Control of miniature proton exchange membrane fuel cells based on fuzzy logic

Schumacher, Jürgen; Gemmar, Peter; Denne, M.; Zedda, Mario; Stueber, M.

doi:10.1016/j.jpowsour.2003.10.010

Cited by 78 publications

(43 citation statements)

References 20 publications

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“…According to the experimental data, Almeda [7] proposed an artificial neural network control method to control fuel cell output voltage and optimize the system parameters. Schumacher [8] proposed a method for PEMFC water management using fuzzy control. Pukrushpan [9,10] used feed-forward and feedback strategies to control the flow rate of the compressor in the air supply system of a PEMFC.…”

Section: Open Accessmentioning

confidence: 99%

Linearization and Input-Output Decoupling for Nonlinear Control of Proton Exchange Membrane Fuel Cells

Chang

Chen

2014

Energies

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This paper presents a nonlinear control strategy utilizing the linearization and input-output decoupling approach for a nonlinear dynamic model of proton exchange membrane fuel cells (PEMFCs). The multiple-input single-output (MISO) nonlinear model of the PEMFC is derived first. The dynamic model is then transformed into a multiple-input multiple-output (MIMO) square system by adding additional states and outputs so that the linearization and input-output decoupling approach can be directly applied. A PI tracking control is also introduced to the state feedback control law in order to reduce the steady-state errors due to parameter uncertainty. This paper also proposes an adaptive genetic algorithm (AGA) for the multi-objective optimization design of the tracking controller. The comprehensive results of simulation demonstrate that the PEMFC with nonlinear control has better transient and steady-state performance compared to conventional linear techniques.

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Section: Open Accessmentioning

confidence: 99%

Linearization and Input-Output Decoupling for Nonlinear Control of Proton Exchange Membrane Fuel Cells

Chang

Chen

2014

Energies

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“…Sie benötigen ein gezieltes Wassermanagement, da ein zu geringer Wassergehalt in der Brennstoffzelle die ohmschen Verluste im Polymerelektrolyten erhöht und ein zu großer Wassergehalt den Antransport von Edukten blockiert [43,44].…”

Section: Brennstoffzellenunclassified

Review on Portable Energy Systems: From Electrochemical Systems to Energy Harvesting

Krewer

2011

Chemie Ingenieur Technik

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“…In [6] an online self tuning PID controller for PEM fuel cell systems is described. However, from the literature it can be seen that the fuzzy controller design is widely used for performance enhancement in fuel cell systems; see for example, [7], [5], [8], [9], [10]. In [11] for a SOFC, a control strategy using fuzzy logic controller to effect voltage control of the fuel cell at the output of the DC/DC converter is presented.…”

Section: Controller Designmentioning

confidence: 99%

Model based controller design for hydrogen fuel cell systems

Thanapalan¹,

Premier²,

Guwy³

2011

REPQJ

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In this paper, fuel cell system modelling alongside controller development for performance improvements, are investigated. Essentially and in general terms there are two approaches to modelling; black box modelling and detailed dynamic modelling. This work addresses both modelling approaches. These models are then used for hydrogen fuel cell (FC) controller development for improved system performance. A fuzzy-PID hybrid controller has been designed and tested and the results indicate that the fuzzy-PID hybrid control strategy can improve the system's performance significantly.

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Control of miniature proton exchange membrane fuel cells based on fuzzy logic

Cited by 78 publications

References 20 publications

Linearization and Input-Output Decoupling for Nonlinear Control of Proton Exchange Membrane Fuel Cells

Linearization and Input-Output Decoupling for Nonlinear Control of Proton Exchange Membrane Fuel Cells

Review on Portable Energy Systems: From Electrochemical Systems to Energy Harvesting

Model based controller design for hydrogen fuel cell systems

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