The level of exhaust gases is rising with increasing usage of internal combustion engine vehicles. In order to reduce carbon emission, researchers and industry head up for improving electric vehicle technologies in all over the word. This paper deals with design and simulation of a bi-directional power converter of electric vehicle. The power electronics block is comprised by batteries, bi-directional dc-dc converter and dc machine. The initial state of battery charge is set around 90% where the discharge current is 44.5 A during motor mode. The nominal voltage of battery stack is 350 V and maximum capacity is 100 Ah. The rated power of dc machine is set to 250 HP with 500 V armature voltage and 300 V field voltage. The operating mode of power converter is determined according to the torque values of dc machine which is operated in motor and generator modes. The charge and discharge conditions of batteries have been controlled regarding to operating modes of dc machine. The bi-directional dc-dc converter is controlled with fuzzy logic controller in both modes. The proposed converter and controller are designed to meet charge control and motor drive requirements of an all-electric vehicle.

Energy benefits both individuals and nations. Humanity's reliance on fossil fuels an inability to respond increases depletion. Energy supplies are rapidly decreasing. Electricity use causes the energy crisis. Sustainable energy largely meets the energy demand of the growing population. In addition, it benefits the environment by reducing carbon emissions. This situation sustainable energy sources have supplemented traditional energy sources and promoted sustainable energy use. Solar, wind, and fuel cell energy are given example of sustainable energy. Power generating facilities are employed nowadays because of their extended lifespan, inexpensive maintenance, no hazardous waste, and independence from dwindling energy sources. Solar power generation depends on environmental circumstances, hence MPP generation must be observed. MPPT follow solar panel highest MPP. This study involves a system is comprised by a DC-DC boost converter, PV panel, and a ohmic load. The duty ratio is generated by the IC and FLC MPPT algorithms, and the PWM signal is generated by comparing it with the triangle wave. This generated signal is applied to the DC-DC boost converter. The aim of this research is to investigate the effectiveness, variability, and duration required to attain the MPP of the implemented MPPT methods. The system has been developed within the MATLAB/Simulink framework. Based on the findings of the simulation, it has been determined that the FLC MPPT algorithm achieves the MPP at a faster rate compared to the IC MPPT algorithm. Consequently, the level of fluctuation is minimum and the efficiency is high.

Due to the increase in electricity consumption in the world, the tendency to increase resource diversity in the electricity generation section has increased. With the decrease in the reserves of petroleum and derivative products used in traditional energy production systems, energy production has turned to renewable energy sources. Examples of renewable energy sources are the sun, wind turbines, and fuel cells. In order to provide sustainable energy production in wind turbines, the blades and body must be protected. In this study, the blade pitch angle control of the wind turbine is realized with the PID controller and the wind turbine is protected from high speeds. The coefficient control of the PID controller is determined by the PSO (Particle Swarm Optimization) and Ziegler Nichols method. Simulation was carried out in MATLAB/Simulink environment. It has been observed that the PID coefficient parameters optimized with PSO in the pitch angle control process reach the reference power value in a shorter time compared to the PID parameter values calculated with Ziegler Nichols. In addition, it was observed that the oscillation value was less at the reference power reached and the pitch angle increased.

Lifestyle changes worldwide are increasing chronic diseases (CD). This research concluded that cardiovascular diseases (CVDs) cause 46% of global mortality, excluding communicable diseases and accidents, and heart attacks are 7.4 million of the 17.5 million CVD deaths. 2030 will see 22.2 million cardiovascular deaths. Avoiding and treating most HD reduces cardiovascular disease fatalities. In this study, the data set for heart disease is optimized with PSO and GWO, and classification is performed with k-NN.

PID controllers are utilised extensively in the domain of electric motors and drives. The values of the PID controller have a direct impact on the controller's characteristics. Establishing optimal values is imperative to enhance the efficacy of control mechanisms. Consequently, a multitude of optimization algorithms have been developed. Employing these algorithms facilitates the optimisation of the controller's optimal values with greater efficiency, requiring less experience and a shorter timeframe. In this study, the parameters of the PID controller employed in the motor drive developed for a direct current (DC) motor are optimised by three distinct heuristic optimisation methods: The following optimization methods are used: Particle Swarm Optimisation (PSO), Ant Colony Optimisation (ACO), and PSO-ACO, which is a combination of these two methods. The execution of simulations is conducted within the MATLAB environment, with a subsequent comparative analysis of control performances. This study proposes a pioneering optimisation approach that integrates the PSO and ACO algorithms. The PID controller attains the reference value in the most efficient timeframe through this methodology. The simulation results show that the PSO-ACO method demonstrates optimal performance, followed by PSO and ACO.