Online stack monitoring tool for dynamically and stationary operated fuel cell systems

Ramschak, Erich; Peinecke, Volker; Prenninger, Peter; Schäffer, Thomas; Baumgartner, Wolfgang Richard; Hacker, Viktor

doi:10.1016/s1464-2859(06)71207-x

Cited by 24 publications

(7 citation statements)

References 3 publications

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“…Even when only single cells show failures THD analysis can reveal problems on the stack level. [95] In a nutshell, it was shown that NL methods can provide information that is hardly, if at all, directly accessible with other electrochemical methods like EIS. [89,91] Besides the direct access to detailed system, material, and reaction information, the results of nonlinear studies can help to understand other electrochemical measurements more in detail or to develop models describing the respective outcomes.…”

Section: Nonlinear Electrochemical Methodsmentioning

confidence: 99%

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Nonlinear Electrochemical Analysis: Worth the Effort to Reveal New Insights into Energy Materials

Schlüter

Novák

Schröder

2022

Advanced Energy Materials

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and the effectiveness and durability of the materials applied, researchers always call for reliable and detailed knowledge of the mechanism(s) of both, main and side reactions, and especially for their kinetics. In order to gain the relevant information, a large number of methods have been proposed to characterize battery materials, battery electrodes and their interfaces, and battery cells. But when samples are taken out of the cells and analyzed later, there is a high risk of both, contamination and changes due to possible relaxation processes, and the challenge is to draw sound conclusions in respect to a "living and breathing" battery cell, i.e., to a cell under current flow (cf. Figure 1). Therefore, the so-called in situ and operando (and often online) methods are preferred. Sometimes they are the only option to collect relevant data to understand and improve materials for energy systems. [5] In many cases, results from different techniques need to be combined to obtain sound conclusions. [6,7] The terminology for the aforementioned methods shifted during the last decade and is not always consistent throughout the literature. Nowadays, the general understanding is as follows: An operando experiment means a characterization experiment in an electrochemical cell (e.g., battery, fuel cell, electrolyzer) under current flow, with either the potential or the current controlled. One of the many examples is the operando neutron powder diffraction, a technique analyzing the changes in the structure of battery materials in "real" battery cells under current flow with the help of neutron diffraction. [9] Another example is the operando X-ray photoelectron spectroscopy experiment (normally limited to vacuum-compatible cells with nonvolatile electrolytes) providing information about the changes of the chemical composition of the interfacial species with the potential. [10,11] An in situ experiment is also an experiment in a battery cell, however, the current flow is interrupted when the data are collected. An example here is the in situ X-ray absorption spectroscopy (XAS) experiment in which the cell has to be disconnected for technical reasons during the operation of the detector [7] or the tomography of energy materials by means of neutron or X-ray source. [12,13] Online techniques are understood here as either continuously or periodically analyzed samples that are taken out of the electrochemical cell. A typical example is the online (differential) electrochemical mass spectrometry (often shortened as online electrochemical mass spectrometry (OEMS)/differential electrochemical mass spectrometry (DEMS)) analyzing the volatile products of electrochemical reactions with the help of a mass spectrometer by

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Section: Nonlinear Electrochemical Methodsmentioning

confidence: 99%

Section: Nonlinear Electrochemical Methodsmentioning

confidence: 99%

Nonlinear Electrochemical Analysis: Worth the Effort to Reveal New Insights into Energy Materials

Schlüter

Novák

Schröder

2022

Advanced Energy Materials

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“…One of the most important parts of the control system is the Cell Voltage Monitoring (CVM). As cell voltage is a very important indicator of the performance as well as of the state of health of cells [36,37], this value must be carefully handled and considered. The CVM chosen by Nuvera Fuel Cells acquires every second the voltage value of each cell in the stacks; therefore, it saves and communicates to the control system -for each FC stack -the following data:…”

Section: System Layout and Controlmentioning

confidence: 99%

Response Surface Methodology for 30 kW PEMFC stack characterization

et al. 2022

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Hydrogen is a promising energy carrier to allow the reach of the zero-emission targets established for the next years. Polymeric Electrolyte Membrane FC are studied inside the HI-SEA laboratory of the University of Genoa, to assess the opportunities of this technology on marine applications. Here, 8 PEMFC stacks, sized 30 kW each for a total power installation of 240 kW, have been tested to draw guidelines for the best system design onboard ships and to deepen the know-how on the experimental management of the technology. During the tests, it was possible to observe the reciprocal influence of some parameters, which may influence the system efficiency. In this work, a statistical investigation is developed to quantify the cell voltage variation correlated to the values of temperature and current. This has been possible thanks to Design Expert (DE), a software developed by Stat-EASE, Inc. Through the Design of Experiment approach, it is possible to evaluate the significance of variables in the FC system, called factors. The experiment under consideration is also characterized by non-controllable factors, cause of disturbances that induce further variability in the response. Eventually, it was possible to analyse the significance of the parameters involved, to build a regression model by performing the analysis of variance with which the significant values are identified, and to assess the presence of outliers.

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“…In 1985, NFRA was used for the first time to determine the SoC of lead-acid batteries [27]. In the field of fuel cells, at the stack and cell level, NFRA was successfully applied for system monitoring and analysis [28][29][30], but it was also used in more general kinetic studies [31,32]. Over the last decade, data mining approaches have become popular for diagnosing and predicting the cell state of LIBs in BMSs.…”

Section: Introductionmentioning

confidence: 99%

State-of-Health Identification of Lithium-Ion Batteries Based on Nonlinear Frequency Response Analysis: First Steps with Machine Learning

et al. 2018

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Abstract:In this study, we show an effective data-driven identification of the State-of-Health of Lithium-ion batteries by Nonlinear Frequency Response Analysis. A degradation model based on support vector regression is derived from highly informative Nonlinear Frequency Response Analysis data sets. First, an ageing test of a Lithium-ion battery at 25 • C is presented and the impact of relevant ageing mechanisms on the nonlinear dynamics of the cells is analysed. A correlation measure is used to identify the most sensitive frequency range for ageing tests. Here, the mid-frequency range from 1 Hz to 100 Hz shows the strongest correlation to Lithium-ion battery degradation. The focus on the mid-frequency range leads to a dramatic reduction in measurement time of up to 92% compared to standard measurement protocols. Next, informative features are extracted and used to parametrise the support vector regression model for the State of Health degradation. The performance of the degradation model is validated with additional cells and validation data sets, respectively. We show that the degradation model accurately predicts the State of Health values. Validation data demonstrate the usefulness of the Nonlinear Frequency Response Analysis as an effective and fast State of Health identification method and as a versatile tool in the diagnosis of ageing of Lithium-ion batteries in general.

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Online stack monitoring tool for dynamically and stationary operated fuel cell systems

Cited by 24 publications

References 3 publications

Nonlinear Electrochemical Analysis: Worth the Effort to Reveal New Insights into Energy Materials

Nonlinear Electrochemical Analysis: Worth the Effort to Reveal New Insights into Energy Materials

Response Surface Methodology for 30 kW PEMFC stack characterization

State-of-Health Identification of Lithium-Ion Batteries Based on Nonlinear Frequency Response Analysis: First Steps with Machine Learning

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