FI20116256A - A method and arrangement for detecting operating conditions of a solid oxide cell - Google Patents

A method and arrangement for detecting operating conditions of a solid oxide cell Download PDF

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Publication number
FI20116256A
FI20116256A FI20116256A FI20116256A FI20116256A FI 20116256 A FI20116256 A FI 20116256A FI 20116256 A FI20116256 A FI 20116256A FI 20116256 A FI20116256 A FI 20116256A FI 20116256 A FI20116256 A FI 20116256A
Authority
FI
Finland
Prior art keywords
value
stacks
solid oxide
stack
temperature value
Prior art date
Application number
FI20116256A
Other languages
Finnish (fi)
Swedish (sv)
Inventor
Tero Hottinen
Topi Korhonen
Original Assignee
Waertsilae Finland Oy
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Waertsilae Finland Oy filed Critical Waertsilae Finland Oy
Priority to FI20116256A priority Critical patent/FI20116256A/en
Priority to PCT/FI2012/051184 priority patent/WO2013083872A1/en
Publication of FI20116256A publication Critical patent/FI20116256A/en

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Classifications

    • H—ELECTRICITY
    • H01—ELECTRIC ELEMENTS
    • H01M—PROCESSES OR MEANS, e.g. BATTERIES, FOR THE DIRECT CONVERSION OF CHEMICAL ENERGY INTO ELECTRICAL ENERGY
    • H01M8/00—Fuel cells; Manufacture thereof
    • H01M8/04—Auxiliary arrangements, e.g. for control of pressure or for circulation of fluids
    • H01M8/04298—Processes for controlling fuel cells or fuel cell systems
    • H01M8/04313—Processes for controlling fuel cells or fuel cell systems characterised by the detection or assessment of variables; characterised by the detection or assessment of failure or abnormal function
    • H01M8/04664—Failure or abnormal function
    • H01M8/04679—Failure or abnormal function of fuel cell stacks
    • H—ELECTRICITY
    • H01—ELECTRIC ELEMENTS
    • H01M—PROCESSES OR MEANS, e.g. BATTERIES, FOR THE DIRECT CONVERSION OF CHEMICAL ENERGY INTO ELECTRICAL ENERGY
    • H01M8/00—Fuel cells; Manufacture thereof
    • H01M8/04—Auxiliary arrangements, e.g. for control of pressure or for circulation of fluids
    • H01M8/04298—Processes for controlling fuel cells or fuel cell systems
    • H01M8/04992—Processes for controlling fuel cells or fuel cell systems characterised by the implementation of mathematical or computational algorithms, e.g. feedback control loops, fuzzy logic, neural networks or artificial intelligence
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
    • G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
    • G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
    • G01R31/367—Software therefor, e.g. for battery testing using modelling or look-up tables
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B13/00—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion
    • G05B13/02—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
    • G05B13/0265—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric the criterion being a learning criterion
    • G05B13/027—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric the criterion being a learning criterion using neural networks only
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
    • G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
    • G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
    • G01R31/378—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC] specially adapted for the type of battery or accumulator
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
    • G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
    • G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
    • G01R31/382—Arrangements for monitoring battery or accumulator variables, e.g. SoC
    • G01R31/3835—Arrangements for monitoring battery or accumulator variables, e.g. SoC involving only voltage measurements
    • H—ELECTRICITY
    • H01—ELECTRIC ELEMENTS
    • H01M—PROCESSES OR MEANS, e.g. BATTERIES, FOR THE DIRECT CONVERSION OF CHEMICAL ENERGY INTO ELECTRICAL ENERGY
    • H01M8/00—Fuel cells; Manufacture thereof
    • H01M8/10—Fuel cells with solid electrolytes
    • H01M8/12—Fuel cells with solid electrolytes operating at high temperature, e.g. with stabilised ZrO2 electrolyte
    • H01M2008/1293—Fuel cells with solid oxide electrolytes
    • H—ELECTRICITY
    • H01—ELECTRIC ELEMENTS
    • H01M—PROCESSES OR MEANS, e.g. BATTERIES, FOR THE DIRECT CONVERSION OF CHEMICAL ENERGY INTO ELECTRICAL ENERGY
    • H01M8/00—Fuel cells; Manufacture thereof
    • H01M8/04—Auxiliary arrangements, e.g. for control of pressure or for circulation of fluids
    • H01M8/04298—Processes for controlling fuel cells or fuel cell systems
    • H01M8/04313—Processes for controlling fuel cells or fuel cell systems characterised by the detection or assessment of variables; characterised by the detection or assessment of failure or abnormal function
    • H01M8/04664—Failure or abnormal function
    • H—ELECTRICITY
    • H01—ELECTRIC ELEMENTS
    • H01M—PROCESSES OR MEANS, e.g. BATTERIES, FOR THE DIRECT CONVERSION OF CHEMICAL ENERGY INTO ELECTRICAL ENERGY
    • H01M8/00—Fuel cells; Manufacture thereof
    • H01M8/06—Combination of fuel cells with means for production of reactants or for treatment of residues
    • H01M8/0606—Combination of fuel cells with means for production of reactants or for treatment of residues with means for production of gaseous reactants
    • H—ELECTRICITY
    • H01—ELECTRIC ELEMENTS
    • H01M—PROCESSES OR MEANS, e.g. BATTERIES, FOR THE DIRECT CONVERSION OF CHEMICAL ENERGY INTO ELECTRICAL ENERGY
    • H01M8/00—Fuel cells; Manufacture thereof
    • H01M8/24—Grouping of fuel cells, e.g. stacking of fuel cells
    • H01M8/249—Grouping of fuel cells, e.g. stacking of fuel cells comprising two or more groupings of fuel cells, e.g. modular assemblies
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
    • Y02E60/00—Enabling technologies; Technologies with a potential or indirect contribution to GHG emissions mitigation
    • Y02E60/30—Hydrogen technology
    • Y02E60/50—Fuel cells

Landscapes

  • Engineering & Computer Science (AREA)
  • Artificial Intelligence (AREA)
  • Evolutionary Computation (AREA)
  • General Physics & Mathematics (AREA)
  • Automation & Control Theory (AREA)
  • Software Systems (AREA)
  • Physics & Mathematics (AREA)
  • Medical Informatics (AREA)
  • Health & Medical Sciences (AREA)
  • Sustainable Development (AREA)
  • General Chemical & Material Sciences (AREA)
  • Electrochemistry (AREA)
  • Chemical Kinetics & Catalysis (AREA)
  • Chemical & Material Sciences (AREA)
  • Sustainable Energy (AREA)
  • Manufacturing & Machinery (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • Computing Systems (AREA)
  • Fuzzy Systems (AREA)
  • Theoretical Computer Science (AREA)
  • Fuel Cell (AREA)

Abstract

The focus of the invention is a method for indicating solid oxide cell operating conditions in a solid oxide cell system, wherein cells being formatted in cell stacks (103), air being fed into the cell stacks (103) and fuel being fed to the cell stacks (103). In the method is performed neural network stack modelling of the solid oxide cell system stacks (103) by providing one or more of the following input parameters to the neural network individual stack current value, air utilization rate, air flow rate, air inlet temperature value, fuel utilization rate, fuel flow rate, fuel composition information, cell system surroundings temperature value and heat flux to surroundings to define at least one of stack voltage value, air output temperature value, internal temperature value of stack, fuel output temperature value and leakage rate as a simulation value. The neural network stacks are modelled essentially simultaneously during operation of the solid oxide cell system, is measured at least one of stack voltage value and air output temperature value as a measurement value and is compared the simulation value and the measurement value to form a difference value, and is further compared, if the difference value is outside at least one of pre- determined stack specific operating tolerance and group of stacks specific operating tolerance.
FI20116256A 2011-12-09 2011-12-09 A method and arrangement for detecting operating conditions of a solid oxide cell FI20116256A (en)

Priority Applications (2)

Application Number Priority Date Filing Date Title
FI20116256A FI20116256A (en) 2011-12-09 2011-12-09 A method and arrangement for detecting operating conditions of a solid oxide cell
PCT/FI2012/051184 WO2013083872A1 (en) 2011-12-09 2012-11-29 Method and arrangement for indicating solid oxide cell operating conditions

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
FI20116256A FI20116256A (en) 2011-12-09 2011-12-09 A method and arrangement for detecting operating conditions of a solid oxide cell

Publications (1)

Publication Number Publication Date
FI20116256A true FI20116256A (en) 2013-06-10

Family

ID=47603809

Family Applications (1)

Application Number Title Priority Date Filing Date
FI20116256A FI20116256A (en) 2011-12-09 2011-12-09 A method and arrangement for detecting operating conditions of a solid oxide cell

Country Status (2)

Country Link
FI (1) FI20116256A (en)
WO (1) WO2013083872A1 (en)

Families Citing this family (10)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CA2940150C (en) * 2014-02-19 2021-06-08 Farid Golnaraghi Use of neural network and eis signal analysis to quantify h2 crossover in-situ in operating pem cells
CN107329056A (en) * 2017-07-10 2017-11-07 国网浙江省电力公司宁波供电公司 Test method for distribution line natural grounding substance impact characteristics
CN109994760B (en) * 2018-01-03 2022-06-28 通用电气公司 Temperature control system and method for fuel cell system and fuel cell system
CN110065393B (en) * 2018-01-23 2022-07-19 联合汽车电子有限公司 Fault monitoring system, fault monitoring method and vehicle
CN111916791B (en) * 2020-07-31 2021-10-01 上海捷氢科技有限公司 Multi-working-condition multi-sample fuel cell stack testing system and control method thereof
DE102020128268A1 (en) 2020-10-28 2022-04-28 Audi Aktiengesellschaft Method of operating a fuel cell stack
AT524724A1 (en) * 2021-02-08 2022-08-15 Avl List Gmbh Test procedure for virtual testing of a fuel cell
CN114626195B (en) * 2022-01-18 2024-05-03 南昌大学 Modeling method and system for solid oxide fuel cell system by using space-time data
CN114784324B (en) * 2022-04-21 2023-12-22 中汽创智科技有限公司 Fuel cell system control method and device, electronic equipment and storage medium
CN114744254A (en) * 2022-04-28 2022-07-12 武汉雄韬氢雄燃料电池科技有限公司 Modeling method of hydrogen circulating pump in fuel cell system

Family Cites Families (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
DE19624301B4 (en) * 1996-06-18 2006-08-17 Siemens Ag Learning method for a neural network
US8518594B2 (en) * 1999-11-24 2013-08-27 Encite, Llc Power cell and power chip architecture
EP1408384B1 (en) * 2002-10-09 2006-05-17 STMicroelectronics S.r.l. An arrangement for controlling operation of a physical system, like for instance fuel cells in electric vehicles
US7136779B2 (en) * 2004-05-28 2006-11-14 Daimlerchrysler Ag Method for simplified real-time diagnoses using adaptive modeling

Also Published As

Publication number Publication date
WO2013083872A1 (en) 2013-06-13

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