Fuzzy-Energy-Management-Based Intelligent Direct Torque Control for a Battery—Supercapacitor Electric Vehicle

Adel Oubelaid, Hisham Alharbi, Abdullah S. Bin Humayd, Nabil Taib, Toufik Rekioua and Sherif S. M. Ghoneim
Additional contact information
Adel Oubelaid: Laboratoire de Technologie Industrielle et de l’Information, Faculté de Technologie, Université de Bejaia, Bejaia 06000, Algeria
Hisham Alharbi: Department of Electrical Engineering, Taif University, P.O. Box 11099, Taif 21944, Saudi Arabia
Abdullah S. Bin Humayd: Department of Electrical Engineering, Umm Al-Qura University, Makkah 21421, Saudi Arabia
Nabil Taib: Laboratoire de Technologie Industrielle et de l’Information, Faculté de Technologie, Université de Bejaia, Bejaia 06000, Algeria
Toufik Rekioua: Laboratoire de Technologie Industrielle et de l’Information, Faculté de Technologie, Université de Bejaia, Bejaia 06000, Algeria
Sherif S. M. Ghoneim: Department of Electrical Engineering, Taif University, P.O. Box 11099, Taif 21944, Saudi Arabia

Sustainability, 2022, vol. 14, issue 14, 1-20

Abstract: This paper presents a proposed fuzzy energy management strategy developed for a battery−super capacitor electric vehicle. In addition to providing different driving modes, the proposed strategy delivers the suitable type and amount of power to the vehicle. Furthermore, the proposed strategy takes into account possible failures in vehicle power sources. The speed and torque of the HEV traction machine are simultaneously controlled using a genetic algorithm that provides simultaneous tuning via the use of newly proposed cost functions that give the designer the ability to tradeoff and prioritize between the design variables to be minimized. The simulation results show that the intelligent speed and torque control and the fuzzy power management strategy improved the vehicle’s performance in terms of ripple minimization. The real-time simulation is conducted using the RT LAB simulator, and the results obtained correspond to those obtained in the numerical simulation using MATLAB/Simulink.

Keywords: genetic algorithm; hybrid electric vehicle; direct torque control; power management; fuzzy logic control; RT LAB (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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