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Year 2021, Volume: 10 Issue: 1, 33 - 41, 31.03.2021
https://doi.org/10.18245/ijaet.879754

Abstract

References

  • A. Kerem, “Elektrikli araç teknolojisinin gelişimi ve gelecek beklentileri”, Mehmet Akif Ersoy Üniversitesi Fen Bilimleri Enstitüsü Dergisi, 5(1), 1-13, 2014.
  • R. Miceli, F. Viola, “Designing a sustainable university recharge area for electric vehicles: technical and economic analysis”, Energies, 10, 1064, 2017.
  • A. Kerem, H. Gürbak, “Elektrikli araçlar için hızlı şarj istasyonu teknolojileri”, Gazi Üniversitesi Fen Bilimleri Dergisi Part C: Tasarım ve Teknoloji, 8(3), 644-661, 2020.
  • V. Sandeep, S. Shastri, “Analysis and design of PMBLDC motor for three wheeler electric vehicle application”, 1st International Conference on Sustainable Energy and Future Electric Transportation, E3S Web of Conferences 87, 01022, 1-7, 2019.
  • S. Kahourzade, A. Mahmoudi, N. Abdul Rahim, H.W. Ping, “Sizing equation and finite element analysis optimum design of axial-flux permanent- magnet motor for electric vehicle direct drive”, IEEE International Power Engineering and Optimization Conference, June 2012, Melaka, Malaysia, 2012.
  • A. İ. Özkan, M. Ciniviz, F. Candan, “Estimating engine performance and emission values using ANFIS”, International Journal of Automotive Engineering and Technologies, 4(1), 63-67, 2015.
  • H. Harandizadeh, M.M. Toufigh, V. Toufigh, “Application of improved ANFIS approaches to estimate bearing capacity of piles”, Soft Computing, 23: 9537-9549, 2019.
  • M.O. Okwu, O.D. Samuel, D.R.E. Ewim, Z. Huan, “Estimation of biogas yields produced from combination of waste by implementing response surface methodology (RSM) and adaptive neuro‑fuzzy inference system (ANFIS)”, International Journal of Energy and Environmental Engineering, 2021.
  • L. Naderloo, R. Alimardani, M. Omid, F. Sarmadian, P. Javadikia, M.Y Torabi, F. Alimardani, “Application of ANFIS to predict crop yield based on different energy inputs”, Measurement, 45, 1406-1413, 2012.
  • M., Mohandes, S. Rehman, S.M. Rahman, “Estimation of wind speed profile using adaptive neuro-fuzzy inference system (ANFIS)”, Applied Energy, 88, 4024-4032, 2011.
  • B. Najafi, S.F. Ardabili, “Application of ANFIS, ANN, and logistic methods in estimating biogas production from spent mushroom compost (SMC)”, Resources, Conservation & Recycling, 133, 169-178, 2018.
  • P.D. Sreekanth, P.D. Sreedevi, S. Ahmed, N. Geethanjali, “Comparison of FFNN and ANFIS models for estimating groundwater level”, Environ Earth Sci, 62, 1301-1310, 2011.
  • M.A. Raharja, I.D.M.B.A. Darmawan, D.P.E. Nilakusumawati, I.W. Supriana, “Analysis of membership function in implementation of adaptive neuro fuzzy inference system (ANFIS) method for inflation prediction”, Journal of Physics: Conference Series,1722, 2021.
  • A. Toghroli, M. Mohammadhassani, M. Shariati, M. Suhatril, Z. Ibrahim, N.H.R. Sulong, “Prediction of shear capacity of channel shear connectors using the ANFIS model”, Steel and Composite Structures, 17, 5, 2014.
  • A. Baghban, J. Sasanipour, A.M. Goodarzi, “Evolving ANFIS model to estimate sweet natural gas water content”, Petroleum Science and Technology, 35(18), 1807-1813, 2017.
  • P. Abbasi, M. Mohammad, A. Baghban, G. Zargar, “Evolving ANFIS model to estimate density of bitumen-tetradecane mixtures”. Petroleum Science and Technology, 35(2), 120-126, 2017.
  • R. Daneshfar, A. Bemani, M. Hadipoor, M. Sharifpur, H.M. Ali, I. Mahariq, T. Abdeljawad, “Estimating the heat capacity of non-newtonian ionanofluid systems using ANN, ANFIS, and SGB tree algorithms”, Appl. Sci.10, 6432, 2020.
  • V.H. Quej, J. Almorox, J.A. Arnaldo, L. Saito, “ANFIS, SVM and ANN soft-computing techniques to estimate daily global solar radiation in a warm sub-humid environment”, Journal of Atmospheric and Solar–Terrestrial Physics 155, 62-70, 2017.
  • A. Baghban, “Application of the ANFIS strategy to estimate vaporization enthalpies of petroleum fractions and pure hydrocarbons”, Petroleum Science and Technology, 34(15), 1359-1366, 2016.
  • C.H. Cai, D. Du, Z.Y. Liu, “Battery state-of-charge (SOC) estimation using adaptive neuro-fuzzy ınference system (ANFIS)”, The IEEE lntemational Conference on Fuzzy Systems, 1068-1073. May 2003, St Louis, MO, USA, 2003.
  • R. Razavi, A. Sabaghmoghadam, A. Bemani, A. Baghban, K. Chaue, E. Salwana, “Application of ANFIS and LSSVM strategies for estimating thermal conductivity enhancement of metal and metal oxide based nanofluids”, Engineering Applications of Computational Fluid Mechanics, 13(1), 560-578, 2019.
  • B. Najafi, S.F. Ardabili, S. Shamshirband, K. Chaue, T. Rabczuk, “Application of ANNs, ANFIS and RSM to estimating and optimizing the parameters that affect the yield and cost of biodiesel production”, Engineering Applications of Computational Fluid Mechanics, 12(1), 611-624, 2018.
  • D.J. Armaghani, P.G. Asteris, “A comparative study of ANN and ANFIS models for the prediction of cement-based mortar materials compressive strength”, Neural Computing and Applications, 2020.
  • D.J. Armaghani, E. Momeni, S.V.A.N.K. Abad, M. Khandelwal, “Feasibility of ANFIS model for prediction of ground vibrations resulting from quarry blasting”, Environ Earth Sci,74, 2845-2860, 2015.
  • S. Amirkhani, Sh. Nasirivatan, A.B. Kasaeian, A. Hajinezhad, “ANN and ANFIS models to predict the performance of solar chimney power plants”, Renewable Energy, 83, 597-607, 2015.
  • B. Ahmadi-Nedushan, “Prediction of elastic modulus of normal and high strength concrete using ANFIS and optimal nonlinear regression models”, Construction and Building Materials, 36, 665-673, 2012.
  • A. Moghaddamnia, R. Remesan, M.H. Kashani, M. Mohammadi, D. Han, J. Piri, “Comparison of LLR, MLP, Elman, NNARX and ANFIS Models-with a case study in solar radiation estimation”, Journal of Atmospheric and Solar-Terrestrial Physics, 71, 975-982, 2009.
  • A.A.M. Ahmed, S.M.A Shah, “Application of adaptive neuro-fuzzy inference system (ANFIS) to estimate the biochemical oxygen demand (BOD) of Surma River”, Journal of King Saud University-Engineering Sciences, 29, 237-243, 2017.
  • X. Zhuang, T. Yu, Z. Sun, K. Song, “Wear prediction of a mechanism with multiple joints based on ANFIS. Engineering Failure Analysis”, 119 (104958), 1-15, 2021.
  • M.A.A. Al-qaness, H. Fan, A.A. Ewees, D. Yousri, M.A. Elaziz, “Improved ANFIS model for forecasting Wuhan City air quality and analysis COVID-19 lockdown impacts on air quality”, Environmental Research, 194(110607), 1-12, 2021.
  • M.A. Jirdehi, V.S. Tabar, “State estimation in electric power systems based on adaptive neuro-fuzzy system considering load uncertainty and false data”, Iranian Journal of Electrical and Electronic Engineering, 03(1722), 1-10, 2021.
  • V. Nourani, H. Karimzadeh, A.H. Baghanam, “Forecasting CO pollutant concentration of Tabriz city air using artificial neural network and adaptive neuro‑fuzzy inference system and its impact on sustainable development of urban”, Environmental Earth Sciences 80:136, 2021.
  • Y.B. Adyapaka Apatya, A. Subiantoro, F. Yusivar, “Design and Prototyping of 3-Phase BLDC Motor”, 15th International Conference on Quality in Research (QiR): International Symposium on Electrical and Computer Engineering, July 2017, Nusa Dua, Indonesia, 2017.
  • N. Marian-Ştefan, P. Raluca-Cristina, V. Ion, N. Petre-Marian, S. Ionuţ-Dani, “Particular Aspects Concerning the Design of a Brushless DC Electric Motor Driving a Mini-Scooter”, International Conference on Applied and Theoretical Electricity (ICATE), Oct. 2016, Craiova, Romania, 2016.
  • M. Rameli, Y.R. Hais, R.E.A. Kadir, “Design of self commutation BLDC motor with torque control strategy using fuzzy logic in hybrid electric vehicle (HEV)”, International Seminar on Intelligent Technology and Its Application, Aug. 2017, Surabaya, Indonesia, 2017.
  • S.Ganesh, S. Sankar, N. Selvaganesan, “Design and analysis of BLDC motor for aerospace application using FEM”, International Conference on Intelligent Computing,Instrumentation and Control Technologies (ICICICT), July 2017, Kannur, India, 2017.

Torque estimation of electric vehicle motor using adaptive-network based fuzzy inference systems

Year 2021, Volume: 10 Issue: 1, 33 - 41, 31.03.2021
https://doi.org/10.18245/ijaet.879754

Abstract

This paper presents to estimating studies of the torque data of the Electric Vehicle (EV) motor using Adaptive-Network Based Fuzzy Inference Systems (ANFIS). The real-time data set of the Outer-Rotor Permanent Magnet Brushless DC (ORPMBLDC) motor which was designed and manufactured for using in ultra-light EV, was used in these estimation process. The current, the power and the motor speed parameters are defined as input variables, and the torque parameter defined as output variable. Five distinct ANFIS models were designed for torque estimation process and the performances of each model were compared. The most effective model for testing data set among the ANFIS models was anfis: 2 with 98 nodes and 36 fuzzy rules, and the worst model was anfis: 5 with 286 nodes and 125 fuzzy rules. Performance results of all designed models were presented in tables and graphs.

References

  • A. Kerem, “Elektrikli araç teknolojisinin gelişimi ve gelecek beklentileri”, Mehmet Akif Ersoy Üniversitesi Fen Bilimleri Enstitüsü Dergisi, 5(1), 1-13, 2014.
  • R. Miceli, F. Viola, “Designing a sustainable university recharge area for electric vehicles: technical and economic analysis”, Energies, 10, 1064, 2017.
  • A. Kerem, H. Gürbak, “Elektrikli araçlar için hızlı şarj istasyonu teknolojileri”, Gazi Üniversitesi Fen Bilimleri Dergisi Part C: Tasarım ve Teknoloji, 8(3), 644-661, 2020.
  • V. Sandeep, S. Shastri, “Analysis and design of PMBLDC motor for three wheeler electric vehicle application”, 1st International Conference on Sustainable Energy and Future Electric Transportation, E3S Web of Conferences 87, 01022, 1-7, 2019.
  • S. Kahourzade, A. Mahmoudi, N. Abdul Rahim, H.W. Ping, “Sizing equation and finite element analysis optimum design of axial-flux permanent- magnet motor for electric vehicle direct drive”, IEEE International Power Engineering and Optimization Conference, June 2012, Melaka, Malaysia, 2012.
  • A. İ. Özkan, M. Ciniviz, F. Candan, “Estimating engine performance and emission values using ANFIS”, International Journal of Automotive Engineering and Technologies, 4(1), 63-67, 2015.
  • H. Harandizadeh, M.M. Toufigh, V. Toufigh, “Application of improved ANFIS approaches to estimate bearing capacity of piles”, Soft Computing, 23: 9537-9549, 2019.
  • M.O. Okwu, O.D. Samuel, D.R.E. Ewim, Z. Huan, “Estimation of biogas yields produced from combination of waste by implementing response surface methodology (RSM) and adaptive neuro‑fuzzy inference system (ANFIS)”, International Journal of Energy and Environmental Engineering, 2021.
  • L. Naderloo, R. Alimardani, M. Omid, F. Sarmadian, P. Javadikia, M.Y Torabi, F. Alimardani, “Application of ANFIS to predict crop yield based on different energy inputs”, Measurement, 45, 1406-1413, 2012.
  • M., Mohandes, S. Rehman, S.M. Rahman, “Estimation of wind speed profile using adaptive neuro-fuzzy inference system (ANFIS)”, Applied Energy, 88, 4024-4032, 2011.
  • B. Najafi, S.F. Ardabili, “Application of ANFIS, ANN, and logistic methods in estimating biogas production from spent mushroom compost (SMC)”, Resources, Conservation & Recycling, 133, 169-178, 2018.
  • P.D. Sreekanth, P.D. Sreedevi, S. Ahmed, N. Geethanjali, “Comparison of FFNN and ANFIS models for estimating groundwater level”, Environ Earth Sci, 62, 1301-1310, 2011.
  • M.A. Raharja, I.D.M.B.A. Darmawan, D.P.E. Nilakusumawati, I.W. Supriana, “Analysis of membership function in implementation of adaptive neuro fuzzy inference system (ANFIS) method for inflation prediction”, Journal of Physics: Conference Series,1722, 2021.
  • A. Toghroli, M. Mohammadhassani, M. Shariati, M. Suhatril, Z. Ibrahim, N.H.R. Sulong, “Prediction of shear capacity of channel shear connectors using the ANFIS model”, Steel and Composite Structures, 17, 5, 2014.
  • A. Baghban, J. Sasanipour, A.M. Goodarzi, “Evolving ANFIS model to estimate sweet natural gas water content”, Petroleum Science and Technology, 35(18), 1807-1813, 2017.
  • P. Abbasi, M. Mohammad, A. Baghban, G. Zargar, “Evolving ANFIS model to estimate density of bitumen-tetradecane mixtures”. Petroleum Science and Technology, 35(2), 120-126, 2017.
  • R. Daneshfar, A. Bemani, M. Hadipoor, M. Sharifpur, H.M. Ali, I. Mahariq, T. Abdeljawad, “Estimating the heat capacity of non-newtonian ionanofluid systems using ANN, ANFIS, and SGB tree algorithms”, Appl. Sci.10, 6432, 2020.
  • V.H. Quej, J. Almorox, J.A. Arnaldo, L. Saito, “ANFIS, SVM and ANN soft-computing techniques to estimate daily global solar radiation in a warm sub-humid environment”, Journal of Atmospheric and Solar–Terrestrial Physics 155, 62-70, 2017.
  • A. Baghban, “Application of the ANFIS strategy to estimate vaporization enthalpies of petroleum fractions and pure hydrocarbons”, Petroleum Science and Technology, 34(15), 1359-1366, 2016.
  • C.H. Cai, D. Du, Z.Y. Liu, “Battery state-of-charge (SOC) estimation using adaptive neuro-fuzzy ınference system (ANFIS)”, The IEEE lntemational Conference on Fuzzy Systems, 1068-1073. May 2003, St Louis, MO, USA, 2003.
  • R. Razavi, A. Sabaghmoghadam, A. Bemani, A. Baghban, K. Chaue, E. Salwana, “Application of ANFIS and LSSVM strategies for estimating thermal conductivity enhancement of metal and metal oxide based nanofluids”, Engineering Applications of Computational Fluid Mechanics, 13(1), 560-578, 2019.
  • B. Najafi, S.F. Ardabili, S. Shamshirband, K. Chaue, T. Rabczuk, “Application of ANNs, ANFIS and RSM to estimating and optimizing the parameters that affect the yield and cost of biodiesel production”, Engineering Applications of Computational Fluid Mechanics, 12(1), 611-624, 2018.
  • D.J. Armaghani, P.G. Asteris, “A comparative study of ANN and ANFIS models for the prediction of cement-based mortar materials compressive strength”, Neural Computing and Applications, 2020.
  • D.J. Armaghani, E. Momeni, S.V.A.N.K. Abad, M. Khandelwal, “Feasibility of ANFIS model for prediction of ground vibrations resulting from quarry blasting”, Environ Earth Sci,74, 2845-2860, 2015.
  • S. Amirkhani, Sh. Nasirivatan, A.B. Kasaeian, A. Hajinezhad, “ANN and ANFIS models to predict the performance of solar chimney power plants”, Renewable Energy, 83, 597-607, 2015.
  • B. Ahmadi-Nedushan, “Prediction of elastic modulus of normal and high strength concrete using ANFIS and optimal nonlinear regression models”, Construction and Building Materials, 36, 665-673, 2012.
  • A. Moghaddamnia, R. Remesan, M.H. Kashani, M. Mohammadi, D. Han, J. Piri, “Comparison of LLR, MLP, Elman, NNARX and ANFIS Models-with a case study in solar radiation estimation”, Journal of Atmospheric and Solar-Terrestrial Physics, 71, 975-982, 2009.
  • A.A.M. Ahmed, S.M.A Shah, “Application of adaptive neuro-fuzzy inference system (ANFIS) to estimate the biochemical oxygen demand (BOD) of Surma River”, Journal of King Saud University-Engineering Sciences, 29, 237-243, 2017.
  • X. Zhuang, T. Yu, Z. Sun, K. Song, “Wear prediction of a mechanism with multiple joints based on ANFIS. Engineering Failure Analysis”, 119 (104958), 1-15, 2021.
  • M.A.A. Al-qaness, H. Fan, A.A. Ewees, D. Yousri, M.A. Elaziz, “Improved ANFIS model for forecasting Wuhan City air quality and analysis COVID-19 lockdown impacts on air quality”, Environmental Research, 194(110607), 1-12, 2021.
  • M.A. Jirdehi, V.S. Tabar, “State estimation in electric power systems based on adaptive neuro-fuzzy system considering load uncertainty and false data”, Iranian Journal of Electrical and Electronic Engineering, 03(1722), 1-10, 2021.
  • V. Nourani, H. Karimzadeh, A.H. Baghanam, “Forecasting CO pollutant concentration of Tabriz city air using artificial neural network and adaptive neuro‑fuzzy inference system and its impact on sustainable development of urban”, Environmental Earth Sciences 80:136, 2021.
  • Y.B. Adyapaka Apatya, A. Subiantoro, F. Yusivar, “Design and Prototyping of 3-Phase BLDC Motor”, 15th International Conference on Quality in Research (QiR): International Symposium on Electrical and Computer Engineering, July 2017, Nusa Dua, Indonesia, 2017.
  • N. Marian-Ştefan, P. Raluca-Cristina, V. Ion, N. Petre-Marian, S. Ionuţ-Dani, “Particular Aspects Concerning the Design of a Brushless DC Electric Motor Driving a Mini-Scooter”, International Conference on Applied and Theoretical Electricity (ICATE), Oct. 2016, Craiova, Romania, 2016.
  • M. Rameli, Y.R. Hais, R.E.A. Kadir, “Design of self commutation BLDC motor with torque control strategy using fuzzy logic in hybrid electric vehicle (HEV)”, International Seminar on Intelligent Technology and Its Application, Aug. 2017, Surabaya, Indonesia, 2017.
  • S.Ganesh, S. Sankar, N. Selvaganesan, “Design and analysis of BLDC motor for aerospace application using FEM”, International Conference on Intelligent Computing,Instrumentation and Control Technologies (ICICICT), July 2017, Kannur, India, 2017.
There are 36 citations in total.

Details

Primary Language English
Journal Section Article
Authors

Alper Kerem 0000-0002-9131-2274

Publication Date March 31, 2021
Submission Date February 13, 2021
Published in Issue Year 2021 Volume: 10 Issue: 1

Cite

APA Kerem, A. (2021). Torque estimation of electric vehicle motor using adaptive-network based fuzzy inference systems. International Journal of Automotive Engineering and Technologies, 10(1), 33-41. https://doi.org/10.18245/ijaet.879754
AMA Kerem A. Torque estimation of electric vehicle motor using adaptive-network based fuzzy inference systems. International Journal of Automotive Engineering and Technologies. March 2021;10(1):33-41. doi:10.18245/ijaet.879754
Chicago Kerem, Alper. “Torque Estimation of Electric Vehicle Motor Using Adaptive-Network Based Fuzzy Inference Systems”. International Journal of Automotive Engineering and Technologies 10, no. 1 (March 2021): 33-41. https://doi.org/10.18245/ijaet.879754.
EndNote Kerem A (March 1, 2021) Torque estimation of electric vehicle motor using adaptive-network based fuzzy inference systems. International Journal of Automotive Engineering and Technologies 10 1 33–41.
IEEE A. Kerem, “Torque estimation of electric vehicle motor using adaptive-network based fuzzy inference systems”, International Journal of Automotive Engineering and Technologies, vol. 10, no. 1, pp. 33–41, 2021, doi: 10.18245/ijaet.879754.
ISNAD Kerem, Alper. “Torque Estimation of Electric Vehicle Motor Using Adaptive-Network Based Fuzzy Inference Systems”. International Journal of Automotive Engineering and Technologies 10/1 (March 2021), 33-41. https://doi.org/10.18245/ijaet.879754.
JAMA Kerem A. Torque estimation of electric vehicle motor using adaptive-network based fuzzy inference systems. International Journal of Automotive Engineering and Technologies. 2021;10:33–41.
MLA Kerem, Alper. “Torque Estimation of Electric Vehicle Motor Using Adaptive-Network Based Fuzzy Inference Systems”. International Journal of Automotive Engineering and Technologies, vol. 10, no. 1, 2021, pp. 33-41, doi:10.18245/ijaet.879754.
Vancouver Kerem A. Torque estimation of electric vehicle motor using adaptive-network based fuzzy inference systems. International Journal of Automotive Engineering and Technologies. 2021;10(1):33-41.