A comparative study on time-frequency distribution techniques for battery parameters estimation system

Due to the degradation in battery lifetime directly impacts by load performance, reliability and safety operation of the battery cannot be guaranteed. In turn, safety precautions can be taken by monitoring battery performance from charging/discharging signals behaviour. Analyse the battery charging/...

Full description

Saved in:

Bibliographic Details
Main Author:	Mohamad Basir, Muhammad Sufyan Safwan
Format:	Thesis
Language:	English English
Published:	2017
Subjects:	T Technology (General)
Online Access:	http://eprints.utem.edu.my/id/eprint/20624/1/A%20Comparative%20Study%20On%20Time-Frequency%20Distribution%20Techniques%20For%20Battery%20Parameters%20Estimation%20System.pdf http://eprints.utem.edu.my/id/eprint/20624/2/A%20comparative%20study%20on%20time-frequency%20distribution%20techniques%20for%20battery%20parameters%20estimation%20system.pdf
Tags:	Add Tag No Tags, Be the first to tag this record!

id	my-utem-ep.20624
record_format	uketd_dc
institution	Universiti Teknikal Malaysia Melaka
collection	UTeM Repository
language	English English
advisor	Abdullah, Abdul Rahim
topic	T Technology (General) T Technology (General)
spellingShingle	T Technology (General) T Technology (General) Mohamad Basir, Muhammad Sufyan Safwan A comparative study on time-frequency distribution techniques for battery parameters estimation system
description	Due to the degradation in battery lifetime directly impacts by load performance, reliability and safety operation of the battery cannot be guaranteed. In turn, safety precautions can be taken by monitoring battery performance from charging/discharging signals behaviour. Analyse the battery charging/discharging signals become challenging as the signal characteristic appears at very low frequency. Therefore, fast and accurate analysis in estimating battery parameters for real-time monitoring system should be proposed and developed. This research presents analysis of the battery charging/discharging signals using a spectral analysis technique, namely periodogram and time-frequency distributions (TFDs) which are spectrogram and S-transform techniques. The analysed batteries are lead acid (LA), nickel-metal hydride (Ni-MH) and lithium-ion (Li-ion). From the equivalent circuit model (ECM) simulated using MATLAB, constant charging/discharging signals are presented, jointly, in time-frequency representation (TFR). From the TFR, battery signal characteristics are determined from the estimated parameters of instantaneous of total voltage (VTOT (t)), instantaneous of average voltage (VAVG (t)) and instantaneous of ripple factor voltage (VRF (t)). Hence, an equation for battery remaining capacity as a function of estimated parameter of VRF (t) using curve fitting tool is presented. In developing a real time automated battery parameters estimation system, best TFD is chosen in terms of accuracy of battery parameters, computational complexity in signal processing and memory size. Advantages in high accuracy for battery parameters estimation and low in memory size requirement makes S-transform technique is selected to be the best TFD. The accuracy of the system is verified with parameters estimation using ECM for each type of battery at a different capacity. The field testing results show that average mean absolute percentage error (MAPE) is around four percent. Thus, implementation of S-transform technique for real-time automated battery parameters estimation system is very appropriate for battery signal analysis.
format	Thesis
qualification_name	Master of Philosophy (M.Phil.)
qualification_level	Master's degree
author	Mohamad Basir, Muhammad Sufyan Safwan
author_facet	Mohamad Basir, Muhammad Sufyan Safwan
author_sort	Mohamad Basir, Muhammad Sufyan Safwan
title	A comparative study on time-frequency distribution techniques for battery parameters estimation system
title_short	A comparative study on time-frequency distribution techniques for battery parameters estimation system
title_full	A comparative study on time-frequency distribution techniques for battery parameters estimation system
title_fullStr	A comparative study on time-frequency distribution techniques for battery parameters estimation system
title_full_unstemmed	A comparative study on time-frequency distribution techniques for battery parameters estimation system
title_sort	comparative study on time-frequency distribution techniques for battery parameters estimation system
granting_institution	Universiti Teknikal Malaysia Melaka
granting_department	Faculty of Electrical Engineering
publishDate	2017
url	http://eprints.utem.edu.my/id/eprint/20624/1/A%20Comparative%20Study%20On%20Time-Frequency%20Distribution%20Techniques%20For%20Battery%20Parameters%20Estimation%20System.pdf http://eprints.utem.edu.my/id/eprint/20624/2/A%20comparative%20study%20on%20time-frequency%20distribution%20techniques%20for%20battery%20parameters%20estimation%20system.pdf
_version_	1747833988433575936
spelling	my-utem-ep.206242022-04-20T11:55:17Z A comparative study on time-frequency distribution techniques for battery parameters estimation system 2017 Mohamad Basir, Muhammad Sufyan Safwan T Technology (General) TK Electrical engineering. Electronics Nuclear engineering Due to the degradation in battery lifetime directly impacts by load performance, reliability and safety operation of the battery cannot be guaranteed. In turn, safety precautions can be taken by monitoring battery performance from charging/discharging signals behaviour. Analyse the battery charging/discharging signals become challenging as the signal characteristic appears at very low frequency. Therefore, fast and accurate analysis in estimating battery parameters for real-time monitoring system should be proposed and developed. This research presents analysis of the battery charging/discharging signals using a spectral analysis technique, namely periodogram and time-frequency distributions (TFDs) which are spectrogram and S-transform techniques. The analysed batteries are lead acid (LA), nickel-metal hydride (Ni-MH) and lithium-ion (Li-ion). From the equivalent circuit model (ECM) simulated using MATLAB, constant charging/discharging signals are presented, jointly, in time-frequency representation (TFR). From the TFR, battery signal characteristics are determined from the estimated parameters of instantaneous of total voltage (VTOT (t)), instantaneous of average voltage (VAVG (t)) and instantaneous of ripple factor voltage (VRF (t)). Hence, an equation for battery remaining capacity as a function of estimated parameter of VRF (t) using curve fitting tool is presented. In developing a real time automated battery parameters estimation system, best TFD is chosen in terms of accuracy of battery parameters, computational complexity in signal processing and memory size. Advantages in high accuracy for battery parameters estimation and low in memory size requirement makes S-transform technique is selected to be the best TFD. The accuracy of the system is verified with parameters estimation using ECM for each type of battery at a different capacity. The field testing results show that average mean absolute percentage error (MAPE) is around four percent. Thus, implementation of S-transform technique for real-time automated battery parameters estimation system is very appropriate for battery signal analysis. 2017 Thesis http://eprints.utem.edu.my/id/eprint/20624/ http://eprints.utem.edu.my/id/eprint/20624/1/A%20Comparative%20Study%20On%20Time-Frequency%20Distribution%20Techniques%20For%20Battery%20Parameters%20Estimation%20System.pdf text en public http://eprints.utem.edu.my/id/eprint/20624/2/A%20comparative%20study%20on%20time-frequency%20distribution%20techniques%20for%20battery%20parameters%20estimation%20system.pdf text en validuser https://plh.utem.edu.my/cgi-bin/koha/opac-detail.pl?biblionumber=106132 mphil masters Universiti Teknikal Malaysia Melaka Faculty of Electrical Engineering Abdullah, Abdul Rahim 1. Abdullah, A.R. and Sha'ameri, A.Z., 2011. Power quality analysis using bilinear time-frequencydistributions. EURASIP Journal on Advances in Signal Processing, 2010(1), pp.1-18. 2. Abdullah, A.R., Ahmad, N.H.T.H., Abidullah, N.A., Shamsudin, N.H. and Jopri, M.H., 2015.Performance Evaluation of Real Power Quality Disturbances Analysis using Stransform.Applied Mechanics & Materials. 3. Adi, V.S.K. and Chang, C.T., 2015. Development of flexible designs for PVFC hybrid powersystems. Renewable Energy, 74, pp.176-186. 4. Aggarwal, R., Singh, J.K., Gupta, V.K., Rathore, S., Tiwari, M. and Khare, A., 2011. Noisereduction of speech signal using wavelet transform with modified universalthreshold. International Journal of Computer Applications, 20(5), pp.14-19. 5. Aizpuru, I., Iraola, U., Canales, J.M., Echeverria, M. and Gil, I., 2013, June. Passive balancingdesign for Li-ion battery packs based on single cell experimental tests for a CCCV chargingmode. In 2013 International Conference on Clean Electrical Power (ICCEP), pp. 93-98. IEEE. 6. Aneke, M. and Wang, M., 2016. Energy storage technologies and real life applications.a state ofthe art review. Applied Energy, 179, pp.350-377. 7. Arepalli, S., Fireman, H., Huffman, C., Moloney, P., Nikolaev, P., Yowell, L., Kim, K., Kohl,P.A., Higgins, C.D., Turano, S.P. and Ready, W.J., 2005. Carbon-nanotube-basedelectrochemical double-layer capacitor technologies for spaceflight applications. Jom, 57(12),pp.26-31. 8. Auger, F., Flandrin, P., Lin, Y.T., McLaughlin, S., Meignen, S., Oberlin, T. and Wu, H.T., 2013.Time-frequency reassignment and synchrosqueezing: An overview. IEEE Signal ProcessingMagazine, 30(6), pp.32-41. 9. Aurilio, G., Gallo, D., Landi, C., Luiso, M., Rosano, A., Landi, M. and Paciello, V., 2015, May.A battery equivalent-circuit model and an advanced technique for parameter estimation. In 2015IEEE International Instrumentation and Measurement Technology Conference (I2MTC)Proceedings, pp.1705-1710. IEEE. 10. Aviyente, S. and Williams, W.J., 2004. A centrosymmetric kernel decomposition for timefrequencydistribution computation. IEEE Transactions on Signal Processing, 52(6), pp.1574-1584. 11. Bae, K.C., Choi, S.C., Kim, J.H., Won, C.Y. and Jung, Y.C., 2014, February. LiFePO4 dynamicbattery modeling for battery simulator. In 2014 IEEE International Conference on IndustrialTechnology (ICIT), pp.354-358. IEEE. 12. Barcellona, S., Brenna, M., Foiadelli, F., Longo, M. and Piegari, L., 2015. Analysis of ageingeffect on Li-polymer batteries. The Scientific World Journal, 2015.Battery Association of Japan, 2017. Total battery production statistics. [online] Available at:http://www.baj.or.jp/e/statistics/01.html [Accessed on 15 July 2017]. 13. Behera, H.S., Dash, P.K. and Biswal, B., 2010. Power quality time series data mining using Stransformand fuzzy expert system. Applied Soft Computing, 10(3), pp.945-955. 14. Bera, T.K., Saikia, M. and Nagaraju, J., 2013, July. A battery-based constant current source (Bb-CCS) for biomedical applications. In 2013 Fourth International Conference on Computing,Communications and Networking Technologies (ICCCNT), pp.1-5. IEEE. 15. Bhuvaneswari, P.T.V., Balakumar, R., Vaidehi, V. and Balamuralidhar, P., 2009, July. Solarenergy harvesting for wireless sensor networks. In Computational Intelligence, CommunicationSystems and Networks, 2009. CICSYN'09. First International Conference on (pp. 57-61). IEEE. 16. Biswal, M. and Dash, P.K., 2013. Detection and characterization of multiple power qualitydisturbances with a fast S-transform and decision tree based classifier. Digital SignalProcessing, 23(4), pp.1071-1083. 17. Biswal, M. and Dash, P.K., 2013. Measurement and Classification of simultaneous power signalpatterns with an S-transform variant and Fuzzy Decision Tree. IEEE Transactions on IndustrialInformatics, 9(4), pp.1819-1827. 18. Boashash, B., 2015. Time-frequency signal analysis and processing: a comprehensive reference.Academic Press. Elservier Ltd, pp.9. 19. Brodd, R.J. ed., 2012. Batteries for sustainability: selected entries from the encyclopedia ofsustainability science and technology. Springer Science & Business Media. 20. Brown, R.A., Lauzon, M.L. and Frayne, R., 2010. A general description of linear time-frequencytransforms and formulation of a fast, invertible transform that samples the continuous Stransformspectrum nonredundantly. IEEE Transactions on Signal Processing, 58(1), pp.281-290. 21. Bui, V.X. and Vyssotski, N.V., Dell Products LP, 2005. Information handling system including apower management apparatus capable of independently switching between a primary andsecondary battery. U.S. Patent 6,892,147. 22. Cai, C., Du, D., Liu, Z. and Ge, J., 2002, November. State-of-charge (SOC) estimation of highpower Ni-MH rechargeable battery with artificial neural network. In Proceedings of the 9thInternational Conference on Neural Information Processing (ICONIP'02), 2, pp.824-828. IEEE. 23. Cardoso, R.T., Tibola, J.R., Pai, M.D., Andrade, A.M., Martins, M.L.D.S. and Schuch, L., 2015,September. Modified current pulse charging method for lead-acid batteries based on phase-shiftfull-bridge converter in UPSs family applications. In 2015 17th European Conference on PowerElectronics and Applications (EPE'15 ECCE-Europe), pp.1-7. IEEE. 24. Ceylan, M. and Balikci, A., 2014, September. Design and implementation of an electronicconstant current DC load for battery discharge and power supply test systems. In 2014 16thInternational Power Electronics and Motion Control Conference and Exposition (PEMC),pp.924-927. IEEE. 25. Chan, C.C., Lo, E.W.C. and Weixiang, S., 2000. The available capacity computation modelbased on artificial neural network for lead.acid batteries in electric vehicles. Journal of PowerSources, 87(1), pp.201-204. 26. Chan, H.L. and Sutanto, D., 2000. A new battery model for use with battery energy storagesystems and electric vehicles power systems. In Power Engineering Society Winter Meeting, 1,pp.470-475. IEEE. 27. Chen, H., Cong, T.N., Yang, W., Tan, C., Li, Y. and Ding, Y., 2009. Progress in electrical energystorage system: A critical review. Progress in Natural Science, 19(3), pp.291-312. 28. Chen, J.J., Yang, F.C., Lai, C.C., Hwang, Y.S. and Lee, R.G., 2009. A high-efficiency multimodeLi.ion battery charger with variable current source and controlling previous-stage supplyvoltage. IEEE Transactions on Industrial Electronics, 56(7), pp.2469-2478. 29. Chen, L.R., 2007. A design of an optimal battery pulse charge system by frequency-variedtechnique. IEEE Transactions on Industrial Electronics, 54(1), pp.398-405. 30. Chen, M. and Rincon-Mora, G.A., 2006. Accurate, compact, and power-efficient Li-ion batterycharger circuit. IEEE Transactions on Circuits and Systems II: Express Briefs, 53(11), pp.1180-1184. 31. Chen, Y. and Evans, J.W., 1994. Three�]Dimensional Thermal Modeling of Lithium�]PolymerBatteries under Galvanostatic Discharge and Dynamic Power Profile. Journal of theElectrochemical Society, 141(11), pp.2947-2955. 32. Chen, Z., Fu, Y. and Mi, C.C., 2013. State of charge estimation of lithium-ion batteries in electricdrive vehicles using extended Kalman filtering. IEEE Transactions on VehicularTechnology, 62(3), pp.1020-1030. 33. Cho, J., Jeong, S. and Kim, Y., 2015. Commercial and research battery technologies for electricalenergy storage applications. Progress in Energy and Combustion Science, 48, pp.84-101. 34. Clark, M.S., 2007. Rate adjusted battery capacity testing and calculations, including what to dowhen someone says oops!. The Battcon Proceedings. 35. Coleman, M., Lee, C.K., Zhu, C. and Hurley, W.G., 2007. State-of-charge determination fromEMF voltage estimation: Using impedance, terminal voltage, and current for lead-acid andlithium-ion batteries. IEEE Transactions on industrial electronics, 54(5), pp.2550-2557. 36. Cowell, D.M. and Freear, S., 2010. Separation of overlapping linear frequency modulated (LFM)signals using the fractional Fourier transform. IEEE transactions on ultrasonics, ferroelectrics,and frequency control, 57(10), pp.2324-2333. 37. De Godoi, F.C., Wang, D.W., Zeng, Q., Wu, K.H. and Gentle, I.R., 2015. Dependence of LiNO 3decomposition on cathode binders in Li.S batteries. Journal of Power Sources, 288, pp.13-19. 38. Debnath, L. and Shah, F.A., 2002. Wavelet transforms and their applications, pp. 10. Boston:Birkhauser. 39. Di Rienzo, R., Baronti, F., Vellucci, F., Cignini, F., Ortenzi, F., Pede, G., Roncella, R. andSaletti, R., 2016, November. Experimental analysis of an electric minibus with small battery andfast charge policy. In Electrical Systems for Aircraft, Railway, Ship Propulsion and RoadVehicles & International Transportation Electrification Conference (ESARS-ITEC),International Conference on (pp. 1-6). IEEE 40. Di Yin, M., Youn, J., Park, D. and Cho, J., 2015, August. Dynamic Frequency and Duty CycleControl Method for Fast Pulse-Charging of Lithium Battery Based on Polarization Curve.In 2015 Ninth International Conference on Frontier of Computer Science and Technology,pp.40-45. IEEE. 41. Di Yin, Meng, Jiae Youn, Daejin Park, and Jeonghun Cho., 2015. "Efficient Frequency and DutyCycle Control Method for Fast Pulse-Charging of Distributed Battery Packs by Sharing CellStatus." In 2015 IEEE 12th Intl Conf on Ubiquitous Intelligence and Computing and 2015 IEEE12th Intl Conf on Autonomic and Trusted Computing and 2015 IEEE 15th Intl Conf on ScalableComputing and Communications and Its Associated Workshops (UIC-ATC-ScalCom), , pp.1813-1818. IEEE. 42. Doerffel, D. and Sharkh, S.A., 2006. A critical review of using the Peukert equation fordetermining the remaining capacity of lead-acid and lithium-ion batteries. Journal of powersources, 155(2), pp.395-400. 43. Doyle, M., Fuller, T.F. and Newman, J., 1993. Modeling of galvanostatic charge and discharge ofthe lithium/polymer/insertion cell. Journal of the Electrochemical Society, 140(6), pp.1526-1533. 44. Dubarry, M. and Liaw, B.Y., 2009. Identify capacity fading mechanism in a commercial LiFePO4 cell. Journal of Power Sources, 194(1), pp.541-549. 45. Dyer, C.K., Moseley, P.T., Ogumi, Z., Rand, D.A. and Scrosati, B. eds., 2013. Encyclopedia ofelectrochemical power sources. Newnes, pp.510 . 511. 46. El-Habrouk, M. and Darwish, M.K., 2001. Design and implementation of a modified Fourieranalysis harmonic current computation technique for power active filters using DSPs. IEEProceedings-Electric Power Applications, 148(1), pp.21-28. 47. Erjavec, J. and Thompson, R., 2014. Automotive technology: a systems approach. CengageLearning, pp.490. 48. Eskra, M., Ralston, P., Klein, M., Johnson, W., Erbacher, J. and Newman, B., 2001. Nickel-metalhydride replacement for VRLA and vented nickel-cadmium aircraft batteries. In The SixteenthAnnual Battery Conference on Applications and Advances, pp.11-15. IEEE. 49. Fang, W., Kwon, O.J. and Wang, C.Y., 2010. Electrochemical.thermal modeling of automotiveLi�]ion batteries and experimental validation using a three�]electrode cell. In International journalof energy research, 34(2), pp.107-115. 50. Farid, M.M., Khudhair, A.M., Razack, S.A.K. and Al-Hallaj, S., 2004. A review on phase changeenergy storage: materials and applications. Energy conversion and management, 45(9), pp.1597-1615. 51. Flores, R.A., 2002, October. State of the art in the classification of power quality events, anoverview. In Proc. 10th Int. Conf. Harmonics Quality of Power, 1, pp.17-20. 52. Fotouhi, A., Auger, D.J., Propp, K., Longo, S. and Wild, M., 2016. A review on electric vehiclebattery modelling: From Lithium-ion toward Lithium.Sulphur. Renewable and SustainableEnergy Reviews, 56, pp.1008-1021. 53. Fuhs, A., 2008. Hybrid vehicles: and the future of personal transportation. CRC Press. pp.118. 54. Gaines, L., Sullivan, J., Burnham, A. and Belharouak, I., 2011, January. Life-cycle analysis forlithium-ion battery production and recycling. In Transportation Research Board 90th AnnualMeeting, Washington, DC, pp.23-27. 55. Gao, Z., Chin, C.S., Woo, W.L., Jia, J. and Da Toh, W., 2015, April. Lithium-ion batterymodeling and validation for smart power system. In Computer, Communications, and ControlTechnology (I4CT), 2015 International Conference, pp.269-274. IEEE. 56. Gogus, Y. ed., 2009. Energy Storage Systems-Volume I. EOLSS Publications. 57. Gu, Y.H. and Bollen, M.H., 2000. Time-frequency and time-scale domain analysis of voltagedisturbances. IEEE Transactions on Power Delivery, 15(4), pp.1279-1284. 58. Hadjipaschalis, I., Poullikkas, A. and Efthimiou, V., 2009. Overview of current and future energystorage technologies for electric power applications. Renewable and sustainable energyreviews, 13(6), pp.1513-1522. 59. Hageman, S.C., 1993. Simple PSpice models let you simulate common battery types. ElectronicDesign News (EDN), 38(22), p.117-129. 60. Han, X., Chen, X., Li, J. and Hui, D., 2011, July. Application of wavelet analysis theory instorage energy system of lithium battery. In Control Conference (CCC), 2011 30thChinese (pp.5175-5177). IEEE. 61. Haq, I.N., Leksono, E., Iqbal, M., Sodami, F.N., Kurniadi, D. and Yuliarto, B., 2014, November.Development of battery management system for cell monitoring and protection. In 2014International Conference on Electrical Engineering and Computer Science (ICEECS), pp.203-208. IEEE. 62. He, H., Xiong, R. and Fan, J., 2011. Evaluation of lithium-ion battery equivalent circuit modelsfor state of charge estimation by an experimental approach. Energies, 4(4), pp.582-598. 63. He, H., Xiong, R., Guo, H. and Li, S., 2012. Comparison study on the battery models used for theenergy management of batteries in electric vehicles. Energy Conversion and Management, 64,pp.113-121. 64. He, H., Xiong, R., Zhang, X., Sun, F. and Fan, J., 2011. State-of-charge estimation of thelithium-ion battery using an adaptive extended Kalman filter based on an improved Theveninmodel. IEEE Transactions on Vehicular Technology, 60(4), pp.1461-1469. 65. Hekkala, A., Harjula, I., Panaitopol, D., Rautio, T. and Pacalet, R., 2011, June. Cooperativespectrum sensing study using welch periodogram. In Telecommunications (ConTEL),Proceedings of the 2011 11th international conference, pp.67-74. IEEE. 66. Holan, S.H., Wikle, C.K., Sullivan�]Beckers, L.E. and Cocroft, R.B., 2010. Modeling complexphenotypes: generalized linear models using spectrogram predictors of animal communicationsignals. Biometrics, 66(3), pp.914-924. 67. Hou, T.Y. and Shi, Z., 2013. Data-driven time.frequency analysis. Applied and ComputationalHarmonic Analysis, 35(2), pp.284-308. 68. Hu, X., Li, S. and Peng, H., 2012. A comparative study of equivalent circuit models for Li-ionbatteries. Journal of Power Sources, 198, pp.359-367. 69. Hua, A.C.C. and Syue, B.Z.W., 2010, June. Charge and discharge characteristics of lead-acidbattery and LiFePO4 battery. In 2010 International Power Electronics Conference (IPEC),pp.1478-1483. IEEE. 70. Huda, N.H.T., Abdullah, A.R. and Jopri, M.H., 2013, June. Power quality signals detection usingS-transform. In 2013 IEEE 7th International Power Engineering and Optimization Conference(PEOCO), pp.552-557. IEEE. 71. Hussein, A.A., Fardoun, A.A. and Stephen, S.S., 2016. An Online Frequency TrackingAlgorithm Using Terminal Voltage Spectroscopy for Battery Optimal Charging. IEEETransactions on Sustainable Energy, 7(1), pp.32-40. 72. Hussein, A.A.H. and Batarseh, I., 2011. A review of charging algorithms for nickel and lithiumbattery chargers. IEEE Transactions on Vehicular Technology, 60(3), pp.830-838. 73. Imamoglu, N., Lin, W. and Fang, Y., 2013. A saliency detection model using low-level featuresbased on wavelet transform. IEEE transactions on multimedia, 15(1), pp.96-105. 74. Jha, A.R., 2012. Next-generation batteries and fuel cells for commercial, military, and spaceapplications. CRC Press. 75. Jiang, J. and Zhang, C., 2015. Fundamentals and Application of Lithium-ion Batteries in ElectricDrive Vehicles. John Wiley & Sons. 76. Jiang, S., 2011. A parameter identification method for a battery equivalent circuit model. (No.2011-01-1367). SAE Technical Paper.Jing, Y.P., 2005. Correcting for the alias effect when measuring the power spectrum using a fastFourier transform. The Astrophysical Journal, 620(2), pp.559. 77. Joesten, M.D., Hogg, J.L. and Castellion, M.E., 2006. The world of chemistry: essentials:essentials. Cengage Learning. pp.212. 78. Jongerden, M.R. and Haverkort, B.R., 2009. Which battery model to use? IET software, 3(6),pp.445-457. 79. Kasim, R., Abdullah, A.R., Selamat, N.A., Abidullah, N.A. and T. N. S. T. Zawawi, 2015,August. Lead Acid Battery Analysis Using Spectrogram. In Applied Mechanics and Materials,785, pp.692-696. Trans Tech Publications. 80. Khan, M.A.S., Hinchey, M.J. and Rahman, M.A., 2009, October. Implementation of WaveletController for Battery Storage System of Hybrid Electric Vehicle. In Industry ApplicationsSociety Annual Meeting, 2009. IAS 2009, pp.1-8. IEEE. 81. Khan, M.R., Mulder, G. and Van Mierlo, J., 2014. An online framework for state of chargedetermination of battery systems using combined system identification approach. Journal ofPower Sources, 246, pp.629-641. 82. Kia, S.H., Henao, H. and Capolino, G.A., 2013, March. Efficient digital signal processingtechniques for induction machines fault diagnosis. In Electrical Machines Design Control andDiagnosis (WEMDCD), 2013 IEEE Workshop, pp.232-246. IEEE. 83. Kim, J., Chun, C.Y. and Cho, B.H., 2015, May, Experiment-based Analysis between the WaveletTransform and the Discrete Wavelet Packet Transform. In EVS28 International Electric VehicleSymposium and Exhibition, pp.1-5. 84. Kim, T. and Qiao, W., 2011. A hybrid battery model capable of capturing dynamic circuitcharacteristics and nonlinear capacity effects. IEEE Transactions on Energy Conversion, 26(4),pp.1172-1180. 85. Kim, T. and Qiao, W., 2011. A hybrid battery model capable of capturing dynamic circuitcharacteristics and nonlinear capacity effects. IEEE Transactions on Energy Conversion, 26(4),pp.1172-1180. 86. Kim, Y.H. and Ha, H.D., 1997. Design of interface circuits with electrical battery models. IEEETransactions on Industrial Electronics, 44(1), pp.81-86. 87. Klein, R., Chaturvedi, N.A., Christensen, J., Ahmed, J., Findeisen, R. and Kojic, A., 2010, June.State estimation of a reduced electrochemical model of a lithium-ion battery. In Proceedings ofthe 2010 American Control Conference, pp.6618-6623. IEEE. 88. Krieger, E.M., 2013. Effects of variability and rate on battery charge storage andlifespan (Doctoral dissertation, Princeton University). 89. Kwok, H.K. and Jones, D.L., 2000. Improved instantaneous frequency estimation using anadaptive short-time Fourier transform. IEEE Transactions on Signal Processing, 48(10),pp.2964-2972. 90. Kyung, C.M, Yoo, S, 2011. Energy-Aware System Design: Algorithms and Architectures.Springer Science & Business Media. 91. Lahyani, A., Venet, P., Guermazi, A. and Troudi, A., 2013. Battery/supercapacitors combinationin uninterruptible power supply (UPS). IEEE transactions on power electronics, 28(4), pp.1509-1522. 92. Larminie, J. and Lowry, J., 2003. Electric vehicle modelling. Electric Vehicle TechnologyExplained, pp.58. 93. Le Roux, J., Kameoka, H., Ono, N. and Sagayama, S., 2010, September. Fast signalreconstruction from magnitude STFT spectrogram based on spectrogram consistency. In Proc.Int. Conf. Digital Audio Effects (Vol. 10). 94. Leadbetter, J. and Swan, L.G., 2012. Selection of battery technology to support grid-integratedrenewable electricity. Journal of Power Sources, 216, pp.376-386. 95. Lee, I.W. and Dash, P.K., 2003. S-transform-based intelligent system for classification of powerquality disturbance signals. IEEE Transactions on Industrial Electronics, 50(4), pp.800-805. 96. Lee, S. and Kim, J., 2015. Discrete wavelet transform-based denoising technique for advancedstate-of-charge estimator of a lithium-ion battery in electric vehicles. Energy, 83, pp.462-473. 97. Li, B., Zhang, P.L., Liu, D.S., Mi, S.S., Ren, G.Q. and Tian, H., 2011. Feature extraction forrolling element bearing fault diagnosis utilizing generalized S transform and two-dimensionalnon-negative matrix factorization. Journal of Sound and Vibration, 330(10), pp.2388-2399. 98. Li, P., Pan, Y., Ma, Y. and Qin, Q., 2011, August. Study on an active voltage equalization chargesystem of a series battery pack. In 2011 International Conference on Electronic and MechanicalEngineering and Information Technology (EMEIT), 1, pp.141-144. IEEE. 99. Li, X., Xiao, M. and Choe, S.Y., 2011, June. Reduced order of electrochemical model for apouch type high power Li-polymer battery. In 2011 International Conference on Clean ElectricalPower (ICCEP), pp.593-599. IEEE. 100. Lin, C.H., Hsieh, C.Y. and Chen, K.H., 2010. A Li-ion battery charger with smooth controlcircuit and built-in resistance compensator for achieving stable and fast charging. IEEETransactions on Circuits and Systems I: Regular Papers, 57(2), pp.506-517. 101. Lopatka, M., Olivier, A., Laplanche, C., Zarzycki, J. and Motsch, J.F., 2005. An attractivealternative for sperm whale click detection using the wavelet transform in comparison to theFourier spectrogram. Aquatic Mammals, 31(4), p.463. 102. Lu, L., Han, X., Li, J., Hua, J. and Ouyang, M., 2013. A review on the key issues for lithium-ionbattery management in electric vehicles. Journal of power sources, 226, pp.272-288. 103. Lu, W.K. and Zhang, Q., 2009. Deconvolutive short-time Fourier transform spectrogram. IEEESignal Processing Letters, 16(7), pp.576-579. 104. Lucas, A. and Chondrogiannis, S., 2016. Smart grid energy storage controller for frequencyregulation and peak shaving, using a vanadium redox flow battery. International Journal ofElectrical Power & Energy Systems, 80, pp.26-36. 105. Luo, X., Wang, J., Dooner, M. and Clarke, J., 2015. Overview of current development inelectrical energy storage technologies and the application potential in power systemoperation. Applied Energy, 137, pp.511-536. 106. Lynn, T.J. and Sha'ameri, A.Z., 2007, May. Comparison between the performance ofspectrogram and multi-window spectrogram in digital modulated communication signals.In IEEE International Conference on Telecommunications and Malaysia InternationalConference on Communications, 2007. ICT-MICC 2007, pp.97-101. IEEE. 107. McDonald, J.C. ed., 2013. Fundamentals of digital switching. Springer Science & BusinessMedia. 108. Melentjev, S. and Lebedev, D., 2013, January. Overview of simplified mathematical models ofbatteries. In 13th International Symposium" Topical problems of education in the field ofelectrical and power engineering"..Doctoral school of energy and geotechnology: Parnu,Estonia, pp.231-235. 109. Mellit, A., Benghanem, M. and Kalogirou, S.A., 2007. Modeling and simulation of a stand-alonephotovoltaic system using an adaptive artificial neural network: Proposition for a new sizingprocedure. Renewable energy, 32(2), pp.285-313. 110. Micea, M.V., Ungurean, L., Carstoiu, G.N. and Groza, V., 2011. Online state-of-healthassessment for battery management systems. IEEE Transactions on Instrumentation andMeasurement, 60(6), pp.1997-2006. 111. Montaru, M. and Pelissier, S., 2010. Frequency and temporal identification of a li-ion polymerbattery model using fractional impedance. Oil & Gas Science and Technology.Revue de l�fInstitutFrancais du Petrole, 65(1), pp.67-78. 112. Moura, S.J., Chaturvedi, N.A. and Krsti., M., 2014. Adaptive partial differential equationobserver for battery state of charge/state of health estimation via an electrochemicalmodel. Journal of Dynamic Systems, Measurement, and Control, 136(1), p.011015. 113. Ng, K.S., Moo, C.S., Chen, Y.P. and Hsieh, Y.C., 2009. Enhanced coulomb counting method forestimating state-of-charge and state-of-health of lithium-ion batteries. Applied energy, 86(9),pp.1506-1511. 114. Nishi, Y., 2001. Lithium ion secondary batteries; past 10 years and the future. Journal of PowerSources, 100(1), pp.101-106. 115. Osman, S.R., Rahim, N.A. and Jeyraj, S., 2014, November. Single current sensor with multipleconstant current charging method in solar battery charger. In 3rd IET International Conferenceon Clean Energy and Technology (CEAT), pp.1-5. IET. 116. Pattipati, B., Sankavaram, C. and Pattipati, K., 2011. System identification and estimationframework for pivotal automotive battery management system characteristics. IEEE Transactionson Systems, Man, and Cybernetics, Part C (Applications and Reviews), 41(6), pp.869-884. 117. Pavkovi., D., Hrgeti., M., Komljenovi., A. and Smetko, V., 2014, October. Battery current andvoltage control system design with charging application. In 2014 IEEE Conference on ControlApplications (CCA), pp.1133-1138. IEEE. 118. Pavlov, D., 2011. Lead-acid batteries: science and technology. Elsevier. 119. Pei, S.C. and Huang, S.G., 2012. STFT with adaptive window width based on the chirprate. IEEE Transactions on Signal Processing, 60(8), pp.4065-4080. 120. Peng, Z.K. and Chu, F.L., 2004. Application of the wavelet transform in machine conditionmonitoring and fault diagnostics: a review with bibliography. Mechanical systems and signalprocessing, 18(2), pp.199-221. 121. Pitas, I., 2000. Digital image processing algorithms and applications. John Wiley & Sons.,pp.97. 122. Plett, G.L., 2004. Extended Kalman filtering for battery management systems of LiPB-basedHEV battery packs: Part 2. Modeling and identification. Journal of power sources, 134(2),pp.262-276. 123. Pop, V., Bergveld, H.J., Notten, P.H.L. and Regtien, P.P., 2005. State-of-the-art of battery stateof-charge determination. Measurement Science and Technology, 16(12), pp.R93. 124. Praisuwanna, N. and Khomfoi, S., 2013, September. A seal lead-acid battery charger forprolonging battery lifetime using superimposed pulse frequency technique. In 2013 IEEE EnergyConversion Congress and Exposition. pp.1603-1609. IEEE. 125. Rakhmatov, D.N., 2005, May. Battery voltage prediction for portable systems. In 2005 IEEEInternational Symposium on Circuits and Systems, pp.4098-4101. IEEE. 126. Rao, V., Singhal, G., Kumar, A. and Navet, N., 2005, January. Battery model for embeddedsystems. In VLSI Design, 2005. 18th International Conference on (pp.105-110). IEEE. 127. Ray, P.K., Mohanty, S.R. and Kishor, N., 2011. Disturbance detection in grid-connecteddistributed generation system using wavelet and S-transform. Electric Power SystemsResearch, 81(3), pp.805-819. 128. Rodriguez, A., Aguado, J.A., Martin, F., Lopez, J.J., Munoz, F. and Ruiz, J.E., 2012. Rule-basedclassification of power quality disturbances using S-transform. Electric power systemsResearch, 86, pp.113-121. 129. Rong, P. and Pedram, M., 2006. An analytical model for predicting the remaining batterycapacity of lithium-ion batteries. IEEE Transactions on Very Large Scale Integration (VLSI)Systems, 14(5), pp.441-451. 130. Samadi, M.F., Alavi, S.M. and Saif, M., 2012, December. An electrochemical model-basedparticle filter approach for Lithium-ion battery estimation. In 2012 IEEE 51st IEEE Conferenceon Decision and Control (CDC), pp.3074-3079. IEEE. 131. Santhanagopalan, S., Zhang, Q., Kumaresan, K. and White, R.E., 2008. Parameter estimation andlife modeling of lithium-ion cells. Journal of The Electrochemical Society, 155(4), pp.A345-A353. 132. Schiffer, J., Sauer, D.U., Bindner, H., Cronin, T., Lundsager, P. and Kaiser, R., 2007. Modelprediction for ranking lead-acid batteries according to expected lifetime in renewable energysystems and autonomous power-supply systems. Journal of Power Sources, 168(1), pp.66-78. 133. Sejdi., E., Djurovi., I. and Jiang, J., 2009. Time--frequency feature representation using energyconcentration: An overview of recent advances. Digital Signal Processing, 19(1), pp.153-183. 134. Shahzad, M.I., Iqbal, S., Taib, S. and Masri, S., 2015, November. Design of a PEV batterycharger with high power factor using half-bridge LLC-SRC operating at resonance frequency.In 2015 IEEE International Conference on Control System, Computing and Engineering(ICCSCE) (pp.424-429). IEEE. 135. Slater, M.D., Kim, D., Lee, E. and Johnson, C.S., 2013. Sodium�]ion batteries. AdvancedFunctional Materials, 23(8), pp.947-958. 136. Slepski, P., Darowicki, K. and Andrearczyk, K., 2009. On-line measurement of cell impedanceduring charging and discharging process. Journal of Electroanalytical Chemistry, 633(1),pp.121-126. 137. Sullivan, J.L. and Gaines, L., 2010. A review of battery life-cycle analysis: state of knowledgeand critical needs (No. ANL/ESD/10-7). Argonne National Laboratory (ANL). 138. Szmajda, M., Gorecki, K. and Mroczka, J., 2010. Gabor transform, spwvd, gabor-wignertransform and wavelet transform-tools for power quality monitoring. Metrology andMeasurement Systems, 17(3), pp.383-396. 139. Tang, B., Liu, W. and Song, T., 2010. Wind turbine fault diagnosis based on Morlet wavelettransformation and Wigner-Ville distribution. Renewable Energy, 35(12), pp.2862-2866. 140. Tarabay, J. and Karami, N., 2015, April. Nickel Metal Hydride battery: Structure, chemicalreaction, and circuit model. In 2015 Third International Conference on Technological Advancesin Electrical, Electronics and Computer Engineering (TAEECE), pp.22-26. IEEE. 141. Tremblay, O. and Dessaint, L.A., 2009. Experimental validation of a battery dynamic model forEV applications. World Electric Vehicle Journal, 3(1), pp.1-10. 142. Trobs, M. and Heinzel, G., 2006. Improved spectrum estimation from digitized time series on alogarithmic frequency axis. Measurement, 39(2), pp.120-129. 143. Van Schalkwijk, W. and Scrosati, B. eds., 2007. Advances in lithium-ion batteries. SpringerScience & Business Media. 144. Vasebi, A., Bathaee, S.M.T. and Partovibakhsh, M., 2008. Predicting state of charge of lead-acidbatteries for hybrid electric vehicles by extended Kalman filter. Energy Conversion andManagement, 49(1), pp.75-82. 145. Verbrugge, M. and Tate, E., 2004. Adaptive state of charge algorithm for nickel metal hydridebatteries including hysteresis phenomena. Journal of Power Sources, 126(1), pp.236-249. 146. Wai, R.J. and Jhung, S.J., 2012. Design of energy-saving adaptive fast-charging control strategyfor Li-Fe-PO 4 battery module. IET Power Electronics, 5(9), pp.1684-1693. 147. Wang, B.C., 2008. Digital signal processing techniques and applications in radar imageprocessing (Vol. 91). John Wiley & Sons. 148. Wang, D., Miao, Q. and Pecht, M., 2013. Prognostics of lithium-ion batteries based on relevancevectors and a conditional three-parameter capacity degradation model. Journal of PowerSources, 239, pp.253-264. 149. Wang, J., Zou, K., Chen, C. and Chen, L., 2010, February. A high frequency battery model forcurrent ripple analysis. In Applied Power Electronics Conference and Exposition (APEC), 2010Twenty-Fifth Annual IEEE, pp.676-680. IEEE. 150. Wang, M. and Mamishev, A.V., 2004. Classification of power quality events using optimal timefrequencyrepresentations-Part 1: theory. IEEE Transactions on Power Delivery, 19(3), pp.1488-1495. 151. Wang, Q., Ping, P., Zhao, X., Chu, G., Sun, J. and Chen, C., 2012. Thermal runaway caused fireand explosion of lithium ion battery. Journal of power sources, 208, pp.210-224. 152. Wang, W., Chung, H.S.H. and Zhang, J., 2014. Near-real-time parameter estimation of anelectrical battery model with multiple time constants and SOC-dependent capacitance. IEEETransactions on Power Electronics, 29(11), pp.5905-5920. 153. Weicker, P., 2013. A systems approach to Lithium-Ion battery management. Artech house,pp.203. 154. Welch, P.D., 1967. The use of fast Fourier transform for the estimation of power spectra: Amethod based on time averaging over short, modified periodograms. IEEE Transactions on audioand electroacoustics, 15(2), pp.70-73. 155. Wen, H., Teng, Z. and Guo, S., 2010. Triangular self-convolution window with desirablesidelobe behaviors for harmonic analysis of power system. IEEE Transactions onInstrumentation and Measurement, 59(3), pp.543-552. 156. Wey, C.L. and Jui, P.C., 2013, December. A unitized charging and discharging smart batterymanagement system. In 2013 International Conference on Connected Vehicles and Expo(ICCVE), pp.903-909. IEEE. 157. Winter, M. and Brodd, R.J., 2004. What are batteries, fuel cells, and supercapacitors?.Wood, E., Alexander, M. and Bradley, T.H., 2011. Investigation of battery end-of-life conditionsfor plug-in hybrid electric vehicles. Journal of Power Sources, 196(11), pp.5147-5154. 158. Wu, Y. ed., 2015. Lithium-Ion Batteries: Fundamentals and Applications (Vol. 4). CRC PXia, Z., Qahouq, J.A.A., Phillips, E. and Gentry, R., 2017, March. A simple and upgradableautonomous battery aging evaluation and test system with capacity fading and AC impedancespectroscopy measurement. In Applied Power Electronics Conference and Exposition (APEC),2017 IEEE (pp. 951-958). IEEE. 159. Xiang, C., Wu, S., Wang, W. and Zhao, F., 2013. A research on charge and discharge strategy ofhybrid batteries based on the electrochemical characteristics. Intelec 2013 160. .Xiong, R., He, H., Guo, H. and Ding, Y., 2011. Modeling for lithium-ion battery used in electricvehicles. Procedia Engineering, 15, pp.2869-2874. 161. Xiong, R., He, H., Sun, F. and Zhao, K., 2013. Evaluation on state of charge estimation ofbatteries with adaptive extended Kalman filter by experiment approach. IEEE Transactions onVehicular Technology, 62(1), pp.108-117. 162. Xiong, R., Sun, F., Chen, Z. and He, H., 2014. A data-driven multi-scale extended Kalmanfiltering based parameter and state estimation approach of lithium-ion olymer battery in electricvehicles. Applied Energy, 113, pp.463-476. 163. Yan, R., Gao, R.X. and Chen, X., 2014. Wavelets for fault diagnosis of rotary machines: Areview with applications. Signal processing, 96, pp.1-15. 164. Yao, L.W., Aziz, J.A., Kong, P.Y. and Idris, N.R.N., 2013, November. Modelling of lithium-ionbattery using MATLAB/simulink. In IECON 2013-39th Annual Conference of the IndustrialElectronics Society, pp.1729-1734. IEEE. 165. Yoshio, M., Brodd, R.J. and Kozawa, A., 2009. Lithium-Ion Batteries (Vol. 1). New York:Springer. 166. Young, K., Wang, C., Wang, L.Y. and Strunz, K., 2013. Electric vehicle battery technologies.In Electric Vehicle Integration into Modern Power Networks (pp.15-56). Springer New York. pp27. 167. Zawawi, T.N.S.T., Abdullah, A.R., Shair, E.F., Halim, I. and Rawaida, O., 2013, December.Electromyography signal analysis using spectrogram. In 2013 IEEE Student Conference onResearch and Development (SCOReD), pp.319-324. IEEE. 168. Zhang, H. and Chow, M.Y., 2010, July. Comprehensive dynamic battery modelling for PHEVapplications. In IEEE PES General Meeting, pp.1-6. IEEE. 169. Zhang, J. and Lee, J., 2011. A review on prognostics and health monitoring of Li-ion battery.Journal of Power Sources, 196(15), pp.6007-6014. 170. Zhao, K., Pharr, M., Vlassak, J.J. and Suo, Z., 2010. Fracture of electrodes in lithium-ionbatteries caused by fast charging. Journal of Applied Physics, 108(7), p.073517. 171. Zheng, Y., Ouyang, M., Li, X., Lu, L., Li, J., Zhou, L. and Zhang, Z., 2016. Recording frequencyoptimization for massive battery data storage in battery management systems. AppliedEnergy, 183, pp.380-389. 172. Zhu, W.H., Zhu, Y. and Tatarchuk, B.J., 2014. Self-discharge characteristics and performancedegradation of Ni-MH batteries for storage applications. International Journal of HydrogenEnergy, 39(34), pp.19789-19798. 173. Zin, Z.M., Salleh, S.H., Daliman, S. and Sulaiman, M.D., 2003, August. Analysis of heart soundsbased on continuous wavelet transform. In Research and Development, 2003. SCORED 2003.Proceedings. Student Conference on (pp. 19-22). IEEE.

A comparative study on time-frequency distribution techniques for battery parameters estimation system

Similar Items