基于伪随机二进制序列的阻抗谱快速重构及其在电化学能源领域的应用
收稿日期: 2019-03-11
修回日期: 2019-04-17
网络出版日期: 2020-01-16
基金资助
基金国家自然科学基金项目(11674086)
Recent Advancement in Pseudo-Random Binary Sequence Signals-Based Fast Reconstruction of Impedance Spectrum and Its Applications in Electrochemical Energy Sources
Received date: 2019-03-11
Revised date: 2019-04-17
Online published: 2020-01-16
阻抗谱的应用范围越来越广,其传统测试方法耗时长的问题也日益突出. 提高阻抗谱测量速度的各种尝试中,合成宽带激励信号和设计高效率估计算法被认为是最具潜力的解决方案,由于伪随机二进制序列（pseudo-random binary sequence,PRBS）具有功率谱平坦和易生成等优点,它在阻抗谱快速测试中具有独特优势. 本文综述了快速阻抗谱测试中三个核心问题：PRBS信号类型、不同快速算法及其在电化学能源领域的典型应用. 对于PRBS信号类型,即最大长度序列信号、混合PRBS、离散区间二进制序列和正交PRBS,本文讨论了它们各自的特点和应用范围;对于不同的PRBS激励信号的快速算法,即离散傅里叶变换/快速傅里叶变换、小波变换、快速m序列变换、基于系统辨识的参数估计算法以及这些算法各自的特点和应用范围,本文进行了深入的分析;对于PRBS阻抗谱快速测量的应用,本文以铅酸电池、锂离子电池、质子交换膜燃料电池和超级电容器等电化学能源为例,验证了其应用的可行性. 为促进技术的进一步完善,本文总结和分析了PRBS阻抗谱快速测量存在的挑战,并提出了克服这些挑战所必需的未来研究方略.
李伟恒 , 黄秋安 , 杨维明 , 杨昌平 , 张久俊 . 基于伪随机二进制序列的阻抗谱快速重构及其在电化学能源领域的应用[J]. 电化学, 2020 , 26(3) : 370 -388 . DOI: 10.13208/j.electrochem.190309
With the extensive application of impedance spectroscopy, the time-consuming issue of its traditional testing methods has become more and more serious, which limits its application range. In the study of accelerating impedance measurement or reconstruction, the synthesis of wideband excitation signals and the design of high efficiency estimation algorithms have been identified as important ways. In view of the purpose of rapid impedance reconstruction, Pseudo-Random Binary Sequence (PRBS) has the advantages of flat power spectrum and easy generation, and has a good application prospect. This paper reviews three core issues in rapid reconstruction of impedance spectrum: PRBS signal types, different fast algorithms, and their typical applications in the field of electrochemical energy. For the PRBS signal types, namely, the maximum length sequence signal, the hybrid PRBS, the discrete interval binary sequence and the orthogonal PRBS, their respective characteristics and application ranges are discussed. For the fast algorithms corresponding to different PRBS excitation signals, namely, the discrete Fourier transform/Fast Fourier transform, wavelet transform, fast m-sequence transform, parameter estimation algorithm based on system identification, and their respective characteristics and application scope, this paper has carried out in-depth analysis on computation efficiency and calculation precision for fast reconstruction of impedance spectrum. For the application of rapid impedance spectrum measurement based on PRBS, the electrochemical energy sources such as lead-acid batteries, lithium-ion batteries, proton exchange membrane fuel cells and supercapacitors are taken as examples to verify the feasibility of its application. In order to promote the further improvement of technology, this paper summarizes and analyzes the challenges in rapid measurements or reconstruction of impedance spectrum based on PRBS signals, and proposes the future research strategy necessary to overcome these challenges: 1) design hardware test system according to specific application scenarios; 2) choose the optimal estimation algorithms based on the test object; 3) balance the complexity between excitation signal generation and impedance estimation algorithms.
| [1] | Fairweather A J, Foster M P, Stone D A. Battery parameter identification with Pseudo Random Binary Sequence excitation (PRBS)[J]. Journal of Power Sources, 2011,196(22):9398-9406. |
| [2] | Fairweather A J, Foster M P, Stone D A. Modelling of VRLA batteries over operational temperature range using Pseudo Random Binary Sequences[J]. Journal of Power Sources, 2012,207:56-59. |
| [3] | Weddle P J, Kee R J, Vincent T. A stitching algorithm to identify wide-bandwidth electrochemical impedance spectra for Li-ion batteries using binary perturbations[J]. Journal of The Electrochemical Society, 2018,165(9):A1679-A1684. |
| [4] | Sihvo J, Messo T, Roinila T, et al. Online internal impedance measurements of Li-ion battery using PRBS broadband excitation and fourier techniques: methods and injection design[C]//IEEE. 2018 International Power Electronics Conference (IPEC-Niigata 2018-ECCE Asia), May 20-24, 2018, Niigata, Japan. New York: IEEE, 2018: 2470-2475. |
| [5] | Dai H F, Xu T J, Zhu L T, et al. Adaptive model parameter identification for large capacity Li-ion batteries on separated time scales[J]. Applied Energy, 2016,184:119-131. |
| [6] | Al Nazer R, Cattin V, Granjon P, et al. Broadband identification of battery electrical impedance for HEVs[J]. IEEE Transactions on Vehicular Technology, 2013,62(7):2896-2905. |
| [7] | Shi Z, Auger F, Schaeffer E, et al. Interconnected observers for online supercapacitor ageing monitoring[C]//IEEE. IECON 2013-39th Annual Conference of the IEEE, November 10-13, 2013, Vienna, Austria. New York: IEEE, 2013: 6746-6751. |
| [8] | Fairweather A J, Stone D A, Foster M P. Evaluation of UltraBatteryTM performance in comparison with a battery-supercapacitor parallel network [J]. Journal of Power Sources, 2013,226:191-201. |
| [9] | Ritzberger D, Striednig M, Simon C, et al. Online estimation of the electrochemical impedance of polymer electrolyte membrane fuel cells using broad-band current excitation[J]. Journal of Power Sources, 2018,405:150-161. |
| [10] | Manganiello P, Petrone G, Giannattasio M, et al. FPGA implementation of the EIS technique for the on-line diagnosis of fuel-cell systems[C]//IEEE. 2017 IEEE 26th International Symposium on Industrial Electronics (ISIE 2017), June 19-21, 2017, Edinburgh, United Kingdom. New York: IEEE, 2017: 981-986. |
| [11] | Debenjak A, Boskoski P, Musizza B, et al. Fast measurement of proton exchange membrane fuel cell impedance based on pseudo-random binary sequence perturbation signals and continuous wavelet transform[J]. Journal of Power Sources, 2014,254:112-118. |
| [12] | Forrai A, Funato H, Yanagita Y, et al. Fuel-cell parameter estimation and diagnostics[J]. IEEE Transactions on Energy Conversion, 2005,20(3):668-675. |
| [13] | Hamilton T J, Van Schaik A, Cornell B. Measuring the impedance of a tethered bilayer membrane biosensor[C]//IEEE. 2008 IEEE Biomedical Circuits and Systems Conference - Intelligent Biomedical Systems (BioCAS), November 20-22, 2008, Baltimore, Maryland. New York: IEEE, 2008: 361-364. |
| [14] | Min M, Ojarand J, M?rtens O, et al. Binary signals in impedance spectroscopy[C]//IEEE. 2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, August 28-September 1, San Diego, California USA, New York: IEEE, 2012: 134-137. |
| [15] | Sanchez B, Vandersteen G, Bragos R, et al. Basics of broadband impedance spectroscopy measurements using periodic excitations[J]. Measurement Science and Technology, 2012,23(10):105501. |
| [16] | Min M, Paavle T. Broadband discrete-level excitations for improved extraction of information in bioimpedance measurements[J]. Physiological Measurement, 2014,35(6):997-1010. |
| [17] | Roinila T, Messo T, Santi E. MIMO-identification techniques for rapid impedance-based stability assessment of three-phase systems in DQ domain[J]. IEEE Transactions on Power Electronics, 2018,33(5):4015-4022. |
| [18] | Roinila T, Messo T. Online grid-impedance measurement using ternary-sequence injection[J]. IEEE Transactions on Industry Applications, 2018,54(5):5097-5103. |
| [19] | AlyanNezhadi M M, Hassanpour H, Zare F. A new high frequency grid impedance estimation technique for the frequency range of 2 to150 kHz[J]. International Journal of Engineering, 2018,31(10):1666-1674. |
| [20] | Messo T, Luhtala R, Roinila T, et al. Real-time impedance-based stability assessment of grid converter interactions[C]//IEEE. 2017 IEEE 18th Workshop on Control and Modeling for Power Electronics (COMPEL 2017), July 9-12, 2017, Stanford, California, USA. New York: IEEE, 2017: 1-8. |
| [21] | Luhtala R, Messo T, Reinikka T, et al. Adaptive control of grid-connected inverters based on real-time measurements of grid impedance: DQ-domain approach [C]// IEEE. 2017 IEEE Energy Conversion Congress and Exposition (ECCE 2017), October 1-5, 2017, Cincinnati, Ohio, USA. New York: IEEE, 2017: 69-75. |
| [22] | Tewari U, Neshvad S, Goldbach D, et al. Verification and implementation of pseudo-random-binary-sequences for online determination of grid impedance spectrum[J]. The Renewable Energy & Power Quality Journal, 2015,1(13):596-601. |
| [23] | Roinila T, Messo T, Suntio T, et al. Pseudo-random sequences in DQ-domain analysis of feedforward control in grid-connected inverters[J]. IFAC-PapersOnLine, 2015,48(28):1301-1306. |
| [24] | Neshvad S, Chatzinotas S, Sachau J. Online determination of grid impedance spectrum through pseudo-random excitation of a pulse width modulator[J]. Renewable Energy & Power Quality Journal, 2014,1(12):316-321. |
| [25] | Roinila T, Vilkko M, Sun J. Online grid impedance measurement using discrete-interval binary sequence injection[J]. IEEE Journal of Emerging and Selected Topics in Power Electronics, 2014,2(4):985-993. |
| [26] | Huang Q A, Hui R, Wang B, et al. A review of AC im-pedance modeling and validation in SOFC diagnosis[J]. Electrochimica Acta, 2007,52(28):8144-8164. |
| [27] | Yuan X, Wang H, Sun J C, et al. AC impedance technique in PEM fuel cell diagnosis — A review[J]. International Journal of Hydrogen Energy, 2007,32(17):4365-4380. |
| [28] | Yuan X Z R, Song C, Wang H, et al. Electrochemical im-pedance spectroscopy in PEM fuel cells: fundamentals and applications[M]. London: Springer Science & Business Media, 2009. |
| [29] | Huang Q A, Park S M. Unified model for transient faradaic impedance spectroscopy: theory and prediction[J]. The Journal of Physical Chemistry C, 2012,116(32):16939-16950. |
| [30] | Huang Q A, Shen Y, Huang Y, et al. Impedance characteristics and diagnoses of automotive lithium-ion batteries at 7.5% to 93.0% state of charge[J]. Electrochimica Acta, 2016,219:751-765. |
| [31] | Huang Q A, Li Y, Tsay K C, et al. Multi-scale impedance model for supercapacitor porous electrodes: Theoretical prediction and experimental validation[J]. Journal of Power Sources, 2018,400:69-86. |
| [32] | Li W H, Huang Q A, Yang C P, et al. A fast measurement of Warburg-like impedance spectra with Morlet wavelet transform for electrochemical energy devices[J]. Electrochimica Acta, 2019, 322 : UNSP 134760. |
| [33] | Xiong R, Tian J P, Mu H, et al. A systematic model-based degradation behavior recognition and health monitoring method for lithium-ion batteries[J]. Applied Energy, 2017,207:372-383. |
| [34] | Zhou X, Pan Z Q, Han X B, et al. An easy-to-implement multi-point impedance technique for monitoring aging of lithium ion batteries[J]. Journal of Power Sources, 2019,417:188-192. |
| [35] | Yoo J S, Park S M. An electrochemical impedance measurement technique employing Fourier transform[J]. Analytical Chemistry, 2000,72(9):2035-2041. |
| [36] | Nam K M, Shin D H, Jung N, et al. Development of galvanostatic Fourier transform electrochemical impedance spectroscopy[J]. Analytical Chemistry, 2013,85(4):2246-2252. |
| [37] | Itagaki M, Ueno M, Hoshi Y, et al. Simultaneous determination of electrochemical impedance of lithium-ion rechargeable batteries with measurement of charge-discharge curves by wavelet transformation[J]. Electrochimica Acta, 2017,235:384-389. |
| [38] | de Beer C, Barendse P S, Pillay P. Fuel cell condition monitoring using optimized broadband impedance spectroscopy[J]. IEEE Transactions on Industrial Electronics, 2015,62(8):5306-5316. |
| [39] | Slepski P, Darowicki K. Optimization of impedance measurements using ‘chirp’type perturbation signal[J]. Measurement, 2009,42(8):1220-1225. |
| [40] | Pintelon R, Schoukens J. System identification: a frequency domain approach[M]. Hoboken: John Wiley & Sons, 2012. |
| [41] | Tan A H, Godfrey K R. The generation of binary and near-binary pseudorandom signals: An overview[J]. IEEE Tran-sactions on Instrumentation and Measurement, 2002,51(4):583-588. |
| [42] | Sun T, Gawad S, Bernabini C, et al. Broadband single cell impedance spectroscopy using maximum length sequences: theoretical analysis and practical considerations[J]. Measurement Science and Technology, 2007,18(9):2859-2868. |
| [43] | Davies W. D T. System identification for self-adaptive control[M]. Hoboken: Wiley-Interscience, 1970. |
| [44] | Davidson J N, Stone D A, Foster M P, et al. Improved bandwidth and noise resilience in thermal impedance spectroscopy by mixing PRBS signals[J]. IEEE transactions on Power Electronics, 2014,29(9):4817-4828. |
| [45] | Bos A V D, Krol R. Synjournal of discrete-interval binary signals with specified Fourier amplitude spectra[J]. International Journal of Control, 1979,30(5):871-884. |
| [46] | Briggs P A N, Godfrey K R. Pseudorandom signals for the dynamic analysis of multivariable systems[J]. Procee-dings of the Institution of Electrical Engineers. 1966,113(7):1259-1267. |
| [47] | Sung S W, Lee J H. Pseudo-random binary sequence design for finite impulse response identification[J]. Control Engineering Practice, 2003,11(8):935-947. |
| [48] | Amrani M E H, Dowdeswell R M, Payne P A, et al. Pseudo-random binary sequence interrogation technique for gas sensors[J]. Sensors and Actuators B: Chemical, 1998,47(1/3):118-124. |
| [49] | Rivera D E, Jun K S. An integrated identification and control design methodology for multivariable process system applications[J]. IEEE Control Systems, 2000,20(3):25-37. |
| [50] | Yao L, Zhao J, Qian J. An improved pseudo-random binary sequence design for multivariable system identification (A16-395)[C]//IEEE. 2006 6th World Congress on In-telligent Control and Automation, June 21-23, 2006, Dalian, China. New York: IEEE, 2006,1:1768-1772. |
| [51] | Briggs P, Godfrey K. Design of uncorrelated signals[J]. Electronics Letters, 1976,12(21):555-556. |
| [52] | Tan A H, Godfrey K R, Barker H A. Design of ternary signals for MIMO identification in the presence of noise and nonlinear distortion[J]. IEEE Transactions on Control Systems Technology, 2009,17(4):926-933. |
| [53] | Tan A H, Barker H A, Godfrey K R. Identification of multi-input systems using simultaneous perturbation by pseudorandom input signals[J]. IET Control Theory & Applications, 2015,9(15):2283-2292. |
| [54] | Godfrey K. Perturbation signals for system identification[M]. Hemel Hempstead: Prentice Hall International (UK) Ltd., 1993. |
| [55] | Roinila T, Abdollahi H, Arrua S, et al. Online measurement of bus impedance of interconnected power electronics systems: Applying orthogonal sequences[C]//IEEE.2017 IEEE Energy Conversion Congress and Exposition (ECCE 2017), October 1-5, 2017, Cincinnati, Ohio, USA. New York: IEEE, 2017: 5783-5788. |
| [56] | Roinila T, Abdollahi H, Arrua S, et al. MIMO identification techniques in online measurement of bus impedance of in terconnected power-electronics systems[C]//IEEE.2017 IEEE Electric Ship Technologies Symposium (ESTS 2017), August 14-17, 2017, Arlington, Virginia, USA. New York:IEEE, 2017: 316-321. |
| [57] | Liu Chun-Lin. A tutorial of the wavelet transform[EB/OL]. (2010-02-23). http://disp.ee.ntu.edu.tw/tutorial/WaveletTutorial.pdf |
| [58] | Hoshi Y, Yakabe N, Isobe K, et al. Wavelet transformation to determine impedance spectra of lithium-ion rechargeable battery[J]. Journal of Power Sources, 2016,315:351-358. |
| [59] | Cohn M, Lempel A. On fast M-sequence transforms (Corresp.)[J]. IEEE Transactions on Information Theory, 1977,23(1):135-137. |
| [60] | Xiang N. Using M-sequences for determining the impulse responses of LTI-systems[J]. Signal Processing, 1992,28(2):139-152. |
| [61] | Gawad S, Sun T, Green N G, et al. Impedance spectro-scopy using maximum length sequences: Application to single cell analysis[J]. Review of Scientific Instruments, 2007,78(5):054301. |
| [62] | Rife D D, Vanderkooy J. Transfer-function measurement with maximum-length sequences[J]. Journal of the Audio Engineering Society, 1989,37(6):419-444. |
| [63] | Huang X D( 黄翔东) , Li W Y( 李文元) , Wang Z H( 王兆华) . Determination algorithm for impulse responses of LTI-meshwork based on fast m-sequence transform[J]. Journal of Data Acquisition & Processingl( 数据采集与处理), 2006,21(2):142-148. |
| [64] | Chu W T. Impulse-response and reverberation-decay mea-surements made by using a periodic pseudorandom sequence[J]. Applied Acoustics, 1990,29(3):193-205. |
| [65] | Manz J. A sequency-ordered fast Walsh transform[J]. IEEE Transactions on Audio and Electroacoustics, 1972,20(3):204-205. |
| [66] | Harwit M. Hadamard transform optics[M]. Amsterdam: Elsevier, 2012. |
| [67] | Berlekamp E. Algebraic coding theory[M]. Singapore: World Scientific Publishing Co Pte Ltd, 1968. |
| [68] | Delsarte P, Goethals J M, Mac Williams F J. On generalized Reed Muller codes and their relatives[J]. Information and Control, 1970,16(5):403-442. |
| [69] | Al Nazer R, Cattin V, Granjon P, et al. Classical EIS and square pattern signals comparison based on a well-known reference impedance[J]. World Electric Vehicle Journal, 2013,6(3):800-806. |
| [70] | Nejad S, Gladwin D T, Stone D A. A hybrid battery parameter identification concept for lithium-ion energy storage applications[C]//IEEE. IECON 2016 - 42nd Annual Conference of the IEEE Industrial Electronics Society, October 24-27, 2016, Florence, Italy. New York: IEEE, 2016: 1980-1985. |
| [71] | Ding F( 丁锋). System identification. Part B: Basic models for system description[J]. Journal of Nanjing University of Information Science and Technology: Natural Science Editionl( 南京信息工程大学学报(自然科学版)), 2011,3(2):97-117. |
| [72] | Fridholm B, Wik T, Nilsson M. Robust recursive impedance estimation for automotive lithium-ion batteries[J]. Journal of Power Sources, 2016,304:33-41. |
| [73] | Van Huffel S, Lemmerling P. Total least squares and errors-in-variables modeling: analysis, algorithms and applications[M]. London: Springer Science & Business Media, 2013. |
| [74] | Huffel S V, Vandewalle J. Analysis and properties of the generalized total least squares problem AX≈B when some or all columns in A are subject to error[J]. SIAM Jour-nal on Matrix Analysis and Applications, 1989,10(3):294-315. |
| [75] | Paige C C, Wei M. Analysis of the generalized total least squares problem AX≈ B when some columns of A are free of error[J]. Numerische Mathematik, 1993,65(1):177-202. |
| [76] | Jakubek S, Hametner C. Identification of neurofuzzy models using GTLS parameter estimation[J]. IEEE Transactions on Systems Man And Cybernetics Part B - Cybernetics, 2009,39(5):1121-1133. |
| [77] | Hu G X( 胡广侠), Xie J Y( 解晶莹). Some consideration for lithium-ion cells’ safety[J]. Journal of Electrochemistryl( 电化学), 2002,8(3):245-251. |
| [78] | Alavi S M M, Birkl C R, Howey D A. Time-domain fitting of battery electrochemical impedance models[J]. Journal of Power Sources, 2015,288:345-352. |
| [79] | Guo J W( 郭建伟), Wang J L( 王建龙). The pilot application of electrochemical impedance spectroscopy on dynamic proton exchange membranefuel cell[J]. Journal of Electrochemistryl( 电化学), 2018,24(6):687-696. |
| [80] | Xiao Y( 肖燕), Chang Y J( 常英杰), Zhang W( 张伟). Simulation analysis in dynamic performance of proton exchange membrane fuel cell under starting condition[J]. Journal of Electrochemistryl( 电化学), 2018,24(2):166-173. |
/
| 〈 |
|
〉 |
