5 research outputs found

    On-line Parameter Estimation of the Polarization Curve of a Fuel Cell with Guaranteed Convergence Properties: Theoretical and Experimental Results

    Full text link
    In this paper, we address the problem of online parameter estimation of a Proton Exchange Membrane Fuel Cell (PEMFC) polarization curve, that is the static relation between the voltage and the current of the PEMFC. The task of designing this estimator -- even off-line -- is complicated by the fact that the uncertain parameters enter the curve in a highly nonlinear fashion, namely in the form of nonseparable nonlinearities. We consider several scenarios for the model of the polarization curve, starting from the standard full model and including several popular simplifications to this complicated mathematical function. In all cases, we derive separable regression equations -- either linearly or nonlinearly parameterized -- which are instrumental for the implementation of the parameter estimators. We concentrate our attention on on-line estimation schemes for which, under suitable excitation conditions, global parameter convergence is ensured. Due to these global convergence properties, the estimators are robust to unavoidable additive noise and structural uncertainty. Moreover, their on-line nature endows the schemes with the ability to track (slow) parameter variations, that occur during the operation of the PEMFC. These two features -- unavailable in time-consuming off-line data-fitting procedures -- make the proposed estimators helpful for on-line time-saving characterization of a given PEMFC, and the implementation of fault-detection procedures and model-based adaptive control strategies. Simulation and experimental results that validate the theoretical claims are presented.Comment: 16 pages, 18 figures, requires IEEEtran.cls 2015/08/26 version V1.8

    Input-parallel output-series DC-DC boost converter with a wide input voltage range, for fuel cell vehicles

    Get PDF
    An input-parallel, output-series DC-DC Boost converter with a wide input voltage range is proposed in this paper. An interleaved structure is adopted in the input side of this converter to reduce input current ripple. Two capacitors are connected in series on the output side to achieve a high voltage-gain. The operating principles and steady-state characteristics of the converter are presented and analyzed in this paper. A 400V/1.6kW prototype has been created which demonstrates that a wide range of voltage-gain can be achieved by this converter and it is shown that the maximum efficiency of the converter is 96.62%, and minimum efficiency is 94.14% The experimental results validate the feasibility of the proposed topology and its suitability for fuel cell vehicles

    Predicting Performance Degradation of Fuel Cells in Backup Power Systems

    Get PDF

    Converter based electrochemical impedance spectroscopy for fuel cell stacks

    Get PDF
    Fuel cells are important devices in a hydrogen-based chain of energy conversion. They have distinctive advantages over batteries with their higher energy density and faster refueling speed, which make them attractive in stationary power supplies and heavy-duty vehicles. However, the high cost and low durability associated with modern fuel cells are still hindering their wider commercialization. Besides developing more reliable and lower cost materials and advanced assemblies of cells and stacks, a practical and effective diagnostic tool is highly needed for fuel cells to identify any abnormal internal conditions and assist with maintenance scheduling or application of on-board mitigating schemes. Conventionally, linear instruments were used for fuel cell EIS, however, limited to single cells or short stacks only as a laboratory testing method. With recent developments, EIS enabled by switching power converters are capable of being applied to a high-power stack directly. This approach has the potential for practical field applications such as a servicing tool for fuel cell manufacturers or an on-board diagnostic tool of a moving vehicle. Previous works on converter based EIS have made a few different attempts at conceptually realizing this solution while several significant issues were not well recognized and resolved yet. As such, this thesis explores further on this topic to address the flexibility of EIS perturbation generation, the perturbation frequency range, and the linkage between fuel cell EIS requirements and the converter design to push for its readiness for practical implementations. Several new solutions are proposed and discussed in detail, including a total software approach for existing high-power converters to enable wide-frequency-range EIS, a redesign of the main dc/dc converter enabling wide-frequency-range perturbations, and a separate auxiliary converter as a standalone module for EIS operation. A detailed analysis of oscillations brought by converter based EIS in powertrains is also presented

    ๋ณต์žกํ•œ ๊ณตํ•™ ์‹œ์Šคํ…œ์— ๋Œ€ํ•œ ์˜ค๊ฒฝ๋ณด๋ฅผ ๊ณ ๋ คํ•œ ๋ฆฌ์งˆ๋ฆฌ์–ธ์Šค ํ•ด์„ ๋ฐ ์„ค๊ณ„ ๋ฐฉ๋ฒ•๋ก  ์—ฐ๊ตฌ

    Get PDF
    ํ•™์œ„๋…ผ๋ฌธ (๋ฐ•์‚ฌ)-- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ๊ณต๊ณผ๋Œ€ํ•™ ๊ธฐ๊ณ„ํ•ญ๊ณต๊ณตํ•™๋ถ€, 2018. 2. ์œค๋ณ‘๋™.it estimates a healthy engineered to be faulty, resulting unnecessary system shutdown, inspection, and โ€“ in the case of incorrect inspection โ€“ unnecessary system repair or replacement. Although false alarms make a system unavailable with capital loss, it has not been considered in resilience engineering. To cope with false alarm problems, this research is elaborated to advance the resilience engineering considering false alarms. Specifically, this consists of three research thrusts: 1) resilience analysis considering false alarms, 2) resilience-driven system design considering false alarms (RDSD-FA), and 3) resilience-driven system design considering time-dependent false alarms (RDSD-TFA). In the first research thrust, a resilience measure is newly formulated considering false alarms. This enables the evaluation of resilience decrease due to false alarms, resulting in accurate analysis of system resilience. Based upon the new resilience measure, RDSD-FA is proposed in the second research thrust. This aims at designing a resilient system to satisfy a target resilience level while minimizing life-cycle cost. This is composed of three hierarchical tasks: resilience allocation problem, reliability-based design optimization (RBDO), and PHM design. The third research thrust presents RDSD-TFA that considers time-dependent variability of an engineered system. This makes one to estimate life-cycle cost in an accurate and rigorous manner, and to design an engineered system more precisely while minimizing its life-cycle cost. The framework of RDSD-TFA consists of four tasks: system analysis, PHM analysis, life-cycle simulation, and design optimization. Through theoretical analysis and case studies, the significance of false alarms in engineering resilience and the effectiveness of the proposed ideas are demonstrated.๊ณตํ•™ ์‹œ์Šคํ…œ์€ ์ƒ์• ์ฃผ๊ธฐ์— ๊ฑธ์ณ ๋‹ค์–‘ํ•œ ๋ถˆํ™•์‹ค์„ฑ์— ๋…ธ์ถœ๋˜๋ฉฐ, ์ด๋กœ ์ธํ•ด ๋ชฉํ‘œ ์„ฑ๋Šฅ์„ ์ถฉ์กฑ์‹œํ‚ค์ง€ ๋ชปํ•  ๊ฒฝ์šฐ ์‚ฌํšŒ์ , ๊ฒฝ๊ณ„์ , ์ธ์  ์†Œ์‹ค์„ ์•ผ๊ธฐํ•˜๊ฒŒ ๋œ๋‹ค. ์ด์— ๋Œ€ํ•œ ํ•ด๊ฒฐ ๋ฐฉ์•ˆ ์ค‘ ํ•˜๋‚˜๋กœ ๋ฆฌ์งˆ๋ฆฌ์–ธ์Šค ์ฃผ๋„ ์„ค๊ณ„ ๊ธฐ์ˆ  (resilience-driven system design์ดํ•˜ RDSD)์ด ๊ฐœ๋ฐœ๋˜์—ˆ๋‹ค. RDSD๋Š” ๊ฑด์ „์„ฑ ์˜ˆ์ธก ๋ฐ ๊ด€๋ฆฌ ๊ธฐ์ˆ  (prognostics & health management์ดํ•˜ PHM)์„ ์„ค๊ณ„์— ๋„์ž…ํ•จ์œผ๋กœ์จ ๋น„์šฉ ํšจ์œจ์ ์ธ ๊ณ ์žฅ ์˜ˆ๋ฐฉ์„ ๊ฐ€๋Šฅ์ผ€ ํ•˜์˜€๋‹ค. ํ•˜์ง€๋งŒ, RDSD๋Š” PHM์˜ ๊ณ ์žฅ ์˜ค๊ฒฝ๋ณด ํ˜„์ƒ์„ ๊ณ ๋ คํ•˜์ง€ ์•Š๋Š” ํ•œ๊ณ„์ ์„ ๊ฐ–๋Š”๋‹ค. ๊ณ ์žฅ ์˜ค๊ฒฝ๋ณด๋Š” ๊ฑด์ „ํ•œ ์‹œ์Šคํ…œ์„ ๊ณ ์žฅ์ด๋ผ ์ถ”์ •ํ•˜๋Š” ํ˜„์ƒ์œผ๋กœ, ๋ถˆํ•„์š”ํ•œ ์‹œ์Šคํ…œ ์ •์ง€ ๋ฐ ๊ฒ€์‚ฌ ๋น„์šฉ์„ ์•ผ๊ธฐํ•˜์—ฌ, PHM๊ณผ RDSD์˜ ๊ธฐ์ˆ ์  ํšจ์šฉ์„ฑ์„ ๋–จ์–ดํŠธ๋ฆฌ๊ฒŒ ๋œ๋‹ค. ๋”ฐ๋ผ์„œ, RDSD์˜ ๊ธฐ์ˆ ์  ์•ฝ์ง„๊ณผ ์‹ค์ ์šฉ์„ ๋„๋ชจํ•˜๊ธฐ ์œ„ํ•ด์„œ๋Š” ๊ณ ์žฅ ์˜ค๊ฒฝ๋ณด ํ˜„์ƒ์„ ํ•ด๊ฒฐํ•ด์•ผ ํ•œ๋‹ค. ๋ณธ ๋…ผ๋ฌธ์—์„œ๋Š” ๊ณ ์žฅ ์˜ค๊ฒฝ๋ณด์˜ ๊ณ ๋ ค๋ฅผ ํ†ตํ•ด ๋ฆฌ์งˆ๋ฆฌ์–ธ์Šค ํ•ด์„ ๋ฐ ์„ค๊ณ„ ๋ฐฉ๋ฒ•๋ก ์„ ๊ฐœ์„ ํ•˜๊ณ ์ž ํ•˜๋ฉฐ, ์ด๋ฅผ ์œ„ํ•ด ์„ธ ๊ฐ€์ง€ ์—ฐ๊ตฌ ์ฃผ์ œ๋ฅผ ์ œ์•ˆํ•œ๋‹ค. ์ฒซ ๋ฒˆ์งธ ์ฃผ์ œ๋Š” ์˜ค๊ฒฝ๋ณด๋ฅผ ๊ณ ๋ คํ•œ ๋ฆฌ์งˆ๋ฆฌ์–ธ์Šค ๋ถ„์„์œผ๋กœ, ๊ณตํ•™ ์‹œ์Šคํ…œ์˜ ๋ฆฌ์งˆ๋ฆฌ์–ธ์Šค ์‹œ๋‚˜๋ฆฌ์˜ค ๋ถ„์„์— ๊ธฐ๋ฐ˜ํ•ด ๋ฆฌ์งˆ๋ฆฌ์–ธ์Šค ์ง€์ˆ˜๋ฅผ ์ƒˆ๋กญ๊ฒŒ ์ •์‹ํ™” ํ•œ๋‹ค. ์ด ์ง€์ˆ˜๋Š” ๊ณ ์žฅ ์˜ค๊ฒฝ๋ณด๋กœ ์ธํ•œ ๋ฆฌ์งˆ๋ฆฌ์–ธ์Šค์˜ ์ €ํ•˜๋ฅผ ๋ถ„์„ํ•จ์œผ๋กœ์จ, ์ •ํ™•ํ•œ ๋ฆฌ์งˆ๋ฆฌ์–ธ์Šค ์ถ”์ •์„ ๊ฐ€๋Šฅ์ผ€ ํ•œ๋‹ค. ๋‘ ๋ฒˆ์งธ ์ฃผ์ œ๋Š” ๊ณ ์žฅ ์˜ค๊ฒฝ๋ณด๋ฅผ ๊ณ ๋ คํ•œ ๋ฆฌ์งˆ๋ฆฌ์–ธ์Šค ์ฃผ๋„ ์„ค๊ณ„ ๋ฐฉ๋ฒ•๋ก ์ด๋‹ค. ์ด๋Š” 3๋‹จ๊ณ„์˜ ๊ณ„์ธต์  ์š”์†Œ๋กœ ๊ตฌ์„ฑ๋œ๋‹ค. ๋จผ์ € ๋ชฉํ‘œ ๋ฆฌ์งˆ๋ฆฌ์–ธ์Šค ์ง€์ˆ˜๋ฅผ ๋งŒ์กฑํ•˜๋ฉด์„œ ์ƒ์• ์ฃผ๊ธฐ๋น„์šฉ์„ ์ตœ์†Œํ™”ํ•˜๊ธฐ ์œ„ํ•ด, ๋ชฉํ‘œ ์‹ ๋ขฐ๋„์™€ ๋ชฉํ‘œ ์˜ค๊ฒฝ๋ณด ๋ฐ ์œ ์‹ค๊ฒฝ๋ณด์œจ์„ ์ตœ์ ํ™”ํ•œ๋‹ค. ์ดํ›„ ์‹ ๋ขฐ์„ฑ ๊ธฐ๋ฐ˜ ์ตœ์  ์„ค๊ณ„ (reliability-based design optimization)๋ฅผ ํ†ตํ•ด ๋ชฉํ‘œ ์‹ ๋ขฐ๋„๋ฅผ ํ™•๋ณดํ•˜๊ณ , PHM ์„ค๊ณ„๋ฅผ ํ†ตํ•ด ํ• ๋‹น๋œ ๋ชฉํ‘œ ์˜ค๊ฒฝ๋ณด ๋ฐ ์œ ์‹ค๊ฒฝ๋ณด์œจ์„ ์ถฉ์กฑ์‹œํ‚จ๋‹ค. ์„ธ ๋ฒˆ์งธ ์ฃผ์ œ๋Š” ์‹œ๋ณ€(ๆ™‚่ฎŠ) ์˜ค๊ฒฝ๋ณด๋ฅผ ๊ณ ๋ คํ•œ ๋ฆฌ์งˆ๋ฆฌ์–ธ์Šค ์ฃผ๋„ ์„ค๊ณ„ ๋ฐฉ๋ฒ•๋ก ์ด๋‹ค. ๊ธฐ์กด์˜ ์„ค๊ณ„ ๋ฐฉ๋ฒ•๋ก ๋“ค์€ ์‹œ์Šคํ…œ์˜ ๊ฑด์ „์„ฑ ์ƒํƒœ๋ฅผ ์‹œ๋ถˆ๋ณ€(ๆ™‚๏ฅง่ฎŠ)ํ•˜๋‹ค ๊ฐ„์ฃผํ•˜์˜€์œผ๋‚˜, ์‹ค์ œ ์‹œ์Šคํ…œ์€ ์šดํ–‰์— ๋”ฐ๋ผ ์ ์ง„์ ์œผ๋กœ ๊ฑด์ „์„ฑ์ด ์ €ํ•˜๋œ๋‹ค. ๋ณธ ์—ฐ๊ตฌ์—์„œ๋Š” ์‹œ๋ณ€์„ฑ์„ ๋ถ„์„ํ•˜๊ธฐ ์œ„ํ•ด ์‹œ๋ณ€ ์˜ค๊ฒฝ๋ณด์œจ ๋ฐ ์œ ์‹ค๊ฒฝ๋ณด์œจ์— ๋Œ€ํ•œ ๊ฐœ๋…์„ ์ƒˆ๋กญ๊ฒŒ ์ œ์•ˆํ•˜์˜€์œผ๋ฉฐ, ์ƒ์• ์ฃผ๊ธฐ ์‹œ๋ฎฌ๋ ˆ์ด์…˜์„ ํ†ตํ•œ ์ด ์œ ์ง€๋ณด์ˆ˜ ๋น„์šฉ ๋ถ„์„ ๋ฐฉ๋ฒ•๋ก ์„ ๊ฐœ๋ฐœํ•˜์˜€๋‹ค. ์ด๋ฅผ ํ†ตํ•ด ์ƒ์• ์ฃผ๊ธฐ๋น„์šฉ์„ ๋ณด๋‹ค ์—„๋ฐ€ํ•˜๊ณ  ์ •ํ™•ํ•˜๊ฒŒ ์ถ”์ •ํ•  ์ˆ˜ ์žˆ๊ฒŒ ๋˜์—ˆ์œผ๋ฉฐ, ์ด๋ฅผ ์ตœ์†Œํ™”ํ•˜๋Š” ๋ฐฉํ–ฅ์œผ๋กœ ์‹œ์Šคํ…œ๊ณผ PHM์˜ ์„ค๊ณ„๋ฅผ ์ตœ์ ํ™”์˜€๋‹ค. ๋ณธ ์—ฐ๊ตฌ์—์„œ ์ œ์•ˆํ•œ ๋ฐฉ๋ฒ•๋ก ๋“ค์€ ์ด๋ก ์  ๋ถ„์„๊ณผ ์‚ฌ๋ก€ ์—ฐ๊ตฌ๋ฅผ ํ†ตํ•ด ๊ทธ ํšจ์šฉ์„ฑ์„ ์ž…์ฆํ•˜์˜€๋‹ค.Most engineered systems are designed with a passive and fixed design capacity and, therefore, may become unreliable in the presence of adverse events. In order to handle this issue, the resilience-driven system design (RDSD) has been proposed to make engineered systems adaptively reliable by incorporating the prognostics and health management (PHM) method. PHM tracks the health degradation of an engineered system, and provides health state information supporting decisions on condition-based maintenance. Meanwhile, one of the issues awaiting solution in the field of PHM, as well as in RDSD, is to address false alarms. A false alarm is an erroneous report on the health state of an engineered systemChapter 1. Introduction 1 1.1 Motivation 1 1.2 Research Scope and Overview 3 1.3 Dissertation Layout 6 Chapter 2. Literature Review 7 2.1 Resilience Engineering (Analysis and Design) 7 2.1.1 Resilience Analysis for Mechanical Systems 8 2.1.2 Resilience-Driven System Design (RDSD) for Mechanical Systems 15 2.2 False and Missed Alarms in Prognostics and Health Management 27 2.2.1 Definition of False and Missed Alarms 27 2.2.2 Quantification of False and Missed Alarms 32 2.3 Summary and Discussion 35 Chapter 3. Resilience Analysis Considering False Alarms 37 3.1 Resilience Measure Considering False Alarms 37 3.2 Case Studies 42 3.2.1 Numerical ample 42 3.2.2 Electro-Hydrtatic Actuator (EHA) 44 3.3 Summary and Discussion 53 Chapter 4. Resilience-Driven System Design Considering False Alarms (RDSD-FA) 55 4.1 Overview of RDSD-FA Framework 55 4.2 Resilience Allocation Problem Considering False Alarms 56 4.3 Prognostics and Health Management (PHM) Design Considering False Alarms 60 4.4 Case study: Electro-Hydrostatic Actuator (EHA) 61 4.4.1 Step 1: Resilience Allocation Considering False Alarms 61 4.4.2 Step 2: Reliability-Based Design Optimization 64 4.4.3 Step 3: PHM Design Considering False Alarms 68 4.4.4 Comparison of Design Results from RDSD and RDSD-FA 73 4.5 Summary and Discussion 75 Chapter 5. Resilience-Driven System Design Considering Time-Dependent False Alarms (RDSD-TFA) 77 5.1 Time-Dependent False and Missed Alarms in PHM 79 5.2 Resilience-Driven System Design Considering Time-Dependent False Alarms (RDSD-TFA) 83 5.2.1 Overview of RDSD-TFA Framework 83 5.2.2 Task 1: System Analysis 86 5.2.3 Task 2: PHM Analysis 89 5.2.4 Task 3: Life-Cycle Simulation 91 5.2.5 Task 4: Design Optimization 97 5.3 Case studies 98 5.3.1 Numerical Example of Life-Cycle Simulation 98 5.3.2 Electro-Hydrostatic Actuator (EHA) 107 5.4 Summary and Discussion 123 Chapter 6. Conclusions 126 6.1 Summary and Contributions 126 6.2 Suggestions for Future Research 129 References 132 Appendix 154 Abstract(Korean) 157Docto
    corecore