مقاله انگلیسی رایگان در مورد کنترل سیستم پیشرانش توان خودرو برقی مبتنی بر محاسبات انتگرال کسری متغیر با زمان – ۲۰۱۹ IEEE

I- Introduction

II- Hybrid Electric Vehicle Modelling

III- A Non-Integer Type-II Fuzzy P + ID Controller

IV- Optimization Algorithm Optimization Algorithm and Objective Function

V- Simulation and Experimental Results

VI- Conclusion

References

Abstract

Nowadays, due to the fact that motor efficiency is strictly related to the diminution of emissions, researchers pay much attention to find a robust and efficient motor control technique for Hybrid Electric Vehicles (HEVs). In this study, an optimal type-2 fuzzy fractional P + ID (IT2FOFP+ID) controller is applied to solve the throttle position and speed control problem of the HEVs. It is undeniable that the performance and effectiveness of the fuzzy-based PID controllers are depended on its gains’ value. Hence, a novel improved heuristic technique, called IJAYA algorithm, is employed for the online tuning of the coefficients embedded in the specific controller structure. In contrast with the classical control methodologies that suffer from the lack of the self-regulating feature, the established controller has been adjusted on-line automatically. As another advantage of this control strategy, it is a model-free scheme and does not need the mathematical computational to identify the system model. To appraise the supremacy of the optimal IT2FOFP+ID controller than the other prevalent methodologies, a highly nonlinear EV model is utilized as a case study. In addition, the usefulness and robustness of the proposed method are tested by the experimental data, the EPA New York City Cycle (NYCC). In the end, the new time-varying proposed technique is validated and implemented in hardware-in-the-loop (HiL) real-time simulation based on OPAL-RT to study the feasibility of the designed IT2FP+ID controller with check outcomes on a physical platform.

INTRODUCTION

DUE to the shortages of fossil fuel reserves, environmental emission, increasing costs of electric transportation, Hybrid Electric Vehicles (HEVs) has become an emerging trend [1-3]. HEVs can potentially offer great benefits including smooth operation, high energy efficiency, safety improvement and energy security [4, 5]. In addition, the whole HEVs produce zero tailpipe emissions, which reduce local air pollution especially when they are widely used in urban areas. Owing to the above excellent properties, it is anticipated that the future automotive industry will be dominated by the HEVs. One of the key ingredients for the establishment of an HEV system is the control mechanism. Since such systems have the time-variant nature, the conventional deterministic methodologies are not resilient enough to ensure the excellent performance for both the dynamic and steady-state requirements.