><sup>b</sup> Arts et Métiers Institute of Technology, CNAM, CNRS, PIMM, F-75013, Paris, France</footnote>

<footnote><sup>c</sup> Arts et Métiers Institute of Technology, LCFC, F-75013, Paris, France</footnote>

<footnote><sup>d</sup> Arts et Métiers Institute of Technology, LEM3–UMR CNRS 7239, 4 Rue Augustin Fresnel, 57078, Metz, France</footnote>

<footnote>* Corresponding author.</footnote>

<footnote>E-mail addresses: pedram.aminharati@ensam.eu (P. Aminharati), mohammadali.shirinbayan@ensam.eu (M. Shirinbayan), khaled.benfriha@ensam.eu (K. Benfriha), foudil.meraghni@ensam.eu (F. Meraghni), joseph.fitoussi@ensam.eu (J. Fitoussi).</footnote>
List of abbreviations

1. **AI** Artificial Intelligence

2. **ANN** Artificial Neural Network

3. **Bi-LSTM** Bidirectional Long Short-Term Memory

4. **BP ANN** Backpropagation Artificial Neural Network

5. **BOSS** Boss Structure (critical connection area between HSV and valves)

6. **CGCNN** Crystal Graph Convolutional Neural Network

7. **CFRP** Carbon Fiber Reinforced Polymer

8. **CGH** Compressed Gaseous Hydrogen

9. **CNG** Compressed Natural Gas

10. **COPV** Composite Overwrapped Pressure Vessel

11. **DL** Deep Learning

12. **DNN-DEO** Deep Neural Network with Differential Evolutionary Optimization

13. **DMTA** Dynamic Mechanical Thermal Analysis

14. **DSC** Differential Scanning Calorimetry

15. **DR** Dome Reinforcement

16. **DTW** Dynamic Time Warping

17. **FEA** Finite Element Analysis

18. **FFNN** Feedforward Neural Network

19. **GA** Genetic Algorithm

20. **GHG** Greenhouse Gas

21. **GISSMO** Generalized Incremental Stress-State dependent

damage Model

22. **GCMC** Grand Canonical Monte Carlo

23. **GPR** Gaussian Process Regression

24. **HSV** Hydrogen Storage Vessel

25. **HTS** High-Throughput Screening

26. **IDEM** Inducible Design Exploration Method

27. **ISO** International Organization for Standardization

28. **k-NN** k-Nearest Neighbors

29. **kPCA-Lasso** Kernel Principal Component Analysis - Lasso

30. **MOGA** Multi-Objective Genetic Algorithm

31. **MOF** Metal-Organic Framework

32. **ML** Machine Learning

33. **MMF** Micromechanics Fracture

34. **PCM** Phase Change Material

35. **PSO** Particle Swarm Optimization

36. **PSED** Plastic Strain Energy Density

37. **RBTO** Reliability-Based Topology Optimization

38. **RMSE** Root Mean Square Error

39. **RVE** Representative Volume Element

40. **SEM** Scanning Electron Microscopy

41. **SHM** Structural Health Monitoring

42. **SOC** State of Charge

43. **TTSP** Time-Temperature Superposition Principle

44. **VMS** Von Mises Stress

unique technology for energy storage without CO₂ and greenhouse gas (GHG) emissions, has always sparked curiosity and hope among researchers. It has been proposed as a potential solution for transportation applications. Besides the possibility of decarbonizing road transport, hydrogen energy can significantly reduce local air pollution [9,10]. Hydrogen is a non-toxic, colorless, and odorless gas that possesses the highest energy density per unit mass compared to standard fuel [11,12]. Table 1 presents a comparison of the properties of hydrogen fuel with other conventional fuels, highlighting the superiority of hydrogen gas in this field. Most importantly, its fueling infrastructure is comparable to that of traditional road fuels. More specifically, the transportation and industrial sectors are heavily dependent on fossil fuels, leading to CO₂ emissions and air pollution. If hydrogen is produced from renewable sources (green hydrogen), it will be considered a carbon-free energy source. Accordingly, replacing fossil fuels requires extensive hydrogen storage and transportation. However, hydrogen is extremely light and flammable, necessitating robust and secure storage and transport vessels [13,14]. On the other hand, the design and construction of hydrogen

<table><caption>**Table 1**
A comparison of different types of fuels [28,29].</caption><thead><tr><th>Fuel</th><th>Gravimetric Energy Density (MJ/kg)</th><th>Boiling Point (°C)</th><th>Storage Condition</th><th>CO<sub>2</sub> Emissions (kg CO<sub>2</sub> per MJ)</th></tr></thead><tbody><tr><td>Hydrogen</td><td>120</td><td>-252.2</td><td>High-Pressure Vessel</td><td>0</td></tr><tr><td>Petrol</td><td>44</td><td>25-215</td><td>Liquid at Ambient Temperature</td><td>0.069</td></tr><tr><td>Diesel</td><td>46</td><td>180-360</td><td>Liquid at Ambient Temperature</td><td>0.074</td></tr><tr><td>Compressed Natural Gas (CNG)</td><td>50</td><td>-161</td><td>High-Pressure Vessel</td><td>0.056</td></tr><tr><td>Ethanol</td><td>30</td><td>78</td><td>Liquid at Ambient Temperature</td><td>0.073</td></tr></tbody></table>

<table><caption>**Table 2**
Comparative summary of prior reviews on HSVs with the present work.</caption><thead><tr><th>Authors</th><th>Year</th><th>Primary Focus</th><th>AI & Data-Driven</th><th>Scope & Novelty</th></tr></thead><tbody><tr><td>Bouhala et al. [32]</td><td>2024</td><td>Multiscale simulation, failure/damage, fatigue, SHM</td><td>Minimal</td><td>Comprehensive physics-based review; lacks AI method comparison</td></tr><tr><td>Eko et al. [33]</td><td>2025</td><td>Type IV COPV materials, components, and manufacturing</td><td>None</td><td>Strong on engineering practice; no structured AI framework</td></tr><tr><td>Nachtane et al. [34]</td><td>2023</td><td>Materials, manufacturing, sustainability, brief AI overview</td><td>optimization, prediction</td><td>AI survey, no deep AI mechanics integration</td></tr><tr><td>Ding et al. [35]</td><td>2025</td><td>AI/HTS/ML for hydrogen storage materials (hydrides, MOFs, carbon), incl. underground storage</td><td>High (materials-focused)</td><td>Advanced materials-AI methods; no COPV structural scope</td></tr><tr><td>Present Review</td><td>2025</td><td>Hybrid conventional-AI across the full COPV lifecycle: design, optimization, prediction; links to standards/ certification</td><td>High (application-oriented)</td><td>First to integrate full COPV lifecycle with task-specific AI mapping, hybrid physics-AI workflows, explicit certification/testing context</td></tr></tbody></table>

storage vessels present multiple critical challenges that researchers strive to overcome as much as possible. In designing and manufacturing hydrogen tanks, researchers face challenges such as ensuring high safety and preventing leakage, withstanding high pressures (350 to 700 bar), reducing weight while increasing strength for use in vehicles, and
managing the high costs of materials and production [15,16]. Additionally, these vessels must be resistant to temperature variations and harsh environmental conditions while maintaining a long lifespan, as material fatigue and cracking can compromise their safety and performance [17,18]. In addition to these physical safety concerns, chemical degradation mechanisms such as hydrogen embrittlement also present significant challenges for long-term vessel performance [19]. This phenomenon, caused by the diffusion of hydrogen into the vessel's metallic or composite structures [20], can lead to reduced ductility, premature cracking, and even catastrophic failure under pressure [21,22]. Moreover, the recycling of composite-based vessels and the development of more sustainable materials are among the environmental concerns. Addressing these challenges is essential for the safe and efficient storage and transport of hydrogen [23,24]. These challenges are tackled through proposed durability performance tests conducted on hydrogen storage vessels. Designers of these vessels apply external stresses sequentially to create a complete simulation of the actual stresses encountered under real-world conditions (Fig. 1) (see Table 2).

One of the solutions to mitigate the challenges as much as possible is the integration of artificial intelligence (AI) methods with conventional computational approaches (such as the FEA, finite volume method, thermo-mechanical modeling, computational fluid dynamics modeling, and analytical methods) in this field. AI methods consist of a set of computational techniques and algorithms designed to solve complex and uncertain problems that traditional computational methods are unable to address [25,26]. Unlike precise computational methods that require high accuracy and mathematical models, AI techniques can handle uncertainty, search for near-optimal solutions to complex problems, and work with incomplete or ambiguous data. These methods are widely applied in various fields, such as optimization, image processing, and big data analysis [27].

In conclusion, the research trend on the evaluation and optimization of hydrogen storage vessels from 1965 to 2024 can be examined. Fig. 2 illustrates a sudden surge in research within this field from 2020 to 2024, primarily driven by the increasing global focus on the scarcity and limitations of fossil fuels. In this regard, researchers from the United States and China have been at the forefront of investigations in this area (Fig. 3).

Based on the provided explanations, this review provides a

comprehensive overview of conventional and AI-based approaches for modeling, optimization, and performance prediction of hydrogen storage vessels, particularly COPVs. It is intended for researchers and industrial stakeholders seeking to combine classical mechanical modeling with emerging intelligent tools while also examining these methods through the lens of standardization and certification requirements, unlike previous reviews, which either focus on structural analysis and material behavior (e.g., Bouhala et al. [32] and Eko et al. [33]) or survey AI for HSV design with limited emphasis on certification/standards and lifecycle integration (e.g., Nachtane et al. [34] and Ding et al. [35]). This work integrates the entire lifecycle from multi-physical modeling to data-driven techniques. It also critically examines how AI can complement traditional design and monitoring methods to improve accuracy, reduce cost, and accelerate development.

## 2. Hydrogen storage vessels (HSV)

As stated, hydrogen storage vessels (HSV) are specialized and advanced equipment designed for storing hydrogen under pressure or specific conditions. These vessels are critical in supplying hydrogen for various industries, particularly in hydrogen-powered vehicles and industrial applications. [36,37]. Hydrogen storage vessels are typically made from high-pressure-resistant materials such as aluminum, steel, or advanced composite materials to enable hydrogen storage at pressures ranging from 200 to 1100 bar [38]. The design conditions of these vessels must ensure their capability to withstand various challenges, including pressure, temperature fluctuations, fatigue, and damage caused by phenomena such as hydrogen embrittlement. The design and material selection for these vessels, especially in hydrogen-powered vehicles, must be carried out with high precision to ensure both performance and safety throughout their operational lifespan [39,40]. Hydrogen can be stored through various techniques, including compressed hydrogen gas, liquid and cryogenic hydrogen, adsorbents, metal hydrides, and chemical hydrides [41].

### 2.1. Types of HSV

Hydrogen storage vessels are categorized into five main types based on their material composition, as illustrated schematically in Fig. 4.

<figure><img src="image_3.png" /><figcaption>Fig. 1. Sequential durability performance test protocol for hydrogen storage vessels [30].</figcaption></figure>
<figure><img src="image_4.png" /><figcaption>Fig. 2. Research trends on hydrogen storage vessels from 1965 to 2024 [31].</figcaption></figure>

<figure><img src="image_5.png" /><figcaption>Fig. 3. Research on hydrogen storage vessels by country [31].</figcaption></figure>

According to this figure, Type 1 hydrogen storage vessels are entirely made of metals such as steel or aluminum, resulting in a high weight. However, they have a low manufacturing cost and are primarily suitable for storing hydrogen at low pressures (up to 300 bar). Typical mechanical properties for steel Type 1 vessels include tensile strengths in the range of 800–1200 MPa and yield strengths around 500–900 MPa, while aluminum variants exhibit tensile strengths of 300–500 MPa with

higher corrosion resistance but lower density [42,43]. It is worth mentioning that these vessels are more appropriate for traditional storage and stationary industrial applications due to their high weight [44,45]. HSVs Type 2 consists of a metallic inner liner (steel or aluminum) with a partial composite fiber wrap to enhance strength and reduce weight. These HSVs are lighter than Type 1 and can withstand pressures of up to 300 bar. The composite overwrap typically provides
<figure><img src="image_6.png" /><figcaption>Fig. 4. – Five types of HSVs (Hydrogen storage vessels) categorized according to their material composition.</figcaption></figure>

additional hoop tensile strengths exceeding 1500 MPa, improving fatigue resistance and delaying crack propagation under cyclic loading [46,47]. In Type 3 hydrogen storage vessels, a metallic inner liner is combined with a full composite outer layer. More specifically, this type consists of an aluminum or steel inner liner with a complete composite wrap (such as carbon fiber) to reduce weight and enhance resistance to high pressure. These vessels can endure pressures between 350 and 700 bar, although their production cost is higher than that of Type 2 vessels [48,49]. Carbon fiber composites used in Type 3 vessels generally exhibit tensile strengths of 3000–5500 MPa and elastic moduli of 200–300 GPa, enabling superior burst pressure resistance while maintaining a favorable strength-to-weight ratio [50,51]. Type 4 HSVs are composed of a polymer inner liner with a fully composite structure. Commonly employed polymer liners encompass high-density polyethylene (HDPE) variants, valued for their low hydrogen permeability and cost-effectiveness in high-pressure applications, as well as polymides (PA), such as PA6 or PA11, have tensile strengths of ~20–80 MPa (HDPE on the lower end; PA6/PA11 higher) which provide enhanced resistance to hydrogen diffusion, improved mechanical durability under cyclic loading, and superior compatibility with composite overwraps. They are considered suitable for storing hydrogen at high pressures (up to 700 bar) [52,53]. The advantages of these vessels include lightweight construction, corrosion resistance, and, consequently, a longer service life. HSVs Type 5 are entirely made of composite materials, making them the lightest type available. However, their manufacturing cost is significantly higher [41,54]. With no

metallic liner, Type 5 vessels rely solely on advanced composites such as ultra-high-strength PAN-based carbon fibers (≥6 GPa) and high-modulus grades (≥300 GPa), delivering exceptional specific strength and fatigue performance under severe cyclic pressure [55,56]. Overall, Type 4 and Type 5 vessels are the most suitable options for the transportation industry due to their lower weight and higher pressure tolerance. As a result, they are widely used in critical sectors such as aerospace, military applications, advanced transportation projects, and large-scale hydrogen storage [57,58]. The characteristics of each vessel type are summarized in Table 3.

## 2.2. Challenges of HSV

Hydrogen gas, as a high-energy and clean fuel, is an ideal option for transportation and energy production. However, its storage presents several challenges, which are discussed below.

The first major challenge in hydrogen storage vessels is ensuring high safety standards to withstand high pressures. In case of vessel damage, there is a risk of leakage and explosion. Therefore, the vessel design must ensure resistance to impacts, high temperatures, and cracking [64,65]. A key solution in this regard is using strong composite materials (such as glass fibers, resins, and carbon fibers). The second challenge is the heavyweight and thickness of hydrogen storage vessels, which makes their installation in hydrogen-powered vehicles difficult [66,67]. Researchers suggest using Type 4 and Type 5 vessels, as they offer a lighter weight while maintaining high-pressure tolerance. The third challenge

<table><caption>**Table 3**
Specifications of HSVs [43,49,52,53,56,59–63].</caption><thead><tr><th>HSV Type</th><th>Structure</th><th>Operating pressure (bar)</th><th>Refill time (min)</th><th>Advantages</th><th>Typical mechanical properties (representative)</th><th>Disadvantages</th></tr></thead><tbody><tr><td>Type 1</td><td>Aluminum or Steel</td><td>200 to 300</td><td>5–10</td><td>Low cost, strong structure, recyclable</td><td>Steel cylinders: σ<sub>u</sub> ≈ 800–1200 MPa, σ<sub>y</sub> ≈ 500–900 MPa.<br>Al alloys: σ<sub>u</sub> ≈ 300–550 MPa, E ≈ 70 GPa [43,49]</td><td>Heavyweight, limited storage capacity</td></tr><tr><td>Type 2</td><td>Steel or Aluminum liner, Composite hoop wrap</td><td>Up to 300</td><td>3–5</td><td>Lighter than Type 1, lower cost than Type 3</td><td>Hoop overwrap (CFRP): laminate σ<sub>u</sub> (hoop) often ≥1.5 GPa; fatigue performance improved vs. bare metal [49, 62]</td><td>Still heavy, limited storage capacity</td></tr><tr><td>Type 3</td><td>Steel or Aluminum liner, Composite full wrap</td><td>Up to 1100</td><td>2–3</td><td>Lighter than Type 2, higher pressure capability, suitable for vehicles</td><td>Carbon fibers (PAN-based): fiber σ<sub>u</sub> ≈ 3–5.5 GPa, E ≈ 200–300 GPa; full-wrap CFRP laminates deliver high burst margins [56,63]</td><td>Higher cost, more complex manufacturing</td></tr><tr><td>Type 4</td><td>polymer liner, Composite full wrap</td><td>Up to 700</td><td>3–5</td><td>Very lightweight, corrosion-resistant, and high storage capacity</td><td>Liners (HDPE/PA6/PA11): σ<sub>u</sub> ≈ 20–80 MPa; CFRP overwrap: carries pressure (fiber σ<sub>u</sub> multi-GPa). Liner performance & permeability are key design limits [52, 53]</td><td>Expensive, long-term use limitations</td></tr><tr><td>Type 5</td><td>All Composite</td><td>Up to 1100</td><td>2–3</td><td>Extremely lightweight, maximum storage capacity</td><td>All-composite shells (high-strength carbon fibers): fiber σ<sub>u</sub> often ≥ 5–6 GPa, high-modulus grades ≥300 GPa, enabling top specific strength [56,62]</td><td>High production cost, developing technology</td></tr></tbody></table>
arises from the intrinsic properties of hydrogen gas. As the lightest element with the smallest molecular size, hydrogen can easily leak from storage vessels. Given its highly flammable nature, this significantly increases the risk of explosion [68,69]. A solution to mitigate this issue is the use of multi-layer protective coatings and optimized seals and connections to minimize gas leakage. The fourth challenge is the high cost of production and maintenance of HSVs [70,71]. This issue can be addressed by optimizing the manufacturing process to reduce raw material consumption. The fifth challenge is hydrogen embrittlement, where hydrogen gas penetrates the metal structure, causing cracking and structural failure. This problem is more prevalent in Type 1 and Type 2 vessels. To minimize this challenge, it is necessary to use hydrogen-resistant alloys or apply protective coatings to prevent hydrogen diffusion into the metal. Additionally, optimizing composite materials can help reduce hydrogen permeation. Another significant challenge is environmental concerns. This issue primarily affects Type 4 and Type 5 vessels, as they are usually composed of non-recyclable composite materials [72,73]. Thermal management is another critical challenge in HSV. Temperature fluctuations can lead to reduced efficiency and safety hazards. There are also additional challenges in the design and manufacturing of HSV, which are illustrated in Fig. 5.

### 3. Hydrogen storage vessels design

The design of HSV is a critical aspect of hydrogen energy technologies [75]. Based on the discussions presented in the previous section, these vessels must be capable of storing hydrogen either under high pressure or in liquid form while meeting specific safety and performance requirements, as outlined in Section 2-1. To achieve this, researchers employ analytical methods and various testing procedures to design and evaluate the performance of hydrogen storage vessels. The following sections will provide an in-depth review and introduction to these

<figure><img src="image_7.png" /><figcaption>Fig. 5. The main challenges of hydrogen storage vessels [45,54,74].</figcaption></figure>

methods.

#### 3.1. Conventional methods, finite element analysis, empirical and experimental approaches, analytical methods

Common methods for designing hydrogen storage vessels have traditionally relied on empirical experience and experimental testing. These methods typically include manual calculations to evaluate internal pressures and structural integrity, the application of mechanics of materials principles to predict material behavior under various loads, and simplified simulations to assess vessel stability under different conditions. However, these approaches may lack precision and are not always sufficient for cases requiring highly accurate designs. One of the most important methods used in the design of HSV is Finite Element Analysis (FEA). This technique is employed for simulating and analyzing complex structures subjected to various loads, including pressure, temperature variations, external forces, and mechanical stresses. [60,76]. In FEA, the storage vessel is divided into small elements, and the behavior of each element is individually simulated to predict the overall performance of the vessel. This analysis is particularly useful for evaluating conditions such as high-pressure resistance, corrosion effects, thermal expansion, and mechanical forces. The following section will explore existing research and studies conducted in this field.

FEA has been widely utilized to investigate various factors affecting hydrogen storage vessels. In this context, Xu et al. [77] Employed a three-dimensional parametric FEA to predict damage evolution and fracture strength of HSV. Their study proposed a solution algorithm to assess progressive damage and failure characteristics of composite structures subjected to increasing internal pressure. Failure indices such as Maximum Stress, Hoffman, Tsai-Hill, and Tsai-Wu were employed to determine the failure characteristics of HSV. Finally, simulation results were compared with experimental findings to validate the model (see Fig. 6).

In another study, Takalkar Atul et al. [78] Investigated the effect of winding angle on the HSV developed using the filament winding process through FEA (Fig. 7). The analysis focused on COPVs made of T300 carbon fiber/epoxy. A FEA was then used to propose an optimal winding angle, and overall deformation, stress generation, and failure analysis of the HSV were examined.

Park et al. [79] investigated crack behavior in an HSV Type 3 utilizing layered modeling and the Extended FEA. Their results identified a weak point in the CFRP composite layers, specifically in the 17th transverse layer at a 35-degree winding angle, located at the interface between the dome and cylindrical regions of the tank. Further analysis revealed that crack propagation was driven by exceeding the allowable transverse stress at this specific winding angle. William et al. [80] utilized FEA to analyze an HSV Type 3. Their study demonstrated that continuous variation of fiber orientation and local thickness in the dome regions could be uniquely modeled using appropriate transformations for macro-mechanical composite properties and a local coordinate system. A comparative study of different metallic liner configurations and positions within the dome revealed that extending the metallic liner across the entire dome geometry led to increased stress at the cylinder-dome interface. Nguyen et al. [81] performed a predictive modeling tool based on 3D FEA to study damage analysis and design optimization of HSV. Their multiscale modeling framework, implemented in a 3D finite element formulation, enabled damage evaluation under thermomechanical loading conditions. The tool was applied to cryogenic compressed hydrogen tanks, examining the effect of fiber orientation in helical layers and different loading scenarios on damage evolution, structural integrity, and burst pressure. Wu et al. [82] focused on fatigue life prediction of a hydrogen storage tank (Fig. 8) using 3D FEA and experimental validation. Their study first established an FEA model incorporating winding angle variations in the dome region, liner thickness, and number of composite layers. The autofrettage pressure, metallic liner thickness, and fiber thickness were then examined for their
<figure><img src="image_8.png" /><figcaption>Fig. 6. Three-dimensional parametric finite element model to predict damage evolution. (a) Element failure time–internal pressure curve. (b) Distributions of the Mises stress using the maximum stress failure criterion [77].</figcaption></figure>

<figure><img src="image_9.png" /><figcaption>Fig. 7. Effect of winding angle on HSV using the filament winding process through FEA. (a) Schematic diagram of different winding angle sub-laminates for COPV. (b) Stress distribution for various winding angles of COPV [78].</figcaption></figure>

influence on fatigue life under cyclic internal pressure loading. Experimental validation showed that initial fatigue failure occurred in the mid-section of the cylinder, with simulation results closely matching experimental data, achieving an average error of 6.33 %.

Kartav et al. [83] designed composite HSV type 3, modeled them using the FEA method, produced them using the fiber winding method, and tested them for burst pressure. In this work, the HSV type 3 was subjected to increasing internal pressure until it reached the burst pressure. During loading, local deformations of the HSV were measured using strain gauges. The performance of the HSV type 3 with different numbers of helical, circumferential, and reinforcement layers was studied both experimentally and numerically. The results showed that adding reinforcement layers (doily layers) to the fiber winding process

improved the performance of composite HSV type 3. Zhang et al. [84] presented a solution using the FEA to study the thickness distribution of the dome and liner charge pressure in an HSV type 4 at 70 MPa (Fig. 9). To accurately predict the dome thickness, cubic spline functions were used. Furthermore, variable polar radii were utilized to decrease fiber stacking and thickness accumulation near the polar openings. To obtain the optimal internal pressure range during hydrogen gas injection in the fiber winding process, the pressure applied on the liner was simulated, considering the changes in torsional stress, and buckling and static analyses of the liner were performed.

Seunghyun et al. [85] evaluated the behavior of an HSV type 1 under pressure and temperature conditions employing the FEA. According to the outcomes, the safety of the studied vessel at a pressure of 40 MPa or
<figure><img src="image_10.png" /><figcaption>Fig. 8. Schematic diagram of a Type III hydrogen storage vessel for fatigue life prediction under cyclic internal pressure, incorporating variable winding angles in dome regions, liner thickness, and composite layers [82].</figcaption></figure>

<figure><img src="image_11.png" /><figcaption>Fig. 9. Using the FEA to study the thickness distribution of the dome and liner charge pressure in an HSV type 4 at 70 MPa. (a) Flowchart of progressive failure analysis. (b) Finite element model of a type 4 HSV [84].</figcaption></figure>

higher was not guaranteed, as the VMS value at the vessel's bottom exceeded the yield strength. The maximum pressure at which the VMS/YS ratio remained below 1 was simulated to be about 30 MPa, indicating that the vessel should be operated with a hydrogen gas pressure below 30 MPa. Xiong et al. [74] experimentally and numerically investigated the free and constrained modes of an HSV. They then performed random vibration simulations to study the stress distribution and used the Steinberg and Dirichlet methods to predict the fatigue life

of the HSV based on the stress distribution outcomes. Finally, they optimized the parameters of the composite winding layers to improve the stress distribution and fatigue life of the hydrogen storage vessel. The outcomes showed that the vibration patterns and frequencies of the free and constrained modes of the composite hydrogen storage vessel obtained from experiments and numerical predictions were aligned. Zhu et al. [86] employed the FEA to assess the critical connection area between HSV type 4 and external valves, which are commonly known as

<figure><img src="image_12.png" /><figcaption>Fig. 10. Finite element assessment of the boss structure in a Type IV hydrogen storage vessel, evaluating fatigue life (>30,000 cycles) and sealing performance [86].</figcaption></figure>
the BOSS structure (Fig. 10). In this work, the aim was to propose a new BOSS structure to enhance the safety of pressurized vessels. The outcomes depicted that the fatigue life of the presented BOSS structures could exceed the 30,000-cycle design life when using austenitic stainless steel S31603. In addition, the BOSS model, designed with a rotational stop platform with a 65-degree slope angle and two sealing grooves, had the lowest contact stress standard deviation of 13.47 MPa, indicating reliable sealing performance.

Yuan et al. [87] developed an FEA employing ACP software and Static Mechanical analysis to gain a better understanding of the deformation, stress, strain, and failure modes of an HSV type 4 during discharging and charging. They comprehensively examined parameters such as layering angles, layer thickness, mesh generation, and boundary conditions. The outcomes showed that high temperature and pressure during fast fueling in HSV rarely led to yielding failure in the internal, external, and plug layers. This paper also stated that the maximum stress in the external layer could be decreased by changing the direction of the circumferential layers from 90° to 70° or by optimizing the layer's direction, increasing it to 110°. Kangal et al. [88] conducted a comparative investigation on the burst pressure performance of aluminum (Al) liners in HSV type 3 utilizing the FEA. In this work, the vessels were subjected to increasing internal pressure until they reached the burst pressure. The burst pressure of the cylindrical section was predicted based on modified von Mises, Tresca, and average shear stress (ASSC) criteria. In numerical analysis, an FEA model was developed to predict the behavior of the vessel under increasing internal pressure and determine the final burst point. This study showed that the FEA and the elastoplastic analytical approach based on ASSC provided the most accurate predictions, with only a 2.2% deviation from experimental burst values. Furthermore, it was shown that elastic analysis underpredicts burst pressure but is effective in determining critical regions on the HSV structure. Cho et al. [89], utilizing the FEA, analyzed the deformation of an HSV type 1 considering the VMS created in the vessel based on the standard ISO 18119 groove shape [90]. Based on the analysis results, the maximum VMS created in the longitudinal groove was greater than in the transverse groove. Additionally, this paper analyzed the vessel's stress using the stress ratio, which indicates the ratio of the material's yield strength to the generated VMS. The analysis results showed that for the longitudinal groove vessel, the stress ratio inside and outside the vessel increased to 0.85 and 0.50, respectively, at a gas pressure of 50 MPa, but remained below 1, indicating that the vessel had not reached its yield strength under these conditions. Xiao et al. [91] examined the performance of HSV units based on adsorption from a systems perspective using the FEA method. For this, a two-dimensional axisymmetric geometric model encompassing mass, momentum, and

energy equations according to thermodynamic conservation laws was developed employing the FEA in COMSOL. In this study, the discharging and charging process of the vessel was analyzed at 9 MPa and an initial temperature of 302 K. The results showed that the simulated pressure and changes in temperature during the discharging and charging process matched well with the experimental data. Bie et al. [92], combining micromechanics and continuous damage mechanics, proposed a theoretical technique to evaluate the fatigue of HSV under cyclic internal pressure, focusing specifically on the fatigue characteristics of aluminum liners (Fig. 11).

The outcomes showed that the vessel's fatigue life depends on various factors such as mesh size in the FEA model, crack density, cyclic loading amplitude, and stress state in the liner. The numerical outcomes acquired from the modeling were compared with practical data, showing good consistency between theoretical predictions and experimental results.

Kim et al. [93] used the FEA to assess the structural integrity of the neck mounting block in an HSV type 4 to improve its strength and stiffness. In this paper, a structural analysis process was developed to assess the vulnerabilities of this block under forces caused by bolt tightening and loading conditions in the direction of vehicle motion. Finally, a comparative analysis was conducted between the improved design and the existing model, showing an increase in strength and stiffness in the new model. Yao et al. [94] presented an FEA model to simulate the dynamic responses of vessels subjected to projectile impacts. This study analyzed factors such as the number of winding layers, impact velocity, piece mass, and shape. The results showed that projectile impacts resulted in a specific local damage behavior with two distinct stages: impact and rebound. The study also showed that impact velocity and piece mass had a direct effect on deformation and damage levels. Liu et al. [95], combining the FEA method and GISMO damage model (a generalized stress-based damage model) with a submodeling strategy, evaluated damage in hydrogen storage vessels. Using this method and calibrated material parameters from a large set of 5083 aluminum alloy samples with grooves, liner damage in an HSV type 3 under localized compressive loading was simulated. This study demonstrated how material stress and damage characteristics interact. Hu et al. [96] studied dome reinforcement (DR) technology to reduce the carbon fiber content in HSV type 4 (Fig. 12). The influence of dome reinforcement angle and thickness on stress distribution in the dome section was investigated using FEA. Subsequently, the reduction in carbon fiber composite weight based on dome reinforcement technology was examined.

Finally, the DR technology was validated through burst tests, showing that the advanced damage model could effectively represent

<figure><img src="image_13.png" /><figcaption>**Fig. 11.** Combining micromechanics and continuous damage mechanics to propose a theoretical technique to evaluate the fatigue of HSV under cyclic internal pressure. (a) Schematic representation of a cracked cell in an element, 30° axisymmetric finite element model of a vessel. (b) S-N fatigue curves by numerical calculation and experiments [92].</figcaption></figure>
<figure><img src="image_14.png" /><figcaption>Fig. 12. Application of dome reinforcement technology to reduce carbon fiber usage in a Type IV hydrogen storage vessel under 700 bar. (a) Fiber damage distribution without reinforcement. (b) Improved fiber damage with reinforcement, showing stress transfer to the cylindrical section and a safer failure mode [96].</figcaption></figure>

matrix cracking and fiber failure, and the predicted burst pressure and failure mode matched experimental results. Based on the results, the fiber stress near the equator in the dome section had a significant effect on the failure mode, and smaller reinforcement angles provided better reinforcement effects. Additionally, DR technology can enhance the fiber damage condition in the dome, transfer maximum stress to the cylindrical section of the HSV, and convert the failure mode into a safer one.

Chapelle [97] studied the cylindrical section of an HSV type 3, including an aluminum liner to prevent gas leakage and a wound composite layer to reinforce the structure. In this study, the layered composite material was considered an elastoplastic material, while the liner was treated as an elastoplastic material. The proposed model, based on classical lamination theory and Hill's criterion, provides an exact solution for stresses and strains in the cylindrical section under thermal-mechanical static loading. The first part of the paper is dedicated to theoretical fundamentals. Then, the impact of layer arrangement on crack occurrence, residual stress levels, and structural stiffness was examined. Park et al. [98] conducted an optimization assessment and investigated an HSV to minimize trial and error and enhance structural reliability. Fatigue assessment was also conducted for all patterns obtained from the optimization assessment. The outcomes of this research can help improve the strength and life design of HSVs reinforced with composite materials. Kang et al. [99] an optimized

design using the FEA has been presented for an HSV type 4 made of epoxy carbon fiber (Fig. 13). The self-catalytic kinetic model was extracted through dynamic and isothermal calorimetry (DSC) experiments by varying the curing temperature. Additionally, a thermal-statistical coupling analysis of the composite curing process was performed utilizing the ACCS program. In this study, the input parameters affecting the curing process include curing temperature (T), heating rate to the target temperature (H), dwell time at the target temperature (D), and cooling time (C). Also, the output parameters affecting layer separation and process time include $σ_z$, $τ_{xz}$, and total time (TT). The optimal model, which satisfies the objective function, was proposed utilizing the MOGA algorithm. Finally, the thermal-statistical coupling analysis was conducted on the final model to confirm the optimal design.

This section reviewed conventional HSV design methods, high-lighting FEA's effectiveness in optimizing geometry, layup, and process parameters, with good experimental agreement. While valuable for identifying stress hotspots and improving performance, FEA remains sensitive to material data, boundary conditions, and may overlook manufacturing defects or long-term degradation. These gaps motivate the shift toward multi-scale modeling explored in the next section.

At the end, a summary of the research performed in this section can be seen in Table 4.

<figure><img src="image_15.png" /><figcaption>Fig. 13. The optimal design of the curing process performed for an HSV type 4 through FEA. (a) Flowchart of curing process optimization. (b) Residual stress distribution of the optimal curing process [99].</figcaption></figure>
<table><caption>**Table 4**
A summary of research conducted on hydrogen storage vessels using conventional methods such as FEA.</caption><thead><tr><th>Authors</th><th>HSV Type</th><th>Year</th><th>Composite/liner</th><th>Maximum Pressure (bar)</th><th>Note</th></tr></thead><tbody><tr><td>Xu et al. [77]</td><td>3</td><td>2009</td><td>T 700/6061-T6 Aluminum</td><td>300</td><td>Predicting damage progression and failure strength of the HSV</td></tr><tr><td>Takalkar Atul et al. [78]</td><td>4</td><td>2016</td><td>T 300/LLDPE + HDPE</td><td>400</td><td>Investigating the effect of winding angle on HSV</td></tr><tr><td>Park et al. [79]</td><td>3</td><td>2018</td><td>T 800/ Aluminum</td><td>700</td><td>Evaluation of the crack behavior in the HSV</td></tr><tr><td>William et al. [80]</td><td>3</td><td>2013</td><td>T800/ 6061Aluminum</td><td>700</td><td>Selecting the proper loading, boundary conditions, and material representation</td></tr><tr><td>Nguyen et al. [81]</td><td>3</td><td>2021</td><td>T 300/ Aluminum</td><td>500</td><td>Damage analysis and design of HSV</td></tr><tr><td>Wu et al. [82]</td><td>3</td><td>2021</td><td>T 700/ Aluminum</td><td>–</td><td>Fatigue life prediction and verification of HSV</td></tr><tr><td>Kartav et al. [83]</td><td>3</td><td>2021</td><td>T 700/6061-T6 Aluminum</td><td>1100</td><td>Development and analysis of the HSV</td></tr><tr><td>Zhang et al. [84]</td><td>4</td><td>2020</td><td>T 700/polymer</td><td>700</td><td>Dome thickness prediction</td></tr><tr><td>Seunghyun et al. [85]</td><td>1</td><td>2020</td><td>–</td><td>300</td><td>Study of the behavioral characteristics that occur in the HSV type 1 Prediction and Optimization of HSV</td></tr><tr><td>Xiong et al. [74]</td><td>3</td><td>2025</td><td>T 700/6061-T6 Aluminum</td><td>–</td><td>BOSS structure</td></tr><tr><td>Zhu et al. [86]</td><td>4</td><td>2023</td><td>T 700/ Polyamide</td><td>580</td><td>Discharging and charging</td></tr><tr><td>Yuan et al. [87]</td><td>4</td><td>2024</td><td>T 700</td><td>700</td><td>Burst pressure performance</td></tr><tr><td>Kangal et al. [88]</td><td>3</td><td>2021</td><td>–</td><td>300</td><td>Evaluating the deformation characteristics of the vessel</td></tr><tr><td>Cho et al. [89]</td><td>1</td><td>2023</td><td>–</td><td>300</td><td>Discharging and charging</td></tr><tr><td>Xiao et al. [91]</td><td>3</td><td>2012</td><td>T 700</td><td>–</td><td>Fatigue life evaluation</td></tr><tr><td>Bie et al. [92]</td><td>3</td><td>2010</td><td>T 700</td><td>–</td><td>Assessing the structural integrity of the neck mount block</td></tr><tr><td>KIM et al. [93]</td><td>4</td><td>2024</td><td>T 700</td><td>–</td><td>Modeling the dynamic behaviors of HSV subjected to the impact of fragments</td></tr><tr><td>Yao et al. [94]</td><td>1</td><td>2024</td><td>–</td><td>500</td><td>Forecasting liner damage in an HSV</td></tr><tr><td>Liu et al. [95]</td><td>3</td><td>2024</td><td>–</td><td>–</td><td>Reduce the value of carbon fiber in the HSV</td></tr><tr><td>Hu et al. [96]</td><td>4</td><td>2021</td><td>T 700</td><td>700</td><td></td></tr></tbody></table>

<table><caption>**Table 4 (continued)**</caption><thead><tr><th>Authors</th><th>HSV Type</th><th>Year</th><th>Composite/liner</th><th>Maximum Pressure (bar)</th><th>Note</th></tr></thead><tbody><tr><td>Chapelle [97]</td><td>3</td><td>2006</td><td>T 700</td><td>700</td><td>Study the cylindrical section of an HSV</td></tr><tr><td>Park et al. [98]</td><td>3</td><td>2019</td><td>T 800</td><td>–</td><td>To reduce the trial and error and enhance the reliability of the structure</td></tr><tr><td>Kang et al. [99]</td><td>4</td><td>2023</td><td>T 700</td><td>–</td><td>The optimal design of the curing process is carried out for an HSV.</td></tr></tbody></table>

### 3.2. Multi-scale modeling of composite hydrogen storage vessels

Multiscale modeling combines micro, meso, and macro analyses to more accurately evaluate HSV performance under high pressures (350–700 bar). By linking material-level behavior (fibers, matrix), ply-level arrangements (resin–fiber interactions, defects), and full-vessel response under thermal and mechanical loads. By linking these scales, as illustrated in Fig. 14, it overcomes the limitations of single-scale models. This approach enables more precise and comprehensive assessment of strength, weight, and safety in composite hydrogen storage vessels.

So far, this method has been used in scientific research, and some of these studies are reviewed below. Feki et al. [101] presented a multi-scale experimental study on epoxy composites reinforced with carbon fibers (CFRP) employed in HSV produced by the filament winding method. This research evaluates the effect of porosity on mechanical performance by using a combination of fatigue tests under tensile-tensile loading and advanced physical-chemical identification methods to determine porosity. The findings of this paper show that porosity has a detrimental effect on mechanical characteristics and acts as a nucleation point for damage mechanisms, including crack initiation, fiber-matrix debonding, and fiber failure. Additionally, at the mesoscale, microscopic defects turn into transverse cracks and delamination, creating complex failure modes under cyclic loading. The tensile tests also showed that fiber orientation has a significant impact on the material’s mechanical behavior. Based on these results, it was found that the ±15° configuration has the highest tensile strength and modulus, while the ±30° and multi-layer configurations exhibited greater formability. Fatigue test results confirmed that fiber orientation significantly affects fatigue life, with the ±15° configuration offering the most resistant structure. Finally, microscopic analysis was performed on the vessel. Shirinbayan et al. [102] investigated the correlation between the microstructure and mechanical characteristics of epoxy composites reinforced with carbon fibers, particularly in the context of lightweight HSV produced by the filament winding method. By examining the effect of dynamic loading over a range of strain rates from quasi-static to 100 s⁻¹, this research explores the effects of different fiber orientations (±15° and ±30°) and multi-layer structures on the performance of these composites. In this regard, using a dynamic tensile testing device (IDTT), the evolution of damage mechanisms during rapid tensile deformation was studied. The results of this research show that porosity has a significant effect on mechanical performance, especially at high strain rates, where cracks initiate at the cavity locations and propagate through fiber-matrix debonding and fiber failure. Additionally, the study shows that these microstructural defects lead to mesoscale damage mechanisms, such as transverse cracking and micro-debonding between the composite layers. Furthermore, the study reveals that mechanical properties are strain rate sensitive, and the viscoelastic degradation
<figure><img src="image_16.png" /><figcaption>Fig. 14. Multiscale modeling links micro-, meso-, and macro-analyses for better HSV performance evaluation under high pressures [100].</figcaption></figure>

phenomenon plays an important role in determining how damage progresses under dynamic loading. Feki et al. [103] examined the correlation between the microstructure and mechanical characteristics of epoxy composites reinforced with carbon fibers utilized in the construction of lightweight HSV under pressure through the filament winding method. In this study, tubes made of epoxy resin reinforced with carbon fibers with different angular orientations (±15° and ±30°) and multi-layer structures were used to assess the effect of these changes on mechanical properties and the vulnerability behavior of the composites. The outcomes of this study depict that porosity, measured in the range of 5% to 7%, has a significant effect on mechanical performance. Additionally, a 40% reduction in Young's modulus was observed between the ±15° and ±30° configurations, and a 65% reduction was noted in the multi-layer structure. At the microscopic scale, the presence of porosity initiated cracks and led to fiber-matrix debonding and fiber failure. At the mesoscale, these defects can transform into transverse cracks and micro-debonding between layers, revealing the complexity of the behavior of these composites under loading. Schlegel et al. [104] examined the main aim of this article, which is to reduce safety margins by creating a fundamental method for systematizing overlapping loads and how they are distributed across longitudinal scales and loading domains. This approach enables the reliability analysis and development of CGH vessels in aircraft, ensuring high reliability and maximum weight storage density. Accordingly, the goals of this article include analyzing existing development strategies, extracting requirements for a new method, and defining its logic and underlying process. The result of this work is the presentation of a method to systematize potential loading conditions, corresponding stresses, and the failure mechanisms resulting from them in CGH aircraft vessels, from the aircraft system scale to the material level. Ruderman et al. [105] presented an integrated product design framework that incorporates simulation-based tools in the early design stages to obtain optimized multi-scale systems. The technique for determining appropriate structure-property relationships at the mid-scale for the material's internal structure is based on topology optimization and a multi-scale design process. In this article, the RBTO method was used to obtain optimal topologies at the mid-scale. IDEM facilitates concurrent design at different scales and provides a method for integrating the other two approaches. This article provides an example of the presented multi-scale design framework in the form of an HSV utilized in hydrogen fuel cell vehicle applications. The multi-scale vessel design features a high-strength mid-wall structure that results in a significant weight reduction. Rafiee and Salehi [106] developed a new multi-scale recursive modeling approach for predicting failure pressure in fiber-wound HSV. In this work, modeling starts at the micro-scale, where fiber spacing and their continuity are simulated. Then, at the meso-scale as an intermediate scale, the effect of waviness in fiber bundles, intersection points, and overlaps is investigated. Finally, at the macro-scale, stress/strain distribution in each layer is evaluated. After completing the bottom-up modeling, a top-down modeling approach was performed. At this stage, stress/strain values

are scaled from macro-to meso- and from meso-to micro-scales to obtain the individual components of stress on the fibers and resin. The occurrence of failure is examined utilizing fracture criteria at the micro-scale and progressive damage modeling. This recursive modeling process continues until the complete failure of the vessel.

In summary, multi-scale modeling offers a detailed framework for correlating microstructural characteristics, ply-level configurations, and overall vessel performance, enabling a better understanding of damage evolution and failure mechanisms. To ensure these models contribute to safe and operational designs, their predictions must be verified through standardized experimental procedures. The next section reviews the test methods and associated standards that are applied to validate hydrogen storage vessels for service conditions.

In conclusion, a summary of the research conducted on hydrogen storage vessels using multi-scale modeling can be seen in Table 5.

### 3.3. Test procedures and standards for vessels: impact test/static fatigue

Building on the design and analysis approaches discussed in the previous sections, ranging from conventional methods and FEA to multi-scale modeling, studies must ultimately align with established standards to ensure the safe operation of HSVs. Among the most critical evaluations are impact testing and static fatigue testing, which assess mechanical strength and long-term durability under realistic service conditions. Impact testing, conducted by ISO 11119-3 [107], examines vessel resistance to sudden loads or drops, with acceptance criteria of no hydrogen leakage and preservation of structural integrity. Static fatigue testing, specified in ISO 11439 [108], evaluates vessel performance under constant or cyclic pressure in a hydrogen environment at defined temperatures and pressures, monitoring for fatigue cracks, changes in mechanical properties, and ensuring no excessive strength loss or leakage. Aligning research with these standards not only verifies the performance improvements achieved through design and simulation but also supports certification requirements and operational reliability over the vessel's service life. The following section reviews studies applying these testing methods to hydrogen storage vessels.

<caption>**Table 5**</caption>

A summary of research conducted on hydrogen storage vessels using multiscale modeling.

<table><thead><tr><th>Author</th><th>HSV Type</th><th>Year</th><th>Composite type</th><th>Maximum pressure</th></tr></thead><tbody><tr><td>Feki et al. [101]</td><td>4</td><td>2024</td><td>T 700</td><td>700</td></tr><tr><td>Shirinbayan et al. [102]</td><td>4</td><td>2024</td><td>T 700</td><td>700</td></tr><tr><td>Feki et al. [103]</td><td>4</td><td>2024</td><td>-</td><td>700</td></tr><tr><td>Schlegel et al. [104]</td><td>4</td><td>2024</td><td>-</td><td>875</td></tr><tr><td>Ruderman et al. [105]</td><td>4</td><td>2009</td><td>-</td><td>-</td></tr><tr><td>Rafiee and Salehi [106]</td><td>4</td><td>2022</td><td>T 700</td><td>950</td></tr></tbody></table>
Zheng et al. [109] studied the thermal-mechanical fatigue characteristics and failure behavior of HSV (type 4) by creating a real hydrogen fatigue testing system. The outcomes of this study demonstrated that the ultimate strength of the HSV was reduced by 15 % after 500 cycles of hydrogen discharging and charging fatigue testing. Wang et al. [110] presented a combined micromechanics fracture approach and time-temperature superposition principle for evaluating the fatigue life of HSV under cyclic loading and operating conditions with varying temperatures (Fig. 15). In this work, the first step was to obtain the elastic solution of cylindrical layers under internal pressure based on classical lamination theory. Then, the MMF method was employed to calculate the component strength from the layer strength at different temperatures, and the primary matrix fatigue curves were developed with the TTSP. Finally, a lifetime prediction method based on the primary component curves was explained and implemented in MATLAB® software to predict the long-term fatigue life of HSV.

Ataabadi and Ahmadi Jeyed [111] predicted the fatigue life of HSV with different materials by conducting critical stress and strain energy analysis. The results of this work showed the minimum fatigue life and the determining factors. According to the findings of this study, during long-term service with repeated filling and emptying, the mechanical properties of HSV inevitably decrease. Liao and Perillo et al. [112,113] studied the failure mechanisms and mechanical properties of HSV under low-velocity impact using a combination of interlaminar and intralaminar damage models. In this work, the Puck and Hashin failure criteria were utilized for matrix cracking and fiber failure, and the cohesive zone model was employed to assess delamination damage. Rafiei et al. [114,115] presented a technique to predict the failure of composite vessels under low-velocity impact. Compared to the FEA results, the theoretical technique for predicting impact-induced failure is more accurate. Long et al. [116] performed low-velocity impact tests on wound composite pressure vessels. The outcomes showed that delamination damage primarily happens between the wound layers and the hoop layers, and the fiber overlap region is the weak spot in the HSV. Finally, a summary of the research methods conducted on HSV using impact tests and static fatigue tests can be seen in Table 6.

This section investigated recent studies on HSVs using FEA, multi-scale modeling, and experimental testing. The importance of material behavior, layer orientation, and defect growth on vessel performance was emphasized. Multi-scale approaches helped link microstructural damage to overall structural response. Impact and static fatigue tests validated simulation outcomes and evaluated long-term durability. Building on these foundations, AI-based methods can further enhance design optimization and predictive accuracy by complementing established modeling and testing workflows.

<caption>**Table 6**</caption>

A summary of research conducted on hydrogen storage vessels using Impact Test/Static Fatigue.

<table><thead><tr><th>Author</th><th>Type of HSV</th><th>Year</th><th>Composite type</th><th>Maximum pressure (bar)</th><th>Test</th></tr></thead><tbody><tr><td>Zheng et al. [109]</td><td>4</td><td>2013</td><td>—</td><td>700</td><td>Fatigue test</td></tr><tr><td>Wang et al. [110]</td><td>4</td><td>2016</td><td>T 700</td><td>750</td><td>Fatigue test</td></tr><tr><td>Ataabadi and Ahmadi Jeyed [111]</td><td>4</td><td>2021</td><td>T 700</td><td>—</td><td>Fatigue test</td></tr><tr><td>Liao and Perillo et al. [112, 113]</td><td>4</td><td>2020</td><td>—</td><td>—</td><td>impact test</td></tr><tr><td>Rafiei et al. [114,115]</td><td>4</td><td>2020</td><td>T 700</td><td>—</td><td>impact test</td></tr><tr><td>Long et al. [116]</td><td>4</td><td>2022</td><td>—</td><td>—</td><td>impact test</td></tr></tbody></table>

## 4. Application of artificial intelligence methods in HSV optimization

Artificial intelligence methods can generally be divided into four main categories: fuzzy logic, evolutionary computation (such as genetic algorithms, PSO, etc.), artificial neural networks (ANN), and deep learning (DL) [117,118] (Fig. 16). This section aims to review the research conducted in the field of HSV that has utilized artificial intelligence methods. The studies will be reviewed and examined in three categories: deep learning, neural networks, and fuzzy logic-evolutionary computation. The processes of the mentioned methods are schematically illustrated in Figs. 16–19.

<figure><img src="image_17.png" /><figcaption>**Fig. 16.** Classification of artificial intelligence methods.</figcaption></figure>

<figure><img src="image_18.png" /></figure>

<figure><img src="image_19.png" /><figcaption>**Fig. 15.** The stress and fatigue analysis of the composite cylindrical laminates is carried out under the cyclic internal pressure $P = 0.75$ MPa. (a) Evolution of fatigue damage in $T = 85$ °C (b) Evolution of fatigue damage in $T = 25$ °C [110].</figcaption></figure>
<figure><img src="image_20.png" /><figcaption>Fig. 17. Mechanism of an artificial neural network, illustrating input layers, hidden layers with activation functions.</figcaption></figure>

<figure><img src="image_21.png" /><figcaption>Fig. 18. Working principles of GA.</figcaption></figure>

## 4.1. Machine learning & deep learning

Recent studies have shown that ML and DL methods play a crucial role in optimizing the design, safety analysis, and fatigue life prediction of HSV. In this regard, Zhang et al. [119] proposed a DL method that can rapidly design the layup pattern of an HSV type 4. This model, called Physically Constrained Generative Adversarial Networks (PCGANs), was introduced. In this work, a third NN was created to assess the output of the trained model, and a specific penalty was added to the generator's cost function to enforce physical constraints. This penalty is applied when the design criteria do not meet the requirements, thereby correcting the model's training path (Fig. 20). The results of this study show that PCGANs have performed remarkably; not only can they generate designs that achieve the desired goals, but they also have high accuracy in predictions. Based on the method presented in this paper, using the DL model, the best design among the options provided by PCGANs was selected, and its burst pressure is 158 MPa, which is 2.25 times the working pressure. In this optimized design, 33 hoop layers are considered, with a weight 16.33% lower than the traditional network theory, and the maximum cumulative fiber thickness is 24.68 mm, which is a 42.11% reduction.

Nachtane et al. [34] proposed a community-based method using a DL model to predict damage in high-pressure composite HSV and optimize their design to prevent failure. In this paper, a new strategy was developed that combines deep NN with differential evolutionary optimization (DNN-DEO) to predict damage and stress in HSV and identify optimal design parameters (radius, pressure, and thickness) to reduce failure risks and maintain high performance. This integrated technique presents a powerful procedure to optimize the design and performance of HSVs and ensure the reliability and efficiency of HSV systems. Kadri et al. [120] studied the degradation of composite material under static pressure using ML methods. In this regard, damage modeling was employed to evaluate the behavior of hydrogen vessels under high pressure. This paper proposes a practical approach that combines FEA simulations with ML algorithms. In this approach, the representative volume element (RVE), along with a selected behavior law and damage law, was used as input data. Classification ML algorithms, such as k-nearest neighbors (k-NN) and a specific type of k-NN with a dynamic time warping (DTW) metric, were used. Park and Chae [121] proposed a polynomial equation in the temperature range 223.15K < T < 373.15K and pressure range 0.1 MPa < P < 100.1 MPa to provide various thermophysical characteristics of hydrogen using different coefficients. The
<figure><img src="image_22.png" /><figcaption>Fig. 19. Working principles of PSO.</figcaption></figure>

coefficients of this equation were determined using ML methods and extensive reference data. The equation was separately trained, tested, and validated for each property. The equation's order was varied between 2 and 5, and their accuracy was evaluated and compared. The average relative error for the fifth-order equation was less than 0.3 %, except for molar volume and entropy. The accuracy of the proposed equation was also compared with practical data and other correlation equations for density, viscosity, and thermal conductivity required in hydrogen refueling process simulations. The results showed that the proposed equation provides better accuracy for viscosity and thermal conductivity compared to existing equations in scientific sources. The outcomes of this work can provide accurate and reliable values of hydrogen properties quickly and stably, especially for hydrogen refueling process simulations. Kadri et al. [122] examined the degradation process of composite materials used in HSV using advanced computational techniques. In this work, a recurrent NN, specifically a bidirectional long short-term memory (Bi-LSTM) network, was used to predict the temporal evolution of soft damage. Key degradation features were extracted from FEA calculations using data clustering algorithms and processed as time-series data. The results of this paper showed that the Bi-LSTM network can accurately simulate the elastic and plastic behaviors of composites under tensile strength. Kaoutari et al. [123] provided a comprehensive analysis combining experimental research, numerical simulations, and ML models for simulating the performance

of metal hydride systems in HSV under various circumstances. This study used a LaNi5 metal hydride cylindrical vessel with a capacity of 500 NL and focused on improvements in PCM thermal management and surface water cooling and heating. Numerical simulations provided insights into thermal dynamics and reactions, while practical data were utilized to validate the model. The data were combined with additional numerical data to train a feedforward neural network (FFNN) model for predicting hydrogen dynamic flow, surface temperature, and heat flux. The average RMSE attained for practical results during the charging/-discharging process was 2.8 % for the state of charge (SOC) and 0.3 °C for surface temperatures. El Moutaouakil et al. [124] investigated structural health monitoring (SHM) according to guided ultrasonic waves in HSV reinforced with hydrogen and used ML methods. In this paper, a 25-transducer network, including five loops with five sensors per loop, was installed on a vessel. Guided ultrasonic waves were employed using the Pitch-Catch method, where the transducers acted in pairs to perform the transmitter and receiver roles for measuring all possible combinations. This method led to the recording of 600 signal paths, which were captured by the Verasonics Vantage 64 LF data acquisition system. Finally, the effects of temperature and pressure on the recorded dataset were visualized. Jiang et al. [125] developed various ML models for structural health monitoring (SHM) and damage prediction in composite pressure vessels (COPV). These models were trained using a large volume of acoustic emission data collected during explosion tests and pressure cycles. Test outcomes were used as labels for the training process. Statistical features from acoustic emission data were derived over specified time intervals and then trained using models such as Naive Bayes, logistic regression, decision tree, XGBoost, and TabNet. After training with explosion pressure test data, TabNet, decision tree, and XGBoost models achieved an accuracy higher than 0.94. In this context, the TabNet model showed the best performance in pressure cycle testing with an accuracy of 0.98. This research is the first development of an ML model for predicting the damage status of COPV vessels in commercial applications related to mandatory safety testing. Santos et al. [126] designed and built several HSVs using the filament winding process and considered parameters controlling winding and curing as time signals. Process parameters include fiber tension, winding speed, layer pressure, fiber volume ratio, winding time, and curing pressure. The constructed HSV was tested for burst pressure. Several configurations were defined, each with one manufacturing parameter changed. The recorded production data were fed into ML algorithms and trained to predict the burst pressure of the vessel. These ML algorithms included NN (Fig. 21), Gaussian process regression (GPR), kernel principal component analysis-lasso (KPCA-Lasso), and group regression. The results showed that the KPCA-Lasso and GPR models exhibited a good correlation. On the other hand, the outcomes showed that the group regression model, by combining KPCA-Lasso and GPR, provided better results.

Islam et al. [127] performed a practical study utilizing feature selection techniques, GA, and DL to categorize cracks in a pressure vessel. In this paper, Acoustic Emission signals were utilized to identify the presence of cracks in HSV. Using acoustic emission data obtained from a pressure vessel designed in this work, the effectiveness of the presented technique (GA + DNN) was demonstrated. The practical outcomes of this paper show that the presented technique was highly effective in selecting distinctive features. These features were fed into a DNN classifier, and a classification accuracy of 94.67 % was achieved. Lu et al. [128] addressed the screening and prediction of HSV performance by combining Targeted Screening Systems (HTS) with ML. In this context, GCMC simulations were used for screening Metal-Organic Frameworks (MOFs) for HSV, and a dataset was created for this purpose. In addition, a DL classification model supported by the Crystal Graph Convolutional NN (CGCNN) was presented to predict optimal HSV. The results of this paper showed that the classification model based on the CGCNN algorithm provided high prediction accuracy for MOF HSV performance, with an area under the curve (AUC) of 0.921 (positive threshold 45 g/L).
<figure><img src="image_23.png" /><figcaption>Fig. 20. The design process of an HSV Type 4 using deep learning methods in [119].</figcaption></figure>

Finally, a summary of the studies conducted in this field can be found in Table 7.

## 4.2. Artificial neural network

As mentioned, the ANN method can be an efficient solution for investigating and optimizing hydrogen storage tanks. In this regard, Li et al. [129] proposed a combined framework of ANN and FEA for Type 4 vessels with a pressure of 70 MPa. This method allows the optimization of fiber winding parameters considering the appropriate dome geometry. In this process, the non-uniform material distribution in the cylindrical and dome areas was fully considered. Additionally, the winding parameters, which are affected through practical manufacturing modifications such as transitional areas and layer end settings, were added as modified inputs to the ANN to maintain the integrity of the complex geometric details. Simulation results showed that the mechanical response of the tank to explosive pressure was improved. Moreover, prototype tanks were produced, and comparative tests were performed on them, indicating that the explosion pressure of the Type 4 tank increased from 145 MPa to 157.74 MPa. Anderson et al. [130] presented for the first time an ANN capable of predicting hydrogen loading under various temperature and pressure conditions using all the obtained data. By leveraging this ANN, it was estimated that in the non-isothermal oscillation from 77 K to 160 K, the reduction in storage pressure from 100 to 35 bar reduces the achievable delivered capacity by only 59 g/L, which could be an acceptable trade-off considering safety considerations and compression costs. Since this ANN uses only simple descriptors as inputs, researchers can use it for rapid evaluation of hydrogen storage capabilities in novel porous crystal designs under different oscillatory conditions. Yang et al. [131] investigated creep-fatigue behavior in HSV under high-temperature loading and internal pressure caused by the repeated desorption and absorption of hydrogen. They calculated heat

<figure><img src="image_24.png" /><figcaption>Fig. 21. Schematic of applying deep learning methods in the hydrogen storage vessel in [126].</figcaption></figure>
<table><caption>**Table 7**
Summary of research conducted on hydrogen storage tanks using Machine Learning & Deep Learning.</caption><thead><tr><th>Author</th><th>HSV Type</th><th>Year</th><th>Composite</th><th>Maximum Pressure (bar)</th><th>Note</th></tr></thead><tbody><tr><td>Zhang et al. [119]</td><td>4</td><td>2024</td><td>T 700</td><td>700</td><td>Designed the lay-up scheme of the HSV</td></tr><tr><td>Nachtane et al. [34]</td><td>4</td><td>2024</td><td>T 700</td><td>700</td><td>Predicting the damage in lightweight HSVs and optimizing their design to prevent failure</td></tr><tr><td>Kadri et al. [120]</td><td>4</td><td>2024</td><td>-</td><td>-</td><td>Analysed composite degradation</td></tr><tr><td>Park and Chae [121]</td><td>4</td><td>2022</td><td>T 700</td><td>-</td><td>Evaluated the coefficients of hydrogen property correlation equations</td></tr><tr><td>Kadri et al. [122]</td><td>4</td><td>2025</td><td>T 800</td><td>-</td><td>Prediction of Ductile Damage in Composite Material Used HSV</td></tr><tr><td>Kaoutari et al. [123]</td><td>1</td><td>2025</td><td>-</td><td>-</td><td>to improve the performance of metal hydride systems for HSV</td></tr><tr><td>El Moutaouakil et al. [124]</td><td>4</td><td>2024</td><td>T 700</td><td>700</td><td>structural health monitoring, according to guided ultrasonic waves</td></tr><tr><td>Jiang et al. [125]</td><td>4</td><td>2024</td><td>T 700</td><td>1050</td><td>Damage prediction and structure health monitoring of HSVs</td></tr><tr><td>Santos et al. [126]</td><td>4</td><td>2025</td><td>T 700</td><td>-</td><td>Prediction of the vessel's burst pressure</td></tr><tr><td>Islam et al. [127]</td><td>1</td><td>2018</td><td>-</td><td>300</td><td>Proposed a robust crack identification method for pressure vessels</td></tr><tr><td>Lu et al. [128]</td><td>-</td><td>2022</td><td>-</td><td>-</td><td>for the discovery of the optimal HSV</td></tr></tbody></table>

transfer properties and creep-fatigue life in a full-service process in HSV utilizing Ansys® nCode software and Workbench. In this paper, the dependability of the computed life results using the FEA method was validated with the ASME code. Additionally, an optimization technique based on OED and Backpropagation Artificial Neural Networks (BP ANNs) was presented to find the best process parameters. Xiao et al. [132] presented an optimization method using Artificial Neural Networks to identify the initial thermodynamic conditions of the filling system that result in the lowest final hydrogen temperature in the HSV with minimal energy consumption. In this work, a zero-dimensional aggregate parameter model was first created. This simplified model,

developed in MATLAB/Simulink, is utilized to simulate the flow of hydrogen from cascade pressure vessels to an internal HSV. Then, an ANN was trained using model computation outcomes and experimental results for multi-objective optimization. The results of this paper show that the ANN has good predictive capabilities and provides the ability to determine optimal charging parameters. Additionally, the findings show that the cascade filling system can efficiently fill the internal storage tank with a fixed average pressure rise rate. Utilizing the presented ANN, the optimization method predicts that for charge times less than 183 s, the input temperature ranges from 259.99 to 266.58 K, which can decrease energy consumption by about 2.5 %. Collantes [133] trained a

<figure><img src="image_25.png" /><figcaption>**Fig. 22.** The studied hydrogen storage tank and the neural network structure used for predicting tank failure in [134].</figcaption></figure>
set of fully connected neural networks and systems formed from them using experimental data to predict the State of Charge of an HSV with metal hydride. In this work, the input variables of the ANN include time, pressure, temperature, and derived values. Various configurations of neural networks, cost functions, and input variables were used, and the performance of the resulting systems was evaluated using a validation dataset. Chen et al. [134] developed an ANN model to predict the failure of hydrogen composite cylinders (Fig. 22) under thermal-mechanical loading. In this work, a backpropagation NN model was designed to predict the failure of composite cylinders. The inputs to this model include layer thickness, twist angle, and temperature, while the model output is the failure pressure of the cylinder. The FEA model of the cylinder is based on the shell theory, considering shear effects and geometric non-linearity. The ANN was trained using failure results obtained from simulations under thermal conditions and different configurations. The results of this paper show that the developed ANN model has a high capability in determining the failure of HSVs.

Liu et al. [135] used an Artificial Immune System (AIS) for the optimization of the weight of aluminum-carbon fiber/epoxy composite HSVs under burst pressure constraints. The optimization method used in this paper simulates processes such as antigen production, antibody and antigen combination, selection, cloning, and hypermutation of B cells, antibody cell diversity preservation, creation of memory cells with high affinity, and the elimination of antibody cells with low affinity. Comparisons showed that the AIS outperforms the GA and simulated annealing in terms of search speed and accuracy.

Finally, a summary of the research conducted that utilized NNs for the optimization and evaluation of hydrogen storage vessels is presented in Table 8.

### 4.3. Evolutionary computing & fuzzy logic

Evolutionary methods can be applied in the optimization of hydrogen storage vessels from various perspectives. In this regard, Li et al. [139] developed an integrated reliability index associated with combined random-fuzzy uncertainties and proposed a modified PSO for optimal design based on reliability. In this work, the minimum composite shell weight was considered as the objective, and the reliability of the HSV with combined random-fuzzy uncertainties was set as the constraint. The outcomes of this study showed that the hoop layer of the HSV is the main load-bearing structure. Under combined random-fuzzy uncertainties, the reliability of the HSV behaves ambiguously with fuzzy uncertainty, and at a low cutting level of $\lambda$, the reliability will be 0, making it challenging to meet the reliability requirements. Increasing the longitudinal tensile strength, hoop layer thickness, and spiral layer thickness can enhance the HSV vessel's reliability. Additionally, the optimization technique presented in this work improved the reliability from 0.70 to 1. Xu et al. [140] addressed the optimization (minimum weight) of HSV under burst pressure constraints. In this regard, an adaptive GA was presented for the optimal design of HSV. In the presented optimization algorithm used in this paper, adaptive probabilities of mutation and crossover were considered, which change based on the fitness function of individuals, and a penalty function was introduced to enforce the burst pressure constraint. In this work, the thickness and angles of composite layers were selected as design variables. The results obtained from the adaptive genetic algorithm were compared with results from a simple GA and the Monte Carlo technique. Bai et al. [141] proposed a GA for optimizing the design of an HSV made of metal hydride material. Comparative outcomes depicted that the charging time for 90 % saturation in the optimized fin-foam reactor was reduced by 6.9 % and 38 % compared to the metal foam reactor and fin reactor, respectively. It was shown that the optimal volume ratios of fins in the optimized fin-foam reactors, at different volume fractions of high thermal conductivity materials, remain about 0.4 on average. Lin et al. [142] focused on the optimization of the design of a layered composite HSV to reduce stress concentration. The optimal design in this work was carried

<table><caption>**Table 8**
Summary of research conducted on HSVs using ANN.</caption><thead><tr><th>Authors</th><th>HSV Type</th><th>Year</th><th>Composite type</th><th>Maximum Pressure (bar)</th><th>Note</th></tr></thead><tbody><tr><td>Li et al. [129]</td><td>4</td><td>2023</td><td>T 700</td><td>700</td><td>ANN-based ML algorithms are used to optimize 70 MPa type IV vessels.</td></tr><tr><td>Li et al. [136]</td><td>4</td><td>2024</td><td>T 700</td><td>700</td><td>Providing a technique that enables the winding parameters optimization process to incorporate the corresponding feasible dome geometry</td></tr><tr><td>Anderson et al. [130]</td><td>1</td><td>2018</td><td>-</td><td>700</td><td>Predicting hydrogen loadings at multiple T, P conditions</td></tr><tr><td>Yang et al. [131]</td><td>4</td><td>2024</td><td>-</td><td>-</td><td>Predicting parameters regarding the creep-fatigue behavior of HSV</td></tr><tr><td>Xiao et al. [132]</td><td>3</td><td>2021</td><td>-</td><td>700</td><td>Prediction and optimization of cascade filling system (for HSVs)</td></tr><tr><td>Collantes [133]</td><td>1</td><td>2024</td><td>-</td><td>300</td><td>Forecast the state of charge (SOC) of a metal</td></tr><tr><td>Chen et al. [134]</td><td>3</td><td>2008</td><td>T 700</td><td>300</td><td>Predicting the failure of composite HSV subjected to thermo-mechanical loading</td></tr><tr><td>Liu et al. [135]</td><td>4</td><td>2009</td><td>T 700</td><td>600</td><td>Optimizing the weight of Al-carbon fiber/epoxy HSVs under burst pressure constraint</td></tr><tr><td>Ghaznavi et al. [137]</td><td>4</td><td>2025</td><td>CFRP</td><td>-</td><td>DNN-based deep learning for damage monitoring and classification in COPVs using acoustic emission data.</td></tr><tr><td>Lee et al. [138]</td><td>4</td><td>2025</td><td>-</td><td>-</td><td>Dynamic ANN model for predicting ultralow temperatures in hydrogen storage tanks during operation.</td></tr></tbody></table>

out using the FEA, combined with a simplified conjugate gradient method and GA, to minimize von Mises stress in the HSV. Twist angle and layer thickness were considered as design variables. Additionally, a comparison between SCGM and GA methods was made to achieve an optimal and efficient design. By means of this optimization process, the best possible combination of twist angle and composite layer thickness was obtained, minimizing stress concentration in the vessel. Lee et al. [143] optimized the weight of Type 4 pressure vessels (Fig. 23) through burst pressure constraints and using FEA based on a GA. In this work, the optimal design variables included composite layer thickness and twist angles. The optimized design variables were verified through numerical simulations for the pressure vessel. The outcomes showed that the weight of the Type 4 hydrogen pressure vessel was reduced by approximately 6.5 % compared to previously reported outcomes for HSV
<figure><img src="image_26.png" /><figcaption>**Fig. 23.** Type-IV vessel composition and FE model: (a) composition of a Type-IV pressure vessel; (b) FE mesh and boundary conditions, as studied in Ref. [143].</figcaption></figure>

Type 4. In addition, a technique to reduce the overall optimization time was presented. In the initial technique, the population size was fixed throughout all generations, but in the presented technique, the population size was halved every 25 generations, which resulted in reduced computational volume. Although the proposed technique caused an increase in weight by about 0.1 %, it reduced the computational time for optimization by approximately 46.5 %.

Hu et al. [144] presented a design technique for Type 4 automotive HSV based on the strength and stiffness of the reinforcing layer. In this project, the stress and strain of the vehicle's HSV under both operational and burst internal pressure were assessed through FEA simulation. Finally, GA was utilized to identify the best carbon fiber for the automotive HSV. The outcomes of this paper depict that the proposed design can meet the vehicle's usage requirements; the most suitable carbon fiber modulus for the twisting of the HSV bottle is 288.8 GPa, and the weight of the reinforcing layer is reduced by approximately 32 % compared to the T700S carbon fiber-wound tank. In conclusion, a summary of the research conducted on HSV that utilized metaheuristic methods can be presented in Table 9.

<caption>**Table 9**</caption>

Summary of research conducted on hydrogen storage tanks using Evolutionary Computing & Fuzzy Logic.

<table><thead><tr><td>Author</td><td>HSV type</td><td>Year</td><td>Composite type</td><td>Maximum pressure</td><td>Soft computing method</td><td>Note</td></tr></thead><tbody><tr><td>Li et al. [139]</td><td>3</td><td>2023</td><td>-</td><td>700</td><td>PSO-Fuzzy</td><td>Applying PSO-Fuzzy to carry out reliability-based optimization design</td></tr><tr><td>Xu et al. [140]</td><td>3</td><td>2010</td><td>-</td><td>-</td><td>GA</td><td>Optimization (Minimum Weight) of a Composite HSV Under Burst Pressure Constraint</td></tr><tr><td>Bai et al. [141]</td><td>1</td><td>2022</td><td>-</td><td>-</td><td>GA</td><td>Optimization of the ratios of metal foam in HSV</td></tr><tr><td>Lin et al. [142]</td><td>3</td><td>2013</td><td>T 700</td><td>400</td><td>GA</td><td>Optimization of the design of an HSV to minimize stress concentration</td></tr><tr><td>Lee et al. [143]</td><td>4</td><td>2019</td><td>T 700</td><td>-</td><td>GA</td><td>Optimization of the weight of HSV</td></tr><tr><td>Hu et al. [144]</td><td>4</td><td>2024</td><td>T 700</td><td>288</td><td>GA</td><td>Utilized to find the best carbon fiber for HSV</td></tr></tbody></table>

## 4.4. AI in composite mechanics

Although the central focus of this review lies in the application of AI to hydrogen storage vessels, the broader field of composite materials has also benefited substantially from the integration of artificial intelligence, particularly in the prediction and modeling of mechanical behavior. Valuable research has been conducted in this field, the information of which is presented in Table 10.

This section studied the use of AI techniques in HSV design and analysis. methods like deep learning, neural networks, and evolutionary algorithms were used for performance prediction and structural optimization. These techniques improved reliability, reduced weight, lowered production costs through faster iterations, and decreased waste via predictive failure models that extend vessel lifespan. It promotes sustainability by enabling recyclable material selection and efficient resource distribution, supporting green hydrogen infrastructure. And enhanced design efficiency.

# 5. Critical analysis of AI applications in hydrogen storage vessels

AI is now demonstrably effective for COPV design exploration and predictive performance modeling. However, real industrial adoption hinges on four key pressure points. Model reliability, data governance, robust generalization, and workflow fit. Physics-constrained DL for lay-up synthesis shows how priors can be embedded to limit non-physical designs while accelerating iterations, yet training such models still requires curated labels and careful penalty design to avoid “physics in name only” [119]. At the other end of the pipeline, manufacturing data, ML studies indicate that process signals (tension, winding speed, cure traces) carry enough information to predict burst strength with competitive accuracy, evidence that factory data can materially reduce trial-and-error, provided logging is standardized and drift is monitored [126]. Likewise, AE-based classifiers trained on pressure cycling and burst tests show high accuracy for damage state detection, but class imbalance (rare critical failures) and label noise remain limiting. Careful feature governance and out-of-distribution (OOD) checks are required before such models can inform certification decisions [125].

Experimental approaches, though essential for certification and rich in real-world fidelity, are costly and time-consuming. AI can mitigate these demands by guiding targeted test selection through predictive modeling. Simulation-based methods enable scalable, controlled parametric studies but may omit critical failure modes. Integrating AI allows for calibration and refinement using empirical data. Purely AI-driven methods offer rapid, multi-objective optimization but remain heavily dependent on high-quality datasets. Their most impactful role emerges in hybrid frameworks that fuse experimental and simulation data to deliver physically consistent and data-efficient models.

On explainability, state-of-the-art Explainable AI(XAI) frameworks for FRP/composites demonstrate that saliency, SHAP, and concept attribution can expose input-output pathways in strength and defect
<table><caption>**Table 10**
Summary of research conducted on composite using AI.</caption><thead><tr><td>Author</td><td>Year</td><td>Soft computing method</td><td>Note</td></tr></thead><tbody><tr><td>Chen et al. [145]</td><td>2025</td><td>Fourier Homogenization Network (FHN)</td><td>Proposed a data-driven micromechanics model to predict stress fields and effective moduli, capturing interfacial damage.</td></tr><tr><td>Jiang et al. [146]</td><td>2022</td><td>Physics-informed DNN</td><td>Proposed a DNN integrating surface elasticity to capture size-dependent stress-strain responses in nanostructures.</td></tr><tr><td>El Fallaki Idrissi et al. [147]</td><td>2024</td><td>Thermodynamics-informed ANN (MuTINN)</td><td>Proposed a multiscale ANN surrogate incorporating thermodynamic variables for predicting inelastic behavior under complex loading.</td></tr><tr><td>Wu et al. [148]</td><td>2023</td><td>Deep Homogenization Network (DHN)</td><td>Proposed a neural network for stress prediction in periodic composites using two-scale displacement expansions and automatic differentiation.</td></tr><tr><td>Wu et al. [149]</td><td>2024</td><td>Adaptive DHN</td><td>Proposed an adaptive DHN with trainable collocation weights to improve predictions near phase interfaces.</td></tr><tr><td>Jiang et al. [150]</td><td>2023</td><td>Physics-informed DNN</td><td>Proposed a DNN to solve heat conduction problems in composites with built-in periodicity and automatic differentiation.</td></tr><tr><td>El Fallaki Idrissi et al. [151]</td><td>2022</td><td>Non-intrusive PGD</td><td>Proposed a real-time stiffness predictor using PGD with a few high-fidelity simulations for woven composites.</td></tr><tr><td>El Fallaki Idrissi et al. [152]</td><td>2025</td><td>POD + Random Forest</td><td>Proposed a hybrid meta-model for fast nonlinear stress prediction using microstructural parameters.</td></tr><tr><td>Chen et al. [153]</td><td>2024</td><td>Physics-informed DNN</td><td>Proposed a homogenization model capturing size effects and stress fields using surface-aware DNN.</td></tr><tr><td>Gavallas et al. [154]</td><td>2024</td><td>CNN</td><td>Proposed a CNN to predict spatial stiffness from microstructure images for property field generation.</td></tr><tr><td>Nastos et al. [155]</td><td>2023</td><td>CNN + Stochastic FE</td><td>Proposed a CNN-based framework to estimate residual strength from partial tests with uncertainty quantification.</td></tr></tbody></table>

prediction. This supports traceability demanded by vessel standards, but reproducibility across geometries and scales is still immature [156,157]. Physics-informed learning offers a promising remedy. PINNs for polymers/laminates and theory-constrained variants can encode constitutive laws, interface conditions, or failure envelopes directly into the loss, improving data efficiency and physical plausibility, particularly valuable when proprietary datasets limit sample sizes [158,159].

A parallel thread is digital twins. recent RB/DFOS-enabled twins for hydrogen vessels show early feasibility of condition-based fatigue life monitoring and real-time strain mapping, pointing to closed-loop maintenance where AI prioritizes inspections and updates remaining-life estimates on-the-fly [160-162].

Two integration risks persist. First, standards alignment, black-box predictions without uncertainty bounds or physical rationale face headwinds against the minimum design/test requirements under ISO 11119-3 [107] and related pressure-vessel standards. Future AI must pair predictions with calibrated Uncertainty Quantification (UQ), physically consistent counterfactuals, and auditable data lineage. Second, dataset realism, many studies still rely on simulated or lab-scale data without domain randomization and continual learning, and models can overfit to specific lay-ups, liners, or duty cycles. Initial work

on probabilistic burst modeling and multi-sensor SHM data acquisition suggests a path toward more representative training corpora and risk-aware decisions, but shared benchmarks for COPVs are still missing. A visual summary of AI's strengths, weaknesses, opportunities, and threats in hydrogen storage is provided in Table 11, while Fig. 24 outlines integration challenges and potential hybrid workflows.

## 6. Conclusion

The present review has examined the role of AI in optimizing and predicting key performance parameters of HSVs. While conventional methods such as finite element analysis, multi-scale modeling, and static or dynamic testing are effective for evaluating fracture strength, liner damage, dome thickness, torsion effects, crash behavior, crack propagation, charging/discharging processes, burst pressure, durability, and fatigue life, they remain limited in delivering multi-objective optimization or rapid predictive capabilities. Integrating these established techniques with AI methods, including deep learning, artificial neural networks, and evolutionary algorithms, enables substantial advancements in areas such as failure prediction, structural health monitoring, material property estimation, burst pressure prediction, crack detection, composite lay-up optimization, weight minimization, and optimal material selection. Such hybrid approaches have demonstrated the potential to reduce design cycles, enhance reliability, and improve cost-effectiveness. Looking ahead, progress in this domain will depend on building standardized, open-access datasets that combine experimental, simulation, and operational data to overcome current limitations in AI model training and validation. Expanding the use of physics-informed AI, where governing equations and failure criteria are embedded directly into learning architectures, will further enhance physical fidelity and reduce data requirements. Advances in AI-enabled digital twin systems, particularly those leveraging distributed sensing and robust uncertainty quantification, can enable real-time monitoring, predictive maintenance, and adaptive control of HSVs throughout their lifecycle. At the same time, aligning AI-based predictions with international pressure vessel standards by embedding explainability, traceability, and uncertainty bounds into outputs will be essential for certification and industry trust. Equally important will be the development of AI workflows that integrate seamlessly into established engineering processes, ensuring adoption without disrupting compliance, safety, or production efficiency.

The next stage in HSV design evolution lies not in replacing conventional methods but in coupling them with AI to enable intelligent, data-driven, and physically consistent optimization strategies. Achieving this will require coordinated efforts between academia, industry, and regulatory bodies to address data governance, model credibility, and standardization, ensuring that AI transitions from a

<table><caption>**Table 11**
The main challenges in the integration of AI with COPV design.</caption><thead><tr><td>Challenge Domain</td><td>Implication</td></tr></thead><tbody><tr><td>Black-box opacity</td><td>Limits interpretability; lacks mechanistic linkage, impeding compliance under ISO 11119-3 and ISO 11439 [107,108].</td></tr><tr><td>Poor generalization</td><td>Models often degrade in response to new geometries, materials, or operating conditions.</td></tr><tr><td>Data scarcity/quality</td><td>Industrial and test data are limited, expensive, and often proprietary. Synthetic datasets are non-standardized.</td></tr><tr><td>Integration complexity</td><td>Mismatch between deterministic FEA/multiscale workflows and probabilistic AI pipelines.</td></tr><tr><td>Computational cost/overfitting</td><td>Deep or large models can overfit to limited datasets and require significant computational resources.</td></tr><tr><td>Regulatory concerns</td><td>Safety standards demand transparent, auditable models with uncertainty quantification.</td></tr><tr><td>Dataset biases & sustainability gaps</td><td>Underemphasis on lifecycle, multi-physics phenomena, and recyclable designs.</td></tr></tbody></table>
Strengths

* Solves high-dimensional, multi-objective problems.

* Enables rapid prediction and optimization once trained.

* Complements FEA and experimental data for hybrid modeling.

* Enhances economic viability by minimizing costs and material waste.

Opportunities

* Adoption of explainable and physics-informed AI.

* Development of standard datasets and benchmarking tasks.

* Integration into digital twin environments for real-time health monitoring.

* Accelerating material discovery and innovation in composites.

Weaknesses

* Requires large, domain-specific datasets.

* Often lacks transparency and interpretability.

* Generalization across vessel types or loading conditions remains limited.

* Introduces uncertainties from input parameter dependencies.

Threats

* Over-reliance on synthetic data or simulated outputs can lead to error drift.

* Regulatory constraints on black-box models in certification workflows.

* Model drift due to material variability and environmental fluctuations.

<caption>Fig. 24. – SWOT Analysis for integration of AI with COPV design.</caption>

promising tool to a fully trusted asset in hydrogen storage vessel design.

CRediT authorship contribution statement

**Pedram Aminharati:** Writing – review & editing, Writing – original draft, Visualization, Validation, Conceptualization. **Mohammadali Shirinbayan:** Writing – review & editing, Writing – original draft, Visualization, Validation, Supervision, Project administration, Conceptualization. **Khaled Benfriha:** Writing – review & editing, Writing – original draft, Visualization, Validation, Supervision. **Fodil Meraghi:** Writing – review & editing, Writing – original draft, Visualization, Validation, Supervision. **Joseph Fitoussi:** Writing – review & editing, Writing – original draft, Visualization, Validation, Supervision, Project administration, Conceptualization.

Declaration of competing interest

We would like to submit an original research article entitled "AI-Driven Advances in Composite Materials for Hydrogen Storage Vessels: A Review" for consideration by International Journal of Hydrogen Energy.

The authors certify that they have NO affiliations with or involvement in any organization or entity with any financial interest, in the subject matter or materials discussed in this manuscript.

We have no conflicts of interest to disclose.

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