Adaptive Fuzzy Logic Controller-Based Intelligent Energy Management System Scheme for Hybrid Electric Vehicles

Abstract

Hybrid Electric Vehicles (HEVs) are affected to a high extent by Intelligent Energy Management Systems (IEMS), especially during situations that are challenging and unpredictable including changes in traffic patterns, road gradients, and speed. These uncertainties are not easily solved using the existing energy management systems; therefore, this paper presents the design of an AFLC-IEMS employing Type 1 and Interval Type 2 Fuzzy Logic Controllers for energy distribution improvement. The AFLC-IEMS sustains the combustion of fuel and discharge of battery in a way that promotes efficiency in switching between the internal combustion engine and the electric motor. The simulation results with the one-way analysis of variance test confirm our finding that the proposed system is far superior to the traditional ones. The savings achieved by the AFLC-IEMS are a decrease in fuel consumption from 7.26 Liters/100 km down to 6.69 Liters/100 km, as well as an increase in the battery State of Charge (SoC) from 72.7% to 75.8%. The ANOVA analysis shows that the fuel consumption (p < 0.01), the motor torque (p < 0.01), as well as the SoC of the battery (p < 0.05) in the developed FLC are statistically superior to the Type 1 FLC and Type 2 FLC. These improvements are achieved by adapting the technology to the situation to adjust the control strategy; hence, the efficiency of the energy management system is optimized. Therefore, the AFLC-IEMS is more effective in improving the fuel economy and reducing emissions under various conditions.

Keywords

• State-of-charge (SoC)
• fuzzy logic controller
• intelligent energy management system (IEMS)
• hybrid electric vehicle (HEV)
• engine torque
• motor torque