Low power proton exchange membrane fuel cell system identification and adaptive control

Yang, Yee‐Pien; Wang, Fu-Cheng; Chang, Hsin-Ping; Ma, Ying-Wei; Weng, Biing-Jyh

doi:10.1016/j.jpowsour.2006.11.038

Cited by 42 publications

(21 citation statements)

References 14 publications

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“…Unfortunately, despite a large number of theoretical contributions e the majority of which have focused on control [1e4], and only a few on optimization [5,6] e there still remains a significant gap between simulation studies and reported experimental results. To the best of the authors' knowledge, all experimental studies so far have been limited to proton exchange membrane (PEM) cells [3,4,7]. In these, many have sought efficiency based on specific criteria that were already known in advance.…”

Section: Introductionmentioning

confidence: 99%

Experimental real-time optimization of a solid oxide fuel cell stack via constraint adaptation

Bunin

Wuillemin

François

et al. 2012

Energy

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a b s t r a c tThe experimental validation of a real-time optimization (RTO) strategy for the optimal operation of a solid oxide fuel cell (SOFC) stack is reported in this paper. Unlike many existing studies, the RTO approach presented here utilizes the constraint-adaptation methodology, which assumes that the optimal operating point lies on a set of active constraints and then seeks to satisfy those constraints in practice via the addition of a correction term to each constraint function. These correction terms, also referred to as "modifiers", correspond to the difference between predicted and measured constraint values and are updated at each steady-state iteration, thereby allowing the RTO to iteratively meet the optimal operating conditions of an SOFC stack despite significant plant-model mismatch. The effects of the filter parameters used in the modifier update and of the RTO frequency on the general performance of the algorithm are also investigated.

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Section: Introductionmentioning

confidence: 99%

Experimental real-time optimization of a solid oxide fuel cell stack via constraint adaptation

Bunin

Wuillemin

François

et al. 2012

Energy

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“…Up to now, a number of fuel cell models have been built for different purposes [4,13], among which the models reported in [8,[11][12][13][14] belong to the control-oriented model. The fuel cell system model in this paper is a theoretical model developed from [8,11], and adopts a twin screw compressor.…”

Section: Fuel Cell System For Vehiclementioning

confidence: 99%

Air flow control based on optimal oxygen excess ratio in fuel cells for vehicles

Chen

et al. 2013

J. Mod. Transport.

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Air flow control is one of the most important control methods for maintaining the stability and reliability of a fuel cell system, which can avoid oxygen starvation or oxygen saturation. The oxygen excess ratio (OER) is often used to indicate the air flow condition. Based on a fuel cell system model for vehicles, OER performance was analyzed for different stack currents and temperatures in this paper, and the results show that the optimal OER was affected weakly by the stack temperature. In order to ensure the system working in optimal OER, a control scheme that includes an optimal OER regulator and a fuzzy control was proposed. According to the stack current, a reference value of air flow rate was obtained with the optimal OER regulator and then the air compressor motor voltage was controlled with the fuzzy controller to adjust the air flow rate provided by the air compressor. Simulation results show that the control method has good dynamic and static characteristics.

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“…Several commercially available battery testers diagnose battery aging by measuring the AC impedance of batteries at a frequency of 1 kHz on the basis of the fact that the real part of the complex impedance of a battery at 1 kHz is almost equal to its ohmic resistance [15], [16]. However, the method may lead to inaccurate internal resistance calculations because ohmic resistance does not always match the real part of the impedance at 1 kHz; this inaccuracy may lead to inaccurate internal resistance calculations, which affect aging estimation.…”

Section: Introductionmentioning

confidence: 99%

Development of an Intelligent Charger with a Battery Diagnosis Function Using Online Impedance Spectroscopy

Nguyen¹,

Doan²,

Lee³

et al. 2016

Journal of Power Electronics

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Battery diagnosis is vital to battery-based applications because it ensures system reliability by avoiding battery failure. This paper presents a novel intelligent battery charger with an online diagnosis function to circumvent interruptions in system operation. The charger operates in normal charging and diagnosing modes. The diagnosis function is performed with the impedance spectroscopy technique, which is achieved by injecting a sinusoidal voltage excitation signal to the battery terminals without the need for additional hardware. The impedance spectrum of the battery is calculated based on voltage excitation and current response with the aid of an embedded digital lock in amplifier in a digital signal processor. The measured impedance data are utilized in the application of the complex nonlinear least squares method to extract the battery parameters of the equivalent circuit. These parameters are then compared with the reference values to reach a diagnosis. A prototype of the proposed charger is applied to four valve-regulated lead-acid batteries to measure AC impedance. The results are discussed.

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Low power proton exchange membrane fuel cell system identification and adaptive control

Cited by 42 publications

References 14 publications

Experimental real-time optimization of a solid oxide fuel cell stack via constraint adaptation

Experimental real-time optimization of a solid oxide fuel cell stack via constraint adaptation

Air flow control based on optimal oxygen excess ratio in fuel cells for vehicles

Development of an Intelligent Charger with a Battery Diagnosis Function Using Online Impedance Spectroscopy

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