Multi-stack Fuel Cell Research Articles

Efficiency maximization of fuel cell electric bus using asymmetric multi-stack fuel cells

Fuel cell electric vehicles (FCEVs) have been widely studied as alternative ecofriendly vehicles based on multi-stack fuel cell (MFC) systems. An MFC system has the advantages of considerable efficiency, reliability, and durability compared with a single-stack fuel cell system. In this study, a novel MFC concept with an asymmetric structure is proposed to maximize the efficiency of a fuel cell electric bus (FCEB). The proposed concept was designed with a cascade electrical architecture composed of differently rated power stacks, and the optimal energy distribution between the stacks was determined to leverage the asymmetric structure. This facilitates the use of high-efficiency areas of the fuel cell stacks throughout most of the output power range. To confirm the performance of the proposed mechanism, dynamic programming, a global optimization technique, was used as the energy management strategy, and the FCEB was tested on Manhattan, Hanoi, and Seoul bus driving cycles. As a result, a significant reduction in the total fuel consumption of the proposed MFC electric bus compared with that of the conventional FCEB was verified.

A Coordinated Control Strategy for Efficiency Improvement of Multistack Fuel Cell Systems in Electric–Hydrogen Hybrid Energy Storage System

A two-layer coordinated control strategy is proposed to solve the power allocation problem faced by electric–hydrogen hybrid energy storage systems (HESSs) when compensating for the fluctuating power of the DC microgrid. The upper-layer control strategy is the system-level control. Considering the energy storage margin of each energy storage system, fuzzy logic control (FLC) is used to make the initial power allocation between the battery energy storage system (BESS) and the multistack fuel cell system (MFCS). The lower-layer control strategy is the device-level control. Considering the individual differences and energy-storage margin differences of the single-stack fuel cell (FC) in an MFCS, FLC is used to make the initial power allocation of the FC. To improve the hydrogen-to-electricity conversion efficiency of the MFCS, a strategy for optimization by perturbation (OP) is used to adjust the power allocation of the FC. The output difference of the MFCS before and after the adjustment is compensated for by the BESS. The simulation and experiment results show that the mentioned coordinated control strategy can enable the HESS to achieve adaptive power allocation based on the energy storage margin and obtain an improvement in the hydrogen-to-electricity conversion efficiency of the MFCS.

Simulation of a Novel Integrated Multi-Stack Fuel Cell System Based on a Double-Layer Multi-Objective Optimal Allocation Approach

A multi-stack fuel cell system (MFCS) is a promising solution for high-power PEM fuel cell applications. This paper proposes an optimized stack allocation approach for power allocation, considering economy and dynamics to establish integrated subsystems with added functional components. The results show that an MFCS with target powers of 20 kW, 70 kW, and 120 kW satisfies lifetime and efficiency factors. The common rail buffer at the air supply subsystem inlet stabilizes pressure, buffers, and diverts. By adjusting the volume of the common rail buffer, it is possible to reduce the maximum instantaneous power and consumption of the air compressor. The integrated hydrogen supply subsystem improves hydrogen utilization and reduces parasitic power consumption. However, the integrated thermal subsystem does not have the advantages of integrated gas supply subsystems, and its thermal management performance is worse than that of a distributed thermal subsystem. This MFCS provides a solution for high-power non-average distribution PEM fuel cell systems.

A Novel Perspective of Energy Management Strategies on Multistack Fuel Cell Hybrid Electric Vehicles: Trends and Challenges

A Novel Perspective of Energy Management Strategies on Multistack Fuel Cell Hybrid Electric Vehicles: Trends and Challenges

Control Method of Temperature for Multi-stack Fuel Cell System

Control Method of Temperature for Multi-stack Fuel Cell System

An Energy Management Strategy for Multi-Stack Fuel Cell Hybrid Locomotives with Integrated Optimization of System Service Life

An Energy Management Strategy for Multi-Stack Fuel Cell Hybrid Locomotives with Integrated Optimization of System Service Life

Predictive Health-Conscious Energy Management Strategy of a Hybrid Multi-Stack Fuel Cell Vehicle

Predictive Health-Conscious Energy Management Strategy of a Hybrid Multi-Stack Fuel Cell Vehicle

Fault Diagnosis of Sensors for Multi-stack Fuel Cell Thermal Management Subsystem Based on UKF

Fault Diagnosis of Sensors for Multi-stack Fuel Cell Thermal Management Subsystem Based on UKF

Distributed Power Management Strategy for Multi-Stack Fuel Cell Systems in Electric Propulsion Aircraft with Efficiency Reinforcement

Distributed Power Management Strategy for Multi-Stack Fuel Cell Systems in Electric Propulsion Aircraft with Efficiency Reinforcement

Speedy Hierarchical Eco-Planning for Connected Multi-Stack Fuel Cell Vehicles via Health-Conscious Decentralized Convex Optimization

<div>Connected fuel cell vehicles (C-FCVs) have gained increasing attention for solving traffic congestion and environmental pollution issues. To reduce operational costs, increase driving range, and improve driver comfort, simultaneously optimizing C-FCV speed trajectories and powertrain operation is a promising approach. Nevertheless, this remains difficult due to heavy computational demands and the complexity of real-time traffic scenarios. To resolve these issues, this article proposes a two-level eco-driving strategy consisting of speed planning and energy management layers. In the top layer, the speed planning predictor first predicts dynamic traffic constraints using the long short-term memory (LSTM) model. Second, a model predictive control (MPC) framework optimizes speed trajectories under dynamic traffic constraints, considering hydrogen consumption, ride comfort, and traffic flow efficiency. A multivariable polynomial hydrogen consumption model is also introduced to reduce computational time. In the bottom layer, the decentralized MPC framework uses the calculated speed trajectory to figure out how to allocate the power optimally between the fuel cell modules and the battery pack. The objective of the optimization problem is to reduce hydrogen consumption and mitigate component degradation by focusing on targets such as the operating range of state of charge (SoC), as well as battery and fuel cell degradation. Simulation results show that the proposed decentralized eco-planning strategy can optimize the speed trajectory to make the ride much more comfortable with a small amount of jerkiness (−0.18 to 0.18 m/s<sup>3</sup>) and reduce the amount of hydrogen used per unit distance by 7.28% and the amount of degradation by 5.33%.</div>

주택용 연료전지 효율 향상을 위한 다중 스택 연료전지 시스템의 전력 분배 최적화

주택용 연료전지 효율 향상을 위한 다중 스택 연료전지 시스템의 전력 분배 최적화

Q-learning based energy management strategy for a hybrid multi-stack fuel cell system considering degradation

The use of multi-stack fuel cells (FCs) is attracting considerable attention in electrified vehicles due to the added degrees of freedom in terms of efficiency and survivability. In a multi-stack FC hybrid electric vehicle, the power sources (FCs and the battery pack) have different energetic characteristics and their operation is influenced by the performance drifts caused by degradation. Hence, efficient power distribution for such a multi-source system is a critical issue. This paper proposes a three-layer online EMS for a recreational vehicle composed of three FCs and a battery pack. In the first layer, two online estimators are responsible for constantly updating the characteristics of each FC and the battery to be used by the power distribution algorithm. In the second layer, a rule-based method is developed to improve the calculation speed of the power distribution algorithm by deciding when it should be activated. The last layer performs the power distribution between FCs and battery using a model-free reinforcement learning (RL) algorithm called Q-learning. The proposed RL-based EMS attempts to meet the requested power while minimizing the costs of hydrogen consumption and degradation of all power sources. To justify the performance of the proposed strategy, a comprehensive benchmark with an offline EMS and two online strategies is performed under two driving cycles. In comparison with the online strategies, the proposed method based on RL reduces the defined trip cost up to 11.5 % and 13.08 % under the Real driving cycle while having a higher cost than the offline strategy by 4.78 %.

An Energy Management Strategy Considering the Economy and Lifetime of Multistack Fuel Cell Hybrid System

Due to technical limitations, it is difficult for single stack fuel cell (FC) system to be applied in high-power applications. Therefore, multi-stack FC hybrid system (MS-FCHS) is attracting more and more attention. In order to improve the system economy, this work proposes a real-time energy management strategy (EMS) that minimizes system operating cost. Specifically, the proposed strategy considers not only fuel cost but also FC and battery degradation cost. Secondly, during the operation of MS-FCHS, the performance of each stack varies with operating conditions and time, this work also considers the impact of inconsistent FC performance on the system lifetime. Finally, compared with the power following strategy (PF) and the hydrogen consumption minimizing strategy (H2-minimization), RT-LAB experimental results show that the proposed strategy can reduce the system operating cost by 15.85% and 9.75%, and improve system efficiency. In addition, since the system life depends on the stack with the worst service performance, the proposed strategy can also realize the convergent control of the system performance when the stacks operate in parallel, prolong the life of the system, and maintain battery state of charge (SOC) within a certain range, which is conducive to the stable operation of the system.

Electrical, thermal and degradation modelling of PEMFCs for naval applications

This article presents a semi-empirical model of Proton Exchange Membrane Fuel Cell (PEMFC), combining electrical, thermal and ageing phenomena. This modelling approach links activation and diffusion phenomena in order to create a new PEMFC model, close to reality in steady state and enabling fast simulation (since the dynamics are of first order). The purpose of this model is to enable energy management studies to optimise the design, lifetime and consumption of a PEMFC system. The degradation model is defined and fitted according to a PEM fuel cell datasheet. The design aims to study fault-tolerant multi stack Fuel Cell (MFC) systems, to consider their interactions with power converters and to design optimal control and adaptive management rules. The degradation model helps to define management rules according to the state of health and to the degradation speed of each stack. This work also presents an analysis of the existing models available in the literature and more specifically models compatible with the needs of Modular Fuel Cells (MFC) studies.

Data-Driven Load-Current Sharing Control for Multi-Stack Fuel Cell System with Circulating Current Mitigation

The global trend toward renewable power generation has drawn great attention to hydrogen Fuel Cells (FCs), which have a wide variety of applications, from utility power stations to laptops. The Multi-stack Fuel Cell System (MFCS), which is an assembly of FC stacks, can be a remedy for obstacles in high-power applications. However, the output voltage of FC stacks varies dramatically under variable load conditions; hence, in order for MFCS to be efficiently operated and guarantee an appropriate load-current sharing among the FC stacks, advanced converter controllers for power conditioning need to be designed. An accurate circuit model is essential for controller design, which accounts for the fact that the parameters of some converter components may change due to aging and repetitive stress in long-term operations. Existing control frameworks and parametric and non-parametric system identification techniques do not consider the aforementioned challenges. Thus, this paper investigates the potential of a data-driven method that, without system identification, directly implements control on paralleled converters using raw data. Based on pre-collected input/output trajectories, a non-parametric representation of the overall circuit is produced for implementing predictive control. To achieve proper current sharing, the proposed method considers also the mitigation of circulating current within the MFCS. Simulation results verify the effectiveness of the proposed method.

Model Predictive Control of Hydrogen Pressure of Multi-Stack Fuel Cell System

Model Predictive Control of Hydrogen Pressure of Multi-Stack Fuel Cell System

Minimum hydrogen consumption power allocation strategy for the multi-stack fuel cell (MFC) system based on a discrete approach

In high-powered application scenarios, a multi-stack fuel cell system (MFCS) could have advantages such as higher robustness, lifetime, and reliability than a single-stack fuel cell system. In particular, MFCS could maintain a high efficiency and increase system redundancy by power configuration between subsystems. In order to reduce the operational expenses for systems, a reasonable power management strategy is necessary to minimize the hydrogen consumption of MFCS. First, the power-hydrogen consumption curve of the single-stack fuel cell system is discretized from experimental measurements. Next, the discrete data are reassembled by the inverse derivation of the dynamic programming method to produce a minimum solution for the hydrogen consumption in the output power range. It is found that the strategy varies depending on activated state On or Off. Finally, two power allocation strategies are developed and modeled in a lookup-table block considering the activated state. The optimal stack output power strategy is analyzed with four stacks. The results indicate that the hydrogen consumption is smaller and more efficient than the other allocation strategies. It can respond to the load demand with a high efficiency sooner than the average strategy.

Comparison of Different Topologies of Thermal Management Subsystems in Multi-Stack Fuel Cell Systems

The performance of a fuel cell stack is affected by the operating temperature of the stack. The thermal management subsystem of a multi-stack fuel cell system (MFCS) is particularly significant for the operating temperature control of each stack in the MFCS. To study the influence of different topologies of a MFCS thermal management subsystem, this paper proposes and establishes two different topologies. Firstly, the integrated topology is proposed. Secondly, seven component models, namely the mixer, thermostat, radiator, tank, pump, bypass value, and proton exchange membrane fuel cell stack temperature models, are described in detail. Finally, the performance of the two topologies of the MFCS thermal management subsystem under two working conditions, steady (200 A) and variable (China heavy-duty commercial test cycle, C-WTVC), is compared. Furthermore, there are two evaluating indicators, including the stability duration and deviation of the operating temperatures of the single stack in the MFCS. Results show that when the MFCS operates under steady working conditions, the integrated topology is superior in operating temperature control accuracy (ΔT<0.5 K), while the distributed topology is superior in the adjustment process (t ≤ 100 s). Moreover, when the MFCS operates under variable working conditions, the distributed topology is superior in operating temperature control accuracy.

Online identification of optimal efficiency of multi-stack fuel cells(MFCS)

With the development of fuel cells, the demand for high-power fuel cells has gradually increased, but the multi-stack fuel cell system(MFCS) has received wide attention due to the existing technical barriers. The optimal efficiency of the multi-stack fuel cell system is the target, and the power distribution point of the multi-stack fuel cell is obtained by combining the KKT condition. Equipped with the recursive gradient correction method, the online identification of the optimal efficiency of the system is realized. The results are compared with the average distribution and Daisy-chain methods, and the online identification algorithm results in the highest optimal efficiency and the lowest hydrogen consumption. It was validated on two 1kW air-cooled fuel cell systems.

Performance evaluation of a solid oxide fuel cell multi-stack combined heat and power system with two power distribution strategies

Solid oxide fuel cell (SOFC) combined heat and power (CHP) system is a promising candidate for future energy conversion systems. Large power SOFC-CHP system needs multi-stack fuel cell (MFC) to solve the problems of thermal stress and system stability because MFC systems can achieve better performances for maximum power output and average efficiency than single stack fuel cell systems. This study develops an energy management strategy (EMS) for a SOFC multi-stack CHP system coupled with a methane reformer. SOFC multi-stacks are connected in parallel and verified by the published results. The effect of pressure and temperature on the overall multi-stack CHP system electrical efficiency and heat power is investigated with chain and adaptive EMS. The results show that as temperature and pressure increase, the system power increases. The temperature is more effective for the multi-stack system peak efficiency. Up to 11% of electrical system efficiency can be improved when the temperature rises from 700 °C (973 K) to 900 °C (1173 K). However, higher pressure reduces the peak efficiency by 1.5%. Both temperature and pressure can enlarge the output power range. The proposed model can serve as an effective solution for optimizing the SOFC multi-stack CHP system running on methane.