Determination of Power Transmission Scenarios Via Decision Algorithms in Plug-In Hybrid Vehicles with Dual Electric Motors and Semi-Active Suspension Systems

AFRICA 11TH INTERNATIONAL CONFERENCE ON SCIENTIFIC RESEARCHES, Cairo, Mısır, 15 - 20 Ekim 2026, ss.639-640, (Özet Bildiri)

Conventional vehicles using internal combustion engines to generate mechanical power offer

good performance and a long range by leveraging the high energy density of the petroleum they

use as fuel. However, traditional vehicles have disadvantages such as low fuel efficiency and

causing environmental pollution. On the other hand, battery-powered electric vehicles offer

advantages such as high energy efficiency and zero environmental pollution compared to

traditional internal combustion engine vehicles. However, it is difficult for them to compete

with internal combustion engine vehicles, particularly due to the distance they can cover on a

single charge and the fact that batteries have a much lower energy density than gasoline. Hybrid

electric vehicles, which utilize two power sources, combine the advantages of both internal

combustion engine vehicles and electric vehicles, thereby eliminating the shortcomings

associated with these systems. The present study6 focuses on the control strategies of a full

hybrid vehicle equipped with a semi-active suspension system. Dual-electric-motor plug-in

hybrid electric vehicles (PHEVs) are advanced powertrains that feature two independent

electric motors alongside an internal combustion engine (gasoline or diesel). In these systems,

the motors operate synchronously to maximize efficiency and performance. Powertrain delivery

scenarios are managed by the main control unit known as the PCM (Powertrain Control

Module). The PCM continuously collects input (sensor) data, processes it through mathematical

algorithms, and generates output (action) commands. In this study, too, a control block diagram

has also been designed in the MATLAB/Simulink environment for use in the PCM. In this

diagram, rule-based (logical) decision algorithms and machine learning-based decision

algorithms are compared. Simulation results have shown that rule-based decision algorithms

achieve more successful results in determining vehicle powertrain scenarios.