| Titre : | Modern energy management techniques for a hybrid storage system dedicated to electric vehicles |
| Auteurs : | HASROURI Malika, Auteur ; Omar Charrouf, Auteur |
| Type de document : | Monographie imprimée |
| Editeur : | Université Mohamed Kheider, 2025 |
| Langues: | Anglais |
| Mots-clés: | Electric vehicle, Energy Management System, Adaptive wavelet transform, Adaptive fuzzy logic, Driving pattern recognition |
| Résumé : |
This thesis presents the development and the experimental validation of an Energy
Management System for a real Hybrid Energy Storage System using lithium-ion batteries and supercapacitors in an electric vehicle emulator. The EMS allocates power based on source dynamics, managing real-time power distribution between the battery and SC, with the SC assisting the battery during high demands and recovering braking energy. Frequency-sharing techniques have been proposed to achieve this goal, including two innovative adaptive Wavelet Transform based on the development of the conventional versions. The first method employs an adaptive wavelet technique within the EMS, adjusting the wavelet decomposition level according to the state of charge of the supercapacitor. This allows for dynamic changes in the wavelet decomposition during high power peaks and when the supercapacitor has a high charge, reducing the battery’s workload. The second method integrates an adaptive wavelet with adaptive fuzzy logic within the EMS. This approach modifies wavelet transform levels and fuzzy logic outputs using a k-means-SVM pattern recognition system. The combination of k-means clustering and Support Vector Machine classification enhances driving pattern recognition, enabling real-time decision-making. The adaptive wavelet dynamically manages power distribution between the battery and SC, while the fuzzy logic maintains the supercapacitor at optimal levels and shields the battery from peak current surges. These proposed systems maintain the supercapacitor charge by dynamically managing power between the battery and SC, taking into account real-time driving conditions. As a result, it reduces battery aging, extends battery lifespan, and lowers overall operational costs in electric vehicles |
| Type de document : | Thése doctorat |
Disponibilité (1)
| Cote | Support | Localisation | Statut | Emplacement | |
|---|---|---|---|---|---|
| Th/1417 | Livre | BIB.FAC.ST. | Empruntable | Magazin |
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