On-board System Research Articles

Theoretical aspects of designing the components of an aircraft onboard cable network

The article presents theoretical foundations of designing the components of an aircraft onboard cable network, proposed as a methodology for solving the problems of designing onboard equipment. The composition of the initial and developed technical documentation for the aircraft onboard cable network is determined. The article also shows the relationship between the stages of designing the onboard cable network and the stages of designing the aircraft. The formulation of the general task of creating the components of an onboard cable network is presented as a number of particular subtasks of a lower level of complexity. The method for solving the problem is based on a number of transformations performed with the original documentation in order to develop technical documentation for the introduction of the onboard system into the complex of onboard equipment. A number of successive transformations of the proposed bundle model using the apparatus of topology and multiset theory made it possible to form a model of the components of the onboard cable network of an aircraft that gives a detailed view of its future design. The proposed scientific approach to the design of aircraft onboard cable network components created the prerequisites for minimizing the heuristic component in the process of designing aircraft onboard cable network components.

A Digital Track Map-Assisted SINS/OD Fusion Algorithm for Onboard Train Localization

Accurate and reliable speed and position estimation plays an important role in the safety and efficiency of intelligent railway vehicles. Due to the level required of safety, reliability, and strong norms in the current practical application, intelligent railway vehicle positioning heavily relies on a large number of balises laid on the track and the onboard odometer (OD), while the other position method, GNSS introduction, is relatively slow. This article proposed a digital track map-assisted onboard railway location system using strapdown inertial navigation system (SINS) and OD. The proposed method consists of two steps. First, an SINS- and OD-integrated navigation method based on OD velocity integration is in the inner circle. Then, a map-matching algorithm based on vertical projection and heading weighting was employed, and when the matching outer circle results were obtained, the positions obtained from the matching outer circles were used to replace the positions obtained from SINS/OD for the Kalman filter combination. The performance of our algorithm was verified using field tests, and SINS/OD and SINS/OD/MM comparison data processing results prove that our proposed digital track map-assisted SINS/OD algorithm can effectively suppress the accumulation of train position errors. After nearly 80 km of navigation, the position error is 24 m, and the relative mileage accuracy is less than or equal to 0.03% distance.

Customized Research on Fault Diagnosis of Civil Aircraft Onboard Maintenance System Based on Big Data Technology

Abstract This paper dissects the BP neural network and RNN on the basis of existing research, obtains the improved long and short-term memory network, introduces the attention mechanism into it, and designs the fault diagnosis model of airborne maintenance system based on AM-LITM with big data technology. Focusing on the use of the BP learning algorithm to obtain the reference error of each unit needed to adjust the weight of each connection, real-time updating of information through the long- and short-term memory network to solve the problem of gradient dispersion of RNN, and at the same time, the method designed in this paper is compared with the traditional method from the aspects of fault diagnosis and prediction. The results show that the average prediction error of the traditional fault diagnosis algorithm is around 0.06, and it only has good diagnosis performance for the A-type fault, except for the classification accuracy rate of 0.9845. The rest of the performance index is 1. The diagnosis accuracy of the AM-LSTM fault diagnosis algorithm for three types of faults can reach 1, and all the indexes for the A, B, and C faults are 1, which shows that it greatly improves the performance of the traditional fault diagnosis algorithm. Diagnostic accuracy of civil aircraft on-board maintenance system faults is verified to verify the effectiveness and superiority of the designed model.

Fault detection of high-speed train axle bearings based on a hybridized physical and data-driven temperature model

As a key component connecting axle and frame, axle bearings of the high-speed train are essential for the safe operation of modern high-speed trains. The current on-board fault detection system, which compares the axle box temperature measured from the sensor to a predefined threshold value, faces problems such as poor adaptability to varied service conditions, undesired high rate of false alarms and missing alarms, and lack of potential to be optimized. To solve these problems, in the present study, a novel hybrid model is proposed which predicts the temperature of a healthy bearing under the given service condition, and based on that detects the bearing fault. The hybrid model includes a physical model reflecting the thermal behavior of the axle box and two data-driven models adopting the same inputs but representing the relationship by means of back-propagation (BP) neural network and long-short term memory neural network (LSTM). After calibrating these models by real-life operation data collected from 2.25 million kilometers and using all results as inputs, a fault discriminant mechanism is established. The performance of this new mechanism is superior than the current on-board system and all stand-alone models in terms of indicators such as precision, recall and F1 score. This way, the study shows that similar hybrid models can be a viable solution for the future on-board fault detection system.

Hydrogen Fuel Cell Aiming at Vehicle Propulsion: Onboard or Off-Board Electricity Generation?

In response to the global energy crisis and climate change, hydrogen has been pointed out as promising fuel to be part of the green energy transition. Thus, countries worldwide have released policies to support and increase the hydrogen industry. Therefore, investigations regarding how hydrogen can be utilized best as a fuel should be addressed to evaluate its potential applications. Since transportation is responsible for 37% of the carbon emissions from the end-use sectors, it is of interest to consider hydrogen fuel for vehicle propulsion. There are two main alternatives that might be considered for the electricity generation from hydrogen: 1) a hydrogen fuel cell onboard the electric vehicle or 2) a hydrogen fuel cell off-board powering the electric vehicle.Therefore, in this work, we propose the modeling and simulation from hydrogen fuel to wheels of the two different systems using the WLTP as the driving cycle reference. For the onboard system, the following components were considered: the vehicle dynamics, the gear, the motor, the inverter, the battery, and the fuel cell system on board (fuel cell vehicle powertrain). For the off-board system, the following components were considered: the vehicle dynamics, the gear, the motor, and the inverter for the battery electric vehicle powertrain, followed by the charger, the grid, and the fuel cell system off-board. The components were modeled as further described.The vehicle dynamics were modeled mathematically and the weight difference between the vehicles was taken into account. The gear was a single-step gear. The motor was an eight-pole PMSM and the inverter three-phase IGBT. The battery for both was modeled as a lithium-ion battery with different sizes, with the battery for the fuel cell vehicle being much smaller. Capacity values similar to commercialized vehicles were used (4 and 300 Ah). The fuel cell was modeled as a commercialized fuel cell with 63% peak efficiency. For the fuel cell vehicle, a control strategy was implemented to split the power demand between the fuel cell and the battery. The control strategy was based on the state of charge of the battery and the power demand for each point of the cycle.The simulation results have shown a slightly higher efficiency and lower hydrogen consumption for the onboard system compared to the off-board system. Even though there were more losses associated with the battery and the fuel cell in the onboard system, the losses associated with vehicle weight and the off-board power electronics related to the off-board system were more significant to the overall energy consumption. Thus, the results suggest that if hydrogen is available as a fuel aimed at vehicle propulsion using it onboard is more efficient than using it off-board.

Effect of pH on the Selectivity of γ-MnO2 Electrocatalysts Towards Oxygen Evolution Reaction in the Presence of Chloride Ions in Alkaline Environment

Pt-Nb anodes utilized in the cathodic protection system onboard of Naval ships generate NaOCl (and eventually Cl2) that (1) limits the materials that can be used in the vicinity of the anodes and (2) has a severe impact on living organisms in the marine environment. The environmentally-friendly oxygen evolution reaction (OER) from H2O is thermodynamically more favorable due to a lower equilibrium potential [1]. However, chlorine evolution successfully competes with the OER due to faster kinetics. One of a few catalysts that demonstrates high selectivity towards the OER in the presence of chloride ions in alkaline environment (pH 12) is g-MnO2 [2]. In this presentation, we will (1) compare activities and selectivities towards the OER of electrodeposited and chemically synthesized g-MnO2 and (2) determine how pH affects selectivity in the pH range from 8 to 12.

Autonomous method for a strapdown inertial navigation system calibration in operation in operation

Strapdown inertial navigation systems (SINS) are widely used in modern air transport. The service life of such systems is decades. During this time, the characteristics of such systems may deteriorate due to the degradation of inertial sensors - gyroscopes and accelerometers. Under these conditions, in order to maintain the required accuracy of the SINS during the entire period of operation, it is necessary either to periodically carry out routine maintenance with the onboard system, or to continuously adapt the used sensor measurement model in terms of compensating for errors that arise. The second direction, which we will call calibration, has the advantage over the first one that it is carried out automatically by the system itself and does not require any additional equipment or additional maintenance work. The known method of SINS recalibration is based on the integration of information from the SINS and from the receiver of satellite radio navigation signals (SRNS). Such calibration, firstly, significantly depends on the availability and quality of satellite information, and secondly, it is possible only for integrated navigation systems in which the inertial subsystem is supplemented by a SRNS receiver. In contrast to the known method, this article solves the problem of autonomous SINS calibration, which is carried out after each flight. The solution is based on a linearized inertial navigation error model based on the boundary conditions of the state vector (at the beginning and end of the flight). The external information used consists only of the initial values of the coordinates and the current measurements of the baroaltimeter. As a result of the decision at the end of the flight, the errors of gyroscopes and accelerometers are determined, as well as the error in determining the final values of latitude and longitude in the SINS. Sensor errors can be used in subsequent system activations, which improves the accuracy of its operation as a whole. The analysis of the suitability and effectiveness of the developed methodology was carried out on a typical flight simulation program.

Quantitative Failure Analysis of the Ballast Pump System Onboard a Ship under HAZOP and the Extended CREAM Approach

Quantitative Failure Analysis of the Ballast Pump System Onboard a Ship under HAZOP and the Extended CREAM Approach

Neuromorphic Analog Machine Vision Enabled by Nanoelectronic Memristive Devices

Arrays of memristive devices coupled with photosensors can be used for capturing and processing visual information, thereby realizing the concept of “in-sensor computing”. This is a promising concept associated with the development of compact and low-power machine vision devices, which is crucial important for bionic prostheses of eyes, on-board image recognition systems for unmanned vehicles, computer vision in robotics, etc. This concept can be applied for the creation of a memristor based neuromorphic analog machine vision systems, and here, we propose a new architecture for these systems in which captured visual data are fed to a spiking artificial neural network (SNN) based on memristive devices without analog-to-digital and digital-to-analog conversions. Such an approach opens up the opportunities of creating more compact, energy-efficient visual processing units for wearable, on-board, and embedded electronics for such areas as robotics, the Internet of Things, and neuroprosthetics, as well as other practical applications in the field of artificial intelligence.

IMAGE FEATURE EXTRACTION METHODS FOR STRUCTURE DETECTION FROM UNDERWATER IMAGERY

Abstract. The use of autonomous underwater vehicles (AUVs) for surveying underwater infrastructure presents a potential cost saving in comparison to remotely operated vehicles (ROVs). One of the challenges when processing images of underwater structures captured by an AUV, is that vast number of images captured during the mission usually do not show the structure. For instance, images captured during the dive to the structure or of the sea floor, or of the deep sea facing away from the structure. Too many images captured, without relevant information for a 3D reconstruction of the structure, leads to increased processing time and issues during the reconstruction process. There are two solutions to reduce the images to only images showing the structure. Firstly, only images of the structure are captured in the first place or remove images that are not useful after the capture and before further processing. This study developed and evaluated techniques that would enable the first strategy to be applied in an AUV. To apply this strategy in an AUV, would require an on-board structure detection system to ensure that they are correctly orientated for capturing useful footage during a survey mission. However, the marine environment poses several challenges to image-based object detection. Furthermore, small AUVs have limited power and computational resources available while deployed on a mission. To investigate the suitability of creating a lightweight structure detection model for the purpose of image evaluation, three computationally efficient image feature extraction methods (colour moments, local binary patterns (LBP), and Haar wavelet decomposition) were evaluated for their ability to distinguish underwater structures from background areas using unsupervised k-means models. LBP was found to be an effective method for identifying underwater structures in open water conditions. For identifying a structure against the seabed, colour moments were identified as the most effective method.

Electromagnetic-thermal co-optimization to minimize case-to-winding temperature rise in permanent magnet machines

Electrification of aerospace platforms requires efficient heat dissipation from electric machines to meet their temperature requirements while minimizing the weight and size of the onboard thermal management system. It is critical to couple available electromagnetic design optimization approaches with complementary thermal models, so that these thermal management system objectives can be considered, namely to reduce the temperature rise between the sources of heat generation and coolant. In this work, a thermal equivalent circuit model for the stator of a permanent magnet synchronous machine is developed and validated. The thermal network includes a discretization of individual conductors and considers their placement within a slot, compared to previous approaches that assumed a lumped/homogenized representation that is not versatile for robust optimization. The temperature predictions reveal that the choice of conductor sizing and packing within the stator slot significantly affects their peak temperatures. After validation, the thermal model is coupled to an electromagnetic model to perform multi-physics multi-objective design that minimizes machine mass and the temperature rise between the conductors and coolant. This approach contrasts a traditional electromagnetic design trade off between loss and mass. Design studies were performed to generate Pareto-optimal designs for comparison between these objectives. By using the co-optimization design framework with an integrated thermal objective function, the machine operating temperature can be significantly reduced while delivering the same power density at the cost of machine efficiency. The detailed machine designs are analyzed to explain the thermal advantages of the co-optimization design.

Diagnostics of inter-turn short circuits in phase stator windings of permanent magnet synchronous machines

BACKGROUND: Currently, electric buses are becoming more widespread in the segment of urban passenger transportation due to the absence of harmful emissions and low noise levels. Permanent magnet synchronous machines (PMSM) are widely used as a traction motor.
 One of the most promising methods of monitoring the technical condition of traction motors is diagnostics using monitoring tools installed on board of a vehicle. The advantages of such methods include the possibility of regular frequent condition checks (as opposed to much rarer diagnostics in stationary maintenance points) and a more rapid response to the development of identified malfunctions, which significantly reduces the cost of maintenance and repair of expensive equipment. The disadvantages include limitations associated with a shortage of layout space for the placement of diagnostic equipment, with the limited capabilities of on-board computer facilities for the transmission and storage of diagnostic information and with the complexity of computational procedures of built-in expert diagnostic systems.
 Concerning the types of operational failures of PMSMs, which are the most common and the most severe in terms of the cost of restoring operability, these are inter-turn short circuits in the stator windings, increased eccentricity of the air gap between the stator and the rotor, bearing damage, as well as damage of the mechanical gearbox coupled with the electric motor.
 AIM: Identification the diagnostic signs of inter-turn short circuits in the phase windings of a permanent magnet synchronous machine, helping to detect these malfunctions on board of route urban vehicles at an early stage.
 METHODS: A mathematical model of a permanent magnet synchronous machine with short-circuited turns of the phase winding, which makes it possible to study the behavior of the motor with a various number of short-circuited turns, has been developed.
 RESULTS: For the first time, the requirements for diagnostic features for the on-board diagnostic system of an urban electric bus, making it possible to detect inter-turn short circuits in the phase windings of traction electric motors at the early stages of the origin of a malfunction, are formulated.
 The main parameters of the process of digital registration of phase currents have been obtained, helping to do measurements and to transmit signals through the onboard information network of the electric bus.
 Using simulation modeling methods, it was found that the peak values of the linear spectrum of the total current vector are stable diagnostic signs that make it possible to detect inter-turn short circuits in the phase windings of traction motors at the early stages of the origin of the malfunction.
 CONCLUSION: The practical value of the study lies in the opportunity of using the proposed methods for diagnosing the technical condition of traction electric drive units in urban passenger electric transport.

A Novel Efficient Dual-Gate Mixed Dilated Convolution Network for Multi-Scale Pedestrian Detection

With the increasing use of onboard high-speed computing systems, vehicle manufacturers are offering significant advanced features of driver assistance systems. Pedestrian detection is one of the major requirements of such systems, which commonly use cameras, radar, and ultrasonic sensors. Image recognition based on captured image streams is one of the powerful tools used for the detection of pedestrians, which exhibits similarities and distinguishing features compared to general object detection. Although pedestrian detection has advanced significantly along with deep learning, some issues still need to be addressed. Pedestrian detection is essential for several real-world applications and is an initial step in outdoor scene analysis. Typically, in a crowded situation, conventional detectors are unable to distinguish persons from each other successfully. This study presents a novel technique, based on the Dual Gate Mixed Dilated Convolution Network, to address this problem by adaptively filtering spatial areas where the patterns are still complicated and require further processing. The proposed technique manages obscured patterns while offering improved multiscale pedestrian recognition accuracy.

Improvement of 2D cine image quality using 3D priors and cycle generative adversarial network for low field MRI-guided radiation therapy.

Cine magnetic resonance (MR) images have been used for real-time MR guided radiation therapy (MRgRT). However, the onboard MR systems with low-field strength face the problem of limited image quality. To improve the quality of cine MR images in MRgRT using prior image information provided by the patient planning and positioning MR images. This study employed MR images from 18 pancreatic cancer patients who received MR-guided stereotactic body radiation therapy. Planning 3D MR images were acquired during the patient simulation, and positioning 3D MR images and 2D sagittal cine MR images were acquired before and during the beam delivery, respectively. A deep learning-based framework consisting of two cycle generative adversarial networks (CycleGAN), Denoising CycleGAN and Enhancement CycleGAN, was developed to establish the mapping between the 3D and 2D MR images. The Denoising CycleGAN was trained to first denoise the cine images using the time domain cine image series, and the Enhancement CycleGAN was trained to enhance the spatial resolution and contrast by taking advantage of the prior image information from the planning and positioning images. The denoising performance was assessed by signal-to-noise ratio (SNR), structural similarity index measure, peak SNR, blind/reference-less image spatial quality evaluator (BRISQUE), natural image quality evaluator, and perception-based image quality evaluator scores. The quality enhancement performance was assessed by the BRISQUE and physician visual scores. In addition, the target contouring was evaluated on the original and processed images. Significant differences were found for all evaluation metrics after Denoising CycleGAN processing. The BRISQUE and visual scores were also significantly improved after sequential Denoising and Enhancement CycleGAN processing. In target contouring evaluation, Dice similarity coefficient, centroid distance, Hausdorff distance, and average surface distance values were significantly improved on the enhanced images. The whole processing time was within 20ms for a typical input image size of 512 × 512. Taking advantage of the prior high-quality positioning and planning MR images, the deep learning-based framework enhanced the cine MR image quality significantly, leading to improved accuracy in automatic target contouring. With the merits of both high computational efficiency and considerable image quality enhancement, the proposed method may hold important clinical implication for real-time MRgRT.

SUBSTANTIATION OF GENERAL REQUIREMENTS TO THE INFORMATION AND MEASURING SYSTEM FOR RECORDING PARAMETERS OF AUTOMOTIVE (ARMORED) VEHICLES

The article examines the possibilities of using a modern on-board recording system of the VBOX type and the Quantumx series for testing automotive and armored vehicles.
 Research conducted by the method of system analysis of technical characteristics of the system.
 The purpose of the research was to study the technical characteristics, principles of construction of a modern on-board recording system of foreign production of the VBOX and Quantumx series, regarding the registration of the necessary operating parameters of automotive and armored vehicle systems, the possibilities of application for conducting tests and the formulation of the main General requirements for this system.
 Analysis of the technical characteristics and features of the use of modern data recorders during testing of automotive and armored vehicles showed that:
 
 modern measuring systems (recorders) can be used to measure the performance parameters of automobiles and armored vehicles of domestic production, which will simplify and automate the process of performing measurements, and minimize the number of errors that occur during their performance;
 measuring systems (recorders) are universal and will allow to ensure the measurement process during testing of automotive and armored vehicles, their components, as well as to measure parameters in various systems (static, dynamic characteristics of objects, temperature, pressure, vibrations, overloads and other indicators);
 using the capabilities of measuring systems (recorder) will allow to reduce the time for making a decision on the feasibility of supplying new (modernized) samples of automobile and armored vehicles of the Armed Forces of Ukraine and to extend the resource of existing samples of
 
 The results of the work should be used when studying the possibilities of purchasing laboratory measuring equipment for testing automotive and armored vehicles by research institutions and industrial enterprises.

A new design method for the vortex hydrogen circulating pump system

Against the backdrop of “carbon peak and carbon neutrality,” the hydrogen and fuel cell vehicle industry are rapidly developing. Within the on-board hydrogen supply system, the hydrogen circulation pump serves as an essential component of the hydrogen fuel cell system. The spiral disk, as a core part of the hydrogen fuel cell system’s vortex hydrogen circulation pump (VHCP), plays a crucial role in determining the performance of the hydrogen circulation system in hydrogen fuel cell vehicles. To meet the requirements for high-performance and high-reliability development of the hydrogen circulation pump, the VHCP scheme is adopted as the choice for the hydrogen pump solution. Through the magnetic suspension and no connection shaft structural design, the feasibility of applying the high speed and high flow hydrogen turbine was initially validated. Utilizing Fluent analysis software and high precision performance test bench, a comprehensive three-dimensional numerical simulation of the turbine design under various operating conditions was conducted and performance test verification, demonstrating that the performance meets the required specifications. By conducting research in both strength optimization design and performance requirement, two major technical challenges in turbine pump application were overcome. Combined with the experimental results of the turbine medium, it is concluded that the vortex pump can meet the flow and pressure rise under the premise of low power consumption in the hydrogen circulation system so as to perfectly increase the hydrogen return amount. Based on these findings, recommendations are proposed for the future development direction of hydrogen supply systems in hydrogen fuel cell vehicles.

СONSTRUCTION OF AN OPTIMAL ROUTE FOR OVERFLYING UNPREPARED LANDING PADS

The problem of constructing an optimal route for overflying unprepared landing pads by the unmanned aerial vehicle (UAV) helicopter type in purpose to choose a useful unprepared landing to make an emergency landing is considered. The description of mathematical models that form the basis of the optimal algorithm of unprepared landing pads overflying is given. The decision to fly to an unprepared landing pad is based on the probabilistic criterion that considers the next following information: the probability of availability of an unprepared landing pad, the probability of failure of the UAV’s helicopter type onboard system, the error of a digital elevation map (DEM) positional information, the error of the UAV’s helicopter type coordinates information and the flight specifications of the UAV helicopter type.The comparison of the computational time of the algorithms of unprepared landing pads overflying is completed. The combined approach to find an optimal route for overflying unprepared landing pads depending on the computational time to solve it is proposed to use. The results of a simulation modelling of the proposed optimal algorithm for the estimation of its efficiency under conditions of variability of parameters of the probabilistic loss function are presented.

Development and verification of a virtual prototype of a vehicle

BACKGROUND: Methods of mathematical modelling are widely used in vehicle development. In order to study vehicle dynamics, stability and handling, as well as to accelerate and to reduce the cost of on-board software development, it is necessary to build a digital twin which contains description of special motion of a vehicle with models of units and subsystems as parts of the vehicle. AIMS: Development and verification of a virtual prototype of a vehicle. METHODS: Development of the virtual prototype and vehicle modelling were done in the MATLAB/Simulink software package. Main derivation of the equations necessary to build the models of vehicle’s units and subsystems is given. Verification testing was conducted using special measuring equipment. RESULTS: The vehicle virtual prototype containing description of combined dynamics of bodyframe, transmission elements, suspension and wheels was developed. Comparison of results of field and virtual testing was made in order to confirm operability and adequacy of the virtual prototype. Main graphs showing dynamics of real and virtual vehicles are presented. CONCLUSIONS: Practical value of development and study lies in ability of using a virtual prototype in vehicle dynamic studies and development of on-board control systems.

On the problems of determining the trajectory of an aerial vehicle based on inertial navigation system data

Navigation in military UAVs requires the use of onboard inertial systems. An approach is proposed for noise filtering, refinement of initial and intermediate values when applying calculation algorithms based on the estimates of loads and angular velocity taken from sensors.

Methods for Estimating Hydrogen Fuel Tank Characteristics

Abstract The pressure vessels needed to store hydrogen for next-generation hydrogen fuel cell vehicles are expected to be a substantial portion of the total system mass, volume, and cost. Gravimetric capacity, volumetric capacity, and cost per kilogram of usable hydrogen are key performance metrics that the U.S. Department of Energy (DOE) uses to determine the viability of hydrogen fuel cell systems. Research and development related to hydrogen storage systems covers a wide range of potential operating conditions, from cryogenic temperatures to high temperatures (above ambient) and low pressure to high pressure. Researchers at PNNL have developed methods for estimating these key pressure vessel characteristics to support on-board hydrogen storage system design and performance evaluation and to support decision-making about DOE hydrogen storage system research investments. This article describes the pressure tank estimation methodology that has been used as a stand-alone calculation and has been incorporated into larger system evaluation tools. The methodology estimates the geometry, mass, and material cost of type I, type III, and type IV pressure vessels based on operating pressure and material strength at the system's operating temperature, using classical thin-wall and thick-wall pressure vessel stress calculations. The geometry, mass, and material cost requirements of the pressure vessel have significant impacts on the total system performance. For example, hydrogen storage materials that can separately achieve a very high hydrogen density can be deemed impractical for use in fuel cell vehicle hydrogen storage systems because the pressure tank containing them is too large, heavy, or expensive. This article describes the design philosophy and analytical process of the tank characteristic estimation methodology, which has been implemented in spreadsheet calculation tools and system-level analysis tools used by DOE researchers. Each of the three tank types (type I, type III, and type IV) uses a different analysis methodology with some common elements. This article also provides examples of implementing the methodology to perform parametric studies of all three pressure vessel types. The goal of this article is to present the methodology in sufficient detail so it can be implemented in other hydrogen fuel cell vehicle design and analysis tools.