ANFIS Based Parameter Estimating of a Two-Phase Interleaved Dual Cascaded DC-DC Boost Converter for Fuel Cell Supplied Electric Vehicles

Yıl 2021, Cilt: 9 Sayı: 4, 410 - 416, 30.10.2021

Selami Balcı , Ahmet Kayabaşı , Berat Yıldız

https://doi.org/10.17694/bajece.940791

Cited By: 1

Öz

Fuel cells (FCs), one of the renewable energy sources, have started to be preferred as a power source in electric vehicles in recent years and research studies are continuing on designs in this direction. Although their efficiency is low <50%, they produce Direct Current (DC) electrical energy by electrochemical conversion without requiring battery systems, which can be used in electric vehicle drive systems. There are zero-emission effects such as water and temperature rise with waste and environmental aspects. One of the major disadvantages is the DC voltage amplitude they produce is inversely proportional to the temperature increase. In this context, parameter estimation is required to adapt the fluctuating FC voltage to a certain value adaptively with the DC-DC boost converter circuit. In this study, parametric simulation studies were carried out with Ansys-Electronics 2019-R3 software to determine the DC voltage level of a certain number of series and parallel connected FC cells depending on different temperature values. Duty ratio values of two-phase interleaved dual cascaded DC-DC boost converter circuit for desired output voltage were determined by using Adaptive Nero-Fuzzy Inference System (ANFIS) modeling of 1300 data determined by simulation studies. Thus, the output voltage of the converter is adaptively fixed at a certain value.

Anahtar Kelimeler

ANFIS, Electric Vehicle, Fuel Cell, Interleaved Cascaded Boost Converter.

Kaynakça

• [1] W. Choi, PN. Enjeti, JW. Howze, Development of an equivalent circuit model of a fuel cell to evaluate the effects of inverter ripple current, Nineteenth Annual IEEE Applied Power Electronics Conference and Exposition. 1 (2004) 355-361.
• [2] JW. Jung, A. Keyhani, Control of a fuel cell-based Z-source converter, IEEE Transactions on Energy Conversion. 22(2) (2007) 467-476.
• [3] Z. Zhang, et al., A review and design of power electronics converters for fuel cell hybrid system applications, Energy Procedia. 20 (2012) 301-310.
• [4] HJ. Chiu, LW. Lin, A bidirectional DC-DC converter for fuel cell electric vehicle driving system, IEEE Transactions on Power Electronics. 21(4) (2006) 950-958.
• [5] A. Shahin, M. Hinaje, J.P. Martin, S. Pierfederici, S. Raël, B. Davat, High Voltage Ratio DC–DC Converter for Fuel-Cell Applications, IEEE Transactions on Industrial Electronics. 57(12) (2010) 3944-3955.
• [6] M. Bahrami, J.P. Martin, G. Maranzana, S. Pierfederici, M. Weber, F. Meibody-Tabar, M. Zandi, Design and modeling of an equalizer for fuel cell energy management systems, IEEE Transactions on Power Electronics. DOI 10.1109/TPEL.2019.2899150, (2019).
• [7] C. Liu, A. Johnson, JS. Lai, A novel three-phase high-power soft-switched DC/DC converter for low-voltage fuel cell applications, IEEE Transactions on Industry Applications. 41(6) (2005) 1691-1697.
• [8] KC. Tseng, JT. Lin, CC. Huang, High step-up converter with three-winding coupled inductor for fuel cell energy source applications, IEEE Transactions on Power Electronics. 30(2) (2004) 574-581.
• [9] M. Nymand, MA. Andersen, High-efficiency isolated boost DC-DC converter for high-power low-voltage fuel-cell applications, IEEE Transactions on industrial electronics 57(2) (2010) 505-514.
• [10] DM. Bellur, MK. Kazimierczuk, DC-DC converters for electric vehicle applications 2007, Electrical Insulation Conference and Electrical Manufacturing Expo 2007. (2007) IEEE.
• [11] CT. Pan, CM. Lai. A high-efficiency high step-up converter with low switch voltage stress for fuel-cell system applications, IEEE Transactions on Industrial Electronics. 57(6) (2009) 1998-2006.
• [12] P. Thounthong, B. Davat, Study of a multiphase interleaved step-up converter for fuel cell high power applications, Energy Conversion and Management. 51(4) (2010) 826-832.
• [13] T. Takiguchi, K. Furukawa, K. Matsumura, H. Koizumi, Interleaved Soft-Switching Multilevel Boost Converter with Coupled Inductor, 41st Annual Conference of the IEEE Industrial Electronics Society, IECON, Yokohama, Japan. (2015).
• [14] S. Balci, N. Altin, H. Komurcugil, Sefa I, Performance Analysis of Interleaved Quadratic Boost Converter with Coupled Inductor for Fuel Cell Applications, 42st Annual Conference of the IEEE Industrial Electronics Society, IECON, Italy. (2016) 3541-3546.
• [15] MIRZAEI, Amin, et al. Analysis and design of a high efficiency bidirectional DC–DC converter for battery and ultracapacitor applications. Simulation Modelling Practice and Theory, 2011, 19.7: 1651-1667.
• [16] AU. Chávez-Ramírez AU et al., High power fuel cell simulator based on artificial neural network, International Journal of Hydrogen Energy. 35(21) (2010) 12125-12133.
• [17] X. Kong, AM. Khambadkone, Modeling of a PEM fuel-cell stack for dynamic and steady-state operation using ANN-based submodels, IEEE Transactions on Industrial Electronics. 56(12) (2009) 4903-4914.
• [18] Sabancı K , Balcı S , Aslan M . Estimation of the switching losses in DC-DC boost converters by various machine learning methods. Journal of Energy Systems. 2020; 4(1): 1-11.
• [19] Balci, S., Kayabasi, A., Yildiz, B. Estimation of Fuel Cell Parameters with ANFIS. 7th Eur. Conf. Ren. Energy Sys. (2019), Madrid, Spain.
• [20] AM. Azmy, I. Erlich, Online optimal management of PEM fuel cells using neural networks, IEEE Transactions on Power Delivery. 20(2) (2005) 1051-1058.
• [21] JS. Jang, ANFIS: adaptive-network-based fuzzy inference system. IEEE Transactions on Systems, Man, and Cybernetics. 23(3) (1993) 665-685.
• [22] JSR. Jang, Fuzzy modeling using generalized neural networks and the Kalman filter algorithm, In: AAAI Association for the Advancement of Artificial Intelligence. 91 (1991) 762-767.
• [23] MT. Güneşer, Artificial intelligence solution to extract the dielectric properties of materials at sub-THz frequencies, IET Science, Measurement & Technology. (2019). DOI:10.1049/iet-smt.2018.5356.
• [24] S. Balci, A. Kayabasi, B. Yildiz, ANN-based estimation of the voltage ripple according to the load variation of battery chargers, International Journal of Electronics. (2019). Doi:https://doi.org/10.1080/00207217.2019.1591530.