Search Results (52)

This paper presents a hierarchical model architecture for the analysis and optimization of Fuel Cell Electric Vehicles (FCEVs). The model encompasses the levels of cell, stack, and complete vehicle, which are interconnected through clearly defined transfer parameters. At the cell level, electrochemical and thermodynamic processes are mapped, the results of which are aggregated at the stack level into characteristic maps such as current–voltage curves and efficiency profiles. These maps serve as interfaces to the vehicle level, where the electric powertrain—comprising the fuel cell, energy storage, electric motor, and auxiliary consumers—is integrated. Special attention is given to the trade-off between the lifetime and dynamics of the fuel cell, which is methodically captured through variable parameter vectors. The transfer parameters enable consistent and scalable modelling that considers both detailed cell and stack information as well as vehicle-side requirements. On this basis, various vehicle configurations can be evaluated and optimized with regard to efficiency, lifetime, and drivability. Full article

Electric vehicles are becoming more commonplace as we shift towards cleaner transportation. However, current charging infrastructure is immature, especially in remote and off-grid regions, making electric vehicle adoption challenging. This study presents an architecture for a standalone renewable energy-based electric vehicle charging station. The proposed renewable energy system comprises wind turbines, solar photovoltaic panels, fuel cells, and a hydrogen tank. As an energy storage system, second-life electric vehicle batteries are considered. This study investigates the feasibility and performance of the charging station with respect to two vastly different Canadian regions, Windsor, Ontario (urban), and Eagle Plains, Yukon (remote). In modeling these two regions using HOMER Pro software, this study concludes that due to its higher renewable energy availability, Windsor shows a net-present cost of $2.80 million and cost of energy of $0.201/kWh as compared to the severe climate of Eagle Plains, with a net-present cost of $3.61 million and cost of energy of $0.259/kWh. In both cases, we see zero emissions in off-grid configurations. A sensitivity analysis shows that system performance can be improved by increasing wind turbine hub heights and solar photovoltaic panel lifespans. With Canada’s goal of transitioning towards 100% zero-emission vehicle sales by 2035, this study provides practical insights regarding site-specific resource optimization for electric vehicle infrastructure that does not rely on grid energy. Furthermore, this study highlights a means to progress the sustainable development goals, namely goals 7, 9, and 13, through the development of more accessible electric vehicle charging stations. Full article

This study presents the design, real-time implementation, and full-scale experimental validation of a rule-based Energy Management Strategy (EMS) for a dual-stack Fuel Cell Hybrid Electric Vehicle (FCHEV) developed on a Jeep Wrangler platform. Unlike previous studies, predominantly focused on simulation-based analysis or single-stack architectures, this work provides comprehensive vehicle-level experimental validation of a deterministic real-time EMS applied to a dual fuel cell system in an SUV-class vehicle. The control algorithm, deployed on a National Instruments CompactRIO embedded controller, ensures deterministic real-time energy distribution and stable hybrid operation under dynamic load conditions. Simulation analysis conducted over eight consecutive WLTC cycles shows that both fuel cell stacks operate predominantly within their optimal efficiency range (25–35 kW), achieving an average DC efficiency of 68% and a hydrogen consumption of 1.35 kg/100 km under idealized conditions. Experimental validation on the Wrangler FCHEV demonstrator yields a hydrogen consumption of 1.67 kg/100 km, corresponding to 1.03 kg/100 km·m² after aerodynamic normalization (Cd·A = 1.624 m²), reflecting real-world operating constraints. The proposed EMS promotes fuel-cell durability by reducing current cycling amplitude and maintaining operation within high-efficiency regions for the majority of the driving cycle. By combining deterministic real-time embedded control with vehicle-level experimental validation, this work strengthens the link between EMS design and practical deployment and provides a scalable reference framework for future hydrogen powertrain control systems. Full article

Fuel cell electric vehicles (FCEVs) rely on a battery system to manage transient load demands and to recover braking energy. In recent years, hybrid topologies that also integrate supercapacitors have gained considerable attention, since they can improve system efficiency, driving dynamics, and component lifetime. Supercapacitors, thanks to their much higher power density compared to conventional batteries, are particularly promising for adoption in FCEVs. Most studies in the literature, however, evaluate these architectures under standardized homologation driving cycles. While such cycles provide a common benchmark for comparison, they generally exhibit less energy-intensive profiles and therefore do not fully capture the real operating demands of a vehicle. For this reason, the present work investigates the use of batteries and supercapacitors in FCEVs under an actual urban driving mission, where the route includes an experimentally measured altitude profile. This approach allows for a more realistic assessment of energy requirements. Furthermore, the analysis carried out in this study considers different powertrain configurations: the exclusive use of a battery, the sole use of a supercapacitor, and a hybrid combination of both systems. These scenarios are evaluated both for an FCEV that can only be refueled with hydrogen and for a plug-in hybrid version of the vehicle that can also recharge its battery from an external charging station. Full article

At present, the decarbonization of the public transport sector plays a key role in international and regional policies. Among the various energy vectors being considered for future clean bus fleets, green hydrogen and electricity are gaining significant attention thanks to their minimal carbon footprint. However, a comprehensive Life Cycle Assessment (LCA) is essential to compare the most viable solutions for public mobility, accounting for variations in weather conditions, geographic locations, and time horizons. Therefore, the present work compares the life cycle environmental impact of different powertrain configurations for urban buses. In particular, a series hybrid architecture featuring two possible hydrogen-fueled Auxiliary Power Units (APUs) is considered: an H2-Internal Combustion Engine (ICE) and a Fuel Cell (FC). Furthermore, a Battery Electric Vehicle (BEV) is considered for the same application. The global warming potential of these powertrains is assessed in comparison to both conventional and hybrid diesel over a typical urban mission profile and in a wide range of external ambient conditions. Given that cabin and battery conditioning significantly influence energy consumption, their impact varies considerably between powertrain options. A sensitivity analysis of the BEV battery size is conducted, considering the effect of battery preconditioning strategies as well. Furthermore, to evaluate the potential of hydrogen and electricity in achieving cleaner public mobility throughout Europe, this study examines the effect of different grid carbon intensities on overall emissions, based also on a seasonal variability and future projections. Finally, the present study demonstrates the strong dependence of the carbon footprint of various technologies on both current and future scenarios, identifying a range of boundary conditions suitable for each analysed powertrain option. Full article

This study investigates the operational behavior and voltage stability of a 1 kW-class AIP PEMFC stack under high-pressure H₂ and O₂ conditions. AIP PEMFCs, unlike conventional air-based systems, operate in enclosed environments using stored O₂, requiring designs that minimize parasitic power losses while ensuring stable operation. To establish a performance baseline, single cell tests were conducted to isolate the effects of in-plane components, including the MEA, GDL, and flow field geometry. Results indicated that temperature and pressure significantly influenced performance, whereas humidity and flow rate had minimal effects under the tested conditions. A 27-cell stack was then assembled and evaluated under various current densities, flow rates, and humidity levels. Time-resolved voltage measurements revealed that low flow rates (stoichiometry ≤ 1.5) led to voltage instability, particularly at high humidity and current density. Instability was more pronounced in cells positioned farthest from the inlet and outlet ports. These findings underscore the importance of optimizing operational parameters and stack architecture to achieve stable AIP PEMFC performance under reduced flow conditions. The results provide key insights for developing compact, efficient, and durable AIP fuel cell systems for use in enclosed or submerged environments such as submarines or unmanned underwater vehicles, while highlighting key challenges associated with AIP-targeted applications. Full article

Rail transit as a high-energy consumption field urgently requires the adoption of clean energy innovations to reduce energy consumption and accelerate the transition to new energy applications. As an energy-saving fluid machinery, the ejector exhibits significant application potential and academic value within this field. This paper reviewed the recent advances, technical challenges, research hotspots, and future development directions of ejector applications in rail transit, aiming to address gaps in existing reviews. (1) In waste heat recovery, exhaust heat is utilized for propulsion in vehicle ejector refrigeration air conditioning systems, resulting in energy consumption being reduced by 12~17%. (2) In vehicle pneumatic pressure reduction systems, the throttle valve is replaced with an ejector, leading to an output power increase of more than 13% and providing support for zero-emission new energy vehicle applications. (3) In hydrogen supply systems, hydrogen recirculation efficiency exceeding 68.5% is achieved in fuel cells using multi-nozzle ejector technology. (4) Ejector-based active flow control enables precise ± 20 N dynamic pantograph lift adjustment at 300 km/h. However, current research still faces challenges including the tendency toward subcritical mode in fixed geometry ejectors under variable operating conditions, scarcity of application data for global warming potential refrigerants, insufficient stability of hydrogen recycling under wide power output ranges, and thermodynamic irreversibility causing turbulence loss. To address these issues, future efforts should focus on developing dynamic intelligent control technology based on machine learning, designing adjustable nozzles and other structural innovations, optimizing multi-system efficiency through hybrid architectures, and investigating global warming potential refrigerants. These strategies will facilitate the evolution of ejector technology toward greater intelligence and efficiency, thereby supporting the green transformation and energy conservation objectives of rail transit. Full article

Today, hydrogen fuel cells occupy a crucial position in sustainable energy systems. However, a precise model of their performance is needed to improve their efficiency and integrate them into hydrogen electric vehicles. This paper presents a hydrogen fuel cell model based on artificial neural networks (ANNs) to predict its performance characteristics. Using experimental data from a PEMFC NEXA 1200 hydrogen fuel cell in the ISA laboratory, an ANN model optimized by deep learning was developed, integrating advanced training techniques. The model’s performance was evaluated on independent test sets, revealing predictive precision with a low mean squared error (MSE) of 0.0429, a low Mean Absolute Percentage Error (MAPE) of 1.05%, a low Root-Mean-Square Error (RMSE) of 0.2071, and a high coefficient of determination (R²) of 0.9071. The model’s development and evaluation will be reviewed here in order to visualize the training progress and the results of the simulation. The main advantages of the proposed ANN model lie in both its flexible architecture, which can capture complex relationships without the need for explicit physical models, and its predictive and optimization capability. Full article

Battery longevity and hydrogen consumption efficiency are primary optimization goals for EMS in high-performance fuel cell hybrid electric vehicles (FCHEVs). This article provides an overview of an FCHEV powertrain and a hierarchical control scheme that includes low-level controllers for key components. Finally, a higher-level control architecture for power management combines a fuzzy logic controller with an H-infinity controller to ensure reliable power management. The aim is to enhance EMS performance and overall robustness to uncertainties by implementing the higher-level control architecture. The effectiveness of the proposed strategy is demonstrated through simulations in the MATLAB/SIMULINK 2024a environment. Full article

To enable climate-neutral aviation, improving the energy efficiency of aircraft is essential. The research project Synergies of Highly Integrated Transport Aircraft investigates cross-disciplinary synergies in aircraft and propulsion technologies to achieve energy savings. This study examines a fuel cell electric powered configuration with distributed electric propulsion. For this, a reverse-engineered ATR 72-500 serves as a reference model for calibrating the methods and ensuring accurate performance modeling. A baseline configuration featuring a state-of-the-art turboprop engine with the same entry-into-service is also introduced for a meaningful performance comparison. The analysis uses an enhanced version of the Stanford University Aerospace Vehicle Environment (SUAVE), a Python-based aircraft design environment that allows for novel energy network architectures. This paper details the preliminary aircraft design process, including calibration, presents the resulting aircraft configurations, and examines the integration of a fuel cell-electric energy network. The results provide a foundation for higher fidelity studies and performance comparisons, offering insights into the trade-offs associated with hydrogen-based propulsion systems. All fundamental equations and methodologies are explicitly presented, ensuring transparency, clarity, and reproducibility. This comprehensive disclosure allows the broader scientific community to utilize and refine these findings, facilitating further progress in hydrogen-powered aviation technologies. Full article

Proton-Exchange Membrane Fuel Cells (PEMFCs), as efficient and environmentally friendly energy conversion devices, have wide application potential in areas such as transportation, mobile power, and distributed energy. However, the remaining useful life (RUL) issue of PEMFCs has been one of the main challenges limiting their commercialization. The RUL prediction problem of PEMFCs exhibits characteristics of time series forecasting, but its data possess multidimensional features and non-stationarity, which limits the applicability of classical time series forecasting models like the Transformer in solving the RUL prediction problem. In this paper, we propose a PEMFC RUL prediction model based on the Crossformer for non-stationary time series (De-stationary-Crossformer). Firstly, the overall architecture adopts the Crossformer model to extract dependencies between different features and temporal dependencies. Secondly, adaptive normalization is applied to the data to mitigate the non-stationarity in the original data, thereby increasing their predictability. Subsequently, a non-stationary attention mechanism is introduced in the model to simultaneously utilize the non-stationarity in the original data when extracting deep information. Additionally, manual features are introduced through mathematical statistics to enhance the predictive performance of the model. During the training process, the TILDE-Q loss function is used to focus on the similarity between the predicted sequence and the true sequence. The model proposed in this paper improves the MSE by 31% compared to the Transformer and 23% compared to the Crossformer in the experimental prediction of the RUL of PEMFCs in actual vehicles. Full article

In this paper, a methodology for selecting the Energy Management Strategy (EMS) that best suits a heavy-duty Fuel Cell Electric Vehicle (FCEV) operating under specific conditions along a given driving cycle is proposed. Using a simulation model that incorporates the powertrain architecture and components of a specific FCEV—validated through a more detailed model operating at the power converter switching level—the performance of the entire system can be tested under different EMSs. The multicriteria evaluation system developed in this study enables the calculation of hydrogen and energy consumption, as well as the aging of the battery and fuel cell associated with each EMS. The proposed methodology serves as an evaluation tool for both the dimensioning of powertrain components and the selection of the EMS that best meets the operational requirements of a given FCEV. Results demonstrate that applying this methodology to a use case tailored for commercial devices and a standard driving cycle enables the identification of the most suitable EMS, minimizing hydrogen and energy consumption while reducing battery and fuel cell aging. Full article

This paper investigates the feasibility of hydrogen-powered hybrid electric vehicles as a solution to transportation-related pollution. It focuses on optimizing energy use to improve efficiency and reduce emissions. The study details the creation and real-time performance assessment of a hydrogen hybrid electric vehicle (HHEV)system using an STM32F407VG board. This system includes a fuel cell (FC) as the main energy source, a battery (Bat) to provide energy during hydrogen supply disruptions and a supercapacitor (SC) to handle power fluctuations. A multi-agent-based artificial intelligence tool is used to model the system components, and an energy management algorithm (EMA) is applied to optimize energy use and support decision-making. Real Global Positioning System (GPS) data are analyzed to estimate energy consumption based on trip and speed parameters. The EMA, developed and implemented in real-time using Matlab/Simulink(2016), identifies the most energy-efficient routes. The results show that the proposed vehicle architecture and management strategy effectively select optimal routes with minimal energy use. Full article

In the context of intelligent and connected transportation, obtaining the real-time vehicle status and comprehensive traffic data is crucial for addressing challenges related to speed optimization and energy regulation in intricate transportation situations. This paper introduces a control method for the speed optimization and energy management of a fuel cell hybrid bus (FCHB) based on the Deep Deterministic Policy Gradient (DDPG) algorithm. The strategy framework is built on a dual-objective optimization deep reinforcement learning (D-DRL) architecture, which integrates traffic signal information into the energy management framework, in addition to conventional state spaces to guide control decisions. The aim is to achieve “green wave” traffic while minimizing hydrogen consumption. To validate the effectiveness of the proposed strategy, simulation tests were conducted using the SUMO platform. The results show that in terms of speed planning, the difference between the maximum and minimum speeds of the FCHB was reduced by 21.66% compared with the traditional Intelligent Driver Model (IDM), while the acceleration and its variation were reduced by 8.89% and 13.21%, respectively. In terms of the hydrogen fuel efficiency, the proposed strategy achieved 95.71% of the performance level of the dynamic programming (DP) algorithm. The solution proposed in this paper is of great significance for improving passenger comfort and FCHB economy. Full article

Hydrogen, with its high energy density and zero greenhouse gas emissions, is an exceptional energy vector, pivotal for a sustainable energy future. Ammonia, serving as a practical and cost-effective hydrogen carrier, offers a secure method for hydrogen storage and transport. The decomposition of ammonia into hydrogen is a crucial process for producing green hydrogen, enabling its use in applications ranging from clean energy generation to fueling hydrogen-powered vehicles, thereby advancing the transition to a carbon-free energy economy. This study investigates the catalytic performance of various 3D-printed porous supports based on periodic open cellular structures (POCS) and triply periodic minimal surface (TPMS) architecture manufactured from IN625 nickel alloy powder using the laser powder bed fusion (LPBF) technique. The POCS and TPMS, featuring geometries including BCC, Kelvin, and Gyroid, were analyzed for cell size, strut/sheet diameter, porosity, and specific surface area. Pressure drop analyses demonstrated correlations between structural parameters and fluid dynamics, with BCC structures exhibiting lower pressure drops due to their higher porosity and the open channel network. The dip/spin coating method was successfully applied to activate the supports with a commercial Ru/Al₂O₃ catalyst, achieving uniform coverage crucial for catalytic performance. Among the tested geometries, the Gyroid structure showed superior catalytic activity towards ammonia decomposition, attributed to its efficient mass transfer pathways. This study highlights the importance of structural design in optimizing catalytic processes and suggests the Gyroid structure as a promising candidate for improving reactor efficiency and compactness in hydrogen production systems. Full article