| Titre : | Contribution to the Improvement of Electric Vehicle Performance Via Optimization Methods |
| Auteurs : | Mohamed Fadi KETHIRI, Auteur ; Omar Charrouf, Directeur de thèse |
| Type de document : | Monographie imprimée |
| Editeur : | Biskra [Algerie] : Université Mohamed Kheider, 2026 |
| Langues: | Anglais |
| Mots-clés: | Electric Vehicles, BLDC Motor, Fractional-Order Control, Metaheuristic Algorithms, Fuzzy Logic, Energy Management |
| Résumé : |
The rapid growth of electric vehicles (EVs) has created pressing challenges in efficiency, re?liability, and energy management. Conventional control strategies often fall short in fully
exploiting the potential of modern electric powertrains, especially under variable and uncer?tain operating conditions. This thesis proposes advanced control and optimization strategies for brushless DC (BLDC) motors and hybrid energy storage systems in EV applications. The research introduces a novel framework that integrates fractional-order control theory, fuzzy logic, and metaheuristic optimization algorithms such as Particle Swarm Optimiza?tion (PSO) to design hybrid self-tuning controllers capable of real-time adaptation to system uncertainties. A hybrid fuzzy–PSO-based fractional-order PI controller (A-HFOPI) was de?veloped and validated not only through simulations and Hardware-in-the-Loop (HIL) testing but also on a real experimental test bench designed as an electric vehicle traction system. Furthermore, an adaptive flux control strategy based on fuzzy logic and the Incremental Conductance (IncCond) method was implemented to dynamically regulate the stator flux, thereby reducing copper losses, lowering input power consumption, and improving overall energy efficiency during BLDC motor operation. The results demonstrate that the proposed strategies significantly reduce power losses, enhance torque control precision, improve en?ergy allocation between storage devices, and extend battery life, while ensuring robustness and stability across operating conditions. The originality of this work lies in the explicit integration of adaptive flux regulation with intelligent, optimization-based control, a novel approach not previously applied in this context. This thesis thus advances the state of the art in electric vehicle control, offering both a practical framework for modern EV powertrains and a solid foundation for future research on adaptive, real-time, and energy-aware control architectures. |
| Type de document : | Thése doctorat |
Disponibilité (1)
| Cote | Support | Localisation | Statut | Emplacement | |
|---|---|---|---|---|---|
| Th/1440 | Thèse de doctorat | BIB.FAC.ST. | Empruntable | Magazin |
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