Fuel Cell Lifetime Research Articles

A health index-based approach for fuel cell lifetime estimation

A health index-based approach for fuel cell lifetime estimation

Self-learning Markov prediction algorithm based aging-oriented gradient drop power control strategy for the transient modes of fuel cell hybrid electric vehicles

The power transients caused by switching from drive mode to brake mode in fuel cell hybrid electric vehicles (FCHEV) can result in significant degradation cost losses to the fuel cell. To address this issue, this study proposes a self-learning Markov prediction algorithm based aging-oriented gradient drop power control strategy. First, a real-time self-learning Markov predictor (SLMP) based on the traditional offline training Markov improvement is designed to predict the demand power and combined with the sequential quadratic programming (SQP) optimization algorithm to solve for the inner optimal demand power based on its global cost function minimization characteristic. On this basis, the fuel cell gradient drop power (FGDP) strategy is proposed to optimize the operating state of the vehicle powertrain under vehicle mode switching. This involves establishing a power gradient drop step based on considering the fuel cell hydrogen consumption cost and its lifetime degradation cost to further obtain the outer fuel cell demand power at the optimal step. And three execution modes are designed to trigger the FGDP strategy. Finally, by combining the above efforts, the SLMP-FGDP optimization control strategy is constructed. The numerical verification and hardware in loop experiments results show that the proposed improved SLMP can predict the vehicle demand power more accurately. Compared with the non-FGDP system, the SLMP-FGDP strategy can effectively near-eliminate the fuel cell power transient due to any braking scenario, thus effectively controlling the fuel cell lifetime degradation cost in a lower range and realizing a reduction of up to 52.21% of the fuel cell usage costs without significantly sacrificing the hydrogen fuel economy.

(Invited) Highly Water-Soluble Fullerene Derivatives for Durable Polymer Electrolyte Membrane Fuel Cells

Recently, the focus of proton-exchange membrane fuel cell (PEMFC) applications has shifted from fuel cell vehicles (FCVs) to heavy-duty vehicles (HDVs), necessitating enhanced fuel cell durability. The degradation of carbon-supported metal nanoparticle catalysts and electrolyte membranes are the primary factors responsible for fuel cell durability. Particularly in the context of HDVs, augmenting the chemical stability of electrolyte membranes, such as Nafion, stands as a crucial factor in improving fuel cell lifetime. The degradation mechanism of Nafion is believed to involve the cleavage of polymer chains by OH∙ radicals, formed by crossover hydrogen and crossover oxygen in the presence of trace transition metal ions. A common strategy to mitigate Nafion's chemical degradation involves adding radical quenchers like cerium ion (Ce+) to Nafion. However, Ce+ may migrate out of Nafion during fuel cell operation, leading to a decline in its highly efficient radical quenching ability over extended operational periods.In this study, we demonstrate that a fullerene-derived radical quencher with multiple hydroxy groups significantly contributes to enhancing the durability of Nafion. Our approach involves synthesizing fullerene derivatives with various kind of hydroxy and functional groups for remarkable water solubility to realize their homogeneous dispersion within the Nafion membrane. The abundant functional groups also aid in improving proton conductivity. Moreover, the chelating effect of the spherically distributed functional groups immobilizes themselves, preventing any observed leakage from the Nafion membrane. These fullerene-derivative radical quenchers effectively enhance fuel cell durability and hold promise for future applications in HDVs.This study was supported by the New Energy and Industrial Technology Development Organization (NEDO), Japan (JPNP20003).

Optimal energy management strategy based on neural network algorithm for fuel cell hybrid vehicle considering fuel cell lifetime and fuel consumption

Optimal energy management strategy based on neural network algorithm for fuel cell hybrid vehicle considering fuel cell lifetime and fuel consumption

A real-time energy management strategy for fuel cell vehicle based on Pontryagin’s minimum principle

This paper proposes a Pontryagin's minimum principle (PMP) energy management strategy (EMS) based on driving cycle recognition for fuel cell vehicle powertrains, aiming to minimize hydrogen consumption and fuel cell degradation. Firstly, the neural network-based driving cycle recognizer is optimized using the tuna swarm optimization (TSO) algorithm and trained under four typical driving cycles. Then, the optimal co-state variables for the four driving cycles are obtained by iteration. Finally, the co-state variables are dynamically updated based on real-time driving cycle recognition results. Comparative analysis demonstrates that the PMP-DCR effectively improves fuel cell lifetime and vehicle economy under short-distance driving cycles. Based on the combined driving cycle, the proposed PMP-DCR EMS exhibits similar economy performance to optimal dynamic programming (DP) EMS, reducing equivalent hydrogen consumption by 13.8% and 9.2%, and decreasing fuel cell degradation rates by 93% and 8.7% in comparison to the conventional power-following and PMP EMS, respectively.

State-of-Health observer for PEM fuel cells—A novel approach for real-time online analysis

Determining the State-of-Health (SoH) of fuel cell systems during operation is vital for implementing model-based control strategies that aim to minimize degradation and extend the fuel cell lifetime. This paper introduces a novel observer architecture designed to estimate the internal states as well as the SoH of fuel cell systems in real time. The proposed observer offers three key advantages: (1) it decouples the dynamics of internal states from SoH, thus enhancing numerical stability, (2) by incorporating multiple measurement points, assessing the SoH for various components within the fuel cell (e.g. membrane, catalyst layer) becomes possible, and (3) in combination with the used physical real-time capable model, the observer architecture facilitates real-time evaluation of the SoH during dynamic operation. The observer’s benefits are demonstrated through simulations and measurement data.

Trend‐focused dynamic degradation prediction based on echo state networks in automotive fuel cells

AbstractFuel cell technology is a promising alternative to traditional internal combustion engines in various applications, especially in transportation applications. However, the high cost and limited lifetime of fuel cells have hindered their widespread commercialization. Accurately predicting fuel cell lifetime is crucial for reducing the cost of ownership, ensuring safety, and promoting the adoption of this technology. The objective of the present work is to develop a tool that is able to estimate the lifespan of a proton exchange membrane fuel cell and to predict its behavior to anticipate failures. Therefore, this paper contributes to proposing a multi‐input time‐series prediction network based on an echo state network, which takes the future current into consideration. A degradation trend extraction method is proposed in this paper and the remaining useful life of the fuel cell is predicted. Results have shown that the proposed methods in both short‐term and long‐term prediction have achieved satisfying prediction accuracy.

Utilising Digital Twins for Smart Maintenance Planning of Fuel Cell in Electric Vehicles

This paper presents a framework utilising digital twins for predictive maintenance planning of fuel cells in electric vehicles, focusing on real-time condition monitoring and Remaining Useful Lifetime (RUL) prediction. By integrating advanced algorithms, it optimises maintenance schedules to reduce downtime and extend fuel cell lifetime. Despite relying on simulated data, the findings highlight the potential of digital twins to improve fuel cell reliability, and sustainability, illustrating their transformative impact on smart urban transportation systems.

Present and Future Perspectives on Fuel Cell Membrane Chemical Stability

Durability is key to the value proposition of proton exchange membrane (PEM) fuel cells for transportation applications. Ultimately, fuel cell vehicles need to match the lifetimes of diesel powered trucks, and last longer than lithium batteries, for widespread adoption[1]. The burden of reaching these aggressive targets falls on all components of the system, stack, and membrane electrode assembly. Of these, membrane durability remains an important area of research, in particular the chemical stability of the ionomer. The recognition of the ‘simple unzipping’ of the perfluorosulfonic acid (PFSA) backbone by hydroxyl radicals (HO·)[2],[3] and the subsequent introduction of cerium radical scavengers[4],[5] mark significant advancements in fuel cell lifetimes. Yet, even with this technology, it is unclear if this class of membranes can meet the 25,000+ hours required for heavy duty applications without additional improvements. Furthermore, recent proposals to restrict usage of fluorochemicals in many applications[6] presents a risk to the rapidly growing PEM fuel cell and water electrolysis markets. This talk will outline recent work that looks beyond simple peroxide degradation of PFSA ionomers and highlight the role of other reaction mechanisms suspected in accelerated fuel cell durability testing. Analysis of effluent water for ionomer fragments during these tests provides a greater understanding of the degradation mechanisms and suggests additional pathways beyond back-bone unzipping. Not only are these fragments useful in inferring reaction mechanisms, but there is also evidence they adsorb to the platinum surface, ultimately adding to performance decay. In other words, increased membrane durability may also result in increased catalyst stability. Finally, chemical stability of hydrocarbon membranes will be considered in light of the advances over the last two decades in understanding PFSA stability. Similarities and differences will be discussed between these two classes of PEM membrane with suggestions on how the techniques used to understand PFSA stability could be applied to hydrocarbon ionomers and advance the understanding of chemical stability in these systems.[1] Cullen, D. A.; Neyerlin, K. C.; Ahluwalia, R. K.; Mukundan, R.; More, K. L.; Borup, R. L.; Weber, A. Z.; Myers, D. J.; Kusoglu, A. New Roads and Challenges for Fuel Cells in Heavy-Duty Transportation. Nat Energy 2021, 6 (5), 462–474. .[2] Curtin, D. E.; Lousenberg, R. D.; Henry, T. J.; Tangeman, P. C.; Tisack, M. E. Advanced Materials for Improved PEMFC Performance and Life. Journal of Power Sources 2004, 131 (1), 41–48.[3] Coms, F. D. The Chemistry of Fuel Cell Membrane Chemical Degradation. ECS Trans. 2008, 16 (2), 235.[4] Frey, M. H., Pierpont, D. M., Hamrock, S. J., US 8,367,267 and US 9,431,670[5] Coms, F. D.; Liu, H.; Owejan, J. E. Mitigation of Perfluorosulfonic Acid Membrane Chemical Degradation Using Cerium and Manganese Ions. ECS Trans. 2008, 16 (2), 1735.[6] European Chemicals Agency (ECHA) proposal submitted 7th February 2023

Powering the Future: Progress and Hurdles in Developing Proton Exchange Membrane Fuel Cell Components to Achieve Department of Energy Goals—A Systematic Review

This comprehensive review explores recent developments in Proton Exchange Membrane Fuel Cells (PEMFCs) and evaluates their alignment with the ambitious targets established by the U.S. Department of Energy (DOE). Notable advancements have been made in developing catalysts, membrane technology advancements, gas diffusion layers (GDLs), and enhancements in bipolar plates. Notable findings include using carbon nanotubes and graphene oxide in membranes, leading to substantial performance enhancements. Innovative coatings and materials for bipolar plates have demonstrated improved corrosion resistance and reduced interfacial contact resistance, approaching DOE targets. Nevertheless, the persistent trade-off between durability and cost remains a formidable challenge. Extending fuel cell lifetimes to DOE standards often necessitates higher catalyst loadings, conflicting with cost reduction objectives. Despite substantial advancements, the ultimate DOE goals of USD 30/kW for fuel cell electric vehicles (FCEVs) and USD 600,000 for fuel cell electric buses (FCEBs) remain elusive. This review underscores the necessity for continuous research and innovation, emphasizing the importance of collaborative efforts among academia, industry, and government agencies to overcome the remaining technical barriers.

How can we design anion-exchange membranes to achieve longer fuel cell lifetime?

The substantial advancements and availability of new cost-effective materials have drawn significant attention to the anion-exchange membrane fuel cell (AEMFC) technology. The anion exchange membrane (AEM) is a core component in AEMFCs, essential for conducting hydroxide ions and controlling water transport between the fuel cell electrodes. In this study, we apply a numerical model to investigate the relationship and sensitivity of AEMFC performance and its stability to key AEM properties. Our findings show that membrane maximum hydration level (λmax) has the most significant impact on AEMFC lifetime, followed by membrane ion-exchange capacity, water diffusivity, and membrane thickness. We also demonstrate the significance of improving the stability of the functional group to AEMFC lifetime, while AEM hydroxide conductivity shows a negligible effect on AEMFC lifetime. Finally, we provide a simple algebraic functional relationship between key dimensionless parameters as well as a machine learning-based analysis of the relationship between AEM parameters and AEMFC lifetime. Through these analyses, we calculate the lifetime of selected membranes from the literature and compare them to the measured operation time. This study summarizes and highlights important AEM property targets suggested to improve AEM design to boost the performance stability of AEMFCs.

Effective Energy Management Strategy with Model-Free DC-Bus Voltage Control for Fuel Cell/Battery/Supercapacitor Hybrid Electric Vehicle System

This article presents a new design method of energy management strategy with model-free DC-Bus voltage control for the fuel-cell/battery/supercapacitor hybrid electric vehicle (FCHEV) system to enhance the power performance, fuel consumption, and fuel cell lifetime by considering regulation of DC-bus voltage. First, an efficient frequency-separating based-energy management strategy (EMS) is designed using Harr wavelet transform (HWT), adaptive low-pass filter, and interval type–2 fuzzy controller (IT2FC) to determine the appropriate power distribution for different power sources. Second, the ultra-local model (ULM) is introduced to re-formulate the FCHEV system by the knowledge of the input and output signals. Then, a novel adaptive model-free integral terminal sliding mode control (AMFITSMC) based on nonlinear disturbance observer (NDO) is proposed to force the actual values of the DC-link bus voltage and the power source’s currents track their obtained reference trajectories, wherein the NDO is used to approximate the unknown dynamics of the ULM. Moreover, the Lyapunov theorem is used to verify the stability of AMFITSMC via a closed-loop system. Finally, the FCHEV system with the presented method is modeled on a Matlab/Simulink environment, and different driving schedules like WLTP, UDDS, and HWFET driving cycles are utilized for investigation. The corresponding simulation results show that the proposed technique provides better results than the other methods, such as operational mode strategy and fuzzy logic control, in terms of the reduction of fuel consumption and fuel cell power fluctuations.

Health-aware model predictive energy management for fuel cell electric vehicle based on hybrid modeling method

Fuel cell lifetime is strongly affected by dynamic conditions. Most existing energy management works only focus on the fuel cell durability protection from the perspective of output power slope, without deeply considering the influence of the important parameters inside the stack. However, considering the variation of stack internal parameters (mechanism analysis) is more significant for fuel cell lifetime evaluation. In this paper, a health-aware model predictive control (HA-MPC) energy management strategy is proposed for fuel cell electric vehicle. A fuel cell health state model is established from the perspective of stack hydrogen excess ratio (HER), oxygen excess ratio (OER) and humidity through the hybrid modeling method. The fuel cell mechanism model and the low-dimensional data-driven model are established through the grey-box model estimation method and genetic algorithm-based radial basis function (GA-RBF) neural network. Then the objective function of energy management strategy is developed considering the total equivalent hydrogen consumption and stack improper parameter changes of HER, OER and humidity. Comparing with model predictive control strategy based on the typical power cost function, the HA-MPC can effectively reduce the steep drop of HER and OER in low power region by 3.58% and 4.41%, which can protect the fuel cell system lifetime.

Sensitivity analysis of current distribution to critical operating parameters for polymer electrolyte membrane fuel cells

Suitable operating parameters can not only improve the uniformity of current distribution, but also extend the lifetime of fuel cells. Existing studies of critical operating parameters on the uniformity of local current distribution tend to be qualitative and lack quantitative indicators that affect the sensitivity. In addition, there is almost no physical quantity that can characterize the uniformity of current distribution, though the uniformity of current distribution is an important factor that affects the fuel cell lifetime. In this paper, a multi-domain physical model of a proton exchange membrane fuel cell based on segmentation and resistive grid is developed. A physical quantity index that can reflect the uniformity of current distribution is further proposed. In addition, the effects of five critical parameters on the uniformity of current distribution are investigated. Besides, the specific sensitivity values of each parameter to the proposed index are also quantitatively calculated. The research content in this paper can make up for the current vacancy research on the impacts of operating parameters on the uniformity of fuel cell current distribution. These results provide a reference for the performance optimization and critical operating parameter control strategies for proton exchange membrane fuel cells.

Deep reinforcement learning based energy management strategy for range extend fuel cell hybrid electric vehicle

To meet the power and long-range driving requirements of the vehicle, this paper presents a dual mode operation scheme for a range extend fuel cell hybrid vehicle for the first time, with an in-depth study of the pure electric mode and the range extend mode. The deep deterministic policy gradient algorithm is a well-known deep reinforcement learning algorithm that can solve complex nonlinear problems. To achieve the optimal power distribution among energy sources in the two modes, a dual deep deterministic policy gradient algorithm framework is proposed for the first time in this paper. In addition, a pervious action guidance mechanism is proposed to enable networks to approximate the action value function more efficiently in training. The training results show that the adopted previous action guidance mechanism helps to improve the learning convergence and exploration ability. The validation results show that the proposed strategy improves the operating economy by about 30% compared to the rule-based strategy, reduces the average fuel cell output fluctuation to less than 100 W, and reduces the fuel cell lifetime loss greatly. It is hoped that the proposed new structure, patterns, and energy management strategy will provide more ideas for scholars in future research.

High Step-Up SEPIC-Based Trans-Inverse DC–DC Converter With Quasi-Resonance Operation for Renewable Energy Applications

This article proposes a new single switch trans-inverse high step-up converter applicable in sustainable sources of energies such as photovoltaic (PV) and fuel cell (FC). Through developing a single-ended primary-inductor converter by a three-winding built-in transformer (BIT) mixed with a switched-capacitor voltage multiplier cell, high voltage gains as well as low voltage stress across the <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">mosfet</small> can be achieved to reduce the required duty cycle and the conduction losses. The third winding of the BIT acts in a trans-inverse manner whose turns ratio should be lower than unity. Hence, with a lower number of windings the voltage gain can also be improved. Furthermore, the quasi-resonance operation of the proposed converter, reduces the associated switching losses of the <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">mosfet</small> and also guarantees zero current switching of diodes through the whole switching cycle. Meanwhile, low input current of the proposed converter serves as an interesting feature to maintain PV and FC lifetime. The detailed steady-state analysis of the proposed high-efficiency converter is presented with an extensive performance comparison to explore its advantages. Finally, a 200-W prototype with 20–250-V voltage conversion is developed in the laboratory to examine the carried steady-state analysis.

Performance and Total Cost of Ownership of a Fuel Cell Hybrid Mining Truck

The main objective of this work was to investigate the potential of hydrogen and fuel cells replacing diesel and internal combustion engines in the ultraclass haul trucks deployed in the mining sector. Performance, range, durability, and cost are the main criteria considered for comparing the two fuels and engine options. Fuel cell system (FCS) performance is characterized in terms of heat rejection, efficiency, and fuel consumption for a hybrid platform equivalent to a 3500 hp diesel engine operating on a representative open pit mining duty cycle. A hybrid platform was chosen because the heat rejection, with a constrained radiator frontal area, limits the maximum fuel cell-rated power by about 50% compared to that of the diesel truck. The hybrid powertrain was 81–88% more efficient than the diesel powertrain on the truck duty cycle. A liquid hydrogen storage system is required for an equal range or time between refilling, but the packaging remains a challenge. Fuel cell and battery durability were evaluated for their performance degradation and lifetime. Achieving a fuel cell lifetime comparable to the time between major overhauls for diesel trucks necessitates the oversizing of the membrane-active area, catalyst overloading, and voltage clipping. For an equal lifetime, the battery must be oversized to control its depth of discharge and charge/discharge rates. A total cost of ownership (TCO) analysis considering the initial capital expenditures, as well as the lifetime cost of fuel, operation, and maintenance, indicates that fuel cells and hydrogen can compete with diesel. A breakeven fuel cost for TCO parity is obtained if H2 is available at USD 5.79–6.85/kg vs. diesel at USD 3.25/gal and the FCS-specific cost is USD 323/kWe relative to USD 250/kW for a diesel genset. Volume manufacturing is required for FCS cost reduction. High volume is possible through the standardization, modularity, and proliferation of class 8 long-haul truck systems across different heavy-duty applications.

Multihorizon predictive energy optimization and lifetime management for connected fuel cell electric vehicles

A reliable energy optimization strategy incorporating vehicle connectivity is of great importance for the performance enhancement of fuel cell electric vehicles. In this paper, a multihorizon hierarchical model predictive control framework is proposed, which reduces energy consumption while incorporating fuel cell lifetime management though real-time speed preview. Specifically, the trajectories of battery state of charge are explored via convex optimization in the upper layer to provide a suboptimal reference for real-time optimization, and the concept of multihorizon is introduced into convex optimization for the first time. At the lower level, an equivalent consumption minimum strategy-based model predictive control is designed, which improves energy utilization efficiency and prolongs the lifetime of fuel cells. The main contribution of this paper is to use multihorizon optimization to solve the energy optimization and lifetime management of fuel cell electric vehicles over different timescales. Experimental results show that the proposed strategy has great potential in cost saving, which can reduce 10.10% to 16.95% of the total cost in real driving conditions compared with the rule-based strategy.

Lifetime prediction method of proton exchange membrane fuel cells based on current degradation law

Lifetime evaluation and prediction of proton exchange membrane fuel cells (PEMFCs) are essential for the lifetime extension and commercialization of fuel cells. Under the background that the accelerated stress test (AST) is difficult to evaluate the lifetime accurately, and the actual road test is time-consuming and costly, it is necessary to propose a quick lifetime prediction method of fuel cells. In this study, the current degradation law is analyzed based on the first-order kinetic model, and the lifetime prediction method based on the current degradation law is proposed. Moreover, the scale factor k is proposed to completely describe the fuel cell degradation due to complex degradation causes, and its calculation and selection methods are discussed. Finally, the accuracy of the proposed method is verified by the experimental results of single cells and fuel cell stacks, and the maximum relative error is less than 5% when the fuel cells conform to the first-order kinetic model. The lifetime prediction method of PEMFCs based on current degradation law improves the utilization of degradation information in current density, which is a feasible way for automotive fuel cell lifetime evaluation and prediction.

Rapid synchronous state-of-health diagnosis of membrane electrode assemblies in fuel cell stacks

Consistency evaluation and degradation diagnosis of membrane electrode assemblies (MEAs) in fuel cell stacks are critical issues for fuel cell lifetime extension and commercialization. This study aims to develop a high-efficiency, high-precision, and synchronous state-of-health (SOH) diagnosis methodology for MEAs at the stack level. The micro-current excitation (MCE) method based on multiple excitations is used to evaluate the MEA inconsistency of a 22-cell commercial stack, involving hydrogen crossover, short-circuit resistance, electrochemical surface area, and double-layer capacitance. High precision is demonstrated by the consistency in the membrane-related parameters and the difference in the electrode-related parameters between the cathode and anode tests. Herein, an efficient single excitation and natural discharging (SEND) analytical method based on the excitation-discharging-combined equivalent circuit model is originally proposed. The accuracy of SEND can be demonstrated by comparing its results with the standard results of the MCE, which fit the baseline y = x with high linearity and identical evaluation of inconsistency. Moreover, the stability of the SEND is proved by its independence from the excitation current, which is reflected by the presence of tiny standard deviations. In addition, the bifurcation feature in the integrated net charge curves of the excitation and natural discharging processes proves the irreversibility of partial oxidation reactions in the high-potential region of excitation. The SEND significantly increases the testing efficiency using only a single excitation. It has promising prospects for the rapid consistency-based MEA screening, degradation evaluation, and recombination of MEAs.