Based on the electromechanical equivalent circuit theory, equations related to the resonance frequency and the magnifying coefficient of a quarter-wave vibrator and a quarter-wave taper transition horn were deduced, respectively. A series of 3D models of ultrasonic composite transducers with various conical section length was also established. To reveal the influences of the conical section length and the prestressed bolt on the dynamic characteristics (resonance frequency, amplitude, displacement node, and the maximum equivalent stress) of the models and the design accuracy, finite element (FE) analyses were carried out. The results show that the addition of prestressed bolt increases the resonance frequency and causes the displacement node on the center axis to move towards the small cylindrical section. As the conical section length rises, the increment of resonance frequency reduces and tends to a stable value of 360 Hz while the displacement of the node on the center axis becomes lager and gradually approaches 1.5 mm. Furthermore, the amplitude of the output terminal is stable at 16.18 μm under 220 V peak-topeak (77.8 VRMS) sinusoidal potential excitation. After that, a prototype was fabricated and validated experiments were conducted. The experimental results are consistent with that of theory and simulations. It provides theoretical basis for the design and optimization of small-size, large-amplitude, and high-power composite transducers.
The paper presents a new electromechanical amplifying device i.e., an electromechanical biological transistor. This device is located in the outer hair cell (OHC), and constitutes a part of the Cochlear amplifier. The physical principle of operation of this new amplifying device is based on the phenomenon of forward mechanoelectrical transduction that occurs in the OHC's stereocilia. Operation of this device is similar to that of classical electronic Field Effect Transistor (FET). In the considered electromechanical transistor the input signal is a mechanical (acoustic) signal. Whereas the output signal is an electric signal. It has been shown that the proposed electromechanical transistor can play a role of the active electromechanical controlled element that has the ability to amplify the power of input AC signals. The power required to amplify the input signals is extracted from a battery of DC voltage. In the considered electromechanical transistor, that operates in the amplifier circuit, mechanical input signal controls the flow of electric energy in the output circuit, from a battery of DC voltage to the load resistance. Small signal equivalent electrical circuit of the electromechanical transistor is developed. Numerical values of the electrical parameters of the equivalent circuit were evaluated. The range, which covers the levels of input signals (force and velocity) and output signals (voltage, current) was determined. The obtained data are consistent with physiological data. Exemplary numerical values of currents, voltages, forces, vibrational velocities and power gain (for the assumed input power levels below 1 picowatt (10-12 W)), were given. This new electromechanical active device (transistor) can be responsible for power amplification in the cochlear amplifier in the inner ear.
[1] Komarnicki P., Kranhold M., Styczynski Z., Sektorenkopplung. Energetisch-nachhaltige Wirtschaft der Zukunft, ISBN: 978-3-658-33559-5, Springer Verlag (2021), DOI: 10.1007/978-3-658-33559-5.
[2] Komarnicki P., Lombardi P., Styczynski Z., Elektrische Energiespeichersysteme - Flexibilitätsoptionen für Smart Gridshardcover, ISBN 978-3-662-62801-0, Springer Verlag (2021), DOI: 10.1007/978-3- 662-62802-7.
[3] Forschungsstelle für Energiewirtschaft e.V. (FfE), Abschlussbericht zum Projekt: Kurzstudie Elektromobilität Modellierung für die Szenarienentwicklung des Netzentwicklungsplan, München (2019).
[4] Dechent P., Epp A., Jöst D., Preger Y., Attia P., Li W., Sauer D.U., ENPOLITE: Comparing lithium-ion cells across energy, power, lifetime, and temperature, ACS Energy Letters, vol. 6, pp. 2351–2335 (2021), DOI: 10.1021/acsenergylett.1c00743.
[5] Sterner M., Stadler I., Handbook of energy storage. Demand, technologies, integration, Springer Verlag (2019), DOI: 10.1007/978-3-662-55504-0.
[6] Rudnicki T., Wojcicki S., Metody wyznaczania stanu naladowania akumulatorow stosowane w pojazdach elektrycznych, Informatyka, Automatyka, Pomiary w Gospodarce i Ochronie Środowiska (in Polish), vol. 3, ISSN 2083-0157 (2014), DOI: 10.5604/20830157.1121381.
[7] Hannam M.A., Lipu M.S.H., Hussain A., Mohamed A., A review of lithium-ion battery state of charge estimation and management system in electric vehicle applications: Challenges and recommendations, Renewable and Sustainable Energy Review, vol. 78 pp. 834–854 (2017), DOI: 10.1016/j.rser.2017.05.001.
[8] Dai H., Jiang B., Hu X.-S., Lin X., Wei X., Pecht M., Advance battery management strategies for sustainable energy future: Multilayer design concept and research trends, Renewable and Sustainable Energy Review, vol. 138, p. 110480 (2021), DOI: 10.1016/j.rser.2020.110480.
[9] Waag W., Fleicher C., Sauer D.U., Critical review of the methods for monitoring of lithium-ion batteries in electric and hybrid vehicles, Journal of Power Sources, vol. 258, pp. 321–339 (2014), DOI: 10.1016/j.jpowsour.2014.02.064.
[10] Zhang Y.-J., Guo C., Liu Y.-G., Ding F., Chen Z., Hao W., A novel strategy for power sources management in connected plug-in hybrid electric vehicles based on mobile edge computation framework, Journal of Power Sources, vol. 477, p. 228650 (2020), DOI: 10.1016/j.jpowsour.2020.228650.
[11] Styczynski P., Lombardi P., Styczynski Z., Electric energy storage systems, Report CIGRE WG C6.15, ISBN: 978-2-85873-147-3, no. 458, CIGRE, Paris (2011), DOI: 10.1007/978-3-662-53275-1.
[12] Rancillo G., Pocha Pinto Lucas A., Kotsakis E., Fulli G., Merlo M., Delfanti M., Masera M., Modelling a large-scale battery energy storage system for power grid application analysis, Energies, vol. 12, no. 17, p. 3312 (2019), DOI: 10.3390/en12173312.
[13] Zeh A., Müller M., Naumann M., Hesse H.C., Jossen A., Witzmann R., Fundamentals of using battery energy storage systems to provide primary control reserves in Germany, Batteries, vol. 2, p. 49 (2016), DOI: 10.3390/batteries2030029.
[14] Komarnicki P., Energy storage systems: power grid and marked use cases, Archives of Electrical Engineering, vol. 65, no. 3, pp. 495–511 (2016), DOI: 10.1515/aee-2016-0036.
[15] Ceran B., A comparative analysis of energy storage technologies, Energy Policy Journal, vol. 21, no. 3, pp. 97–110 (2018), DOI: 10.24425/124498.
[16] Parol M., Rokicki L., Parol S., Towards optimal operation in rural low voltage microgrids, Bul- letin of Polish Academy of Sciences, Technical Sciences, vol. 67, no. 4, pp. 799–812 (2019), DOI: 10.24425/bpasts.2019.130189.
[17] Paliwal N.K., Singh A.K., Singh N.K., Short-term optimal energy management in stand-alone mi- crogrid with battery energy storage, Archives of Electrical Engineering, vol. 67, no. 3, pp. 499–513 (2017), DOI: 10.3390/en13061454.
[18] Kucevica D., Tepe B., Englberger S., Parlikar A., Mühlbauer M., Bohlen O., Jossen A., Hesse H., Standard battery energy storage system profiles: analysis of various applications for stationary energy storage systems using a holistic simulation framework, Journal of Energy Storage, vol. 28, no. 4, p. 101077 (2020), DOI: 10.1016/j.est.2019.101077.
[19] Ghazavidozein M., Gomis-Bellmunt O., Mancarella P., Simultaneous provision of dynamic active and reactive power response from utility-scale battery energy storage system in weak grids, IEEE Transactions on Power System (2021), DOI: 110.1109/TPWRS.2021.3076218.
[20] European Commission, Commission Regulation (EU) 2017/1485 of establishing a guideline on electricity transmission system operation, Official Journal of the European Union, vol. 220, pp. 1–120 (2017).
[21] Li X.-J., Yao L.-Z., Hui D., Optimal control and management of a large-scale battery energy storage system to mitigate fluctuation and intermittence of renewable generations, Journal of Modern Power Systems and Clean Energy, vol. 4, no. 4, pp. 593–603 (2016), DOI: 10.1007/s40565-016-0247-y.
[22] Podder S., Khan M.Z.R., Comparison of lead acid and Li-ion battery in solar home system of Bangladesh, 5th International Conference on Informatics, Electronics and Vision (ICIEV), pp. 434–438 (2016), DOI: 10.1109/ICIEV.2016.7760041.
[23] Hoppmann J., Volland J., Schmidt T.S., Hoffmann V.H., The economic viability of battery storage for residential solar photovoltaic systems – a review and a simulation model, Renewable and Sustainable Energy Reviews, vol. 39, pp. 1101–1118 (2014), DOI: 10.1016/j.rser.2014.07.068.
[24] Zhang R., Xia B., Li B., Cao L., Lai Y., Zheng W., Wang H., Wang W., State of the art of lithium-ion battery SOC estimation for electrical vehicles, Energies, vol. 11, no. 7, p. 1820 (2018), DOI: 10.3390/en11071820.
[25] Hallmann M., Wenge C., Komarnicki P., Evaluation methods for battery storage systems, IEEE 12th International Conference on Electrical Power Quality and Utilization (EPQU) (2020), DOI: 10.1109/EPQU50182.2020.9220321.
[26] Khandorin M.M., Estimation of the residual capacity of a lithium-ion battery in real time, (in Russian), in Khandorin M.M., Bukreev V.G. (eds.), Electrochemical power engineering (in Russian), pp. 65–693 (2014).
[27] May G.J., Standby batteries requirements for telecommunications power, Journal of Power Sources, vol. 158, no. 2, pp. 1117–1123 (2006), DOI: 10.1016/j.jpowsour.2006.02.083.
[28] Wikipedia, Electrical System of the International Space Station, https://en.wikipedia.org/wiki/Elect rical_system_of_the_International_Space_Station, accessed April 2021.
[29] Heussen K., Koch S., Ubig A., Anderson G., Unified system-level modeling of intermittent renewable energy sources and energy storage for power system operation, IEEE System Journal, vol. 6, no. 1, pp. 140–151 (2011), DOI: 10.1109/JSYST.2011.2163020.
[30] Buchholz B., Frey H., Lewaldt N., Stephanblome T., Schwagerl C., Styczynski Z.A., Advanced planning and operation of dispersed generation ensuring power quality, security and efficiency in distribution systems, CIGRE 2004, Invited paper C6-206, CD-ROM, Paris (2004).
[31] Codeca F., Savaresi S.M., Manzoni V., The mix estimation algorithm for battery state-of-charge estimator: analysis of the sensitivity to measurement errors, Proceedings of the 48th IEEE Con- ference on Decision and Control, held jointly with 28th Chinese Control Conference (2009), DOI: 10.1109/CDC.2009.5399759.
[32] Nejad S., Gladwin D.T., Stone D.A., Enhanced state-of-charge estimation for lithium-ion iron phosphate cells with flat open-circuit voltage curves, IECON2015-Yokohama, Japan (2015), DOI: 10.1109/IECON.2015.7392591.
[33] Huria T., Ceraolo M., Gazzarri J., Jackey R., Simplified extended Kalman filter observer for SOC estimation of commercial power-oriented LFP lithium battery cells, SAE World Congress, Technical Paper Series (2013), DOI: 10.4271/2013-01-1544.
[34] Baccouche I., Jemmali S., Manai B., Omar N., Amara N., Improved OCV model of a li-ion NMC battery for online SOC estimation using the extended Kalman filter, Energies, vol. 10, no. 6, p. 764 (2017), DOI: 10.3390/en10060764.
[35] Zhang C., Jiang J., Zhang L., Liu S., Wang L., Loh P., A generalized SOC-OCV model for lithium- ion batteries and the SOC estimation for LNMCO battery, Energies, vol. 9, no. 11, p. 900 (2016), DOI: 10.3390/en9110900.
[36] Zheng Y., Ouyang M., Han X., Lu L., Li J., Investigating the error sources of the online state of charge estimation methods for lithium-ion batteries in electric vehicles, Journal of Power Sources, vol. 377, pp. 161–188 (2018), DOI: 10.1016/j.jpowsour.2017.11.094.
[37] Chen M., Rincon-Mora G.A., Accurate electrical battery model capable of predicting runtime and I–V performance, IEEE Transactions on Energy Conversion, vol. 21, no. 2, pp. 504–511 (2006), DOI: 10.1109/TEC.2006.874229.
[38] Thanagasundram S., Arunachala R., Makinejad K., Teutsch T., Jossen A., A cell level model for battery simulation, European Electric Vehicle Congress Brussels, Belgium (2012).
[39] Feng J.-H., Yang L., Zhao X.-W., Zhang H.-D., Qiang J., Online identification of lithium-ion bat- tery parameters based on an improved equivalent-circuit model and its implementation on battery state-of-power prediction, Journal of Power Sources, vol. 281, pp. 192–203 (2015), DOI: 10.1016/j.jpowsour.2015.01.154.
[40] Rivera-Barrera J., Muñoz-Galeano N., Sarmiento-Maldonado H., SoC Estimation for lithium-ion Bat- teries: review and future challenges, Electronics, vol. 6, no. 4, p. 102 (2017), DOI: 10.3390/electronics6040102.
[41] He H., Xiong R., Fan J., Evaluation of lithium-ion battery equivalent circuit models for state of charge estimation by an experimental approach, Energies, vol. 4, no. 4, pp. 582–598 (2011), DOI: 10.3390/en4040582.
[42] Li Z., Huang J., Kiaw B.Y., Zjhang J., On state of charge determination for lithium-ion batteries, Journal of Power Sources, vol. 348, pp. 281–301 (2017), DOI: 10.1016/j.jpowsour.2017.03.001.
<[43] Attanayaka A., Karunadasa J.P., Hemapala K., Estimation of state of charge for lithium-ion batteries – a review, AIMS Energy, vol. 7, no. 2, pp. 186–210 (2019), DOI: 10.3934/energy.2019.2.186.
[44] Fleicher C., Waag W., Hey H.-M., Sauer D.U., On-line adaptive impedance parameter and state estimation considering physical principles in reduced order equivalent circuit battery models: Part 2. Parameter and state estimation, Journal of Power Sources, vol. 262, pp. 457–482 (2014), DOI: 10.1016/j.jpowsour.2014.03.046.
[45] Zhang C., Allafi W., Dinh Q., Ascencio P., Marco J., Online estimation of battery equivalent circuit model parameters and state of charge using decoupled least squares technique, Energy, vol. 142, pp. 678–688 (2018), DOI: 10.1016/j.energy.2017.10.043.
[46] Keil P., Jossen A., Aufbau und Parametrierung von Batteriemodellen. 19. DESIGN&ELEKTRONIK- Entwicklerforum Batterien & Ladekonzepte, München (2012), https://mediatum.ub.tum.de/doc/1162416/1162416.pdf, accessed April 2021.
[47] El Mejdoubi A., Oukaour A., Chaoui H., Gualous H., Sabor J., Slamani Y., State-of-charge and state-of- health lithium-ion batteries’ diagnosis according to surface temperature variation, IEEE Transactions on Industrial Electronics, vol. 63, no. 4, pp. 2391–2402 (2016), DOI: 10.1109/TIE.2015.2509916.
[48] Chang W.-Y., The state of charge estimating methods for battery: a review, ISRN Applied Mathematics, pp. 1–7 (2013), DOI: 10.1155/2013/953792.
[49] Kalman R.E., A new approach to linear filtering and prediction problems, Journal of Basic Engineering, vol. 82, no. 1, pp. 35–45 (1960), DOI: 10.1115/1.3662552.
[50] Meng J., Ricco M., Luo G., Swierczynski M., Stroe D.-I., Stroe A.-I., Teodorescu R., An overview and comparison of online implementable SOC estimation methods for lithium-ion battery, IEEE Transactions on Industry Applications, vol. 54, no. 2, pp. 1583–1591 (2018), DOI: 10.1109/TIA.2017.2775179.
[51] Duong V.H., Bastawrous H.A., Lim K.C., See K.W., Zhang P., Dou S.X., SOC estimation for LiFePO4 battery in EVs using recursive least-squares with multiple adaptive forgetting factors, 2014 International Conference on Connected Vehicles and Expo (ICCVE) (2014), DOI: 10.1109/IC-CVE.2014.7297603.
[52] Xia B., Huang R., Lao Z., Zhang R., Lai Y., Zheng W., Wang M., Online parameter identification of lithium-ion batteries using a novel multiple forgetting factor recursive least square algorithm, Energies, vol. 11, no. 11, p. 3180 (2018), DOI: 10.3390/en11113180.
[53] Sun X., Ji J., Ren B., Xie C., Yan D., Adaptive forgetting factor recursive least square algorithm for online identification of equivalent circuit model parameters of a lithium-ion battery, Energies, vol. 12, no. 12, p. 2242 (2019), DOI: 10.3390/en12122242.
[54] Chandra Shekar A., Anwar S., Real-time state-of-charge estimation via particle swarm optimization on a lithium-ion electrochemical cell model, Batteries, vol. 5, no. 1, p. 4 (2019), DOI: 10.3390/batteries5010004.
[55] Qays M.O., Buswig Y., Anyi M., Active cell balancing control method for series-connected lithium-ion battery, International Journal of Innovative Technology and Exploring Engineering (IJITEE) (2019), DOI: 10.35940/ijitee.i8905.078919.
[56] Zhang C.-W., Chen S.-R., Gao H.-B., Xu K.-J., Yang M.-Y., State of charge estimation of power battery using improved back propagation neural network, Batteries, vol. 4, no. 4, p. 69 (2018), DOI: 10.3390/batteries4040069.
[57] Jiménez-Bermejo D., Fraile-Ardanuy J., Castaño-Solis S., Merino J., Álvaro-Hermana R., Using dynamic neural networks for battery state of charge estimation in electric vehicles, Procedia Computer Science, vol. 130, pp. 533–540 (2018), DOI: 10.1016/j.procs.2018.04.077.
[58] Thirugnanam K., Ezhil Reena Joy T.P., Singh M., Kumar P., Mathematical modeling of li-ion battery using genetic algorithm approach for V2G applications, IEEE Transactions on Energy Conversion, vol. 29, no. 2, pp. 332–343 (2014), DOI: 10.1109/TEC.2014.2298460.
[59] Liu F., Ma J., Su W., Unscented particle filter for SOC estimation algorithm based on a dynamic parameter identification, Mathematical Problems in Engineering, no. 6, pp. 1–14 (2019), DOI: 10.1155/2019/7452079.
[60] Rozaqi L., Rijanto E., SOC estimation for li-ion battery using optimum RLS method based on genetic algorithm, 8th International Conference on Information Technology and Electrical Engineering (ICITEE) (2016), DOI: 10.1109/ICITEED.2016.7863224.
[61] Styczynski Z., Rudion K., Naumann A., Einführung in Expertensysteme, Springer Verlag (2018).
[62] Wei K., Wu J., Ma W., Li H., State of charge prediction for UAVs based on support vector machine, 7th International Symposium on Test Automation and Instrumentation (ISTAI) (2018), DOI: 10.1049/joe.2018.9201.
[63] Zhang W., Wang W., Lithium-ion battery SoC estimation based on online support vector regression, 33rd Youth Academic Annual Conference of Chinese Association of Automation (YAC) (2018), DOI: 10.1109/YAC.2018.8406438.
[64] Alvarez Anton J.C., Garcia Nieto P.J., Blanco Viejo C., Vilan Vilan J.A., Support vector machines used to estimate the battery state of charge, IEEE Transactions on Power Electronics, vol. 28, no. 12, pp. 5919–5926 (2013), DOI: 10.1109/TPEL.2013.2243918.
[65] Rupp S., Modellierung von Anlagen und Systemen Teil 1, DHBW, CAS 2017, www.srupp.de/ENT/ TM20305_1_Modellierung_von_Anlagen_und_Systemen.pdf+&cd=1&hl=en&ct=clnk&gl=de, ac- cessed July 2021,
[66] Wenge C., Pietracho R., Balischewski S., Arendarski B., Lombardi P., Komarnicki P., Kasprzyk L., Multi Usage Applications of Li-Ion Battery Storage in a Large Photovoltaic Plant: A Practical Experience, Energies, vol. 13, no. 18, 4590 (2020), DOI: 10.3390/en13184590.
[67] Dambrowski J., Methoden der Ladezustandsbestimmung – mit Blick auf LiFePO4=Li4Ti5O 12-Systeme.
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