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

Nonlinear predictive control for durability enhancement and efficiency improvement in a fuel cell power system

© . This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/In this work, a nonlinear model predictive control (NMPC) strategy is proposed to improve the efficiency and enhance the durability of a proton exchange membrane fuel cell (PEMFC) power system. The PEMFC controller is based on a distributed parameters model that describes the nonlinear dynamics of the system, considering spatial variations along the gas channels. Parasitic power from different system auxiliaries is considered, including the main parasitic losses which are those of the compressor. A nonlinear observer is implemented, based on the discretised model of the PEMFC, to estimate the internal states. This information is included in the cost function of the controller to enhance the durability of the system by means of avoiding local starvation and inappropriate water vapour concentrations. Simulation results are presented to show the performance of the proposed controller over a given case study in an automotive application (New European Driving Cycle). With the aim of representing the most relevant phenomena that affects the PEMFC voltage, the simulation model includes a two-phase water model and the effects of liquid water on the catalyst active area. The control model is a simplified version that does not consider two-phase water dynamics.Peer ReviewedPostprint (author's final draft

Nonlinear model predictive control methodology for efficiency and durability improvement in a fuel cell power system

The main contribution of this work is the improvement of the efficiency of a PEMFC power system while guaranteeing conditions that also improve its durability. Adopting the NMPC scheme with the distributed parameter model and the nonlinear observer, the efficiency of the PEMFC-based system can be maximized guaranteeing at the same time the appropriate internal gas concentration profiles to avoid global and local hydrogen and oxygen starvation and proper membrane humidification.Peer ReviewedPostprint (author's final draft

Volterra Model Based Predictive Control, application to a Pem Fue Cell

14th Nordic Process Control Workshop - Espoo, Finland Duration: 23 Aug 2007 → 25 Aug 2007This paper presents a non linear model predictive controller for a PEM fuel cell for which the starvation control is the main objective. A second order Volterra model for control is obtained using input/output data for which the power supplied by the fuel cell is considered as a measurable disturbance. The controller developed allows to solve the nonlinear objective function in a way that it can be actually implemented in fast systems like Fuel cells. The use of a nonlinear controller is justified while comparing the outcome obtained with a linear controller of the same class

Identification of PEM fuel cells based on support vector regression and orthonormal bases

© 20xx IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.Polymer Electrolyte Membrane Fuel Cells (PEMFC) are efficient devices that convert the chemical energy of the reactants in electricity. In this type of fuel cells, the performance of the air supply system is fundamental to improve their efficiency. An accurate mathematical model representing the air filling dynamics for a wide range of operating points is then necessary for control design and analysis. In this paper, a new Wiener model identification method based on Support Vector (SV) Regression and orthonormal bases is introduced and used to estimate a nonlinear dynamical model for the air supply system of a laboratory PEMFC from experimental data. The method is experimentally validated using a PEMFC system based on a ZB 8-cell stack with Nafion 115 membrane electrode assembliesPeer ReviewedPostprint (author's final draft

Air flow regulation in fuel cells: an efficient design of hybrid fuzzy-PID control

This paper presents a hybrid fuzzy-PID controller for air flow supply on a Proton Exchange Membrane fuel cell (PEMFC) system. The control objective is to adjust the oxygen excess ratio at a given setpoint in order to prevent oxygen starvation and damage of the fuel-cell stack. The proposed control scheme combines a fuzzy logic controller (FLC) and classical PID controller with a view to benefit the advantages of both controllers. The results show that the proposed technique performs significantly better than the classical PID controller and the FLC in terms of several key performances indices such as the Integral Squared Error (ISE), the Integral Absolute Error (IAE) and the Integral Time-weighted Absolute Error (ITAE) for the closed-loop control system.Peer ReviewedPostprint (author's final draft

Model predictive control using machine learning for voltage control of a PEM fuel cell stack

Abstract: In this paper, a Nonlinear Model Predictive Control (NMPC) is designed using a data-based model of Proton Exchange Membrane Fuel cell (PEMFC) for output voltage control. To capture PEMFC complex dynamics and non-linearities, Machine Learning (ML) algorithms are utilized to model the behavior of the system. This model is then embedded inside the NMPC controller to provide the predictions required for solving the optimization problem. The NMPC not only provides precise output voltage tracking, but also can simultaneously reduce the fuel consumption of the stack as one additional term in the cost function. Moreover, the possible upper and lower bounds of the control effort generated by actuators are set as the hard constraints of NMPC. The simulation results show that while these constraints are not violated, the desired output voltage is generated with less fuel being consumed comparing to the case that fuel consumption is not controlled.Communication présentée lors du congrès international tenu conjointement par Canadian Society for Mechanical Engineering (CSME) et Computational Fluid Dynamics Society of Canada (CFD Canada), à l’Université de Sherbrooke (Québec), du 28 au 31 mai 2023

Modeling and control of PEM fuel cells

Aplicat embargament des del moment de la defensa fins al 5 de juliol de 2019.In recent years, the PEM fuel cell technology has been incorporated to the R&D plans of many key companies in the automotive, stationary power and portable electronics sectors. However, despite current developments, the technology is not mature enough to be significantly introduced into the energy market. Performance, durability and cost are the key challenges. The performance and durability of PEM fue! cells significantly depend on variations in the concentrations of hydrogen and oxygen in the gas channels, water activity in the catalyst layers and other backing layers, water content in the polymer electrolyte membrane, as well as temperature, among other variables. Such variables exhibit intemal spatial dependence in the direction of the fuel and air streams of the anode and cathode. Highly non-uniform spatial distributions in PEM fuel cells result in local over-heating, cell flooding, accelerated ageing, and lower power output than expected. Despite the importance of spatial variations of certain variables in PEM fuel cells, not many works available in the literature target the control of spatial profiles. Most control-oriented designs use lumped-parameter models because of their simplicity and convenience for controller performance. In contrast, this Doctoral Thesis targets the distributed parameter modelling and control of PEM fuel cells. In the modelling part, the research addresses the detailed development of a non-linear distributed parameter model of a single PEM fuel cell, which incorporates the effects of spatial variations of variables that are relevant to its proper performance. The model is first used to analyse important cell intemal spatial profiles, and it is later simplified in arder to decrease its computational complexity and make it suitable for control purposes. In this task, two different model order reduction techniques are applied and compared. The purpose of the control part is to tackle water management and supply of reactants, which are two major PEM fuel cell operation challenges with important degradation consequences. In this part of the Thesis, two decentralised control strategies based on distributed parameter model predictive controllers are designed, implemented and analysed via simulation environment State observers are also designed to estímate intemal unmeasurable spatial profiles necessary for the control action. The aim of the first strategy is to monitor and control observed water activity spatial profiles on both sides of the membrana to appropriate levels. These target values are carefully chosen to combine proper membrane, catalyst layer and gas diffusion layer humídification, whilst the rate of accumulation of excess liquid water is reduced. The key objective of this approach is to decrease the frequency of water removal actions that cause disruption in the power supplied by the cell, increased parasitic losses or degradation of cell efficiency. The second strategy is a variation of the previous water activity control strategy, which includes the control of spatial distribution of gases in the fuel and air channels. This integrated solution aims to avoid starvation of reactants by controlling corresponding concentration spatial profiles. This approach is intended to prevent PEM fuel cell degradation due to corrosion mechanisms, and thennal stress caused by the consequences of reactant starvation.A pesar de los avances actuales, la tecnología de celdas de hidrógeno tipo PEM no está suficientemente preparada para ser ampliamente introducida en el mercado energético. Rendimiento, durabilidad y costo son los mayores retos. El rendimiento y la durabilidad de las celdas dependen significativamente de las variaciones en las concentraciones de hidrógeno y oxígeno en los canales de alimentación de gases, la humedad relativa en las capas catalizadoras, el contenido de agua de la membrana polimérica, así como la temperatura, entre otras variables. Dichas variables presentan dependencia espacial interna en la dirección del flujo de gases del ánodo y del cátodo. Distribuciones espaciales altamente no uniformes en algunas variables de la celda resultan en sobrecalentamiento local, inundación, degradación acelerada y menor potencia de la requerida. Muy pocos trabajos disponibles en la literatura se ocupan del control de perfiles espaciales. La mayoría de los diseños orientados a control usan modelos de parámetros concentrados que ignoran la dependencia espacial de variables internas de la celda, debido a la complejidad que añaden al funcionamiento de controladores. En contraste, esta Tesis Doctoral trata la modelización y control de parámetros distribuidos en las celdas de hidrógeno tipo PEM. En la parte de modelización, esta tesis presenta el desarrollo detallado de un modelo no lineal de parámetros distribuidos para una sola celda, el cual incorpora las variaciones espaciales de todas las variables que son relevantes para su correcto funcionamiento. El modelo se usa primero para analizar importantes perfiles espaciales internos, y luego se simplifica para reducir su complejidad computacional y adecuarlo a propósitos de control. En esta tarea se usan y se comparan dos técnicas de reducción de orden de modelos. El propósito de la parte de control es abordar la gestión de agua y el suministro de reactantes, que son dos grandes retos en el funcionamiento de las celdas con importantes consecuencias para su vida útil. En esta parte de la tesis, dos estrategias de control descentralizadas, basadas en controladores predictivos de modelos de referencia con parámetros distribuidos, son diseñadas, implementadas y analizadas en un entorno de simulación. Estas tareas incluyen también el diseño de observadores de estado que estiman los perfiles espaciales internos necesarios para la acción de control. El objetivo de la primera estrategia es monitorear y controlar perfiles espaciales observados de la humedad relativa en las capas catalizadoras para mantenerlos en niveles apropiados. Estos niveles son escogidos cuidadosamente para combinar la correcta humidificación de la membrana y las capas catalizadoras, reduciendo la velocidad de acumulación de agua líquida. El objetivo clave de este enfoque es disminuir la frecuencia de las acciones de remoción de agua dentro de la celda, ya que estas acciones causan interrupción en la potencia suministrada, aumento de las cargas parasitarias y disminución de la eficiencia. La segunda estrategia es una variación de la estrategia anterior que considera adicionalmente el control de la distribución espacial de los gases en los canales del ánodo y cátodo. Esta solución integrada tiene como objetivo evitar la ausencia local de reactantes mediante el control de perfiles espaciales de concentración de gases. Este enfoque pretende prevenir la degradación de las celdas debido a mecanismos de corrosión. Los resultados muestran un mayor rendimiento de la celda considerando los enfoques de control de perfiles espaciales propuestos en esta tesis, en comparación con técnicas de control que ignoran dichos perfiles. Además, la característica descentralizada de los esquemas de control, combinada con el uso de modelos reducidos dentro de los controladores predictivos, tiene un impacto positivo importante en el rendimiento general del control.Postprint (published version

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

Water Management in PEM Fuel Cells: Controllability Analysis and Steady-state Optimization for Temperature Control

This paper presents a controllability study of the water management inside anode channel by regulating the stack temperature for PEM fuel cell systems with dead-ended anode. Moreover, this work includes the design of a steady-state target optimizer which calculates the temperature set-point profiles that minimize the stack degradation and the hydrogen leaks. The control architecture is successfully simulated and the results show promising performanc