Smart and sustainable wireless electric vehicle charging strategy with renewable energy and internet of things integration

This study addresses the challenges associated with electric vehicle (EV) charging in office environments. These challenges include (1) reliance on manual cable connections, (2) constrained charging options, (3) safety concerns with cable management, and (4) the lack of dynamic charging capabilities. This research focuses on an innovative wireless power transfer (WPT) system specifically designed for use in office parking areas. This system incorporates renewable energy resources (RERs) and uses the transformative power of the Internet of Things (IoT). It employs a mix of solar energy systems and battery storage solutions to facilitate a sustainable and efficient energy supply to EVs. The integration of IoT technology allows for the automatic initiation of charging as soon as an EV is parked. Additionally, the implementation of the Blynk application offers users real-time access to information regarding the operational status of the photovoltaic system and the battery levels of their EVs. The system is further enhanced with IoT and RFID technologies to provide dynamic updates on the availability of charging slots and to implement strict security protocols for user authentication and protection. The research also includes a case study focusing on the application of this charging system in office settings. The case study achieves a 95.9% IRR, lower NPC of USD 1.52 million, and 56.7% power contribution by RERs, and it reduces annual carbon emissions to 173,956 kg CO2

Unified power quality conditioner-based solar EV charging station using the GBDT–JS technique

This manuscript proposes a novel hybrid artificial intelligence (AI) approach for a unified power quality conditioner (UPQC) designed specifically for electric vehicle charging stations (EVCSs). The aim is to integrate multiple vehicle-to-grid (V2G) functionalities, thereby mitigating the challenges associated with electric vehicle (EV) grid integration and the incorporation of distributed energy resources (DERs). The hybrid technique presented in this manuscript combines the gradient boosting decision tree (GBDT) algorithm and the jellyfish search (JS) algorithm, referred to as the GBDT–JS technique. This innovative approach involves utilizing the charging station to offer EV charging services and facilitating the discharge of EVs to the power grid. Integration of the UPQC with DERs, such as photovoltaic (PV), is implemented to decrease the power rating of converters and fulfill power demand requirements. The initial converter within the UPQC is employed to manage the direct current (DC) voltage, while the second converter oversees the power charging or discharging processes of EVs. Additionally, it mitigates the impact of battery voltage fluctuations. The UPQC with vehicle-to-grid functionality minimizes the load pressure on the grid, preventing over-current issues. The presented approach regulates the UPQC converters to mitigate power quality issues such as harmonic currents and voltage sags. Subsequently, the effectiveness of this technique is demonstrated using the MATLAB/Simulink operating platform. The evaluation of GBDT–JS performance involves a comparative analysis with existing techniques. This assessment reveals that the proposed method effectively alleviates power quality issues, specifically reducing total harmonic distortion (THD), and delivers optimal outcomes

Unified power quality conditioner-based solar EV charging station using the GBDT–JS technique

This manuscript proposes a novel hybrid artificial intelligence (AI) approach for a unified power quality conditioner (UPQC) designed specifically for electric vehicle charging stations (EVCSs). The aim is to integrate multiple vehicle-to-grid (V2G) functionalities, thereby mitigating the challenges associated with electric vehicle (EV) grid integration and the incorporation of distributed energy resources (DERs). The hybrid technique presented in this manuscript combines the gradient boosting decision tree (GBDT) algorithm and the jellyfish search (JS) algorithm, referred to as the GBDT–JS technique. This innovative approach involves utilizing the charging station to offer EV charging services and facilitating the discharge of EVs to the power grid. Integration of the UPQC with DERs, such as photovoltaic (PV), is implemented to decrease the power rating of converters and fulfill power demand requirements. The initial converter within the UPQC is employed to manage the direct current (DC) voltage, while the second converter oversees the power charging or discharging processes of EVs. Additionally, it mitigates the impact of battery voltage fluctuations. The UPQC with vehicle-to-grid functionality minimizes the load pressure on the grid, preventing over-current issues. The presented approach regulates the UPQC converters to mitigate power quality issues such as harmonic currents and voltage sags. Subsequently, the effectiveness of this technique is demonstrated using the MATLAB/Simulink operating platform. The evaluation of GBDT–JS performance involves a comparative analysis with existing techniques. This assessment reveals that the proposed method effectively alleviates power quality issues, specifically reducing total harmonic distortion (THD), and delivers optimal outcomes