Abstract:
This study presents a comprehensive simulation and analysis of the Nissan Qashqai e-Power, a series hybrid electric vehicle (HEV), with a focus on optimizing power distribution among the internal combustion engine (ICE), electric motor, and battery system using a fuzzy logic controller (FLC). Implemented in MATLAB Simulink and Simscape, the model incorporates real-time operational parameters and manufacturer specifications to accurately reflect the vehicle’s dynamic behavior under various driving conditions. Key performance indicators such as power demand, vehicle speed, and battery charge are continuously monitored and processed by the FLC, which intelligently manages the interplay between the ICE and electric motor. The primary objective is to improve the vehicle’s powertrain efficiency while simultaneously reducing fuel consumption and emissions, thereby enhancing both fuel economy and environmental performance.
The fuzzy logic controller operates by evaluating current driving conditions—such as speed, load, and state of battery charge—and applying a set of heuristic fuzzy rules to determine the optimal power distribution. This dynamic decision-making enables the vehicle to adapt its power usage across a wide range of real-world scenarios, ensuring smooth operation and efficient energy use. The effectiveness of this approach is demonstrated through a case study on the Nissan Qashqai e-Power, where various driving cycles are simulated to evaluate performance outcomes. The results indicate that fuzzy logic-based control significantly improves hybrid system responsiveness and energy optimization, highlighting its potential as a robust solution for next-generation hybrid vehicles. This work also contributes to the ongoing effort in the automotive sector to integrate renewable energy strategies and reduce reliance on fossil fuels through smarter hybrid powertrain management.
Aim:
This study is focused on the development of an advanced fuzzy logic-based supervisory controller tailored specifically for Series Hybrid Electric Vehicles (HEVs). The primary function of this controller is to intelligently manage and optimize the power distribution between the internal combustion engine (ICE) and the electric motor, ensuring that energy is used in the most efficient way possible. By dynamically adjusting power flow in response to real-time driving conditions such as vehicle speed, load demand, and battery state of charge, the controller aims to significantly reduce fuel consumption and lower harmful emissions. This not only enhances the vehicle’s overall energy efficiency but also contributes to improved environmental performance, aligning with global efforts to promote cleaner, more sustainable transportation solutions. The controller’s adaptability across various driving scenarios makes it a promising solution for next-generation HEV systems seeking to balance performance, efficiency, and eco-friendliness.
Objectives:
The objectives of this study are centered on advancing the efficiency and sustainability of Series Hybrid Electric Vehicles (HEVs) through a systematic and innovative approach. Initially, the research aims to examine HEVs both in the UK and globally, identifying various classifications—such as series, parallel, and plug-in hybrids—and analyzing their unique operational characteristics and regional applications. This global perspective helps in understanding how HEV technologies are adapted to different transportation needs and regulatory environments. The study then delves into current supervisory control methods used in HEVs, assessing their efficiency and adaptability in managing power distribution and overall system performance. This critical review highlights strengths and exposes areas that require improvement, especially in real-time energy management. Building on these insights, the core objective is to design a fuzzy logic-based control strategy specifically for Series HEVs. This strategy aims to intelligently manage power distribution between the internal combustion engine and the electric motor, with the goal of reducing fuel consumption and lowering emissions under diverse driving conditions. Finally, the proposed controller is rigorously validated through simulation-based testing using tools like MATLAB Simulink and Simscape, evaluating its effectiveness in optimizing energy use and achieving environmental performance goals across a range of realistic driving scenarios.
