In light of the energy and environment issues, fuel cell vehicles have many advantages, including high efficiency, low-temperature operation, and zero greenhouse gas emissions, making them an excellent choice for urban environments where air pollution is a significant problem. The dynamics of fuel cells, on the other hand, are relatively slow, owing principally to the dynamics of the air compressor and the dynamics of manifold filling. Because these dynamics can limit the overall performance of fuel cell vehicles, two key technologies that have emerged as critical components of electric vehicle powertrains are batteries and supercapacitors. However, choosing the best hybrid energy storage system that combines a battery and a supercapacitor is a critical task nowadays. An electric vehicle simulated application by MATLAB Code is modeled in this article using the multi-objective particle swarm optimization technique (MOPSO) to determine the appropriate type of batteries and supercapacitors in the SFTP-SC03 drive cycle. This application optimized both component sizing and power management at the same time. Batteries of five distinct types (Lithium, Li-ion, Li-S, Ni-Nicl2, and Ni-MH) and supercapacitors of two different types (Maxwell BCAP0003 and ESHSR-3000CO) were used. Each storage component is distinguished by its weight, capacity, and cost. As a consequence, using a Li-ion battery with the Maxwell BCAP0003 represented the optimal form of hybrid storage in our driving conditions, reducing fuel consumption by approximately 0.43% when compared to the ESHSR-3000CO.

Currently, providing water in developing countries, especially in dry and hot rural areas, is a significant challenge. However, creating new electric grids is often expensive. Therefore, the use of low-cost photovoltaic (PV) panels in water pumping systems, without chemical energy storage, based on high-performance and more efficient power converters with increased time life and lower maintenance interventions is needed. In this study, a photovoltaic water pumping system with two power converters, the first is used to extract the maximum power using the maximum power point tracking (MPPT) algorithm, and the second is a three-cell multicellular power converter used to control the DC motor with a submerged pump. Meanwhile, the serial connection and redundant topology of multicellular converters render the system more vulnerable to failure. fault diagnosis-based machine learning approach and fault tolerant control (FTC) are proposed for multicellular power converters. Simulation results with MATLAB show the effectiveness and practicability of the proposed structure and control to isolate the faulty capacitor, increase the sustainability of the system, assure the supply of water under faulty conditions, minimize the mechanical vibrations in electric DC motors, and avoid PV system shutdown.

Wind energy takes first place in many countries among other renewable energy. However, the power quality problems such as nonlinear and unbalanced loads, unbalanced grid, can increase the mechanical vibrations as well as the risk of overheating in Wind Energy Conversion System (WECS). Therefore, the objective of this work is the use of robust flying capacitor converter in order to enhance the WECS efficiency by minimizing mechanical vibrations and reducing thermal stress with minimum voltage stress of power switches. To this end, this paper proposes a WECS based on doubly fed induction generator (DFIG) with the stator connected directly to non-ideal power grid, grid side converter (GSC) with flying capacitor topology and robust input output linearization control used to regulate the dc side capacitor voltage and also compensate the current harmonics in power grid in order to avoid excessive heating and mechanical vibrations in WECS. Simulation using MATLAB demonstrate that the proposed approach improves the power quality by reducing the total harmonic distortion (THD) of grid current to a value which satisfies the limits of IEEE standard, and allows the 8 KW DFIG to operate with reduced mechanical vibration and minimum heating stress in addition to the generation of power.