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A novel ML-fuzzy control system for optimizing PHEV fuel efficiency and extending electric range under diverse driving conditions by Raeesi, Mehrdad; Mansour, Saba; Changizian, Sina is a scholarly article available to read on EtoBox.

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Aiming for a greener transportation future, this study introduces an innovative control system for plug-in hybrid electric vehicles (PHEVs) that utilizes machine learning (ML) techniques to forecast energy usage in the pure electric mode of the vehicle and optimize power allocation across different operational modes, including pure electric, series hybrid, parallel hybrid, and internal combustion operation. The fuzzy logic decision-making process governs the vehicle control system. The performance was assessed under various driving conditions. Key findings include a significant enhancement in pure electric mode efficiency, achieving an extended full-electric range of approximately 84 kilometers on an 80% utilization of a 20-kWh battery pack. During the WLTC driving cycle, the control system reduced fuel consumption to 2.86 L/100km, representing a 20% reduction in gasoline-equivalent fuel consumption. Evaluations of vehicle performance at discrete driving speeds, highlighted effective energy management, with the vehicle battery charging at lower speeds and discharging at higher speeds, showing optimized energy recovery and consumption strategies. Initial battery charge levels notabl

Author

Raeesi, Mehrdad; Mansour, Saba; Changizian, Sina

Published

2024

Language

EN