Abstract
The main objective of the paper is to acclimatize the throttle accommodated in the internal combustion engine (ICE) to attain maximum output torque while alternating fuel consumed by adaptive neuro-fuzzy inference system (ANFIS) controller. This paper describes various techniques to acclimatize the throttle in ICE. The throttle manages the flow of fluid and can increase or decrease the engine’s power, and the air fuel ratio is checked and adjusted. Constantly maintain the air fuel ratio of the vehicle to attain the improved performance of the maximum possible torque demand from the engine. ANFIS control technique is an optimal method for controlling the hybrid vehicles. Hybrid vehicles are propelled by two sources. In this paper, hybrid vehicle uses two sources, that is, ICE and battery which is being charged with wind turbine.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
S. Sowmini priya, S. Rajakumar, An energy storage system for wind turbine generators-battery and supercapacitor. Int. J. Eng. Res. Appl. (IJERA) (ISSN: 2248–9622) (2013)
M. Mohebbi, M. Charkhgard, M. Farrokhi, Optimal neuro-fuzzy control of parallel hybrid electric vehicles, in Proceedings of IEEE Conference on Vehicle Power and Propulsion (2005), pp. 26–30
D. pelusi, Genetic neuro-fuzzy controllers for second order control system, in 2011 UKsim 5th European Symposium on Computer Modeling and Simulation (2011)
A. Boyali, M. Demirci, T. Acarman, L. Guvenc, O. Tur, H. Ucarol, B. Kirey, E. Ozatey, Modeling and control of a four wheel drive parallel hybrid electric vehicle, in Proceedings of IEEE International Conference on Control Applications (Munich, Germany, 4–6 Oct 2006)
K. Zhang, J. Li, M. Ouyang, J. Gu, Y. Ma, Electric braking performance analysis of PMSM for electric vehicle applications, in 2011 International Conference on Electronic & Mechanical Engineering and Information Technology (2011)
J. Osorio, A. Molina, P. Ponce, D. Romero, A supervised adaptive neuro-fuzzy inference system controller for a hybrid electric vehicle’s power train system, in 9th IEEE International Conference on Control and Automation (ICCA) (Santiago, Chile, Dec 2011) pp. 19–21
A. Ntirnberger, D. Nauck, R. Kruse, L. Merz, A neuro-fuzzy development tool for fuzzy controllers under MATLAB/SIMULINK, in Proceedings of the 5’th European Congress on Intelligent Techniques & Soft Computing (EUFIT’97) (Aachen, Germany, 1997)
Y. Mihoub, B. Mazari, S. Fasla, Neuro-Fuzzy Controller Used to Control, the Speed of an Induction Motor (University of science and technology of Oran Mohamed Bodied Faculty of Electrical Engineering-Electro technical Department B.P1505,31000 Oran ElMnaouar Algeria) (2007)
C. Abbey, G. Joos, Supercapacitor energy storage for wind energy applications. IEEE Trans. Ind. Appl. 43(3), 769–776 (2007)
W. Li, G. Xu, Z. Wang, Y. Xu, A hybrid controller design for parallel hybrid electric vehicle, in Proceedings of IEEE International Conference on Integration Technology (Shenzhen, China, 20–24 Mar 2007)
L.K Butler, M. Ehsani, P. Kamath, A mat lab-based modeling and simulation package for electric and hybrid electric vehicle design. IEEE Trans. Veh. Technol. 48(6) (Nov 1999)
H. Lee, E. Koo, S. Sul, J. Kim, Torque control strategy for a parallel-hybrid vehicle using fuzzy logic, in IEEE Industry Applications Magazine (Nov 2000)
C. Xia,* C. Zhang, Power management strategy of hybrid electric vehicles based on quadratic performance index. Energies 8 pp. 12458–12473 (2015)
D. Sukumar, J. Jithendranath, S. Saranu, Three level Inverter fed Induction motor drive performance improvement with neuro-fuzzy space vector modulation. Electr. Power Compon. Syst (Taylor and Francis) (ISSN 1532-5008) pp. 1633–1646 (2014)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Rachananjali, K., Krishna, K.B., Suman, S., Tejasree, V. (2018). Modeling of an ANFIS Controller for Series–Parallel Hybrid Vehicle. In: Dash, S., Naidu, P., Bayindir, R., Das, S. (eds) Artificial Intelligence and Evolutionary Computations in Engineering Systems. Advances in Intelligent Systems and Computing, vol 668. Springer, Singapore. https://doi.org/10.1007/978-981-10-7868-2_60
Download citation
DOI: https://doi.org/10.1007/978-981-10-7868-2_60
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-10-7867-5
Online ISBN: 978-981-10-7868-2
eBook Packages: EngineeringEngineering (R0)