Search Results (548)

The increasing depletion of fossil fuels and their environmental impact have led to the development of fuel cell hybrid electric vehicles. By combining fuel cells with batteries, these vehicles offer greater efficiency and zero emissions. However, their energy management remains a challenge requiring advanced strategies. This paper presents a comparative study of two developed energy management strategies: a deterministic rule-based approach and a fuzzy logic approach. The proposed system consists of a proton exchange membrane fuel cell (PEMFC) as the primary energy source and a lithium-ion battery as the secondary source. A comprehensive model of the hybrid powertrain is developed to evaluate energy distribution and system behaviour. The control system includes a model predictive control (MPC) method for fuel cell current regulation and a PI controller to maintain DC bus voltage stability. The proposed strategies are evaluated under standard driving cycles (UDDS and NEDC) using a simulation in MATLAB/Simulink. Key performance indicators such as fuel efficiency, hydrogen consumption, battery state-of-charge, and voltage stability are examined to assess the effectiveness of each approach. Simulation results demonstrate that the deterministic strategy offers a structured and computationally efficient solution, while the fuzzy logic approach provides greater adaptability to dynamic driving conditions, leading to improved overall energy efficiency. These findings highlight the critical role of advanced control strategies in improving FCHEV performance and offer valuable insights for future developments in hybrid-vehicle energy management. Full article

Inspired by the Toyota Mirai, this study presents a high-fidelity data-driven approach for modelling and simulation of a fuel cell hybrid electric powertrain. This study utilises technical assessment data sourced from Argonne National Laboratory’s publicly available report, faithfully modelling most of the vehicle subsystems as data-driven entities. The simulation framework is developed in the MATLAB/Simulink environment and is based on a power dynamics approach, capturing nonlinear interactions and performance intricacies between different powertrain elements. This study investigates subsystem synergies and performance boundaries under a combined driving cycle composed of the NEDC, WLTP Class 3 and US06 profiles, representing urban, extra-urban and aggressive highway conditions. To emulate the real-world load-following strategy, a state transition power management and allocation method is synthesised. The proposed method dynamically governs the power flow between the fuel cell stack and the traction battery across three operational states, allowing the battery to stay within its allocated bounds. This simulation framework offers a near-accurate and computationally efficient digital counterpart to a commercial hybrid powertrain, serving as a valuable tool for educational and research purposes. Full article

This work proposes a new method to refill fuel cell electric vehicle hydrogen tanks from a storage system in hydrogen refueling stations. The new method uses the storage tanks in cascade to supply hydrogen to the refueling station dispensers. This method reduces the hydrogen compressor power requirement and the energy consumption for refilling the vehicle tank; therefore, the proposed alternative design for hydrogen refueling stations is feasible and compatible with low-intensity renewable energy sources like solar photovoltaic, wind farms, or micro-hydro plants. Additionally, the cascade method supplies higher pressure to the dispenser throughout the day, thus reducing the refueling time for specific vehicle driving ranges. The simulation shows that the energy saving using the cascade method achieves 9% to 45%, depending on the vehicle attendance. The hydrogen refueling station design supports a daily vehicle attendance of 9 to 36 with a complete refueling process coverage. The carried-out simulation proves that the vehicle tank achieves the maximum attainable pressure of 700 bars with a storage system of six tanks. The data analysis shows that the daily hourly hydrogen demand follows a sinusoidal function, providing a practical tool to predict the hydrogen demand for any vehicle attendance, allowing the planners and station designers to resize the elements to fulfill the new requirements. The proposed system is also applicable to hydrogen ICE vehicles. Full article

This paper presents a data-driven energy management scheme for fuel cell and battery electric vehicles, formulated as a constrained optimal control problem. The proposed method employs a co-state network trained using real-time measurements to estimate the control law without requiring prior knowledge of the system model or a complete dataset across the full operating domain. In contrast to conventional reinforcement learning approaches, this method avoids the issue of high dimensionality and does not depend on extensive offline training. Robustness is demonstrated by treating uncertain and time-varying elements, including power consumption from air conditioning systems, variations in road slope, and passenger-related demands, as unknown disturbances. The desired state of charge is defined as a reference trajectory, and the control input is computed while ensuring compliance with all operational constraints. Validation results based on a combined driving profile confirm the effectiveness of the proposed controller in maintaining the battery charge, reducing fluctuations in fuel cell power output, and ensuring reliable performance under practical conditions. Comparative evaluations are conducted against two benchmark controllers: one designed to maintain a constant state of charge and another based on a soft actor–critic learning algorithm. Full article

Driven by carbon neutrality and peak carbon policies, hydrogen energy, due to its zero-emission and renewable properties, is increasingly being used in hydrogen fuel cell vehicles (H-FCVs). However, the high cost and limited durability of H-FCVs hinder large-scale deployment. Hydrogen internal combustion engine hybrid electric vehicles (H-HEVs) are emerging as a viable alternative. Research on the techno-economics of H-HEVs remains limited, particularly in systematic comparisons with H-FCVs. This paper provides a comprehensive comparison of H-FCVs and H-HEVs in terms of total cost of ownership (TCO) and hydrogen consumption while proposing a multi-objective powertrain parameter optimization model. First, a quantitative model evaluates TCO from vehicle purchase to disposal. Second, a global dynamic programming method optimizes hydrogen consumption by incorporating cumulative energy costs into the TCO model. Finally, a genetic algorithm co-optimizes key design parameters to minimize TCO. Results show that with a battery capacity of 20.5 Ah and an H-FC peak power of 55 kW, H-FCV can achieve optimal fuel economy and hydrogen consumption. However, even with advanced technology, their TCO remains higher than that of H-HEVs. H-FCVs can only become cost-competitive if the unit power price of the fuel cell system is less than 4.6 times that of the hydrogen engine system, assuming negligible fuel cell degradation. In the short term, H-HEVs should be prioritized. Their adoption can also support the long-term development of H-FCVs through a complementary relationship. Full article

At present, transportation energy comes primarily from fossil fuels. In order to mitigate the effects of greenhouse gas emissions, it is necessary to transition to low-carbon transportation technologies. These technologies can include battery electric vehicles, fuel cell vehicles and biofuel vehicles. This transition includes not only the development and production of suitable vehicles, but also the development of appropriate infrastructure. For example, in the case of battery electric vehicles, this infrastructure would include additional grid capacity for battery charging. For fuel cell vehicles, infrastructure could include facilities for the production of suitable electrofuels, which, again, would require additional grid capacity. In the present paper, we look at some specific examples of infrastructure requirements for battery electric vehicles and vehicles using hydrogen and other electrofuels in either internal combustion engines or fuel cells. Analysis includes the necessary additional grid capacity, energy storage requirements and land area associated with renewable energy generation by solar photovoltaics and wind. The present analysis shows that the best-case scenario corresponds to the use of battery electric vehicles powered by electricity from solar photovoltaics. This situation corresponds to a 47% increase in grid electricity generation and the utilization of 1.7% of current crop land. Full article

Hydrogen is widely recognized as a versatile energy carrier with significant potential to support the decarbonization of the power, transport, and industrial sectors. This paper analyzes the integration of hydrogen into power systems and offers an overview of the operation of electrolyzers and fuel cells for readers with limited background in these technologies. Applications of hydrogen beyond the scope of power systems are not considered. Then, this paper explores the mathematical modeling of hydrogen-related technologies, including electrolyzers and fuel cells, to assess their impact on hydrogen production and electricity generation. The paper also reviews recent developments in electricity storage through power-to-gas systems and examines planning models for integrating hydrogen into power systems. Furthermore, the role of hydrogen facilities in power system operations is analyzed in depth. The integration of hydrogen vehicles into power grids is also discussed, emphasizing their diverse applications. Additionally, the paper examines the production of ammonia, which can be used as a fuel for electricity generation. Finally, the most important conclusions of the literature review are summarized, offering an overview of the main findings and identified research gaps. Full article

Due to the high demand for fuel efficiency, electric vehicles have come into the picture, as they only use batteries to power the vehicle. This requires constant charging of the batteries at charging stations, which are costly and impractical to install. But it is possible to install charging stations by making use of photovoltaic (PV) cells and demagnetization currents to self-charge batteries under stand-still conditions. The design of a bidirectional converter with asymmetrical half-bridge converter based on a switched reluctance motor (SRM) drive, using PV for electric vehicles, is implemented in this paper. It consists of developing a control unit (GCU), Li-ion battery pack, and photovoltaic (PV) solar cells that are integrated with a bidirectional converter and asymmetrical half-bridge converter (AHBC) to provide power to the SRM drive. The solar-assisted SRM drive can be operated in either the motoring mode or charging mode. In the motoring-mode GCU, the battery or PV energy can be used in any combination to power the SRM. In the charging-mode PV, the GCU and AC grids are used to charge the battery under stand-still conditions. This work helps in the self-charging of batteries using either the GCU or PV cells, as well as aids in the improvement in the performance characteristics. Also, this work compares the performance metrics for the proposed system and conventional system. The performance of the drive system using PV cells/GCU is evaluated and verified through MatLab/Simulink and experimental results. Full article

Due to climate change, the electric vehicle (EV) industry is rapidly growing and drawing researchers interest. Driving conditions like mountainous roads, slick surfaces, and rough terrains illuminate the vehicles inherent nonlinearities. Under such scenarios, the behavior of power sources (fuel cell, battery, and super-capacitor), power processing units (converters), and power consuming units (traction motors) deviates from nominal operation. The increasing demand for FCHEVs necessitates control systems capable of handling nonlinear dynamics, while ensuring robust, precise energy distribution among fuel cells, batteries, and super-capacitors. This paper presents a DSMC strategy enhanced with Robust Uniform Exact Differentiators for FCHEV energy management. To optimally tune DSMC parameters, reduce chattering, and address the limitations of conventional methods, a hybrid metaheuristic framework is proposed. This framework integrates moth flame optimization (MFO) with the gravitational search algorithm (GSA) and Fractal Heritage Evolution, implemented through three spiral-based variants: MFOGSAPSO-A (Archimedean), MFOGSAPSO-H (Hyperbolic), and MFOGSAPSO-L (Logarithmic). Control laws are optimized using the Integral of Time-weighted Absolute Error (ITAE) criterion. Among the variants, MFOGSAPSO-L shows the best overall performance with the lowest ITAE for the fuel cell (56.38), battery (57.48), super-capacitor (62.83), and DC bus voltage (4741.60). MFOGSAPSO-A offers the most accurate transient response with minimum RMSE and MAE FC (0.005712, 0.000602), battery (0.004879, 0.000488), SC (0.002145, 0.000623), DC voltage (0.232815, 0.058991), and speed (0.030990, 0.010998)—outperforming MFOGSAPSO, GSA, and PSO. MFOGSAPSO-L further reduces the ITAE for fuel cell tracking by up to 29% over GSA and improves control smoothness. PSO performs moderately but lags under transient conditions. Simulation results conducted under EUDC validate the effectiveness of the MFOGSAPSO-based DSMC framework, confirming its superior tracking, faster convergence, and stable voltage control under transients making it a robust and high-performance solution for FCHEV. Full article

This paper presents a methodology for systematically scaling vehicle powertrain and other models and an approach for using model parameters and scaling variables to perform controller design. The parameter scaling method allows for high degrees of scaling while maintaining the target performance metrics, such as vehicle speed tracking, with minimal changes to the model code by the researcher. A comparison of proportional-integral, Youla parameterization, ${\mathrm{\text{H}}}_{\infty }$, and hybrid Youla-${\mathrm{H}}_{\infty }$ controllers is provided, along with the respective methods for maintaining controller performance metrics across degrees of model scaling factors. The application of the scaling and various control design methods to an existing model of a hydrogen fuel cell and a battery electric vehicle powertrain allows for the development of a representative scale model to be compared with experimental data generated by an actual scale vehicle. The comparison between scaled simulation and experimental data will eventually be used to inform the expected performance of the full-size electric vehicle based on full-size simulation results. Full article

Fuel cell electric vehicles (FCEVs) offer a promising solution for energy saving and emission reduction in transportation. However, several challenges must be addressed for their application. This study conducts a full life cycle assessment (LCA) of FCEVs, dividing it into the fuel cycle and vehicle cycle to separately assess energy consumption (EC) and emissions. The fuel cycle examined 18 hydrogen production–storage–transport pathways, while the vehicle cycle evaluates energy use and emissions associated with vehicle component production, assembly, disposal, battery production, and fluid consumption. Based on the GREET database, total energy consumption and emissions over a lifetime were calculated. Five environmental impact indicators were used for evaluation, and a comprehensive environmental assessment (CEA) indicator was established for different scenarios. Results indicate that nuclear thermochemical water splitting is the best hydrogen production method, and pipeline transportation is the most efficient for hydrogen transport. Additionally, water electrolysis for hydrogen production is only practical when paired with renewable energy. The study also identified that the Hydrogen production method, “Body”, “Proton Exchange Membrane Fuel Cells (PEMFCs) System”, “Chassis”, “Hydrogen Storage System” and lifetime significantly impact energy consumption and emissions. These stages or products represent high-impact leverage points for enhancing the lifecycle sustainability evaluation of FCEVs. Full article

This study presents the design of a hybrid powerplant for unmanned aerial vehicles (UAVs), improving its autonomy compared to power systems based solely on batteries. The powerplant is designed for the Mugin EV-350 aircraft. Using experimental data from electric motors in a wind tunnel and fuel cells, a comparative analysis of different energy management strategies, such as fuzzy logic and passive, is conducted to reduce the operational and maintenance costs. A Python-based software program is developed and utilized for the real-time implementation and simulation of energy management strategies, with data collected in databases. This study integrates experimental data (wind tunnel and fuel cells) with real-time EMS strategies, and simulation-based predictions indicate practical improvements in endurance and cost reduction, as well as an increase in flight autonomy of 50%. Full article

In this study, the cooling performance of fuel cell electric vehicles (FCEVs) with regard to thermal derating is investigated. Particularly in hot climate conditions, low operating temperature of the fuel cell stack and hence low temperature difference to the environment can result in thermal derating of the fuel cell stack. Experimental investigations on a production vehicle with a fuel cell drive (Hyundai Nexo) are conducted to analyze the influence of climatic boundary conditions and a dynamic driving scenario on the thermal management system of the vehicle. Therefore, a new method based on energy balances is introduced to indirectly measure the average cooling air velocity at the cooling module. The results indicate that the two high-power radiator fans effectively maintain a high cooling airflow between a vehicle speed of approximately 30 and 100 $\mathrm{k}\mathrm{m}/\mathrm{h}$, leading to efficient heat rejection at the cooling module largely independent of vehicle speed. Furthermore, this study reveals that the efficiency of the fuel cell system is notably affected by ambient air temperature, attributed to the load on the electric air compressor (EAC) as well as on cooling system components like cooling pump and radiator fans. However, at the stack level, balance of plant (BoP) components demonstrate the ability to ensure ambient temperature-independent performance, likely due to reliable humidification control up to 45 °C. Additionally, a new method for determining thermal derating of FCEVs on roller dynamometer tests is presented. A real-world uphill drive under ambient temperatures exceeding 40 °C demonstrates derating occurring in 6.3% of the time, although a worst case with an aged stack and high payload is not investigated in this study. Finally, a time constant of 50 $\mathrm{s}$ is found to be suitable to correlate the average fuel cell stack power with a coolant temperature at the stack inlet, which gives information on the thermal inertia of the system observed and can be used for future simulation studies. Full article

In response to the global imperative to reduce greenhouse gas emissions and fossil fuel dependency, electric vehicles (EVs) have emerged as a sustainable transportation alternative, primarily utilizing lithium-ion batteries (LIBs) due to their high energy density and efficiency. However, LIBs are highly sensitive to temperature fluctuations, significantly affecting their performance, lifespan, and safety. One of the most critical threats to the safe operation of LIBs is thermal runaway (TR), an uncontrollable exothermic process that can lead to catastrophic failure under abusive conditions. Moreover, thermal runaway propagation (TRP) can rapidly spread failures across battery cells, intensifying safety threats. To address these challenges, developing advanced battery thermal management systems (BTMS) is essential to ensure optimal temperature control and suppress TR and TRP within LIB modules. This review systematically evaluates advanced cooling strategies, including indirect liquid cooling, water mist cooling, immersion cooling, phase change material (PCM) cooling, and hybrid cooling based on the latest studies published between 2020 and 2025. The review highlights their mechanisms, effectiveness, and practical considerations for preventing TR initiation and suppressing TRP in battery modules. Finally, key findings and future directions for designing next-generation BTMS are proposed, contributing valuable insights for enhancing the safety and reliability of LIB applications. Full article

Decarbonizing Oregon’s heavy-duty trucking sector, which accounts for 24% of the state’s transportation emissions, is essential for meeting carbon reduction targets. Drop-in fuels such as renewable diesel, biodiesel, and synthetic fuels provide an immediate and effective solution, reducing emissions by up to 80% while utilizing the existing diesel infrastructure. In 2023, Oregon’s heavy-duty trucks consumed 450 million gallons of diesel, with drop-in fuels making up 15% of the fuel mix. Renewable diesel, which is growing at a rate of 30% annually, accounted for 10% of this volume, thanks to incentives from Oregon’s Clean Fuels Program. By 2030, drop-in fuels could capture 40% of the market, reducing CO₂ emissions by 3.5 million metric tons annually, assuming continued policy support and advancements in feedstock sourcing. Meeting the projected demand of 200 million gallons annually and securing sustainable feedstock remain critical challenges. Advances in synthetic fuels, like Power-to-Liquids (PtL) from renewable energy, may further contribute to decarbonization, with costs expected to decrease by 20% over the next decade. Oregon aims for a 50% reduction in emissions from heavy-duty trucks by 2050, using a mix of drop-in fuels and emerging technologies. While hydrogen fuel cells and electric trucks face challenges, innovations in infrastructure and vehicle design will be key to the success of Oregon’s long-term decarbonization strategy. Full article