The emerging bidirectional wireless power transfer (BWPT) technology provides improved flexibility without requiring human intervention by facilitating smooth Grid to Vehicle (G2V) and Vehicle to Grid (V2G) operations. Nevertheless, BWPT systems have issues with power factor management, efficiency, and power transfer rate. In order to address these issues, sophisticated control systems are essential. While unidirectional Wireless Power Transfer (WPT) are adequately managed by conventional control techniques and power factor corrector (PFC) approaches, whereas bidirectional systems require dual-side control of power converters in order to efficiently handle G2V and V2G operations. Furthermore, the existing literature predominantly focuses on power factor improvement for unidirectional WPT systems. This paper presents a dual-phase shift Pulse Width Modulation (PWM) technique for an improved PFC control for BWPT. A thorough analysis of the suggested approach is conducted, looking into power factor, bifurcation, total harmonic distortion (THD), and power losses. Experimentation and verification together with simulation studies at an operating frequency of 85 kHz and a power rating of 3.7 kW demonstrate the effectiveness of the system. Dual-phase shift control yields 94.4% power transfer efficiencies in simulations and 90.1% in experimental results in both power transfer directions.

Regenerative braking is of paramount importance both in terms of energy recovery and from the perspective of hybrid electric vehicles (HEVs). It allows HEVs to recover energy during braking which in turn enhances the range, efficiency and sustainability by reducing electricity demand and environmental impacts. Upon considering the demand for high-performance regenerative braking in HEVs, the development and optimization of advanced control strategies become mandatory. The study presents a model-based regenerative braking system that integrates tire-road coefficients with an intelligent control algorithm to enhance the efficiency and sustainability of HEVs. The literature review provides insights into existing hybrid power systems, tire-road friction estimation, braking force distribution, and regenerative braking optimization strategies. Different control strategies including lookup table, fuzzy logic control (FLC) and adaptive neuro-fuzzy inference system (ANFIS) are analyzed for their impact on the efficiency of regenerative braking systems under various driving conditions and their results have been presented. The study reveals the supremacy of the ANFIS control algorithm across multiple driving cycles, demonstrating its potential to enhance fuel efficiency and state of charge (SOC). The study involves several input and output parameters of a vehicle to arrive at the desired outcome, and this also showcases ANFIS’s capabilities in providing a better and smoother driving behaviour for the driver, indicating its adaptability and proficiency in energy recovery. FLC strategy excelled in μ- estimation with lower estimation errors of about 0.15%, 0.16% and 0.13% across various driving cycles when compared to the look-up table approach. ANFIS exhibited fuel economy, improvements of about 0.282%, 0.437%, and 0.345% over the look-up table approach in the same cycles. In terms of SOC optimization, ANFIS achieved the highest final SOC values, surpassing the look-up table strategy by 2.082%, 6.799%, and 0.702% in the same cycles.

In response to the escalating global energy crisis, the motivation for this research has been derived from the need for sustainable and efficient energy solutions. A gap in existing renewable energy systems, particularly in terms of stability and efficiency under variable environmental conditions, has been recognized, leading to the introduction of a novel hybrid system that combines photovoltaic (PV) and wind energy. The innovation of this study lies in the methodological approach that has been adopted, integrating dynamic modeling with a sophisticated control mechanism. This mechanism, a blend of model predictive control (MPC) and particle swarm optimization (PSO), has been specifically designed to address the fluctuations inherent in PV and wind power sources. The methodology involves a detailed stability analysis using Lyapunov’s theorem, a critical step distinguishing this system from conventional renewable energy solutions. The integration of MPC and PSO, pivotal in enhancing the system’s adaptability and optimizing the maximum power point tracking (MPPT) process, improves control efficiency across key components like the doubly fed induction generator (DFIG), rectifier-sourced converter (RSC), and grid-side converter (GSC). Through rigorous MATLAB simulations, the system’s robust response to changing solar irradiance and wind velocities has been demonstrated. The key findings confirm the system’s ability to maintain stable power generation, underscoring its practicality and efficiency in renewable energy integration. Not only has this study filled a crucial gap in renewable energy control systems, but it has also set a precedent for future research in sustainable energy technologies.

The human intervention free bidirectional efficient energy exchange between the grid and Electric Vehicles (EVs) relies on effective bidirectional power flow control in Wireless Power Transfer (WPT) system. This essential bidirectional control feature enables the grid to charge the EV’s battery and, during surplus energy periods, allows the EV to feed power back to the grid. This capability facilitates the implementation of Vehicle-to-Grid (V2G) functionalities. Implementing this bidirectional flow requires sophisticated modulation techniques to ensure seamless power transfer and optimize overall efficiency. In comparison to pulse width modulation, pulse density modulation (PDM) proves more effective in minimizing switching losses in the converter. PDM efficiently manages the amplitude of high-frequency pulses generated by the inverter and converter systems for WPT applications. However, the PDM techniques are not effectively investigated in the bidirectional wireless power transfer system (BWPT). In this paper, Fuzzy Logic Controlled Pulse Density Modulation (FLC-PDM) approach for the BWPT is proposed for the first time. This approach uses dual side pulse density control for high-frequency converters with the fuzzy rule base to adjust the pulse density and duty ratio based on the load demand. The proposed approach aiming to optimize efficiency across diverse loads and regulate the output voltage and provides effective control approach for the BWPT system. This method ensures constant switching frequency output voltage regulation and delicate shifting operation for high-frequency converter switches, requiring no additional components. The circuit simulations and hardware prototype testing are performed for the 3.7 kW power rating and 85 kHz operating frequency to validate the proposed FLC-PDM approach for the BWPT circuit and yielded an efficiency over 93% across diverse load resistances and pulse densities.

The main issue is that photovoltaic (PV) systems have their power output reduced due to partial shading. Less energy is produced by photovoltaic modules when partial shading causes an imbalance in the levels of irradiation. Array reconfigurations, both static and dynamic, are suggested as a strategy to improve power capture and reduce the impact of partial shading. Furthermore, it is recognized that irradiance fluctuations, snow, ice, and dust are environmental elements that impact the efficiency of PV arrays. Using white shark optimization (WSO), this research introduces a new method for optimizing the power reconfiguration of PV arrays. Minimizing the disparity in row currents and maximizing power production are the main goals of this WSO-based technique. Four different types of shade patterns are considered in the research: short wide (SW), long wide (LW), short narrow (SN), and long narrow (LN). In order to confirm that this method works, we ran simulations in MATLAB-Simulink and compared the outcomes to those of other configurations, such as Total Cross Tied (TCT), Butterfly Optimization Algorithm (BOA), Harris Hawks Optimization (HHO), and Flower Pollination Algorithm (FPA). Simulation results show the effectiveness of the WSO method in enhancing power extraction from the PV array, especially under partial shading conditions. Notably, the WSO method significantly increases the Global Maximum Power (GMP) output across different scenarios: by 26.22% in SW, 18.51% in LW, 10.95% in SN, and 10% in LN. This confirms the ability of this method to improve the PV power generation in diverse operating environments.