Simulation and Modelling of Math Function Based Controller Implemented With Fuzzy and Artificial Neural Network for a Smooth Transition Between Battery and Ultracapacitor

Simulation and Modelling of Math Function Based Controller Implemented With Fuzzy and Artificial Neural Network for a Smooth Transition Between Battery and Ultracapacitor

Raghavaiah Katuri* Srinivasarao Gorantla

Department of Electrical and Electronics Engineering, Vignan’s Foundation for Science, Technology and Research, Vadlamudi, Guntur 522213, Andhra Pradesh, India

Electrical and Electronics Engineering, Vignan’s Foundation for Science, Technology and Research, Vadlamudi, Guntur 522213, Andhra Pradesh, India

Corresponding Author Email: 
rk_eeep@vignanuniversity.org
Page: 
45-52
|
DOI: 
https://doi.org/10.18280/ama_c.730203
Received: 
9 April 2018
|
Accepted: 
13 June 2018
|
Published: 
30 June 2018
| Citation

OPEN ACCESS

Abstract: 

Electric vehicles (EVs)/Hybrid electric vehicles (HEVs) are implemented with Hybrid Energy Storage System (HESS) to obtain the effective results. HESS has been framed by combining battery with ultracapacitor (UC). Here the battery is used to supply the average power whereas UC can meet the transient power requirement of an electric vehicle. UC always assists the battery during peak power requirements and starting of the motor can also be done. The problem associated with HESS powered vehicle is switching between battery and UC depending upon vehicle road conditions. The main aim of this work is to design a controller for proper switching of energy sources in HESS. With four individual math function, one controller has been designed based on the speed of the electric motor, named as Math Function Based (MFB) controller, further, this has been integrated with ANN as well as Fuzzy logic made two new hybrid controllers. After that two-hybrid controllers have been implemented for the electric motor, thereafter comparative analysis has been made between them and suggested one good controller based on different comparative factors. The two-hybrid controllers have been implemented in four modes and results are discussed in the simulation results and discussion section.

Keywords: 

Hybrid Electric Vehicles(HEVs), Bidirectional Converter (BDC), Unidirectional Converter (UDC), battery, Ultracapacitor (UC), Math Function Based (MFB) controller, Artificial Neural Network controller (ANN), fuzzy logic controller

1. Introduction
2. Proposed System Model
3. Math Function Based Controller (MFB)
4. Modes of Operation of Converter Model
5. Proposed Model Control Strategy
6. Simulation Results and Discussions
7. Conclusions
  References

[1] Golchoubian P, Azad NL. (2017). Real-time nonlinear model predictive control of a battery–supercapacitor hybrid energy storage system in electric vehicles. IEEE Transactions on Vehicular Technology 66(11): 9678-88. http://dx.doi.org/ 10.1109/TVT.2017.2725307

[2] Katuri R, Gorantla SR. (2018). Math function based controller applied to the electric/hybrid electric vehicle. Modeling, Measurement and Control A 91(1): 15-21.

[3] Katuri R, Rao G. (2018). Design of math function based controller for smooth switching of hybrid energy storage system. Majlesi Journal of Electrical Engineering 12(2): 47-54.

[4] Shen J, Khaligh A. (2015). A supervisory energy management control strategy in a battery/ultracapacitor hybrid energy storage system. IEEE Transactions on Transportation Electrification 1(3): 223-31. http://dx.doi.org/ 10.1109/TTE.2015.2464690

[5] Wu D, Todd R, Forsyth AJ. (2015). Adaptive rate-limit control for energy storage systems. IEEE Transactions on Industrial Electronics 62(7): 4231-40. http://dx.doi.org/10.1109/TIE.2014.2385043

[6] Emadi A, Lee YJ, Rajashekara K. (2008). Power electronics and motor drives in electric, hybrid electric, and plug-in hybrid electric vehicles. IEEE Transactions on industrial electronics 55(6): 2237-2245. http://dx.doi.org/10.1109/TIE.2008.922768

[7] Chan CC, Bouscayrol A, Chen K. (2010). Electric, hybrid, and fuel-cell vehicles: Architectures and modelling. IEEE transactions on vehicular technology 59(2): 589-598. http://dx.doi.org/ 10.1109/TVT.2009.2033605

[8] Xiang C, Wang Y, Hu S, Wang W. (2014). A new topology and control strategy for a hybrid battery-ultra-capacitor energy storage system. Energies 7(5): 2874-96. http://dx.doi.org/3390/en7052874

[9] Gholizadeh M, Salmasi FR. (2014). Estimation of state of charge, unknown nonlinearities, and state of health of a lithium-ion battery based on a comprehensive unobservable model. IEEE Transactions on Industrial Electronics 61(3): 1335-1344. http://dx.doi.org/ 10.1109/TIE.2013.2259779 

[10] Sánchez Ramos L, Blanco Viejo CJ, Álvarez Antón JC, García García VG, González Vega M, Viera Pérez JC. (2015). A variable effective capacity model for LiFePO4 traction batteries using computational intelligence techniques. IEEE Transactions on Industrial Electronics 62 (1). http://dx.doi.org/10.1109/TIE.2014.2327552

[11] de Castro R, Araujo RE, Trovao JPF, Pereirinha PG, Melo P, Freitas D. (2012). Robust DC-link control in EVs with multiple energy storage systems. IEEE Transactions on Vehicular Technology 61(8): 3553-3565. http://dx.doi.org/10.1109/TVT.2012.2208772

[12] Carter R, Cruden A, Hall PJ. (2012). Optimizing for efficiency or battery life in a battery/supercapacitor electric vehicle. IEEE Transactions on Vehicular Technology 61(4): 1526-33. http://dx.doi.org/10.1109/TVT.2012.2188551

[13] Ferreira AA, Pomilio JA, Spiazzi G, de Araujo Silva L. (2008). Energy management fuzzy logic supervisory for electric vehicle power supplies system. IEEE Transactions on Power Electronics 23(1). http://dx.doi.org/107-115.10.1109/TPEL.2007.911799

[14] Choi ME, Kim SW, Seo SW. (2012). Energy management optimization in a battery/supercapacitor hybrid energy storage system. IEEE Transactions on Smart Grid 3(1): 463-72. http://dx.doi.org/10.1109/TSG.2011.2164816

[15] Trovao JPF, Santos VD, Antunes CH, Pereirinha PG, Jorge HM. (2015). A real-time energy management architecture for multisource electric vehicles. IEEE Trans. Industrial Electronics 62(5): 3223-3233. http://dx.doi.org/10.1109/TIE.2014.2376883

[16] Cao J, Emadi A. (2012). A new battery/ultracapacitor hybrid energy storage system for electric, hybrid, and plug-in hybrid electric vehicles. IEEE Transactions on power electronics 27(1): 122-132. http://dx.doi.org/10.1109/TPEL.2011.2151206

[17] Zhang Y, Sen PC. (2003). A new soft-switching technique for buck, boost, and buck-boost converters. IEEE Transactions on Industry Applications 39(6): 1775-1782. http://dx.doi.org/ 10.1109/TIA.2003.818964

[18] Moreno J, Ortúzar ME, Dixon JW. (2006). Energy-management system for a hybrid electric vehicle, using ultracapacitors and neural networks. IEEE Transactions on Industrial Electronics 53(2): 614-623. http://dx.doi.org/10.1109/TIE.2006.870880

[19] Camara MB, Gualous H, Gustin F, Berthon A. (2008). Design and new control of DC/DC converters to share energy between supercapacitors and batteries in hybrid vehicles. IEEE Trans. vehicular technology 57(5): 2721-2735. http://dx.doi.org/10.1109/TVT.2008.915491

[20] Camara MB, Gualous H, Gustin F, Berthon A, Dakyo B. (2010). DC/DC converter design for supercapacitor and battery power management in hybrid vehicle applications—Polynomial control strategy. IEEE Transactions on Industrial Electronics 57(2): 587-597. http://dx.doi.org/10.1109/TIE.2009.2025283

[21] Liu X, He R, Song YD. (2017). Clutch displacement servo control in gear-shifting process of electric vehicles based on two-speed DCT. Advances in Modelling and Analysis C 72(2): 140-155.