On-line nonlinear model predictive control of a PEM fuel cell system

Ziogou, Chrysovalantou; Papadopoulou, Simira; Georgiadis, Michael C.

doi:10.1016/j.jprocont.2013.01.011

Cited by 81 publications

(43 citation statements)

References 35 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Overall the online energy management framework used in this work, has a number of interacting entities that form a computer-aided platform for the monitoring and control of the PEMFC unit [9]. The information flow, from the I/O field to the end user, is shown at Fig.…”

Section: Integrated Online Energy Management Frameworkmentioning

confidence: 99%

Optimum Energy Management of PEM Fuel Cell Systems Based on Model Predictive Control

2017

Self Cite

View full text Add to dashboard Cite

This work presents an optimum energy management framework, which is developed for integrated Polymer Electrolyte Membrane (PEM) fuel cell systems. The objective is to address in a centralized manner the control issues that arise during the operation of the fuel cell (FC) system and to monitor and evaluate the system's performance at real time. More specifically the operation objectives are to deliver the demanded power while operating at a safe region, avoiding starvation, and concurrently minimize the fuel consumption at stable temperature conditions. To achieve these objectives a novel Model Predictive Control (MPC) strategy is developed and demonstrated. A semiempirical experimentally validated model is used which is able to capture the dynamic behaviour of the PEMFC. Furthermore, the MPC strategy was integrated in an industrial-grade automation system to demonstrate its applicability in realistic environment. The proposed framework relies on a novel nonlinear MPC (NMPC) formulation that uses a dynamic optimization method that recasts the multivariable control problem into a nonlinear programming problem using a warm-start initialization method and a search space reduction technique which is based on a piecewise affine approximation of the variable's feasible space. The behaviour of the MPC framework is experimentally verified through the online deployment to a small-scale fully automated PEMFC unit. During the experimental scenarios the PEMFC system demonstrated excellent response in terms of computational effort and accuracy with respect to the control objectives.

show abstract

Section: Integrated Online Energy Management Frameworkmentioning

confidence: 99%

Optimum Energy Management of PEM Fuel Cell Systems Based on Model Predictive Control

2017

Self Cite

View full text Add to dashboard Cite

show abstract

“…In [14,15,16], different topologies of fuzzy-logic control (FLC) are proposed such as adaptive PID-based FLC, optimal PID plus fuzzy controller and feed-forward fuzzy PID. Other control strategies, as gain scheduled Linear Parameter-Varying (LPV) control [17], fault tolerant unfalsified control [18], Model Predictive Control (MPC) [19,20] and optimal control [21,22] were also reported to control the air supply PEMFC-based systems. All these control strategies are applied for the regulation of the oxygen excess ratio in the PEMFC with different degrees of success.…”

Section: Introductionmentioning

confidence: 99%

Novel hybrid fuzzy-PID control scheme for air supply in PEM fuel-cell-based systems

Baroud

Benmiloud

Benalia

et al. 2017

International Journal of Hydrogen Energy

127

View full text Add to dashboard Cite

This paper proposes a novel hybrid fuzzy-PID controller for air supply on Proton Exchange Membrane fuel cell (PEMFC) systems. 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 consists of three parts: a fuzzy-logic controller (FLC), a fuzzy-based self-tuned PID (FSTPID) controller and a fuzzy selector. Depending on the value of the error between the current value of oxygen excess ratio and its setpoint value, the fuzzy selector decides which controller should play the greatest effect on the control system. The performance of the proposed control strategy is analysed through simulations for different load variations and for parameter uncertainties. The results show that the novel hybrid fuzzy-PID controller performs significantly better than the classical PID controller and the FLC in terms of several key performance indices such as the Integral Squared Error (ISE), the Integral Absolute Error (IAE) and the Integral Time-weighted Absolute Error (ITAE), as well as the overshoot, settling and rise time for the closed-loop control system.

show abstract

“…Many control strategies for PEMFC have been proposed in the recent literature, e.g. optimal control [3], model predictive control [4] and sliding mode control [5].…”

Section: Introductionmentioning

confidence: 99%

Robust fault diagnosis of proton exchange membrane fuel cells using a Takagi-Sugeno interval observer approach

Rotondo

Fernandez-Canti

Tornil-Sin

et al. 2016

International Journal of Hydrogen Energy

View full text Add to dashboard Cite

In this paper, the problem of robust fault diagnosis of proton exchange mem- ments lie inside their corresponding estimated interval bounds. When a fault is detected, the measurements that are inconsistent with their corresponding estimations are annotated and a fault isolation procedure is triggered. By using the theoretical fault signature matrix (FSM), which summarizes the effects of the different faults on the available residuals, the fault is isolated by means of a logic reasoning that takes into account the bounded uncertainty, and if the number of candidate faults is more than one, a correlation analysis is used to obtain the most likely fault candidate. Finally, the proposed approach is tested using a PEM fuel cell case study proposed in the literature.

show abstract

On-line nonlinear model predictive control of a PEM fuel cell system

Cited by 81 publications

References 35 publications

Optimum Energy Management of PEM Fuel Cell Systems Based on Model Predictive Control

Optimum Energy Management of PEM Fuel Cell Systems Based on Model Predictive Control

Novel hybrid fuzzy-PID control scheme for air supply in PEM fuel-cell-based systems

Robust fault diagnosis of proton exchange membrane fuel cells using a Takagi-Sugeno interval observer approach

Contact Info

Product

Resources

About