fuzzy logic controller

Electric vehicles (EVs) have become a vital solution for environmental transportation; however, challenges related to battery life and power density persist. In pursuit of enhanced EV performance and cost-effectiveness, researchers advocate for Hybrid Energy Storage Systems (HESS), integrating various Energy Storage Systems (ESS). An efficient Energy Management Strategy (EMS) is crucial for optimal power distribution within the HESS. This study introduces a real-time, simple, and practical EMS using a low-pass filter (LPF). However, the LPF lacks State of Charge (SoC) control, necessitating the addition of a SoC Limiter. The static SoC Limiter, while effective, faces challenges in predicting peak loads, leading to suboptimal power-sharing performance. To address this limitation, LPF with Adaptive SoC Limiter (LPF-ASL) is proposed. The LPF-ASL accommodates the peak load by saving some portion of supercapacitor (SC) power for peak load. In an unpredictable initial SC SoC test, LPF-ASL achieves substantial reductions in maximum battery current compared to LPF and Fuzzy Logic Control (FLC) by up to 21.30% and 21.14%, respectively. This underscores the effectiveness of LPF-ASL in optimizing battery life and enhancing power distribution within HESS-equipped EVs.

This article proposes a technique to improve power quality and eliminate current and voltage harmonics. The robustness of the system functioning in various operating modes is the key contribution of this research. Research findings demonstrating the PV array insulation, load variation, and grid unavailability are employed for transitioning between modes and are used to assess the performance of a grid-interfaced PV-BES (Battery Energy Storage) system. The system also operates in constant and variable power modes to provide power smoothening and reduce the load on the distribution grid during peak demand. This system also turns out to be capable of operating in an islanding mode to provide the load with uninterrupted power. For this, a grid-tied voltage source converter (VSC) with effective control logic is designed. A PI controller was used in the existing model to enhance the system's performance, but due to various disadvantages, a fuzzy logic controller has been implemented in the proposed system, which has more advantages and aids in suppressing harmonics and enhancing the reliability of the power. Consequently, a comparison of the controller is done in this article. For implementing the performance of this system, MATLAB/Simulink software is used.

Wind turbines can be controlled by controlling the generator speed and adjusting the blade angle and the total rotation of a turbine. Wind energy is one of the main types of renewable energy and is geographically extensive, scattered and decentralized and is almost always available. Pitch angle control in wind turbines with Doubly Fed Induction Generator (DFIG) has a direct impact on the dynamic performance and oscillations of the power system. Due to continuous changes in wind speed, wind turbines have a multivariate nonlinear system. The purpose of this study is to design a pitch angle controller based on fuzzy logic. According to the proposed method, nonlinear system parameters are automatically adjusted and power and speed fluctuations are reduced. The wind density is observed by the fuzzy controller and the blade angle is adjusted to obtain appropriate power for the system. Therefore, the pressure on the shaft and the dynamics of the turbine are reduced and the output is improved, especially in windy areas. Finally, the studied system is simulated using Simulink in MATLAB and the output improvement with the fuzzy controller is shown in the simulation results compared to the PI controller. Fuzzy control with the lowest cost is used to control the blade angle in a wind turbine. Also, in this method, the angle is adjusted automatically and it adapts to the system in such a way that the input power to the turbine is limited. Compared to the PI controller, by calculating different parameters, the power quality for fuzzy controller is enhanced from 2.941 % to 4.762 % for wind with an average speed of 12 meters per second.

The purpose of designing the active suspension systems is providing comfort riding and good handling in different road disturbances. In this paper, a novel control method based on adaptive neuro fuzzy system in active suspension system is proposed. Choosing the proper database to train the ANFIS has an important role in increasing the suspension system’s performance. The database used to train the proposed ANFIS system is extracted from the outputs of fuzzy, LQR and sliding mode controllers. A quarter-car model is considered to study the performance of the proposed controller. Performance of this controller is compared with the passive system, and active suspension systems with fuzzy and LQR controllers. The results demonstrate that proposed ANFIS controller is better than passive suspension system and active fuzzy and LQR based suspension systems in suspension deflection, body acceleration, settling time and also control force.