Energy-Saving of Battery Electric Vehicle Powertrain and Efficiency Improvement during Different Standard Driving Cycles

Khairy Sayed, Ahmed Kassem, Hedra Saleeb, Ali S. Alghamdi and Ahmed G. Abo-Khalil
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Khairy Sayed: Electrical Engineering Department, Faculty of Engineering, Sohag University, Sohag 82524, Egypt
Ahmed Kassem: Electrical Engineering Department, Faculty of Engineering, Sohag University, Sohag 82524, Egypt
Hedra Saleeb: Electrical Department, Faculty of Technology and Education, Sohag University, Sohag 82524, Egypt
Ali S. Alghamdi: Electrical Engineering Department, College of Engineering, Majmaah University, Almajmaah 15341, Saudi Arabia
Ahmed G. Abo-Khalil: Electrical Engineering Department, College of Engineering, Majmaah University, Almajmaah 15341, Saudi Arabia

Sustainability, 2020, vol. 12, issue 24, 1-26

Abstract: This article focuses on the energy-saving of each driving distance for battery electric vehicle (BEV) applications, by developing a more effective energy management strategy (EMS), under different driving cycles. Fuzzy logic control (FLC) is suggested to control the power management unit (PMU) for the battery management system (BMS) for BEV applications. The adaptive neural fuzzy inference system (ANFIS) is a modeling technique that is mainly based on data. Membership functions and FLC rules can be improved by simply training the ANFIS with real driving cycle data gathered from the MATLAB/SIMULINK program. Then, FLC console blocks are rewritten by enhanced membership functions by ANFIS traineeship. Two different driving cycles are chosen to check the improvement in the efficiency of this proposed system. The suggested control system is validated by simulation and comparison with the traditional proportional-integral (PI) control. The optimized FLC shows better energy-saving.

Keywords: electric vehicle; energy management; fuzzy logic control; driving cycles; energy saving (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

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