A control system for reducing the hydrogen consumption of PEM fuel cells under parametric uncertainties

Este artículo presenta un sistema de control para reducir el consumo de hidrogeno para una celda de combustible de Membrana de Intercambio Protónico, considerando incertidumbres paramétricas. El sistema de control incluye un modelo no lineal en el espacio de estado para la celda de combustible, un filtro de Kalman/estimador, un regulador óptimo cuadrático y algoritmo de seguimiento de puntos de máxima potencia (MPP). El objetivo de control es suministrar la potencia de carga demandada, evitando el agotamiento del oxígeno y minimizando el consumo de hidrógeno por medio de un algoritmo de Perturbación y Observación (P&O). El desempeño del sistema de control es evaluado ante incertidumbres paramétricas al simular escenarios de perdida de desempeño como producto del envejecimiento del compresor. De esta forma, dos escenarios fueron simulados: un primer escenario simula un error entre la ganancia (de lazo abierto) del compresor de la celda de combustible y la del modelo; y un segundo escenario, con un error entre la corriente de pérdidas y del compresor de la celda de combustible con respecto al modelo. Los resultados de simulación muestran que el filtro Kalman/estimador logra contrarrestar las incertidumbres producidas por los cambios paramétricos del sistema. Igualmente, el algoritmo MPP logra suministrar el voltaje del compresor adecuado sin necesidad de un perfil óptimo en condiciones ideales.This paper presents a control system for reducing the hydrogen consumption for a Polymer Electrolyte Membrane fuel cell, also considering parametric uncertainties. The control system is based on a non-linear state space model of the fuel cell, a Kalman filter/estimator, a linear state feedback controller and a Maximum Power Point (MPP) tracking algorithm. The control objective is to supply the requested load power, avoiding oxygen starvation with minimum fuel consumption using a Perturb and Observe (P&O) algorithm. The performance of the control system was assessed under parametric uncertainties by simulating a performance degradation of the compressor due to aging. Thus, two cases were simulated: first, a mismatch between the system and the linear model in the (open-loop) air compressor gain; and second, a mismatch between the system and the linear model in the current compressor and losses. The simulation results showed that the Kalman filter/estimator overcome the uncertainties produced by the parametrical variations. Besides, the P&O algorithm accomplished to provide the suitable compressor voltage without identifying an optimal profile under ideal operating conditions and empirical data

PROGNOSTIC AND HEALTH-MANAGEMENT ORIENTED FUEL CELL MODELING AND ON-LINE SUPERVISORY SYSTEM DEVELOPMENT

Of the fuel cells being studied, the proton exchange membrane fuel cell (PEMFC) is viewed as the most promising for transportation. Yet until today, the commercialization of the PEMFC has not been widespread in spite of its large expectation. Poor long term performances or durability, and high production and maintenance costs are the main reasons. For the final commercialization of fuel cells in the transportation field, durability issues must be addressed, while costs should be further brought down. At the same time, health-monitoring and prognosis techniques are of great significance in terms of scheduling condition-based maintenance (CBM) to minimize repair and maintenance costs, the associated operational disruptions, and also the risk of unscheduled downtime for the fuel cell systems. This dissertation presents a comprehensive on-line supervisory system to address the important issues related to the PEMFC durability, including: 1) diagnosis of critical operating conditions, 2) optimization of the operating conditions, and 3) health monitoring (or damage tracking) and remaining useful life (RUL) prediction. In order to design and implement this supervisory system, a comprehensive fuel cell model is developed that integrates a control/diagnostic oriented dynamic fuel cell model and a prognostic oriented fuel cell degradation model, due to a lack of such models in the existing literature. To address the first issue, a model-based on-line diagnostics system is developed for fuel cell flooding and drying diagnosis, thanks to the incorporation of the diagnostic feature in the dynamic fuel cell model. The channel flooding diagnostic problem is decoupled from the gas diffusion layer (GDL) flooding and membrane drying diagnostic problem. Simultaneous state and parameter estimation problems are formulated for both cases. Dual extended Kalman filter (EKF) and dual unscented Kalman filter (UKF) techniques are applied respectively to solve the problems. The second issue is addressed by a diagnostic based control design for the air supply of the fuel cell system. The design concept allows selection of the most suitable controller in a controller bank that delivers the best performance under specific operating conditions and that mitigates the faulty condition based on the feedback of the diagnosis results. The control problem is reformulated as an H-infinity robust control problem, the objective of which is to minimize the difference between the desired and actual excess O2 ratio, thus preventing and minimizing oxidant starvation at the cathode. Finally, an UKF-based health-monitoring and prognostic scheme is proposed and applied to the damage tracking and RUL prediction for the fuel cell. The developed aging model is employed as the kernel for this scheme, which utilizes the fuel cell output voltage as the only feature for the prognostic and health management task

Performance improvement in polymer electrolytic membrane fuel cell based on nonlinear control strategies—A comprehensive study

A Polymer Electrolytic Membrane Fuel Cell (PEMFC) is an efficient power device for automobiles, but its efficiency and life span depend upon its air delivery system. To ensure improved performance of PEMFC, the air delivery system must ensure proper regulation of Oxygen Excess Ratio (OER). This paper proposes two nonlinear control strategies, namely Integral Sliding Mode Control (ISMC) and Fast Terminal ISMC (FTISMC). Both the controllers are designed to control the OER at a constant level under load disturbances while avoiding oxygen starvation. The derived controllers are implemented in MATLAB/ Simulink. The corresponding simulation results depict that FTISMC has faster tracking performance and lesser fluctuations due to load disturbances in output net power, stack voltage/power, error tracking, OER, and compressor motor voltage. Lesser fluctuations in these parameters ensure increased efficiency and thus extended life of a PEMFC. The results are also compared with super twisting algorithm STA to show the effectiveness of the proposed techniques. ISMC and FTISMC yield 7% and 20% improved performance as compared to STA. The proposed research finds potential applications in hydrogen-powered fuel cell electric vehicles

Machine Learning Approach for Modeling and Control of a Commercial Heliocentris FC50 PEM Fuel Cell System

In recent years, machine learning (ML) has received growing attention and it has been used in a wide range of applications. However, the ML application in renewable energies systems such as fuel cells is still limited. In this paper, a prognostic framework based on artificial neural network (ANN) is designed to predict the performance of proton exchange membrane (PEM) fuel cell system, aiming to investigate the effect of temperature and humidity on the stack characteristics and on tracking control improvements. A large part of the experimental database for various operating conditions has been used in the training operation to achieve an accurate model. Extensive tests with various ANN parameters such as number of neurons, number of hidden layers, selection of training dataset, etc., are performed to obtain the best fit in terms of prediction accuracy. The effect of temperature and humidity based on the predicted model are investigated and compared to the ones obtained from real-time experiments. The control design based on the predicted model is performed to keep the stack operating point at an adequate power stage with high-performance tracking. Experimental results have demonstrated the effectiveness of the proposed model for performance improvements of PEM fuel cell system.This research was funded by the Basque Government, Diputación Foral de Álava and UPV/EHU, respectively, through the projects EKOHEGAZ (ELKARTEK KK-2021/00092), CONAVANTER and GIU20/063

Hierarchical Model Predictive Control for the Dynamical Power Split of a Fuel Cell Hybrid Vehicle

In order to reduce emissions of the transport sector, fuel cell hybrid vehicles (FCHVs) constitute a promising alternative as they have zero local emissions and overcome the limited range of electric vehicles. The power management of the propulsion system poses many challenges since it is a highly nonlinear, constrained, strongly coupled, multiple-input multiple-output (MIMO) system. The control objectives aim at dynamic power delivery, minimization of hydrogen consumption and charge sustainability of the battery. This thesis presents a hierarchical model predictive control (MPC) with three levels approaching the control problem on different time scales. The high-level control (HLC) implemented as a nonlinear MPC optimizes the static power split between battery and fuel cell system. The intermediate-level control (ILC) uses static optimization to determine the optimal operating point of the air supply. The lowlevel control (LLC) is a nonlinear MPC and tracks the reference trajectories received from the higher levels. The hierarchical MPC is evaluated on a detailed model of an FCHV using the worldwide harmonized light vehicles test cycle. Utilizing predictive information about the power demand, the HLC provides a power split that assures charge sustainability of the battery and only deviates by 0.2% from the optimal solution in terms of hydrogen consumption. Due to the predictive behavior and inherent decoupling capability of an MPC, the LLC achieves dynamic power delivery while explicitly considering the system constraints caused by prevention of oxygen starvation and limited operating range of the compressor. Moreover, the actual hydrogen consumption deviates only by 1% from the hydrogen consumption that is predicted by the HLC. Even for uncertain power demand prediction, the LLC attains dynamic power delivery by deviating from the reference trajectories to relieve the fuel cell system when operating under system constraints.In order to reduce emissions of the transport sector, fuel cell hybrid vehicles (FCHVs) constitute a promising alternative as they have zero local emissions and overcome the limited range of electric vehicles. The power management of the propulsion system poses many challenges since it is a highly nonlinear, constrained, strongly coupled, multiple-input multiple-output (MIMO) system. The control objectives aim at dynamic power delivery, minimization of hydrogen consumption and charge sustainability of the battery. This thesis presents a hierarchical model predictive control (MPC) with three levels approaching the control problem on different time scales. The high-level control (HLC) implemented as a nonlinear MPC optimizes the static power split between battery and fuel cell system. The intermediate-level control (ILC) uses static optimization to determine the optimal operating point of the air supply. The lowlevel control (LLC) is a nonlinear MPC and tracks the reference trajectories received from the higher levels. The hierarchical MPC is evaluated on a detailed model of an FCHV using the worldwide harmonized light vehicles test cycle. Utilizing predictive information about the power demand, the HLC provides a power split that assures charge sustainability of the battery and only deviates by 0.2% from the optimal solution in terms of hydrogen consumption. Due to the predictive behavior and inherent decoupling capability of an MPC, the LLC achieves dynamic power delivery while explicitly considering the system constraints caused by prevention of oxygen starvation and limited operating range of the compressor. Moreover, the actual hydrogen consumption deviates only by 1% from the hydrogen consumption that is predicted by the HLC. Even for uncertain power demand prediction, the LLC attains dynamic power delivery by deviating from the reference trajectories to relieve the fuel cell system when operating under system constraints

Design and Experimental Validation of the Temperature Control of a PEMFC Stack by Applying Multiobjective Optimization

[EN] The current environmental challenges require the implementation of environmentally friendly energy production systems. In this context, proton exchange membrane fuel cell stacks (PEMFC) represent, due to their high electrical efficiency and their low level of CO2 emissions, a promising alternative technology. However, there are still many technical aspects that need to be improved before they become a commercial reality. One of them is the temperature control of the stack, since its electrical efficiency and its lifetime depend on the performance of this control. In this work, we design a multiloop PID control of the temperature of a PEMFC stack and validate it experimentally. The stack is the prime mover of a micro combined heat and power system (micro-CHP). For this task, we use a previously developed nonlinear model and apply a multiobjective optimization methodology. To assess its performance, the PID control is compared to a second PID control designed with a linearized model. The results show, on the one hand, the importance of having a nonlinear model valid in a wide operation range for the correct design of the temperature control of a PEMFC stack and, on the other hand, the advantages of applying a multiobjective optimization methodology to this problem.This work was supported in part by the Spanish Ministry of Science, Innovation, and Universities under Grant RTI2018-096904-B-I00, and in part by the Generalitat Valenciana Regional Government under Project AICO/2019/055.Navarro-Giménez, S.; Herrero Durá, JM.; Blasco, X.; Simarro Fernández, R. (2020). Design and Experimental Validation of the Temperature Control of a PEMFC Stack by Applying Multiobjective Optimization. IEEE Access. 8:183324-183343. https://doi.org/10.1109/ACCESS.2020.3029321S183324183343

Montecarlo based quantitative Kramers-Kronig test for PEMFC impedance spectrum validation

Electrochemical Impedance Spectroscopy (EIS) is a very powerful tool to study the behaviour of electrochemical systems. At present, it is widely used in the fuel cell field in order to study challenging cutting edge issues as membrane drying or gas diffusion layer flooding amongst others. The proper analysis of impedance data requires the fulfilment of four fundamental conditions: causality, linearity, stability and finiteness. The non compliance with any of these conditions may lead to biased, or even misguided, conclusions. Therefore it is critical to verify the compliance of these conditions before accepting any analysis performed on an experimental spectrum. This is even more important in a fuel cell experimental spectrum analysis, since fuel cells are markedly non stationary systems. The aim of this work is to establish an impedance spectrum quantitative validation technique to validate the whole experimental spectrum and to identify the individual points within a spectrum that do not comply any of the four conditions, in order to remove these inconsistent points from the analysis. The designed validation method consists in a Kramers Kronig (KK) validation test, by equivalent electrical circuit fitting, coupled with a Montecarlo error propagation method. In a first step, the experimental spectrum is fitted to a particular electrical equivalent circuit, which satisfies the KK relations. Then, in a second step, a statistical Montecarlo method is used in order to propagate the model fitting parameter uncertainty through the model. Using this approach, a consistency region is built for a given confidence level: the experimental points inside this region are considered consistent for the given confidence level, whereas the outside points are rejected. The method was used on PEMFC experimental impedance spectra; and it successfully managed to identify inconsistent points, associated to no stationarities.The authors are very grateful to the Generalitat Valenciana for its economic support in form of Vali+d grant (Ref: ACIF-2013-268).Giner Sanz, JJ.; Ortega Navarro, EM.; Pérez-Herranz, V. (2015). Montecarlo based quantitative Kramers-Kronig test for PEMFC impedance spectrum validation. International Journal of Hydrogen Energy. 40(34):11279-11293. https://doi.org/10.1016/j.ijhydene.2015.03.135S1127911293403

Real-Time Power Management of A Fuel Cell/Ultracapacitor Hybrid

This thesis presents the system architecture design, system integration methodology, and real-time control of a fuel cell/ultracapacitor hybrid power system. The main objective is for the hybrid system to respond to real-world fluctuations in power without negatively impacting fuel cell life. A Proton Exchange Membrane (PEM) Fuel Cell is an electrochemical device which converts the chemical energy of pure hydrogen into electricity through a chemical reaction with oxygen. The high conversion efficiency, zero harmful emissions, high power-to-weight ratio, scalability, and low temperature operation make PEM fuel cells very attractive for stationary and portable power applications. However, fuel cells are limited in responding to fast transients in power demand, moreover power fluctuations have negative impact on fuel cell durability. This motivates the use of a supplementary energy storage device to assist the fuel cell by buffering sharp transients in power demand. The high power density, long cycle life, and efficiency of ultracapacitors make them an ideal solution for such auxiliary energy storage in a hybrid fuel cell system. The power management strategy that determines the power split between the fuel cell and ultracapacitor is key to the power following capability, long-term performance, and life-time of the fuel cell. In this thesis, a rule-based and a model predictive control strategy are designed, implemented and evaluated for power management of a fuel cell/ultracapacitor hybrid. The high-level control objectives are to respond to rapid variations in load while minimizing damaging fluctuations in fuel cell current and maintaining ultracapacitor charge (or voltage) within allowable bounds. An experimental test stand was created to evaluate the performance of the controllers. The test stand connects the fuel cell and ultracapacitor to an electronic load through two dc/dc converters, which provide two degrees of freedom, enabling independent low-level control of the DC BUS voltage and the current split between the fuel cell and ultracapacitor. Experiments show that both rule-based and model predictive power management strategies can be tuned to meet both high and low-level control objectives for a given power demand profile. However, the capability to explicitly enforce the constraints in model predictive scheme and its predictive nature in meeting power demands enables a more systematic design and results in general in smoother performance

Constraint-Aware and Efficiency-Aware Control of Air-Path in Fuel Cell Vehicles

Fuel cell technology offers the potential for clean, efficient, robust energy productionfor both stationary and mobile applications. But without fast and robust control systems, fuel cells cannot hope to maintain real-life efficiencies near enough to their theoretical potential. This work studies control and constraint management techniques to regulate a nonlinear multivariable air-path system for a proton exchange membrane fuel cell (PEMFC). The control objectives are to avoid oxygen starvation, run at the maximum net efficiency, achieve fast tracking of air flow and pressure set-points, and be easy to calibrate. To operate at maximum efficiency, a set-point map is generated for air pressure at the cathode inlet. Considering that the conventional PEMFC system cannot independently control the inlet pressure using only the compressor motor, a new multivariable analysis and control scheme is formulated by considering an electronic throttle body (ETB) valve downstream of the cathode as a new degree of freedom in the control problem. Based on this new configuration of the fuel cell model, an internal model control (IMC) controller is designed with intuitive tuning parameters to simultaneously control airflow and pressure, and achieves a fast and smooth response despite strongly coupled plant dynamics. Further, a reference governor (RG) using a computationally tractable linear prediction model is included with IMC-based Multi-Input Multi-Output (MIMO) controller to satisfy the constraint on oxygen level. Compared with a Single-Input Single-Output (SISO) air-flow control approach, the proposed MIMO control approach demonstrated up to 7.36 percent lower hydrogen fuel consumption