Journal of Electronics and Informatics
IRO Journals
Volume 4 — Issue 4 — December 2022

Electric Bike Range Estimation using Fuzzy Logic Controller

https://doi.org/10.36548/jei.2022.4.002
G. S. Naganathan
Department of Electrical and Electronics Engineering, Syed Ammal Engineering College, Tamil Nadu, India
S. Selvaperumal
Department of Electrical and Electronics Engineering, Syed Ammal Engineering College, Tamil Nadu, India
R. Nagarajan
Department of Electrical and Electronics Engineering, Syed Ammal Engineering College, Tamil Nadu, India
P. Nedumal Pugazhenthi
Department of Electrical and Electronics Engineering, Syed Ammal Engineering College, Tamil Nadu, India
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How to Cite

Naganathan, G. S., S. Selvaperumal, R. Nagarajan, and P. Nedumal Pugazhenthi. 2022. “Electric Bike Range Estimation Using Fuzzy Logic Controller”. Journal of Electronics and Informatics 4 (4): 225-36. https://doi.org/10.36548/jei.2022.4.002.
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Keywords

— Fuzzy Logic Controller (FLC)
— Electric Vehicle (EV)
— Electric Bike
— State of Charge (SOC)
Published: 30-11-2022

Abstract

Electric Vehicles (EV) are the prompt solution to significantly lowering the use of fossil fuels and CO2 emissions from the transport industry. There is a continuing growth in the number of EVs in use, but their huge acceptance by customers is associated to the quality they can provide. Nowadays, different types of electric vehicles are moving toward green awareness, and one among them is the electric motorcycle which is a considerable vehicle in India. Though there are many benefits of driving electric motorcycles, because of the limited driving range and inadequate charging stations, it is still not generally accepted in the industry. Range anxiety is the major market concern that is solved by the implementation of an additional range estimating technique that can ease the "range anxiety" caused by the restricted range of EVs. Therefore, this paper proposes a fuzzy logic controller model for the estimation of the EV range based on the battery's state of charge and the load's power usage. In this work, the load power consumption of the vehicle and the status of the battery charge are selected as inputs and the EV range is selected as a Fuzzy Logic Controller output. This model is implemented in the Matlab/Simulink environment.

References

  1. Z. Guirong, Z. Henghai, L. Houyu, The driving control of pure electric vehicle, Proc. Environ. Sci, 2011, pp 433–438.
  2. Sarrafan K, Muttaqi KM, Sutanto D, Town GE, A real-time range indicator for EV susing web-based environmental data and sensorless estimation of regenerative braking power. IEEE Trans Veh Technol, 2018.
  3. Pan C, Dai W, Chen L,Wang L, Driving range estimation for electric vehicles based on driving condition identification and forecast, AIPAdv 2017.
  4. C. Gribben, Debunking the myth of EVs and smokestacks, Electro Automotive ,1996.
  5. M. NC Onat, O.T. Kucukvar, Conventional, hybrid, plug-in hybrid or electric vehicles? State-based comparative carbon and energy footprint analysis in the United States. Appl. Energy,2015, pp 36–49.
  6. S. Heath, P. Sant, B. Allen, Do you feel lucky? Why current range estimation methods are holding back EV adoption, 2013.
  7. J.G. Hayes, R.P.R. de Oliveira, S. Vaughan, M.G. Egan, Simplified electric vehicle power train models and range estimation, in: Vehicle Power and Propulsion Confere (VPPC), 2011 IEEE, 2011, pp 1–5.
  8. Franke T, Rauh N, Krems JF, Individual differences in BEV drivers’ range stress during first encounter of a critical range situation. Appl Ergo, 2016, pp 28–35.
  9. Bi J, Wang Y, Sai Q, Ding C, Estimating remaining driving range of battery electric vehicles based on real-world data: a case study of Beijing, China, Energy, 2019, pp 833–43.
  10. Masjosthusmann C, Kohler U, Decius N, Buker U, A vehicle energy management system for a battery electric vehicle. 2012 IEEE Veh Power Propuls Conf (VPPC 2012), 2012, pp 339–344.
  11. Y. Zhang, W. Wang, Y. Kobayashi, K. Shirai, Remaining driving range estimation of electric vehicle, in: Electric Vehicle Conference (IEVC), 2012 IEEE International, IEEE, 2012, pp 1–7.
  12. E. Kim, J. Lee, K.G. Shin, Real-time prediction of battery power requirements for electric vehicles, in: Proceedings of the ACM/IEEE 4th International Conference on Cyber-Physical Systems, ACM, 2013, pp 11–20
  13. I. Cunningham, K. Burnham, Online use of the fuzzy transform in the estimation of electric vehicle range. Meas. Control, 2013, pp 277–282.
  14. W. X, J. Shen, Y. Li, K.Y. Lee, Data-driven modeling and predictive control for boiler–turbine unit. IEEE Trans. Energy. Convers, 2013, pp 470–481.
  15. Erdelic T, Caric T, A survey on the electric vehicle routing problem: variants and solution approaches. J Adv Transp 2019, 2019, pp 1–48.
  16. De Nunzio G, Thibault L, Energy-optimal driving range prediction for electric vehicles, IEEE Intell Veh Symp Proc 2017, pp 1608–1613.
  17. Hong J, Park S, Chang N, Accurate remaining range estimation for electric vehicles. Proc Asia South Pacific Des Autom Conf ASP-DAC 2016, pp 781–786.
  18. Qi, Chen Y, Li J, Control of electric vehicle, 2012.
  19. N. Watrin, B. Blunier, and A. Miraoui, Review of adaptive systems for lithium batteries state-of-charge and state-of-health estimation, in Proceedings of IEEE Transportation Electrification Conference and Expo,2012, pp. 1–6,
  20. V. Prajapati, H. Hess, E. J. William, A literature review of state of-charge estimation techniques applicable to lithium poly-carbon monoflouride (LI/CFx) battery, in Proceedings of the India International Conference on Power Electronics (IICPE '10), 2011, pp 1–8.
  21. N. Watrin, B. Blunier, A. Miraoui, Review of adaptive systems for lithium batteries state-of-charge and state-of-health estimation, in Proceedings of IEEE Transportation Electrification Conference and Expo, 2012, pp 1-6.
  22. V. Prajapati, H. Hess, E. J. William et al., A literature review of state of-charge estimation techniques applicable to lithium poly-carbon monoflouride (LI/CFx) battery, in Proceedings of the India International Conference on Power Electronics (IICPE '10), 2011, pp. 1–8.
  23. L.A.Zadeh, Fuzzy sets, Inform Control,1965, pp 338.