Search Results (42)

Energy management strategies (EMSs) play a critical role in improving both the efficiency and durability of fuel cell electric vehicles (FCEVs). To overcome the limited adaptability and insufficient durability consideration of existing deep reinforcement learning-based EMSs, this study develops a degradation-aware energy management strategy based on the Soft Actor–Critic (SAC) algorithm. By leveraging SAC’s maximum-entropy framework, the proposed method enhances exploration efficiency and avoids premature convergence to operating patterns that are unfavorable to fuel cell durability. A reward function explicitly penalizing hydrogen consumption, power fluctuation, and degradation-related operating behaviors is designed, and the influences of reward weighting and key hyperparameters on learning stability and performance are systematically analyzed. The proposed SAC-based EMS is evaluated against Deep Q-Network (DQN) and Proximal Policy Optimization (PPO) strategies under both training and unseen driving cycles. Simulation results demonstrate that SAC achieves a superior and robust trade-off between hydrogen economy and degradation mitigation, maintaining improved adaptability and durability under varying operating conditions. These findings indicate that integrating degradation awareness with entropy-regularized reinforcement learning provides an effective framework for practical EMS design in FCEVs. Full article

In terms of their high efficiency and low environmental impact, proton exchange membrane fuel cells (PEMFC) are becoming increasingly essential in the development of hydrogen electric vehicles. Despite these advantages, optimizing hydrogen consumption remains difficult because of the highly nonlinear behavior of PEMFC systems and their sensitivity to variations in operating conditions. This article outlines an intelligent control approach based on extremum seeking control (ESC), based on an artificial neural network (ANN) model, to improve hydrogen utilization in hydrogen electric vehicles. Experimental data on current, voltage, and temperature are collected, preprocessed, and used to train the ANN model of the PEMFC. The ESC algorithm uses this predictive ANN model to adjust the fuel cell current in real time, ensuring voltage stability while reducing hydrogen consumption. The simulation results demonstrate that the ANN-based ESC system provides voltage stability under dynamic load variations and achieves approximately 2.7% hydrogen savings without affecting the experimental current profile, validating the efficacy of the suggested strategy for effective hydrogen management in fuel cell electric vehicles. Full article

The present study proposes an integrated real-time energy management strategy (EMS) that combines an adaptive neuro-fuzzy inference system (ANFIS) with dynamic programming (DP) to enhance the energy efficiency and system durability of fuel cell electric commercial vehicles (FCECVs). Firstly, a comprehensive DP framework was established to optimize the EMS offline, which simultaneously considers power allocation and automated manual transmission (AMT) gear-shifting to minimize hydrogen consumption (HC). Then, the DP framework was employed to determine optimal power allocation patterns of the FCECVs under various initial state-of-charge (SOC) battery conditions. Based on the DP results, a novel real-time EMS integrating ANFIS with DP solution was developed to formulate an efficient fuzzy inference system (FIS), where the ANFIS model was trained using the particle swarm optimization (PSO) algorithm. The proposed ANFIS-DP EMS was evaluated through extensive simulations under stochastic driving cycles, with performance comparisons against both the DP method and conventional charge-depleting and charge-sustaining (CD-CS) strategies. The experimental results demonstrate that the ANFIS-DP maintains efficient FCS operation across diverse driving conditions while effectively controlling the rate of power change within optimal ranges. Compared to the CD-CS strategy, the proposed method achieves a substantial 14.98% reduction in HC, approaching the performance of DP (only 5.40% higher). Most notably, the ANFIS-DP strategy demonstrates remarkable computational efficiency improvements, outperforming DP by 96.13% and CD-CS by 22.05%. These findings collectively validate the effectiveness of our proposed approach in achieving real-time energy management optimization for FCECVs. Full article

Shunting locomotives exhibit a wide and unpredictable range of power profiles. This unpredictability makes it impossible to rely on offline optimizations or predictive methods combined with online optimization. To maintain optimal performance across this broad range of operating conditions, the online control strategy must be robust. This article proposes a robust method to determine the optimal parameter combinations for an online energy management strategy of a hybrid fuel cell battery shunting locomotive, ensuring optimality across all scenario conditions. The first step involves extracting a statistically representative subspace for simulation, both in terms of parameter combinations and scenario conditions. A response surface model (numerical twin) is then constructed to extrapolate results across the entire space based on this simulated subspace. Using this model, the optimal solution is identified through metaheuristic algorithms (minimization search). Finally, the proposed solution is validated against a set of expert-defined scenarios. The result of the methodology ensures robust optimization across an infinite number of scenarios by minimizing the impact on both the fuel cell and the battery, without increasing mission costs. Full article

Fuel-cell flying vehicles suffer from limited endurance, while ammonia, decomposed onboard to supply hydrogen, offers a carbon-free, high-density solution to extend flight missions. However, the system’s performance is governed by a multi-scale coupling between propulsion and control systems. To this end, this paper introduces a novel optimization paradigm, termed physics-informed gradient-enhanced multi-objective optimization (PI-GEMO), to simultaneously optimize the ammonia decomposition unit (ADU) catalyst composition, powertrain sizing, and flight control parameters. The PI-GEMO framework leverages a physics-informed neural network (PINN) as a differentiable surrogate model, which is trained not only on sparse simulation data but also on the governing differential equations of the system. This enables the use of analytical gradient information extracted from the trained PINN via automatic differentiation to intelligently guide the evolutionary search process. A comprehensive case study on a flying vehicle demonstrates that the PI-GEMO framework not only discovers a superior set of Pareto-optimal solutions compared to traditional methods but also critically ensures the physical plausibility of the results. Full article

Hydrogen-powered hybrid trains equipped with fuel cells (FC) and batteries represent a promising alternative to diesel traction on non-electrified railway lines and have significant potential to support modal shifts toward more sustainable transport systems. This study presents the development of a flexible MATLAB-based tool for the dynamic simulation of fuel cell–battery hybrid powertrains. The model integrates train dynamics, rule-based energy management, system efficiencies, and component degradation, enabling both energy and cost analyses over the vehicle’s lifetime. The objective is to assess the techno-economic performance of different powertrain configurations. Sensitivity analyses were carried out by varying two sizing parameters: the nominal power of the fuel cell (parameter m) and the total battery capacity (parameter n), across multiple real-world railway routes. Results show a slight reduction in lifecycle costs as m increases (5.1 €/km for m = 0.50) mainly due to a lower FC degradation. Conversely, increasing battery capacity (n) lowers costs by reducing cycling stress for both battery and FC, from 5.3 €/km (n = 0.10) to 4.5 €/km (n = 0.20). In general, lowest values of m and n provide unviable solutions as the battery discharges completely before the end of the journey. The study highlights the critical impact of the operational profile: for a fixed powertrain configuration (m = 0.45, n = 0.20), the specific cost dramatically increases from 4.44 €/km on a long, flat route to 15.8 €/km on a hilly line and up to 76.7 €/km on a mountainous route, primarily due to severe fuel cell degradation under transient loads. These findings demonstrate that an “all-purpose” train sizing approach is inadequate, confirming the necessity of route-specific powertrain optimization to balance techno-economic performance. 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

The demand for green hydrogen (H₂) and related technologies is expected to increase in the coming years, driven by climate changes and energy security of supply issues, amid the European and global energy crises. The European Green Deal and REpowerEU Plan have identified H₂ as a key pillar for reaching climate neutrality by 2050 and for the intensification of hydrogen delivery targets, bringing the large-scale adoption of hydrogen production and applications, and stressing the need for a skilled workforce in emergent H₂ markets. To that end, the H2Excellence project will establish a Platform of Vocational Excellence in the field of fuel cells and green hydrogen technologies, with an educational and training scheme to tackle identified skill gaps and to implement life-long learning opportunities. This project aims to become a European benchmark in training and knowledge transfer, incorporating the entire hydrogen value chain. The work is supported by the Knowledge Triangle Model, integrating education, research, and innovation efforts to build a dynamic ecosystem in the green hydrogen sector. In this work, activities conducted so far by LNEG as a project partner and expected impacts are highlighted. Those activities are based on a stakeholder needs assessment conducted by project partners and on the knowledge and experience accumulated in research activities developed in the Materials for Energy research area. Full article

Hydrogen safety is a critical issue during the construction and development of the hydrogen energy industry. Hydrogen refueling stations play a pivotal role in the hydrogen energy chain. In the event of an accidental hydrogen leak at a hydrogen refueling station, the ability to quickly predict the leakage location is crucial for taking immediate and effective measures to prevent disastrous consequences. Therefore, the development of precise and efficient technologies to predict leakage locations is vital for the safe and stable operation of hydrogen refueling stations. This paper studied the localization technology of high-risk leakage locations in the fuel cell system of a skid-mounted hydrogen refueling station. The hydrogen leakage and diffusion processes in the fuel cell system were predicted using CFD simulations, and the hydrogen concentration data at various monitoring points were obtained. Then, a multilayer feedforward neural network was developed to predict leakage locations using simulated concentration data as training samples. After multiple adjustments to the network structure and hyperparameters, a final model with two hidden layers was selected. Each hidden layer consisted of 10 neurons. The hyperparameters included a learning rate of 0.0001, a batch size of 32, and 10-fold cross-validation. The Softmax classifier and Adam optimizer were used, with a training set for 1500 epochs. The results show that the algorithm can predict leakage locations not included in the training set. The accuracy achieved by the model was 95%. This approach addresses the limitations of sensor detection in accurately locating leaks and mitigates the risks associated with manual inspections. This paper provides a feasible method for locating hydrogen leakage in hydrogen energy application scenarios. Full article

Solid oxide fuel cells (SOFC) could facilitate the green energy transition as they can produce high-temperature heat and electricity while emitting only water when supplied with hydrogen. Additionally, when operated with natural gas, these systems demonstrate higher thermoelectric efficiency compared to traditional microturbines or alternative engines. Within this context, although digitalisation has facilitated the acquisition of extensive data for precise modelling and optimal management of fuel cells, there remains a significant gap in developing digital twins that effectively achieve these objectives in real-world applications. Existing research predominantly focuses on the use of machine learning algorithms to predict the degradation of fuel cell components and to optimally design and theoretically operate these systems. In light of this, the presented study focuses on developing digital twin-oriented models that predict the efficiency of a commercial gas-fed solid oxide fuel cell under various operational conditions. This study uses data gathered from an experimental setup, which was employed to train various machine learning models, including artificial neural networks, random forests, and gradient boosting regressors. Preliminary findings demonstrate that the random forest model excels, achieving an R² score exceeding 0.98 and a mean squared error of 0.14 in estimating electric efficiency. These outcomes could validate the potential of machine learning algorithms to support fuel cell integration into energy management systems capable of improving efficiency, pushing the transition towards sustainable energy solutions. Full article

In this work, three commercially available Membrane Electrode Assemblies (MEAs) from Advent Technology Inc. and Danish Power Systems, developed for a use in High Temperature Proton Exchange Membrane Fuel Cell (HT-PEMFC), were tested under various Operating Conditions (OCs): over-stoichiometry of hydrogen gas (1.05, 1.2, 1.35), over-stoichiometry of air gas (1.5, 2, 2.5), gas oxidant (O₂ or air) and temperature (140 °C, 160 °C, 180 °C). For each set of operating conditions, a polarization curve (V–I curve) was performed. A semi-empirical and macroscopic (0D) model of the fuel cell voltage was established in steady state conditions in order to model some of these experimental data. The proposed parameterization approach for this model (called here the “multi-VI” approach) is based on the sensitivity to the operating conditions specific to each involved physicochemical phenomenon. According to this method, only one set of parameters is used in order to model all the experimental curves (optimization is performed simultaneously on all curves). A model depending on air over-stoichiometry was developed. The main objective is to validate a simple (0D) and fast-running model that considers the impact of air over-stoichiometry on cell voltage regarding all commercially available MEAs. The obtained results are satisfying with Advent^PBI MEA and Danish Power Systems MEA: an average error less than 1.5% and a maximum error around 15% between modelled and measured voltages with only nine parameters to identify. However, the model was not as adapted to Advent TPS^® MEA: average error and maximum error around 4% and 21%, respectively. Most of the obtained parameters appear consistent regardless of the OCs. The predictability of the model was also validated in the explored domain during the sensibility study with an interesting accuracy for 27 OCs after being trained on only nine curves. This is attractive for industrial application, since it reduces the number of experiments, hence the cost of model development, and is potentially applicable to all commercial HT-PEMFC MEAs. Full article

This study presents a Two-Layer Deep Deterministic Policy Gradient (TL-DDPG) energy management strategy for Hydrogen fuel cell hybrid train, that aims to solve the problem that traditional reinforcement learning strategies require high initial values and are difficult to optimize global variables. Augmenting the optimization capabilities of the inner layer, a frequency decoupling algorithm integrates into the outer layer, furnishing a fitting initial value for strategy optimization. This addition aims to bolster the stability of fuel cell output, thereby enhancing the overall efficiency of the hybrid power system. In comparison with the traditional reinforcement learning algorithm, the proposed approach demonstrates notable improvements: a reduction in hydrogen consumption per 100 km by 16.3 kg, a 9.7% increase in the output power stability of the fuel cell, and a 1.8% enhancement in its efficiency. Full article

Recently, with the fourth industrial revolution, the research cases that search for optimal design points based on neural networks or machine learning have rapidly increased. In addition, research on optimization is continuously reported in the field of fuel cell research using hydrogen as fuel. However, in the case of optimization research, it often requires a large amount of training data, which means that it is more suitable for numerical research such as CFD simulation rather than time-consuming research such as actual experiments. As is well known, the design range of fuel cell flow channels is extremely small, ranging from hundreds of microns to several millimeters, which means the small tolerance could cause fatal performance loss. In this study, the general optimization study was further improved in terms of reliability by considering stochastic tolerances that may occur in actual industry. The optimization problem was defined to maximize stack power, which is employed as objective function, under the constraints such as pressure drop and current density standard deviation; the performance of the optimal point through general optimization was about 3.252 kW/L. In the reliability-based optimization problem, the boundary condition for tolerance was set to 0.1 mm and tolerance was assumed to occur along a normal distribution. The optimal point to secure 99% reliability for the given constraints was 2.918 kW/L, showing significantly lower performance than the general optimal point. Full article

Booming population and tourism have increased congestion, collisions, climate-harming emissions, and transport inequities in The Okanagan Valley, Canada. Surveys suggest that over 30% of residents would shift from cars back to public transit and intercity tram–trains if regional service and connections were improved. Intercity streetcars (aka light-rail tram–trains) have not run in Canada since their replacement in the 1950′s by the national highway system. UBC researchers analyzed a tram–train service fashioned after the current Karlsruhe model but powered by zero-emission hydrogen fuel cell/battery hybrid rail power (hydrail) technology, along a 342 km route between Osoyoos, B.C. at the US Border and Kamloops, B.C., the Canadian VIA rail hub. Hydrail trains have operated successfully since 2018 in Germany and were demonstrated in Quebec, Canada in 2023. However, hydrail combined with tram–train technology has never been tried in Canada. Single-train simulations (STSs) confirmed its technical feasibility, showing a roughly 8 h roundtrip travel time, at an average train velocity of 86 km/h. Each hydrail tram–train consumed 2400 kWh of energy, translating to 144 kg of hydrogen fuel per roundtrip. In total, five tons of H₂/day would be consumed over 16 h daily by the 16-tram–train-vehicle fleet. The results provide valuable insights into technical aspects and energy requirements, serving as a foundation for future studies and decision-making processes in developing zero-emission passenger tram–train services not just for Okanagan Valley communities but all of Canada and NA. Full article

The increase in greenhouse gas emissions, as well as the risk of fossil fuel depletion, has prompted a transition to electric transportation. The European Union aims to substantially reduce pollutant emissions by 2035 through the use of renewable energies. In aviation, this transition is particularly challenging, mainly due to the weight of onboard equipment. Traditional electric motors with radial magnetic flux have been replaced by axial magnetic flux motors with reduced weight and volume, high efficiency, power, and torque. These motors were initially developed for electric vehicles with in-wheel motors but have been adapted for aviation without modifications. Worldwide, there are already companies developing propulsion systems for various aircraft categories using such electric motors. One category of aircraft that could benefit from this electric motor development is traditionally constructed training aircraft with significant remaining flight resource. Electric repowering would allow their continued use for pilot training, preparing them for future electrically powered aircraft. This article presents a study on the feasibility of repowering a classic training aircraft with an electric propulsion system. The possibilities of using either a battery or a hybrid source composed of a battery and a fuel cell as an energy source are explored. The goal is to utilize components already in production to eliminate the research phase for specific aircraft components. Full article