Browse Topic: Fuel cells

Items (1,675)
The utilization of hydrogen in low-temperature Proton Exchange Membrane Fuel Cells (PEMFCs) stands out as a compelling prospect for driving a widespread shift towards green industry practices. Despite significant advancements, a comprehensive understanding of water behaviour and dynamics within PEMFCs remains crucial for their extensive integration in propulsion applications. Striking a delicate balance between flooding and drying conditions poses a challenge for achieving stable and efficient PEMFC operation. In this study, a preliminary experimental investigation was conducted focusing on carbon-paper Gas Diffusion Layer (GDL) and gas channel walls. The static, advancing and receding contact angles were measured and utilized as boundary conditions for simulations. The influence of membrane humidity was also examined during the experimental campaign. 3D CFD simulations were performed on a straight portion of a PEMFC channel with a selected domain length of 5 mm and a section of 1x1 mm
Merola, S. S.Antetomaso, C.Irimescu, A.Vaglieco, B. M.Jannelli, E.
As the automotive sector shifts towards cleaner and more sustainable technologies, fuel cells and batteries have emerged as promising technologies with revolutionary potential. Hydrogen fuel cell vehicles offer faster refueling times, extended driving ranges, and reduced weight and space requirements compared to battery electric vehicles, making them highly appealing for future transportation applications. Despite these advantages, optimizing electrode structures and balancing various transport mechanisms are crucial for improving PEFCs’ performance for widespread commercial viability. Previous research has utilized topology optimization (TO) to identify optimal electrode structures and attempted to establish a connection between entropy generation and topographically optimized structures, aiming to strengthen TO numerical findings with a robust theoretical basis. However, existing studies have often neglected the coupling of transport phenomena. Typically, it is assumed that a single
Tep, Rotanak Visal SokLong, MenglyAlizadeh, MehrzadCharoen-amornkitt, PatcharawatSuzuki, TakahiroTsushima, Shohji
Fuel cell vehicles (FCVs) offer a promising solution for achieving environmentally friendly transportation and improving fuel economy. The energy management strategy (EMS), as a critical technology for FCVs, faces significant challenges of achieving a balanced coordination among the fuel economy, power battery life, and durability of fuel cell across diverse environments. To address these challenges, a learning-based EMS for fuel cell city buses considering power source degradation is proposed. First, a fuel cell degradation model and a power battery aging model from the literature are presented. Then, based on the deep Q-network (DQN), four factors are incorporated into the reward function, including comprehensive hydrogen consumption, fuel cell performance degradation, power battery life degradation, and battery state of charge deviation. The simulation results show that compared to the dynamic programming–based EMS (DP-EMS), the proposed EMS improves the fuel cell durability while
Song, DafengYan, JinxingZeng, XiaohuaZhang, Yunhe
The deployment of PEM fuel cell systems is becoming an increasingly pivotal aspect of the electrification of the transport sector, particularly in the context of heavy-duty vehicles. One of the principal constraints to market penetration is durability of the fuel cell which hardly meets the expected targets set by the vehicle manufacturers and regulatory bodies. Over the years, researchers and companies have faced the challenge of developing reliable diagnostic and condition monitoring tools to prevent early degradation and efficiency losses of fuel cell stack. The diagnostic tools for fuel cell rely usually on model-based, data driven and hybrid approaches. Most of these are mainly developed for stationary and offline applications, with a lack of suitable methods for real-time and vehicle applications. The work presented is divided into two parts: the first part explores the main degradation conditions for a PEMFC and characteristics, advantages, and application limits of the main
Di Napoli, LucaMazzeo, Francesco
This work presents a computationally inexpensive but effective method for an initial assessment of component sizing and power-split for fuel cell hybrid electric heavy-duty trucks. As a first step, the proposed method employs a prototypical longitudinal vehicle model to generate power demand at every instant of a representative drive cycle. Subsequently, six fuel cell and battery sizing combinations, each providing a peak continuous system power of 400 kW, are identified based on drive cycle power demands, commercially available fuel cell sizes, and Department of Energy (DOE) sizing targets. Ultimately, for each sizing combination, a proportional-integral (PI) controller with anti-windup is implemented to split power between the fuel cell and battery. In this study, the controller is tuned to reduce hydrogen consumption while meeting the instantaneous power demand and maintaining the battery state-of-charge (SOC) between 0.3 and 0.7. The results indicate that increasing the fuel cell
Mandviwala, AliYesilyurt, SerhatStefanopoulou, Anna
In addition to electric vehicles (EVs), hydrogen fuel cell systems are gaining attention as energy-efficient propulsion options. However, designing fuel cell vehicles presents unique challenges, particularly in terms of storage systems for heavy hydrogen tanks. These challenges impact factors such as NVH (noise, vibration, and harshness) and safety performance. This study presents a topology optimization study for Hydrogen Energy Storage System (HESS) tank structure in Class 5 trucks, with a focus on enhancing the modal frequencies. The study considers a specific truck configuration with a HESS structure located behind the crew cab, consisting of two horizontally stacked hydrogen tanks and two tanks attached on both sides of the frame. The optimization process aimed to meet the modal targets of this hydrogen tank structure in the fore-aft (X) and lateral (Y) directions, while considering other load cases such as a simplified representation of GST (global static torsion), simplified
Yoo, Dong YeonChavare, SudeepViswanathan, SankarMouyianis, Adam
The performance of a second-generation Toyota Mirai fuel cell was characterized as part of the SwRI internal research program. This data was used to develop a supervisory controller scheme designed to balance the plant for the fuel cell system during steady-state and transient vehicle conditions. This was accomplished using a Supervisory Integrated Controller (SIC) implemented on a Real-time Power Electronics Control System (RPECS) with a Simulink-based control algorithm. The actuators of interest are the three hydrogen injectors at anode inlet, air compressor and three air side valves on at the cathode inlet. The FC power measurement and pressure sensor readings at the anode and cathode were utilized as real-time feedback for the controller operation. The aim of the controller was to achieve and maintain the power target set by the hybrid powertrain ECU present on the vehicle, which is responsible for balancing power on the fuel cell and battery over the high-voltage bus. These
Chundru, Venkata RajeshKubesh, MatthewLegala, Adithya
This study evaluates the performance of alternative powertrains for Class 8 heavy-duty trucks under various real-world driving conditions, cargo loads, and operating ranges. Energy consumption, greenhouse gas emissions, and the Levelized Cost of Driving (LCOD) were assessed for different powertrain technologies in 2024, 2035, and 2050, considering anticipated technological advancements. The analysis employed simulation models that accurately reflect vehicle dynamics, powertrain components, and energy storage systems, leveraging real-world driving data. An integrated simulation workflow was implemented using Argonne National Laboratory's POLARIS, SVTrip, Autonomie, and TechScape software. Additionally, a sensitivity analysis was performed to assess how fluctuations in energy and fuel costs impact the cost-effectiveness of various powertrain options. By 2035, battery electric trucks (BEVs) demonstrate strong cost competitiveness in the 0-250 mile and 250-500 mile ranges, especially when
Mansour, CharbelBou Gebrael, JulienKancharla, AmarendraFreyermuth, VincentIslam, Ehsan SabriVijayagopal, RamSahin, OlcayZuniga, NataliaNieto Prada, DanielaAlhajjar, MichelRousseau, AymericBorhan, HoseinaliEl Ganaoui-Mourlan, Ouafae
With the growing energy crisis, people urgently need green energy sources to replace fossil ones. As a zero-emission clean energy source, the proton-exchange membrane fuel cell (PEMFC) has received growing attention from researchers due to its broad practical application. However, the large-scale application of PEMFC is currently impeded by their unsatisfying power output and high cost. PEMFC is composed of multiple components, among which the catalyst layer significantly affects the output power and cost of PEMFC. Drastically reducing the amount of platinum in the catalyst layer can bring great benefits to PEMFC, yet causing the large voltage loss associated with enlarged local oxygen molecule transport. Cutting down the platinum content in the catalyst layer can yield substantial cost savings for PEMFC. Developing an efficient catalyst possessing enhanced oxygen reduction reaction (ORR) catalytic performance is conducive to the commercialization of low-Pt proton exchange membrane
Liu, YuchenLiu, XinCai, XinDu, AiminLin, Rui
Due to advantages such as high efficiency, low emissions, and fuel flexibility, solid oxide fuel cells (SOFCs) have garnered significant attention as promising power sources for automotive applications. Nickel/yttria-stabilized zirconia (Ni/YSZ) is one of the most widely used anode materials in SOFCs, as it can catalyze both chemical and electrochemical reactions of carbon-containing fuels. However, the direct use of carbon-containing fuels can lead to carbon deposition on the Ni/YSZ anode, negatively impacting the performance and reliability of automotive SOFC systems. The diffusion of carbon atoms within nickel plays a crucial role in the carbon deposition process and requires further investigation. The oxygen atoms that spillover from YSZ also participate in main reactions such as carbon deposition and electrochemical reactions in Ni. Molecular dynamics (MD) is one of the main methods for studying atomic diffusion in crystalline structures. In this study, reactive force field
Du, HaoyuZhang, KaiqiXiao, MaZhang, XiaoqingShuai, Shijin
The accurate extraction of internal operating parameters associated with multi-physicochemical processes forms the basis for precise modelling of solid oxide fuel cells (SOFCs), which serves as the foundation for predicting performance degradation and estimating the lifespan of SOFCs. In this work, a novel integration of the teaching-learning based optimization (TLBO) and collective intelligence (CI), referred as the teaching-learning based collective intelligence algorithm (TLBCI), is introduced. This algorithm utilizes diverse characteristic patterns, including current-voltage (I-V) curves and sequential output data, to enhance the overall identification of degradation process. Experimental data was gathered from a 3-cell SOFC short stack during a 640-hour durability test. The proposed parameter identification algorithm employs a collective intelligence framework, wherein sub-optimizers are based on genetic algorithm (GA) and individually tasked with processing specific formats of
Wang, ZheyuShen, YitaoSun, AoTongHan, BeibeiMa, XiaoShuai, Shijin
Fuel cell electric vehicles (FCEVs) are gaining increasing interest due to contributions to zero emissions and carbon neutrality. Thermal management of FCEVs is essential for fuel cell lifespan and vehicle driving performance, but there is a lack of specialized thermal balance test standards for FCEVs. Considering differences in heat generating mechanism between FCEVs and internal combustion engine vehicles (ICEVs), current thermal balance method for ICEVs should be amended to suit for FCHVs. This study discussed thermal balance performance of ICEV and FCHVs under various regulated test conditions based on thermal balance tests in wind tunnel of two FCEVs and an ICEV. FCEVs reported overheat risk during low-speed climbing test due to continuous large power output from fuel cell (FC). Frequent power source switches between FC and battery were observed under dual constrains of fuel cell temperature and battery state of charge (SOC). Significant temperature exceedance of ICEV occurred
Fang, YanhuaMin, YihangMing, ChenLi, HongtaoLi, DongshengHe, ChongMao, Zhifei
This paper aims to model and simulate a design specification for a fuel cell electric powertrain tailored for Extreme H motorsport applications. A comprehensive numerical model of the powertrain was constructed using GT-SUITE v2024, integrating the 2025 Extreme H regulations, which include specifications for the fuel cell stack, electric motors, hydrogen storage, and battery systems. A detailed drive cycle representing the real-world driving patterns of Extreme E vehicles was developed, utilizing kinematic parameters derived from literature and real-world data. The performance of the Extreme H powertrain was benchmarked against the Toyota Mirai fuel cell vehicle to validate the simulation accuracy under the same racing conditions. The proposed design delivers a maximum power output of 400 kW, with 75 kW supplied by the fuel cell and 325 kW by the battery, ensuring optimal performance within the constraints set by the Extreme H 2025 regulations. Additionally, the design maintains an
Moreno Medina, JavierSamuel, Stephen
Diverse solutions will likely be needed to decarbonize the commercial truck sector in the United States. Battery-powered vehicles play a predominant role but in some cases, fuel cell trucks are more advantageous for the consumer. This study examines several medium- and heavy-duty applications designed for different driving range requirements to identify the design space where battery and fuel cell trucks are attractive. Also considered are the impacts of purchase price, fuel cost, and vehicle usage. We examine the top 10 truck classes as well as bus applications based on vehicle population, fuel usage, and driving distances. We assume a 2030 scenario where both batteries and FC systems become less costly and more efficient, as targeted by the U.S. Department of Energy. Even for smaller-class vehicles, where battery electric vehicles are expected to be the most economical among clean vehicle solutions, the results are not straightforward. Based on vehicle design, usage, and external
Vijayagopal, RamBirky, Alicia
Optimizing energy providers like fuel cells and engines involves considering various factors, constraints, and requirements. These include NVH (Noise, Vibration, and Harshness), durability, operating point efficiency, and customer expectations. Different energy providers prioritize these factors differently. For instance, NVH is crucial for engines due to customer expectations regarding start-up, sound, and power delivery based on accelerator input. In contrast, fuel cells face fewer constraints but must consider noise from electrical AC compressors and other devices, especially at lower vehicle speeds. However, operating point efficiency and durability are paramount for fuel cells, as they are expected to last as long as engines in conventional vehicles sold today. This paper proposes a holistic approach that begins at the vehicle or powertrain architecture level and designs an operating strategy that integrates all the aforementioned factors to enhance the operation of a fuel cell
Patel, NadirshKudupley, Harshal
Fuel economy and the ability to maintain the state of charge (SOC) of the battery are two key metrics for the energy management of a full-power fuel cell hybrid vehicle fitted with a small-capacity battery pack. To achieve stable maintenance of SOC and near-optimal fuel consumption, this paper proposes an adaptive equivalent consumption minimization strategy (PA-ECMS) based on power prediction. The strategy realizes demand power prediction through a hybrid deep learning model, and periodically updates the optimal equivalent factor (EF) based on the predicted power to achieve SOC convergence and ensure fuel economy. Simulation results show that the hybrid deep learning network model has high prediction accuracy with a root mean square error (RMSE) of only 0.733 m/s. Compared with the traditional ECMS based on SOC feedback, the PA-ECMS effectively maintains the battery SOC in a more reasonable range, reduces the situation of the fuel cell directly charging the power cell in the high
Gao, XinyuJu, FeiChen, GangZong, YuhuaWang, Liangmo
In the electrification of automotive and commercial vehicles, batteries are replacing internal combustion engines (ICE) with a battery only power system. The current process uses Linear Circuit Analysis (LCA) and assumes a passive load. The electronics are also assumed to have a constant input voltage from the source. A battery is not capable of providing constant input voltage under automotive use cases resulting in LCA not be applicable for all cases. The non-constant battery voltage will also influence the way electronics are modelled. One specific instance is an EV especially with the traction drive motors where the power demands are considered non-passive. The research will show the discharge behavior of batteries and the results of each of these discharge modes. The research will classify loads as either passive loads or non-passive loads and use the conservation of energy to model non-passive loads with a battery.
Ingarra, Nicholas
The depletion of fossil fuels and the emergence of global warming propel public sectors to explore alternative energy such as renewable electricity and hydrogen to reduce greenhouse gas (GHG) emissions. Numerous studies have demonstrated substantial environmental benefits of electric light-duty vehicles. However, research focusing on heavy-duty vehicles is still relatively scarce, and the transition to zero emissions heavy-duty trucks is facing enormous technical and economic challenges. This work investigated GHG emissions during the manufacturing and assembly phase of heavy-duty vehicles (HDVs), including battery electric trucks (BETs) and gaseous hydrogen fuel cell electric trucks (FCETs) using SimaPro software package with wildly accepted Ecoinvent database based on UK grid mix scenarios. A comparative analysis of greenhouse gas (GHG) emissions during the production phase of 700 bar- and 350 bar-H2 FCETs and their battery electric counterparts (eqBETs) was conducted under two UK
Zhao, JianboLi, HuBabaie, MeisamLi, Kang
This paper presents the development of a new vehicle simulation software, the Power- and Usage-Based Simulator Tool (referred to as the Power-Based Model), designed to predict fuel consumption and evaluate advanced powertrain technologies for off-road mobile machinery. The Power-Based Model integrates current research on fuel consumption simulation in the off-road vehicle sector and serves as a platform for development of advanced powertrain technologies such as battery-electric and fuel cell powertrains. The tool predicts the battery capacity and hydrogen storage required for the transition to these advanced powertrains, allowing users to accurately calculate component sizes and reductions in fuel consumption. The Power-Based Model was developed with a strong focus on the unique operational characteristics of off-road machinery, ensuring that it realistically reflects real-world energy consumption and the competitive advantages of various fuel-saving technologies. This paper describes
Kim, NamdooSeo, JiguVijayagopal, RamBurnham, Andrewmakarczyk, DavidFreyermuth, Vincent
Decarbonizing regional and long-haul freight is challenging due to the limitations of battery-electric commercial vehicles and infrastructure constraints. Hydrogen fuel cell medium- and heavy-duty vehicles (MHDVs) offer a viable alternative, aligning with the decarbonization goals of the Department of Energy and commercial entities. Historically, alternative fuels like compressed natural gas and liquefied propane gas have faced slow adoption due to barriers like infrastructure availability. To avoid similar issues, effective planning and deploying zero-emission hydrogen fueling infrastructure is crucial. This research develops deployment plans for affordable, accessible, and sustainable hydrogen refueling stations, supporting stakeholders in the decarbonized commercial vehicle freight system. It aims to benefit underserved and rural energy-stressed communities by improving air quality, reducing noise pollution, and enhancing energy resiliency. This research also provides a blueprint
Sujan, VivekSun, RuixiaoJatana, GurneeshFan, Junchuan
Proton exchange membrane fuel cell (PEMFC) is widely used in transportation and high-efficiency energy systems for their high power density and rapid start-up capability. The temperature control of its thermal management system is characterized by slow response and system oscillation, and the temperature control process suffers from problems such as large temperature fluctuations and slow temperature rise during cold starts. To effectively control the fuel cell thermal management system, this paper proposes a fuzzy PID-based control strategy to optimize the temperature control of the stack by comprehensively controlling the cooling fan, thermostat, temperature control valve, and heat components. By modeling the 60kW PEMFC thermal management system on the MATLAB/Simulink platform, the flow distribution and heat exchange of each component are analyzed and the optimized fuzzy control strategy is compared with the traditional PID control strategy. The simulation results show that the
Zhang, YilongZhang, YunqingGuo, JunWu, Jinglai
Fuel cells offer several advantages, including extended range, rapid refueling, and clean and efficient, making them well-suited for long-distance transportation in commercial vehicles. A multi-objective real-time optimization energy management strategy is proposed based on the comprehensive consideration of the equivalent hydrogen consumption and energy source lifetime. Power distribution among the energy sources is achieved by minimizing the vehicle's instantaneous comprehensive operational cost. Two coefficients are employed to restrict the fuel cell's start-stop frequency and load variation range. Additionally, two control coefficients are introduced in the objective function to regulate the battery's state of charge. The analysis shows that multi-objective real-time optimization energy management strategy is 10% and 14% less economical than conventional rule-based energy management strategy in both operating conditions and 5% and 7.8% higher than dynamic programming. However, the
You, JianhuiGu, ZhuangzhuangWu, JinglaiZhang, Yunqing
Electrochemical model of a fuel cell involves several parameters which influence its polarization curve. For a numerical fuel cell model to match experimental polarization curve, it is critical to find the right values of these parameters. It is hard to find the values of all the parameters experimentally, and hence parameter calibration is required. A fully automated workflow for calibration of fuel cell model parameters in a three-dimensional Computational Fluid Dynamic (CFD) simulation is created. The CFD model captures detailed electrochemistry and water phase change. The CFD polarization curve is generated by sequentially running a series of simulations starting from low current densities to high current densities. Experimental polarization curve is used as the validation target. An objective function is defined as the L2 norm of the difference between the experimental and the CFD generated polarization curve measured at various current densities. For calibration, eight fuel cell
Champhekar, OmkarJanakiraman, ArunGondipalle, SreekanthAjotikar, NikhilZehr, Randall
This study numerically analyzed the gas diffusion layer (GDL) in proton exchange membrane fuel cells (PEMFCs). The GDL, composed of carbon fibers and binder, plays a critical role in facilitating electron, heat, gas, and water transport while cushioning under cell compression. Its microstructure significantly influences these properties, requiring precise design. Using simulations, this study explored GDL designs by varying fiber and binder parameters and calculated gas diffusivity under wet conditions. Unlike previous studies, a novel model treated carbon fibers as beam elements with elastic binder connections, closely replicating structural changes under compression. Key properties analyzed include permeability, electrical conductivity, and gas diffusion efficiency under wet conditions. The optimized designs enhanced these properties while balancing trade-offs between electrical conductivity and mass transport. These findings provide valuable guidelines for advancing PEMFC technology
Ota, YukiDobashi, ToshiyukiNomura, KumikoHattori, TakuyaMaekawa, Ryosuke
Hydrogen fuel cell is one of paths to achieve carbon neutrality transportation. In the last two decades, significant improvements have been made in compactness, efficiency and durability of fuel cell systems. For heavy duty truck applications, a life span similar to heavy duty diesel engines is required. As a critical component in the fuel cell system, air compressors play an important role to meet fuel cell systems’ high efficiency and durability requirements. In this paper, a holistic approach has been taken to develop a series of airfoil bearing centrifugal compressors for a wide range of applications from forklift, passenger vehicles to commercial vehicles, and achieve high efficiency and durability of one million start-stops. In the new platform development, cooling circuit was optimized so that the external cooling air circuit for the rotor and air bearings is no longer needed, which resulted in 4% efficiency improvement. Hollow rotor structure was adopted to achieve lightweight
Wang, QianzhenYuan, XixinTao, ZhangFeng, Jin ZengWang, JuanXiao, YongZhou, LeiXin, Jun
The need for clean mobility launched multiple research directions in the powertrain field. While initially the battery electric vehicle (BEV) seemed the universal solution, the succession of pandemic emergencies and the resulting energetic crisis have defined a new scenario based on the multi-energy approach. One of the most promising technologies is the use of hydrogen in a fuel cell to generate electricity. This type of electric vehicle guarantees a shorter refueling time and a longer driving range than the battery electric one, becoming an enabling solution for long-haul or high-energy applications. In this study a combined 3D-CFD and 0D system analysis of an automotive Proton Exchange Membrane Fuel Cell (PEMFC) and system was carried out to provide a multi-scale analysis. In the first part, starting from a conventional parallel channel flow field configuration, the use of an optimization tool coupled with 3D-CFD simulations allowed to identify the optimal configuration in terms of
Martoccia, LorenzoAntetomaso, ChristianMerola, SimonaMarra, CarmineBreda, SebastianoD'Adamo, Alessandro
From automakers to companies in the wider mobility industry, hydrogen power is seeing no shortage of investment and research even as some remain unconvinced of its future. Most outsiders to the transportation industry don't know much about rapid developments in hydrogen fuel-cell and hydrogen internal-combustion. There just aren't the large-scale commercial and public efforts to inform the public as exist for the battery-electric vehicle market. Still, 50% of people in a recent Department of Energy survey said they understood that hydrogen has a chance to be a clean alternative source of power for vehicles and even for homes. Spotlight or no, progress is being made. And though much of it is outside the United States, American cities and companies have absolutely not given up on the technology. SAE Media wanted to check in and note recent transportation developments that use the earth's most abundant element.
Clonts, Chris
Since the 1860 Hippomobile, hydrogen has been a part of powered mobility. Today, most hydrogen storage applications use cylindrical tanks, but other solutions are available. At a recent Bosch-sponsored event, SAE Media noted Linamar's Flexform conformable storage, which the company says uses the same or less material for a given storage volume while delivering anywhere from 5-25% more volumetric efficiency than conventional cylindrical tanks within that volume. “We see space as a regular bounding box where all you're losing is this area around the corners, closer to five to 10% [loss]. Where Flexform really shines and where the value proposition really is, is irregular spaces, such as between frame rails,” said representatives from the Linamar engineering team.
Cannell, Thom
O-rings are essential components in engineering products as they ensure leak-proof sealing and hinders amalgamation of various fluids in the system. O-rings in general have lot of factors that go into deciding the right design for a system. With the help of FEA, O-ring design is varied to ensure optimal results. However, this process is time and resource consuming. Considering this situation, an alternative approach to predict the outcome with the help of DOE study is chosen in this paper. It leverages the Machine Learning models to predict the output parameters effectively with less resources. With the help of performance parameters, this paper proposes a comparison of various native ML models like Linear Regression, Random Forrest, SVM, KNN, Boosting, Artificial Neural Networks and Kriging [7]. The Goal is to systematically compare the prediction performance of various models based on bootstrapping and hypothesis testing techniques to identify the most effective approach. This
Mallu, Venkata ReddyPenumatsa, Venkata Ramana RajuChirravuri, BhaskaraDuddu, VaraprasadMiller, RonaldSahu, Abhishek
Considered as one of the most promising technology pathways for the transport sector to realize the target of “carbon neutral,” fuel cell vehicles have been seriously discussed in terms of its potential for alleviating environmental burden. Focused on cradle-to-gate (CtG) stage, this article evaluates the environmental impacts of fuel cell heavy-duty vehicles of three size classes and three driving ranges to find the critical components and manufacturing processes in the energy context of China. The findings show that the greenhouse gas (GHG) emissions of the investigated fuel cell heavy-duty vehicle range from 47 ton CO2-eq to 162 ton CO2-eq, with the fuel cell system and hydrogen storage system collectively contributing to 37%–56% of the total. Notably, as the driving range increases, the proportion of GHG emissions stemming from fuel cell-related components also rises. Within the fuel cell system, the catalyst layer and bipolar plate are identified as the components with the most
Mu, ZhexuanDeng, YunFengBai, FanlongZhao, FuquanLiu, ZongweiHao, HanLiu, Ming
Electrochemical impedance spectroscopy (EIS) is often used for fault diagnosis as an important parameter to characterize the state of fuel cells. However, online diagnosis requires high real-time performance and usually can only measure single-frequency or dual-frequency impedance. Too few diagnostic features make it difficult for traditional fault diagnosis methods based on EIS to ensure high accuracy. Therefore, this paper proposes a fault diagnosis method based on fast EIS measurement and an optimized random forest algorithm. Firstly, using a multi-sine excitation signal to realize the simultaneous measurement of multi-frequency impedance, provides more health status information in a single measurement. To solve the problem of large signal peaks caused by the superimposed signals, the phase is optimized by the genetic algorithm, which reduces the crest factor of the excitation signal. Then, multi-frequency impedance is used as a training feature for the random forest (RF) algorithm
Ni, ShengqiZhang, CunmanZhu, YuanZhong, Xiaolong
Hydrogen fuel cell trucks have enormous development potential in the pursuit of global carbon neutrality and sustainable development. However, their commercialization and mass production are facing challenges in various aspects, especially the durability problem of fuel cells. This paper is intended to set up a high-power hydrogen fuel cell system (FCS) model, considering the fuel cell degradation factors, and based on this, proposes a two-layer fuzzy energy management strategy (EMS) to optimize the life of fuel cell and the total energy consumption of the vehicle. The first control layer provides real-time energy distribution efficiently from multiple sources and thus allows flexibility in energy supply. The second layer regulates the dynamic adjustment of fuel cell output power with degradation of both fuel cells and batteries considered, to make the prolonging of system lifetime possible. In this respect, the equivalent hydrogen consumption, which incorporates fuel cell degradation
Hou, QuanWang, HanZhu, Dan
To accurately identify the fault types of proton exchange membrane fuel cell (PEMFC) systems under continuously varying operating currents, this study develops a comprehensive PEMFC system model and proposes a robust fault diagnosis method based on the ResNet50 convolutional neural network (CNN) and transfer learning (TL). Initially, using Matlab/Simulink, a PEMFC model is constructed based on the electrochemical reaction mechanisms and empirical formulas that characterize the operation of the fuel cell. This model primarily includes the fuel cell stack and various auxiliary systems, such as the thermal management system, air supply system, and hydrogen supply system, each crucial for optimal performance. By varying the model parameters, sensor data is generated for five distinct operating conditions. After preprocessing the data, the Gramian Angular Field (GAF) technique is utilized to convert the time series data from each sensor into fault data images, which then serve as input for
Zhu, ShaopengWang, YifengXiong, QinghuiGeng, JunChen, Huipeng
An effective vehicle integrated thermal management system (ITMS) is critical for the safe and efficient operation of proton exchange membrane fuel cell (PEMFC) vehicles. This paper takes a fuel cell vehicle (FCV) as the research object, comprehensively considers the vehicle layout environment and thermal management requirements, and designs a complete thermal management system for FCV. The key components are selected and designed to match the performance and the control strategy of ITMS of fuel cell vehicle is developed. To do that, the ITMS model is established based on the heating principle and heat transfer theory of each key component. Then, the ITMS under Worldwide Harmonized Light Vehicles Test Cycle (WLTC) operating conditions at different ambient temperatures are simulated and analyzed by selecting indicators such as coolant flow rate and temperature. Under the ambient temperature of 40°C, the temperature of PEMFC is basically stable between 78 °C and 83°C, the coolant outlet
Jiang, QiXiong, ShushengWang, YupengZhu, ShaopengChen, Huipeng
The selection of the key components of proton exchange membrane fuel cell (PEMFC) crucially impacts the performance. This work developed a model of the fuel cell system model to simulate the power consumption of component and system and the temperature dynamic response of stack in real systems. A PEMFC simulation model was developed based on AMESim, encompassing the air supply subsystem, hydrogen supply subsystem, and the hydrothermal management subsystem. The parameters for the flow and pressure of hydrogen, air, and water were established based on the operational requirements to ensure efficient stack performance. Furthermore, a PID control model was employed to regulate the flow and pressure parameters of hydrogen, air, and water, in accordance with the operational requirements, to ensure optimal PEMFC system performance.The purpose of this study is to predict the power consumption of the key components and the overall system, as well as to analyze the compliance with fuel supply
Yu, PeiwenWang, YanboZhao, XiaojunPan, FengwenShi, BaofanYang, FengQiao, XingnianShan, FengxiangCheng, XiaoxianZhang, YaranZhang, ChunSun, YulingGao, YongFeng, Gang
State of health (SOH) estimation is essential to ensure safety and reliability in the operation of Proton Exchange Membrane Fuel Cells (PEMFCs). The aging of fuel cells results from the deterioration of multiple internal components, and the aging degree of some key components even directly determines the end of cell life. Due to the complexity of the internal reactions in fuel cell, many internal parameters cannot be measured or recorded during aging tests. In addition, external characteristics do not reflect the internal changes in the cell. Therefore, establishing a multi-scale metric based on fuel cell components is very important for fuel cell life research. During the aging process of a fuel cell, the contributions of different components to the overall aging vary significantly. Additionally, the allocation of indicator parameters presents a challenge in multi-scale modeling. To address these issues, this paper proposes a method to construct multi-scale indicators for fuel cells
Lin, YipengMin, HaitaoSheng, XiaZhang, ZhaopuSun, Weiyi
Energy management strategy (EMS) based on vehicle speed prediction has been widely used in fuel cell vehicles (FCVs). Actually, not only the actual power demand but also other factors affect the optimal power allocation between fuel cell system (FCS) and battery. However, this relationship is difficult to express in formulas especially under urban conditions because the power demand fluctuates greatly under the above conditions. To address the issue, a novel EMS for FCV based on short-term power demand and FCS output power is proposed. In the offline part, the short-term SOC change rate is used to characterize short-term power allocation. Besides, the average of short-term power demand and the FCS output power are selected as input factors. The feedforward neural network is used to learn the relationship of the above three state variables based on historical driving cycles. In the online part, a long short-term memory (LSTM) network is used to predict the short-term speed based on the
Wu, HuiduoMin, HaitaoZhao, HonghuiSun, Weiyi
This paper proposes a method that speeds up the Model Predictive Control (MPC) algorithm in the thermal management system of air-cooled Proton Exchange Membrane Fuel Cell (PEMFC), with an integration of machine learning and Active Set Method (ASM) of quadratic programming. Firstly, the parameters of the electrochemical model and mass transfer model of PEMFC are identified by swarm intelligence algorithms such as particle swarm algorithm and bat algorithm, and a semi-empirical model that can simulate actual dynamics is established. Based on this, a model predictive controller based on Active Set Method (ASM) is designed, and the optimization solution algorithm is optimized to solve the problem of slow and poor real-time performance. Combined with machine learning methods such as K-nearest neighbor algorithm and support vector machine, the warm start of the optimization solution algorithm is realized to improve the solution efficiency. The results show that using the warm-start MPC
Lv, HangChen, FengxiangPei, Yaowang
The thermal management system of fuel cells poses considerable challenges, particularly due to large time delays and nonlinear behaviors that complicate effective temperature control of the stack. In response to these challenges, this study introduces a novel fuel cell inlet temperature feedback control method based on the internal model principle, designed to enhance control accuracy. Simulations were conducted using MATLAB/Simulink® to evaluate the performance of both Proportional-Integral (PI) and internal model controllers through various tests, including step response and random condition assessments. The results demonstrated that the proposed internal model controller significantly outperformed traditional PID control in both static and dynamic scenarios. Specifically, during step response testing, the maximum temperature overshoot was minimized to just 1.5°C, with a steady-state error of less than 0.5°C. In dynamic performance testing, the inlet temperature exhibited a rapid
Liu, Shiguang
Currently, the application scope of fuel cell vehicles is gradually expanding. There is currently no durability testing method for the entire vehicle level in its research and development design process. In this article, a certain fuel cell passenger car is taken as the research object. The load spectrum data of its key components is collected. A ‘user goal test field’ multi-channel multi-dimensional load correlation optimization model is established. The goal is to minimize the difference in pseudo damage of special components such as the fuel cell vehicle stack structure under the user’s full life cycle target load and the test field test load. The characteristics of the multi-dimensional load of the fuel cell components corresponding to the optimized solution in the rainflow distribution and frequency domain distribution are calculated. And a durability reliability acceleration testing specification for fuel cell vehicle test fields for special components such as the stack structure
Wu, ShiyuGuo, TingWang, YupengWu, ZhenWang, Guozhuo
In this paper, a hybrid model based on deep reinforcement learning (DRL) is proposed for predicting the degradation process of the fuel cell stack. The model integrates the interpretability of mechanism models with the strengths of data-driven approaches in capturing nonlinear dynamics. Voltage is selected as an indicator for predicting the performance degradation of the stack. By utilizing DRL, a dynamic weighting process is achieved, enhancing both the accuracy and robustness of the model. The model is validated by the IEEE 2014 dataset. The results show that the hybrid model achieves high accuracy with the R2 value of 0.875 (30% of the data used as a training set). Moreover, when the training set is 7:3 compared to the test set, the accuracy of the hybrid model is 14.18% higher than that of the long short-term memory network (LSTM) model. The DRL model has the highest accuracy for different percentages of the training set in the total data set, which further verifies the
Qin, ZhikunYin, YanZhang, FanYao, JunqiGuo, TingWang, Bowen
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