Modelling and Analysis of a Hybrid Controller Applied to the Ultracapacitor Based Solar Powered Electric Vehicle

Modelling and Analysis of a Hybrid Controller Applied to the Ultracapacitor Based Solar Powered Electric Vehicle

Raghavaiah Katuri Srinivasarao Gorantla 

Department of 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: 
114-122
|
DOI: 
https://doi.org/10.18280/mmc_a.910303
Received: 
25 July 2018
| |
Accepted: 
15 September 2018
| | Citation

OPEN ACCESS

Abstract: 

The high power density capability of Ultracapacitor (UC) can be utilized by developing of Hybrid Energy Storage System (HESS) with a conventional power source, battery. UC power mainly used during peak power requirement of electric vehicle (EV) / Hybrid electric vehicle (HEV) on the other hand side battery is treated as a main source of the entire system and it serves the average power to the load. Energy management between the sources, is the primary difficulty associated with HESS powered electric vehicles. The main aim of this work is to design a new control strategy approach which is used to switch the power sources according to the electric vehicle dynamics. Four math functions are created individually according to the speed of an electric motor, named as Math Function Based (MFB) controller. The designed MFB controller is combined with a Proportional Integral (PI) controller in order to achieve the main objective and applied to the solar-powered electric vehicles for a smooth transition between battery and UC. The principal goal of the designed MFB controller always regulates the pulse signals generated by the conventional PI controller, and this scenario happens with respect to the speed of an electric motor. A solar panel is connected to the electric vehicle which is used to charge the battery charge based on irradiance, temperature available conditions and discharge the same amount of energy during unavailable timings of sunlight All modes of the circuit is simulated in MATLAB and results are plotted, discussed in simulation results and discussion section.

Keywords: 

solar power, Hybrid Electric Vehicles (HEVs), Bidirectional Converter (BDC), Unidirectional Converter (UDC), battery, ultracapacitor, Math Function Based (MFB) controller, Proportional Integral (PI) controller

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

[1] Khan S, Ahmad A, Ahmad F, Shafaati Shemami M, Saad Alam M, Khateeb S. (2018). A comprehensive review on solar-powered electric vehicle charging system. Smart Science 6(1): 54-79. http://dx.doi.org/10.1080/23080477.2017.1419054

[2] Sadagopan S, Banerji S, Vedula P, Shabin M, Bharatiraja C. (2014). A solar power system for electric vehicles with maximum power point tracking for novel energy sharing. In India Educators' Conference (TIIEC), 2014 Texas Instruments, pp. 124-130. IEEE. http://dx.doi.org/10.1109/TIIEC.2014.029

[3] Bhavnani SH. (1994). Design and construction of a solar-electric vehicle. Journal of Solar Energy Engineering 116(1): 28-34. http://dx.doi.org/10.1115/1.2930061

[4] 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

[5] 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.

[6] 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.

[7] 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

[8] 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

[9] 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

[10] 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

[11] 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

[12] 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

[13] 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

[14] 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

[15] 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

[16] 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

[17] 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

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

[19] 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

[20] 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

[21] Katuri R, Gorantla S. (2018). Simulation and modelling of Math Function Based controller implemented with fuzzy and artificial neural network for a smooth transition between battery and ultracapacitor. Advances in Modelling and Analysis C 73(2): 45-52.