Implementation of Adaptive Neuro Fuzzy Controller for Fuel Cell Based Electric Vehicles

Year 2021, Volume: 34 Issue: 1, 112 - 126, 01.03.2021

Harshavardhana Reddy , Sachin Sharma , Shiva Rama Krishna K

https://doi.org/10.35378/gujs.698272

Cited By: 4

Abstract

The global concern for clean energy generation paved the way for technological inventions and provided scope for researchers. More prominently, integration of heterogeneous renewable sources, storage systems, and electric vehicles became the pioneer solutions. In this article, a soft computing based ANFIS method has been proposed to execute the rapid speed response in electric vehicle. Here, Brushless DC motor was used as a propulsion system to drive the vehicle. Electric Vehicle is basically a time variant system, whose operating parameters and road conditions vary continuously. To address these uncertainties, a novel control strategy is proposed. The fuel cell battery is used as the auxiliary power supply for the electric vehicle. To demonstrate the performance of the controllers, a case study has been considered with parameter uncertainties for an ECE-15 test cycle. To evaluate the proficiency of the proposed soft computing control method, the speed response results are evaluated and compared with existing methods like conventional PI and fuzzy based tuned PID controllers. In addition, the performance of proposed technique is benchmarked with other controllers reported in the literature.

Keywords

Electric Vehicle, BLDC Motor, Speed control, Fuzzy PID Controller

References

• [1] Chan, C.C., “The state of the art of electric and hybrid vehicles”, Proc. IEEE, 90(2): 247–275, (2002).
• [2] Leduc, P., Dubar, B., “Downsizing of gasoline engine: an efficient way to reduce CO2 emissions”. Oil Gas Science Technology, 58: 115–27, (2003).
• [3] Yang, Z., Shang, F., Brown, I. P., & Krishnamurthy, M., “Comparative study of interior permanent magnet, induction, and switched reluctance motor drives for EV and HEV applications”. IEEE Transactions on Transportation Electrification, 1(3): 245-254, (2015).
• [4] Kim, K. C., “A novel magnetic flux weakening method of permanent magnet synchronous motor for electric vehicles”, IEEE Transactions on Magnetics, 48(11): 4042-4045.
• [5] Chan, C. C., Chau, K. T., Jiang, J. Z., Xia, W. A. X. W., Zhu, M., Zhang, R., “Novel permanent magnet motor drives for electric vehicles”, IEEE Transactions on Industrial Electronics, 43(2): 331-339, (1996).
• [6] Wang, J., Atallah, K., Zhu, Z. Q., Howe, D., “Modular 3-phase permanent magnet brushless machines for in-wheel applications”, IEEE Vehicle Power and Propulsion Conference,1-6, (2006).
• [7] Konstantinos, L.,I., Kladas A.,G., “Internal permanent magnet motor design for electric vehicle drive”, IEEE Transactions on Industrial Electronics, 57(1): 138-145,(2010).
• [8] Mura, R., Vadim, U., Simona, O., “Energy management design in hybrid electric vehicles: A novel optimality and stability framework”, IEEE Transactions on Control Systems Technology, 23(4): 1307-1322, (2015).
• [9] Overington, S., Sumedha, R., “High-efficiency control of internal combustion engines in blended charge depletion/charge sustenance strategies for plug-in hybrid electric vehicles”, IEEE Transactions on Vehicular Technology, 64(1): 48-64, (2015).
• [10] Sharma, I., Shubhi, P., “Nonlinear controllers for a light-weigted all-electric vehicle using Chebyshev neural network”, International Journal of Veicular Technology, (2014)
• [11] Ziegler, I. G., Nicholas, N B., “Optimum settings for automatic controllers”. Trans. ASME, 64(11), (1942).
• [12] Ramanathan, P., Arjun A, Mampilly., K.J, Marimuthu R. Ramasamy S., “Study and comparison of fuzzy logic and PI controller based on pressure control system”, International Review on Modeling and Simulations, 5: 1356-1359,(2012).
• [13] Brown, K., E., Rafael, Inigo R.,M., Johnson,B.W., “Design, implementation, and testing of an adaptable optimal controller for an electric wheelchair”, IEEE Transactions on Industry Applications, 26 (6):1144-1157,(1990).
• [14] Er, M. J., Sun, Y. L., “Hybrid fuzzy proportional-integral plus conventional derivative control of linear and nonlinear systems”. IEEE Transactions on Industrial Electronics, 48(6),1109-1117, (2001).
• [15] Kasa, S., Ramasamy, S., Ramanathan, P., “Hybrid fuzzy-ZN PID control based grid interfaced distribution level renewable energy source with power quality”, International Conference on Circuits, Power and Computing Technologie, 1-7, (2015).
• [16] Ramanathan, P., Sukanya, K. C., Mishra, S., Ramasamy., “Study on Fuzzy Logic and PID Controller for temperature regulation of a system with time delay”, International Conference on Energy Efficient Technologies for Sustainability, 274-277, (2013).
• [17] Srikar, S., B., Sarath K., M., Ramasamy, S., “Design and Implementation of fuzzy logic control based speed control of Industrial conveyor”, ARPN Journal of Engineering and Applied Sciences, 9.(9): 1547-1553, (2014).
• [18] Schouten, N. J., Salman, M. A., Kheir, N. A., “Fuzzy logic control for parallel hybrid vehicles”, IEEE Transactions on Control Systems Technology, 10(3): 460-468, (2002).
• [19] Wu, H. X., Cheng, S. K., Cui, S. M, “A controller of brushless DC motor for electric vehicle”, IEEE Transactions on Magnetics, 41(1): 509-513, (2005).
• [20] Huang, Q., Huang, Z., Zhou, H., “Nonlinear optimal and robust speed control for a light-weighted all-electric vehicle”,IET Control Theory & Applications, 3(4): 437-444, (2009).
• [21] Ye, M., Bai, Z., Cao, B., “Robust control for regenerative braking of battery electric vehicle”. IET Control Theory & Applications, 2(12): 1105-1114, (2008).
• [22] Khooban, M., H., Taher,N., Mokhtar S., “Speed control of electrical vehicles: A Time-Varying Proportional–Integral Controller-based Type-2 Fuzzy Logic”, IET Science, Measurement & Technology, 10(3): 185-192,(2016).
• [23] Khooban, M., H., Navid,V., Taher,N., “T–S fuzzy model predictive speed control of electrical vehicles”, ISA Transactions,(2016).
• [24] Syed, F., U., Kuang, M.L., Smith,M., Okubo,S., Ying,H., “Fuzzy gain-scheduling proportional–integral control for improving engine power and speed behavior in a hybrid electric vehicle”, IEEE Transactions on Vehicular Technology, 58(1): 69-84,(2009).
• [25] Petković, D., P., Cojbasic, Z., Nikolic.V.,Shamsirband,S., Kiah, M.L.M., Anur,N.B., Wahab, A.W.A., “Adaptive neuro-fuzzy maximal power extraction of wind turbine with continuously variable Transmission” Energy, 64: 868-874,(2014).
• [26] Hafiz, F., Abdennour, A., “An adaptive neuro-fuzzy inertia controller for variable-speed wind turbines”. Renewable Energy, 92: 136-146, (2016).
• [27] Petković, D., Issa, M., Pavlović, N. D., Pavlović, N. T., Zentner, L., “Adaptive neuro-fuzzy estimation of conductive silicone rubber mechanical properties”. Expert Systems with Applications, 39(10): 9477-9482, (2012).
• [28] Petković, D., Issa, M., Pavlović, N. D., Zentner, L., Ćojbašić, Ž., “Adaptive neuro fuzzy controller for adaptive compliant robotic gripper”. Expert Systems with Applications, 39(18): 3295-13304,(2012).
• [29] Vatankhah, R., Broushaki, M., Alasty, A., “Adaptive optimal multi-critic based neuro-fuzzy control of MIMO human musculoskeletal arm model”. NeuroComputing, 173: 1529-1537, (2016).
• [30] Topalov, A., Oniz, Y., Kayacan,E., Kayank, O., “Neuro-Fuzzy control of antilock braking system using sliding mode inceremental learning alogorithm”,Neurocomputing, 74(11): 1883-1893,(2011).
• [31] Liu, Y., Si-Yuan, L., Ning, W., “Fully-tuned fuzzy neural network based robust adaptive tracking control of unmanned underwater vehicle with thruster dynamics”, Neuro Computing, 19(6): 1-13, (2016).
• [32] Sarhadi, P., Behrooz, R.., Zahra, R., “Adaptive predictive control based on adaptive neuro-fuzzy inference system for a class of nonlinear industrial processes” Journal of the Taiwan Institute of Chemical Engineers,132-137, (2015).
• [33] Jia, Li., Kai, Yuan., “The probability density function based neuro-fuzzy model and its application in batch processes”, Neurocomputing, 148: 216-221, (2015).
• [34] Wang, W., De, Z. L., Joe, V., “An evolving neuro-fuzzy technique for system state forecasting”, Neurocomputing, 87:111-119, (2012).
• [35] Petković, D., Pavlovic, N.T., Samshirband, S., Kiah, M.L.M., Anuar, N.B., Idris, M.Y.I., “Adaptive neuro-fuzzy estimation of optimal lens system parameters”, Optical Laser Engineering, 55: 84-93, (2014).
• [36] Hasanien, H., M., Muyeen S.,M., Junji, T., “Speed control of permanent magnet excitation transverse flux linear motor by using adaptive neuro-fuzzy controller”, Energy Conversion Management, 51(12) :2762-2768, (2012).
• [37] Premkumar, K., B. V. Manikandan., “Adaptive Neuro-Fuzzy Inference System based speed controller for brushless DC moto” Neuro Computing,138: 260-270, (2014).
• [38] Premkumar, K., Manikandan B.V., “Fuzzy PID supervised online ANFIS based speed controller for brushless dc motor”, Neuro Computing,157: 76-90, (2015).
• [39] Reddy, K. H., Sudha, R., Prabhu, R., “Hybrid Adaptive Neuro Fuzzy based speed Controller for Brushless DC Motor”. Gazi University Journal of Science, 30(1): 93-110, (2017).
• [40] Ozturk, S., B., Hamid, A. Toliyat., “Direct torque and indirect flux control of brushless DC motor” IEEE/ASME Transactions on Mechatronics,16(2): 351-360, (2011).
• [41] Pillay, P., Ramu, K., “Modeling, simulation, and analysis of permanent-magnet motor drives. I. The permanent-magnet synchronous motor drive”, IEEE Transactions on Industry Applications,25(2): 265-273, (1989).
• [42] Alphonse, I., Hosimin, T., F. Bright, S., “Design of solar powered BLDC motor driven electric vehicle”, International Journal of Renewable Energy Research (IJRER), 2(3): 456-462, (2012).
• [43] Rekioua, T., Rekioua D., “Direct torque control strategy of permanent magnet synchronous machines.” 2003 IEEE Bologna Power Tech Conference Proceedings, 2. IEEE, (2003).
• [44] Tsotoulidis, S., N., Athanasios, N., S., “Analysis of a drive system in a fuel cell and battery powered electric vehicle” International Journal of Renewable Energy Research (IJRER), 1(3): 140-151,(2011).
• [45] Benmouna, A., Becherif, M., Depernet, D., Depature, C., Boulon, L., “Nonlinear control and optimization of hybrid electrical vehicle under sources limitation constraints.” International Journal of Hydrogen Energy 45(19): 11255-11266, (2020).
• [46] Moshkbar-Bakhshayesh, K., Mohammad, B., G., “Development of an efficient identifier for nuclear power plant transients based on latest advances of error back-propagation learning algorithm”, IEEE Transactions on Nuclear Science, 61(1): 602-610, (2014).