Feasibility Study of Efficient and Adaptive Interval Type-2.0 Fuzzy Logic Controller for Hybrid Electric Vehicle Performance Optimization

Abstract This research explores the feasibility of employing an efficient and adaptive Type-1 Fuzzy Logic Controller (T-1 FLC) and Interval Type-2.0 Fuzzy Logic Controller (IT2.0 FLC) to optimize the performance of hybrid electric vehicles (HEVs). The research problem focuses on enhancing the robustness and efficiency of T-1 FLC in HEVs to match the superior performance of IT-2.0 FLC in handling uncertainties and dynamic conditions. Interval Type-2.0 Fuzzy Logic Controllers are a popular option because of their capacity to manage uncertainties inherent in real-world driving conditions. Type-1 Fuzzy Logic Controller-based HEVs have limited ability to handle uncertainties and variations robustly, and they may lack the adaptability required to optimize performance under diverse driving conditions effectively. An electric powertrain permanent magnet synchronous motor (PMSM) and 4 energy storage systems (battery, solar PV, fuel cell, and super-capacitor power structure) are features of an atypical HEV. The PMSM drive, which is the most effective and popular, is utilized in the proposed work. A HEV has been implemented using T-1 and IT-2.0 FLCs for battery and fuel cell storage system along with a solar PV system with an maximum power point tracker (MPPT) controller and a supercapacitor storage system with a PI controller. The efficiency, mileage, and energy consumption of energy of each system are assessed using a combination of plausible driving scenarios and extensive simulations. The IT-2.0 FLC-driven HEV demonstrates outstanding performance by enhancing system Total Harmonic Distortions (THDs), achieving energy savings, optimizing torque output with minimal speed deviation, and extending mileage range. The IT-2.0 FLC outperforms nearly 89.478% in output torque, 95.202% in speed, and nearly 97.26% in battery state of charge preservation. MATLAB/Simulink 2018a was used to implement the entire proposed scheme. Cite this article as: Y. Shekhar, C. Yadav, O. Singh, A. Uddin Ahmad, and K. Kumar Bharati, "Feasibility study of efficient and adaptive interval type-2.0 fuzzy logic controller for hybrid electric vehicle performance optimization," Electrica, 24(3), 640-653, 2024.