The Impact Of Hybrid Storage On Enhancing The Autonomy Of Electric Vehicles And On Their Energy Management

Résumé: The seamless integration of energy storage systems into electrical grids necessitates the deployment of a highly efficient energy management system (EMS) to enhance the stability, reliability, and resilience of interconnected electric vehicle networks. This research investigates the optimization of a photovoltaic (PV) system interfaced with a hybrid energy storage system (HESS) combining lithium-ion batteries and supercapacitors. The development of an effective EMS must overcome several challenges, including peak current limitations in supercapacitors, the inherently slow dynamic response of batteries, and their vulnerability to environmental stressors such as solar irradiation fluctuations and load variations. Metaheuristic optimization techniques have demonstrated significant potential in addressing complex multi-objective optimization problems. In this context, this thesis proposes the modeling and optimization of an EMS for HESS using three advanced metaheuristic algorithms: Genetic Algorithm (GA), Ant Colony Optimization (ACO), and Grey Wolf Optimization (GWO). Furthermore, the study presents the design, modeling, and simulation-based validation of a battery electric vehicle (BEV), wherein speed variation is utilized as the reference input to achieve optimal acceleration and deceleration performance. Control is achieved through both conventional PI regulation and PI tuning via the ACO algorithm to enhance dynamic behavior. Finally, a comprehensive comparative analysis is performed to assess the strengths and limitations of each proposed strategy, substantiating the superior performance and robustness of the ACO-based approach, particularly under varying road gradient conditions.

Publié dans la revue: